Subjects -> EARTH SCIENCES (Total: 771 journals)
    - EARTH SCIENCES (527 journals)
    - GEOLOGY (94 journals)
    - GEOPHYSICS (33 journals)
    - HYDROLOGY (29 journals)
    - OCEANOGRAPHY (88 journals)

EARTH SCIENCES (527 journals)            First | 1 2 3     

Showing 401 - 371 of 371 Journals sorted alphabetically
Physical Geography     Hybrid Journal   (Followers: 8)
Physical Science International Journal     Open Access  
Physics in Medicine & Biology     Full-text available via subscription   (Followers: 15)
Physics of Life Reviews     Hybrid Journal   (Followers: 1)
Physics of Metals and Metallography     Hybrid Journal   (Followers: 18)
Physics of Plasmas     Hybrid Journal   (Followers: 10)
Physics of the Earth and Planetary Interiors     Hybrid Journal   (Followers: 34)
Physics of the Solid State     Hybrid Journal   (Followers: 6)
Physics of Wave Phenomena     Hybrid Journal  
Physics World     Full-text available via subscription   (Followers: 18)
Physik in unserer Zeit     Hybrid Journal   (Followers: 9)
Pirineos     Open Access  
Planet     Open Access   (Followers: 4)
Plasma Physics and Controlled Fusion     Hybrid Journal   (Followers: 6)
Plasma Physics Reports     Hybrid Journal   (Followers: 7)
Polar Record     Hybrid Journal   (Followers: 2)
Positioning     Open Access   (Followers: 4)
Pramana     Open Access   (Followers: 13)
Precambrian Research     Hybrid Journal   (Followers: 7)
Preview     Hybrid Journal  
Proceedings of the Geologists' Association     Full-text available via subscription   (Followers: 6)
Proceedings of the Linnean Society of New South Wales     Full-text available via subscription   (Followers: 2)
Proceedings of the Yorkshire Geological Society     Hybrid Journal   (Followers: 1)
Progress in Earth and Planetary Science     Open Access   (Followers: 16)
Pure and Applied Geophysics     Hybrid Journal   (Followers: 12)
Quarterly Journal of Engineering Geology and Hydrogeology     Hybrid Journal   (Followers: 4)
Quaternary     Open Access  
Quaternary Australasia     Full-text available via subscription  
Quaternary Geochronology     Hybrid Journal   (Followers: 8)
Quaternary International     Hybrid Journal   (Followers: 14)
Quaternary Research     Full-text available via subscription   (Followers: 19)
Quaternary Science Advances     Open Access  
Quaternary Science Reviews     Hybrid Journal   (Followers: 26)
Radiocarbon     Hybrid Journal   (Followers: 12)
Remote Sensing     Open Access   (Followers: 57)
Remote Sensing Applications : Society and Environment     Full-text available via subscription   (Followers: 9)
Remote Sensing in Earth Systems Sciences     Hybrid Journal   (Followers: 5)
Remote Sensing Letters     Hybrid Journal   (Followers: 45)
Remote Sensing Science     Open Access   (Followers: 29)
Rendiconti Lincei     Hybrid Journal  
Reports on Geodesy and Geoinformatics     Open Access   (Followers: 8)
Reports on Mathematical Physics     Full-text available via subscription   (Followers: 2)
Research & Reviews : Journal of Space Science & Technology     Full-text available via subscription   (Followers: 18)
Resource Geology     Hybrid Journal   (Followers: 6)
Resources, Environment and Sustainability     Open Access   (Followers: 1)
Results in Geochemistry     Open Access  
Results in Geophysical Sciences     Open Access  
Reviews in Mineralogy and Geochemistry     Hybrid Journal   (Followers: 4)
Reviews of Modern Physics     Full-text available via subscription   (Followers: 31)
Revista Cerrados     Open Access  
Revista de Ciências Exatas Aplicadas e Tecnológicas da Universidade de Passo Fundo : CIATEC-UPF     Open Access  
Revista de Ingenieria Sismica     Open Access  
Revista de Investigaciones en Energía, Medio Ambiente y Tecnología     Open Access  
Revista de la Academia Colombiana de Ciencias Exactas, Físicas y Naturales     Open Access  
Revista de Teledetección     Open Access  
Revista Geológica de Chile     Open Access  
Revue Française de Géotechnique     Hybrid Journal  
Rock Mechanics and Rock Engineering     Hybrid Journal   (Followers: 7)
Rocks & Minerals     Hybrid Journal   (Followers: 3)
Russian Geology and Geophysics     Hybrid Journal   (Followers: 2)
Russian Journal of Mathematical Physics     Full-text available via subscription  
Russian Journal of Pacific Geology     Hybrid Journal  
Russian Physics Journal     Hybrid Journal   (Followers: 1)
Science China Earth Sciences     Hybrid Journal   (Followers: 3)
Science News     Hybrid Journal   (Followers: 11)
Science of Remote Sensing     Open Access   (Followers: 7)
Scientific Annals of Stefan cel Mare University of Suceava. Geography Series     Open Access  
Scientific Journal of Earth Science     Open Access   (Followers: 1)
Scientific Reports     Open Access   (Followers: 85)
Sedimentary Geology     Hybrid Journal   (Followers: 20)
Sedimentology     Hybrid Journal   (Followers: 15)
Seismic Instruments     Hybrid Journal   (Followers: 1)
Seismological Research Letters     Full-text available via subscription   (Followers: 12)
Soil Dynamics and Earthquake Engineering     Hybrid Journal   (Followers: 14)
Soil Security     Open Access   (Followers: 3)
Solid Earth     Open Access   (Followers: 5)
Solid Earth Discussions     Open Access   (Followers: 1)
Solid Earth Sciences     Open Access   (Followers: 1)
South African Journal of Geomatics     Open Access   (Followers: 2)
Standort - Zeitschrift für angewandte Geographie     Hybrid Journal   (Followers: 2)
Stratigraphy and Geological Correlation     Full-text available via subscription   (Followers: 2)
Studia Geophysica et Geodaetica     Hybrid Journal   (Followers: 1)
Studia Geotechnica et Mechanica     Open Access  
Studia Universitatis Babes-Bolyai, Geologia     Open Access  
Survey Review     Hybrid Journal   (Followers: 6)
Surveys in Geophysics     Hybrid Journal   (Followers: 3)
Swiss Journal of Palaeontology     Hybrid Journal   (Followers: 4)
Tectonics     Full-text available via subscription   (Followers: 15)
Tectonophysics     Hybrid Journal   (Followers: 24)
Tellus A     Open Access   (Followers: 21)
Tellus B     Open Access   (Followers: 20)
Terra Latinoamericana     Open Access  
Terra Nova     Hybrid Journal   (Followers: 5)
The Compass : Earth Science Journal of Sigma Gamma Epsilon     Open Access  
The Holocene     Hybrid Journal   (Followers: 16)
The Leading Edge     Hybrid Journal   (Followers: 1)
Transportation Infrastructure Geotechnology     Hybrid Journal   (Followers: 8)
Turkish Journal of Earth Sciences     Open Access  
UD y la Geomática     Open Access  
Unconventional Resources     Open Access  
Underwater Technology: The International Journal of the Society for Underwater     Full-text available via subscription   (Followers: 1)
Universal Journal of Geoscience     Open Access  
Unoesc & Ciência - ACET     Open Access  
Vadose Zone Journal     Open Access   (Followers: 5)
Volcanica     Open Access  
Water     Open Access   (Followers: 10)
Water International     Hybrid Journal   (Followers: 19)
Water Resources     Hybrid Journal   (Followers: 21)
Water Resources Research     Full-text available via subscription   (Followers: 94)
Watershed Ecology and the Environment     Open Access  
Weather, Climate, and Society     Hybrid Journal   (Followers: 15)
Wiley Interdisciplinary Reviews - Climate Change     Hybrid Journal   (Followers: 33)
World Environment     Open Access   (Followers: 1)
Yearbook of the Association of Pacific Coast Geographers     Full-text available via subscription   (Followers: 2)
Yugra State University Bulletin     Open Access   (Followers: 1)
Zeitschrift der Deutschen Gesellschaft für Geowissenschaften     Full-text available via subscription   (Followers: 3)
Zeitschrift für Geomorphologie     Full-text available via subscription   (Followers: 5)
Zitteliana     Open Access  
Землеустрій, кадастр і моніторинг земель     Open Access   (Followers: 1)

  First | 1 2 3     

Similar Journals
Journal Cover
The Leading Edge
Journal Prestige (SJR): 0.386
Citation Impact (citeScore): 1
Number of Followers: 1  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1070-485X
Published by Society of Exploration Geophysicists Homepage  [3 journals]
  • A roadmap to accelerate OSDU adoption

    • Free pre-print version: Loading...

      Abstract: AbstractThe upstream oil and gas subsurface industry traditionally has invested in rich and complex data sets to solve geoscience challenges. The benefit of those data sets is often realized by domain experts applying specialist workflows to drive exploration, appraisal, and production decisions. However, the siloed nature of the industry's workflows often results in knowledge being stranded in specialist applications, which prevents the value from being shared with a wider team to build upon and drive more informed business decisions. In recent years, key trends within the industry have included the transition of data and applications from on premises to cloud hosting and the collective investment in the Open Subsurface Data Universe (OSDU) initiative. The overall objective of these efforts is to unlock the value of data by liberating knowledge from specialist silos and empower end users to apply best-in-class tools, workflows, or artificial intelligence/machine learning algorithms to solve complex subsurface problems. This paper focuses on the practical considerations of implementing subsurface digital transformation and the challenges of providing high-quality curated data to end users. A case study demonstrates delivering a digital core database, integrating it with corporate well-log data stored in OSDU, and making it available through a common platform to enable easy end-user access to unlock the value of the data in driving more accurate decisions in the field.
      PubDate: Thu, 01 Sep 2022 00:00:00 GMT
       
  • Automated active learning in seismic image interpretation

    • Free pre-print version: Loading...

      Abstract: AbstractThree-dimensional seismic interpretation has been significantly accelerated by the recent implementation of various machine learning algorithms, particularly supervised convolutional neural networks (CNNs). CNNs are able to parse seismic data from the perspective of pattern recognition, extract seismic features at multiple scales, and provide acceptable predictions. The performance of a supervised CNN in seismic image interpretation greatly depends on its training labels, which are usually a set of seismic sections with expert annotations. Among the thousands of sections in a typical 3D seismic cube, effectively selecting those that are most representative is a challenging task. A common approach is to have an experienced interpreter visually screen all of the sections and make their selection. To improve the efficiency of training section selection and to avoid introducing bias from manual screening, this work proposes an automated-active-learning (AutoAL) workflow for interactive seismic image interpretation. This enables quantitatively evaluating the machine performance after one iteration and efficiently recommending the sections to be labeled for learning in the next iteration. The added value of the proposed approach is validated through an application of seismic facies classification to the Parihaka data set in the northwest part of offshore Taranaki Basin in New Zealand. Starting from four initial sections, in three iterations the proposed AutoAL automatically recommends 14 from more than 1300 sections as training data. This improves the accuracy and average F1 of the machine prediction over 0.9. Comparisons demonstrate better prediction by the proposed scheme over traditional training section selection schemes, such as manual screening and clustering-based recommendation.
      PubDate: Thu, 01 Sep 2022 00:00:00 GMT
       
  • Predicting sonic and density logs from drilling parameters using temporal
           convolutional networks

    • Free pre-print version: Loading...

      Abstract: AbstractSonic and bulk density logs are crucial inputs for many subsurface tasks including formation identification, completion design, and porosity estimation. Economic and operational concerns restrict the acquisition of these logs, meaning the overburden and sometimes entire wells are completely unlogged. In contrast, parameters that monitor drilling operations, such as weight on bit and torque, are recorded for every borehole. Previous studies have applied supervised machine learning approaches to predict these missing logs from the drilling parameters. While the results are promising, they often do not investigate the importance of different features and the corresponding practical implications. Here, we explored the feasibility of predicting compressional slowness and bulk density logs using various combinations of formation markers, gamma-ray logs, and drilling data recorded at the rig. Our tests utilized a temporal convolutional network to allow the model to learn from sequences of input features. Bayesian-based hyperparameter tuning found the optimum set of parameters for each experiment before producing the final log predictions. Finally, a permutation feature importance analysis revealed which input variables contributed most to the outputs. Although drilling parameters contain some insight into the mechanical rock properties, we found that they cannot produce the high-quality log predictions required for many tasks. Supplementing the drilling parameters with a gamma-ray log and formation data produces good-quality log predictions, with the additional inputs helping to constrain the model outputs.
      PubDate: Thu, 01 Sep 2022 00:00:00 GMT
       
  • Acoustic impedance to outcrop: Presenting near-surface seismic data as a
           virtual outcrop in carbonate analog studies

    • Free pre-print version: Loading...

      Abstract: AbstractOutcrop analogs play a central role in understanding subseismic interwell depositional facies heterogeneity of carbonate reservoirs. Outcrop geologists rarely utilize near-surface seismic data due to the limited vertical resolution and difficulty visualizing seismic signals as “band-limited rocks.” This study proposes a methodology using a combination of forward modeling and conditional generative adversarial network (cGAN) to translate seismic-derived acoustic impedance (AI) into a pseudo-high-resolution virtual outcrop. We tested the methodology on the Hanifa reservoir analog outcropping in Wadi Birk, Saudi Arabia. We interpret a 4 km long outcrop photomosaic from a digital outcrop model (DOM) for its depositional facies, populate the DOM with AI properties, and forward calculate the band-limited AI of the DOM facies using colored inversion. We pair the synthetic band-limited AI with DOM facies and train them using a cGAN. Similarly, we pair the DOM facies with outcrop photos and train them using a cGAN. We chain the two trained networks and apply them to the approximately 600 m long seismic-derived AI data acquired just behind the outcrop. The result translates AI images into a virtual outcrop “behind-the-outcrop” model. This virtual outcrop model is a visual medium that operates at a resolution and format more familiar to outcrop geologists. This model resolves subseismic stratigraphic features such as the intricate downlap-onlap stratal termination at scales of tens of centimeters and the outline of buildup facies, which are otherwise unresolvable in the band-limited AI.
      PubDate: Thu, 01 Sep 2022 00:00:00 GMT
       
  • Digital transformation in rock physics: Deep learning and data fusion

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      Abstract: AbstractRock physics plays an essential role in geophysical reservoir characterization. It aims to build a bridge between geophysical measurements and in-situ rock and fluid properties. With the advancement of microscopic imaging and computer science, rock physics is transitioning to the digital age. This is referred to as digital rock physics (DRP). DRP provides a nondestructive and efficient way to determine physical rock properties directly from digital images. Over the last decades, it has become a routine tool in reservoir characterization by complementing or replacing expensive and time-consuming laboratory measurements. With the emergence of deep learning, DRP has advanced significantly from image processing to physical simulation. This paper presents an application of deep learning in multiscale fusion of digital rock images. It aims to overcome the trade-off between image resolution and field of view (FoV) by integrating imaging data from multiple sources including (1) 3D microcomputed tomography images at micronscale with a large FoV and (2) 2D scanning electron microscopy images at nanoscale with a small FoV. The reconstructed image integrates information of microstructures at different scales and helps characterize heterogeneous porous rocks more accurately. It is helpful to improve the prediction accuracy of effective rock properties and to have deeper insight into physical processes at pore scale. Data fusion based on deep learning would unlock new pathways for geophysical characterization of porous rocks, with broad implications for various subsurface applications such as groundwater transport, enhanced oil recovery, and geologic carbon sequestration.
      PubDate: Thu, 01 Sep 2022 00:00:00 GMT
       
  • Digitalization of asset surveillance through distributed fiber-optic
           sensing: Geophysics and engineering diagnostics and streaming

    • Free pre-print version: Loading...

      Abstract: AbstractFiber-optic distributed acoustic sensing (DAS) can listen to a wide range of signals. This listening takes place at high sampling rates with fine spatial resolution, resulting in large data volumes. Data streaming solutions are available but result in large transmission and storage costs. In this paper, we describe strategies to convert large data streams from DAS interrogator units to diagnostics or processed products. Optimizing DAS systems results in higher signal-to-noise ratio for signals while extracting diagnostic features out of the noise that could be related to production or well engineering. DAS has sensitivity to diverse signals, and the first goal of edge processing is to separate them for consumption by various disciplines. Focusing the processing on specific aspects in the DAS recordings provides data products that are streamed in efficient ways. We show how DAS processing can deploy fast algorithms so that data diagnostics are sent to remote locations. This enables real-time-diagnostics and event-detection tools. By providing the bulk of computing in the field, data upload to remote servers is efficient and targeted. We show how this managed data stream enables digitalization of engineering and geoscience assets.
      PubDate: Thu, 01 Sep 2022 00:00:00 GMT
       
  • Method of evaluating cement-to-formation bond strength with computed
           tomography image analysis

    • Free pre-print version: Loading...

      Abstract: AbstractInvention of new cements for effective zonal isolation requires bond strength evaluation of the cement to formation. We present a method to evaluate the cement-to-formation bond strength based on the flow of cement through a formation using image analysis. Most researchers perform image analysis on a single slice. This is the first known nondestructive method devised using a quantitative analysis on a sequence of slices from X-ray computed tomography (CT) scans. The number of CT scan slices in which the porosity changes is used to evaluate the bond strength of cement. Our findings demonstrate that the cement-to-formation bond strength is affected by permeability of a formation and viscosity of cement if other physical (sample preparation and measuring conditions) and chemical (cement additives) variables remain constant.
      PubDate: Thu, 01 Sep 2022 00:00:00 GMT
       
  • Introduction to this special section: Digital transformation

    • Free pre-print version: Loading...

      Abstract: AbstractThe evolution of computational hardware and software, complemented by large volumes of usable data, creates new opportunities for technological innovation in the energy industry. Digital transformation is the process of adopting existing technologies (such as cloud services) and developing new technologies (such as machine learning applications) that improve business processes. Energy companies must follow the digital transformation to stay competitive (e.g., Kraus et al., 2022).
      PubDate: Thu, 01 Sep 2022 00:00:00 GMT
       
  • The oil and gas cybersecurity enigma

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      Abstract: AbstractThe digitization of the oil and gas industry creates potentially detrimental opportunities for terrorists, criminals, insiders, and activists to exploit. Due to the COVID-19 pandemic, working remotely has become the norm, and remote collaboration has been enabled by such Internet-based applications as Microsoft Teams, Zoom, and others. Remote employees may be more casual with cybersecurity, which further increases the risk of cyberattacks. Successful cyberattacks against oil and gas assets or operations have the capacity to cripple economies, disrupt power grids, and initiate political or public unrest and chaos. Cybersecurity defense should be as central to our organizational culture as turning on our workplace computer. We discuss the most likely weak points in our systems and possible solutions.
      PubDate: Thu, 01 Sep 2022 00:00:00 GMT
       
 
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