for Journals by Title or ISSN
for Articles by Keywords
help
  Subjects -> ENGINEERING (Total: 2266 journals)
    - CHEMICAL ENGINEERING (190 journals)
    - CIVIL ENGINEERING (181 journals)
    - ELECTRICAL ENGINEERING (100 journals)
    - ENGINEERING (1197 journals)
    - ENGINEERING MECHANICS AND MATERIALS (390 journals)
    - HYDRAULIC ENGINEERING (55 journals)
    - INDUSTRIAL ENGINEERING (64 journals)
    - MECHANICAL ENGINEERING (89 journals)

ENGINEERING (1197 journals)                  1 2 3 4 5 6 | Last

Showing 1 - 200 of 1205 Journals sorted alphabetically
3 Biotech     Open Access   (Followers: 7)
3D Research     Hybrid Journal   (Followers: 19)
AAPG Bulletin     Full-text available via subscription   (Followers: 5)
AASRI Procedia     Open Access   (Followers: 14)
Abstract and Applied Analysis     Open Access   (Followers: 3)
Aceh International Journal of Science and Technology     Open Access   (Followers: 2)
ACS Nano     Full-text available via subscription   (Followers: 207)
Acta Geotechnica     Hybrid Journal   (Followers: 6)
Acta Metallurgica Sinica (English Letters)     Hybrid Journal   (Followers: 5)
Acta Polytechnica : Journal of Advanced Engineering     Open Access   (Followers: 1)
Acta Scientiarum. Technology     Open Access   (Followers: 3)
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Active and Passive Electronic Components     Open Access   (Followers: 7)
Adaptive Behavior     Hybrid Journal   (Followers: 10)
Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi     Open Access  
Adsorption     Hybrid Journal   (Followers: 4)
Advanced Engineering Forum     Full-text available via subscription   (Followers: 4)
Advanced Science     Open Access   (Followers: 4)
Advanced Science Focus     Free   (Followers: 3)
Advanced Science Letters     Full-text available via subscription   (Followers: 4)
Advanced Science, Engineering and Medicine     Partially Free   (Followers: 6)
Advanced Synthesis & Catalysis     Hybrid Journal   (Followers: 17)
Advances in Artificial Neural Systems     Open Access   (Followers: 3)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Complex Systems     Hybrid Journal   (Followers: 7)
Advances in Engineering Software     Hybrid Journal   (Followers: 25)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 14)
Advances in Fuzzy Systems     Open Access   (Followers: 5)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 9)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 18)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 22)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 7)
Advances in Natural Sciences: Nanoscience and Nanotechnology     Open Access   (Followers: 28)
Advances in Operations Research     Open Access   (Followers: 11)
Advances in OptoElectronics     Open Access   (Followers: 5)
Advances in Physics Theories and Applications     Open Access   (Followers: 13)
Advances in Polymer Science     Hybrid Journal   (Followers: 40)
Advances in Porous Media     Full-text available via subscription   (Followers: 4)
Advances in Remote Sensing     Open Access   (Followers: 34)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Aerobiologia     Hybrid Journal   (Followers: 1)
African Journal of Science, Technology, Innovation and Development     Hybrid Journal   (Followers: 4)
AIChE Journal     Hybrid Journal   (Followers: 28)
Ain Shams Engineering Journal     Open Access   (Followers: 5)
Akademik Platform Mühendislik ve Fen Bilimleri Dergisi     Open Access  
Alexandria Engineering Journal     Open Access  
AMB Express     Open Access   (Followers: 1)
American Journal of Applied Sciences     Open Access   (Followers: 28)
American Journal of Engineering and Applied Sciences     Open Access   (Followers: 11)
American Journal of Engineering Education     Open Access   (Followers: 9)
American Journal of Environmental Engineering     Open Access   (Followers: 16)
American Journal of Industrial and Business Management     Open Access   (Followers: 23)
Analele Universitatii Ovidius Constanta - Seria Chimie     Open Access  
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Regional Science     Hybrid Journal   (Followers: 7)
Annals of Science     Hybrid Journal   (Followers: 7)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applicable Analysis: An International Journal     Hybrid Journal   (Followers: 1)
Applied Catalysis A: General     Hybrid Journal   (Followers: 5)
Applied Catalysis B: Environmental     Hybrid Journal   (Followers: 6)
Applied Clay Science     Hybrid Journal   (Followers: 4)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Applied Magnetic Resonance     Hybrid Journal   (Followers: 3)
Applied Nanoscience     Open Access   (Followers: 8)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Physics Research     Open Access   (Followers: 4)
Applied Sciences     Open Access   (Followers: 3)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 4)
Arabian Journal for Science and Engineering     Hybrid Journal   (Followers: 5)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4)
Archives of Foundry Engineering     Open Access  
Archives of Thermodynamics     Open Access   (Followers: 8)
Arkiv för Matematik     Hybrid Journal   (Followers: 1)
ASEE Prism     Full-text available via subscription   (Followers: 2)
Asian Engineering Review     Open Access  
Asian Journal of Applied Science and Engineering     Open Access   (Followers: 1)
Asian Journal of Applied Sciences     Open Access   (Followers: 2)
Asian Journal of Biotechnology     Open Access   (Followers: 7)
Asian Journal of Control     Hybrid Journal  
Asian Journal of Current Engineering & Maths     Open Access  
Asian Journal of Technology Innovation     Hybrid Journal   (Followers: 9)
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
ATZagenda     Hybrid Journal  
ATZextra worldwide     Hybrid Journal  
Australasian Physical & Engineering Sciences in Medicine     Hybrid Journal   (Followers: 1)
Australian Journal of Multi-Disciplinary Engineering     Full-text available via subscription   (Followers: 2)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 7)
Avances en Ciencias e Ingeniería     Open Access  
Balkan Region Conference on Engineering and Business Education     Open Access   (Followers: 1)
Bangladesh Journal of Scientific and Industrial Research     Open Access  
Basin Research     Hybrid Journal   (Followers: 3)
Batteries     Open Access   (Followers: 3)
Bautechnik     Hybrid Journal   (Followers: 1)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 24)
Beni-Suef University Journal of Basic and Applied Sciences     Open Access   (Followers: 3)
BER : Manufacturing Survey : Full Survey     Full-text available via subscription   (Followers: 2)
BER : Motor Trade Survey     Full-text available via subscription   (Followers: 1)
BER : Retail Sector Survey     Full-text available via subscription   (Followers: 2)
BER : Retail Survey : Full Survey     Full-text available via subscription   (Followers: 2)
BER : Survey of Business Conditions in Manufacturing : An Executive Summary     Full-text available via subscription   (Followers: 3)
BER : Survey of Business Conditions in Retail : An Executive Summary     Full-text available via subscription   (Followers: 3)
Bharatiya Vaigyanik evam Audyogik Anusandhan Patrika (BVAAP)     Open Access   (Followers: 1)
Biofuels Engineering     Open Access  
Biointerphases     Open Access   (Followers: 1)
Biomaterials Science     Full-text available via subscription   (Followers: 8)
Biomedical Engineering     Hybrid Journal   (Followers: 16)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering Letters     Hybrid Journal   (Followers: 5)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 16)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 31)
Biomedical Engineering: Applications, Basis and Communications     Hybrid Journal   (Followers: 5)
Biomedical Microdevices     Hybrid Journal   (Followers: 8)
Biomedical Science and Engineering     Open Access   (Followers: 4)
Biomedizinische Technik - Biomedical Engineering     Hybrid Journal  
Biomicrofluidics     Open Access   (Followers: 4)
BioNanoMaterials     Hybrid Journal   (Followers: 1)
Biotechnology Progress     Hybrid Journal   (Followers: 39)
Boletin Cientifico Tecnico INIMET     Open Access  
Botswana Journal of Technology     Full-text available via subscription  
Boundary Value Problems     Open Access   (Followers: 1)
Brazilian Journal of Science and Technology     Open Access   (Followers: 2)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 10)
Bulletin of Canadian Petroleum Geology     Full-text available via subscription   (Followers: 14)
Bulletin of Engineering Geology and the Environment     Hybrid Journal   (Followers: 3)
Bulletin of the Crimean Astrophysical Observatory     Hybrid Journal  
Cahiers, Droit, Sciences et Technologies     Open Access  
Calphad     Hybrid Journal  
Canadian Geotechnical Journal     Full-text available via subscription   (Followers: 14)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 40)
Case Studies in Engineering Failure Analysis     Open Access   (Followers: 7)
Case Studies in Thermal Engineering     Open Access   (Followers: 4)
Catalysis Communications     Hybrid Journal   (Followers: 6)
Catalysis Letters     Hybrid Journal   (Followers: 3)
Catalysis Reviews: Science and Engineering     Hybrid Journal   (Followers: 8)
Catalysis Science and Technology     Free   (Followers: 6)
Catalysis Surveys from Asia     Hybrid Journal   (Followers: 3)
Catalysis Today     Hybrid Journal   (Followers: 5)
CEAS Space Journal     Hybrid Journal  
Cellular and Molecular Neurobiology     Hybrid Journal   (Followers: 4)
Central European Journal of Engineering     Hybrid Journal   (Followers: 1)
CFD Letters     Open Access   (Followers: 6)
Chaos : An Interdisciplinary Journal of Nonlinear Science     Hybrid Journal   (Followers: 2)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
Chinese Journal of Engineering     Open Access   (Followers: 2)
Chinese Science Bulletin     Open Access   (Followers: 1)
Ciencia e Ingenieria Neogranadina     Open Access  
Ciencia en su PC     Open Access   (Followers: 1)
Ciencias Holguin     Open Access   (Followers: 1)
CienciaUAT     Open Access  
Cientifica     Open Access  
CIRP Annals - Manufacturing Technology     Full-text available via subscription   (Followers: 10)
CIRP Journal of Manufacturing Science and Technology     Full-text available via subscription   (Followers: 13)
City, Culture and Society     Hybrid Journal   (Followers: 20)
Clay Minerals     Full-text available via subscription   (Followers: 9)
Clean Air Journal     Full-text available via subscription   (Followers: 2)
Coal Science and Technology     Full-text available via subscription   (Followers: 4)
Coastal Engineering     Hybrid Journal   (Followers: 10)
Coastal Engineering Journal     Hybrid Journal   (Followers: 3)
Coatings     Open Access   (Followers: 2)
Cogent Engineering     Open Access   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 4)
Color Research & Application     Hybrid Journal   (Followers: 1)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 13)
Combustion, Explosion, and Shock Waves     Hybrid Journal   (Followers: 13)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Numerical Methods in Engineering     Hybrid Journal   (Followers: 2)
Components, Packaging and Manufacturing Technology, IEEE Transactions on     Hybrid Journal   (Followers: 23)
Composite Interfaces     Hybrid Journal   (Followers: 5)
Composite Structures     Hybrid Journal   (Followers: 241)
Composites Part A : Applied Science and Manufacturing     Hybrid Journal   (Followers: 174)
Composites Part B : Engineering     Hybrid Journal   (Followers: 215)
Composites Science and Technology     Hybrid Journal   (Followers: 160)
Comptes Rendus Mécanique     Full-text available via subscription   (Followers: 2)
Computation     Open Access  
Computational Geosciences     Hybrid Journal   (Followers: 12)
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computational Science and Discovery     Full-text available via subscription   (Followers: 2)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 6)
Computer Science and Engineering     Open Access   (Followers: 17)
Computers & Geosciences     Hybrid Journal   (Followers: 25)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 5)
Computers and Electronics in Agriculture     Hybrid Journal   (Followers: 4)
Computers and Geotechnics     Hybrid Journal   (Followers: 8)
Computing and Visualization in Science     Hybrid Journal   (Followers: 6)
Computing in Science & Engineering     Full-text available via subscription   (Followers: 25)
Conciencia Tecnologica     Open Access  
Concurrent Engineering     Hybrid Journal   (Followers: 3)
Continuum Mechanics and Thermodynamics     Hybrid Journal   (Followers: 6)
Control and Dynamic Systems     Full-text available via subscription   (Followers: 7)
Control Engineering Practice     Hybrid Journal   (Followers: 40)
Control Theory and Informatics     Open Access   (Followers: 7)
Corrosion Science     Hybrid Journal   (Followers: 24)
CT&F Ciencia, Tecnologia y Futuro     Open Access  
CTheory     Open Access  
Current Applied Physics     Full-text available via subscription   (Followers: 4)

        1 2 3 4 5 6 | Last

Journal Cover Computers & Geosciences
  [SJR: 1.268]   [H-I: 78]   [25 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0098-3004
   Published by Elsevier Homepage  [3043 journals]
  • Augmenting comprehension of geological relationships by integrating 3D
           laser scanned hand samples within a GIS environment
    • Abstract: Publication date: June 2017
      Source:Computers & Geosciences, Volume 103
      Author(s): A.S. Harvey, G. Fotopoulos, B. Hall, K. Amolins
      Geological observations can be made on multiple scales, including micro- (e.g. thin section), meso- (e.g. hand-sized to outcrop) and macro- (e.g. outcrop and larger) scales. Types of meso-scale samples include, but are not limited to, rocks (including drill cores), minerals, and fossils. The spatial relationship among samples paired with physical (e.g. granulometric composition, density, roughness) and chemical (e.g. mineralogical and isotopic composition) properties can aid in interpreting geological settings, such as paleo-environmental and formational conditions as well as geomorphological history. Field samples are collected along traverses in the area of interest based on characteristic representativeness of a region, predetermined rate of sampling, and/or uniqueness. The location of a sample can provide relative context in seeking out additional key samples. Beyond labelling and recording of geospatial coordinates for samples, further analysis of physical and chemical properties may be conducted in the field and laboratory. The main motivation for this paper is to present a workflow for the digital preservation of samples (via 3D laser scanning) paired with the development of cyber infrastructure, which offers geoscientists and engineers the opportunity to access an increasingly diverse worldwide collection of digital Earth materials. This paper describes a Web-based graphical user interface developed using Web AppBuilder for ArcGIS for digitized meso-scale 3D scans of geological samples to be viewed alongside the macro-scale environment. Over 100 samples of virtual rocks, minerals and fossils populate the developed geological database and are linked explicitly with their associated attributes, characteristic properties, and location. Applications of this new Web-based geological visualization paradigm in the geosciences demonstrate the utility of such a tool in an age of increasing global data sharing.

      PubDate: 2017-03-27T12:35:58Z
       
  • Towards semi-automatic rock mass discontinuity orientation and set
           analysis from 3D point clouds
    • Abstract: Publication date: June 2017
      Source:Computers & Geosciences, Volume 103
      Author(s): Jiateng Guo, Shanjun Liu, Peina Zhang, Lixin Wu, Wenhui Zhou, Yinan Yu
      Obtaining accurate information on rock mass discontinuities for deformation analysis and the evaluation of rock mass stability is important. Obtaining measurements for high and steep zones with the traditional compass method is difficult. Photogrammetry, three-dimensional (3D) laser scanning and other remote sensing methods have gradually become mainstream methods. In this study, a method that is based on a 3D point cloud is proposed to semi-automatically extract rock mass structural plane information. The original data are pre-treated prior to segmentation by removing outlier points. The next step is to segment the point cloud into different point subsets. Various parameters, such as the normal, dip/direction and dip, can be calculated for each point subset after obtaining the equation of the best fit plane for the relevant point subset. A cluster analysis (a point subset that satisfies some conditions and thus forms a cluster) is performed based on the normal vectors by introducing the firefly algorithm (FA) and the fuzzy c-means (FCM) algorithm. Finally, clusters that belong to the same discontinuity sets are merged and coloured for visualization purposes. A prototype system is developed based on this method to extract the points of the rock discontinuity from a 3D point cloud. A comparison with existing software shows that this method is feasible. This method can provide a reference for rock mechanics, 3D geological modelling and other related fields.

      PubDate: 2017-03-27T12:35:58Z
       
  • A machine learning approach to the potential-field method for implicit
           modeling of geological structures
    • Abstract: Publication date: June 2017
      Source:Computers & Geosciences, Volume 103
      Author(s): Ítalo Gomes Gonçalves, Sissa Kumaira, Felipe Guadagnin
      Implicit modeling has experienced a rise in popularity over the last decade due to its advantages in terms of speed and reproducibility in comparison with manual digitization of geological structures. The potential-field method consists in interpolating a scalar function that indicates to which side of a geological boundary a given point belongs to, based on cokriging of point data and structural orientations. This work proposes a vector potential-field solution from a machine learning perspective, recasting the problem as multi-class classification, which alleviates some of the original method's assumptions. The potentials related to each geological class are interpreted in a compositional data framework. Variogram modeling is avoided through the use of maximum likelihood to train the model, and an uncertainty measure is introduced. The methodology was applied to the modeling of a sample dataset provided with the software Move™. The calculations were implemented in the R language and 3D visualizations were prepared with the rgl package.

      PubDate: 2017-03-27T12:35:58Z
       
  • Connotations of pixel-based scale effect in remote sensing and the
           modified fractal-based analysis method
    • Abstract: Publication date: June 2017
      Source:Computers & Geosciences, Volume 103
      Author(s): Guixiang Feng, Dongping Ming, Min Wang, Jianyu Yang
      Scale problems are a major source of concern in the field of remote sensing. Since the remote sensing is a complex technology system, there is a lack of enough cognition on the connotation of scale and scale effect in remote sensing. Thus, this paper first introduces the connotations of pixel-based scale and summarizes the general understanding of pixel-based scale effect. Pixel-based scale effect analysis is essentially important for choosing the appropriate remote sensing data and the proper processing parameters. Fractal dimension is a useful measurement to analysis pixel-based scale. However in traditional fractal dimension calculation, the impact of spatial resolution is not considered, which leads that the scale effect change with spatial resolution can't be clearly reflected. Therefore, this paper proposes to use spatial resolution as the modified scale parameter of two fractal methods to further analyze the pixel-based scale effect. To verify the results of two modified methods (MFBM (Modified Windowed Fractal Brownian Motion Based on the Surface Area) and MDBM (Modified Windowed Double Blanket Method)); the existing scale effect analysis method (information entropy method) is used to evaluate. And six sub-regions of building areas and farmland areas were cut out from QuickBird images to be used as the experimental data. The results of the experiment show that both the fractal dimension and information entropy present the same trend with the decrease of spatial resolution, and some inflection points appear at the same feature scales. Further analysis shows that these feature scales (corresponding to the inflection points) are related to the actual sizes of the geo-object, which results in fewer mixed pixels in the image, and these inflection points are significantly indicative of the observed features. Therefore, the experiment results indicate that the modified fractal methods are effective to reflect the pixel-based scale effect existing in remote sensing data and it is helpful to analyze the observation scale from different aspects. This research will ultimately benefit for remote sensing data selection and application.

      PubDate: 2017-03-27T12:35:58Z
       
  • Effects of acid dissolution capacity on the propagation of an
           acid-dissolution front in carbonate rocks
    • Abstract: Publication date: May 2017
      Source:Computers & Geosciences, Volume 102
      Author(s): Chongbin Zhao, B.E. Hobbs, A. Ord
      Acid dissolution capability plays a considerable role in controlling the propagation of an acid-dissolution front in the carbonate rocks that are saturated by pore fluids. This capability can be represented by a dimensionless number, known as the acid dissolution capability number, by which we mean the quotient of the volume of an acid-dissolved carbonate rock divided by that of the acid itself. This paper aims primarily to investigate why and how the acid dissolution capacity can affect the behaviors of the acid-dissolution front propagation in the carbonate rocks that are saturated by pore fluids. If the acid dissolution capacity number is a nonzero finite number, as in a general case, then the computational simulation method needs to be employed to get numerical solutions for the acid-dissolution system. The relevant computational simulation results have demonstrated that: (1) with an increase in the value of the Zhao number (namely another dimensionless number), which is used to denote the dynamic characteristics of an acid-dissolution system, the acid-dissolution front becomes more unstable in the corresponding supercritical acid-dissolution system. (2) When the acid dissolution capacity number is small enough, the propagating speed of a planar acid-dissolution front in the corresponding subcritical acid-dissolution system is linearly dependent on the acid dissolution capacity number, indicating that the smaller the acid dissolution capacity, the slower the propagating speed of a planar acid-dissolution front in the corresponding subcritical acid-dissolution system. (3) With a decrease in the acid dissolution capacity number, the acid-dissolution front can exhibit more unstable behavior in the corresponding supercritical acid-dissolution system.

      PubDate: 2017-03-27T12:35:58Z
       
  • TsuPy: Computational robustness in Tsunami hazard modelling
    • Abstract: Publication date: May 2017
      Source:Computers & Geosciences, Volume 102
      Author(s): Andreas M. Schäfer, Friedemann Wenzel
      Modelling wave propagation is the most essential part in assessing the risk and hazard of tsunami and storm surge events. For the computational assessment of the variability of such events, many simulations are necessary. Even today, most of these simulations are generally run on supercomputers due to the large amount of computations necessary. In this study, a simulation framework, named TsuPy, is introduced to quickly compute tsunami events on a personal computer. It uses the parallelized power of GPUs to accelerate computation. The system is tailored to the application of robust tsunami hazard and risk modelling. It links up to geophysical models to simulate event sources. The system is tested and validated using various benchmarks and real-world case studies. In addition, the robustness criterion is assessed based on a sensitivity study comparing the error impact of various model elements e.g. of topo-bathymetric resolution, knowledge of Manning friction parameters and the knowledge of the tsunami source itself. This sensitivity study is tested on inundation modelling of the 2011 Tohoku tsunami, showing that the major contributor to model uncertainty is in fact the representation of earthquake slip as part of the tsunami source profile. TsuPy provides a fast and reliable tool to quickly assess ocean hazards from tsunamis and thus builds the foundation for a globally uniform hazard and risk assessment for tsunamis.

      PubDate: 2017-03-27T12:35:58Z
       
  • Ontology-based classification of remote sensing images using spectral
           rules
    • Abstract: Publication date: May 2017
      Source:Computers & Geosciences, Volume 102
      Author(s): Samuel Andrés, Damien Arvor, Isabelle Mougenot, Thérèse Libourel, Laurent Durieux
      Earth Observation data is of great interest for a wide spectrum of scientific domain applications. An enhanced access to remote sensing images for “domain” experts thus represents a great advance since it allows users to interpret remote sensing images based on their domain expert knowledge. However, such an advantage can also turn into a major limitation if this knowledge is not formalized, and thus is difficult for it to be shared with and understood by other users. In this context, knowledge representation techniques such as ontologies should play a major role in the future of remote sensing applications. We implemented an ontology-based prototype to automatically classify Landsat images based on explicit spectral rules. The ontology is designed in a very modular way in order to achieve a generic and versatile representation of concepts we think of utmost importance in remote sensing. The prototype was tested on four subsets of Landsat images and the results confirmed the potential of ontologies to formalize expert knowledge and classify remote sensing images.

      PubDate: 2017-03-27T12:35:58Z
       
  • A transfer learning method for automatic identification of sandstone
           microscopic images
    • Abstract: Publication date: June 2017
      Source:Computers & Geosciences, Volume 103
      Author(s): Na Li, Huizhen Hao, Qing Gu, Danru Wang, Xiumian Hu
      Classification of sandstone microscopic images is an essential task in geology, and the classical method is either subjective or time-consuming. Computer aided automatic classification has been proved useful, but it seldom considers the situation where sandstone images are collected from separated regions. In this paper, we provide a method called Festra, which uses transfer learning to handle the problem of interregional sandstone microscopic image classification. The method contains two parts: one is feature selection, which aims to screen out features having great difference between the regions, the other is instance transfer using an enhanced TrAdaBoost, whose object is to mitigate the difference among thin section images collected from the regions. Experiments are conducted based on the sandstone images taken from four regions in Tibet to study the performance of Festra. The experimental results have proved both effectiveness and validity of Festra, which provides competitive prediction performance on all the four regions, with few target instances labeled suitable for the field use.

      PubDate: 2017-03-20T03:59:36Z
       
  • Rule-based topology system for spatial databases to validate complex
           geographic datasets
    • Abstract: Publication date: Available online 18 March 2017
      Source:Computers & Geosciences
      Author(s): J. Martinez-Llario, E. Coll, M. Núñez-Andrés, C. Femenia-Ribera
      A rule-based topology software system providing a highly flexible and fast procedure to enforce integrity in spatial relationships among datasets is presented. This improved topology rule system is built over the spatial extension Jaspa. Both projects are open source, freely available software developed by the corresponding author of this paper. Currently, there is no spatial DBMS that implements a rule-based topology engine (considering that the topology rules are designed and performed in the spatial backend). If the topology rules are applied in the frontend (as in many GIS desktop programs), ArcGIS is the most advanced solution. The system presented in this paper has several major advantages over the ArcGIS approach: it can be extended with new topology rules, it has a much wider set of rules, and it can mix feature attributes with topology rules as filters. In addition, the topology rule system can work with various DBMSs, including PostgreSQL, H2 or Oracle, and the logic is performed in the spatial backend. The proposed topology system allows users to check the complex spatial relationships among features (from one or several spatial layers) that require some complex cartographic datasets, such as the data specifications proposed by INSPIRE in Europe and the Land Administration Domain Model (LADM) for Cadastral data.

      PubDate: 2017-03-20T03:59:36Z
       
  • Quasi-equal area subdivision algorithm for uniform points on a sphere with
           application to any geographical data distribution
    • Abstract: Publication date: Available online 18 March 2017
      Source:Computers & Geosciences
      Author(s): Sanghyun Lee, Daniele Mortari
      This paper describes a quasi-equal area subdivision algorithm based on equal area spherical subdivision to obtain approximated solutions to the problem of uniform distribution of points on a 2-dimensional sphere, better known as Smale's seventh problem. The algorithm provides quasi-equal area triangles, starting by splitting the Platonic solids into subsequent spherical triangles of identical areas. The main feature of the proposed algorithm is that the final adjacent triangles share common vertices that can be merged. It applies reshaping to the final triangles in order to remove obtuse triangles. The proposed algorithm is fast and efficient to generate a large number of points. Consequently, they are suitable for various applications requiring a large number of distributed points. The proposed algorithm is then applied to two geographical data distributions that are modeled by quasi-uniform distribution of weighted points.

      PubDate: 2017-03-20T03:59:36Z
       
  • 3D Voronoi GRID DEDICATED SOFTWARE FOR MODELING GAS MIGRATION IN DEEP
           LAYERED SEDIMENTARY FORMATIONS WITH TOUGH2-TMGAS
    • Abstract: Publication date: Available online 18 March 2017
      Source:Computers & Geosciences
      Author(s): Stefano Bonduà, Alfredo Battistelli, Paolo Berry, Villiam Bortolotti, Alberto Consonni, Carlo Cormio, Claudio Geloni, Ester Maria Vasini
      As is known, a full three-dimensional (3D) unstructured grid permits a great degree of flexibility when performing accurate numerical reservoir simulations. However, when the Integral Finite Difference Method (IFDM) is used for spatial discretization, constraints (arising from the required orthogonality between the segment connecting the blocks nodes and the interface area between blocks) pose difficulties in the creation of grids with irregular shaped blocks. The full 3D Voronoi approach guarantees the respect of IFDM constraints and allows generation of grids conforming to geological formations and structural objects and at the same time higher grid resolution in volumes of interest. In this work, we present dedicated pre- and post-processing gridding software tools for the TOUGH family of numerical reservoir simulators, developed by the Geothermal Research Group of the DICAM Department, University of Bologna. VORO2MESH is a new software coded in C++, based on the voro++ library, allowing computation of the 3D Voronoi tessellation for a given domain and the creation of a ready to use TOUGH2 MESH file. If a set of geological surfaces is available, the software can directly generate the set of Voronoi seed points used for tessellation. In order to reduce the number of connections and so to decrease computation time, VORO2MESH can produce a mixed grid with regular blocks (orthogonal prisms) and irregular blocks (polyhedron Voronoi blocks) at the point of contact between different geological formations. In order to visualize 3D Voronoi grids together with the results of numerical simulations, the functionality of the TOUGH2Viewer post-processor has been extended. We describe an application of VORO2MESH and TOUGH2Viewer to validate the two tools. The case study deals with the simulation of the migration of gases in deep layered sedimentary formations at basin scale using TOUGH2-TMGAS. A comparison between the simulation performances of unstructured and structured grids is presented.

      PubDate: 2017-03-20T03:59:36Z
       
  • The Application of Artificial Intelligence for the Identification of the
           Maceral Groups and Mineral Components of Coal
    • Abstract: Publication date: Available online 16 March 2017
      Source:Computers & Geosciences
      Author(s): Mariusz Mlynarczuk, Marta Skiba
      The correct and consistent identification of the petrographic properties of coal is an important issue for researchers in the fields of mining and geology. As part of the study described in this paper, investigations concerning the application of artificial intelligence methods for the identification of the aforementioned characteristics were carried out. The methods in question were used to identify the maceral groups of coal, i.e. vitrinite, inertinite, and liptinite. Additionally, an attempt was made to identify some non-organic minerals. The analyses were performed using pattern recognition techniques (NN, kNN), as well as artificial neural network techniques (a multilayer perceptron – MLP). The classification process was carried out using microscopy images of polished sections of coals. A multidimensional feature space was defined, which made it possible to classify the discussed structures automatically, based on the methods of pattern recognition and algorithms of the artificial neural networks. Also, the authors of the study assessed the impact of the parameters for which the applied methods proved effective upon the final outcome of the classification procedure. The result of the analyses was a high percentage (over 97%) of correct classifications of maceral groups and mineral components. The paper discusses also an attempt to analyze particular macerals of the inertinite group. It was demonstrated that using artificial neural networks to this end makes it possible to classify the macerals properly in over 91 percent of cases. Thus, it was proved that artificial intelligence methods can be successfully applied for the identification of selected petrographic features of coal.

      PubDate: 2017-03-20T03:59:36Z
       
  • Development of a data-driven forecasting tool for hydraulically fractured,
           horizontal wells in tight-gas sands
    • Abstract: Publication date: Available online 12 March 2017
      Source:Computers & Geosciences
      Author(s): B. Kulga, E. Artun, T. Ertekin
      Tight-gas sand reservoirs are considered to be one of the major unconventional resources. Due to the strong heterogeneity and very low permeability of the formation, and the complexity of well trajectories with multiple hydraulic fractures; there are challenges associated with performance forecasting and optimum exploitation of these resources using conventional modeling approaches. In this study, it is aimed to develop a data-driven forecasting tool for tight-gas sands, which are based on artificial neural networks that can complement the physics-driven modeling approach, namely numerical simulation models. The tool is designed to predict the horizontal-well performance as a proxy to the numerical model, once the initial conditions, operational parameters, reservoir/hydraulic-fracture characteristics are provided. The data-driven model, that the forecasting tool is based on, is validated with blind cases by estimating the cumulative gas production after 10 years with an average error of 3.2%. A graphical-user-interface application is developed that allows the practicing engineer to use the developed tool in a practical manner by visualizing estimated performance for a given reservoir within a fraction of a second. Practicality of the tool is demonstrated with a case study for the Williams Fork Formation by assessing the performance of various well designs and by incorporating known uncertainties through Monte Carlo simulation. P10, P50 and P90 estimates of the horizontal-well performance are quickly obtained within acceptable accuracy levels.

      PubDate: 2017-03-13T02:45:54Z
       
  • Development of a GIS-based integrated framework for coastal seiches
           monitoring and forecasting: A North Jiangsu shoal case study
    • Abstract: Publication date: Available online 11 March 2017
      Source:Computers & Geosciences
      Author(s): Rufu Qin, Liangzhao Lin
      Coastal seiches have become an increasingly important issue in coastal science and present many challenges, particularly when attempting to provide warning services. This paper presents the methodologies, techniques and integrated services adopted for the design and implementation of a Seiches Monitoring and Forecasting Integration Framework (SMAF-IF). The SMAF-IF is an integrated system with different types of sensors and numerical models and incorporates the Geographic Information System (GIS) and web techniques, which focuses on coastal seiche events detection and early warning in the North Jiangsu shoal, China. The in situ sensors perform automatic and continuous monitoring of the marine environment status and the numerical models provide the meteorological and physical oceanographic parameter estimates. A model outputs processing software was developed in C# language using ArcGIS Engine functions, which provides the capabilities of automatically generating visualization maps and warning information. Leveraging the ArcGIS Flex API and ASP.NET web services, a web based GIS framework was designed to facilitate quasi real-time data access, interactive visualization and analysis, and provision of early warning services for end users. The integrated framework proposed in this study enables decision-makers and the publics to quickly response to emergency coastal seiche events and allows an easy adaptation to other regional and scientific domains related to real-time monitoring and forecasting.

      PubDate: 2017-03-13T02:45:54Z
       
  • EdgeDetectPFI: an algorithm for automatic edge detection in potential
           field anomaly images – application to dike-like magnetic structures
    • Abstract: Publication date: Available online 10 March 2017
      Source:Computers & Geosciences
      Author(s): Saulo P. Oliveira, Francisco J.F. Ferreira, Jeferson de Souza
      We propose an algorithm to automatically locate the spatial position of anomalies in potential field images, which can be used to estimate the depth and width of causative sources. The magnetic anomaly is firstly enhanced using an edge detection filter based on a simple transformation (the Signum transform) which retains only the signs of the anomalous field. The theoretical edge positions can be recognized from the locations where one of the spatial field derivatives (or a function of them) change its sign: the zero crossover points. These points are easily identified from the Signum transformed spatial derivatives. The actual sources depths and widths are then estimated using the widths of the positive plateaus obtained from two different Signum transformed data: one based on the vertical derivative and the other using the vertical derivative minus the absolute value of the horizontal derivative. Our algorithm finds these widths in an automatic fashion by computing the radius of the largest circles inside the positive plateaus. Numerical experiments with synthetic data show that the proposed approach provides reliable estimates for the target parameters. Additional testing is carried out with aeromagnetic data from Santa Catarina, Southern Brazil, and the resulting parameter maps are compared with Euler deconvolution.

      PubDate: 2017-03-13T02:45:54Z
       
  • Software for determining the direction of movement, shear and normal
           stresses of a fault under a determined stress state
    • Abstract: Publication date: Available online 9 March 2017
      Source:Computers & Geosciences
      Author(s): A. Álvarez del Castillo, S.A. Alaniz-Álvarez, A.F. Nieto-Samaniego, S-S. Xu, G.H. Ochoa-González, L.G. Velasquillo-Martínez
      In the oil, gas and geothermal industry, the extraction or the input of fluids induces changes in the stress field of the reservoir, if the in-situ stress state of a fault plane is sufficiently disturbed, a fault may slip and can trigger fluid leakage or the reservoir might fracture and become damaged. The goal of the SSLIPO 1.0 software is to obtain data that can reduce the risk of affecting the stability of wellbores. The input data are the magnitudes of the three principal stresses and their orientation in geographic coordinates. The output data are the slip direction of a fracture in geographic coordinates, and its normal (σn) and shear (τ) stresses resolved on a single or multiple fracture planes. With this information, it is possible to calculate the slip tendency (τ/σn) and the propensity to open a fracture that is inversely proportional to σn. This software could analyze any compressional stress system, even non-Andersonian. An example is given from an oilfield in southern Mexico, in a region that contains fractures formed in three events of deformation. In the example SSLIPO 1.0 was used to determine in which deformation event the oil migrated. SSLIPO 1.0 is an open code application developed in MATLAB. The URL to obtain the source code and to download SSLIPO 1.0 are: http://www.geociencias.unam.mx/~alaniz/main_code.txt, http://www.geociencias.unam.mx/~alaniz/ SSLIPO_pkg.exe.

      PubDate: 2017-03-13T02:45:54Z
       
  • RINGMesh: A programming library for developing mesh-based geomodeling
           applications
    • Abstract: Publication date: Available online 9 March 2017
      Source:Computers & Geosciences
      Author(s): Jeanne Pellerin, Arnaud Botella, François Bonneau, Antoine Mazuyer, Benjamin Chauvin, Bruno Lévy, Guillaume Caumon
      RINGMesh is a C++ open-source programming library for manipulating discretized geological models. It is designed to ease the development of applications and workflows that use discretized 3D models. It is neither a geomodeler, nor a meshing software. RINGMesh implements functionalities to read discretized surface-based or volumetric structural models and to check their validity. The models can be then exported in various file formats. RINGMesh provides data structures to represent geological structural models, either defined by their discretized boundary surfaces, and/or by discretized volumes. A programming interface allows to develop of new geomodeling methods, and to plug in external software. The goal of RINGMesh is to help researchers to focus on the implementation of their specific method rather than on tedious tasks common to many applications. The documented code is open-source and distributed under the modified BSD license. It is available at https://www.ring-team.org/index.php/software/ringmesh.

      PubDate: 2017-03-13T02:45:54Z
       
  • Fractal parameters and well-logs investigation using automated
           well-to-well correlation
    • Abstract: Publication date: Available online 7 March 2017
      Source:Computers & Geosciences
      Author(s): Seyyed Mohammad Amin Partovi, Saeid Sadeghnejad
      The aim of well-to-well correlation is to detect similar geological boundaries in two or more wells across a formation, which is usually done manually. The construction of such a correlation by hand for a field with several wells is quite complex and also time-consuming as well. The aim of this study is to speed up the well-to-well correlation process by providing an automated approach. The input data for our algorithm is the depths of all geological boundaries in a reference well. The algorithm automatically searches for similar depths associated with those geological boundaries in other wells (i.e., observation wells). The fractal parameters of well-logs, such as wavelet exponent (Hw), wavelet standard deviation exponent (Hws), and Hausdorff dimension (Ha), which are calculated by wavelet transform, are considered as pattern recognition dimensions during the well-to-well correlation. Finding the proper fractal dimensions in the automatic well-to-well correlation approach that provide the closest geological depth estimation to the results of the manual interpretation is one of the prime aims of this research. To validate the proposed technique, it is implemented on the well-log data from one of the Iranian onshore oil fields. Moreover, the capability of gamma ray, density, and sonic log in automatic detection of geological boundaries by this novel approach is also analyzed in detail. The outcome of this approach shows promising results.

      PubDate: 2017-03-08T13:40:06Z
       
  • Mechanical Properties and Energy Conversion of 3D Close-packed Lattice
           Model for Brittle Rocks
    • Abstract: Publication date: Available online 3 March 2017
      Source:Computers & Geosciences
      Author(s): Chun Liu, Qiang Xu, Bin Shi, Shang Deng, Honghu Zhu
      Numerical simulations using the 3D discrete element method can yield mechanical and dynamic behaviors similar to rocks and grains. In the model, rock is represented by bonded elements, which are arranged on a tetrahedral lattice. The conversion formulas between inter-element parameters and rock mechanical properties were derived. By using the formulas, inter-element parameters can be determined according to mechanical properties of model, including Young's modulus, Poisson's ratio, tensile strength (T u), compressive strength (C u) and coefficient of internal friction. The energy conversion rules of the model are proposed. Based on the methods, a Matlab code “MatDEM” was developed. Numerical models of quartzite were used to validate the formulas. The tested mechanical properties of a single unit correspond reasonably well with the values of quartzite. Tested T u and C u with multiple elements are lower than the values predicted by the formulas. In the simulation of rock failure processes, mechanical energy conversed between different forms and heat is generated, but the mechanical energy plus heat always remains constant. Variations of breaking heat and frictional heat provide clues of the fracturing and slipping behaviors of the T u and C u tests. The model may be applied to a wide range of geological structures that involve breakage at multiple scales, heat generation and dynamic processes.

      PubDate: 2017-03-08T13:40:06Z
       
  • Optimal Ordering of Realizations for Visualization and Presentation
    • Abstract: Publication date: Available online 2 March 2017
      Source:Computers & Geosciences
      Author(s): George de Barros, Clayton V. Deutsch
      In geostatistical simulation, a realization represents one possible outcome of the spatial uncertainty model. Tens to hundreds of realizations are generated in order to understand response property variation. There are ways to summarize local uncertainty, but visualizing all realizations is important to understand joint uncertainty between multiple locations. There is no straightforward manner to visualize all realizations at the same time or in sequence. This paper presents a new method to sequentially display multiple geostatistical realizations. The proposed algorithm performs an ordering of the visible portion of the realizations (images), according to the distance between realizations. The concept of distance corresponds to the differences computed cell by cell for every realization pair or to the differences computed from a moving window filtering applied to each realization. To define an optimal sequence of realizations, the shortest path route through the realizations is established by a simulated annealing technique. The gradual transition between realizations is enhanced by an image morphing technique where intermediate images are introduced between the original images. The final result consists of an animation that shows the sequence of realizations and allows better understanding of the uncertainty model.

      PubDate: 2017-03-08T13:40:06Z
       
  • An integrated workflow for stress and flow modelling using outcrop-derived
           discrete fracture networks
    • Abstract: Publication date: Available online 2 March 2017
      Source:Computers & Geosciences
      Author(s): K. Bisdom, H.M. Nick, G. Bertotti
      Fluid flow in naturally fractured reservoirs is often controlled by subseismic-scale fracture networks. Although the fracture network can be partly sampled in the direct vicinity of wells, the inter-well scale network is poorly constrained in fractured reservoir models. Outcrop analogues can provide data for population of domains of the reservoir model where no direct measurements are available. However, extracting relevant statistics from large outcrops representative of inter-well scale fracture networks remains challenging. Recent advances in outcrop imaging provide high-resolution datasets that can cover areas of several hundred by several hundred meters, i.e. the domain between adjacent wells, but even then, data from the high-resolution models is often upscaled to reservoir flow grids, resulting in loss of accuracy. We present a workflow that uses photorealistic georeferenced outcrop models to construct geomechanical and fluid flow models containing thousands of discrete fractures covering sufficiently large areas, that does not require upscaling to model permeability. This workflow seamlessly integrates geomechanical Finite Element models with flow models that take into account stress-sensitive fracture permeability and matrix flow to determine the full permeability tensor. The applicability of this workflow is illustrated using an outcropping carbonate pavement in the Potiguar basin in Brazil, from which 1082 fractures are digitised. The permeability tensor for a range of matrix permeabilities shows that conventional upscaling to effective grid properties leads to potential underestimation of the true permeability and the orientation of principal permeabilities. The presented workflow yields the full permeability tensor model of discrete fracture networks with stress-induced apertures, instead of relying on effective properties as most conventional flow models do.
      Graphical abstract image

      PubDate: 2017-03-08T13:40:06Z
       
  • Numerical simulation of electro-osmotic consolidation coupling non-linear
           variation of soil parameters
    • Abstract: Publication date: Available online 2 March 2017
      Source:Computers & Geosciences
      Author(s): Wu Hui, Liming Hu, Qingbo Wen
      Electro-osmotic consolidation is an effective method for soft ground improvement. A main limitation of previous numerical models on this technique is the ignorance of the non-linear variation of soil parameters. In the present study, a multi-field numerical model is developed with the consideration of the non-linear variation of soil parameters during electro-osmotic consolidation process. The numerical simulations on an axisymmetric model indicated that the non-linear variation of soil parameters showed remarkable impact on the development of the excess pore water pressure and degree of consolidation. A field experiment with complex geometry, boundary conditions, electrode configuration and voltage application was further simulated with the developed numerical model. The comparison between field and numerical data indicated that the coupling of the non-linear variation of soil parameters gave more reasonable results. The developed numerical model is capable to analyze engineering cases with complex operating conditions.

      PubDate: 2017-03-08T13:40:06Z
       
  • PCTO-SIM: Multiple-point geostatistical modeling using parallel
           conditional texture optimization
    • Abstract: Publication date: May 2017
      Source:Computers & Geosciences, Volume 102
      Author(s): Mohammadreza Pourfard, Mohammad J. Abdollahifard, Karim Faez, Sayed Ahmad Motamedi, Tahmineh Hosseinian
      Multiple‐point Geostatistics is a well-known general statistical framework by which complex geological phenomena have been modeled efficiently. Pixel-based and patch-based are two major categories of these methods. In this paper, the optimization-based category is used which has a dual concept in texture synthesis as texture optimization. Our extended version of texture optimization uses the energy concept to model geological phenomena. While honoring the hard point, the minimization of our proposed cost function forces simulation grid pixels to be as similar as possible to training images. Our algorithm has a self-enrichment capability and creates a richer training database from a sparser one through mixing the information of all surrounding patches of the simulation nodes. Therefore, it preserves pattern continuity in both continuous and categorical variables very well. It also shows a fuzzy result in its every realization similar to the expected result of multi realizations of other statistical models. While the main core of most previous Multiple‐point Geostatistics methods is sequential, the parallel main core of our algorithm enabled it to use GPU efficiently to reduce the CPU time. One new validation method for MPS has also been proposed in this paper.

      PubDate: 2017-03-02T00:25:31Z
       
  • A multi-frequency receiver function inversion approach for crustal
           velocity structure
    • Abstract: Publication date: May 2017
      Source:Computers & Geosciences, Volume 102
      Author(s): Xuelei Li, Zhiwei Li, Tianyao Hao, Sheng Wang, Jian Xing
      In order to constrain the crustal velocity structures better, we developed a new nonlinear inversion approach based on multi-frequency receiver function waveforms. With the global optimizing algorithm of Differential Evolution (DE), low-frequency receiver function waveforms can primarily constrain large-scale velocity structures, while high-frequency receiver function waveforms show the advantages in recovering small-scale velocity structures. Based on the synthetic tests with multi-frequency receiver function waveforms, the proposed approach can constrain both long- and short-wavelength characteristics of the crustal velocity structures simultaneously. Inversions with real data are also conducted for the seismic stations of KMNB in southeast China and HYB in Indian continent, where crustal structures have been well studied by former researchers. Comparisons of inverted velocity models from previous and our studies suggest good consistency, but better waveform fitness with fewer model parameters are achieved by our proposed approach. Comprehensive tests with synthetic and real data suggest that the proposed inversion approach with multi-frequency receiver function is effective and robust in inverting the crustal velocity structures.

      PubDate: 2017-02-23T14:01:04Z
       
  • Numerical observation of the equipartition regime in a 3D random elastic
           medium, and discussion of the limiting parameters
    • Abstract: Publication date: May 2017
      Source:Computers & Geosciences, Volume 102
      Author(s): Shahram Khazaie, Régis Cottereau, Didier Clouteau
      At long lapse times in the weakly scattering regime, the energy of the coda in a randomly fluctuating isotropic medium is equipartitioned between P and S modes. This behavior is well understood mathematically and physically for full spaces. For realistic domains, analytical results are more scarce and numerical simulations become a valuable tool. This paper discusses, based on numerical simulations of wave propagation in a 3D randomly heterogeneous elastic medium, the transition to an equipartitioned regime of the wave field. Both the time to transition and the value of the ratio of energies after transition are evaluated. Several influencing parameters are discussed, either physical (ratio of background P- and S- velocities, propagation length, variance of the heterogeneities) or numerical (influence of Perfectly Matched Layers). Setting up of a localization regime, inefficient mixture of body waves and small propagation length compared to the transport mean free paths are identified as constraining for the transition toward an equipartition regime.

      PubDate: 2017-02-23T14:01:04Z
       
  • A practical implementation of 3D TTI reverse time migration with
           multi-GPUs
    • Abstract: Publication date: May 2017
      Source:Computers & Geosciences, Volume 102
      Author(s): Chun Li, Guofeng Liu, Yihang Li
      Tilted transversely isotropic (TTI) media are typical earth anisotropy media from practical observational studies. Accurate anisotropic imaging is recognized as a breakthrough in areas with complex anisotropic structures. TTI reverse time migration (RTM) is an important method for these areas. However, P and SV waves are coupled together in the pseudo-acoustic wave equation. The SV wave is regarded as an artifact for RTM of the P wave. We adopt matching of the anisotropy parameters to suppress the SV artifacts. Another problem in the implementation of TTI RTM is instability of the numerical solution for a variably oriented axis of symmetry. We adopt Fletcher's equation by setting a small amount of SV velocity without an acoustic approximation to stabilize the wavefield propagation. To improve calculation efficiency, we use NVIDIA graphic processing unit (GPU) with compute unified device architecture instead of traditional CPU architecture. To accomplish this, we introduced a random velocity boundary and an extended homogeneous anisotropic boundary for the remaining four anisotropic parameters in the source propagation. This process avoids large storage memory and IO requirements, which is important when using a GPU with limited bandwidth of PCI-E. Furthermore, we extend the single GPU code to multi-GPUs and present a corresponding high concurrent strategy with multiple asynchronous streams, which closely achieved an ideal speedup ratio of 2:1 when compared with a single GPU. Synthetic tests validate the correctness and effectiveness of our multi-GPUs-based TTI RTM method.

      PubDate: 2017-02-23T14:01:04Z
       
  • Estimating permeability from thin sections without reconstruction: Digital
           rock study of 3D properties from 2D images
    • Abstract: Publication date: May 2017
      Source:Computers & Geosciences, Volume 102
      Author(s): Nishank Saxena, Gary Mavko, Ronny Hofmann, Nattavadee Srisutthiyakorn
      We present a new approach for predicting permeability of natural rocks using thin sections. Our approach involves two steps: (1) computing permeability of the thin sections for flow normal to the face, and (2) application of new robust 2D-3D transforms that relate thin section permeability to 3D rock permeability using calibration parameters. We perform step 1 using Lattice-Boltzmann and finite difference schemes, which are memory efficient. We discuss two models to perform step 2. Our two-step approach is fast and efficient, since it does not require reconstruction of the unknown 3D rock using 2D thin section information. We establish the applicability of this new approach using a dataset comprised of LBM-computed permeability of rock samples from various geologic formations, including Fontainebleau sandstone, Berea sandstone, Bituminous sand, and Grosmont carbonate. We find that for sandstones our approach predicts fairly accurate permeability with little calibration. Predicting permeability of carbonates from thin sections is more challenging due to microstructural complexity thus model parameters require more calibration. For general workflow, we propose to first calibrate the proposed models using the available 3D information on the rock microstructure (from microCT, SEM, etc.) and then predict the permeability for rocks from the same geological formation for which only 2D thin sections are available.

      PubDate: 2017-02-23T14:01:04Z
       
  • WSJointInv2D-MT-DCR: An efficient joint two-dimensional magnetotelluric
           and direct current resistivity inversion
    • Abstract: Publication date: May 2017
      Source:Computers & Geosciences, Volume 102
      Author(s): Puwis Amatyakul, Chatchai Vachiratienchai, Weerachai Siripunvaraporn
      An efficient joint two-dimensional direct current resistivity (DCR) and magnetotelluric (MT) inversion, referred to as WSJointInv2D-MT-DCR, was developed with FORTRAN 95 based on the data space Occam's inversion algorithm. Our joint inversion software can be used to invert just the MT data or the DCR data, or invert both data sets simultaneously to get the electrical resistivity structures. Since both MT and DCR surveys yield the same resistivity structures, the two data types enhance each other leading to a better interpretation. Two synthetic and a real field survey are used here to demonstrate that the joint DCR and MT surveys can help constrain each other to reduce the ambiguities occurring when inverting the DCR or MT alone. The DCR data increases the lateral resolution of the near surface structures while the MT data reveals the deeper structures. When the MT apparent resistivity suffers from the static shift, the DCR apparent resistivity can serve as a replacement for the estimation of the static shift factor using the joint inversion. In addition, we also used these examples to show the efficiency of our joint inversion code. With the availability of our new joint inversion software, we expect the number of joint DCR and MT surveys to increase in the future.

      PubDate: 2017-02-23T14:01:04Z
       
  • A scalable approach for tree segmentation within small-footprint airborne
           LiDAR data
    • Abstract: Publication date: Available online 22 February 2017
      Source:Computers & Geosciences
      Author(s): Hamid Hamraz, Marco A. Contreras, Jun Zhang
      This paper presents a distributed approach that scales up to segment tree crowns within a LiDAR point cloud representing an arbitrarily large forested area. The approach uses a single-processor tree segmentation algorithm as a building block in order to process the data delivered in the shape of tiles in parallel. The distributed processing is performed in a master-slave manner, in which the master maintains the global map of the tiles and coordinates the slaves that segment tree crowns within and across the boundaries of the tiles. A minimal bias was introduced to the number of detected trees because of trees lying across the tile boundaries, which was quantified and adjusted for. Theoretical and experimental analyses of the runtime of the approach revealed a near linear speedup. The estimated number of trees categorized by crown class and the associated error margins as well as the height distribution of the detected trees aligned well with field estimations, verifying that the distributed approach works correctly. The approach enables providing information of individual tree locations and point cloud segments for a forest-level area in a timely manner, which can be used to create detailed remotely sensed forest inventories. Although the approach was presented for tree segmentation within LiDAR point clouds, the idea can also be generalized to scale up processing other big spatial datasets.

      PubDate: 2017-02-23T14:01:04Z
       
  • Statistical Modeling of Geopressured Geothermal Reservoirs
    • Abstract: Publication date: Available online 21 February 2017
      Source:Computers & Geosciences
      Author(s): Esmail Ansari, Richard Hughes, Christopher White
      Identifying attractive candidate reservoirs for producing geothermal energy requires predictive models. In this work, inspectional analysis and statistical modeling are used to create simple predictive models for a line drive design. Inspectional analysis on the partial differential equations governing this design yields a minimum number of fifteen dimensionless groups required to describe the physics of the system. These dimensionless groups are explained and confirmed using models with similar dimensionless groups but different dimensional parameters. This study models dimensionless production temperature and thermal recovery factor as the responses of a numerical model. These responses are obtained by a Box-Behnken experimental design. An uncertainty plot is used to segment the dimensionless time and develop a model for each segment. The important dimensionless numbers for each segment of the dimensionless time are identified using the Boosting method. These selected numbers are used in the regression models. The developed models are reduced to have a minimum number of predictors and interactions. The reduced final models are then presented and assessed using testing runs. Finally, applications of these models are offered. The presented workflow is generic and can be used to translate the output of a numerical simulator into simple predictive models in other research areas involving numerical simulation.

      PubDate: 2017-02-23T14:01:04Z
       
  • Modification of the random forest algorithm to avoid statistical
           dependence problems when classifying remote sensing imagery
    • Abstract: Publication date: Available online 20 February 2017
      Source:Computers & Geosciences
      Author(s): Fulgencio Cánovas-García, Francisco Alonso-Sarría, Francisco Gomariz-Castillo, Fernando Oñate-Valdivieso
      Random forest is a classification technique widely used in remote sensing. One of its advantages is that it produces an estimation of classification accuracy based on the so called out-of-bag cross-validation method. It is usually assumed that such estimation is not biased and may be used instead of validation based on an external data-set or a cross-validation external to the algorithm. In this paper we show that this is not necessarily the case when classifying remote sensing imagery using training areas with several pixels or objects. According to our results, out-of-bag cross-validation clearly overestimates accuracy, both overall and per class. The reason is that, in a training patch, pixels or objects are not independent (from a statistical point of view) of each other; however, they are split by bootstrapping into in-bag and out-of-bag as if they were really independent. We believe that putting whole patch, rather than pixels/objects, in one or the other set would produce a less biased out-of-bag cross-validation. To deal with the problem, we propose a modification of the random forest algorithm to split training patches instead of the pixels (or objects) that compose them. This modified algorithm does not overestimate accuracy and has no lower predictive capability than the original. When its results are validated with an external data-set, the accuracy is not different from that obtained with the original algorithm. We analysed three remote sensing images with different classification approaches (pixel and object based); in the three cases reported, the modification we propose produces a less biased accuracy estimation.

      PubDate: 2017-02-23T14:01:04Z
       
  • ADFNE: Open source software for discrete fracture network engineering, two
           and three dimensional applications
    • Abstract: Publication date: May 2017
      Source:Computers & Geosciences, Volume 102
      Author(s): Younes Fadakar Alghalandis
      Rapidly growing topic, the discrete fracture network engineering (DFNE), has already attracted many talents from diverse disciplines in academia and industry around the world to challenge difficult problems related to mining, geothermal, civil, oil and gas, water and many other projects. Although, there are few commercial software capable of providing some useful functionalities fundamental for DFNE, their costs, closed code (black box) distributions and hence limited programmability and tractability encouraged us to respond to this rising demand with a new solution. This paper introduces an open source comprehensive software package for stochastic modeling of fracture networks in two- and three-dimension in discrete formulation. Functionalities included are geometric modeling (e.g., complex polygonal fracture faces, and utilizing directional statistics), simulations, characterizations (e.g., intersection, clustering and connectivity analyses) and applications (e.g., fluid flow). The package is completely written in Matlab scripting language. Significant efforts have been made to bring maximum flexibility to the functions in order to solve problems in both two- and three-dimensions in an easy and united way that is suitable for beginners, advanced and experienced users.
      Graphical abstract image

      PubDate: 2017-02-09T08:40:04Z
       
  • Double-Sided Sliding-Paraboloid (DSSP): A new tool for preprocessing GPR
           data
    • Abstract: Publication date: May 2017
      Source:Computers & Geosciences, Volume 102
      Author(s): Mohamed Rashed, Essam A. Rashed
      Background noise in Ground Penetrating Radar (GPR) data is a nagging problem that degrades the quality of GPR images and increases their ambiguity. There are several methods adopting different strategies to remove background noise. In this study, we present the Double-Sided Sliding-Paraboloid (DSSP) as a new background removal technique. Experiments conducted on field GPR data show that the proposed DSSP technique has several advantages over existing background removal techniques. DSSP removes background noise more efficiently while preserving first arrivals and other strong horizontal reflections. Moreover, DSSP introduces no artifacts to GPR data and corrects data for DC-shift and wow noise.
      Graphical abstract image

      PubDate: 2017-02-09T08:40:04Z
       
  • Unsupervised detection of topographic highs with arbitrary basal shapes
           based on volume evolution of isocontours
    • Abstract: Publication date: May 2017
      Source:Computers & Geosciences, Volume 102
      Author(s): Costas Panagiotakis, Eleni Kokinou
      In this work, an unsupervised isocontour based segmentation method is proposed, that is applied on the detection of topographic highs with arbitrary basal shapes on Digital Elevation Models (DEMs). A series of isocontour based segmentation maps is computed for decreasing altitude levels. During this process, the isocontours are gradually merged providing a topological hierarchy of highs in an inclusion tree structure. A novel formulation of a topographic high is given taking into account the volume evolution of an isocontour that starts from the top of a high and grows, as decreasing the altitude level of isocontour, until a high of higher altitude is reached. This formulation yields to a robust unsupervised algorithm that can be sequentially applied to automatically recognize and discriminate the topographic highs of a region according to the inclusion tree without any constraint on basal shapes. The proposed method is applied on real and synthetic DEMs, in order to automatically detect the exact shape of complex topographic highs and some geomorphological based features useful for high annotations, yielding high performance results, even if the highs are partially visible in the given DEM.

      PubDate: 2017-02-09T08:40:04Z
       
  • An auxiliary adaptive Gaussian mixture filter applied to flowrate
           allocation using real data from a multiphase producer
    • Abstract: Publication date: May 2017
      Source:Computers & Geosciences, Volume 102
      Author(s): Rolf J. Lorentzen, Andreas S. Stordal, Neal Hewitt
      Flowrate allocation in production wells is a complicated task, especially for multiphase flow combined with several reservoir zones and/or branches. The result depends heavily on the available production data, and the accuracy of these. In the application we show here, downhole pressure and temperature data are available, in addition to the total flowrates at the wellhead. The developed methodology inverts these observations to the fluid flowrates (oil, water and gas) that enters two production branches in a real full-scale producer. A major challenge is accurate estimation of flowrates during rapid variations in the well, e.g. due to choke adjustments. The Auxiliary Sequential Importance Resampling (ASIR) filter was developed to handle such challenges, by introducing an auxiliary step, where the particle weights are recomputed (second weighting step) based on how well the particles reproduce the observations. However, the ASIR filter suffers from large computational time when the number of unknown parameters increase. The Gaussian Mixture (GM) filter combines a linear update, with the particle filters ability to capture non-Gaussian behavior. This makes it possible to achieve good performance with fewer model evaluations. In this work we present a new filter which combines the ASIR filter and the Gaussian Mixture filter (denoted ASGM), and demonstrate improved estimation (compared to ASIR and GM filters) in cases with rapid parameter variations, while maintaining reasonable computational cost.

      PubDate: 2017-02-09T08:40:04Z
       
  • Quantitative thickness prediction of tectonically deformed coal using
           Extreme Learning Machine and Principal Component Analysis: a case study
    • Abstract: Publication date: April 2017
      Source:Computers & Geosciences, Volume 101
      Author(s): Xin Wang, Yan Li, Tongjun Chen, Qiuyan Yan, Li Ma
      The thickness of tectonically deformed coal (TDC) has positive correlation associations with gas outbursts. In order to predict the TDC thickness of coal beds, we propose a new quantitative predicting method using an extreme learning machine (ELM) algorithm, a principal component analysis (PCA) algorithm, and seismic attributes. At first, we build an ELM prediction model using the PCA attributes of a synthetic seismic section. The results suggest that the ELM model can produce a reliable and accurate prediction of the TDC thickness for synthetic data, preferring Sigmoid activation function and 20 hidden nodes. Then, we analyze the applicability of the ELM model on the thickness prediction of the TDC with real application data. Through the cross validation of near-well traces, the results suggest that the ELM model can produce a reliable and accurate prediction of the TDC. After that, we use 250 near-well traces from 10 wells to build an ELM predicting model and use the model to forecast the TDC thickness of the No. 15 coal in the study area using the PCA attributes as the inputs. Comparing the predicted results, it is noted that the trained ELM model with two selected PCA attributes yields better predication results than those from the other combinations of the attributes. Finally, the trained ELM model with real seismic data have a different number of hidden nodes (10) than the trained ELM model with synthetic seismic data. In summary, it is feasible to use an ELM model to predict the TDC thickness using the calculated PCA attributes as the inputs. However, the input attributes, the activation function and the number of hidden nodes in the ELM model should be selected and tested carefully based on individual application.

      PubDate: 2017-02-09T08:40:04Z
       
  • Accurate and efficient maximal ball algorithm for pore network extraction
    • Abstract: Publication date: April 2017
      Source:Computers & Geosciences, Volume 101
      Author(s): Frederick Arand, Jürgen Hesser
      The maximal ball (MB) algorithm is a well established method for the morphological analysis of porous media. It extracts a network of pores and throats from volumetric data. This paper describes structural modifications to the algorithm, while the basic concepts are preserved. Substantial improvements to accuracy and efficiency are achieved as follows: First, all calculations are performed on a subvoxel accurate distance field, and no approximations to discretize balls are made. Second, data structures are simplified to keep memory usage low and improve algorithmic speed. Third, small and reasonable adjustments increase speed significantly. In volumes with high porosity, memory usage is improved compared to classic MB algorithms. Furthermore, processing is accelerated more than three times. Finally, the modified MB algorithm is verified by extracting several network properties from reference as well as real data sets. Runtimes are measured and compared to literature.

      PubDate: 2017-01-29T01:27:35Z
       
  • Automated Detection of Geological Landforms on Mars using Convolutional
           Neural Networks
    • Abstract: Publication date: Available online 16 January 2017
      Source:Computers & Geosciences
      Author(s): Leon F. Palafox, Christopher W. Hamilton, Stephen P. Scheidt, Alexander M. Alvarez
      The large volume of high-resolution images acquired by the Mars Reconnaissance Orbiter has opened a new frontier for developing automated approaches to detecting landforms on the surface of Mars. However, most landform classifiers focus on crater detection, which represents only one of many geological landforms of scientific interest. In this work, we use Convolutional Neural Networks (ConvNets) to detect both volcanic rootless cones and traverse aeolian ridges. Our system, named MarsNet, consists of five networks, each of which is trained to detect landforms of different sizes. We compare our detection algorithm with a widely used method for image recognition, Support Vector Machines (SVMs) using histogram of oriented gradients (HoG) features. We show that ConvNets can detect a wide range of landforms and has better accuracy and recall in testing data than traditional classifiers based on SVMs.

      PubDate: 2017-01-21T22:38:03Z
       
  • Multi-thread parallel algorithm for reconstructing 3D large-scale porous
           structures
    • Abstract: Publication date: Available online 11 January 2017
      Source:Computers & Geosciences
      Author(s): Yang Ju, Yaohui Huang, Jiangtao Zheng, Xu Qian, Heping Xie, Xi Zhao
      Geomaterials inherently contain many discontinuous, multi-scale, geometrically irregular pores, forming a complex porous structure that governs their mechanical and transport properties. The development of an efficient reconstruction method for representing porous structures can significantly contribute toward providing a better understanding of the governing effects of porous structures on the properties of porous materials. In order to improve the efficiency of reconstructing large-scale porous structures, a multi-thread parallel scheme was incorporated into the simulated annealing reconstruction method. In the method, four correlation functions, which include the two-point probability function, the linear-path functions for the pore phase and the solid phase, and the fractal system function for the solid phase, were employed for better reproduction of the complex well-connected porous structures. In addition, a random sphere packing method and a self-developed pre-conditioning method were incorporated to cast the initial reconstructed model and select independent interchanging pairs for parallel multi-thread calculation, respectively. The accuracy of the proposed algorithm was evaluated by examining the similarity between the reconstructed structure and a prototype in terms of their geometrical, topological, and mechanical properties. Comparisons of the reconstruction efficiency of porous models with various scales indicated that the parallel multi-thread scheme significantly shortened the execution time for reconstruction of a large-scale well-connected porous model compared to a sequential single-thread procedure.

      PubDate: 2017-01-14T15:57:45Z
       
  • Benchmarking PET for geoscientific applications: 3D quantitative diffusion
           coefficient determination in clay rock
    • Abstract: Publication date: Available online 4 January 2017
      Source:Computers & Geosciences
      Author(s): J. Lippmann-Pipke, R. Gerasch, J. Schikora, J. Kulenkampff
      The 3D diagonal anisotropic effective diffusion coefficient of Na+, D eff = (D xx , D yy , D zz ), was quantified in a clay material in one single experiment/simulation. That is possible due to the combination of the non-invasive observation of Na+ diffusion in Opalinus clay by means of GeoPET method (PET: positron emission tomography) followed by quantitative 3D+t data evaluation by means of the finite element numerical modelling (FEM). The extracted anisotropic effective diffusion coefficient parallel ( ) and normal ( ⊥ ) to the bedding of the clay rock, D eff =(D , D ⊥ , D ) are comparable to those obtained on earlier experimental studies in the same clay material but with different methods. We consider this study as benchmark for the long-standing development of our GeoPET method, that explicitly includes a resolute and physics based attenuation and Compton scatter correction algorithm (Kulenkampff, J., M. Gründig, A. Zakhnini and J. Lippmann-Pipke (2016). "Geoscientific process monitoring with positron emission tomography (GeoPET)." Solid Earth 7: 1217–1231) We suggest GeoPET based fluid flow transport visualization combined with computer based process simulation henceforth as a qualified way for the quantification of dimensional, effective transport parameter in geosciences.

      PubDate: 2017-01-07T05:23:53Z
       
  • Multi-waveform classification for seismic facies analysis
    • Abstract: Publication date: Available online 28 December 2016
      Source:Computers & Geosciences
      Author(s): Chengyun Song, Zhining Liu, Yaojun Wang, Xingming Li, Guangmin Hu
      Seismic facies analysis provides an effective way to delineate the heterogeneity and compartments within a reservoir. Traditional method is using the single waveform to classify the seismic facies, which does not consider the stratigraphy continuity, and the final facies map may affect by noise. Therefore, by defining waveforms in a 3D window as multi-waveform, we developed a new seismic facies analysis algorithm represented as multi-waveform classification (MWFC) that combines the multilinear subspace learning with self-organizing map (SOM) clustering techniques. In addition, we utilize multi-window dip search algorithm to extract multi-waveform, which reduce the uncertainty of facies maps in the boundaries. Testing the proposed method on synthetic data with different S/N, we confirm that our MWFC approach is more robust to noise than the conventional waveform classification (WFC) method. The real seismic data application on F3 block in Netherlands proves our approach is an effective tool for seismic facies analysis.

      PubDate: 2017-01-07T05:23:53Z
       
  • Development of a coupled wave-flow-vegetation interaction model
    • Abstract: Publication date: Available online 15 December 2016
      Source:Computers & Geosciences
      Author(s): Alexis Beudin, Tarandeep S. Kalra, Neil K. Ganju, John C. Warner
      Emergent and submerged vegetation can significantly affect coastal hydrodynamics. However, most deterministic numerical models do not take into account their influence on currents, waves, and turbulence. In this paper, we describe the implementation of a wave-flow-vegetation module into a Coupled-Ocean-Atmosphere-Wave-Sediment Transport (COAWST) modeling system that includes a flow model (ROMS) and a wave model (SWAN), and illustrate various interacting processes using an idealized shallow basin application. The flow model has been modified to include plant posture-dependent three-dimensional drag, in-canopy wave-induced streaming, and production of turbulent kinetic energy and enstrophy to parameterize vertical mixing. The coupling framework has been updated to exchange vegetation-related variables between the flow model and the wave model to account for wave energy dissipation due to vegetation. This study i) demonstrates the validity of the plant posture-dependent drag parameterization against field measurements, ii) shows that the model is capable of reproducing the mean and turbulent flow field in the presence of vegetation as compared to various laboratory experiments, iii) provides insight into the flow-vegetation interaction through an analysis of the terms in the momentum balance, iv) describes the influence of a submerged vegetation patch on tidal currents and waves separately and combined, and v) proposes future directions for research and development.

      PubDate: 2016-12-20T17:49:46Z
       
  • Linked Data Scientometrics in Semantic e-Science
    • Abstract: Publication date: Available online 13 December 2016
      Source:Computers & Geosciences
      Author(s): Tom Narock, Hayden Wimmer
      The Semantic Web is inherently multi-disciplinary and many domains have taken advantage of semantic technologies. Yet, the geosciences are one of the fields leading the way in Semantic Web adoption and validation. Astronomy, Earth science, hydrology, and solar-terrestrial physics have seen a noteworthy amount of semantic integration. The geoscience community has been willing early adopters of semantic technologies and have provided essential feedback to the broader semantic web community. Yet, there has been no systematic study of the community as a whole and there exists no quantitative data on the impact and status of semantic technologies in the geosciences. We explore the applicability of Linked Data to scientometrics in the geosciences. In doing so, we gain an initial understanding of the breadth and depth of the Semantic Web in the geosciences. We identify what appears to be a transitionary period in the applicability of these technologies.

      PubDate: 2016-12-14T20:42:49Z
       
  • Benchmarking Defmod, an open source FEM code for modeling episodic fault
           rupture
    • Abstract: Publication date: March 2017
      Source:Computers & Geosciences, Volume 100
      Author(s): Chunfang Meng
      We present Defmod, an open source (linear) finite element code that enables us to efficiently model the crustal deformation due to (quasi-)static and dynamic loadings, poroelastic flow, viscoelastic flow and frictional fault slip. Ali (2015) provides the original code introducing an implicit solver for (quasi-)static problem, and an explicit solver for dynamic problem. The fault constraint is implemented via Lagrange Multiplier. Meng (2015) combines these two solvers into a hybrid solver that uses failure criteria and friction laws to adaptively switch between the (quasi-)static state and dynamic state. The code is capable of modeling episodic fault rupture driven by quasi-static loadings, e.g. due to reservoir fluid withdraw or injection. Here, we focus on benchmarking the Defmod results against some establish results.

      PubDate: 2016-12-07T20:16:11Z
       
  • An interactive image segmentation method for lithological boundary
           detection: A rapid mapping tool for geologists
    • Abstract: Publication date: March 2017
      Source:Computers & Geosciences, Volume 100
      Author(s): Yathunanthan Vasuki, Eun-Jung Holden, Peter Kovesi, Steven Micklethwaite
      Large volumes of images are collected by geoscientists using remote sensing platforms. Manual analysis of these images is a time consuming task and there is a need for fast and robust image interpretation tools. In particular the reliable mapping of lithological boundaries is a critical step for geological interpretation. In this contribution we developed an interactive image segmentation algorithm that harnesses the geologist's input and exploits automated image analysis to provide a practical tool for lithology boundary detection, using photographic images of rock surfaces. In the proposed method, the user is expected to draw rough markings to indicate the locations of different geological units in the image. Image segmentation is performed by segmenting regions based on their homogeneity in colour. This results in a high density of segmented regions which are then iteratively merged based on the colour of different geological units and the user input. Finally, a post-processing step allows the user to edit the boundaries. An experiment was conducted using photographic rock surface images collected by a UAV and a handheld digital camera. The proposed technique was applied to detect lithology boundaries. It was found that the proposed method reduced the interpretation time by a factor of four relative to manual segmentation, while achieving more than 96% similarity in boundary detection. As a result the proposed method has the potential to provide practical support for interpreting large volume of complex geological images.

      PubDate: 2016-12-07T20:16:11Z
       
  • An improved lossless group compression algorithm for seismic data in SEG-Y
           and MiniSEED file formats
    • Abstract: Publication date: March 2017
      Source:Computers & Geosciences, Volume 100
      Author(s): Huailiang Li, Xianguo Tuo, Tong Shen, Mark Julian Henderson, Jérémie Courtois, Minhao Yan
      An improved lossless group compression algorithm is proposed for decreasing the size of SEG-Y files to relieve the enormous burden associated with the transmission and storage of large amounts of seismic exploration data. Because each data point is represented by 4 bytes in SEG-Y files, the file is broken down into 4 subgroups, and the Gini coefficient is employed to analyze the distribution of the overall data and each of the 4 data subgroups within the range [0,255]. The results show that each subgroup comprises characteristic frequency distributions suited to distinct compression algorithms. Therefore, the data of each subgroup was compressed using its best suited algorithm. After comparing the compression ratios obtained for each data subgroup using different algorithms, the Lempel-Ziv-Markov chain algorithm (LZMA) was selected for the compression of the first two subgroups and the Deflate algorithm for the latter two subgroups. The compression ratios and decompression times obtained with the improved algorithm were compared with those obtained with commonly employed compression algorithms for SEG-Y files with different sizes. The experimental results show that the improved algorithm provides a compression ratio of 75–80%, which is more effective than compression algorithms presently applied to SEG-Y files. In addition, the proposed algorithm is applied to the miniSEED format used in natural earthquake monitoring, and the results compared with those obtained using the Steim2 compression algorithm, the results again show that the proposed algorithm provides better data compression.

      PubDate: 2016-12-07T20:16:11Z
       
  • 3D Kirchhoff depth migration algorithm: A new scalable approach for
           parallelization on multicore CPU based cluster
    • Abstract: Publication date: Available online 7 December 2016
      Source:Computers & Geosciences
      Author(s): Richa Rastogi, Ashutosh Londhe, Abhishek Srivastava, Kirannmayi M. Sirasala, Kiran Khonde
      In this article, a new scalable 3D Kirchhoff depth migration algorithm is presented on state of the art multicore CPU based cluster. Parallelization of 3D Kirchhoff depth migration is challenging due to its high demand of compute time, memory, storage and I/O along with the need of their effective management. The most resource intensive modules of the algorithm are traveltime calculations and migration summation which exhibit an inherent trade off between compute time and other resources. The parallelization strategy of the algorithm largely depends on the storage of calculated traveltimes and its feeding mechanism to the migration process. The presented work is an extension of our previous work, wherein a 3D Kirchhoff depth migration application for multicore CPU based parallel system had been developed. Recently, we have worked on improving parallel performance of this application by re-designing the parallelization approach. The new algorithm is capable to efficiently migrate both prestack and poststack 3D data. It exhibits flexibility for migrating large number of traces within the available node memory and with minimal requirement of storage, I/O and inter-node communication. The resultant application is tested using 3D Overthrust data on PARAM Yuva II, which is a Xeon E5-2670 based multicore CPU cluster with 16 cores/node and 64GB shared memory. Parallel performance of the algorithm is studied using different numerical experiments and the scalability results show striking improvement over its previous version. An impressive 49.05X speedup with 76.64% efficiency is achieved for 3D prestack data and 32.00X speedup with 50.00% efficiency for 3D poststack data, using 64 nodes. The results also demonstrate the effectiveness and robustness of the improved algorithm with high scalability and efficiency on a multicore CPU cluster.

      PubDate: 2016-12-07T20:16:11Z
       
  • Identifying P phase arrival of weak events: the Akaike Information
           Criterion picking application based on the Empirical Mode Decomposition
    • Abstract: Publication date: Available online 7 December 2016
      Source:Computers & Geosciences
      Author(s): Xibing Li, Xueyi Shang, A. Morales-Esteban, Zewei Wang
      Seismic P phase arrival picking of weak events is a difficult problem in seismology. The algorithm proposed in this research is based on Empirical Mode Decomposition (EMD) and on the Akaike Information Criterion (AIC) picker. It has been called the EMD-AIC picker. The EMD is a self-adaptive signal decomposition method that not only improves Signal to Noise Ratio (SNR) but also retains P phase arrival information. Then, P phase arrival picking has been determined by applying the AIC picker to the selected main Intrinsic Mode Functions (IMFs). The performance of the EMD-AIC picker has been evaluated on the basis of 1938 micro-seismic signals from the Yongshaba mine (China). The P phases identified by this algorithm have been compared with manual pickings. The evaluation results confirm that the EMD-AIC pickings are highly accurate for the majority of the micro-seismograms. Moreover, the pickings are independent of the kind of noise. Finally, the results obtained by this algorithm have been compared to the wavelet based Discrete Wavelet Transform (DWT)-AIC pickings. This comparison has demonstrated that the EMD-AIC picking method has a better picking accuracy than the DWT-AIC picking method, thus showing this method's reliability and potential.

      PubDate: 2016-12-07T20:16:11Z
       
  • REGIONALIZATION OF LOCAL GEOMORPHOMETRIC DERIVATIONS FOR GEOLOGICAL
           MAPPING IN THE SEDIMENTARY DOMAIN OF CENTRAL AMAZÔNIA
    • Abstract: Publication date: Available online 5 December 2016
      Source:Computers & Geosciences
      Author(s): Márcio de Morisson Valeriano, Dilce de Fátima Rossetti
      This paper reports procedures to prepare locally derived geomorphometric data for geological mapping at regional scale in central Amazônia. The size of the study area, approximately 1.5 million km2, and the prevailing flat topography of the targeted environment were the constraints motivating the aims, at spatial and numerical synthesis of the detailed geomorphometric information derived from SRTM DEM. The developed approach consisted in assigning single (average) values to terrain patches, to represent the regional distribution of pixel-based geomorphometric information (slope, profile curvature and relative relief). In analogy to the nature of sedimentary packs, patches were established as contiguous elevation strata, constructed through a procedure combining segmentation, filterings and range compressions. For slope only, pre-processing of locally derived data with median filtering effectively avoided the typical flattening of the regionalized results due to input distribution characteristics. Profile curvature was transformed into absolute values and thus a different meaning from the original (pixel) variable was considered in the interpretation, also avoiding the compensation of original values (positive and negative) tending to zero value when averaged through a regionally flat extension. Examinations near major river valleys showed patched elevation to depict alluvial terraces. In the interfluves and floodplains, contrasting patterns in the averaged variables among patches of similar elevations allowed the recognition of important relief features. In addition to the reduction of the distribution ranges, the correlation between regionalized geomorphometric variables was higher than observed in the originally local data, due to the thematic synthesis following regionalization. Depth of dissection, claimed to be related to the relative age of sedimentary units, was the main factor to explain the overall variations of the geomorphometric results. The developed regionalization process improved the potential of local geomorphometric data for updating and revision of geological maps and for guiding future surveys in the sedimentary domain of Amazônia.

      PubDate: 2016-12-07T20:16:11Z
       
  • A TETRAHEDRAL MESH GENERATION APPROACH FOR 3D MARINE CONTROLLED-SOURCE
           ELECTROMAGNETIC MODELING
    • Abstract: Publication date: Available online 21 November 2016
      Source:Computers & Geosciences
      Author(s): Evan Schankee Um, Seung-Sep Kim, Haohuan Fu
      3D finite-element (FE) mesh generation is a major hurdle for marine controlled-source electromagnetic (CSEM) modeling. In this paper, we present a FE discretization operator (FEDO) that automatically converts a 3D finite-difference (FD) model into reliable and efficient tetrahedral FE meshes for CSEM modeling. FEDO sets up wireframes of a background seabed model that precisely honors the seafloor topography. The wireframes are then partitioned into multiple regions. Outer regions of the wireframes are discretized with coarse tetrahedral elements whose maximum size is as large as a skin depth of the regions. We demonstrate that such coarse meshes can produce accurate FE solutions because numerical dispersion errors of tetrahedral meshes do not accumulate but oscillates. In contrast, central regions of the wireframes are discretized with fine tetrahedral elements to describe complex geology in detail. The conductivity distribution is mapped from FD to FE meshes in a volume-averaged sense. To avoid excessive mesh refinement around receivers, we introduce an effective receiver size. Major advantages of FEDO are summarized as follow. First, FEDO automatically generates reliable and economic tetrahedral FE meshes without adaptive meshing or interactive CAD workflows. Second, FEDO produces FE meshes that precisely honor the boundaries of the seafloor topography. Third, FEDO derives multiple sets of FE meshes from a given FD model. Each FE mesh is optimized for a different set of sources and receivers and is fed to a subgroup of processors on a parallel computer. This divide and conquer approach improves the parallel scalability of the FE solution. Both accuracy and effectiveness of FEDO are demonstrated with various CSEM examples.

      PubDate: 2016-12-01T00:19:21Z
       
 
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
 
Home (Search)
Subjects A-Z
Publishers A-Z
Customise
APIs
Your IP address: 54.157.239.93
 
About JournalTOCs
API
Help
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-2016