Artificial Intelligence in Geosciences最新文献

{"title":"Microleveling aerogeophysical data using deep convolutional network and MoG-RPCA","authors":"Xinze Li ,&nbsp;Bangyu Wu ,&nbsp;Guofeng Liu ,&nbsp;Xu Zhu ,&nbsp;Linfei Wang","doi":"10.1016/j.aiig.2021.08.003","DOIUrl":"10.1016/j.aiig.2021.08.003","url":null,"abstract":"<div><p>Residual magnetic error remains after standard levelling process. The weak non-geological effect, manifesting itself as streaky noise along flight lines, creates a challenge for airborne geophysical data processing and interpretation. Microleveling is the process to eliminate this residual noise and is now a standard areogeophysical data processing step. In this paper, we propose a two-step procedure for single aerogeophysical data microleveling: a deep convolutional network is first adopted as approximator to map the original data into a low-level part with nature geological structures and a corrugated residual which still contains high-level detail geological structures; second, the mixture of Gaussian robust principal component analysis (MoG-RPCA) is then used to separate the weak energy fine structures from the residual. The final microleveling result is the addition of low-level structures from deep convolutional network and fine structures from MoG-RPCA. The deep convolutional network does not need dataset for training and the handcrafted network serves as prior (deep image prior) to capture the low-level nature geological structures in the areogeophysical data. Experiments on synthetic data and field data demonstrate that the combination of deep convolutional network and MoG-RPCA is an effective framework for single areogeophysical data microleveling.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"2 ","pages":"Pages 20-25"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.aiig.2021.08.003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79759464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unilateral Alignment: An interpretable machine learning method for geophysical logs calibration","authors":"Wenting Zhang ,&nbsp;Jichen Wang ,&nbsp;Kun Li ,&nbsp;Haining Liu ,&nbsp;Yu Kang ,&nbsp;Yuping Wu ,&nbsp;Wenjun Lv","doi":"10.1016/j.aiig.2022.02.006","DOIUrl":"10.1016/j.aiig.2022.02.006","url":null,"abstract":"<div><p>Most of the existing machine learning studies in logs interpretation do not consider the data distribution discrepancy issue, so the trained model cannot well generalize to the unseen data without calibrating the logs. In this paper, we formulated the geophysical logs calibration problem and give its statistical explanation, and then exhibited an interpretable machine learning method, i.e., Unilateral Alignment, which could align the logs from one well to another without losing the physical meanings. The involved UA method is an unsupervised feature domain adaptation method, so it does not rely on any labels from cores. The experiments in 3 wells and 6 tasks showed the effectiveness and interpretability from multiple views.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"2 ","pages":"Pages 192-201"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666544122000065/pdfft?md5=d53965cba548dfab0175d6e81309120d&pid=1-s2.0-S2666544122000065-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76990839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification random forest with exact conditioning for spatial prediction of categorical variables","authors":"Francky Fouedjio","doi":"10.1016/j.aiig.2021.11.003","DOIUrl":"10.1016/j.aiig.2021.11.003","url":null,"abstract":"<div><p>Machine learning methods are increasingly used for spatially predicting a categorical target variable when spatially exhaustive predictor variables are available within the study region. Even though these methods exhibit competitive spatial prediction performance, they do not exactly honor the categorical target variable's observed values at sampling locations by construction. On the other side, competitor geostatistical methods perfectly match the categorical target variable's observed values at sampling locations by essence. In many geoscience applications, it is often desirable to perfectly match the observed values of the categorical target variable at sampling locations, especially when the categorical target variable's measurements can be reasonably considered error-free. This paper addresses the problem of exact conditioning of machine learning methods for the spatial prediction of categorical variables. It introduces a classification random forest-based approach in which the categorical target variable is exactly conditioned to the data, thus having the exact conditioning property like competitor geostatistical methods. The proposed method extends a previous work dedicated to continuous target variables by using an implicit representation of the categorical target variable. The basic idea consists of transforming the ensemble of classification tree predictors' (categorical) resulting from the traditional classification random forest into an ensemble of signed distances (continuous) associated with each category of the categorical target variable. Then, an orthogonal representation of the ensemble of signed distances is created through the principal component analysis, thus allowing to reformulate the exact conditioning problem as a system of linear inequalities on principal component scores. Then, the sampling of new principal component scores ensuring the data's exact conditioning is performed via randomized quadratic programming. The resulting conditional signed distances are turned out into an ensemble of categorical outputs, which perfectly honor the categorical target variable's observed values at sampling locations. Then, the majority vote is used to aggregate the ensemble of categorical outputs. The effectiveness of the proposed method is illustrated on a simulated dataset for which ground-truth is available and showcased on a real-world dataset, including geochemical data. A comparison with geostatistical and traditional machine learning methods show that the proposed technique can perfectly match the categorical target variable's observed values at sampling locations while maintaining competitive out-of-sample predictive performance.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"2 ","pages":"Pages 82-95"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666544121000290/pdfft?md5=7f00cfaafe708bd97ae6249d5444d7b5&pid=1-s2.0-S2666544121000290-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91318447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based prediction of trace element concentrations using data from the Karoo large igneous province and its application in prospectivity mapping","authors":"Steven E. Zhang ,&nbsp;Glen T. Nwaila ,&nbsp;Julie E. Bourdeau ,&nbsp;Lewis D. Ashwal","doi":"10.1016/j.aiig.2021.11.002","DOIUrl":"10.1016/j.aiig.2021.11.002","url":null,"abstract":"<div><p>In this study, we present a machine learning-based method to predict trace element concentrations from major and minor element concentration data using a legacy lithogeochemical database of magmatic rocks from the Karoo large igneous province (Gondwana Supercontinent). Wedemonstrate that a variety of trace elements, including most of the lanthanides, chalcophile, lithophile, and siderophile elements, can be predicted with excellent accuracy. This finding reveals that there are reliable, high-dimensional elemental associations that can be used to predict trace elements in a range of plutonic and volcanic rocks. Since the major and minor elements are used as predictors, prediction performance can be used as a direct proxy for geochemical anomalies. As such, our proposed method is suitable for prospective exploration by identifying anomalous trace element concentrations. Compared to multivariate compositional data analysis methods, the new method does not rely on assumptions of stoichiometric combinations of elements in the data to discover geochemical anomalies. Because we do not use multivariate compositional data analysis techniques (e.g. principal component analysis and combined use of major, minor and trace elements data), we also show that log-ratio transforms do not increase the performance of the proposed approach and are unnecessary for algorithms that are not spatially aware in the feature space. Therefore, we demonstrate that high-dimensional elemental associations can be modelled in an automated manner through a data-driven approach and without assumptions of stoichiometry within the data. The approach proposed in this study can be used as a replacement method to the multivariate compositional data analysis technique that is used for prospectivity mapping, or be used as a pre-processor to reduce the detection of false geochemical anomalies, particularly where the data is of variable quality.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"2 ","pages":"Pages 60-75"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666544121000289/pdfft?md5=ab6a53b98e828233b602726ceb4cbfcf&pid=1-s2.0-S2666544121000289-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83179231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards a fully data-driven prospectivity mapping methodology: A case study of the Southeastern Churchill Province, Québec and Labrador","authors":"Steven E. Zhang ,&nbsp;Julie E. Bourdeau ,&nbsp;Glen T. Nwaila ,&nbsp;David Corrigan","doi":"10.1016/j.aiig.2022.02.002","DOIUrl":"10.1016/j.aiig.2022.02.002","url":null,"abstract":"<div><p>Mineral exploration campaigns are financially risky. Several state-of-the-art methods have been developed to mitigate the risk, including predictive modelling of mineral prospectivity using principal component analysis (PCA) and geographic information systems (GIS). The PCA and GIS approach is currently considered acceptable for generating mineral exploration targets. However, some of its limitations are the dependence on sample stoichiometry (e.g., the existence of minerals), the necessity of log-ratio transformations when dealing with compositional data, and manual interpretation and use of principal components to enhance potential geochemical anomalies for prospectivity mapping. In this study, we generalize the fundamental ideas behind the PCA and GIS approach by developing a new data-driven approach using ML. We showcase a new workflow capable of generating either intermediate evidence layers or final prospectivity maps that depict major regional geochemical anomalies using multi-element geochemical data from Southeastern Churchill Province (Québec and Labrador), Canada. The region is known for its REEs endowment and the data were gathered for prospectivity mapping. A comparison with the established multivariate hybrid data- and knowledge-based approach revealed that on a roughly comparable basis of the amount of manual effort, our new data-driven procedure can much more accurately identify geochemical anomalies in both univariate and multivariate applications. The results of our prospectivity mapping corroborate with the ground truth or known geological anomalies in the studied region. These findings have potentially wider implications on exploration target generation, where project risks (financial, environmental, political, etc.) and geochemical anomalies must be quantified using robust and effective data-driven approaches. In addition, our methodology is more replicable and objective, as manual geoscientific interpretation is not required during the detection of geochemical anomalies.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"2 ","pages":"Pages 128-147"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666544122000028/pdfft?md5=ff135cf1926bd59b2e55e86978e74d00&pid=1-s2.0-S2666544122000028-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74983532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The benefits and dangers of using artificial intelligence in petrophysics","authors":"Steve Cuddy","doi":"10.1016/j.aiig.2021.04.001","DOIUrl":"https://doi.org/10.1016/j.aiig.2021.04.001","url":null,"abstract":"<div><p>Artificial Intelligence, or AI, is a method of data analysis that learns from data, identify patterns and makes predictions with the minimal human intervention. AI is bringing many benefits to petrophysical evaluation. Using case studies, this paper describes several successful applications. The future of AI has even more potential. However, if used carelessly there are potentially grave consequences.</p><p>A complex Middle East Carbonate field needed a bespoke shaly water saturation equation. AI was used to ‘evolve’ an ideal equation, together with field specific saturation and cementation exponents. One UKCS gas field had an ‘oil problem’. Here, AI was used to unlock the hidden fluid information in the NMR T1 and T2 spectra and successfully differentiate oil and gas zones in real time. A North Sea field with 30 wells had shear velocity data (Vs) in only 4 wells. Vs was required for reservoir modelling and well bore stability prediction. AI was used to predict Vs in all 30 wells. Incorporating high vertical resolution data, the Vs predictions were even better than the recorded logs.</p><p>As it is not economic to take core data on every well, AI is used to discover the relationships between logs, core, litho-facies and permeability in multi-dimensional data space. As a consequence, all wells in a field were populated with these data to build a robust reservoir model. In addition, the AI predicted data upscaled correctly unlike many conventional techniques. AI gives impressive results when automatically log quality controlling (LQC) and repairing electrical logs for bad hole and sections of missing data.</p><p>AI doesn’t require prior knowledge of the petrophysical response equations and is self-calibrating. There are no parameters to pick or cross-plots to make. There is very little user intervention and AI avoids the problem of ‘garbage in, garbage out’ (GIGO), by ignoring noise and outliers. AI programs work with an unlimited number of electrical logs, core and gas chromatography data; and don’t ‘fall-over’ if some of those inputs are missing.</p><p>AI programs currently being developed include ones where their machine code evolves using similar rules used by life’s DNA code. These AI programs pose considerable dangers far beyond the oil industry as described in this paper. A ‘risk assessment’ is essential on all AI programs so that all hazards and risk factors, that could cause harm, are identified and mitigated.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"2 ","pages":"Pages 1-10"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.aiig.2021.04.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92057493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Capsule network-based approach for estimating grassland coverage using time series data from enhanced vegetation index","authors":"Yaqi Sun,&nbsp;Hailong Liu,&nbsp;Zhengqiang Guo","doi":"10.1016/j.aiig.2021.08.001","DOIUrl":"10.1016/j.aiig.2021.08.001","url":null,"abstract":"<div><p>The degradation and desertification of grasslands pose a daunting challenge to China's arid and semiarid areas owing to the increasing demand for them in light of the rise of animal husbandry. Monitoring grasslands by using big data has emerged as a popular area of research in recent years. As grassland degradation is a slow and gradual process, the accurate identification of grassland cover is key to monitoring it. Vegetation coverage is currently monitored mainly by combining inversion-based methods with field surveys, which requires significant human effort and other resources and is thus unsuitable for use at a large scale. We proposed to use time series from the enhanced vegetation index (EVI) in capsule network-based methods to identify grasslands. The process classified grassland coverage into four levels, high, medium, low, and other, based on Landsat images from 2019. The accuracy in classifying the grasslands at each level was higher than 90%, with an overall accuracy of 96.32% and a kappa coefficient of 0.9508. The proposed method outperformed the SVM, RF, and LSTM algorithms in terms of classification accuracy.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"2 ","pages":"Pages 26-34"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.aiig.2021.08.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91484651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flood susceptibility assessment using artificial neural networks in Indonesia","authors":"Stela Priscillia ,&nbsp;Calogero Schillaci ,&nbsp;Aldo Lipani","doi":"10.1016/j.aiig.2022.03.002","DOIUrl":"10.1016/j.aiig.2022.03.002","url":null,"abstract":"<div><p>Flood incidents can massively damage and disrupt a city economic or governing core. However, flood risk can be mitigated through event planning and city-wide preparation to reduce damage. For, governments, firms, and civilians to make such preparations, flood susceptibility predictions are required. To predict flood susceptibility nine environmental related factors have been identified. They are elevation, slope, curvature, topographical wetness index (TWI), Euclidean distance from a river, land-cover, stream power index (SPI), soil type and precipitation. This work will use these environmental related factors alongside Sentinel-1 satellite imagery in a model intercomparison study to back-predict flood susceptibility in Jakarta for the January 2020 historic flood event across 260 key locations. For each location, this study uses current environmental conditions to predict flood status in the following month. Considering the imbalance between instances of flooded and non-flooded conditions, the Synthetic Minority Oversampling Technique (SMOTE) has been implemented to balance both classes in the training set. This work compares predictions from artificial neural networks (ANN), k-Nearest Neighbors algorithms (k-NN) and Support Vector Machines (SVM) against a random baseline. The effects of the SMOTE are also assessed by training each model on balanced and imbalanced datasets. The ANN is found to be superior to the other machine learning models.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"2 ","pages":"Pages 215-222"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666544122000090/pdfft?md5=be9afd52112c0a20ec31a3de99a5d5da&pid=1-s2.0-S2666544122000090-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86957808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wavefield solutions from machine learned functions constrained by the Helmholtz equation","authors":"Tariq Alkhalifah ,&nbsp;Chao Song ,&nbsp;Umair bin Waheed ,&nbsp;Qi Hao","doi":"10.1016/j.aiig.2021.08.002","DOIUrl":"10.1016/j.aiig.2021.08.002","url":null,"abstract":"<div><p>Solving the wave equation is one of the most (if not the most) fundamental problems we face as we try to illuminate the Earth using recorded seismic data. The Helmholtz equation provides wavefield solutions that are dimensionally reduced, per frequency, compared to the time domain, which is useful for many applications, like full waveform inversion. However, our ability to attain such wavefield solutions depends often on the size of the model and the complexity of the wave equation. Thus, we use here a recently introduced framework based on neural networks to predict functional solutions through setting the underlying physical equation as a loss function to optimize the neural network (NN) parameters. For an input given by a location in the model space, the network learns to predict the wavefield value at that location, and its partial derivatives using a concept referred to as automatic differentiation, to fit, in our case, a form of the Helmholtz equation. We specifically seek the solution of the scattered wavefield considering a simple homogeneous background model that allows for analytical solutions of the background wavefield. Providing the NN with a reasonable number of random points from the model space will ultimately train a fully connected deep NN to predict the scattered wavefield function. The size of the network depends mainly on the complexity of the desired wavefield, with such complexity increasing with increasing frequency and increasing model complexity. However, smaller networks can provide smoother wavefields that might be useful for inversion applications. Preliminary tests on a two-box-shaped scatterer model with a source in the middle, as well as, the Marmousi model with a source at the surface demonstrate the potential of the NN for this application. Additional tests on a 3D model demonstrate the potential versatility of the approach.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"2 ","pages":"Pages 11-19"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.aiig.2021.08.002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80598192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sparse inversion-based seismic random noise attenuation via self-paced learning","authors":"Yang Yang ,&nbsp;Zhiguo Wang ,&nbsp;Jinghuai Gao ,&nbsp;Naihao Liu ,&nbsp;Zhen Li","doi":"10.1016/j.aiig.2022.03.003","DOIUrl":"10.1016/j.aiig.2022.03.003","url":null,"abstract":"<div><p>Seismic random noise reduction is an important task in seismic data processing at the Chinese loess plateau area, which benefits the geologic structure interpretation and further reservoir prediction. The sparse inversion is one of the widely used tools for seismic random noise reduction, which is often solved via the sparse approximation with a regularization term. The <em>ℓ</em>₁ norm and total variation (TV) regularization term are two commonly used regularization terms. However, the <em>ℓ</em>₁ norm is only a relaxation of the <em>ℓ</em>₀ norm, which cannot always provide a sparse result. The TV regularization term may provide unexpected staircase artifacts. To avoid these disadvantages, we proposed a workflow for seismic random noise reduction by using the self-paced learning (SPL) scheme and a sparse representation (i.e. the continuous wavelet transform, CWT) with a mixed norm regularization, which includes the <em>ℓ</em>_<em>p</em> norm and the TV regularization. In the implementation, the SPL, which is inspired by human cognitive learning, is introduced to avoid the bad minima of the non-convex cost function. The SPL can first select the high signal-to-noise ratio (SNR) seismic data and then gradually select the low SNR seismic data into the proposed workflow. Moreover, the generalized Beta wavelet (GBW) is adopted as the basic wavelet of the CWT to better match for seismic wavelets and then obtain a more localized time-frequency (TF) representation. It should be noted that the GBW can easily constitute a tight frame, which saves the calculation time. Synthetic and field data examples are adopted to demonstrate the effectiveness of the proposed workflow for effectively suppressing seismic random noises and accurately preserving valid seismic reflections.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"2 ","pages":"Pages 223-233"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666544122000107/pdfft?md5=f1a54c0d9a60a906b15a366bf305460a&pid=1-s2.0-S2666544122000107-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91264915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}