Computers & Geosciences最新文献_第8页

Low-code framework for IoT data warehousing and visualization 物联网数据仓库和可视化的低代码框架

IF 4.2 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2025-07-11 DOI: 10.1016/j.cageo.2025.105998

Victor Lamas, Alejandro Cortiñas, Miguel R. Luaces

Background:

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.

Objectives:

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.

Methods:

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.

Results:

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.

Conclusion:

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.

背景：物联网通过广泛的传感器网络彻底改变了地球科学的数据收集。然而，为物联网数据开发基于web的数据仓库系统仍然成本高昂且复杂。虽然研究涉及传感器可变性和数据摄取架构，但它们往往忽略了管理物联网数据量和可变性所需的关键数据仓库组件。此外，模型驱动工程技术已用于为城市活动创建仪表板，但缺乏高级的基于地图的可视化，这对于地理空间数据至关重要。目的：本研究旨在解决为地球科学创建物联网数据仓库的挑战，鼓励科学家使用简单，用户友好且具有成本效益的方法共享传感器数据分析结果。方法：提出的框架集成了(i)一个领域特定语言元模型来定义传感器、尺寸和测量参数，（ii）一个用于物联网数据仓库创建的软件产品线，以及（iii）一个具有命令行和web界面的低代码平台。该方法通过气象、交通和空气质量、海岸和海洋监测系统四个案例研究得到了验证。结果：该框架通过自定义的空间、时间和属性聚合实现了高效的物联网数据仓库创建。案例研究展示了跨领域的适应性，支持实时数据摄取、传感器移动性和高级可视化。结论：该研究提出了一个可扩展的、用户友好的框架，用于使用SPL和DSL技术的地球科学物联网数据仓库，解决了特定领域的挑战，并为非专业用户提供了支持。未来的工作包括可用性评估和扩展到其他领域。

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引用次数: 0

Towards an open soil-plant digital twin based on STEMMUS-SCOPE model following open science 基于STEMMUS-SCOPE模型的开放土壤-植物数字孪生模型的研究

IF 4.2 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2025-07-11 DOI: 10.1016/j.cageo.2025.106013

Yijian Zeng , Fakhereh Alidoost , Bart Schilperoort , Yang Liu , Stefan Verhoeven , Meiert Willem Grootes , Yunfei Wang , Zengjing Song , Danyang Yu , Enting Tang , Qianqian Han , Lianyu Yu , Mostafa Gomaa Daoud , Prajwal Khanal , Yunfei Chen , Christiaan van der Tol , Raúl Zurita-Milla , Serkan Girgin , Bas Retsios , Niels Drost , Zhongbo Su

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.

干旱和热浪危及陆地生态系统服务。开发一个开放的数字孪生土壤-植物系统可以帮助监测和预测这些极端事件对生态系统功能的影响。我们说明了我们最近开发的STEMMUS-SCOPE模型- stemmus，非饱和土壤中能量，质量和动量的同时传递；SCOPE，光合作用和能量通量的土壤冠层观测-将土壤-植物过程与新的卫星观测（例如太阳诱导的叶绿素荧光）联系起来，有助于实现这样的数字孪生。这种土壤-植物数字孪生体为利用遥感观测跟踪地上和地下的生态生理过程提供了一个机制窗口。遵循开放科学和公平（可查找，可访问，可互操作，可重用）原则，数据和研究软件，我们提出了土壤-植物数字双胞胎的构建模块。它强调了促进公平的数字技术将研究需求和发展转化为可复制和可重复使用的数据、软件和知识的重要性。

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引用次数: 0

DAM-CGNet: Semantic segmentation-based approach for valley-bottom extraction from digital elevation models 基于语义分割的数字高程模型谷底提取方法

IF 4.2 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2025-07-11 DOI: 10.1016/j.cageo.2025.106012

Yuhan Ren, Hongming Zhang, Liang Dong, Huanyu Yang, Hongyi Li, Lu Du, Qiankun Chen, Songyuan Li

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.

从数字高程模型（dem）中准确提取山谷底部对山地景观的水文和地貌分析至关重要。然而，阈值设置依赖于人工干预；靠近谷底的道路与谷底特征相似，阈值不能有效适应各种形状的山谷，导致现有方法的提取精度较低，特别是在窄v型山谷中。为了解决这些问题，本研究开发了一种语义分割方法，称为基于密集的注意合并上下文引导网络（DAM-CGNet）。该方法在不依赖阈值的情况下，有效地排除了山坡上的道路，增强了对山谷边界陡峭特征变化的识别，能够提取各种形状的谷底。主要改进包括：(1)引入卷积块注意模块（CBAM）增强信息流中的特征重用，采用注意机制抑制无关特征响应，关注谷边界特征；(2)利用DenseNet的密集连接策略重建特征流，帮助模型在深层保留重要的谷底细节，更好地识别小而窄的谷；(3)通过评估各种输入组合来解决单通道DEM表示的局限性，最终选择DEM、地形位置指数（TPI）和坡度作为提取谷底的有效输入。使用语义分割模型和传统方法进行的实验验证了该方法的有效性。具体而言，DAM-CGNet在测试集上取得了较高的准确率（MPA: 90.15%, MIoU: 84.18%, FWIoU: 92.99%），在提取各种形状的谷底方面优于传统方法。该方法不需要像传统方法那样手动设置阈值，提高了谷底提取精度，为后续谷底宽度计算提供了新的视角。

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引用次数: 0

Uncertainty-aware methods for enhancing rainfall prediction with deep-learning based post-processing segmentation 基于深度学习的后处理分割增强降雨预测的不确定性感知方法

IF 4.2 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2025-07-11 DOI: 10.1016/j.cageo.2025.105992

Simone Monaco , Luca Monaco , Daniele Apiletti , Roberto Cremonini , Secondo Barbero

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 (

2.637 \times 1 0^{- 2}

) 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.

降水预报在洪水管理、农业规划、水资源分配和天气预警中至关重要。尽管数值天气预报（NWP）模式取得了重大进展，但这些系统经常表现出严重的偏差和错误，特别是在高空间和时间分辨率下。为了解决这些限制，我们开发并评估了不确定性感知深度学习集成架构，重点是在实现高精度和锐度与可靠性之间的最佳平衡的同时表征预测不确定性。本研究提出了SDE U-Net，这是一种专门为降水预报中的分割任务而设计的SDE- net的新改编。我们对包括SDE U-Net在内的最先进的集合体系结构进行了全面评估，并将其预测不确定性与穷人集合（PME，即NWPs预测平均值）在不同气象条件下（从非强烈降水模式到强烈天气事件）的预测不确定性进行了比较。作为一个例子，我们专注于预测意大利西北部的日累积降水量，尽管我们的方法是广泛推广的。我们的研究结果表明，就非强烈和强烈降水事件的中位数RMSE而言，所有评估的概率深度学习模型都优于PME基准。其中，SDE U-Net实现了最佳的综合性能，对强烈事件提供了最低的RMSE (2.637×10−2)，并且与其他模型相比显示出更稳定的误差分布。对于非激烈事件，SDE U-Net的表现与其他深度学习模型相当，仍然明显超过基线。此外，SDE U-Net有效地平衡了清晰度和可靠性，使其特别适合极端天气的业务预报。将像SDE U-Net这样的不确定性感知模型集成到预报工作流程中，可以加强对天气相关灾害的决策和准备。

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引用次数: 0

Recognition of multiple geochemical anomalies by dual-branch convolutional neural network with adaptive feature fusion 基于自适应特征融合的双分支卷积神经网络识别地球化学多异常

IF 4.2 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2025-07-11 DOI: 10.1016/j.cageo.2025.106011

Jundong He , Weirong Yang , Zhengbo Yu , Cheng Tan , Binbin Li

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.

地球化学异常是矿产勘查和资源评价的重要指标。然而，由于地质过程的多样性和复杂性，识别地球化学异常仍然具有挑战性。本文提出了一种基于自适应特征融合的双分支卷积神经网络（1-2D AFFCNN），用于同时提取地球化学元素的光谱组成关系和空间结构特征。该模型采用自适应特征融合模块（AFFM），有效整合了不同分支的特征，显著提高了预测性能和鲁棒性。实验结果表明，1-2D AFFCNN在准确率（92.3%）、召回率（92.0%）和AUC值（0.98）方面均优于传统的单一模型。三阶段训练策略有效地缓解了梯度消失问题，提高了训练效率和稳定性。在甘肃长坝矿集中地区的应用中，该模型生成的高概率异常带与已知铅锌矿床的空间分布高度吻合，并识别出多个高潜力矿化区。该研究不仅为多维地球化学数据的综合分析提供了一种新的方法，而且为矿产资源预测和靶区定位开辟了新的途径。

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引用次数: 0

A self-attention convolutional long and short-term memory network for correcting sea surface wind field forecasts to facilitate sea ice drift prediction 海面风场预报校正的自注意卷积长短期记忆网络促进海冰漂移预报

IF 4.2 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2025-07-08 DOI: 10.1016/j.cageo.2025.105997

Qing Xu , Qilin Jia , Yongqing Li , Hao Zhang , Peng Ren

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.

准确、及时的海面风场数值预报对海冰漂移预报至关重要。然而，目前的海洋元素预测系统面临着两大挑战。海面风场的数值预报是及时的，但精度往往有限。海面风场再分析虽然精度较高，但时效性较差，限制了其在紧急情况下的适用性。为了应对这些挑战，研究人员开发了一种自关注卷积长短期记忆网络（SaCLN），用于智能校正数值预报的海面风场。该方法将数值预报风场的时效性与再分析风场的准确性相结合，生成与再分析风场非常接近的校正风场。该网络由自注意网络和卷积长短期记忆网络组成。自关注网络捕获数值预报的海面风场序列的全球空间相关性。CLN提取了一个关注加权风场序列的时空特征。经过训练的SaCLN模型可以有效地生成准确、及时的风场校正，从而提高海冰漂移预测的精度。通过预测北极海冰和南极冰山漂移的实验，验证了SaCLN的有效性。实验结果表明，基于SaCLN校正的风场漂移结果比基于数值预报的海面风场漂移结果更准确。该方法已在海冰漂移预测中证明了其有效性，有助于研究人员更好地应对海冰变率带来的挑战。

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引用次数: 0

ElasWave3D: A GPU-accelerated 3D finite-difference elastic wave solver for complex topography using irregular subdomain index arrays ElasWave3D：一个gpu加速的三维有限差分弹性波求解器，用于使用不规则子域索引阵列的复杂地形

IF 4.2 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2025-07-07 DOI: 10.1016/j.cageo.2025.105994

Ivan Javier Sánchez-Galvis , Herling Gonzalez-Alvarez , William Agudelo , Daniel O. Trad , Daniel A. Sierra

Simulating seismic wave propagation in complex geological structures is a challenging task in exploration geophysics, especially in foothill regions characterized by rough topography, irregular bedrock interfaces, low-velocity surface sediments, and significant heterogeneities. Although existing numerical methods can address such scenarios, they often require highly refined grids that lead to elevated computational costs. To address this, we introduce ElasWave3D, a three-dimensional solver based on the finite difference method for elastic wave propagation in the presence of irregular topography, specifically designed for GPU acceleration. The solver employs a novel Irregular Subdomain Index Array (ISIA) strategy to implement the parameter-modified (PM) formulation, thus enforcing the free-surface condition for arbitrary topographic variations. We validated ElasWave3D against the well-known SPECFEM3D solver in scenarios with rough topography and heterogeneous media, observing misfit errors below 1% and correlation values exceeding 99% in most cases. Additionally, our solver achieves more than an order-of-magnitude speedup (13×) over its CPU-OpenMP implementation on 24 cores. Consequently, ElasWave3D enables cost-effective, realistic, and detailed simulations of near-surface seismic scattering in heterogeneous Earth models with irregular topography.

在勘探地球物理中，模拟地震波在复杂地质构造中的传播是一项具有挑战性的任务，特别是在地形粗糙、基岩界面不规则、地表沉积物速度慢、非均质性明显的山麓地区。虽然现有的数值方法可以解决这种情况，但它们通常需要高度精细的网格，从而导致计算成本上升。为了解决这个问题，我们引入了ElasWave3D，这是一个基于有限差分法的三维求解器，用于不规则地形下的弹性波传播，专门为GPU加速设计。求解器采用一种新颖的不规则子域索引阵列（ISIA）策略来实现参数修正（PM）公式，从而实现任意地形变化的自由曲面条件。我们在粗糙地形和非均匀介质的情况下，针对著名的SPECFEM3D求解器验证了ElasWave3D，观察到在大多数情况下，失配误差低于1%，相关值超过99%。此外，我们的求解器在24核的CPU-OpenMP实现上实现了超过一个数量级的加速（13倍）。因此，ElasWave3D能够在具有不规则地形的非均匀地球模型中实现经济、真实和详细的近地表地震散射模拟。

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引用次数: 0

Efficient variable precision reduction in chaotic climate models: Analysis of the NEMO case in the destination earth project 混沌气候模型的有效变精度降低：目的地球项目NEMO案例分析

IF 4.2 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2025-07-05 DOI: 10.1016/j.cageo.2025.105989

Stella V. Paronuzzi-Ticco , Gladys Utrera , Mario C. Acosta

Driven by the need to improve computational efficiency, the technique of reducing variable precision in model calculations has recently attracted a lot of attention, particularly in the field of weather and climate simulations models, where computational gains are crucial to produce operational results faster and make better use of HPC resources.

However, the source of computational improvements resulting from working in reduced precision, an aspect that could help facilitate the transition in many applications, has never been thoroughly explained. In this paper, we make a step in this direction, shedding light on how to efficiently apply variable precision reduction in chaotic applications, and presenting a computational study methodology to make this possible.

For this purpose, we employ a tool for automatic porting of oceanographic code to mixed precision recently developed at the Barcelona Supercomputing Center and consider as case studies one of the most widely employed ocean models, NEMO, in one of the most ambitious initiatives to date, Destination Earth, because it aims at creating interactive digital replicas of the Earth with unprecedented precision, supporting real-time decision-making and long-term adaptation strategies, which also entails an unprecedented computational cost in terms of supercomputing. We analyze in depth the impact of mixed precision on the most representative functions of the model, providing a clear step forward in understanding where to focus efforts in precision reduction. These results can guide scientists in significantly speeding up weather and climate models using mixed precision by targeting computationally intensive functions and optimizing communications.

在提高计算效率的需求的推动下，模型计算中降低变量精度的技术最近引起了很多关注，特别是在天气和气候模拟模型领域，计算增益对于更快地产生操作结果和更好地利用HPC资源至关重要。然而，由于工作精度降低而导致的计算改进的来源，这方面可以帮助促进许多应用程序的过渡，从来没有得到彻底的解释。在本文中，我们朝这个方向迈出了一步，揭示了如何有效地在混沌应用中应用变精度约简，并提出了一种计算研究方法来实现这一目标。为此，我们采用了巴塞罗那超级计算中心最近开发的一种工具，用于自动将海洋代码移植到混合精度，并考虑将最广泛使用的海洋模型之一NEMO作为案例研究，这是迄今为止最雄心勃勃的计划之一，“目的地地球”，因为它旨在以前所未有的精度创建地球的交互式数字复制品，支持实时决策和长期适应策略。就超级计算而言，这也需要前所未有的计算成本。我们深入分析了混合精度对模型中最具代表性的函数的影响，为理解在哪里集中精力降低精度提供了明确的一步。这些结果可以指导科学家通过针对计算密集型功能和优化通信，显著加快使用混合精度的天气和气候模型。

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引用次数: 0

BankfullMapper: a semi-automated MATLAB tool on high-resolution digital terrain models for spatio-temporal monitoring of bankfull geometry and discharge BankfullMapper：基于高分辨率数字地形模型的半自动化MATLAB工具，用于河岸几何形状和流量的时空监测

IF 4.2 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2025-07-05 DOI: 10.1016/j.cageo.2025.106001

Michele Delchiaro , Valeria Ruscitto , Wolfgang Schwanghart , Eleonora Brignone , Daniela Piacentini , Francesco Troiani

Understanding river channel bankfull geometry is crucial for fluvial monitoring and flood prediction. The bankfull stage, typically reached every 1–2 years, marks when water spills onto the floodplain and strongly influences channel morphology. In our study, we present a novel approach for detecting river channel bankfull levels, utilizing a specialized MATLAB tool we developed, called BankfullMapper. The tool divides rivers into evenly spaced sections and computes a hydraulic depth function, plotting elevation above the thalweg against the area-to-width ratio. Bankfull levels are identified through (i) the lowest breakpoints from the thalweg or (ii) the most prominent breakpoints. Using Manning’s equation, the tool also estimates bankfull discharge.

We applied the method to two Italian rivers with contrasting hydrological settings: the single-channel Potenza River and the braided-to-wandering Marecchia River. Potenza was used for checking the tool's spatial analysis capability, while Marecchia served for spatio-temporal testing (2009 vs. 2022). Modelled bankfull extents were validated against expert-mapped active channel polygons using accuracy, precision, sensitivity, and specificity metrics.

For Potenza, bankfull discharges (33.9–52 m³ s⁻¹) closely matched gauge data (2010–2023) using Gumbel distribution. The method showed high accuracy (0.90–0.92), sensitivity (0.94–0.95), and specificity (0.89–0.92), with moderate precision (0.53–0.61). For Marecchia, sensitivity ranged from 0.63 to 0.92, specificity from 0.73 to 0.89, accuracy from 0.80 to 0.83, and precision from 0.56 to 0.65.

Overall, the semi-automated approach reliably captures spatial and temporal changes in bankfull geometry and discharge across diverse river systems. It performs best using the lowest morphological breakpoints and offers a robust, detailed tool for hydrological research and river management.

了解河道堤岸几何形状对河流监测和洪水预测至关重要。堤岸阶段通常每1-2年达到一次，标志着水溢出到洪泛区并强烈影响河道形态。在我们的研究中，我们提出了一种利用我们开发的专门的MATLAB工具BankfullMapper来检测河道堤岸水位的新方法。该工具将河流划分为均匀间隔的部分，并计算水力深度函数，根据面积与宽度的比例绘制出水面以上的高度。通过(i)从thalweg的最低断点或（ii）最突出的断点来确定银行水平。利用曼宁的公式，该工具还可以估算出银行的流量。我们将该方法应用于两条具有不同水文环境的意大利河流：单通道波坦察河和辫状徘徊的马雷基亚河。Potenza用于检查工具的空间分析能力，而Marecchia用于时空测试（2009年与2022年）。利用准确性、精密度、灵敏度和特异性指标，根据专家绘制的主动通道多边形验证建模的河岸范围。对于Potenza， bankfull流量（33.9-52 m3 s毒血症）与使用Gumbel分布的测量数据（2010-2023）非常吻合。该方法准确度高（0.90 ~ 0.92），灵敏度高（0.94 ~ 0.95），特异度高（0.89 ~ 0.92），精密度中等（0.53 ~ 0.61）。对于孕妇，敏感性为0.63 ~ 0.92，特异性为0.73 ~ 0.89，准确度为0.80 ~ 0.83，精密度为0.56 ~ 0.65。总体而言，半自动化方法可靠地捕获了不同河流水系的河岸几何形状和流量的时空变化。它在使用最低形态断点时表现最佳，并为水文研究和河流管理提供了一个强大而详细的工具。

{"title":"BankfullMapper: a semi-automated MATLAB tool on high-resolution digital terrain models for spatio-temporal monitoring of bankfull geometry and discharge","authors":"Michele Delchiaro , Valeria Ruscitto , Wolfgang Schwanghart , Eleonora Brignone , Daniela Piacentini , Francesco Troiani","doi":"10.1016/j.cageo.2025.106001","DOIUrl":"10.1016/j.cageo.2025.106001","url":null,"abstract":"<div><div>Understanding river channel bankfull geometry is crucial for fluvial monitoring and flood prediction. The bankfull stage, typically reached every 1–2 years, marks when water spills onto the floodplain and strongly influences channel morphology. In our study, we present a novel approach for detecting river channel bankfull levels, utilizing a specialized MATLAB tool we developed, called BankfullMapper. The tool divides rivers into evenly spaced sections and computes a hydraulic depth function, plotting elevation above the thalweg against the area-to-width ratio. Bankfull levels are identified through (i) the lowest breakpoints from the thalweg or (ii) the most prominent breakpoints. Using Manning’s equation, the tool also estimates bankfull discharge.</div><div>We applied the method to two Italian rivers with contrasting hydrological settings: the single-channel Potenza River and the braided-to-wandering Marecchia River. Potenza was used for checking the tool's spatial analysis capability, while Marecchia served for spatio-temporal testing (2009 vs. 2022). Modelled bankfull extents were validated against expert-mapped active channel polygons using accuracy, precision, sensitivity, and specificity metrics.</div><div>For Potenza, bankfull discharges (33.9–52 m<sup>3</sup> s⁻<sup>1</sup>) closely matched gauge data (2010–2023) using Gumbel distribution. The method showed high accuracy (0.90–0.92), sensitivity (0.94–0.95), and specificity (0.89–0.92), with moderate precision (0.53–0.61). For Marecchia, sensitivity ranged from 0.63 to 0.92, specificity from 0.73 to 0.89, accuracy from 0.80 to 0.83, and precision from 0.56 to 0.65.</div><div>Overall, the semi-automated approach reliably captures spatial and temporal changes in bankfull geometry and discharge across diverse river systems. It performs best using the lowest morphological breakpoints and offers a robust, detailed tool for hydrological research and river management.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 106001"},"PeriodicalIF":4.2,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632302","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}

引用次数: 0

Physics-embedded deep learning inversion for transient electromagnetic method survey data 瞬变电磁法测量数据的物理嵌入深度学习反演

IF 4.2 2区地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Geosciences

Pub Date : 2025-07-01 DOI: 10.1016/j.cageo.2025.106000

Ruiyou Li, Yong Zhang, Jiayi Ju, Rongqiang Liu

The transient electromagnetic method (TEM) is a widely used geophysical technique for investigating complex geological conditions. Deep learning (DL) provides a novel approach for solving the complex, nonlinear TEM inversion problem. However, most current DL inversion methods for TEM survey data depend heavily on labeled data (real resistivity models), which are difficult to acquire from field surveys. In this study, we propose an unsupervised DL inversion method for TEM survey data based on the physical laws that govern electric field propagation. First, we integrate forward modeling into the training process, allowing the predicted resistivity model to be converted into simulated data. This simulated data is then compared with observed data to calculate a data misfit. Then, unsupervised training (label-independent) is achieved using the data misfit as the loss function, with dynamic smoothing constraints employed to alleviate the ill-posed inversion problem. Furthermore, the DL network incorporates an Attention mechanism to extract crucial feature information for TEM inversion. Finally, the multivariate variational mode decomposition (MVMD) technique optimized by the whale optimization algorithm (WOA) is adopted to reduce noise in the survey data and enhance TEM inversion precision. Both synthetic examples and field surveys show that our proposed approach accurately delineates subsurface model structures, offering an innovative solution for TEM inversion.

瞬变电磁法（TEM）是一种广泛应用于复杂地质条件调查的地球物理技术。深度学习（DL）为解决复杂的非线性瞬变电磁法反演问题提供了一种新的方法。然而，目前大多数深波反演方法严重依赖于标记数据（真实电阻率模型），难以从现场调查中获得。在这项研究中，我们提出了一种基于控制电场传播的物理定律的TEM测量数据的无监督深度反演方法。首先，我们将正演建模集成到训练过程中，允许将预测电阻率模型转换为模拟数据。然后将模拟数据与观测数据进行比较，以计算数据不匹配。然后，利用数据失拟作为损失函数实现无监督训练（标签无关），并采用动态平滑约束来缓解不适定反演问题。此外，DL网络结合了注意机制来提取TEM反演的关键特征信息。最后，采用鲸鱼优化算法（WOA）优化的多元变分模态分解（MVMD）技术，降低调查数据中的噪声，提高瞬变电磁法反演精度。综合算例和现场实测表明，我们的方法能够准确地描绘出地下模型结构，为瞬变电磁法反演提供了一种创新的解决方案。

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引用次数: 0