Applied Computing and Geosciences最新文献_第4页

Sinkhole susceptibility analysis using machine learning for west central Florida 利用机器学习对佛罗里达州中西部进行地陷敏感性分析

IF 2.6

Applied Computing and Geosciences Pub Date : 2025-06-26 DOI: 10.1016/j.acags.2025.100262

Olanrewaju Muili, Hassan A. Babaie

{"title":"Sinkhole susceptibility analysis using machine learning for west central Florida","authors":"Olanrewaju Muili, Hassan A. Babaie","doi":"10.1016/j.acags.2025.100262","DOIUrl":"10.1016/j.acags.2025.100262","url":null,"abstract":"<div><div>This study examined the feasibility and accuracy of applying machine learning for sinkhole classification and prediction and using the results in automated sinkhole susceptibility mapping for west central Florida. A two-stage processing pipeline was developed. In the first stage, we assessed the predictive power of five exemplary machine learning algorithms: random forest (RF), logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), and multilayer perceptron (MLP), and select the best-performing model. The top-performed model was then chosen to develop a sinkhole susceptibility map (SSM) in the second step of the process. Nine feature layers were derived from the collected geospatial data and utilized as conditional variables. Several statistical metrics and receiver operating characteristic curves were utilized to evaluate the accuracy of the models. The results showed that the RF model, with a ROC of 0.984, had the highest prediction capability in the research area.</div><div>We generated a susceptibility map using the RF model, and the study area was classified into high susceptibility (H) and low susceptibility (L) areas. Confusion Matrix (CM) and Matthews Correlation Coefficient (MCC) were used to confirm the results of the sinkhole susceptibility map's classification. We present a model that predicts sinkhole distribution in the study area, and the output of our model is consistent with the sinkhole hazard map that the Florida Division of Emergency Management had previously created. This work can assist the government, community, and land managers in creating plans for mitigating hazards and land degradation.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"27 ","pages":"Article 100262"},"PeriodicalIF":2.6,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144514348","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

Advanced identification of geological discontinuities with deep learning 基于深度学习的地质不连续面高级识别

IF 2.6

Applied Computing and Geosciences Pub Date : 2025-06-21 DOI: 10.1016/j.acags.2025.100256

Rushan Wang , Martin Ziegler , Michele Volpi , Andrea Manconi

{"title":"Advanced identification of geological discontinuities with deep learning","authors":"Rushan Wang , Martin Ziegler , Michele Volpi , Andrea Manconi","doi":"10.1016/j.acags.2025.100256","DOIUrl":"10.1016/j.acags.2025.100256","url":null,"abstract":"<div><div>Rock mass characterization is essential for various applications in geosciences. Traditional methods, such as manual mapping and interpretation, are labor-intensive and prone to inconsistencies. Although machine learning has advanced in many fields, its application in structural geology, especially for distinguishing different discontinuity types, remains limited. This study presents a deep learning-based approach for identifying geological discontinuities in borehole images, classifying features such as intact walls, induced cracks, and tectonic fault planes, among others. We evaluate deep learning architectures, including standard Convolutional Neural Networks and Transformer-based models, and optimize segmentation performance with multi-scale training, tiling strategies, and tailored loss functions. Our results demonstrate that the Transformer model, particularly SegFormer, outperforms U-Net in detecting complex geological features. The combined use of weighted cross-entropy and focal loss further improves model robustness, especially for underrepresented and challenging features. In addition, the choice of the tiling size significantly affects the classification performance of different geological features. This research establishes an efficient and accurate pipeline for automated geological interpretation, with significant implications for subsurface exploration and geotechnical engineering.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"27 ","pages":"Article 100256"},"PeriodicalIF":2.6,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144514346","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

Integrating neuro-symbolic AI and knowledge graph for enhanced geochemical prediction in copper deposits 结合神经符号人工智能和知识图谱增强铜矿地球化学预测

IF 2.6

Applied Computing and Geosciences Pub Date : 2025-06-18 DOI: 10.1016/j.acags.2025.100259

Weilin Chen, Jiyin Zhang, Wenjia Li, Xiang Que, Chenhao Li, Xiaogang Ma

{"title":"Integrating neuro-symbolic AI and knowledge graph for enhanced geochemical prediction in copper deposits","authors":"Weilin Chen, Jiyin Zhang, Wenjia Li, Xiang Que, Chenhao Li, Xiaogang Ma","doi":"10.1016/j.acags.2025.100259","DOIUrl":"10.1016/j.acags.2025.100259","url":null,"abstract":"<div><div>The integration of machine learning (ML) and deep learning (DL) in geoscience has demonstrated great promise for mineral prediction. However, existing approaches are predominantly data-driven and often overlook expert geological knowledge, limiting their interpretability, accuracy, and practical applicability. This study introduces a new method that combines Large Language Models (LLMs), knowledge graphs (KGs), and Neuro-Symbolic AI (NSAI) models to predict mineralization systems in diverse copper deposits, significantly increasing the precision in prediction results. We utilize LLMs to generate KGs from geological literature, extracting symbolic rules that encode domain-specific insights about copper mineralization. These rules, derived dynamically from expert knowledge, are integrated into ML models as guidance during the training and prediction phases. By fusing symbolic reasoning with ML's computational power, our approach overcomes the limitations of black-box models, offering both improved accuracy and transparency in mineral prediction. To validate this method, we apply it to a comprehensive geochemical dataset of global copper deposits. The results show that rule-guided ML models achieve notable performance improvements, outperforming traditional ML methods in accuracy, precision, and robustness. Interpretability is further enhanced by using tools such as SHAP values, which explain the influence of individual geochemical features within the rule-based framework. This combination not only identifies critical geochemical elements like Cu, Fe, and S but also provides coherent, domain-aligned explanations for the predicted mineralization patterns. Our findings demonstrate the transformative potential of combining LLMs, KGs, and ML models for mineral prediction. This hybrid approach enables geoscientists to leverage both computational and expert knowledge, achieving a deeper understanding of mineralization systems.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"27 ","pages":"Article 100259"},"PeriodicalIF":2.6,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331437","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

Application of machine learning-based post-processing to improve crowd-sourced urban rainfall categorizations 基于机器学习的后处理应用于改进众包城市降雨分类

IF 2.6

Applied Computing and Geosciences Pub Date : 2025-06-01 DOI: 10.1016/j.acags.2025.100255

Mohammad Ashar Hussain , Venkatesh Budamala , Rajarshi Das Bhowmik

{"title":"Application of machine learning-based post-processing to improve crowd-sourced urban rainfall categorizations","authors":"Mohammad Ashar Hussain , Venkatesh Budamala , Rajarshi Das Bhowmik","doi":"10.1016/j.acags.2025.100255","DOIUrl":"10.1016/j.acags.2025.100255","url":null,"abstract":"<div><div>In recent years, citizen science has gained significant attention in the hydrometeorological sciences as an alternative to traditional monitoring systems while also raising awareness of natural processes. Crowd participation in reporting rainfall, known as crowdsourcing rainfall, has the potential to provide insights into the spatio-temporal variability of urban rainfall. However, crowdsourcing often suffers from inaccuracies in rainfall classification due to inadequately trained participants. This study investigates whether machine learning models can reduce misclassification in crowd-sourced rainfall reports under a synthetic framework. A state-of-the-art stochastic rainfall generator is deployed to simulate high-resolution rainfall over Bangalore, India, traditionally monitored by only two rain gauge stations. The study assumes that the 'synthetic' crowd reports qualitative descriptions of two rainfall characteristics—intensity and duration—based on which a categorization of a rainfall event (normal/moderate/severe) is issued. Ten scenarios are introduced to represent varying degrees of misclassification in the crowd reports. Two machine learning models, random forest and logistic regression, are employed to address these misclassifications and improve the resulting rainfall categorization. The findings indicate that while the random forest model outperforms logistic regression, its performance declines as misclassification rates increase. Moreover, the study highlights that increasing the number of participants significantly enhances the post-processing performance, emphasizing the importance of properly training the crowd for accurate reporting.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"26 ","pages":"Article 100255"},"PeriodicalIF":2.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243509","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

Comparison of ETAS parameter estimates across different time windows within the North and East Anatolian Fault Zones, Turkey 土耳其北安纳托利亚断裂带和东安纳托利亚断裂带不同时间窗内ETAS参数估计的比较

IF 2.6

Applied Computing and Geosciences Pub Date : 2025-06-01 DOI: 10.1016/j.acags.2025.100253

Suchanun Piriyasatit , Ercan Engin Kuruoglu , Mehmet Sinan Ozeren

{"title":"Comparison of ETAS parameter estimates across different time windows within the North and East Anatolian Fault Zones, Turkey","authors":"Suchanun Piriyasatit , Ercan Engin Kuruoglu , Mehmet Sinan Ozeren","doi":"10.1016/j.acags.2025.100253","DOIUrl":"10.1016/j.acags.2025.100253","url":null,"abstract":"<div><div>Located at the intersection of major lithospheric plates, Turkey is characterized by significant seismic activity, particularly along the North Anatolian Fault (NAF) and East Anatolian Fault (EAF). This paper employs the Epidemic-Type Aftershock Sequence (ETAS) model, fitted using the BFGS quasi-Newton method, to study earthquake triggering processes along these faults from 1990 to 2023. Our findings show distinct temporal variations in seismicity parameters along these faults. Along the NAF, the ETAS model highlighted a lower background seismicity rate (<span><math><mi>μ</mi></math></span>) and aftershock productivity (<span><math><msub><mrow><mi>K</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>) compared to the EAF. In contrast, the EAF exhibits lower magnitude sensitivity (<span><math><mi>α</mi></math></span>), indicating that smaller earthquakes are more likely to trigger aftershocks, due to weaker dependence on mainshock magnitude. The aftershock decay rate (<span><math><mi>p</mi></math></span>) is notably faster in the NAF, suggesting quicker post-event stabilization. Our analysis across different time windows reveals significant non-stationarities in ETAS parameters, indicating that seismic behaviors along these faults do not strictly follow historical patterns. This temporal variability highlights the challenges in short-term seismic forecasting using historical data alone. A detailed comparison of ETAS parameters across time frames showcases the necessity for incorporating dynamic modeling approaches to improve earthquake forecasting in seismically active regions.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"26 ","pages":"Article 100253"},"PeriodicalIF":2.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279364","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

3D clay microstructure synthesis using Denoising Diffusion Probabilistic Models 基于去噪扩散概率模型的三维粘土微观结构综合

IF 2.6

Applied Computing and Geosciences Pub Date : 2025-06-01 DOI: 10.1016/j.acags.2025.100248

Ali Aouf , Eric Laloy , Bart Rogiers , Christophe De Vleeschouwer

引用次数: 0

Enhancing Indian summer monsoon prediction: Deep learning approach for skillful long-lead forecasts of rainfall 加强印度夏季风预测：深度学习方法用于熟练的长期降雨预测

IF 2.6

Applied Computing and Geosciences Pub Date : 2025-06-01 DOI: 10.1016/j.acags.2025.100257

Kalpesh R. Patil, Takeshi Doi, J.V. Ratnam, Swadhin K. Behera

{"title":"Enhancing Indian summer monsoon prediction: Deep learning approach for skillful long-lead forecasts of rainfall","authors":"Kalpesh R. Patil, Takeshi Doi, J.V. Ratnam, Swadhin K. Behera","doi":"10.1016/j.acags.2025.100257","DOIUrl":"10.1016/j.acags.2025.100257","url":null,"abstract":"<div><div>The prediction of the Indian summer monsoon rainfall (ISMR) in the June–September (JJAS) season at long-lead times is challenging. The state-of-the-art dynamical models often fail to capture the sign and amplitude of the rainfall anomalies in the extreme rainfall seasons, limiting the overall skill of the models. We attempted to address this issue using a deep learning model based on convolutional neural networks (CNN). An ensemble of JJAS rainfall predictions using the CNN model with a unique custom function showed high skills in predicting ISMR at a long-lead time of 12 months. The predictions had an anomaly correlation coefficient (ACC) exceeding 0.5 at all the lead times from 2 to 17 months. The CNN model predictions could capture the sign and phase of the extreme rainfall events in the study period realistically. Analysis of saliency-based heatmaps indicated the high skill to be due to the model capturing the leading modes of climate variability, such as the Indian Ocean Dipole and El Niño-Southern Oscillation, realistically. The ensemble of CNN ISMR predictions can supplement the predictions of the forecasting centers.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"26 ","pages":"Article 100257"},"PeriodicalIF":2.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291535","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

Data-driven dynamic friction models based on Recurrent Neural Networks 基于递归神经网络的数据驱动动态摩擦模型

IF 2.6

Applied Computing and Geosciences Pub Date : 2025-06-01 DOI: 10.1016/j.acags.2025.100249

Gaëtan Cortes, Joaquin Garcia-Suarez

引用次数: 0

Prediction of carbon dioxide phase at bottomhole by adaptive factorization network considering well geometry 考虑井形的自适应分解网络预测井底二氧化碳相

IF 2.6

Applied Computing and Geosciences Pub Date : 2025-05-21 DOI: 10.1016/j.acags.2025.100254

Sungil Kim , Tea-Woo Kim , Yongjun Hong , Hoonyoung Jeong

{"title":"Prediction of carbon dioxide phase at bottomhole by adaptive factorization network considering well geometry","authors":"Sungil Kim , Tea-Woo Kim , Yongjun Hong , Hoonyoung Jeong","doi":"10.1016/j.acags.2025.100254","DOIUrl":"10.1016/j.acags.2025.100254","url":null,"abstract":"<div><div>Accurate carbon dioxide (CO<sub>2</sub>) phase prediction at the bottomhole of injection wells is essential for ensuring safe and efficient CO<sub>2</sub> storage and enhanced gas recovery (EGR). Phase misclassification can cause operational inefficiencies, equipment failure, and compromised storage integrity, posing significant risks to CO<sub>2</sub> injection projects. While previous studies have contributed to CO<sub>2</sub> phase prediction, they have overlooked well geometry effects, which can impact reliability in real-world applications. This study addresses these challenges by introducing a deep learning framework based on the adaptive factorization network (AFN), which enhances CO<sub>2</sub> phase prediction accuracy by leveraging feature interactions. The AFN model was trained on ∼43,000 wells across seven major North American shale gas basins, covering a wide range of well geometries and injection conditions. CO<sub>2</sub> phases were classified into supercritical and dense categories, reflecting prevailing flow conditions. To enhance practical applicability, we incorporated real-field wellbore data, ensuring alignment with actual injection environments. The standard AFN model achieved an F1-score of 0.94, with data augmentation further improving performance by reducing false predictions by 50 % and increasing the F1-score to 0.97. Rigorous validation demonstrated the model's robustness for optimizing wellhead temperature to achieve the desired CO<sub>2</sub> phase transition. By explicitly considering well geometry effects and real-field conditions, this study advances data-driven CO<sub>2</sub> injection modeling, providing a scalable, high-accuracy framework for evaluating CO<sub>2</sub> storage and EGR feasibility.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"26 ","pages":"Article 100254"},"PeriodicalIF":2.6,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123572","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

Soil organic carbon retrieval using a machine learning approach from satellite and environmental covariates in the Lower Brazos River Watershed, Texas, USA 基于卫星和环境协变量的机器学习方法在美国德克萨斯州下布拉索斯河流域土壤有机碳检索

IF 2.6

Applied Computing and Geosciences Pub Date : 2025-05-16 DOI: 10.1016/j.acags.2025.100252

Birhan Getachew Tikuye, Ram Lakhan Ray

{"title":"Soil organic carbon retrieval using a machine learning approach from satellite and environmental covariates in the Lower Brazos River Watershed, Texas, USA","authors":"Birhan Getachew Tikuye, Ram Lakhan Ray","doi":"10.1016/j.acags.2025.100252","DOIUrl":"10.1016/j.acags.2025.100252","url":null,"abstract":"<div><div>Soil is critical in global carbon storage, holding more carbon than terrestrial vegetation and the atmosphere combined. Accurate soil organic carbon (SOC) estimation is essential for improving agricultural productivity and mitigating climate change. This study aims to explore the retrieval of SOC using a machine learning (ML) approach, leveraging remote sensing data and environmental covariates, focusing on the Lower Brazos River Watershed, southern Texas, USA. The study used Sentinel 2A satellite data-derived indices such as vegetation and water indices, topographic features, soil properties, and climatic factors. Three ML models, namely Gradient Boosting (GB), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost), were deployed, with performance assessed using the R<sup>2</sup>, RMSE, and MAE. All explanatory variables are geospatial gridded datasets, except for the point-based measurement of SOC on the Prairie View A&M University (PVAMU) research farm plot used to train the model. The RF model demonstrated the best performance in model testing, with the lowest root mean square error (RMSE = 4.17) and mean absolute error (MAE = 3), as well as the highest coefficient of determination (R<sup>2</sup> = 0.78). GB was the second-best performing model, achieving an RMSE of 4.23 and an MAE of 3.12, with similar R<sup>2</sup> values to the RF model. The average SOC throughout the watershed is 45.5 tons/ha, while the total amount of SOC in the watershed is around 4,278,263 tons. These results suggest that integrating satellite data with environmental covariates and machine learning models holds excellent potential for SOC prediction and supports climate change mitigation efforts by improving carbon stock assessments.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"26 ","pages":"Article 100252"},"PeriodicalIF":2.6,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117054","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