Computers & Geosciences最新文献

{"title":"Mineral prospectivity analysis is unstable to changes in pixel size","authors":"Adrian Baddeley ,&nbsp;Warick Brown ,&nbsp;Gopalan Nair ,&nbsp;Robin Milne ,&nbsp;Suman Rakshit ,&nbsp;Shih Ching Fu","doi":"10.1016/j.cageo.2025.105965","DOIUrl":"10.1016/j.cageo.2025.105965","url":null,"abstract":"<div><div>In mineral prospectivity mapping, the spatial coordinates of mineral deposits and other geological features are often recorded originally in vector form, and converted to a grid of cells (a raster of pixels) for analysis. Although the results of the analysis clearly depend on the choice of pixel size, it is widely believed that, if pixel size is progressively reduced, results should converge to a stable value. However, we show that this is not true. Using a database of gold deposits in the Murchison region of Western Australia, the Weights of Evidence (WofE) contrast statistic <span><math><mi>C</mi></math></span> was calculated for raster conversions with pixel widths varying from 5 km to 100 m, using the vector-to-raster conversion algorithms common in mainstream GIS packages. In response to even the slightest changes in pixel width, the calculated value of <span><math><mi>C</mi></math></span> fluctuated by 1.5 units, and the calculated probability of a deposit fluctuated by a factor of 4.5. As pixel size was progressively reduced, the results did not converge. We investigate this instability phenomenon experimentally and theoretically, and establish that it could be widespread. It could arise in any form of prospectivity analysis (including logistic regression, machine learning and deep learning) where the explanatory variables are discontinuous. We have confirmed that it also occurs with logistic regression. Instability is primarily associated with deposit points which lie close to a discontinuity such as a feature boundary, and could be characterised as a failure to respect “ground truth” at the deposit location. Accordingly, instability can persist even with very small pixel sizes (as small as 3 m in the Murchison example). We propose a new algorithm for vector-to-raster conversion which respects ground truth, and produces results which converge rapidly as pixel size decreases. In the Murchison example, this algorithm provides stable results for pixel widths of 500 m or less. Our theoretical results predict the maximum error as a function of pixel width, and allow the geologist to select an appropriate pixel size for the data available. Potential fields of application include species distribution modelling and geospatial risk analysis.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"204 ","pages":"Article 105965"},"PeriodicalIF":4.2,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144320944","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":"TwoStream-EQT: A microseismic phase picking model combining time and frequency domain inputs","authors":"Ling Peng ,&nbsp;Lei Li ,&nbsp;S. Mostafa Mousavi ,&nbsp;Xiaobao Zeng ,&nbsp;Gregory C. Beroza","doi":"10.1016/j.cageo.2025.105991","DOIUrl":"10.1016/j.cageo.2025.105991","url":null,"abstract":"<div><div>Seismic event detection, phase picking, and phase association are the most fundamental and critical steps in seismic network data processing. We propose a two-stream neural network that integrates the time domain and time-frequency domain representations for microseismic phase detection and picking. This model builds on the EQTransformer (EQT) by incorporating an additional time-frequency stream using a Short-Time Fourier Transform as input. This preserves the original time-domain network structure, while enabling the fusion of features from both domains through lateral interactions. We explore two feature-fusion strategies: fixed weighting addition and a cross-attention mechanism, resulting in two two-stream EQT (TS-EQT) models: AddTwoStream-EQT (ATS-EQT) and CrossTwoStream-EQT (CTS-EQT). We enhance the data through a multi-model average picking strategy to reduce the labeling errors. We train the models with the STEAD dataset and test them on the STEAD, DiTing and Geysers datasets. We find that the TS-EQT models are superior to the original EQT model in both learning ability and generalization performance. The cross-attention mechanism feature fusion strategy is superior to the fixed weighting addition strategy. Specifically, ATS-EQT detects 45 % more events than EQT on the Geysers microseismic dataset, the number of P-wave and S-wave picks increases by about 44 % and 48 %, respectively. CTS-EQT detects 48 % more events, and the number of P-wave and S-wave picks increases by about 52 % and 56 %, respectively. This study verifies that the frequency domain features improve the training of the original model and suggests the potential of two-stream approaches for other geophysical tasks.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"204 ","pages":"Article 105991"},"PeriodicalIF":4.2,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307172","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":"A study on grille structure modeling algorithm for fault-controlled fractured-cavity reservoirs: A case study of the shunbei no. 5 fault zone","authors":"Haojie Shang ,&nbsp;Shuyang Chen ,&nbsp;Yunfeng He ,&nbsp;Lixin Wang ,&nbsp;Yanshu Yin ,&nbsp;Pengfei Xie","doi":"10.1016/j.cageo.2025.105988","DOIUrl":"10.1016/j.cageo.2025.105988","url":null,"abstract":"<div><div>Establishing high-resolution 3D geological models of fault-controlled reservoirs is crucial for optimizing well placement and development plan. Carbonate fault-controlled fracture-cavity reservoirs in the Shunbei area of the Tarim Basin, Northwest China, exhibit complex heterogeneity. These reservoirs typically comprise fracture planes, caves and disordered bodies. Fracture planes are narrow and banded, with caves and disordered bodies distributed around them. Within fracture planes and caves, crush belts and bedrock belts alternate to form grille structures. These pose significant challenges for traditional modeling algorithms to characterize accurately. To address this, we proposed a hierarchical object-based modeling algorithm to reproduce fault-controlled fracture-cavity body's grille structural trends and shapes. Using seismic data from the Shunbei No.5 Fault Zone (with a resolution of 25∗25m) and well logging data (including drilling fluid loss data and resistivity logging data), conduct research on grille structure modeling algorithms. First, the fault-controlled fracture-cavity reservoirs are distinguished by fracture planes, caves, and disordered bodies, and contour models are established via seismic attributes threshold truncation. Second, statistics on the scale of development of crush belts and breccia belts under 100 m of fracture planes and caves in different stress sections by logging data. A regional growth tracking algorithm are applied to identify fracture planes trend lines, which can be classified into single, multi, convergent, and branching forms based on contour characteristics. Third, cumulative probability sampling is used to determine the number and scale of the crush and breccia belts. Grille structure models were constructed at three levels: bedrock, crush, and breccia belts. Results indicate successful identification of trend lines matching the structural contours, establishing accurate grille structure models by employing hierarchical simulation strategy under trend line constraints. The models established by traditional methods exhibit significant randomness, making it difficult to control both the variable developmental trajectories of individual belts and the relative positional relationships among multiple belts. Based on these geological facies models, corresponding physical property models were generated, achieving high accuracy in reserve calculations and numerical simulations with less than 10 % error, thus providing valuable guidance for oil and gas development. In the future, more compatible contour models can be established through methods like multi-attribute fusion and deep learning. By integrating production data, the developmental positions and connectivity of grille belts can be constrained.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"204 ","pages":"Article 105988"},"PeriodicalIF":4.2,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291288","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":"Deep learning assisted denoising of fast polychromatic X-ray micro-CT imaging of multiphase flow in porous media","authors":"E.S. Mathew ,&nbsp;S.J. Jackson ,&nbsp;D. Wildenschild ,&nbsp;P. Mostaghimi ,&nbsp;K. Tang ,&nbsp;R.T. Armstrong","doi":"10.1016/j.cageo.2025.105990","DOIUrl":"10.1016/j.cageo.2025.105990","url":null,"abstract":"<div><div>Understanding the flow of fluids in the subsurface and their interaction with different solid surfaces is crucial for addressing challenging geological applications such as CO₂ sequestration, enhanced oil recovery, and environmental remediation of polluted aquifers. Synchrotron-based 3D X-ray micro-computed tomography (micro-CT) has enabled the visualization of dynamic pore-filling events in multiphase flow experiments at sub-second time resolutions. However, the limited accessibility of synchrotron facilities has driven the use of low-flux polychromatic micro-CT systems, which often produce relatively noisy images during fast scans. To overcome this limitation, we propose a deep learning workflow using a cycleGAN network trained on unpaired datasets as no direct pixel-wise correspondence exists between the noisy domain and the high-quality domain. This approach transforms noisy fast polychromatic micro-CT scans into high-quality images, enabling detailed analysis of multiphase flow dynamics. The effectiveness of the denoising process was verified using blind image quality evaluators and Minkowski functionals for the non-wetting phases. The results indicate that the cycleGAN network achieves an average 1 to 6 percentage error difference for 3D morphological analysis parameters and outperforms other filtering methods such as non-local means and the adaptive Weiner filter, demonstrating its potential as a reliable technique for restoring noisy fast scans from polychromatic micro-CT systems.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"204 ","pages":"Article 105990"},"PeriodicalIF":4.2,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338653","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":"Spatio-temporal deep networks with feature disentangling for advancing earthquake monitoring","authors":"Fanchun Meng ,&nbsp;Tao Ren ,&nbsp;Xinyu He ,&nbsp;Zhuoran Dong ,&nbsp;Xinyue Wang ,&nbsp;Hongfeng Chen","doi":"10.1016/j.cageo.2025.105974","DOIUrl":"10.1016/j.cageo.2025.105974","url":null,"abstract":"<div><div>Earthquake monitoring is essential for assessing seismic hazards and involves interconnected tasks such as phase picking and location estimation. Existing single-parameter estimation methods suffer from error accumulation caused by task interdependencies and typically rely on empirical values. Multi-parameter estimation methods often depend on data from multiple stations, posing challenges in modeling and revealing the inter-station relationships. To address these challenges, this study proposes a novel neural network, SINE, designed to simultaneously estimate key parameters in earthquake monitoring, including P-phase arrival time, location, and magnitude. SINE develops a multi-task framework that incorporates Graph Neural Networks (GNNs) and Bidirectional Long Short-Term Memory networks (BI-LSTM) to extract spatio-temporal features, effectively mitigating error accumulation across the tasks. Unlike previous GNN-based models, SINE incorporates a feature disentanglement structure to automatically identify multiple potential relationships between seismic stations. Additionally, the CNN-based parsing unit is employed to regress multiple seismic parameters simultaneously. Evaluation on datasets from Southern California and Italy shows that SINE outperforms existing DL models and traditional seismological methods. Furthermore, SINE effectively reduces inter-task dependencies, enhancing robustness in earthquake monitoring.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"204 ","pages":"Article 105974"},"PeriodicalIF":4.2,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291286","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":"Controlled-source electromagnetic signal detection using a hybrid deep learning model: Convolutional and long short-term memory neural networks","authors":"Yecheng Liu ,&nbsp;Diquan Li ,&nbsp;Jin Li ,&nbsp;Yanfang Hu ,&nbsp;Zijie Liu ,&nbsp;Xian Zhang","doi":"10.1016/j.cageo.2025.105986","DOIUrl":"10.1016/j.cageo.2025.105986","url":null,"abstract":"<div><div>Controlled-source electromagnetic (CSEM) method using a periodic transmitted signal source suppresses random noise by superimposing and averaging the recorded signal over multiple periods. However, it still faces great challenges in strong interference environments. Here we present a CSEM signal detection method based on a hybrid architecture of convolutional neural network (CNN) and long short-term memory neural network (LSTM). Our method improves the quality of the recorded signal by filtering out all the noise periods and retaining the useful signal periods. The core lies in combining the spatial feature extraction capability of CNN with the time series modeling capability of LSTM, which can deeply excavate the feature differences between noise and CSEM useful signal. Meanwhile, a classification model of signal and noise is constructed using a large-scale training dataset. This hybrid model exhibits superior performance compared to other deep learning models such as CNN or LSTM. Also, we propose a novel signal detection mechanism that not only maintains the periodicity of CSEM signal, but also greatly enhances processing efficiency. The results of synthetic and measured data demonstrate that our method can obtain useful signals from CSEM data containing different noise types and significantly improve the quality of sounding curves. In particular, our method is useful for CSEM signal detection in strong interference.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"204 ","pages":"Article 105986"},"PeriodicalIF":4.2,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254094","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":"A novel algorithm and software for efficient global gravimetric forward modeling in the spherical coordinate system","authors":"Wenjin Chen ,&nbsp;Xiaoyu Tang ,&nbsp;Robert Tenzer","doi":"10.1016/j.cageo.2025.105985","DOIUrl":"10.1016/j.cageo.2025.105985","url":null,"abstract":"<div><div>We present a novel algorithm and complementary software for the global gravimetric forward modeling that accommodates masses with complex shapes and density distributions defined in the frame of spherical coordinates. Traditional gravimetric forward modeling techniques often face significant challenges when dealing with irregularly shaped bodies and complex density variations, leading to high computational costs and long processing times. To address these practical limitations, we introduce an innovative algorithm that divides the 3-D mass-density body into spherical concentric rings with equal intervals in the radial direction, while discretizing the latitudinal and longitudinal directions into a grid with equal intervals. After discretization, we assume that each spherical volumetric mass ring has a laterally varying density and constant upper and lower bounds. Based on this approach, the gravitational field at any point outside the Earth is evaluated as the sum of the gravitational contributions generated by each concentric ring. This discretization allows applying the Fast Fourier Transform (FFT) technique to drastically improve computational efficiency of the spherical harmonic analysis and synthesis. Numerical results are validated against corresponding solutions obtained using the tesseroid method in the spatial domain. The comparison of results reassures a high accuracy of proposed method, with relative differences between results obtained from both methods less than 1 % and 4 % for the gravitational attraction and gradient respectively, while significantly improving the numerical efficiency. When modelling the gravitational field quantities of very complex structures by means of their geometry and density distribution, such as the Earth's crustal density structure, the numerical efficiency improved several orders of magnitude.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"204 ","pages":"Article 105985"},"PeriodicalIF":4.2,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291287","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 innovative adaptive mineral segmentation method via augmentation and fusion of single and orthogonal polarized images: AMS-p/xpl","authors":"W. Ma ,&nbsp;P. Lin ,&nbsp;S. Li ,&nbsp;Z.H. Xu","doi":"10.1016/j.cageo.2025.105987","DOIUrl":"10.1016/j.cageo.2025.105987","url":null,"abstract":"<div><div>An adaptive mineral segmentation method is proposed to address the challenges of traditional thin section identification, which is typically expert-dependent, highly subjective, and time-consuming. The method is based on the augmentation and fusion of single polarized (PPL) and orthogonal polarized (XPL) images using an enhanced Deeplabv3-based segmentation model. The Depth Separable Convolution (DSC) is introduced to strengthen edge and texture features from PPL images, and a ColorBoost module is designed to enhance color information from XPL images. An adaptive feature fusion mechanism is employed to integrate complementary polarized features and dynamically adjust their contribution weights. The results demonstrate that the proposed model achieved the highest segmentation performance on the test set, with a mean intersection over union (<em>mIoU</em>) of 89.0 % and an accuracy of 96.7 %. Compared to widely used semantic segmentation networks such as FCN, it demonstrates a notable improvement in <em>mIoU</em>, with a maximum gain of 32.9 %. Additionally, through the integration of feature augmentation and an adaptive fusion mechanism, the model outperforms the baseline DeepLabv3 by 5 % in <em>mIoU.</em> The proposed method provides a more efficient and automated solution for thin section mineral identification, reducing reliance on expert knowledge and improving applicability in practical and non-specialist settings.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"204 ","pages":"Article 105987"},"PeriodicalIF":4.2,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262984","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":"A gradient-optimized least-squares reverse time migration based on the safe type-I anderson acceleration","authors":"Yingming Qu ,&nbsp;Chongpeng Huang","doi":"10.1016/j.cageo.2025.105984","DOIUrl":"10.1016/j.cageo.2025.105984","url":null,"abstract":"<div><div>Least-squares reverse time migration (LSRTM) can generate preferable images for complex media, but faces substantial computational challenges in field data applications, especially in 3D cases. Many optimization algorithms have been proposed to alleviate this problem, such as the limited-memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) method, the restarted generalized minimal residual method, and Anderson acceleration (AA). AA is a popular gradient optimization algorithm that has been widely used in many fields due to its ability to greatly accelerate the convergence of fixed-point iterations and considerably reduce the computational cost. According to Broyden's method, AA is divided into type-I AA (AA-I) and type-II AA (AA-II), with most implementations favoring AA-II due to residual oscillation issues observed in AA-I during data residual minimization. To address the residual vibration issue of AA-I and expedite the convergence of LSRTM, we apply a safe AA-I method to LSRTM, incorporating Powell-type regularization, re-start checking, and safe guarding steps. The Powell-type regularization guarantees the non-singularity of AA-I, while the re-start checking preserves its strong linear independence, both contributing to the stability of AA-I. The safe guarding steps examine the data residual reduction and accelerate the convergence. Our analysis reveals that the optimal step length for the safe AA-I method is approximately 5 times or 10 times the initial steepest descent (SD) iteration. We also derive an exponential scaling law for the safe AA-I step length. In addition, the safe AA-I has faster data residual convergence speed, less computational cost, and higher quality images than SD, conjugate gradient (CG), AA-II, and LBFGS. The safe AA-I is approximately twice as efficient as the LBFGS. Field validation through land seismic data processing shows that LSRTM based on the safe AA-I delivers enhanced structural resolution with sharper imaging events and improved stratigraphic continuity relative to LBFGS-based implementations.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"204 ","pages":"Article 105984"},"PeriodicalIF":4.2,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262986","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":"Lithological mapping using Spatially Constrained Bayesian Network (SCB-Net): A deep learning model for generating field-data-constrained predictions with uncertainty evaluation using remote sensing data","authors":"Victor Silva dos Santos ,&nbsp;Erwan Gloaguen ,&nbsp;Shiva Tirdad","doi":"10.1016/j.cageo.2025.105964","DOIUrl":"10.1016/j.cageo.2025.105964","url":null,"abstract":"<div><div>Geological maps are an important source of information for the Earth sciences. These maps are created using numerical or conceptual models that use geological observations to extrapolate data. Geostatistical techniques have traditionally been used to generate reliable predictions that take into account the spatial patterns inherent in the data. However, as the number of auxiliary variables increases, these methods become more labor-intensive. Additionally, traditional machine learning methods often struggle to capture spatial context and extract valuable non-linear information from geoscientific datasets. To address these challenges, we developed the Spatially Constrained Bayesian Network (SCB-Net)—an architecture designed to effectively integrate auxiliary variables while generating spatially constrained predictions. SCB-Net employs a late-fusion strategy, processing auxiliary data and ground-truth information through two parallel encoding paths. Additionally, it leverages Monte Carlo dropout as a Bayesian approximation to quantify model uncertainty. The SCB-Net has been tested on two real-world datasets from northern Quebec, Canada, demonstrating its effectiveness in generating field-data-constrained lithological maps while providing uncertainty estimates for unsampled locations. Our method outperformed the Attention U-Net – a widely used model in image segmentation – by at least 4.7% in accuracy across all tested datasets.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"204 ","pages":"Article 105964"},"PeriodicalIF":4.2,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144222039","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}