Applied Computing and Geosciences最新文献

{"title":"Predictive regressive models of recent marsh sediment thickness improve the quantification of coastal marsh sediment budgets","authors":"Christopher G. Smith ,&nbsp;Julie Bernier ,&nbsp;Alisha M. Ellis ,&nbsp;Kathryn E.L. Smith","doi":"10.1016/j.acags.2024.100215","DOIUrl":"10.1016/j.acags.2024.100215","url":null,"abstract":"<div><div>Coastal marsh wetlands experience variations in vertical gains and losses through time, which have allowed them to infill relict topography and record variations in drivers. The stratigraphic unit associated with the development of the marsh also reflects the long-term importance of key ecosystem services supplied by the marsh environment, including carbon storage and storm mitigation. Mapping these coastal wetland sediments and the marsh unit thickness is challenging as traditional coastal geophysical tools are not easily deployable (acoustic methods) or are unreliable in saline-soil environments (e.g., ground-penetrating radar), leaving core-based methods the most viable mapping method. In the present study, we utilized prior information on the geologic architecture of the region to select spatial and physical metrics that likely persisted throughout evolution of the marsh during the late Holocene. We then assessed the individual and collective power of these metrics to predict marsh thickness observed from cores. Employing regressive predictive models powered by these data, we improve the quantification of marsh thickness for a coastal fringing marsh within the Grand Bay estuary in Mississippi and Alabama (USA). The information gained from this approach yields improved estimates of the carbon stocks in this environment. Additionally, the stored sediment masses reflect the past, and potential future, persistence of the Grand Bay marsh under historical and present marsh-estuarine sediment exchange fluxes. Such improvements to both the sediment budget of recent marsh stratigraphic units and the spatial extent provide new resources for comparison with large-scale landscape models, the latter of which may be used, when validated, to predict future change and ecosystem transformations.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"25 ","pages":"Article 100215"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166136","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}

{"title":"X-ray Micro-CT based characterization of rock cuttings with deep learning","authors":"Nils Olsen ,&nbsp;Yifeng Chen ,&nbsp;Pascal Turberg ,&nbsp;Alexandre Moreau ,&nbsp;Alexandre Alahi","doi":"10.1016/j.acags.2025.100220","DOIUrl":"10.1016/j.acags.2025.100220","url":null,"abstract":"<div><div>Rock cuttings from destructive boreholes are a common and cheaper source of drilling materials that can be used to determine underground geology compared to rock core samples. Classifying manually the series of cuttings can be a long and tedious process and can also be prone to subjectivity leading to errors. In this paper, a framework for the classification of multiple types of rock structures is introduced based on rock cutting images from X-ray micro-CT technology. The classification is performed using a simple yet effective deep learning model (a ResNet-18 architecture) to categorize five different lithologies: micritic limestone, bioclastic limestone, oolithic limestone, molassic sandstone and gneiss. The proposed network is trained on 2 datasets (laboratory and borehole) both containing the five lithologies and comprise over 10 000 images. The laboratory dataset consists of a well-controlled experiments with homogeneous samples and the borehole dataset with heterogeneous samples corresponding to a real case application. Among all the considered models, including ResNet-34, and SPP-CNN and human experts manual classification, ResNet-18 demonstrates superior performance across multiple evaluation metrics, including precision, recall, and F1-score. It is to our best knowledge, the first time a test comparing deep neural network and human performance is performed for this task. To optimize the performance of the proposed model, the transfer learning method is implemented. Furthermore, the experiments demonstrate that when employing transfer learning, the size of the dataset significantly impacts the performance of the model. In the studied design, the experimental results confirm that the proposed approach is a cost-effective and efficient method for automated rock cutting classification using the micro-CT technique, and it can be easily modified to adapt the rock cutting classification from various types and sources.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"25 ","pages":"Article 100220"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165478","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}

{"title":"Streamlining geoscience data analysis with an LLM-driven workflow","authors":"Jiyin Zhang,&nbsp;Cory Clairmont,&nbsp;Xiang Que,&nbsp;Wenjia Li,&nbsp;Weilin Chen,&nbsp;Chenhao Li,&nbsp;Xiaogang Ma","doi":"10.1016/j.acags.2024.100218","DOIUrl":"10.1016/j.acags.2024.100218","url":null,"abstract":"<div><div>Large Language Models (LLMs) have made significant advancements in natural language processing and human-like response generation. However, training and fine-tuning an LLM to fit the strict requirements in the scope of academic research, such as geoscience, still requires significant computational resources and human expert alignment to ensure the quality and reliability of the generated content. The challenges highlight the need for a more flexible and reliable LLM workflow to meet domain-specific analysis needs. This study proposes an LLM-driven workflow that addresses the challenges of utilizing LLMs in geoscience data analysis. The work was built upon the open data API (application programming interface) of Mindat, one of the largest databases in mineralogy. We designed and developed an open-source LLM-driven workflow that processes natural language requests and automatically utilizes the Mindat API, mineral co-occurrence network analysis, and locality distribution heat map visualization to conduct geoscience data analysis tasks. Using prompt engineering techniques, we developed a supervisor-based agentic framework that enables LLM agents to not only interpret context information but also autonomously addressing complex geoscience analysis tasks, bridging the gap between automated workflows and human expertise. This agentic design emphasizes autonomy, allowing the workflow to adapt seamlessly to future advancements in LLM capabilities without requiring additional fine-tuning or domain-specific embedding. By providing the comprehensive context of the task in the workflow and the professional tool, we ensure the quality of LLM-generated content without the need to embed geoscience knowledge into LLMs through fine-tuning or human alignment. Our approach integrates LLMs into geoscience data analysis, addressing the need for specialized tools while reducing the learning curve through LLM-driven interactions between users and APIs. This streamlined workflow enhances the efficiency of exploratory data analysis, as demonstrated by the several use cases presented. In our future work we will explore the scalability of this workflow through the integration of additional agents and diverse geoscience data sources.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"25 ","pages":"Article 100218"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166138","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}

{"title":"Skillful prediction of Indian Ocean Dipole index using machine learning models","authors":"J.V. Ratnam,&nbsp;Swadhin K. Behera,&nbsp;Masami Nonaka,&nbsp;Kalpesh R. Patil","doi":"10.1016/j.acags.2025.100228","DOIUrl":"10.1016/j.acags.2025.100228","url":null,"abstract":"<div><div>In this study, we evaluated six machine learning models for their skill in predicting the Indian Ocean Dipole (IOD). The results based on the IOD index predictions at 1–8 month lead time indicate that the AdaBoost model with Multi-Layer Perceptron as the base estimator, AdaBoost(MLP), to perform better than the other five models in predicting the IOD index at all lead times. Interestingly, the IOD predictions of AdaBoost(MLP) had an anomaly correlation coefficient above 0.6 at almost all lead times. The results suggest that the AdaBoost(MLP) machine learning model to be a promising tool for predicting the IOD index with a long lead time of 8 months. Analysis revealed that the machine learning model predictions are aided by the signals from the Pacific region, owing to co-occurrences of some of the IODs with El Nino-Southern Oscillations.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"25 ","pages":"Article 100228"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377477","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}

{"title":"Integrating empirical analysis and deep learning for accurate monsoon prediction in Kerala, India","authors":"Yajnaseni Dash,&nbsp;Ajith Abraham","doi":"10.1016/j.acags.2024.100211","DOIUrl":"10.1016/j.acags.2024.100211","url":null,"abstract":"<div><div>Kerala, a coastal state in India characterized by its humid tropical monsoon climate, is profoundly influenced by the Western Ghats and the Arabian Sea. Kerala receives significant rainfall during both the southwest monsoon (June to September, JJAS) and the northeast monsoon (October to December, OND) seasons. Given the substantial impact of rainfall on the state's economy and livelihoods, accurate precipitation forecasting is of critical importance. Although Kerala's annual rainfall is approximately 2.5 times higher than the national average, the state frequently experiences water scarcity due to rapid runoff into the Arabian Sea. This study builds upon previous research concerning Kerala's rainfall patterns and introduces a novel approach to improving rainfall predictions. Usage of a hybrid model that integrates Empirical Mode Decomposition (EMD) with Detrended Fluctuation Analysis (DFA) and deep Long Short-Term Memory (LSTM) neural networks, demonstrates enhanced precision in forecasting. Thus, by integrating empirical data analysis with advanced deep learning techniques, this research offers a robust framework for predicting rainfall in Kerala, making a significant contribution to the field of climate informatics and providing practical benefits for the region's economy and environmental management.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"24 ","pages":"Article 100211"},"PeriodicalIF":2.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137714","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}

{"title":"CloudSense: A model for cloud type identification using machine learning from radar data","authors":"Mehzooz Nizar ,&nbsp;Jha K. Ambuj ,&nbsp;Manmeet Singh ,&nbsp;S.B. Vaisakh ,&nbsp;G. Pandithurai","doi":"10.1016/j.acags.2024.100209","DOIUrl":"10.1016/j.acags.2024.100209","url":null,"abstract":"<div><div>The knowledge of type of precipitating cloud is crucial for radar based quantitative estimates of precipitation. We propose a novel model called CloudSense which uses machine learning to accurately identify the type of precipitating clouds over the complex terrain locations in the Western Ghats (WG) of India. CloudSense uses vertical reflectivity profiles collected during July–August 2018 from an X-band radar to classify clouds into four categories namely stratiform, mixed stratiform-convective, convective and shallow clouds. The machine learning (ML) model used in CloudSense was trained using a dataset balanced by Synthetic Minority Oversampling Technique (SMOTE), with features selected based on physical characteristics relevant to different cloud types. Among various ML models evaluated Light Gradient Boosting Machine (LightGBM) demonstrate superior performance in classifying cloud types with a BAC (Balanced Accuracy) of 0.79 and F1-Score of 0.8. CloudSense generated results are also compared against conventional radar algorithms and we find that CloudSense performs better than radar algorithms. For 200 samples tested, the radar algorithm achieved a BAC of 0.69 and F1-Score of 0.68, whereas CloudSense achieved a BAC of 0.8 and F1-Score of 0.79. Our results show that ML based approach can provide more accurate cloud detection and classification which would be useful to improve precipitation estimates over the complex terrain of the WG.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"24 ","pages":"Article 100209"},"PeriodicalIF":2.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745714","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}

{"title":"Enhancing prediction of fluid-saturated fracture characteristics using deep learning super resolution","authors":"Manju Pharkavi Murugesu ,&nbsp;Vignesh Krishnan ,&nbsp;Anthony R. Kovscek","doi":"10.1016/j.acags.2024.100208","DOIUrl":"10.1016/j.acags.2024.100208","url":null,"abstract":"<div><div>Utilization of subsurface resources is essential to achieve energy sustainability including large-scale CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> sequestration, H<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> storage, geothermal energy extraction, and hydrocarbon recovery. In-situ visualization of fluid flow in geological media is essential to understand complex, coupled, physical and chemical processes underlying fluid injection, storage, extraction. X-ray Computed Tomography (CT) in the laboratory has proven beneficial to visualize changes in the flow field with rapid temporal resolution (10’s s) and moderate spatial resolution (100’s <span><math><mrow><mi>μ</mi><mi>m</mi></mrow></math></span>). There is a trade-off between spatial and temporal resolution that limits accurate characterization of dynamics in rock features that are below spatial resolution of CT. While past literature has offered solutions to improve resolution of CT rock images, including deep learning-based algorithms, our study uniquely focuses on improving dynamic, partially and fully fluid-saturated geological images. Fluid-saturated CT images offer additional information, through augmented signals provided by the presence of fluid. Among challenges, CT images of geological media inherently possess limited information due to their single-channel gray-scale source. Additionally, fluid flows through partially saturated media frustrate existing super resolution techniques because unsaturated CT images are an inaccurate proxy for saturated dynamic rock images. The novelty of this work is the expansion of a generative adversarial network (GAN) for applications involving super resolution of partially saturated low resolution CT images using end-member, unsaturated high resolution <span><math><mi>μ</mi></math></span>CT images. To this end, we acquired multiscale low- and high-resolution CT rock images in unsaturated and saturated states. Among GAN and convolutional neural networks, GAN’s produce realistic high-resolution reconstructions of saturated geological media when trained using high-resolution, unsaturated images and lower resolution images in various saturation states. The model has direct usefulness for interpretation of real-time images.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"24 ","pages":"Article 100208"},"PeriodicalIF":2.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137712","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}

{"title":"An UNet3+ Network based on global pyramid aggregation for change detection in optical remote-sensing images","authors":"Yanbo Sun ,&nbsp;Wenxing Bao ,&nbsp;Wei Feng ,&nbsp;Kewen Qu ,&nbsp;Xuan Ma ,&nbsp;Xiaowu Zhang","doi":"10.1016/j.acags.2024.100210","DOIUrl":"10.1016/j.acags.2024.100210","url":null,"abstract":"<div><div>Change detection (CD) is a meaningful and challenging task for remote sensing (RS) image analysis. Deep learning (DL) based methods have shown great potential in change detection tasks, there are still two problems with existing deep learning methods such as CNN and Transformer: (1) They do not target different depths to extract global semantics in the network; (2) The increase in network depth will lead to uncertainty in the edge pixels of changing targets and the absence of small targets. First, to address this challenge and address these issues, this work proposes a global pyramid aggregation UNet3+ (GPA-UNet3+) change detection model, that uses UNet3+ as the backbone network and connects the encoder and decoder with a pyramid structure. Secondly, a Global Atrous Spatial Pooling Pyramid Module (GASPPM) is proposed. Refined features at different depths and aggregated them to enhance the network’s ability to extract global semantics. Finally, the Edge Enhancement Channel Attention Module (EECAM) is specifically proposed to alleviate the edge pixel uncertainty and spatial position information loss caused by the increase in network depth. Multiple experiments are conducted on two common change detection datasets and a real dataset. Extensive experimental results show that the proposed method outperforms other state-of-the-art methods, achieving the highest F1-score of 90.95%, 95.31%, and 88.32% on the LEVIR-CD dataset, SVCD dataset and Shizuishan Mining Area dataset, respectively.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"24 ","pages":"Article 100210"},"PeriodicalIF":2.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137713","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}

{"title":"A machine learning approach for mapping susceptibility to land subsidence caused by ground water extraction","authors":"Diana Orlandi ,&nbsp;Esteban Díaz ,&nbsp;Roberto Tomás ,&nbsp;Federico A. Galatolo ,&nbsp;Mario G.C.A. Cimino ,&nbsp;Carolina Pagli ,&nbsp;Nicola Perilli","doi":"10.1016/j.acags.2024.100207","DOIUrl":"10.1016/j.acags.2024.100207","url":null,"abstract":"<div><div>Land subsidence is a worldwide threat that may cause irreversible damage to the environment and the infrastructures. Thus, identifying and mapping areas prone to land subsidence with accurate methods such as Land Subsidence Susceptibility Index (LSSI) mapping is crucial for mitigating the adverse impacts of this geohazard. Also, Machine Learning (ML) is now becoming a powerful tool to analyze vast and different datasets such as those necessary for LSSI mapping. In this study, we use the conventional Frequency Ratio (FR) method and ML models to generate LSSI maps of the region of Murcia (Spain) where land subsidence occurred in the past due to groundwater overdraft. A LSSI map was initially generated with known FR. Then, additional Conditioning Factors (CFs) with increased spatial resolution were used to train several ML models and generate a new LSSI map. The Extra-Trees Classifier (ETC) outperformed the other approaches, achieving the best performance with a weighted average precision and F1-Score of 0.96, after optimizing its hyperparameters. Then, a third LSSI map was calculated using the FR method and observations of land subsidence from InSAR (Interferometric Synthetic Aperture Radar). This study shows that the effectiveness of using several CFs depends on the added information of each layer. Moreover, the comparison between the different LSSI maps and InSAR data highlights the crucial role of the spatial resolution for accurate mapping, thus enhancing land subsidence risk assessment.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"24 ","pages":"Article 100207"},"PeriodicalIF":2.6,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699885","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}

{"title":"Revolutionizing the future of hydrological science: Impact of machine learning and deep learning amidst emerging explainable AI and transfer learning","authors":"Rajib Maity,&nbsp;Aman Srivastava,&nbsp;Subharthi Sarkar,&nbsp;Mohd Imran Khan","doi":"10.1016/j.acags.2024.100206","DOIUrl":"10.1016/j.acags.2024.100206","url":null,"abstract":"<div><div>Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are revolutionizing hydrology, driving significant advancements in water resource management, modeling, and prediction. This review synthesizes cutting-edge developments, methodologies, and applications of AI-ML-DL across key hydrological processes. By critically evaluating these techniques against traditional models, we highlight their superior ability to capture complex, nonlinear relationships and adapt to diverse environments. We further explore AI applications in precipitation forecasting, evapotranspiration estimation, groundwater dynamics, and extreme event prediction (floods, droughts, and compound events), showcasing their timely potential in addressing critical water-related challenges. A particular emphasis is placed on Explainable AI (XAI) and transfer learning as essential tools for improving model transparency and applicability, enabling broader stakeholder trust and cross-regional adaptability. The review also addresses persistent challenges, including data limitations, computational demands, and model interpretability, proposing solutions that integrate emerging technologies like quantum computing, the Internet of Things (IoT), and interdisciplinary collaboration. This review charts a strategic course for future research and practice by bridging AI advancements with practical hydrological applications. Our findings highlight the importance of embracing AI-driven approaches for next-generation hydrological modeling and provide actionable understandings for researchers, practitioners, and policymakers. As hydrology faces escalating challenges due to human-induced climate change and growing water demands, the continued evolution of AI-integrated models and innovations in data handling and stakeholder engagement will be imperative. In conclusion, the findings emphasize the critical role of AI-driven hydrological modeling in addressing global water challenges, including climate change adaptation, sustainable water resource management, and disaster risk reduction.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"24 ","pages":"Article 100206"},"PeriodicalIF":2.6,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652751","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}