Artificial Intelligence in Geosciences最新文献

{"title":"Online learning to accelerate nonlinear PDE solvers: Applied to multiphase porous media flow","authors":"Vinicius L.S. Silva ,&nbsp;Pablo Salinas ,&nbsp;Claire E. Heaney ,&nbsp;Matthew D. Jackson ,&nbsp;Christopher C. Pain","doi":"10.1016/j.aiig.2025.100146","DOIUrl":"10.1016/j.aiig.2025.100146","url":null,"abstract":"<div><div>We propose a novel type of nonlinear solver acceleration for systems of nonlinear partial differential equations (PDEs) that is based on online/adaptive learning. It is applied in the context of multiphase flow in porous media. The proposed method rely on four pillars: (i) dimensionless numbers as input parameters for the machine learning model, (ii) simplified numerical model (two-dimensional) for the offline training, (iii) dynamic control of a nonlinear solver tuning parameter (numerical relaxation), (iv) and online learning for real-time improvement of the machine learning model. This strategy decreases the number of nonlinear iterations by dynamically modifying a single global parameter, the relaxation factor, and by adaptively learning the attributes of each numerical model on-the-run. Furthermore, this work performs a sensitivity study in the dimensionless parameters (machine learning features), assess the efficacy of various machine learning models, demonstrate a decrease in nonlinear iterations using our method in more intricate, realistic three-dimensional models, and fully couple a machine learning model into an open-source multiphase flow simulator achieving up to 85% reduction in computational time.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100146"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GeoNeXt: Efficient landslide mapping using a pre-trained ConvNeXt V2 encoder with a PSA-ASPP decoder","authors":"Rodrigo Uribe-Ventura ,&nbsp;Willem Viveen ,&nbsp;Ferdinand Pineda-Ancco ,&nbsp;César Beltrán-Castañon","doi":"10.1016/j.aiig.2025.100172","DOIUrl":"10.1016/j.aiig.2025.100172","url":null,"abstract":"<div><div>Landslides constitute one of the most destructive geological hazards worldwide, causing thousands of casualties and billions in economic losses annually. To mitigate these risks, accurate and efficient pixel-wise mapping of landslides for automatic semantic segmentation is of paramount importance. While recent advances in deep learning, particularly with transformer architectures and large pre-trained models like the Segment Anything Model (SAM), have shown promise, their application to landslide mapping is often hindered by high computational costs, prompt dependency, and challenges with data imbalance. To address these limitations, we propose GeoNeXt, a novel semantic segmentation architecture for intelligent landslide recognition. It combines a scalable, pre-trained ConvNeXt V2 encoder with a decoder that utilizes Pyramid Squeeze Attention (PSA) and Atrous Spatial Pyramid Pooling (ASPP) to capture multi-scale features. Through domain adaptation on the large-scale CAS landslide dataset, we refined the encoder's general pre-trained features to learn robust, landslide-specific features. GeoNeXt exhibited zero-shot transferability, achieving 74–78 % F1 and 64–66 % mIoU across three distinct test datasets from diverse regions, which were entirely excluded from the training process. Ablation studies on decoder variants validated the PSA-ASPP synergy, achieving a superior F1 of 90.39 % and mIoU of 83.18 % on the CAS dataset. Comparative analysis confirmed that GeoNeXt outperformed SAM-based methods, achieving F1 scores of 94.25 %, 86.43 %, and 92.27 % (mIoU: 89.51 %, 78.21 %, 86.02 %) on the Bijie, Landslide4Sense, and GVLM datasets, respectively, with 10× fewer parameters than SAM-based methods and lower computational demands. We showed that modernized convolutions, paired with strategic training, were a viable alternative to resource-intensive transformers. This efficiency facilitated their use in operational intelligent landslide recognition and geohazard monitoring systems.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100172"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Undrained uplift capacity prediction of open-caisson anchors in anisotropic clays using XGBoost integrated with mutation-based genetic algorithms","authors":"Rungroad Suppakul ,&nbsp;Wittaya Jitchaijaroen ,&nbsp;Suraparb Keawsawasvong ,&nbsp;Sutasinee Intui ,&nbsp;Shinya Inazumi","doi":"10.1016/j.aiig.2025.100162","DOIUrl":"10.1016/j.aiig.2025.100162","url":null,"abstract":"<div><div>This study evaluates the undrained uplift capacity of open-caisson anchors embedded in anisotropic clay using Finite Element Limit Analysis (FELA) and a hybrid machine learning framework. The FELA simulations investigate the influence of the radius ratio (<em>R/B</em>), anisotropic ratio (<em>r</em>_<em>e</em>), interface roughness factor (<em>α</em>), and inclination angle (<em>β</em>). Specifically, the results reveal that increasing <em>β</em> significantly enhances <em>N</em>_<em>c</em>, especially as soil behavior approaches isotropy. Higher <em>α</em> improves resistance at steeper inclinations by mobilizing greater interface shear. <em>N</em>_<em>c</em> increases with <em>r</em>_<em>e</em>, reflecting enhanced strength under isotropic conditions. To enhance predictive accuracy and generalization, a hybrid machine learning model was developed by integrating Extreme Gradient Boosting (XGBoost) with Genetic Algorithm (GA) and Mutation-Based Genetic Algorithm (MGA) for hyperparameter tuning. Among the models, MGA-XGBoost outperformed GA-XGBoost, achieving higher predictive accuracy (R² = 0.996 training, 0.993 testing). Furthermore, SHAP analysis consistently identified anisotropic ratio (<em>r</em>_<em>e</em>) as the most influential factor in predicting uplift capacity, followed by interface roughness factor (<em>α</em>), inclination angle (<em>β</em>), and radius ratio (<em>R/B</em>). The proposed framework serves as a scalable decision-support tool adaptable to various soil types and foundation geometries, offering a more efficient and data-driven approach to uplift-resistant design in anisotropic cohesive soils.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100162"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explaining machine learning models trained to predict Copernicus DEM errors in different land cover environments","authors":"Michael Meadows,&nbsp;Karin Reinke,&nbsp;Simon Jones","doi":"10.1016/j.aiig.2025.100141","DOIUrl":"10.1016/j.aiig.2025.100141","url":null,"abstract":"<div><div>Machine learning models are increasingly used to correct the vertical biases (mainly due to vegetation and buildings) in global Digital Elevation Models (DEMs), for downstream applications which need “bare earth” elevations. The predictive accuracy of these models has improved significantly as more flexible model architectures are developed and new explanatory datasets produced, leading to the recent release of three model-corrected DEMs (FABDEM, DiluviumDEM and FathomDEM). However, there has been relatively little focus so far on explaining or interrogating these models, especially important in this context given their downstream impact on many other applications (including natural hazard simulations). In this study we train five separate models (by land cover environment) to correct vertical biases in the Copernicus DEM and then explain them using SHapley Additive exPlanation (SHAP) values. Comparing the models, we find significant variation in terms of the specific input variables selected and their relative importance, suggesting that an ensemble of models (specialising by land cover) is likely preferable to a general model applied everywhere. Visualising the patterns learned by the models (using SHAP dependence plots) provides further insights, building confidence in some cases (where patterns are consistent with domain knowledge and past studies) and highlighting potentially problematic variables in others (such as proxy relationships which may not apply in new application sites). Our results have implications for future DEM error prediction studies, particularly in evaluating a very wide range of potential input variables (160 candidates) drawn from topographic, multispectral, Synthetic Aperture Radar, vegetation, climate and urbanisation datasets.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100141"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning applied to recognition of dinoflagellate cysts: Type study with the species Batioladinium longicornutum","authors":"A. Sanches ,&nbsp;B. Ağbulut ,&nbsp;L. Castro ,&nbsp;M. Vieira","doi":"10.1016/j.aiig.2025.100150","DOIUrl":"10.1016/j.aiig.2025.100150","url":null,"abstract":"<div><div>This study explores the application of YOLOv10, a cutting-edge object detection framework, to automate the identification and classification of <em>Batioladinium longicornutum</em>. Utilizing a dataset of 137 annotated images, we trained and validated the model to distinguish <em>B. longicornutum</em> from other species with a mean Average Precision ([email protected]) of 62.0 %. The methodology incorporated robust data augmentation techniques and evaluation metrics, including precision-recall analysis, confusion matrices, and cross-validation. YOLOv10's architecture facilitated accurate feature extraction and efficient classification, even with a relatively small dataset. While this study focuses on species-level identification, future work will extend to morphological and preservation state classifications, offering broader applications in automated palynology. These findings demonstrate the potential of YOLOv10 to revolutionize taxonomic workflows and enhance the efficiency of paleontological research.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100150"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144842611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Earthquake location and magnitude estimation using seismic arrival times pattern and gradient boosted decision trees","authors":"Saeed SoltaniMoghadam,&nbsp;Anooshiravan Ansari,&nbsp;Leila Etemadsaeed,&nbsp;Mohammad Tatar,&nbsp;Meysam Mahmoodabadi","doi":"10.1016/j.aiig.2025.100149","DOIUrl":"10.1016/j.aiig.2025.100149","url":null,"abstract":"<div><div>We present a machine learning approach for earthquake location and magnitude estimation based on seismic arrival time patterns, using Histogram-Based Gradient Boosting for its high accuracy and computational efficiency. The model is first evaluated using a synthetic earthquake bulletin that simulates realistic network geometry, station-event distributions, and incorporates a 3D velocity model for accurate travel-time computation. Input features include P and S arrival times and amplitudes, while targets consist of location, origin time, magnitude, and uncertainty measures (horizontal and depth errors, azimuthal gap). Model performance is evaluated using <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>, Mean Absolute Error (MAE), and Median Absolute Error (MEDAE), demonstrating high accuracy across datasets with varying levels of completeness. Finally, we validate the model using real-world data from the Ahar-Varzaghan 2012 aftershock sequence in NW Iran. The model accurately recovers key spatial patterns of seismicity despite significant missing data, and the results align with previous high-resolution studies. These findings confirm that the proposed method generalizes well beyond synthetic settings and offers a fast, robust alternative for operational seismic networks and rapid hazard assessment.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100149"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144842609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application research of SSA-RF model in predicting the height of water-conducting fracture zone in deep and thick coal seams","authors":"Li Wang ,&nbsp;Jiming Zhu ,&nbsp;Zhongchang Wang","doi":"10.1016/j.aiig.2025.100154","DOIUrl":"10.1016/j.aiig.2025.100154","url":null,"abstract":"<div><div>The 91 measured values of the development height of the water-conducting fracture zone (WCFZ) in deep and thick coal seam mining faces under thick loose layer conditions were collected. Five key characteristic variables influencing the WCFZ height were identified. After removing outliers from the dataset, a Random Forest (RF) regression model optimized by the Sparrow Search Algorithm (SSA) was constructed. The hyperparameters of the RF model were iteratively optimized by minimizing the Out-of-Bag (OOB) error, resulting in the rapid determination of optimal parameters. Specifically, the SSA-RF model achieved an OOB error of 0.148, with 20 decision trees, a maximum depth of 8, a minimum split sample size of 2, and a minimum leaf node sample size of 1. Cross-validation experiments were performed using the trained optimal model and compared against other prediction methods. The results showed that the mining height had the most significant correlation with the development height of the WCFZ. The SSA-RF model outperformed all other models, with R² values exceeding 0.9 across the training, validation, and test datasets. Compared to other models, the SSA-RF model demonstrates a simpler structure, stronger fitting capacity, higher predictive accuracy, and superior stability and generalization ability. It also exhibits the smallest variation in relative error across datasets, indicating excellent adaptability to different data conditions.Furthermore, a numerical model was developed using the hydrogeological data from the 1305 working face at Wanfukou Coal Mine, Shandong Province, China, to simulate the dynamic development of the WCFZ during mining. The SSA-RF model predicted the WCFZ height to be 69.7 m, closely aligning with the PFC2D simulation result of 65 m, with an error of less than 5 %. Compared to traditional methods and numerical simulations, the SSA-RF model provides more accurate predictions, showing only a 7.23 % deviation from the PFC2D simulation, while traditional empirical formulas yield deviations as large as 19.97 %. These results demonstrate the SSA-RF model's superior predictive capability, reinforcing its reliability and engineering applicability for real-world mining operations. This model holds significant potential for enhancing mining safety and optimizing planning processes, offering a more accurate and efficient approach for WCFZ height prediction.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100154"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning approaches for estimating maximum wall deflection in excavations with inconsistent clay stratigraphy","authors":"Vinh V. Le ,&nbsp;HongGiang Nguyen ,&nbsp;Nguyen Huu Ngu","doi":"10.1016/j.aiig.2025.100140","DOIUrl":"10.1016/j.aiig.2025.100140","url":null,"abstract":"<div><div>This paper presents a deep learning architecture combined with exploratory data analysis to estimate maximum wall deflection in deep excavations. Six major geotechnical parameters were studied. Statistical methods, such as pair plots and Pearson correlation, highlighted excavation depth (correlation coefficient = 0.82) as the most significant factor. For method prediction, five deep learning models (CNN, LSTM, BiLSTM, CNN-LSTM, and CNN-BiLSTM) were built. The CNN-BiLSTM model excelled in training performance (R² = 0.98, RMSE = 0.02), while BiLSTM reached superior testing results (R² = 0.85, RMSE = 0.06), suggesting greater generalization ability. Based on the feature importance analysis from model weights, excavation depth, stiffness ratio, and bracing spacing were ranked as the highest contributors. This point verified a lack of prediction bias on residual plots and high model agreement with measured values on Taylor diagrams (correlation coefficient 0.92). The effectiveness of integrated techniques was reliably assured for predicting wall deformation. This approach facilitates more accurate and efficient geotechnical design and provides engineers with improved tools for risk evaluation and decision-making in deep excavation projects.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100140"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised hierarchical sequence stratigraphy framework of carbonate successions","authors":"Márcio Vinicius Santana Dantas ,&nbsp;Kaio Henrique Masse Vieira ,&nbsp;Thomás Jung Spier ,&nbsp;José Arthur Oliveira Santos ,&nbsp;Alan Cabral Trindade Prado ,&nbsp;Danilo Vomlel ,&nbsp;Mariana Leite ,&nbsp;Felipe Alves Farias ,&nbsp;Daniel Galvão Carnier Fragoso ,&nbsp;Humberto Reis ,&nbsp;Gabriel Coutinho ,&nbsp;Douglas G. Macharet","doi":"10.1016/j.aiig.2025.100160","DOIUrl":"10.1016/j.aiig.2025.100160","url":null,"abstract":"<div><div>Performing the high-resolution stratigraphic analysis may be challenging and time-consuming if one has to work with large datasets. Moreover, sedimentary records have signals of different frequencies and intrinsic noise, resulting in a complex signature that is difficult to identify only through eyes-based analysis. This work proposes identifying transgressive-regressive (T-R) sequences from carbonate facies successions of three South American basins: (i) São Francisco Basin - Brazil, (ii) Santos Basin - Brazil, and (iii) Salta Basin - Argentina. We applied a hidden Markov model in an unsupervised approach followed by a Score-Based Recommender System that automatically finds medium or low-frequency sedimentary cycles from high-frequency ones. Our method is applied to facies identified using Fullbore Formation Microimager (FMI) logs, outcrop description, and composite logs from carbonate intervals. The automatic recommendation results showed better long-distance correlations between medium- to low-frequency sedimentary cycles, whereas the hidden Markov model method successfully identified high-resolution (high-frequency) transgressive and regressive systems tracts from the given facies successions. Our workflow offers advances in the automated analyses and construction of lower- to higher-rank stratigraphic framework and short to long-distance stratigraphic correlation, allowing for large-scale automated processing of the basin dataset. Our approach in this work fits the unsupervised learning framework, as we require no previous input of stratigraphical analysis in the basin. The results provide solutions for prospecting any sediment-hosted mineral resource, especially for the oil and gas industry, offering support for subsurface geological characterization, whether at the exploration scale or for reservoir zoning during production development.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100160"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145264973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a reliable rock slope stability model utilizing field and analytical data – An integration of FE-ML approaches","authors":"Virat Singh Chauhan ,&nbsp;Md. Rehan Sadique ,&nbsp;Mohd. Masroor Alam ,&nbsp;Mohd. Ahmadullah Farooqi","doi":"10.1016/j.aiig.2025.100158","DOIUrl":"10.1016/j.aiig.2025.100158","url":null,"abstract":"<div><div>Slope instability in hilly regions is a highly complex phenomenon, with triggering factors ranging from natural events to anthropogenic activities. Such failures hit disastrous losses both in terms of material as well as life. It is necessary to comprehend the mechanism of these failures to mitigate such events and also to predict their vulnerability for better preparedness. Significant advancements have already been done in the area of slope stability analysis, and scores of valued tools and techniques have been developed, such as limit equilibrium methods, finite element and finite difference methods, stochastic methods, and several of their combinations. In this study, an attempt has been made to capitalize on machine learning tools to predict the factor of safety of rock slope stability in hilly regions. Three road-cut slopes have been considered and their stability is determined using both finite element (FE) and machine learning (ML) techniques. The idea to intertwine these approaches is to supplement each other and enhance the reliability of the results. The geotechnical data was acquired through field investigation trips to the adopted mountainous sites. Since the slopes at the site are rocky, in the FE model, the Generalized Hoek Brown (GHB) material model with shear strength reduction technique have been used. In the implementation of ML models, Random Forest (RF) and Gradient Boosting Machine (GBM) models have been used. For the training of the ML model, ample published data has been utilized, while for testing the ML model, the data from the current slope site is used. The analysis in ML model is carried out in three stages: a) without Hyperparameter tuning, b) with Hyperparameter tuning using GridSearchCV, and c) Pipeline incorporating Recursive Feature Elimination (RFE). Performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and R² score, were evaluated to assess the accuracy of the model. A slight discrepancy within a range of 10 percent has been found, which is rather expected due to factors such as grid refinement and, data volume and variability. Overall, the proposed ML model demonstrates excellent compatibility with the FE model results. This study is an attempt to pick relevant ML techniques to develop a purpose-built framework that has the potential to validate the rock slope stability obtained using the traditional methods.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 2","pages":"Article 100158"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}