Computers & Geosciences最新文献

{"title":"Manifold embedding of geological and geophysical observations for non-stationary subsurface property estimation using geodesic Gaussian processes","authors":"Eungyu Park ,&nbsp;Jize Piao ,&nbsp;Hyunggu Jun ,&nbsp;Yong-Sung Kim ,&nbsp;Heejun Suk ,&nbsp;Weon Shik Han","doi":"10.1016/j.cageo.2025.105958","DOIUrl":"10.1016/j.cageo.2025.105958","url":null,"abstract":"<div><div>Traditional methods for geological characterization often overlook or oversimplify the challenge of subsurface non-stationarity. This study introduces an innovative methodology that uses ancillary data, such as geological insights and geophysical exploration, to accurately delineate the spatial distribution of subsurface petrophysical properties in large, non-stationary geological fields. The approach leverages geodesic distance on an embedded manifold, with the level-set curve linking observed geological structures to intrinsic non-stationarity. Critical parameters <span><math><mrow><mi>ρ</mi></mrow></math></span> and <span><math><mrow><mi>β</mi></mrow></math></span> were identified, influencing the strength and dependence of estimates on secondary data. Comparative evaluations demonstrated that this method outperforms traditional kriging, particularly in representing complex subsurface structures. This enhanced accuracy is crucial for applications such as contaminant remediation and underground repository design. While focused on two-dimensional models, future work should explore three-dimensional applications across diverse geological structures. This research provides novel strategies for estimating non-stationary geologic media, advancing subsurface characterization.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"202 ","pages":"Article 105958"},"PeriodicalIF":4.2,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143905944","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":"IntersecT: a Python script for quantitative isopleth thermobarometry of equilibrium and disequilibrium systems","authors":"Sara Nerone ,&nbsp;Pierre Lanari ,&nbsp;Hugo Dominguez ,&nbsp;Jacob B. Forshaw ,&nbsp;Chiara Groppo ,&nbsp;Franco Rolfo","doi":"10.1016/j.cageo.2025.105949","DOIUrl":"10.1016/j.cageo.2025.105949","url":null,"abstract":"<div><div>Isopleth thermobarometry involves comparing compositional isopleths generated from forward thermodynamic models with the measured mineral compositions in a specific assemblage to retrieve the pressure and temperature conditions of equilibration. This technique has been used extensively in the last two decades to constrain the conditions of metamorphism for natural rock samples. However, this method is often applied qualitatively, relying on the intersection of a limited number of isopleths for a few selected phases. Recent works have introduced software solutions with more quantitative approaches; these use statistical methods to derive optimal <em>P–T</em> conditions and provide a more accurate interpretation of forward modelling results. Despite these advances, these methods are not commonly used. IntersecT aims at distributing a tool for statistically quantifying the quality of fit using the WERAMI output of Perple_X and applying multiple approaches, including the quality factor concept from Bingo-Antidote. This formulation allows the propagation of measurement uncertainty in isopleth thermobarometry. In addition, IntersecT applies reduced <em>χ</em>² statistics to assess the weight of the considered phases, enabling the down-weighting of outlier data due to model inaccuracies or incorrect assumptions, such as disequilibrium features. The quality factor approach helps to visualize discrepancies resulting from these issues. IntersecT provides a quantitative framework to improve the interpretation of Perple_X isopleth thermobarometry results, allowing compositional uncertainties in the measured mineral composition to be considered. This approach can also help interpret how phase equilibrium experiments reproduce the observed compositions for magmatic and metamorphic systems.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"202 ","pages":"Article 105949"},"PeriodicalIF":4.2,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143905945","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":"PyHawk: An efficient gravity recovery solver for low–low satellite-to-satellite tracking gravity missions","authors":"Yi Wu ,&nbsp;Fan Yang ,&nbsp;Shuhao Liu ,&nbsp;Ehsan Forootan","doi":"10.1016/j.cageo.2025.105934","DOIUrl":"10.1016/j.cageo.2025.105934","url":null,"abstract":"<div><div>The low–low satellite-to-satellite tracking (LL-SST) gravity missions, such as the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO), provide an important space-based Essential Climate Variable (ECV) to measure changes in the Terrestrial Water Storage (TWS). Due to the high-precision Global Navigation Satellite System (GNSS) receiver, accelerometers, and inter-satellite ranging instrument, these LL-SST missions are able to sense extremely tiny perturbations on both the orbit and inter-satellite ranges, which can project into the Earth’s time-variable gravity fields. The measurement systems of these LL-SST missions are highly complex; therefore, a data processing chain is required to exploit the potential of their high-precision measurements, which challenges both general and expert users. In this study, we present an open-source, user-friendly, cross-platform and integrated toolbox “PyHawk”, which is the first Python-based software in relevant field, to address the complete data processing chain of LL-SST missions including GRACE, GRACE-FO and probably the future gravity missions. This toolbox provides non-expert users an easy access to the payload data pre-processing, background force modeling, orbit integration, ranging calibration, as well as the ability for temporal gravity field recovery using LL-SST measurements. In addition, a series of high-standard benchmark tests have been provided to evaluate PyHawk, confirming its performance to be comparable with those used to provide the official Level-2 time-variable gravity field solutions of GRACE. Researchers working with orbit determination and gravity field modeling can benefit from this toolbox.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"201 ","pages":"Article 105934"},"PeriodicalIF":4.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863847","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":"Why the relational data model matters for climate data management","authors":"Ezequiel Cimadevilla","doi":"10.1016/j.cageo.2025.105931","DOIUrl":"10.1016/j.cageo.2025.105931","url":null,"abstract":"<div><div>Efficient data management of climate data banks, in particular those generated by Global or Regional Climate Models, is an important requirement for precise understanding of current changes in the climate system. Current data management practices in the climate community are based on the analysis of binary files for storage of multidimensional arrays that require ad hoc software libraries for accessing the data. Several approaches are being developed to ease and facilitate climate data management and data analysis. However, the theoretical foundations that cause climate data manipulation difficulties remain unchallenged. The Relational Data Model was proposed as a formal solution for database management based on mathematical logic. It has been widely accepted in the industry and has survived the test of time. However, the foundational principles of the Relational Data Model have been overlooked by the climate data management community, mostly due to a lack of emphasis in the relevance of mathematical logic for database management and misunderstanding between physical and logical levels of abstraction. As a result, climate data management workflows lack the rigor and formality provided by the Relational Data Model. This work explains the Relational Data Model at the logical level of abstraction and provides the arguments, clarifies the misconceptions, and justifies its adoption for climate data management in the context of gridded data generated by climate models.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"201 ","pages":"Article 105931"},"PeriodicalIF":4.2,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844826","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":"Geoscience popularisation in Geoparks: A common workflow for digital outcrop modelling","authors":"Mark A. Williams ,&nbsp;Gianluca Tronti ,&nbsp;Raiza Sartori Peruzzo ,&nbsp;Manuel García-Rodríguez ,&nbsp;Eugenio Fazio ,&nbsp;Michele Zucali ,&nbsp;Irene Maria Bollati","doi":"10.1016/j.cageo.2025.105945","DOIUrl":"10.1016/j.cageo.2025.105945","url":null,"abstract":"<div><div>Geodiversity has gained increasing attention, prompting geoscientists to advocate for its recognition to promote holistic nature conservation. UNESCO Global Geoparks (UGGps) have expanded globally, promoting geoheritage and contributing to geoconservation and sustainable tourism. In this framework, the IGCP 714 project, \"3GEO – Geoclimbing &amp; Geotrekking in Geoparks,\" launched in 2021, aims to use Geographical Information Technologies (GIT) – including GIS, remote sensing, Unmanned Aerial Vehicles (UAVs), and other geospatial tools - to enhance geoscience communication within UGGps. However, there is still a significant need for a repeatable, accessible, low-cost, and effective workflow to integrate these digital technologies into geoscience communication effectively. This study developed a workflow for creating Digital Outcrop Models (DOMs) of geosites and geodiversity sites used for recreational climbing and trekking. Using technologies such as UAVs and Smartphones equipped with LiDAR sensors, the workflow generates DOMs that can be integrated into web-GIS applications and Virtual Reality experiences, offering interactive educational content. Four examples are described, illustrating the implementation of the DOM workflow from the outcrop scale (La Pedriza Granitic Batholith, Spain, and Etna Volcano Lava Tube, Italy) to the terrane scale (Organ Pipes Columnar Jointing, Australia, and Baceno Tectonic Window, Italy). The workflow is designed to produce DOMs for public and student engagement, demonstrating their potential for broader educational and geoconservation applications. Moreover, the workflow aims to build capacity among Geopark practitioners and researchers by improving techniques for creating content on geoheritage features and enhancing geoscience communication. For this, the workflow is designed to be repeatable by employing common and relatively low-cost GIT tools. We discuss the need for investment in capability, software, and hardware to equip Geopark practitioners with the skills required to implement this workflow. By applying this workflow to create DOMs of geoheritage features, we demonstrate its potential to enhance the appreciation of geodiversity, support education and research, and promote sustainable geotourism within UGGps.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"201 ","pages":"Article 105945"},"PeriodicalIF":4.2,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876830","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":"Domain knowledge-guided geological named entities recognition of rock minerals based on prompt engineering with error feedback mechanism","authors":"Qinjun Qiu ,&nbsp;Yunxia Ma ,&nbsp;Peng Han ,&nbsp;Kai Ma ,&nbsp;Zehua Huang ,&nbsp;Miao Tian ,&nbsp;Qirui Wu","doi":"10.1016/j.cageo.2025.105944","DOIUrl":"10.1016/j.cageo.2025.105944","url":null,"abstract":"<div><div>Geological reports contain a wealth of information about geological entities, such as rocks and minerals, which are of great significance for resource exploration, environmental assessment, 3D geological modeling, and intelligent prospecting. However, existing methods and models (e.g., deep learning-based approaches) for geological named entity recognition (GNER) heavily rely on large manually annotated corpora. This process is time-consuming and labor-intensive, and it faces limitations when dealing with complex entities in geological reports, such as long or nested entities. To address this issue, this paper proposes a rock and mineral NER method based on prompt engineering and domain knowledge guidance. First, preliminary entity recognition is conducted through labeling rather than extraction, mitigating the problem of repetitive recognition of nested entities. Second, we summarize and categorize the types of errors made by large language models(LLMs), incorporating geological knowledge guidance for secondary recognition to reduce common mistakes. Finally, secondary category validation is used to alleviate the “hallucination” problem, where LLMs mistakenly identify non-entities as entities. This method requires only two examples as training samples to guide the model, significantly reducing the workload of corpus annotation. Experiments were conducted on multiple LLMs (e.g., GPT-4o-0513, GPT-4o-0806, and Claude 3.5 sonnet). The results show that on our self-constructed dataset, compared to direct entity extraction, the accuracy of rock and mineral recognition is improved by approximately 17 % and 11 %, respectively, validating the effectiveness of combining domain knowledge with LLMs for GNER.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"201 ","pages":"Article 105944"},"PeriodicalIF":4.2,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850379","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":"OPTIM: A Python-based optimization framework for geophysical problems","authors":"Tao Lei ,&nbsp;Wei Zhang ,&nbsp;Yongming Lu ,&nbsp;Li Yang","doi":"10.1016/j.cageo.2025.105930","DOIUrl":"10.1016/j.cageo.2025.105930","url":null,"abstract":"<div><div>Geophysical inverse problems, such as full waveform inversion, involve significant computational demands and algorithmic complexity. Geophysicists aim to resolve numerous unknown parameters within a limited number of inversion iterations, necessitating both efficient and accurate geophysical modules (e.g., forward modeling and sensitivity kernel calculations) and robust optimization frameworks to drive the inversion process. To facilitate the rapid construction of comprehensive inversion workflows, we present <em>OPTIM</em>, a Python-based open-source local optimization software package. <em>OPTIM</em> structures each optimization step as an independent program, exchanging information between adjacent steps through files and parameters. Its implementation closely follows mathematical formulations, allowing users to easily identify and modify specific modules as needed. Constructing an inversion workflow with the proposed software is analogous to assembling modular components, minimizing concerns about program interfaces and lifecycle management. Through a series of examples, we demonstrate how the proposed software enables efficient inverse workflow construction and large-scale geophysical inversion on multi-node high-performance clusters. <em>OPTIM</em> empowers researchers to rapidly and robustly develop novel geophysical inversion processes without compromising on performance and scalability. This capability significantly streamlines the complexity in solving geophysical inverse problems and accelerates the development cycle.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"201 ","pages":"Article 105930"},"PeriodicalIF":4.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143824416","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":"Hybrid supervised prestack three-parameter inversion method based on physical equation driving","authors":"Yaojie Chen ,&nbsp;Shulin Pan ,&nbsp;Yinghe Wu ,&nbsp;Ziyu Qin ,&nbsp;Shengbo Yi ,&nbsp;Dongjun Zhang","doi":"10.1016/j.cageo.2025.105935","DOIUrl":"10.1016/j.cageo.2025.105935","url":null,"abstract":"<div><div>Seismic inversion methods based on deep learning have made significant progress. However, supervised learning networks face challenges such as limited labeled data and poor generalization in transfer learning. Meanwhile, unsupervised learning inversion methods, due to the absence of labeled constraints, often suffer from insufficient inversion accuracy. In order to further improve the inversion accuracy, a hybrid supervised pre-stack three-parameter inversion method driven by physical equations is proposed. This method integrates supervised and unsupervised learning, driven by physical equations and constrained by low-frequency models, while employing a multi-trace inversion strategy. It effectively enhances the continuity of elastic parameter inversion and addresses the accuracy degradation caused by the scarcity of labeled data in seismic inversion. To fully integrate high- and low-frequency features in the inversion results and further refine accuracy, the Unet-GRU network is introduced, combining the U-shaped Network (Unet) with the Gated Recurrent Unit (GRU). In this method, a supervised network is first trained on the Marmousi2 model to learn the mapping relationship between seismic data and inversion parameters. After training, the network is applied to field seismic data to generate initial inversion results. These results are then used as inputs for the unsupervised network, followed by forward modeling processing of the final output. By minimizing the error between synthetic and observed seismic data through iterative optimization, the final inversion results are obtained. The feasibility of this method is validated using the Overthrust model, and its robustness is further tested by adding noise. Finally, the approach is applied to real field data and compared with traditional inversion methods. The results demonstrate that the proposed method significantly improves inversion accuracy and offers strong practical applicability in seismic exploration and development.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"201 ","pages":"Article 105935"},"PeriodicalIF":4.2,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838898","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 method for preserving three spatial features in the upscaling of categorical raster data","authors":"Xiangyuan He,&nbsp;Chen Zhou,&nbsp;Mingzhu Gao,&nbsp;Saisai Sun,&nbsp;Chiying Lyu,&nbsp;Xiaoyi Han","doi":"10.1016/j.cageo.2025.105933","DOIUrl":"10.1016/j.cageo.2025.105933","url":null,"abstract":"<div><div>Raster resampling can be used to modify the resolution of raster data to satisfy specific application requirements for geographical information systems (GIS). However, with an increase in raster cell size, a process known as upscaling, various spatial features are inevitably lost, resulting in reduced data accuracy. Categorical raster data refer to a raster dataset where each specific raster value corresponds to a category, such as land use types or vegetation cover, rather than continuous numerical values. To improve the accuracy of upscaled data, this study proposes a method for preserving the shape, topological, and area features in categorical raster upscaling. First, we refined the shape index calculation to accurately assess the shape of the raster zones and corrected the shape errors using neighborhood operations. Second, we resolved the topological errors by reassigning the cells between the raster zones. Finally, we calculated the number of cells that needed adjustment in each zone and reassigned the cells on the zone boundaries accordingly, to reduce the overall area error. The results demonstrated a 30.5235 % improvement in the accuracy, compared with the accuracy of the nearest neighbor method for upscaling from 5 to 10 m. The effectiveness of the proposed method decreased with increasing target cell size, with the method being ineffective at 35 m. Furthermore, the method demonstrates wide applicability across different datasets. By efficiently and simultaneously maintaining these spatial features during upscaling, our method can offer users more accurate resampled datasets as input for GIS applications, thereby enhancing the precision of the outputs.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"201 ","pages":"Article 105933"},"PeriodicalIF":4.2,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817292","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 spatiotemporal mixed-enhanced generative adversarial network for radar-based precipitation nowcasting","authors":"Long He ,&nbsp;Kun Zheng ,&nbsp;Huihua Ruan ,&nbsp;Shuo Yang ,&nbsp;Jinbiao Zhang ,&nbsp;Cong Luo ,&nbsp;Siyu Tang ,&nbsp;Yunlei Yi ,&nbsp;Yugang Tian ,&nbsp;Jianmei Cheng","doi":"10.1016/j.cageo.2025.105919","DOIUrl":"10.1016/j.cageo.2025.105919","url":null,"abstract":"<div><div>Skillful precipitation nowcasting with high resolution and detailed information holds promise for providing reliable alerts about severe weather events to society. Radar echo extrapolation is an essential method for precipitation nowcasting, but traditional methods struggle to capture rapidly changing regions. Deep learning (DL)-based methods exhibit superior performance. However, existing DL-based methods face challenges such as low accuracy, particularly in producing clear forecasts over longer lead times and accurately forecasting moderate to heavy rainfall events. To address these challenges, we developed a novel radar-based precipitation nowcasting model, STMixGAN, which can be described as a nonlinear proximity forecasting model. This model effectively aggregates global-to-local information and imposes constraints to represent the complex evolution of rainfall efficiently. Consequently, STMixGAN produces realistic and spatiotemporally consistent predictions. Using radar observations from South China, STMixGAN successfully forecasted radar maps for the next 1 h using 24 min of input data. Two traditional methods (Persistence and Optical flow) and five DL-based methods (ConvLSTM, Rainformer, IAM4VP, REMNet, and GAN-argcPredNet) were employed as benchmarks to validate STMixGAN’s forecasting capabilities. The experimental results demonstrate STMixGAN’s superior performance and provide valuable insights for enhancing heavy rainfall forecasting.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"200 ","pages":"Article 105919"},"PeriodicalIF":4.2,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715501","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}