Computers & Geosciences最新文献

{"title":"A neural network architecture based on attention gate mechanism for 3D magnetotelluric forward modeling","authors":"Xin Zhong ,&nbsp;Weiwei Ling ,&nbsp;Kejia Pan ,&nbsp;Chaofei Liu ,&nbsp;Pinxia Wu ,&nbsp;Jiajing Zhang ,&nbsp;Zhiliang Zhan ,&nbsp;Wenbo Xiao","doi":"10.1016/j.cageo.2025.106079","DOIUrl":"10.1016/j.cageo.2025.106079","url":null,"abstract":"<div><div>Traditional methods of three-dimensional (3D) magnetotelluric (MT) numerical forward modeling, such as the finite element method (FEM) and finite volume method (FVM), suffer from high computational costs and low efficiency due to limitations in mesh refinement and computational resources. We propose a novel neural network architecture named MTAGU-Net, which integrates an attention gating mechanism for 3D MT forward modeling. Specifically, a dual-path attention gating module is designed based on data images of the apparent resistivity and phase and embedded in the skip connections between the encoder and decoder. This module enables the fusion of critical anomaly information from shallow feature maps during the decoding of deep feature maps, significantly enhancing the network’s capability to extract features from anomalous regions. Numerical experiments show that the forward predictions of MTAGU-Net closely match the numerical solutions obtained through FEM. Compared to leading network models such as 3D U-Net and Swin-UNETR, MTAGU-Net not only achieves superior prediction accuracy but also demonstrates robust generalization capabilities when handling model samples that were not part of the training dataset. For medium-scale grid forward modeling, MTAGU-Net delivers a prediction speed more than a hundred times faster than FEM. This remarkable computational efficiency makes MTAGU-Net a highly promising core engine for inversion algorithms, significantly enhancing the computational performance of 3D MT inversion.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"207 ","pages":"Article 106079"},"PeriodicalIF":4.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural network-based framework for signal separation in spatio-temporal gravity data","authors":"Betty Heller-Kaikov,&nbsp;Roland Pail,&nbsp;Martin Werner","doi":"10.1016/j.cageo.2025.106057","DOIUrl":"10.1016/j.cageo.2025.106057","url":null,"abstract":"<div><div>Global, temporal gravity data such as those provided by the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow on (GRACE-FO) satellite missions contain signals from many mass redistribution processes on Earth. These include hydrological, atmospheric, oceanic, cryospheric and solid Earth-related processes. As the measured gravity changes represent the sum of all signals, an optimal exploitation of these data for scientific applications requires strategies for separating the individual contained signals. We provide a neural network algorithm using a multi-channel U-Net architecture that translates the sum of several signals to the individual contained components based on their typical space–time patterns. The software contains strategies for transforming spatio-temporal gravity data depending on latitude, longitude, and time to 2-D “image” training samples. The software also includes implementations of strategies for introducing additional knowledge about the physical behavior of the individual signals as constraints to the training. In a closed-loop simulation example, simulated gravity signals induced by processes in the atmosphere and oceans, hydrosphere, cryosphere and solid Earth are successfully separated at relative RMS prediction errors between 19 and 67%. This shows that neural network-based methods can help solving geodetic tasks if the considered data is transformed into a suitable data format. To apply the framework to real observational data, we suggest training the network on representative, physical forward-modeled signals and subsequently applying the trained network to real data. The latter will additionally require external validation strategies. The software is freely available on GitHub under <span><span>https://github.com/Betty-Heller/neural-gravity</span><svg><path></path></svg></span> and is, in general, also applicable for signal separation in any other dataset depending on three variables.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"207 ","pages":"Article 106057"},"PeriodicalIF":4.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of automated stratigraphic interpretations of boreholes with geology-informed metrics","authors":"Sebastián Garzón ,&nbsp;Willem Dabekaussen ,&nbsp;Freek S. Busschers ,&nbsp;Eva De Boever ,&nbsp;Siamak Mehrkanoon ,&nbsp;Derek Karssenberg","doi":"10.1016/j.cageo.2025.106043","DOIUrl":"10.1016/j.cageo.2025.106043","url":null,"abstract":"<div><div>Stratigraphic interpretation of borehole data is a fundamental aspect of subsurface geological models, providing critical insights into the distribution of stratigraphic units. However, expert interpretation of all available borehole data is impractical for large-scale regional mapping involving thousands of boreholes. Automated interpretations using machine learning models can significantly increase the number of boreholes included in subsurface geological models. Nevertheless, these predictions must adhere to strict spatial and stratigraphic relationships (e.g. superposition) to ensure geological plausibility, which often requires post-processing tasks. Traditional evaluation metrics commonly used for general-domain classification tasks (e.g. accuracy, F1-score) do not necessarily reflect the geological plausibility of predictions, as they fail to account for the sequential nature and spatial relationships inherent in borehole interpretation. To address this limitation, we propose and evaluate a set of geology-informed metrics that focus on three key aspects of stratigraphic interpretation, namely the expected geographical extent of units (extent metrics), their sequential relationships (sequence metrics), and their vertical positioning along boreholes (position metrics). Using a dataset of 1394 boreholes from the Cenozoic Roer Valley Graben (southeast Netherlands), which covers <span><math><mo>∼</mo></math></span>3000 km<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> and includes 15 lithostratigraphic units, we demonstrate that Random Forest and Neural Network models with similar performance on traditional metrics (e.g. accuracy, Cohen’s kappa, and F1-score) can differ significantly in their ability to produce geologically plausible predictions. For example, while many model configurations achieve <span><math><mo>∼</mo></math></span>75%–80% agreement between expected and predicted classes, the Neural Network models better capture the sequential stratigraphic relationships expected in the study area. Our results underscore the need for domain-specific metrics that offer a more accurate and interpretable assessment of model performance.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"207 ","pages":"Article 106043"},"PeriodicalIF":4.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145098952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EFKAN: A KAN-integrated neural operator for efficient magnetotelluric forward modeling","authors":"Feng Wang ,&nbsp;Hong Qiu ,&nbsp;Yingying Huang ,&nbsp;Xiaozhe Gu ,&nbsp;Renfang Wang ,&nbsp;Bo Yang","doi":"10.1016/j.cageo.2025.106052","DOIUrl":"10.1016/j.cageo.2025.106052","url":null,"abstract":"<div><div>Forward modeling is the cornerstone of magnetotelluric (MT) inversion. Neural operators have been successfully applied to solve partial differential equations, demonstrating encouraging performance in rapid MT forward modeling. In particular, they can obtain the electromagnetic field at arbitrary locations and frequencies, which is meaningful for MT forward modeling. In conventional neural operators, the projection layers have been dominated by classical multi-layer perceptrons, which may reduce the precision of solution because they usually suffer from the disadvantages of multi-layer perceptrons, such as lack of interpretability, overfitting, etc. Therefore, to improve the accuracy of the MT forward modeling with neural operators, we integrate the Fourier neural operator with the Kolmogorov–Arnold network (KAN). Specifically, we adopt KAN as the trunk network instead of the classic multi-layer perceptrons to project the resistivity and phase, determined by the branch network-Fourier neural operator, to the desired locations and frequencies. Experimental results demonstrate that the proposed method can achieve high precision in obtaining apparent resistivity and phase at arbitrary frequencies and/or locations with rapid computational speed.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"207 ","pages":"Article 106052"},"PeriodicalIF":4.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145098950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conditioned 3D DeepKriging with locally varying anisotropy","authors":"Gamze Erdogan Erten,&nbsp;Jeff Boisvert","doi":"10.1016/j.cageo.2025.106075","DOIUrl":"10.1016/j.cageo.2025.106075","url":null,"abstract":"<div><div>Deep neural networks (DNNs) are powerful tools for spatial modeling tasks but they often struggle to capture spatial autocorrelation and accurately reproduce observed data, which are crucial in geoscience applications. While traditional methods like Kriging address these challenges effectively, DNNs typically treat spatial coordinates as standard features, missing the full potential of spatial relationships. A Conditioned DeepKriging (C-DK) methodology is proposed to overcome these limitations, which builds on the DeepKriging (DK) model architecture created by Chen et al., (2020). C-DK integrates Locally Dependent Moments (LDM) to ensure reproduction of observed values at sampled locations without increasing computational complexity. An embedding layer of spatial coordinates constructed with kernel basis functions is utilized as features in the DNN, and the resulting model is merged with LDM estimates based on local reliability. A second contribution is the addition of locally varying anisotropy (C-DK+LVA), which improves the ability to model complex geological features by incorporating LVA into the model. LVA parameterizes the spatial continuity of a domain using a vector field. Shortest-path distance (SPD) features are employed to encode the effects of LVA, replacing the Euclidean radial basis function (RBF) embedding used in the original DK model. This adaptation allows the model to incorporate directional continuity structures. To support 3D applications, both the Euclidean RBF embedding and SPD computations are extended to 3D. The proposed models are validated on 2D and 3D datasets and yield performance metrics comparable to Ordinary Kriging (OK). Moreover, C-DK+LVA outperforms both C-DK and OK+LVA in scenarios with significant variation in anisotropy. The proposed methodologies require no assumptions of stationarity or linearity and they eliminate the need for variogram calculations, enabling an automated estimation process.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"207 ","pages":"Article 106075"},"PeriodicalIF":4.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Invertible neural network for real-time inversion and uncertainty quantification of ultra-deep resistivity measurements","authors":"George Bittar ,&nbsp;Sihong Wu ,&nbsp;Yawei Su ,&nbsp;Shubin Zeng ,&nbsp;Jiajia Sun ,&nbsp;Xuqing Wu ,&nbsp;Yueqin Huang ,&nbsp;Jiefu Chen","doi":"10.1016/j.cageo.2025.106067","DOIUrl":"10.1016/j.cageo.2025.106067","url":null,"abstract":"<div><div>Real-time geosteering, formation evaluation, and wellbore placement decisions hinge on the ability to invert electromagnetic (EM) well logging measurements in a fast manner while understanding the associated uncertainties. Conventional deterministic inversion methods, such as the Levenberg–Marquardt algorithm (LMA) and Occam’s inversion, often get trapped in local minima and yield a single optimal solution, neglecting the impact of the non-uniqueness of solutions. Bayesian approaches like Markov Chain Monte Carlo (MCMC) can provide the posterior distribution but are computationally expensive, making them impractical for real-time inversion. In this study, we develop a deep learning-based invertible neural network (INN) that performs rapid approximate Bayesian inversion under a specific likelihood and provides uncertainty quantification (UQ) for ultra-deep resistivity measurements. Synthetic tests demonstrate that the INN recovers the posterior distribution and generates an ensemble of predictions to quantify uncertainty within seconds. We compare its performance with conventional inversion algorithms, including LMA and Occam’s inversion, evaluating accuracy and inference efficiency. The results show that the INN delivers reliable resistivity inversion with uncertainty information at a fraction of the computational cost, highlighting its potential for real-time geosteering and other drilling-related decision-making tasks.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"207 ","pages":"Article 106067"},"PeriodicalIF":4.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145364037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An acceleration method for elastic wave forward modeling based on 1D convolution operator","authors":"Bohan Chen ,&nbsp;Yan Zhang ,&nbsp;Liangliang Yao ,&nbsp;Haichao Wang ,&nbsp;Qifeng Chen ,&nbsp;Miaomiao Wang","doi":"10.1016/j.cageo.2025.106100","DOIUrl":"10.1016/j.cageo.2025.106100","url":null,"abstract":"<div><div>The finite-difference time-domain (FDTD) method incurs significant computational overhead in high-precision, long sampling, and large-scale elastic wave forward numerical simulations, due to its inherent drawbacks, such as the need for repeated wave field iteration and the large number of grid points. To address this problem, this paper proposes a 1D convolution operator elastic wave forward acceleration method based on FDTD theory, the staggered grids, and the first-order velocity-stress equation while maintaining the original wave field iteration and grid point count. The method takes the finite difference coefficients as the 1D convolution kernel weights, transforms the finite difference partial derivative computation into the form of convolution operation, and makes full use of the high arithmetic intensity and parallel characteristics of convolution computation in GPUs to achieve efficient solving of spatial first-order derivatives. The matrix transposition optimization strategy is introduced to reorganise the storage layout of column direction data to improve the efficiency of reading column direction data and maximize the performance of the convolution operation. At the same time, a parallel matrix multiplication mechanism is designed to further improve the performance of convolutional computation. The proposed method achieves comparable numerical simulation accuracy to the FDTD method of the same order. It show a time efficiency improvement of 62.82 % in high-precision imaging, 58.48 % in long sampling, and 44.03 % in large-scale models.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"208 ","pages":"Article 106100"},"PeriodicalIF":4.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parallel finite element forward modeling of 3-D magnetotelluric conductivity and permeability anisotropy with coupled PML boundary conditions","authors":"Shuaiying Qiao ,&nbsp;Tiaojie Xiao ,&nbsp;Junjun Zhou ,&nbsp;Chunye Gong ,&nbsp;Bo Yang ,&nbsp;Jie Liu ,&nbsp;Yun Wang ,&nbsp;Qinglin Wang","doi":"10.1016/j.cageo.2025.106064","DOIUrl":"10.1016/j.cageo.2025.106064","url":null,"abstract":"<div><div>Magnetotelluric sounding (MT) is a crucial geophysical exploration method, with its response primarily influenced by two physical parameters: conductivity and magnetic permeability. MT forward modeling typically presents as a large-scale, open-domain problem, necessitating boundary truncation and computational acceleration for the simulation area. Compared to traditional boundary conditions, the Perfectly Matched Layer (PML) offers a more efficient and accurate truncation method. However, the current application of the PML is confined to scenarios involving variations in conductivity alone, and is unable to accommodate simultaneous variations in both conductivity and permeability, as well as complex anisotropic models. Therefore, this paper proposes a PML that accounts for both conductivity and permeability parameters, as well as anisotropy, making it suitable for complex anisotropic models. Furthermore, by integrating the Multi-Processing Interface (MPI) to design a multi-level parallel processing scheme, we have achieved parallel vector finite element forward modeling of three-dimensional magnetotelluric conductivity and permeability anisotropy with coupled PML boundary conditions. In comparison with previous results, the PML boundary conditions have been validated to possess the advantages of high efficiency, high precision, and stable performance. Numerical experimental results indicate that, compared with traditional boundary conditions, the PML reduces the degrees of freedom (DOFs) by over 85%, and decreases both computation time and memory usage by more than 90%. Compared with the conventional method with 888,822 DOFs, the proposed method, which integrates the PML and a multi-level parallelization strategy, achieves a speedup of approximately 85.24 for a single frequency using 32 processes and approximately 649.63 for 8 frequencies using 512 processes. The PML boasts a wider range of applicability, better performance, and thus holds broader prospects for application.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"207 ","pages":"Article 106064"},"PeriodicalIF":4.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PIKANs: Physics-informed Kolmogorov–Arnold networks for landslide time-to-failure prediction","authors":"Jiashan Wan ,&nbsp;Liangjun Wen ,&nbsp;Ziheng Jian ,&nbsp;Jinhua Wu ,&nbsp;Jingyang Li ,&nbsp;Mengqi Lian ,&nbsp;Kai Wang","doi":"10.1016/j.cageo.2025.106094","DOIUrl":"10.1016/j.cageo.2025.106094","url":null,"abstract":"<div><div>Slope deformation is characterized by pronounced time variability and complexity. Although ground-based synthetic aperture radar (GB-SAR) provides high-frequency, broad monitoring, its strong oscillations and large fluctuations can impair predictive performance. To address this, the raw displacement sequence is first smoothed via misaligned subtraction to suppress high-frequency noise and highlight key deformation trends. A dynamic confidence boundary is then established on the inverse-velocity curve to robustly identify the acceleration start point. Building on prior work on physics-informed Kolmogorov–Arnold networks (PIKANs), we apply a PIKANs framework to landslide early warning, embedding the displacement-time evolution constraint into the basis-function space of Kolmogorov–Arnold network (KAN) to unify nonlinear deformation dynamics with governing physical laws. During model training, an alternating optimization scheme combining Adam and the L-BFGS algorithm accelerates convergence and enhances predictive accuracy. Comparative experiments on field GB-SAR datasets demonstrate that compared with an improved KAN baseline and a physics-informed neural network benchmark, PIKANs reduce the relative error in landslide failure-time prediction by 38.42% and 20.44%, respectively. These results confirm that integrating physical equation constraints into neural network parameter updates substantially improves the precision and efficiency of real-time landslide early warning.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"208 ","pages":"Article 106094"},"PeriodicalIF":4.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GeoFedNet: Federated learning for privacy-aware, robust, and generalizable seismic interpretation","authors":"Muhammad Saif ul Islam,&nbsp;Aamir Wali","doi":"10.1016/j.cageo.2025.106060","DOIUrl":"10.1016/j.cageo.2025.106060","url":null,"abstract":"<div><div>Seismic structural interpretation is crucial for understanding subsurface geology, particularly in hydrocarbon exploration, as it aids in identifying reservoir formations, assessing drilling risks, and optimizing resource extraction. However, developing a widely generalizable model for seismic interpretation remains challenging due to the limited availability of large-scale public datasets, variations in seismic surveys, and privacy constraints that hinder data sharing. These factors lead to inconsistencies in model performance across diverse datasets, limiting the applicability of existing approaches. To address this gap, we propose a federated learning-based framework for seismic interpretation, enabling distributed model training without requiring direct data sharing. In this approach, local models are trained independently across different clients, and a global model is aggregated to improve generalization across heterogeneous datasets. This method not only preserves data confidentiality but also mitigates challenges related to labeled data scarcity and class imbalance, allowing clients with limited data to benefit from collaborative learning. We evaluate GeoFedNet on three key seismic interpretation tasks: seismic structure classification, salt detection, and facies segmentation. Across all tasks, GeoFedNet achieves performance within 1%–3% of centralized models while significantly outperforming isolated local models by up to 15% in accuracy and generalization. These results demonstrate that our framework can effectively learn from non-IID and imbalanced data without compromising performance. GeoFedNet also shows improved robustness to client variability and better minority class recognition, which are critical in real-world subsurface interpretation scenarios. These findings highlight the potential of federated learning in enabling hydrocarbon companies to collaboratively train robust seismic interpretation models while maintaining data privacy, ultimately improving exploration efficiency and informed decision-making.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"207 ","pages":"Article 106060"},"PeriodicalIF":4.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}