Artificial Intelligence in Geosciences最新文献_第8页

Soil liquefaction assessment using machine learning 利用机器学习进行土壤液化评估

Artificial Intelligence in Geosciences Pub Date : 2025-06-01 Epub Date: 2025-05-20 DOI: 10.1016/j.aiig.2025.100122

Gamze Maden Muftuoglu , Kaveh Dehghanian

{"title":"Soil liquefaction assessment using machine learning","authors":"Gamze Maden Muftuoglu , Kaveh Dehghanian","doi":"10.1016/j.aiig.2025.100122","DOIUrl":"10.1016/j.aiig.2025.100122","url":null,"abstract":"<div><div>Liquefaction is one of the prominent factors leading to damage to soil and structures. In this study, the relationship between liquefaction potential and soil parameters is determined by applying feature importance methods to Random Forest (RF), Logistic Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) algorithms. Feature importance methods consist of permutation and Shapley Additive exPlanations (SHAP) importances along with the used model's built-in feature importance method if it exists. These suggested approaches incorporate an extensive dataset of geotechnical parameters, historical liquefaction events, and soil properties. The feature set comprises 18 parameters that are gathered from 161 field cases. Algorithms are used to determine the optimum performance feature set. Compared to other approaches, the study assesses how well these algorithms predict soil liquefaction potential. Early findings show that the algorithms perform well, demonstrating their capacity to identify non-linear connections and improve prediction accuracy. Among the feature set, <em>σ</em><sup><em>,</em></sup><sub><em>v</em></sub> (<em>psf</em>), MSF, <em>CSR</em><sub><em>σ,</em></sub> <sub><em>v</em></sub>, FC%, V<sub>s∗,40f</sub> <sub>t</sub>(f ps) and <em>N</em><sub>1<em>,</em>60<em>,CS</em></sub> are the ones that have the highest deterministic power on the result. The study's contribution is that, in the absence of extensive data for liquefaction assessment, the proposed method estimates the liquefaction potential using five parameters with promising accuracy.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 1","pages":"Article 100122"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134957","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}

引用次数: 0

Self-supervised multi-stage deep learning network for seismic data denoising 地震数据去噪的自监督多阶段深度学习网络

Artificial Intelligence in Geosciences Pub Date : 2025-06-01 Epub Date: 2025-06-02 DOI: 10.1016/j.aiig.2025.100123

Omar M. Saad , Matteo Ravasi , Tariq Alkhalifah

{"title":"Self-supervised multi-stage deep learning network for seismic data denoising","authors":"Omar M. Saad , Matteo Ravasi , Tariq Alkhalifah","doi":"10.1016/j.aiig.2025.100123","DOIUrl":"10.1016/j.aiig.2025.100123","url":null,"abstract":"<div><div>Seismic data denoising is a critical process usually applied at various stages of the seismic processing workflow, as our ability to mitigate noise in seismic data affects the quality of our subsequent analyses. However, finding an optimal balance between preserving seismic signals and effectively reducing seismic noise presents a substantial challenge. In this study, we introduce a multi-stage deep learning model, trained in a self-supervised manner, designed specifically to suppress seismic noise while minimizing signal leakage. This model operates as a patch-based approach, extracting overlapping patches from the noisy data and converting them into 1D vectors for input. It consists of two identical sub-networks, each configured differently. Inspired by the transformer architecture, each sub-network features an embedded block that comprises two fully connected layers, which are utilized for feature extraction from the input patches. After reshaping, a multi-head attention module enhances the model’s focus on significant features by assigning higher attention weights to them. The key difference between the two sub-networks lies in the number of neurons within their fully connected layers. The first sub-network serves as a strong denoiser with a small number of neurons, effectively attenuating seismic noise; in contrast, the second sub-network functions as a signal-add-back model, using a larger number of neurons to retrieve some of the signal that was not preserved in the output of the first sub-network. The proposed model produces two outputs, each corresponding to one of the sub-networks, and both sub-networks are optimized simultaneously using the noisy data as the label for both outputs. Evaluations conducted on both synthetic and field data demonstrate the model’s effectiveness in suppressing seismic noise with minimal signal leakage, outperforming some benchmark methods.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 1","pages":"Article 100123"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243298","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}

引用次数: 0

Enhancing understanding of 3D rectangular tunnel heading stability in c-φ soils with surcharge loading: A comprehensive FELA analysis using three stability factors and machine learning 加强对具有附加荷载的 c-φ 土层中三维矩形隧道顶稳定性的理解：利用三个稳定因子和机器学习进行综合 FELA 分析

Artificial Intelligence in Geosciences Pub Date : 2025-06-01 Epub Date: 2025-03-14 DOI: 10.1016/j.aiig.2025.100111

Suraparb Keawsawasvong , Jim Shiau , Nhat Tan Duong , Thanachon Promwichai , Rungkhun Banyong , Van Qui Lai

{"title":"Enhancing understanding of 3D rectangular tunnel heading stability in c-φ soils with surcharge loading: A comprehensive FELA analysis using three stability factors and machine learning","authors":"Suraparb Keawsawasvong , Jim Shiau , Nhat Tan Duong , Thanachon Promwichai , Rungkhun Banyong , Van Qui Lai","doi":"10.1016/j.aiig.2025.100111","DOIUrl":"10.1016/j.aiig.2025.100111","url":null,"abstract":"<div><div>This study examines the stability of three-dimensional rectangular tunnel headings in drained <em>c-ϕ</em> soils, incorporating surcharge effects using 3D Finite Element Limit Analysis (FELA). It focuses on the upper and lower bound solutions for three stability factors: cohesion, surcharge, and soil unit weight (Nc, Ns, and Nγ). Based on Terzaghi's principle of superposition, the analysis evaluates tunnel stability under varying parameters, such as cover-depth ratio (<em>H/D</em>), width-depth ratio (<em>B/D</em>), and friction angle (<em>ϕ</em>). The results align closely with previous studies, and practical design charts are provided for calculating minimum support pressures. Additionally, machine learning models (ANN and XGBoost) are used to develop accurate correlations between input parameters and stability results. A relative importance index analysis is conducted to assess the impact of these parameters. This research enhances understanding of tunnel stability and offers practical insights for tunnel design.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 1","pages":"Article 100111"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LatentPINNs: Generative physics-informed neural networks via a latent representation learning latentpinn：基于潜在表征学习的生成物理信息神经网络

Artificial Intelligence in Geosciences Pub Date : 2025-06-01 Epub Date: 2025-05-09 DOI: 10.1016/j.aiig.2025.100115

Mohammad H. Taufik, Tariq Alkhalifah

{"title":"LatentPINNs: Generative physics-informed neural networks via a latent representation learning","authors":"Mohammad H. Taufik, Tariq Alkhalifah","doi":"10.1016/j.aiig.2025.100115","DOIUrl":"10.1016/j.aiig.2025.100115","url":null,"abstract":"<div><div>Physics-informed neural networks (PINNs) are promising to replace conventional mesh-based partial differential equation (PDE) solvers by offering more accurate and flexible PDE solutions. However, PINNs are hampered by the relatively slow convergence and the need to perform additional, potentially expensive training for new PDE parameters. To solve this limitation, we introduce LatentPINN, a framework that utilizes latent representations of the PDE parameters as additional (to the coordinates) inputs into PINNs and allows for training over the distribution of these parameters. Motivated by the recent progress on generative models, we promote using latent diffusion models to learn compressed latent representations of the distribution of PDE parameters as they act as input parameters for NN functional solutions. We use a two-stage training scheme in which, in the first stage, we learn the latent representations for the distribution of PDE parameters. In the second stage, we train a physics-informed neural network over inputs given by randomly drawn samples from the coordinate space within the solution domain and samples from the learned latent representation of the PDE parameters. Considering their importance in capturing evolving interfaces and fronts in various fields, we test the approach on a class of level set equations given, for example, by the nonlinear Eikonal equation. We share results corresponding to three Eikonal parameters (velocity models) sets. The proposed method performs well on new phase velocity models without the need for any additional training.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 1","pages":"Article 100115"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143948290","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}

引用次数: 0

Microseismic moment tensor inversion based on ResNet model 基于ResNet模型的微震矩张量反演

Artificial Intelligence in Geosciences Pub Date : 2025-06-01 Epub Date: 2025-03-01 DOI: 10.1016/j.aiig.2025.100107

Jiaqi Yan , Li Ma , Tianqi Jiang , Jing Zheng , Dewei Li , Xingzhi Teng

引用次数: 0

Deep learning based identification of rock minerals from un-processed digital microscopic images of undisturbed broken-surfaces 基于深度学习的岩石矿物识别，从未受干扰的破碎表面的未处理的数字显微图像

Artificial Intelligence in Geosciences Pub Date : 2025-06-01 Epub Date: 2025-06-06 DOI: 10.1016/j.aiig.2025.100127

M.A. Dalhat, Sami A. Osman

{"title":"Deep learning based identification of rock minerals from un-processed digital microscopic images of undisturbed broken-surfaces","authors":"M.A. Dalhat, Sami A. Osman","doi":"10.1016/j.aiig.2025.100127","DOIUrl":"10.1016/j.aiig.2025.100127","url":null,"abstract":"<div><div>This study employed convolutional neural networks (CNNs) for the classification of rock minerals based on 3179 RGB-scale original microstructural images of undisturbed broken surfaces. The image dataset covers 40 distinct rock mineral-types. Three CNN architectures (Simple model, SqueezeNet, and Xception) were evaluated to compare their performance and feature extraction capabilities. Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to visualize the features influencing model predictions, providing insights into how each model distinguishes between mineral classes. Key discriminative attributes included texture, grain size, pattern, and color variations. Texture and grain boundaries were identified as the most critical features, as they were strongly activated regions by the best model. Patterns such as banding and chromatic contrasts further enhanced classification accuracy. Performance analysis revealed that the Simple model had limited ability to isolate fine-grained details, producing broad and less specific activations (0.84 test accuracy). SqueezeNet demonstrated improved localization of discriminative features but occasionally missed finer textural details (0.95 test accuracy). The Xception model outperformed the others, achieving the highest classification accuracy (0.98 test accuracy) by exhibiting precise and tightly focused activations, capturing intricate textures and subtle chromatic variations. Its superior performance can be attributed to its deep architecture and efficient depth-wise separable convolutions, which enabled hierarchical and detailed feature extraction. Results underscores the importance of texture, pattern, and chromatic features in accurate mineral classification and highlights the suitability of deep, efficient architectures like Xception for such tasks. These findings demonstrate the potential of CNNs in geoscience research, offering a framework for automated mineral identification in industrial and scientific applications.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 1","pages":"Article 100127"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255175","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}

引用次数: 0

Thank you reviewers! 谢谢审稿人！

Artificial Intelligence in Geosciences Pub Date : 2025-06-01 Epub Date: 2025-04-03 DOI: 10.1016/j.aiig.2025.100114

引用次数: 0

Enhancing microseismic event detection with TransUNet: A deep learning approach for simultaneous pickings of P-wave and S-wave first arrivals 利用TransUNet增强微地震事件检测：一种深度学习方法，用于同时拾取p波和s波首次到达

Artificial Intelligence in Geosciences Pub Date : 2025-06-01 Epub Date: 2025-06-18 DOI: 10.1016/j.aiig.2025.100129

Kun Chen , Meng Li , Xiaolian Li , Guangzhi Cui , Jia Tian , JiaLe Li , RuoYao Mu , JunJie Zhu

{"title":"Enhancing microseismic event detection with TransUNet: A deep learning approach for simultaneous pickings of P-wave and S-wave first arrivals","authors":"Kun Chen , Meng Li , Xiaolian Li , Guangzhi Cui , Jia Tian , JiaLe Li , RuoYao Mu , JunJie Zhu","doi":"10.1016/j.aiig.2025.100129","DOIUrl":"10.1016/j.aiig.2025.100129","url":null,"abstract":"<div><div>Microseismic monitoring is essential for understanding subsurface dynamics and optimizing oil and gas production. However, traditional methods for the automatic detection of microseismic events rely heavily on characteristic functions and human intervention, often resulting in suboptimal performance when dealing with complex and noisy data. In this study, we propose a novel approach that leverages deep learning frame to extract multiscale features from microseismic data using a TransUNet neural network. Our model integrates the advantages of Transformer and UNet architectures to achieve high accuracy in multivariate image segmentation and precise picking of P-wave and S-wave first arrivals simultaneously. We validate our approach using both synthetic and field microseismic datasets recorded from gas storage monitoring and roof fracturing in a coal seam. The robustness of the proposed method has been verified in the testing of synthetic data with various levels of Gaussian and real background noises extracted from field data. The comparisons of the proposed method with UNet and SwinUNet in terms of the model architecture and classification performance demonstrate the TransUNet achieves the optimal balance in its architecture and inference speed. With relatively low inference time and network complexity, it operates effectively in high-precision microseismic phase pickings. This advancement holds significant promise for enhancing microseismic monitoring technology in hydraulic fracturing and reservoir monitoring applications.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 1","pages":"Article 100129"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314011","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}

引用次数: 0

Automatic classification of Carbonatic thin sections by computer vision techniques and one-vs-all models 基于计算机视觉技术和一对一模型的碳酸盐岩薄片自动分类

Artificial Intelligence in Geosciences Pub Date : 2025-06-01 Epub Date: 2025-06-02 DOI: 10.1016/j.aiig.2025.100117

Elisangela L. Faria , Rayan Barbosa , Juliana M. Coelho , Thais F. Matos , Bernardo C.C. Santos , J.L. Gonzalez , Clécio R. Bom , Márcio P. de Albuquerque , P.J. Russano , Marcelo P. de Albuquerque

{"title":"Automatic classification of Carbonatic thin sections by computer vision techniques and one-vs-all models","authors":"Elisangela L. Faria , Rayan Barbosa , Juliana M. Coelho , Thais F. Matos , Bernardo C.C. Santos , J.L. Gonzalez , Clécio R. Bom , Márcio P. de Albuquerque , P.J. Russano , Marcelo P. de Albuquerque","doi":"10.1016/j.aiig.2025.100117","DOIUrl":"10.1016/j.aiig.2025.100117","url":null,"abstract":"<div><div>Convolutional neural networks have been widely used for analyzing image data in industry, especially in the oil and gas area. Brazil has an extensive hydrocarbon reserve on its coast and has also benefited from these neural network models. Image data from petrographic thin section can be essential to provide information about reservoir quality, highlighting important features such as carbonate lithology. However, the automatic identification of lithology in reservoir rocks is still a significant challenge, mainly due to the heterogeneity that is part of the lithologies of the Brazilian pre-salt. Within this context, this work presents an approach using one-class or specialist models to identify four classes of lithology present in reservoir rocks in the Brazilian pre-salt. The proposed methodology had the challenge of dealing with a small number of images for training the neural networks, in addition to the complexity involved in the analyzed data. An auto-machine learning tool called AutoKeras was used to define the hyperparameters of the implemented models. The results found were satisfactory and presented an accuracy greater than 70% for image samples belonging to other wells not seen during the model building, which increases the applicability of the implemented model. Finally, a comparison was made between the proposed methodology and multiple-class models, demonstrating the superiority of one-class models.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 1","pages":"Article 100117"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144261331","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}

引用次数: 0

Identification of interlayer and connectivity analysis based on machine learning and production data: A case study from M oilfield 基于机器学习和生产数据的层间识别和连通性分析：以M油田为例

Artificial Intelligence in Geosciences Pub Date : 2025-06-01 Epub Date: 2025-05-09 DOI: 10.1016/j.aiig.2025.100119

Xiaoshuai Wu , Yuanliang Zhao , Jianpeng Zhao , Shichen Shuai , Bing Yu , Junqing Rong , Hui Chen

{"title":"Identification of interlayer and connectivity analysis based on machine learning and production data: A case study from M oilfield","authors":"Xiaoshuai Wu , Yuanliang Zhao , Jianpeng Zhao , Shichen Shuai , Bing Yu , Junqing Rong , Hui Chen","doi":"10.1016/j.aiig.2025.100119","DOIUrl":"10.1016/j.aiig.2025.100119","url":null,"abstract":"<div><div>Interlayer is an important factor affecting the distribution of remaining oil. Accurate identification of interlayer distribution is of great significance in guiding oilfield production and development. However, the traditional method of identifying interlayers has some limitations: (1) Due to the existence of overlaps in the cross plot for different categories of interlayers, it is difficult to establish a determined model to classify the type of interlayer; (2) Traditional identification methods only use two or three logging curves to identify the types of interlayers, making it difficult to fully utilize the information of the logging curves, the recognition accuracy will be greatly reduced; (3) For a large number of complex logging data, interlayer identification is time-consuming and labor-intensive. Based on the existing well area data such as logging data and core data, this paper uses machine learning method to quantitatively identify the interlayers in the single well layer of CⅢ sandstone group in the M oilfield. Through the comparison of various classifiers, it is found that the decision tree method has the best applicability and the highest accuracy in the study area. Based on single well identification of interlayers, the continuity of well interval interlayers in the study area is analyzed according to the horizontal well. Finally, the influence of the continuity of interlayers on the distribution of remaining oil is verified by the spatial distribution characteristics of interlayers combined with the production situation of the M oilfield.</div></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"6 1","pages":"Article 100119"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143936977","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}

引用次数: 0