Computers & Geosciences最新文献

{"title":"DisperPy: A machine learning based tool to automatically pick group velocity dispersion curves from earthquakes","authors":"André V.S. Nascimento ,&nbsp;Carlos A.M. Chaves ,&nbsp;Susanne T.R. Maciel ,&nbsp;George S. França ,&nbsp;Giuliano S. Marotta","doi":"10.1016/j.cageo.2025.106015","DOIUrl":"10.1016/j.cageo.2025.106015","url":null,"abstract":"<div><div>Seismology has made significant progress in high-resolution Earth imaging, largely driven by the increasing volume of freely available data. As a result, automated tools and machine learning algorithms are becoming essential for processing this vast amount of information. We present <em>DisperPy</em>, an open-source Python library developed to automatically extract group velocity dispersion curves from earthquake data. The analysis framework of <em>DisperPy</em> is structured around two primary tasks: (1) assessing the quality of waveforms to determine if dispersion extraction is feasible, and (2) measuring the group velocity dispersion curve for suitable waveforms. To address the first task, <em>DisperPy</em> uses a convolutional neural network trained on dispersion spectrograms to classify waveform quality. The model, based on the ResNet-34 architecture, is initialized with ImageNet-pretrained weights and fine-tuned using the fastai deep learning library. In the test set, the network achieves an accuracy of 92 % in distinguishing between high- and low-quality dispersion images. For the second task, <em>DisperPy</em> employs unsupervised learning techniques, starting with a Gaussian mixture model to separate dispersion energy from background noise, followed by <em>k-means</em> to separate the dispersion energy into clusters, making it easier to track amplitude maxima and then construct initial dispersion curves. Finally, a refinement of the initial dispersion is achieved using both the density-based spatial clustering of applications with noise algorithm and data quality criteria to remove possible outliers. To further test <em>DisperPy</em>, we conduct a surface wave tomography experiment across the contiguous United States using freely available vertical-component broadband waveforms. After processing the data with <em>DisperPy</em> and removing low-quality waveforms, the final dataset consisted of 194,325 unique dispersion curves. Consistent with previous studies, our maps reveal a prominent velocity dichotomy, with low velocities in the tectonically active western US and high velocities in the stable central and eastern US.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 106015"},"PeriodicalIF":4.2,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Adaptive Preconditioned Conjugate Gradient Regularization (APCGR) algorithm with Sigmoid Function (SF) constraint for efficient three-dimensional (3D) gravity focusing inversion","authors":"Wenjin Chen,&nbsp;Xiaolong Tan","doi":"10.1016/j.cageo.2025.106014","DOIUrl":"10.1016/j.cageo.2025.106014","url":null,"abstract":"<div><div>We introduce a novel focused gravity inversion algorithm and develop corresponding software, highlighting three key innovations. First, we propose the Adaptive Preconditioned Conjugate Gradient Regularization algorithm, which efficiently and adaptively determines the regularization parameter. Second, we incorporate the Sigmoid Function to stabilize the inversion process, significantly accelerating iterative convergence. Third, we have developed a user-friendly software with a graphical user interface for this new method, utilizing the popular high-level and interactive programming language MATLAB. To promote knowledge sharing and resource accessibility, we have made the software open-source. To validate our approach, we tested the algorithm on both synthetic and real gravity data, demonstrating its exceptional capability to accurately reconstruct the 3D density distribution of complex subsurface structures. Furthermore, we conducted a comparative analysis between the new algorithm, the conjugate gradient method constrained by SF, and the standard conjugate gradient method. The results indicate that the new method requires fewer iterations and exhibits higher computational efficiency.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 106014"},"PeriodicalIF":4.2,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel class-imbalance learning framework for fluid recognition: Application to Qingshimao-Gaoshawo tight-sand gas reservoirs in the Ordos Basin, China","authors":"Jun Yi ,&nbsp;ZhongLi Qi ,&nbsp;XiangChengZhen Li ,&nbsp;Fuqiang Lai ,&nbsp;Wei Zhou","doi":"10.1016/j.cageo.2025.105993","DOIUrl":"10.1016/j.cageo.2025.105993","url":null,"abstract":"<div><div>The mathematical model-based methods used for conventional oil and gas resources often perform poorly in fluid recognition of tight-sand reservoir, due to the mutual interference of various factors such as reservoir lithology and pore structure. Booming artificial intelligence technologies and accumulating logging data provide a solid foundation for the application of machine learning methods as new tools for fluid identification. However, there is often a serious class imbalance, which can easily lead to the inability to achieve ideal classification results, in the proportion of categories of the collected well logging data. Consequently, this issue has become a huge challenge for the academic and industrial communities. To address this, a novel class-imbalance learning framework for fluid recognition (CILF) is proposed to tight-sand gas reservoirs of Qingshimao-Gaoshawo area of Ordos Basin, in China. Specifically, an improved label propagation algorithm based on semi-supervised learning (SS-LPA) is designed at the data level, which can reduce the imbalance rate of raw data to some extent after assigning high-confidence labels to unlabeled samples. At the model level, <span><math><mi>Q</mi></math></span>-network, as an effective reinforcement learning approach, is introduced into ensemble learning framework (QNEL), which can enhance the multi-classification accuracy of fluid identification by training multiple baseline models that are given different weights for feedback on imbalanced data. The experimental results from 35 tight-sand wells in Qingshimao-Gaoshawo area of Ordos Basin validate the effectiveness of the proposed framework. Specifically, the performance of CILF is the best on all three typical evaluation metrics, and it outperforms others in 12 out of a total of 18 categories. In terms of the average scores for six categories, the precision, recall rate, and F1 score of the proposed framework reach 0.988, 0.984, and 0.985, respectively.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 105993"},"PeriodicalIF":4.2,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seismicity-constrained fault detection and characterization with a multitask machine learning model","authors":"Kai Gao,&nbsp;Ting Chen","doi":"10.1016/j.cageo.2025.105999","DOIUrl":"10.1016/j.cageo.2025.105999","url":null,"abstract":"<div><div>Geological fault detection and characterization are crucial for understanding subsurface dynamics across scales. While methods for fault delineation based on either seismicity location analysis or seismic image reflector discontinuity are well-established, a systematic approach that integrates both data types remains absent. We develop a novel machine learning model that unifies seismic reflector images and seismicity location information to automatically identify geological faults and characterize their geometrical properties. The model encodes a seismic image and a seismicity location image separately, and fuses the encoded features with a spatial-channel attention fusion module to improve the learning of important features in both inputs. We design an automated strategy to generate high-quality synthetic training data and labels. To improve the realism of the seismicity location image, we include random seismicity noise and missing seismicity location associated with some of the faults. We validate the model’s efficacy and accuracy using synthetic data examples and two field data examples. Moreover, we show that fine-tuning the trained model with a small, domain-specific dataset enhances its fidelity for field data applications. The results demonstrate that integrating seismicity location and seismic images into a unified framework allows the end-to-end neural network to achieve higher fidelity and accuracy in delineating subsurface faults and their geometrical properties compared with image-only fault detection methods. Our approach offers an adaptive data-driven tool for geological fault characterization and seismic hazard mitigation, bridging the gap between seismicity location and image-based fault detection methods.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 105999"},"PeriodicalIF":4.2,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-code framework for IoT data warehousing and visualization","authors":"Victor Lamas,&nbsp;Alejandro Cortiñas,&nbsp;Miguel R. Luaces","doi":"10.1016/j.cageo.2025.105998","DOIUrl":"10.1016/j.cageo.2025.105998","url":null,"abstract":"<div><h3>Background:</h3><div>The Internet of Things has revolutionized data collection in geosciences through extensive sensor networks. However, developing web-based data warehousing systems for IoT data remains costly and complex. While studies address sensor variability and data ingestion architectures, they often overlook the critical data warehouse component needed to manage IoT data volume and variability. Additionally, Model-Driven Engineering techniques have been used to create dashboards for urban activities but lack advanced map-based visualizations, which are essential for geospatial data.</div></div><div><h3>Objectives:</h3><div>This study aims to address the challenges of creating IoT data warehouses for geosciences, encouraging scientists to share sensor data analysis results using a simple, user-friendly, and cost-effective approach.</div></div><div><h3>Methods:</h3><div>The proposed framework integrates (i) a Domain-Specific Language metamodel to define sensors, dimensions, and measurement parameters, (ii) a Software Product Line for IoT data warehouse creation, and (iii) a low-code platform with command-line and web interfaces. The approach was validated through four case studies: meteorological, traffic and air quality, coastal, and oceanic monitoring systems.</div></div><div><h3>Results:</h3><div>The framework enables efficient IoT data warehouse creation with customized spatial, temporal, and attribute aggregation. Case studies demonstrate adaptability across domains, supporting real-time data ingestion, sensor mobility, and advanced visualization.</div></div><div><h3>Conclusion:</h3><div>The study presents a scalable, user-friendly framework for IoT data warehousing in geosciences using SPL and DSL technologies, addressing domain-specific challenges and empowering non-expert users. Future work includes usability assessments and expansion to other domains.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 105998"},"PeriodicalIF":4.2,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards an open soil-plant digital twin based on STEMMUS-SCOPE model following open science","authors":"Yijian Zeng ,&nbsp;Fakhereh Alidoost ,&nbsp;Bart Schilperoort ,&nbsp;Yang Liu ,&nbsp;Stefan Verhoeven ,&nbsp;Meiert Willem Grootes ,&nbsp;Yunfei Wang ,&nbsp;Zengjing Song ,&nbsp;Danyang Yu ,&nbsp;Enting Tang ,&nbsp;Qianqian Han ,&nbsp;Lianyu Yu ,&nbsp;Mostafa Gomaa Daoud ,&nbsp;Prajwal Khanal ,&nbsp;Yunfei Chen ,&nbsp;Christiaan van der Tol ,&nbsp;Raúl Zurita-Milla ,&nbsp;Serkan Girgin ,&nbsp;Bas Retsios ,&nbsp;Niels Drost ,&nbsp;Zhongbo Su","doi":"10.1016/j.cageo.2025.106013","DOIUrl":"10.1016/j.cageo.2025.106013","url":null,"abstract":"<div><div>Droughts and heatwaves jeopardize terrestrial ecosystem services. The development of an open digital twin of the soil-plant system can help monitor and predict the impact of these extreme events on ecosystem functioning. We illustrate how our recently developed STEMMUS-SCOPE model—STEMMUS, Simultaneous Transfer of Energy, Mass and Momentum in Unsaturated Soil; SCOPE, Soil Canopy Observation of Photosynthesis and Energy fluxes—links soil-plant processes to novel satellite observables (e.g. solar-induced chlorophyll fluorescence), contributing to such a digital twin. This soil-plant digital twin allows a mechanistic window for tracking above- and below-ground ecophysiological processes with remote sensing observations. Following Open Science and FAIR (Findable, Accessible, Interoperable, Reusable) principles, both for data and research software, we present the building blocks of the soil-plant digital twin. It emphasizes the importance of FAIR-enabling digital technologies to translate research needs and developments into reproducible and reusable data, software and knowledge.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 106013"},"PeriodicalIF":4.2,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DAM-CGNet: Semantic segmentation-based approach for valley-bottom extraction from digital elevation models","authors":"Yuhan Ren,&nbsp;Hongming Zhang,&nbsp;Liang Dong,&nbsp;Huanyu Yang,&nbsp;Hongyi Li,&nbsp;Lu Du,&nbsp;Qiankun Chen,&nbsp;Songyuan Li","doi":"10.1016/j.cageo.2025.106012","DOIUrl":"10.1016/j.cageo.2025.106012","url":null,"abstract":"<div><div>The accurate extraction of valley bottoms from digital elevation models (DEMs) is crucial for hydrological and geomorphological analyses of mountainous landscapes. However, threshold settings rely on manual intervention; roads near valley bottoms resemble valley-bottom features, and thresholds cannot effectively adapt to valleys of various shapes, leading to low extraction accuracy in existing methods, particularly in narrow V-shaped valleys. To address these issues, this study developed a semantic segmentation approach called a Dense-based Attention Merging Context Guided Network (DAM-CGNet). Without relying on thresholds, this method effectively excludes roads on hillslopes and enhances the recognition of steep feature changes at valley boundaries, enabling the extraction of valley bottoms of various shapes. Key improvements include: (1) incorporating the convolutional block attention module (CBAM) to enhance feature reuse in the information flow, employing attention mechanisms to suppress irrelevant feature responses and focus on valley boundary features; (2) using the dense connection strategy of DenseNet to rebuild the feature flow, helping the model keep important valley-bottom details in deep layers and better recognize small and narrow valleys; and (3) addressing the limitations of single-channel DEM representation by evaluating various input combinations, ultimately selecting DEM, topographic position index (TPI), and slope as effective inputs for valley-bottom extraction. Experiments using semantic segmentation models and conventional methods validated the effectiveness of the proposed method. Specifically, DAM-CGNet achieved high accuracy on the test set (MPA: 90.15 %, MIoU: 84.18 %, FWIoU: 92.99 %) and outperformed conventional methods in extracting valley bottoms of various shapes. This method, without a manual threshold setting as in conventional approaches, enhances valley bottom extraction precision and provides a new perspective for subsequent valley bottom width calculations.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 106012"},"PeriodicalIF":4.2,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncertainty-aware methods for enhancing rainfall prediction with deep-learning based post-processing segmentation","authors":"Simone Monaco ,&nbsp;Luca Monaco ,&nbsp;Daniele Apiletti ,&nbsp;Roberto Cremonini ,&nbsp;Secondo Barbero","doi":"10.1016/j.cageo.2025.105992","DOIUrl":"10.1016/j.cageo.2025.105992","url":null,"abstract":"<div><div>Precipitation forecast is critical in flood management, agricultural planning, water resource allocation, and weather warnings. Despite significant advancements in Numerical Weather Prediction (NWP) models, these systems often exhibit substantial biases and errors, particularly at high spatial and temporal resolutions. To address these limitations, we develop and evaluate uncertainty-aware deep learning ensemble architectures, focusing on characterizing forecast uncertainties while achieving high accuracy and an optimal balance between sharpness and reliability. This study presents SDE U-Net, a novel adaptation of SDE-Net designed specifically for segmentation tasks in precipitation forecasting. We conduct a comprehensive evaluation of state-of-the-art ensemble architectures, including SDE U-Net, and compare their forecast uncertainty against that of a Poor Man’s Ensemble (PME, i.e. NWPs forecast average) across diverse meteorological conditions, ranging from non-intense precipitation patterns to intense weather events. As an example case, we focus on predicting daily cumulative precipitation in northwest Italy, though our approach is broadly generalizable. Our findings demonstrate that all the evaluated probabilistic deep learning models outperform the PME benchmark in terms of median RMSE for both non-intense and intense precipitation events. Among them, SDE U-Net achieves the best overall performance, delivering the lowest RMSE for intense events (<span><math><mrow><mn>2</mn><mo>.</mo><mn>637</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>2</mn></mrow></msup></mrow></math></span>) and demonstrating a more stable error distribution compared to other models. For non-intense events, SDE U-Net perform comparably to other deep learning models, still notably surpassing the baselines. Moreover, SDE U-Net effectively balances sharpness and reliability, making it particularly suitable for operational forecasting of extreme weather. Integrating uncertainty-aware models like SDE U-Net into forecasting workflows can enhance decision-making and preparedness for weather-related hazards.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 105992"},"PeriodicalIF":4.2,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recognition of multiple geochemical anomalies by dual-branch convolutional neural network with adaptive feature fusion","authors":"Jundong He ,&nbsp;Weirong Yang ,&nbsp;Zhengbo Yu ,&nbsp;Cheng Tan ,&nbsp;Binbin Li","doi":"10.1016/j.cageo.2025.106011","DOIUrl":"10.1016/j.cageo.2025.106011","url":null,"abstract":"<div><div>Geochemical anomalies are critical indicators for mineral exploration and resource evaluation. However, due to the diversity and complexity of geological processes, identifying geochemical anomalies remains challenging. This study proposes a dual-branch convolutional neural network based on adaptive feature fusion (1-2D AFFCNN) to simultaneously extract the spectral compositional relationships and spatial structural features of geochemical elements. The model incorporates an Adaptive Feature Fusion Module (AFFM) to effectively integrate features from different branches, significantly improving predictive performance and robustness. Experimental results demonstrate that the 1-2D AFFCNN outperforms traditional single models in terms of accuracy (92.3 %), recall (92.0 %), and AUC value (0.98). The three-stage training strategy effectively mitigates the vanishing gradient problem, enhancing training efficiency and stability. In the application to the Changba ore-concentrated area in Gansu Province, the high-probability anomaly zones generated by the model are highly consistent with the spatial distribution of known lead-zinc deposits, and several high-potential mineralization areas were identified. This study not only provides a novel approach for the comprehensive analysis of multidimensional geochemical data but also opens new avenues for mineral resource prediction and target area localization.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 106011"},"PeriodicalIF":4.2,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A self-attention convolutional long and short-term memory network for correcting sea surface wind field forecasts to facilitate sea ice drift prediction","authors":"Qing Xu ,&nbsp;Qilin Jia ,&nbsp;Yongqing Li ,&nbsp;Hao Zhang ,&nbsp;Peng Ren","doi":"10.1016/j.cageo.2025.105997","DOIUrl":"10.1016/j.cageo.2025.105997","url":null,"abstract":"<div><div>Accurate and timely correction of numerically forecasted sea surface wind fields is essential for sea ice drift prediction. However, current oceanic element prediction systems face two major challenges. The numerically forecasted sea surface wind fields are timely, but their accuracy is often limited. In contrast, reanalysis sea surface wind fields are more accurate but lack timeliness, limiting their applicability in urgent requirements. To address these challenges, a self-attention convolutional long and short-term memory network (SaCLN) has been developed for intelligently correcting the numerically forecasted sea surface wind fields. This approach combines the timeliness of the numerically forecasted wind fields with the accuracy of reanalysis wind fields to generate corrected wind fields that closely approximate the reanalysis wind fields. This network consists of a self-attention network and a convolutional long and short-term memory network (CLN). The self-attention network captures the global spatial correlations of a numerically forecasted sea surface wind field sequence. The CLN extracts the spatial and temporal characteristics of an attention weighted wind field sequence. The trained SaCLN model can effectively generate accurate and timely corrected wind fields, thereby enhancing the accuracy of sea ice drift prediction. The effectiveness of the SaCLN was validated through experiments predicting the drift of Arctic sea ice and Antarctic icebergs. Experimental results show that the drift results based on wind fields corrected by the SaCLN are more accurate than those based on numerically forecasted sea surface wind fields. This method has demonstrated its effectiveness in sea ice drift prediction, assisting researchers in better addressing the challenges posed by sea ice variability.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 105997"},"PeriodicalIF":4.2,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}