Computers & Geosciences最新文献_第6页

A new multi-expert distance for clustering climate parameters: a Caribbean precipitation case study 聚类气候参数的一种新的多专家距离：加勒比海降水案例研究

IF 4.4 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2025-09-20 DOI: 10.1016/j.cageo.2025.106058

Emmanuel Biabiany , Ruben Bagghi , Didier C. Bernard , Vincent Pagé , Stéphane Cholet , Raphaël Cécé

This study investigates precipitation patterns in the Caribbean region using a novel Multi-Expert Distance (MED) metric for clustering analysis. MED integrates multiple climate parameters, including Sea Surface Temperature (SST), wind components at 925 hPa, and Outgoing Longwave Radiation (OLR), with the objective of enhancing spatiotemporal precipitation analysis. This approach offers an alternative to conventional methods that rely on single datasets and Euclidean distances. It combines physical parameters during clustering to enhance accuracy and insights. The analysis encompasses a 43-year period (1979–2021), extending from the Gulf of Mexico to the Caribbean, with a spatial extent that covers the entire region. The MED metric incorporates zone-specific histograms and Kullback-Leibler divergence, enabling dynamic comparisons of atmospheric configurations. The analysis yielded six distinct clusters, each exhibiting unique seasonal and inter-annual precipitation patterns, influenced by regional atmospheric dynamics. The analysis revealed significant transitions and associations between clusters, precipitation levels, and atmospheric conditions. Clusters representing dry conditions exhibited negative SST anomalies, reflecting reduced moisture production. Conversely, clusters exhibiting high precipitation exhibited positive SST anomalies, which are conducive to moisture accumulation. Furthermore, tropical storms and hurricanes were predominantly observed in wetter clusters, underscoring the utility of MED in linking atmospheric phenomena with climatic impacts. The results highlight the effectiveness of the MED in improving both the accuracy and interpretability of clustering algorithms. Beyond its methodological contributions, this work highlights the MED's potential to advance the understanding and forecasting of precipitation regimes, thereby contributing to more robust climate analyses. Such insights are particularly relevant for informing climate adaptation strategies in vulnerable regions, notably the Caribbean. Future research could investigate automated domain segmentation as a means of further refining and optimizing this approach.

本研究使用一种新的多专家距离（MED）度量进行聚类分析，调查了加勒比地区的降水模式。MED集成了海温（SST）、925 hPa风分量和出射长波辐射（OLR）等多个气候参数，目的是增强时空降水分析。这种方法为依赖单一数据集和欧氏距离的传统方法提供了一种替代方法。它在聚类过程中结合了物理参数，以提高准确性和洞察力。该分析涵盖了43年的时间（1979-2021），从墨西哥湾延伸到加勒比海，空间范围覆盖了整个地区。MED指标结合了特定区域直方图和Kullback-Leibler散度，可以对大气结构进行动态比较。分析得出6个不同的簇，每个簇都表现出受区域大气动力影响的独特的季节和年际降水模式。分析揭示了集群、降水水平和大气条件之间的显著转变和关联。代表干燥条件的集群表现出负海温异常，反映出水分生产减少。相反，高降水的星团呈现海温正异常，有利于水汽积累。此外，热带风暴和飓风主要是在潮湿的集群中观测到的，这强调了MED在将大气现象与气候影响联系起来方面的效用。结果表明MED在提高聚类算法的准确性和可解释性方面是有效的。除了在方法上的贡献之外，这项工作还强调了MED在促进对降水机制的理解和预测方面的潜力，从而有助于更有力的气候分析。这些见解对于为脆弱地区，特别是加勒比地区的气候适应战略提供信息尤其重要。未来的研究可以研究自动领域分割作为进一步改进和优化该方法的手段。

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引用次数: 0

Stratya2D: Enhancing kinematic backstripping through image-based 2D horizon integration Stratya2D：通过基于图像的2D水平整合增强运动学反剥离

IF 4.4 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2025-09-19 DOI: 10.1016/j.cageo.2025.106056

Harikrishnan Nalinakumar , Patrick Makuluni , Juerg Hauser , Stuart R. Clark

The study of sedimentary basins is crucial for understanding Earth’s evolution and geological history. Traditional basin analysis, often constrained by 1D subsidence analysis, limits the spatial understanding of geological processes. This study introduces Stratya2D, a Python-based tool that extends traditional methodologies by extending 1D decompaction and backstripping to a 2D framework allowing for detailed basin analysis. The tool extracts horizon annotations from pre-interpreted seismic images, enabling coordinate-based reconstruction of depositional surfaces. Using advanced image processing techniques, Stratya2D integrates horizon extraction, depth normalisation, and Monte Carlo Simulation (MCS) to quantify uncertainties in tectonic subsidence and layer evolution at each time step, offering a breakthrough in geoscientific analysis. This innovative approach offers a more cost-effective alternative to traditional software and improves prediction reliability. The tool’s effectiveness was validated through comparisons with established literature and specific case studies, including data from the NDI Carrara 1 well in the South Nicholson region, Northern Territory, Australia, along the 17GA-SN1 seismic line. The results closely align with previously published data and PetroMod simulations, accurately replicating the tectonic subsidence curve and offering extended insights into the complex geological context of the South Nicholson Region. Comparative analysis with PetroMod confirms the robustness of Stratya2D, while the inclusion of MCS highlights the critical role of uncertainty quantification in subsurface modelling. Stratya2D offers a robust and versatile tool for regional-scale basin modelling, effectively addressing diverse geoscientific challenges.

沉积盆地的研究对于理解地球的演化和地质历史是至关重要的。传统的盆地分析往往受到一维沉降分析的约束，限制了对地质过程的空间理解。本研究介绍了Stratya2D，这是一种基于python的工具，通过将1D分解和反剥离扩展到2D框架，从而扩展了传统的方法，从而可以进行详细的盆地分析。该工具从预解释的地震图像中提取层位注释，从而实现基于坐标的沉积表面重建。Stratya2D采用先进的图像处理技术，集成了地平线提取、深度归一化和蒙特卡罗模拟（MCS），以量化每个时间步的构造沉降和层演化的不确定性，为地球科学分析提供了突破。这种创新的方法为传统软件提供了一种更具成本效益的替代方案，并提高了预测的可靠性。通过与已有文献和具体案例研究的对比，验证了该工具的有效性，其中包括澳大利亚北部地区South Nicholson地区沿17GA-SN1地震线的NDI Carrara 1井的数据。该结果与先前公布的数据和PetroMod模拟结果密切一致，准确地复制了构造沉降曲线，并为南尼科尔森地区复杂的地质环境提供了更深入的了解。与PetroMod的对比分析证实了Stratya2D的鲁棒性，而MCS的加入则强调了不确定性量化在地下建模中的关键作用。Stratya2D为区域尺度的盆地建模提供了一个强大而通用的工具，有效地解决了各种地球科学挑战。

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引用次数: 0

EFKAN: A KAN-integrated neural operator for efficient magnetotelluric forward modeling EFKAN：一种基于kan的高效大地电磁正演模拟神经算子

IF 4.4 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2025-09-17 DOI: 10.1016/j.cageo.2025.106052

Feng Wang , Hong Qiu , Yingying Huang , Xiaozhe Gu , Renfang Wang , Bo Yang

Forward modeling is the cornerstone of magnetotelluric (MT) inversion. Neural operators have been successfully applied to solve partial differential equations, demonstrating encouraging performance in rapid MT forward modeling. In particular, they can obtain the electromagnetic field at arbitrary locations and frequencies, which is meaningful for MT forward modeling. In conventional neural operators, the projection layers have been dominated by classical multi-layer perceptrons, which may reduce the precision of solution because they usually suffer from the disadvantages of multi-layer perceptrons, such as lack of interpretability, overfitting, etc. Therefore, to improve the accuracy of the MT forward modeling with neural operators, we integrate the Fourier neural operator with the Kolmogorov–Arnold network (KAN). Specifically, we adopt KAN as the trunk network instead of the classic multi-layer perceptrons to project the resistivity and phase, determined by the branch network-Fourier neural operator, to the desired locations and frequencies. Experimental results demonstrate that the proposed method can achieve high precision in obtaining apparent resistivity and phase at arbitrary frequencies and/or locations with rapid computational speed.

正演模拟是大地电磁反演的基础。神经算子已成功地应用于求解偏微分方程，在快速MT正演建模中表现出令人鼓舞的性能。特别是，它们可以获得任意位置和频率的电磁场，这对大地电磁法正演模拟具有重要意义。在传统的神经算子中，投影层一直被经典的多层感知器所主导，这可能会降低解的精度，因为它们通常具有多层感知器的缺点，如缺乏可解释性、过拟合等。因此，为了提高神经算子的MT正演建模精度，我们将傅里叶神经算子与Kolmogorov-Arnold网络（KAN）相结合。具体来说，我们采用KAN作为主干网络来代替经典的多层感知器，将分支网络-傅里叶神经算子确定的电阻率和相位投影到期望的位置和频率上。实验结果表明，该方法可以获得任意频率和任意位置的视电阻率和相位，计算速度快，精度高。

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引用次数: 0

Assessment of automated stratigraphic interpretations of boreholes with geology-informed metrics 利用地质信息指标对钻孔进行自动地层解释的评估

IF 4.4 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2025-09-10 DOI: 10.1016/j.cageo.2025.106043

Sebastián Garzón , Willem Dabekaussen , Freek S. Busschers , Eva De Boever , Siamak Mehrkanoon , Derek Karssenberg

Stratigraphic interpretation of borehole data is a fundamental aspect of subsurface geological models, providing critical insights into the distribution of stratigraphic units. However, expert interpretation of all available borehole data is impractical for large-scale regional mapping involving thousands of boreholes. Automated interpretations using machine learning models can significantly increase the number of boreholes included in subsurface geological models. Nevertheless, these predictions must adhere to strict spatial and stratigraphic relationships (e.g. superposition) to ensure geological plausibility, which often requires post-processing tasks. Traditional evaluation metrics commonly used for general-domain classification tasks (e.g. accuracy, F1-score) do not necessarily reflect the geological plausibility of predictions, as they fail to account for the sequential nature and spatial relationships inherent in borehole interpretation. To address this limitation, we propose and evaluate a set of geology-informed metrics that focus on three key aspects of stratigraphic interpretation, namely the expected geographical extent of units (extent metrics), their sequential relationships (sequence metrics), and their vertical positioning along boreholes (position metrics). Using a dataset of 1394 boreholes from the Cenozoic Roer Valley Graben (southeast Netherlands), which covers

\sim

3000 km

^{2}

and includes 15 lithostratigraphic units, we demonstrate that Random Forest and Neural Network models with similar performance on traditional metrics (e.g. accuracy, Cohen’s kappa, and F1-score) can differ significantly in their ability to produce geologically plausible predictions. For example, while many model configurations achieve

\sim

75%–80% agreement between expected and predicted classes, the Neural Network models better capture the sequential stratigraphic relationships expected in the study area. Our results underscore the need for domain-specific metrics that offer a more accurate and interpretable assessment of model performance.

钻孔资料的地层解释是地下地质模型的一个基本方面，为地层单元的分布提供了重要的见解。然而，对于涉及数千个钻孔的大规模区域测绘来说，专家解释所有可用的钻孔数据是不切实际的。使用机器学习模型的自动解释可以显著增加地下地质模型中包含的钻孔数量。然而，这些预测必须遵循严格的空间和地层关系（例如叠加），以确保地质上的合理性，这通常需要后处理任务。通常用于一般领域分类任务的传统评价指标（例如精度，f1分数）不一定反映预测的地质合理性，因为它们无法考虑井眼解释中固有的序列性质和空间关系。为了解决这一限制，我们提出并评估了一套地质信息指标，这些指标侧重于地层解释的三个关键方面，即单元的预期地理范围（范围指标），它们的序列关系（序列指标），以及它们沿钻孔的垂直定位（位置指标）。使用来自新生代Roer Valley地堑（荷兰东南部）的1394个钻孔数据集，覆盖约3000平方公里，包括15个岩石地层单元，我们证明随机森林和神经网络模型在传统指标（例如精度，Cohen 's kappa和f1分数）上具有相似性能，但在产生地质上合理的预测能力方面存在显着差异。例如，虽然许多模型配置在预期类别和预测类别之间实现了~ 75%-80%的一致性，但神经网络模型更好地捕捉了研究区域预期的顺序地层关系。我们的结果强调了对特定领域的度量的需求，这些度量提供了对模型性能的更准确和可解释的评估。

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引用次数: 0

A novel metric to assess the accuracy of land use change modeling 一种评估土地利用变化模型准确性的新度量

IF 4.4 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2025-09-10 DOI: 10.1016/j.cageo.2025.106053

Youcheng Song , Haijun Wang , Xiaoxu Cao , Bin Zhang , Jialin Xie , Zhijia Gong , Yaotao Liang , Zongyou He , Guanxian Huang

The integration of the first law of geography into land use change simulation models has attracted considerable attention, aiming to improve model accuracy through the enhanced representation of spatial heterogeneity. However, existing evaluation metrics, which primarily focus on cell-to-cell agreements, inadequately capture the models' ability to represent spatial heterogeneity. Consequently, there is a pressing need for updated evaluation metrics that accurately reflect the models' capability to depict spatial features. To address this issue, the Fuzzy Figure of Merit (Fuzzy FoM) grounded in fuzzy theory was proposed. This metric effectively quantifies and visualizes a model's ability to capture spatial features by introducing the notion of degree of membership, facilitating a comprehensive analysis of model accuracy from both statistical and spatial perspectives. This paper demonstrates the metric's utility in the validation process, illustrating four land use change models that incorporate the spatial heterogeneity.

将地理第一定律整合到土地利用变化模拟模型中，旨在通过增强空间异质性的表征来提高模型的准确性，已引起广泛关注。然而，现有的评估指标主要侧重于细胞间的一致性，无法充分捕捉模型表征空间异质性的能力。因此，迫切需要更新评估指标，以准确反映模型描述空间特征的能力。针对这一问题，提出了基于模糊理论的模糊优值图。该度量通过引入隶属度的概念，有效地量化和可视化模型捕捉空间特征的能力，促进从统计和空间角度对模型精度的全面分析。本文以包含空间异质性的四种土地利用变化模型为例，说明了该度量在验证过程中的效用。

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引用次数: 0

Corrigendum to “Seismic random noise attenuation using structure-oriented 3D curvelet transform” [Comput. Geosci. 206 (2026) 106020] “利用面向结构的三维曲线变换来衰减地震随机噪声”的勘误表[计算机]。地球科学进展。206 (2026)106020 [j]

IF 4.4 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2025-09-08 DOI: 10.1016/j.cageo.2025.106044

Minggui Liang , Shaohuan Zu , Zifei Li , Wenlu Liu , Haojun Chen , Zhengyu Tan

引用次数: 0

Uncertainty-aware ensemble learning and dynamic threshold optimization for landslide susceptibility mapping 滑坡易感性制图的不确定性感知集成学习与动态阈值优化

IF 4.4 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2025-08-29 DOI: 10.1016/j.cageo.2025.106042

Ting Xiao , Wei Huang , Lichang Wang , Beibei Yang , Zuohui Qin , Xiaodong Liu , Yingbin Xiao

Landslides represent a prevalent and devastating geological hazard. Identifying areas susceptible to landslides is vital for disaster prevention and reduction. However, traditional models suffer from limited predictive accuracy, strong regularity in breakpoint selection for susceptibility zoning, and inconsistent predictions across different models, resulting in uncertainty in susceptibility assessment. To address these issues, this study proposes an innovative intelligent landslide susceptibility mapping approach that integrates ensemble learning, multi-model uncertainty analysis, and dynamic optimization. Focusing on Linxiang City, Hunan Province, China, this research synthesizes historical landslide inventories and field-identified unstable slopes as positive samples. Three base models were constructed: logistic regression (LR), random forest (RF), and graph neural network (GNN). Ensemble learning using the stacking method was applied to combine these models. The ensemble further incorporates prediction uncertainty estimation and multi-dimensional k-nearest neighbor (KNN) adjacency matrix. Utilizing an attention mechanism, the model dynamically integrates geographic features, environmental factors, and prediction outputs. The final output is a prediction model that synthesizes spatial structure information and prediction uncertainties. For susceptibility mapping, this study proposes a dynamic optimization approach combining Natural Breaks, Frequency Ratio, and Equal Interval methods, determining optimal threshold combinations through relative density distribution of landslide occurrences to enhance susceptibility classification rationality. Model performance was evaluated and compared using area under roc curve (AUC), where a larger AUC signifies higher predictive accuracy. The results show that the ensemble model outperformed all others with an AUC of 0.95, compared to the base models' AUCs of 0.82 (LR), 0.84 (RF), and 0.87 (GNN). This demonstrates that the ensemble learning methods that incorporate uncertainty achieve higher accuracy in risk identification than conventional models. The dynamic classification method also shows a better performance over conventional approaches in high-susceptibility classification precision and landslide density differentiation.

滑坡是一种普遍存在的破坏性地质灾害。确定易受滑坡影响的地区对防灾减灾至关重要。然而，传统模型的预测精度有限，易感性分区断点选择的规律性强，不同模型之间的预测结果不一致，导致易感性评估存在不确定性。为了解决这些问题，本研究提出了一种集成集成学习、多模型不确定性分析和动态优化的滑坡敏感性智能制图方法。本研究以湖南省临乡市为研究对象，综合了历史滑坡清单和现场鉴定的不稳定边坡为阳性样本。构建了逻辑回归（LR）、随机森林（RF）和图神经网络（GNN）三种基本模型。采用集成学习的叠加方法对这些模型进行组合。该集成进一步结合了预测不确定性估计和多维k近邻（KNN）邻接矩阵。利用注意机制，该模型动态集成了地理特征、环境因素和预测输出。最后的输出是一个综合了空间结构信息和预测不确定性的预测模型。对于敏感性映射，本文提出了一种结合自然断裂法、频率比法和等间隔法的动态优化方法，通过滑坡发生点的相对密度分布确定最优阈值组合，以提高敏感性分类的合理性。使用roc曲线下面积（AUC）评估和比较模型性能，其中AUC越大表示预测精度越高。结果表明，与基础模型的AUC分别为0.82 （LR）、0.84 （RF）和0.87 （GNN）相比，集成模型的AUC为0.95，优于其他所有模型。这表明集成学习方法在风险识别方面比传统模型具有更高的准确性。动态分类方法在高敏感性分类精度和滑坡密度分异方面也优于常规方法。

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引用次数: 0

Controlled latent diffusion models for 3D porous media reconstruction 三维多孔介质重建的可控潜扩散模型

IF 4.4 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2025-08-26 DOI: 10.1016/j.cageo.2025.106038

Danilo Naiff , Bernardo P. Schaeffer , Gustavo Pires , Dragan Stojkovic , Thomas Rapstine , Fabio Ramos

Three-dimensional digital reconstruction of porous media presents a fundamental challenge in geosciences, requiring simultaneous resolution of fine-scale pore structures while capturing representative elementary volumes. This work introduces a computational framework that addresses this challenge through latent diffusion models operating within the Elucidated Diffusion Models (EDM) framework. The proposed approach reduces dimensionality via a custom Variational Autoencoder trained in binary geological volumes, improving efficiency and also enabling the generation of larger volumes than previously possible with diffusion models. A key innovation is the controlled unconditional sampling methodology, which enhances distribution coverage by first sampling target statistics from their empirical distributions, and then generating samples conditioned on these values. Extensive testing on four distinct rock types demonstrates that conditioning on porosity – a readily computable statistic – is sufficient to ensure a consistent representation of multiple complex properties, including permeability, two-point correlation functions, and pore size distributions. The framework achieves better generation quality than pixel-space diffusion while enabling significantly larger volume reconstruction (

25 6^{3}

voxels) with substantially reduced computational requirements, establishing a new state-of-the-art for digital rock physics applications.

多孔介质的三维数字重建是地球科学领域的一个基本挑战，它需要在捕获代表性基本体积的同时，对细尺度孔隙结构进行分辨率处理。这项工作引入了一个计算框架，通过在阐明扩散模型（EDM）框架内运行的潜在扩散模型来解决这一挑战。该方法通过自定义的变分自编码器（Variational Autoencoder）减少了二进制地质体积的维数，提高了效率，并且能够生成比以前使用扩散模型更大的体积。一个关键的创新是受控无条件抽样方法，它通过首先从其经验分布中抽样目标统计量，然后根据这些值生成样本来增强分布覆盖率。对四种不同岩石类型的广泛测试表明，孔隙度（一个易于计算的统计数据）的调节足以确保多种复杂属性的一致表示，包括渗透率、两点相关函数和孔隙大小分布。该框架实现了比像素空间扩散更好的生成质量，同时实现了更大的体积重建（2563体素），大大减少了计算需求，为数字岩石物理应用建立了新的技术水平。

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引用次数: 0

Segmentation of stochastic scalar fields in unstructured meshes 非结构化网格中随机标量场的分割

IF 4.4 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2025-08-26 DOI: 10.1016/j.cageo.2025.106041

Tommaso Sorgente , Marianna Miola , Simone Pittaluga , Daniela Cabiddu , Michela Mortara , Marino Vetuschi Zuccolini

We present an algorithm for segmenting a (stochastic) scalar field defined on an unstructured mesh into a given number of parts. It can be applied to any type of mesh, such as triangular/tetrahedral meshes, 2D/3D grids, and generic polygonal/polyhedral meshes, inducing a classification of the mesh elements into regions with limited noise and smooth boundaries. The algorithm offers multiple output options, providing valuable information about the segmentation and the mesh regions in various file formats, thus making it suitable for practical applications. We show the algorithm at work in different application scenarios, ranging from environmental geochemistry to marine sciences and groundwater modeling, proving its efficacy and versatility.

我们提出了一种将定义在非结构化网格上的（随机）标量场分割为给定数量的部分的算法。它可以应用于任何类型的网格，如三角形/四面体网格、2D/3D网格和一般多边形/多面体网格，将网格元素分类到具有有限噪声和光滑边界的区域。该算法提供了多种输出选项，以各种文件格式提供了关于分割和网格区域的有价值的信息，因此适合实际应用。我们展示了该算法在不同应用场景中的工作，从环境地球化学到海洋科学和地下水建模，证明了它的有效性和多功能性。

引用次数: 0

SmartMagDL: Smartphone geomagnetic mapping using deep learning SmartMagDL：使用深度学习的智能手机地磁制图

IF 4.4 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2025-08-23 DOI: 10.1016/j.cageo.2025.106040

Elad Fisher , Roger Alimi , Miki Vizel , Itzik Klein

Magnetic field mapping is an essential tool in geoscience, for identifying anomalies and understanding subsurface structures, requiring systematic and methodical data acquisition. The use of smartphones’ built-in magnetometers for this task offers advantages such as cost-effectiveness, accessibility, and simplicity. Recent works relied on model-based interpolation techniques significantly limited by sparse data collection, sensor noise, orientation-dependent distortions, and overall low data quality. As a result, magnetic maps were often noisy and unreliable for practical applications. In this work, we aim to fill this gap by introducing a deep learning (DL) approach to overcome these challenges and produce accurate, high-resolution magnetic field maps from smartphone data. To address the limitations of extensive real-world data collection, we developed an innovative two-stage simulation framework to generate the required training datasets. First, the theoretical magnetic field produced by ferromagnetic objects in a 30 m × 30 m area was computed to serve as ground truth data for the network. Second, a simulation model was implemented to replicate the data acquisition process of smartphone magnetometers. This model included real-world survey protocols, noise factors, sensor behavior, and simulated trajectories based on real world recorded data. Compared to the model-based baseline, our method improves anomaly localization, reduces noise, and enhances accuracy. At the 80th percentile the MSE and LPIPS metrics showed 75% and 55% improvements respectively, further validated by visual analysis of the reconstructed maps.

磁场测绘是地球科学中识别异常和了解地下结构的重要工具，需要系统和有条不紊的数据采集。使用智能手机内置的磁力计完成这项任务具有成本效益、可访问性和简单性等优势。最近的工作依赖于基于模型的插值技术，这些技术明显受到稀疏数据收集、传感器噪声、方向相关失真和整体低数据质量的限制。因此，磁图在实际应用中往往存在噪声和不可靠性。在这项工作中，我们的目标是通过引入深度学习（DL）方法来填补这一空白，以克服这些挑战，并从智能手机数据中生成准确、高分辨率的磁场图。为了解决广泛的现实世界数据收集的局限性，我们开发了一个创新的两阶段模拟框架来生成所需的训练数据集。首先，计算30 m × 30 m区域内铁磁物体产生的理论磁场，作为网络的地真值数据；其次，建立了智能手机磁强计数据采集过程的仿真模型。该模型包括真实世界的调查协议、噪声因素、传感器行为以及基于真实世界记录数据的模拟轨迹。与基于模型的基线相比，该方法改进了异常定位，降低了噪声，提高了精度。在第80百分位，MSE和LPIPS指标分别显示75%和55%的改善，通过重建地图的视觉分析进一步验证。

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引用次数: 0