Computers & Geosciences最新文献

{"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":"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":"AI-based geological subsurface reconstruction using sparse convolutional autoencoders","authors":"Rodrigo Uribe-Ventura ,&nbsp;Yoan Barriga-Berrios ,&nbsp;Jorge Barriga-Gamarra ,&nbsp;Patrice Baby ,&nbsp;Willem Viveen","doi":"10.1016/j.cageo.2025.105981","DOIUrl":"10.1016/j.cageo.2025.105981","url":null,"abstract":"<div><div>Subsurface reconstruction is critical for geological modeling and resource exploration. Conventional spatial interpolation methods are limited by stationarity and spatial isotropy assumptions, while advanced geostatistical techniques require specialized datasets. Deep learning approaches often need large datasets, which is impractical for geoscientific applications. This study presents an AI-based methodology using a sparse convolutional autoencoder for robust subsurface modeling under data constraints and integrating secondary data sources such as Vertical Electrical Sounding (VES) data. A four-stage testing framework was implemented: (1) emulating conventional interpolation for baseline performance; (2) reconstructing subsurface geometries from synthetic data; (3) incorporating geophysical constraints through VES forward modeling; and (4) validating the methodology using a real-world case study from the Huancayo tectonic basin in the Peruvian Andes, using 41 VES measurements across two cross-sections (12 and 14 km long). Results demonstrate that the proposed model effectively emulates kriging interpolation (mean squared error: 1.5 <span><math><mrow><mo>×</mo></mrow></math></span> 10<span><math><mrow><mo>−</mo><mn>3</mn></mrow></math></span> to 1.2 <span><math><mrow><mo>×</mo></mrow></math></span> 10<span><math><mrow><mo>−</mo><mn>3</mn></mrow></math></span> with 100–800 training examples) through transfer learning from an inverse-distance, pre-trained model. In subsurface reconstruction, the model outperforms kriging (37.4–61.7 % improvement across 1–15 % sampling densities) through its ability to adapt to non-stationary conditions. When incorporating synthetic VES data, the model effectively reconstructed subsurface geometries with error reduction from 4.1 <span><math><mrow><mo>×</mo></mrow></math></span> 10<span><math><mrow><mo>−</mo><mn>1</mn></mrow></math></span> to 9.1 <span><math><mrow><mo>×</mo></mrow></math></span> 10<span><math><mrow><mo>−</mo><mn>3</mn></mrow></math></span> as stations increased from 1 to 40, demonstrating diminishing returns beyond this point. Application to the Huancayo basin case study validated the model's practical applicability by successfully identifying previously unmapped features including the contact between basement and sedimentary infill, folds and faults. The methodology demonstrates the AI's capability to enhance geological understanding in complex tectonic settings, revealing subtle features and refining existing assumptions about subsurface architecture.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"204 ","pages":"Article 105981"},"PeriodicalIF":4.2,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178633","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":"Quadrangular adaptive mesh for elastic wave simulation in smooth anisotropic media","authors":"Marius Rapenne ,&nbsp;Paul Cupillard ,&nbsp;Guillaume Caumon ,&nbsp;Corentin Gouache","doi":"10.1016/j.cageo.2025.105946","DOIUrl":"10.1016/j.cageo.2025.105946","url":null,"abstract":"<div><div>Smooth anisotropic media are often met when implementing effective medium theory, full waveform inversion or seismic imaging. However, computational overburden is often a recurring problem when working with high frequencies or when quantifying uncertainties. In this context, adaptive meshes constitute, in principle, an attractive representation to maximize simulation accuracy while minimizing the computational cost. However, such meshes are difficult to create in the context of smooth anisotropic media as the optimal local size of the elements is not clearly defined. In this work, we present a two-step algorithm to efficiently mesh these media for spectral element method (SEM) simulation in the 2D elastic case. Our algorithm yields quadrangular only meshes which adapt the size of the element to the local and directional S-wave velocity. It relies on a quadtree division introduced by Maréchal (2009) to divide the mesh until the size of each element edge is adapted to the local minimum wavelength that will be propagated. Then, a Laplacian smoothing is applied to further optimize the size of the elements, increasing the global time step and makes the SEM simulation faster while keeping a good accuracy and even improving it in some cases. An application of our method on a 2D section of the homogenized Groningen area shows that simulation time can be reduced by a factor up to 7.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"203 ","pages":"Article 105946"},"PeriodicalIF":4.2,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148034","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":"FourCastLSTM: A precipitation nowcasting model integrating global and local spatiotemporal features","authors":"Chuangwei Xu ,&nbsp;Jie Liu ,&nbsp;Shiyuan Han ,&nbsp;Xiaoqi Duan ,&nbsp;Lei Xiang ,&nbsp;Tong Zhang","doi":"10.1016/j.cageo.2025.105966","DOIUrl":"10.1016/j.cageo.2025.105966","url":null,"abstract":"<div><div>Accurate precipitation nowcasting is crucial for transportation, agriculture, urban planning, and tourism, and it is highly beneficial in disaster prevention, resource allocation, and service optimization. Existing precipitation nowcasting methods often integrate convolution neural networks and recurrent neural networks or employ vision transformers to capture spatiotemporal correlations. However, convolutional operators struggle to capture global information, and vision transformers based global modeling may overemphasize heavy rainfall while neglecting moderate and light precipitation. In this study, Fourier nowCasting LSTM (FourCastLSTM) is introduced to effectively capture and fusion spatiotemporal global and local features of precipitation, enhancing prediction accuracy for different precipitation intensities. A Fourier nowCasting LSTM Cell (FourCastCell), which combine the Adaptive Fourier Neural Operator (AFNO) with a simplified LSTM, is proposed to reinforce the representation of global spatiotemporal precipitation patterns by replacing traditional convolutional layers with AFNO. An Image Detail Enhancement module (IDE) is adopted to strengthen local precipitation detail features by integrating difference convolutional neural network. Finally, the adaptive feature fusion module embedded in the IDE, can dynamically adjust the integration weights of global and local features based on the specific spatiotemporal features of precipitation events, ensuring a balanced fusion of features with different intensities. Experiments on synthetic datasets (MovingMNIST++) and real-world datasets (RadarCIKM) demonstrate that the proposed FourCastLSTM outperforms state-of-the-art approaches by 15.6 % and 9.6 % in B-MAE and B-MSE metrics, respectively.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"204 ","pages":"Article 105966"},"PeriodicalIF":4.2,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338655","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":"Advancing raster DEM generalization with a quadric error metric approach","authors":"Richard Feciskanin,&nbsp;Jozef Minár","doi":"10.1016/j.cageo.2025.105963","DOIUrl":"10.1016/j.cageo.2025.105963","url":null,"abstract":"<div><div>Generalizing Digital Elevation Models (DEMs)—a process that simplifies data while preserving essential features—is crucial for efficient land surface analysis and revealing hierarchical structures of landforms. However, traditional methods often struggle to balance simplification with feature preservation. This paper presents a novel approach for generalizing raster-based DEMs using Quadric Error Metrics (QEM). Traditionally used for polygonal simplification, QEM has been uniquely adapted to operate directly on gridded data, which is required by most geomorphometric calculation and analysis tools. By minimizing geometric distortion, QEM effectively maintains significant land surface features, even at high levels of generalization, where the limitations of existing methods become evident. This was confirmed through a methods comparison, evaluating the generalization level using local roughness measurements based on the circular variance of aspect on four distinct areas that vary considerably in terms of landform type. The QEM approach's implicit evaluation of local surface properties ensures that significant features are preserved without the need for explicit feature detection or extensive parameter tuning. The method employs an adaptive error threshold to progressively remove smaller, non-essential landforms, providing flexible control over the generalization process. The proposed method has significant implications for various applications utilizing DEMs, particularly for analyses for which micro-scale features are undesirable noise, but preservation of the terrain skeleton is especially important. By offering a robust tool for DEM generalization, this research aims to enhance support for digital geomorphological mapping, but it can also be useful for a wider range of geoscientific research.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"202 ","pages":"Article 105963"},"PeriodicalIF":4.2,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068268","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}