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 201 - 371 of 371 Journals sorted alphabetically Hydrological Processes       (Followers: 44) Hydrology and Earth System Sciences       (Followers: 38) ICES Journal of Marine Science: Journal du Conseil       (Followers: 53) IEEE Geoscience and Remote Sensing Letters       (Followers: 151) IEEE Geoscience and Remote Sensing Magazine       (Followers: 6) IEEE Journal of Oceanic Engineering       (Followers: 11) Indian Geotechnical Journal       (Followers: 4) Indonesian Journal on Geoscience       (Followers: 1) Inland Waters Innovative Infrastructure Solutions Interdisciplinary Environmental Review       (Followers: 3) International Geology Review       (Followers: 17) International Journal of Advanced Geosciences       (Followers: 2) International Journal of Advanced Remote Sensing and GIS       (Followers: 50) International Journal of Applied Earth Observation and Geoinformation       (Followers: 36) International Journal of Coal Geology       (Followers: 2) International 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52) Journal on Geoinformatics, Nepal       (Followers: 2) Jurnal Ilmiah Perikanan dan Kelautan / Scientific Journal of Fisheries and Marine Kartografija i geoinformacije (Cartography and Geoinformation) Lake and Reservoir Management       (Followers: 7) Landslides       (Followers: 26) Latin American Journal of Sedimentology and Basin Analysis       (Followers: 1) Lethaia       (Followers: 5) Letters in Mathematical Physics       (Followers: 4) Limnologica       (Followers: 4) Limnology       (Followers: 9) Lithology and Mineral Resources       (Followers: 3) Lithos       (Followers: 9) Malaysian Journal of Geosciences Marine and Freshwater Research       (Followers: 6) Marine and Petroleum Geology       (Followers: 21) Marine Biology Research: New for 2005       (Followers: 2) Marine Economics and Management       (Followers: 3) Marine Environmental Research       (Followers: 31) Marine Geodesy       (Followers: 4) Marine Geology       (Followers: 31) Marine Geophysical Researches     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(Followers: 8) Nature Geoscience       (Followers: 162) Neues Jahrbuch für Geologie und Paläontologie - Abhandlungen       (Followers: 3) Neues Jahrbuch für Mineralogie - Abhandlungen       (Followers: 1) Newsletters on Stratigraphy       (Followers: 2) Nonlinear Processes in Geophysics (NPG) Nonlinear Processes in Geophysics Discussions Ocean & Coastal Management       (Followers: 62) Ocean Development & International Law       (Followers: 15) Ocean Dynamics       (Followers: 6) Ocean Engineering       (Followers: 6) Ocean Modelling       (Followers: 12) Ocean Science (OS)       (Followers: 7) Ocean Science Journal       (Followers: 6) Open Geospatial Data, Software and Standards       (Followers: 3) Open Journal of Earthquake Research       (Followers: 3) Open Journal of Soil Science       (Followers: 9) Ore and Energy Resource Geology Ore Geology Reviews       (Followers: 11) Organic Geochemistry       (Followers: 4) Osterreichische Wasser- und Abfallwirtschaft Paläontologische Zeitschrift       (Followers: 4) Papers in Palaeontology Permafrost and Periglacial Processes       (Followers: 5) Perspectives of Earth and Space Scientists i       (Followers: 1) Petroleum Geoscience       (Followers: 5) Petroleum Science Petrology       (Followers: 6) PFG : Journal of Photogrammetry, Remote Sensing and Geoinformation Science       (Followers: 4) Photogrammetrie - Fernerkundung - Geoinformation Physical Geography       (Followers: 8) Physical Science International Journal Physics of Life Reviews       (Followers: 1) Physics of Metals and Metallography       (Followers: 18) Physics of Plasmas       (Followers: 10) Physics of the Earth and Planetary Interiors       (Followers: 34) Physics of the Solid State       (Followers: 7)

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Similar Journals
 Mathematical GeosciencesJournal Prestige (SJR): 0.76 Citation Impact (citeScore): 2Number of Followers: 4      Hybrid journal (It can contain Open Access articles) ISSN (Print) 1874-8961 - ISSN (Online) 1874-8953 Published by Springer-Verlag  [2469 journals]
• And the 2022 Krumbein Medalist of the IAMG is…

PubDate: 2022-05-13

• Variational Autoencoder or Generative Adversarial Networks' A
Comparison of Two Deep Learning Methods for Flow and Transport Data
Assimilation

Abstract: Abstract Groundwater modeling is an important tool for water resources management and aquifer remediation. However, the inherent strong heterogeneity of the subsurface and scarcity of observed data pose major challenges for groundwater flow and contaminant transport modeling. Data assimilation such as the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) can be used to improve the understanding of the subsurface by integrating a variety of data into the modeling. For highly heterogeneous aquifers such as fluvial deposits, traditional data assimilation methods cannot preserve geological structures. In this work, two of the most popular deep learning methods, Variational Autoencoder (VAE) and Generative Adversarial Network (GAN), are compared for identifying geological structures using flow and transport data assimilation. Specifically, VAE and GAN are used to re-parameterize the hydraulic conductivity fields with low dimensional latent variables. The ES-MDA then is used to update the latent variables by assimilating the observed data such as hydraulic head and contaminant concentration into the model. Synthetic examples of both categorical and continuous variables are conducted to test the performance of coupling ES-MDA with VAE or GAN. The results demonstrate that the generating quality of GAN is better such that channels are generated with similar properties as in the training image, while VAE has the advantage that data assimilation is more successful in allowing better localization of the actual channels for the considered inverse problem.
PubDate: 2022-05-12

• New Validity Conditions for the Multivariate Matérn Coregionalization
Model, with an Application to Exploration Geochemistry

Abstract: Abstract This paper addresses the problem of finding parametric constraints that ensure the validity of the multivariate Matérn covariance for modeling the spatial correlation structure of coregionalized variables defined in an Euclidean space. To date, much attention has been given to the bivariate setting, while the multivariate setting has been explored to only a limited extent. The existing conditions often imply severe restrictions on the upper bounds for the collocated correlation coefficients, which makes the multivariate Matérn model appealing for the case of weak spatial cross-dependence only. We provide a collection of sufficient validity conditions for the multivariate Matérn covariance that allows for more flexible parameterizations than those currently available, and prove that one can attain considerably higher upper bounds for the collocated correlation coefficients in comparison with our competitors. We conclude with an illustration on a trivariate geochemical data set and show that our enlarged parametric space yields better fitting performances.
PubDate: 2022-05-04

• Fracability Evaluation Based on the Three-Dimensional Geological Numerical
Simulation of In Situ Stress: Case Study of the Longmaxi Formation in the
Weirong Shale Gas Field, Southwestern China

Abstract: Abstract Quantitative evaluation of fracability is essential for hydraulic fracturing design, with the distribution of in situ stress being a key parameter. This study focused on evaluating fracability based on three-dimensional (3D) geological numerical simulation of in situ stress. First, a 3D geological model was established based on seismic data and the geological setting of a field of interest. Thereafter, the anisotropy of the mechanical parameters and boundary conditions was used to calculate the in situ stress on the 3D model. The analytic hierarchy process (AHP) method was then used to calculate the fracability related to the horizontal stress difference coefficient, rock mechanical brittleness, silicon content, cohesion, and internal friction angle. The horizontal stress difference coefficient was obtained by 3D geological numerical simulation, and the remaining parameters were obtained from logging data. The results revealed that the mechanical parameters exhibited significant anisotropy within the field of interest. The in situ stress depended on the structural depth and anisotropy of the mechanical parameters. The fracability index along the well trajectory differed at different well depths. The calculation results were verified using the Meijer G-function. With the parameters used in the AHP method, the in situ stress was determined by a 3D geological numerical simulation, logging data, and laboratory experiments, from which the fracability index was obtained. The results verified by the Meijer G-function indicated that obtaining the fracability index based on the 3D geological numerical simulation of in situ stress was advantageous with respect to complex formations.
PubDate: 2022-04-21

• Teaching Numerical Groundwater Flow Modeling with Spreadsheets

Abstract: Abstract The use of spreadsheets for numerical groundwater flow modeling is not a novelty; however, its potential in the classroom has not been emphasized enough. This Teachers Aid provides a step-by-step implementation of a steady-state, vertically integrated two-dimensional groundwater flow model in a confined irregular aquifer with boundary conditions of the three kinds and subject to pumping and recharge that will enhance the learning experience of students that are confronted for the first time with the numerical solution of the groundwater flow partial differential equation.
PubDate: 2022-04-08

• Correction to: Surface Warping Incorporating Machine Learning Assisted
Domain Likelihood Estimation: A New Paradigm in Mine Geology Modeling and
Automation

PubDate: 2022-04-01

• Surface Warping Incorporating Machine Learning Assisted Domain Likelihood
Estimation: A New Paradigm in Mine Geology Modeling and Automation

Abstract: In surface mining, assay measurements taken from production drilling often provide useful information that enables initially inaccurate surfaces (for example, mineralization boundaries) created using sparse exploration data to be revised and subsequently improved. Recently, a Bayesian warping technique was proposed to reshape modeled surfaces using geochemical observations and spatial constraints imposed by newly acquired blasthole data. This paper focuses on incorporating machine learning (ML) into this warping framework to make the likelihood computation generalizable. The technique works by adjusting the position of vertices on the surface to maximize the integrity of modeled geological boundaries with respect to sparse geochemical observations. Its foundation is laid by a Bayesian derivation in which the geological domain likelihood given the chemistry, $$p(g\!\mid \!{\mathbf {c}})$$ , plays a role similar to $$p(y({\mathbf {c}})\!\mid \! g)$$ for certain categorical mappings $$y:{\mathbb {R}}^K\rightarrow {\mathbb {Z}}$$ . This observation allows a manually calibrated process centered on the latter to be automated, since ML techniques may be used to estimate the former in a data-driven way. Machine learning performance is evaluated for gradient boosting, neural network, random forest, and other classifiers in a binary and multi-class context using precision and recall rates. Once ML likelihood estimators are integrated into the surface warping framework, surface shaping performance is evaluated using unseen data by examining the categorical distribution of test samples located above and below the warped surface. Large-scale validation experiments are performed to assess the overall efficacy of ML-assisted surface warping as a fully integrated component within an ore grade estimation system, where the posterior mean is obtained via Gaussian process (GP) inference with a Matérn 3/2 kernel. This article illustrates an application of machine learning within a complex system where grade estimation is accomplished by integrating boundary warping with ML and other components. Graphic
PubDate: 2022-04-01

• Robust Feature Extraction for Geochemical Anomaly Recognition Using a
Stacked Convolutional Denoising Autoencoder

Abstract: Abstract Deep neural networks perform very well in learning high-level representations in support of multivariate geochemical anomaly recognition. Geochemical exploration data typically contain a proportion of large variations and missing values, which motivated us to construct a network architecture optimized to deal with these data. Our approach adopted a stacked convolutional denoising autoencoder (SCDAE) to extract robust features and decreased the level of sensitivity to partially corrupted data, that is, input data that are partially missing. SCDAE parameters, which include the network depth, number of convolution layers, number of convolution kernels, and convolution kernel size, were optimized using trial-and-error experiments. The optimal SCDAE architecture was then used to recognize multivariate geochemical anomalies related to mineralization in a case study in southwestern Fujian Province, based on the differences in the reconstruction errors between sample populations. The spatial distribution of high reconstruction errors in the anomaly map was closely related to most known Fe deposits, indicating the effectiveness of the SCDAE at recognizing geochemical anomalies related to Fe mineralization. A comparative study between the SCDAE and a stacked convolutional autoencoder (SCAE) with different corruption levels showed that the SCDAE exhibited reduced sensitivity to stochastic disturbances with different corruption proportions, and had an enhanced ability to recognize geochemical anomalies varying in a reasonable range. The robustness of the SCDAE makes it applicable to a wide variety of geochemical exploration scenarios, particularly in areas with incomplete or missing data.
PubDate: 2022-04-01

• A Hybrid Estimation Technique Using Elliptical Radial Basis Neural
Networks and Cokriging

Abstract: Abstract Mineral resource estimation is an integral part of making informed decisions while evaluating a mining operation’s feasibility. Geostatistical tools estimate geological features with the assumptions of first and second-order stationarity. Kriging is considered the best linear unbiased estimation technique for modelling geological features; however, in domains where data is non-Gaussian, and features are complex, the assumption of stationarity and the linearity of kriging can lead to suboptimal estimates. This manuscript presents a hybrid machine learning and geostatistical algorithm to improve estimation in complex domains. Elliptical radial basis function neural networks (ERBFN) take advantage of non-stationary functions to generate geological estimates. An ERBFN does not require the assumption of stationarity, and the only input features required are the spatial coordinates of the known data. The proposed hybrid estimation considers the machine learning estimate as exhaustive secondary data in ordinary intrinsic collocated cokriging, taking advantage of kriging’s exactitude while including the non-stationary features modelled in the ERBFN. The principle results of integrating geostatistics and machine learning indicate an improved estimation technique in domains with complex features, poorly defined domains, or non-Gaussian data. The major conclusion from this paper is that using the proposed hybrid algorithm can improve mineral resource estimations.
PubDate: 2022-04-01

• Bridging Deep Convolutional Autoencoders and Ensemble Smoothers for
Improved Estimation of Channelized Reservoirs

Abstract: Abstract One of the main problems associated with applying data assimilation methods for facies models is the lack of geological plausibility in updates. This issue is even more acute for channelized reservoirs, knowing that, without a reliable parameterization, the geometrical structure of the channels can hardly be reproduced in the updated step of any data assimilation method. This paper presents a new methodology for estimation and uncertainty quantification of facies fields in channelized reservoirs, bridging a deep convolutional autoencoder with an ensemble-based method. The proposed methodology is suitable for any geological simulation model and does not use the resampling from the training image when using a multipoint geostatistical simulation model. Besides the channel estimation, the proposed methodology preserves the geological plausibility in the updated step of the history-matching method. The new methodology employs, inside the parameterization, a deep convolutional autoencoder to reconstruct the channel geometry. The convolutional autoencoder is used for image reconstruction purposes. The input of the training set of the autoencoder consists of images (facies fields) generated with a parameterization of the facies fields and perturbed with a Gaussian noise having spatial correlation. This procedure ensures the consistency of the method in the sense that the input fields have a similar structure with the facies fields obtained after the history matching. The methodology is tested for channelized reservoirs, with different levels of complexity, and also by comparison with previous methods that use or not resampling from the training image. The results show an improvement in the geological plausibility, estimation, and uncertainty quantification of the channel distributions while achieving a good data match.
PubDate: 2022-03-24

• Special Issue: Geostatistics and Machine Learning

Abstract: Abstract Recent years have seen a steady growth in the number of papers that apply machine learning methods to problems in the earth sciences. Although they have different origins, machine learning and geostatistics share concepts and methods. For example, the kriging formalism can be cast in the machine learning framework of Gaussian process regression. Machine learning, with its focus on algorithms and ability to seek, identify, and exploit hidden structures in big data sets, is providing new tools for exploration and prediction in the earth sciences. Geostatistics, on the other hand, offers interpretable models of spatial (and spatiotemporal) dependence. This special issue on Geostatistics and Machine Learning aims to investigate applications of machine learning methods as well as hybrid approaches combining machine learning and geostatistics which advance our understanding and predictive ability of spatial processes.
PubDate: 2022-03-21

• A Comparison Between Machine Learning and Functional Geostatistics
Approaches for Data-Driven Analyses of Sediment Transport in a Pre-Alpine
Stream

Abstract: Abstract The problem of providing data-driven models for sediment transport in a pre-Alpine stream in Italy is addressed. This study is based on a large set of measurements collected from real pebbles, traced along the stream through radio-frequency identification tags after precipitation events. Two classes of data-driven models based on machine learning and functional geostatistics approaches are proposed and evaluated to predict the probability of movement of single pebbles within the stream. The first class built upon gradient-boosting decision trees allows one to estimate the probability of movement of a pebble based on the pebbles’ geometrical features, river flow rate, location, and subdomain types. The second class is built upon functional kriging, a recent geostatistical technique that allows one to predict a functional profile—that is, the movement probability of a pebble, as a function of the pebbles’ geometrical features or the stream’s flow rate—at unsampled locations in the study area. Although grounded in different perspectives, both models aim to account for two main sources of uncertainty, namely, (1) the complexity of a river’s morphological structure and (2) the highly nonlinear dependence between probability of movement, pebble size and shape, and the stream’s flow rate. The performance of the two methods is extensively compared in terms of classification accuracy. The analyses show that despite the different perspectives, the overall performance is adequate and consistent, which suggests that both approaches can provide modeling frameworks for sediment transport. These data-driven approaches are also compared with physics-based ones that are classically used in the hydrological literature. Finally, the use of the developed models in a bottom-up strategy, which starts with the prediction/classification of a single pebble and then integrates the results into a forecast of the grain-size distribution of mobilized sediments, is discussed.
PubDate: 2022-03-16

• Application of Bayesian Generative Adversarial Networks to Geological
Facies Modeling

Abstract: Abstract Geological facies modeling is a key component in exploration and characterization of subsurface reservoirs. While traditional geostatistical approaches are still commonly used nowadays, deep learning is gaining a lot of attention within geoscientific community for generating subsurface models, as a result of recent advance of computing powers and increasing availability of training data sets. This work presents a deep learning approach for geological facies modeling based on generative adversarial networks (GANs) combined with training-image-based simulation. In a typical application of learned networks, all neural network parameters are fixed after training, and the uncertainty in the trained model cannot be analyzed. To address this problem, a Bayesian GANs (BGANs) approach is proposed to create facies models. In this approach, a probability distribution is assigned to the neural parameters of the BGANs. Only neural parameters of the generator in BGANs are assigned with a probability function, and the ones in the discriminator are treated as fixed. Random samples are then drawn from the posterior distribution of neural parameters to simulate subsurface facies models. The proposed approach is applied to the two different geological depositional scenarios, fluvial channels and carbonate mounds, and the generated models reasonably capture the variability of the training/testing data. Meanwhile, the model uncertainty of learned networks is readily accessible. To fully sample the spatial distribution in the training image set, a large collection of samples of network parameters is required to be drawn from the posterior distribution, thus significantly increasing computational cost.
PubDate: 2022-02-19
DOI: 10.1007/s11004-022-09994-w

• Acknowledgement for Reviewers for 2021

PubDate: 2022-02-08
DOI: 10.1007/s11004-022-09993-x

• Stochastic Modelling of Mineral Exploration Targets

Abstract: Abstract Mineral deposits are metal enrichment anomalies, occurring as local manifestations of the interplay between various geological processes that operate at a wide range of temporal and spatial scales. Mineral prospectivity maps are generated by integrating several proxy maps that represent critical geological processes in a mineral system conceptual model. The derivation of mineral prospectivity maps is subject to several types of uncertainty, including systematic (inadequate knowledge of mineralisation processes), stochastic (incomplete geoscience data), and model uncertainty (multiple choices for predictive models and their parameters). Traditional approaches to mineral prospectivity mapping often fail to fully appreciate different sources of uncertainty and spatiotemporal interdependencies between proxy maps associated with the mineral system components. Therefore, these traditional approaches are biased and understate the overall uncertainty. For instance, spatial proxies are mapped using univariate deterministic approaches that ignore stochastic uncertainty and spatial dependencies (i.e., auto- and cross-correlations). This study presents a multivariate stochastic model for prediction and uncertainty quantification of mineral exploration targets by combining multivariate geostatistical simulations and spatial machine learning algorithms. The spatial machine learning algorithm used in the stochastic model is a spatially aware random forests algorithm based on higher-order spatial statistics. It is demonstrated that the proposed approach can detect intrinsic heterogeneity, spatial interdependencies, and complex spatial patterns in proxy maps that are related to the mineralisation type of interest. The approach is illustrated using a synthetic case study with multiple geochemical, geophysical, and lithological attributes.
PubDate: 2022-02-03
DOI: 10.1007/s11004-021-09989-z

• Contaminant Source Identification in Aquifers: A Critical View

Abstract: Abstract Forty years and 157 papers later, research on contaminant source identification has grown exponentially in number but seems to be stalled concerning advancement towards the problem solution and its field application. This paper presents a historical evolution of the subject, highlighting its major advances. It also shows how the subject has grown in sophistication regarding the solution of the core problem (the source identification), forgetting that, from a practical point of view, such identification is worthless unless it is accompanied by a joint identification of the other uncertain parameters that characterize flow and transport in aquifers.
PubDate: 2022-02-01
DOI: 10.1007/s11004-021-09976-4

• Quantifying Mineral Resources and Their Uncertainty Using Two Existing
Machine Learning Methods

Abstract: Abstract Mineral resources are typically quantified by estimating the grade–tonnage curve for different resource categories. Statutory resource assessment reports (e.g., NI-43-101), however, do not include a measure of uncertainty for the disclosed resources despite the major investments required for mining projects. Although conditional simulation can provide confidence intervals (CIs) for resource estimation, it requires a strong stationarity assumption and depends on the variogram model selected, which is often poorly defined with the available data. In order to avoid these limitations, this research proposes the use and comparison of two machine learning (ML) methods, multiple linear regression and a multilayer neural network, to generate tonnage curves and their CIs directly from the data. The classical variogram modeling step is replaced by the specification of intervals for each parameter of the selected variogram model. The learning is carried out in a perfectly controlled environment using simulations with known variograms. Numerous reference deposits are sampled, and for each one, a series of conditional realizations define the mean tonnage and CI curves. Different statistics computed for the entire data set are used as input to predict the tonnage and CI curves by the ML methods. The results indicate that there are no significant differences between the ML methods. In addition, ML resource predictions outperform those obtained with ordinary kriging, constrained kriging, uniform conditioning and indirect lognormal correction, being surpassed only by the discrete Gaussian model. Nevertheless, these predictors were favored by the use of true variogram models. Moreover, the coverage probabilities of different CIs reach the nominal levels indicating adequate resource uncertainty quantification. Finally, two case studies validate the effectiveness of the proposed approach for tonnage prediction and uncertainty quantification.
PubDate: 2022-02-01
DOI: 10.1007/s11004-021-09971-9

• Probability Transform of Kriging Estimates and Its Effects on Selection
Bias and Conditional Bias

Abstract: Abstract This study shows the benefits of doing a probability transform of kriging estimates, into the sample data distribution, to improve the selection of spatial locations with values higher than a given threshold. The probability transform restores the data variance in the kriging estimates, which increases the conditional bias but reduces the bias for the expected true values when selection is done on the basis of the transformed estimates.
PubDate: 2022-02-01
DOI: 10.1007/s11004-021-09974-6

• Sequential Simulation of a Conditional Boolean Model

Abstract: Abstract Simulations of object-based models are widely used in various fields of geoscience to represent subsurface heterogeneities. A prototype of such a model is the Boolean model. Based on the stability and decomposition properties, a novel algorithm is proposed to simulate Boolean models, stationary or not, subject to foreground and background point conditions. This algorithm amounts to simulating two independent Boolean models, namely an avoiding Boolean model made of objects that avoid all conditioning data points and a hitting Boolean model, the objects of which hit one or several foreground conditioning data points. The first model is simulated using thinning techniques, and the second model is simulated by particle filtering. Overall, the algorithm produces exact simulations. It is very fast and easy to implement.
PubDate: 2022-02-01
DOI: 10.1007/s11004-021-09977-3

• Bayesian Deep Learning for Spatial Interpolation in the Presence of
Auxiliary Information

Abstract: Earth scientists increasingly deal with ‘big data’. For spatial interpolation tasks, variants of kriging have long been regarded as the established geostatistical methods. However, kriging and its variants (such as regression kriging, in which auxiliary variables or derivatives of these are included as covariates) are relatively restrictive models and lack capabilities provided by deep neural networks. Principal among these is feature learning: the ability to learn filters to recognise task-relevant patterns in gridded data such as images. Here, we demonstrate the power of feature learning in a geostatistical context by showing how deep neural networks can automatically learn the complex high-order patterns by which point-sampled target variables relate to gridded auxiliary variables (such as those provided by remote sensing) and in doing so produce detailed maps. In order to cater for the needs of decision makers who require well-calibrated probabilities, we also demonstrate how both aleatoric and epistemic uncertainty can be quantified in our deep learning approach via a Bayesian approximation known as Monte Carlo dropout. In our example, we produce a national-scale probabilistic geochemical map from point-sampled observations with auxiliary data provided by a terrain elevation grid. By combining location information with automatically learned terrain derivatives, our deep learning approach achieves an excellent coefficient of determination ( $$R^{2} = 0.74$$ ) and near-perfect probabilistic calibration on held-out test data. Our results indicate the suitability of Bayesian deep learning and its feature-learning capabilities for large-scale geostatistical applications where uncertainty matters. Graphic
PubDate: 2022-01-17
DOI: 10.1007/s11004-021-09988-0

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