Computers & Geosciences最新文献

Fire-Image-DenseNet (FIDN) for predicting wildfire burnt area using remote sensing data

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

Computers & Geosciences

Pub Date : 2024-11-27 DOI: 10.1016/j.cageo.2024.105783

Bo Pang , Sibo Cheng , Yuhan Huang , Yufang Jin , Yike Guo , I. Colin Prentice , Sandy P. Harrison , Rossella Arcucci

Predicting the extent of massive wildfires once ignited is essential to reduce the subsequent socioeconomic losses and environmental damage, but challenging because of the complexity of fire behavior. Existing physics-based models are limited in predicting large or long-duration wildfire events. Here, we develop a deep-learning-based predictive model, Fire-Image-DenseNet (FIDN), that uses spatial features derived from both near real-time and reanalysis data on the environmental and meteorological drivers of wildfire. We trained and tested this model using more than 300 individual wildfires that occurred between 2012 and 2019 in the western US. In contrast to existing models, the performance of FIDN does not degrade with fire size or duration. Furthermore, it predicts final burnt area accurately even in very heterogeneous landscapes in terms of fuel density and flammability. The FIDN model showed higher accuracy, with a mean squared error (MSE) about 82% and 67% lower than those of the predictive models based on cellular automata (CA) and the minimum travel time (MTT) approaches, respectively. Its structural similarity index measure (SSIM) averages 97%, outperforming the CA and FlamMap MTT models by 6% and 2%, respectively. Additionally, FIDN is approximately three orders of magnitude faster than both CA and MTT models. The enhanced computational efficiency and accuracy advancements offer vital insights for strategic planning and resource allocation for firefighting operations.

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引用次数: 0

Efficient reservoir characterization using dimensionless ensemble smoother and multiple data assimilation in damaged multilayer systems

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

Computers & Geosciences

Pub Date : 2024-11-26 DOI: 10.1016/j.cageo.2024.105777

Adailton José do Nascimento Sousa , Malú Grave , Renan Vieira Bela , Thiago M.D. Silva , Sinesio Pesco , Abelardo Borges Barreto Junior

The ES-MDA has been extensively applied to address inverse problems related to oil reservoirs, leveraging Bayesian statistics as its cornerstone. This ensemble-based methodology utilizes historical reservoir data to infer its properties such as permeability and skin zone properties. In a recent study , the ES-MDA was utilized to estimate individual skin zone properties using well pressure responses as observed data. However, owing to insufficient reservoir information and the inherent nonlinearity of the problem, their findings lacked precision. This study presents a novel approach to efficiently characterize reservoir skin zones by employing an enhanced ES-MDA implementation and augmenting the observed data vector with flow-rate data. We introduce an analytical method for determining the pressure and flow rate observed at the well during an injectivity test, specifically tailored for multilayer reservoirs with skin zones, utilizing Laplace Transform. To convert the computed data to the real field, we use Stehfest’s algorithm. The analytical model serves a dual purpose: generating artificial data to represent a real field and predicting properties when coupled to the ES-MDA. The new analytical model enables the extraction of flow rates in each layer, which are then integrated as new data into the ES-MDA, thereby bolstering the estimation accuracy of targeted parameters. Both flow rate and pressure are employed as input data and, to alleviate the impact of orders of magnitude disparities on estimates, the ES-MDA is implemented in a dimensionless form. We tested the proposed methodology in four cases to display how adding the flow-rate data could improve results from a previous work. Moreover, the dimensionless ES-MDA offered skin zone properties with lower RMSE compared to the ones obtained in the mentioned study.

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引用次数: 0

Shear wave velocity prediction based on bayesian-optimized multi-head attention mechanism and CNN-BiLSTM

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

Computers & Geosciences

Pub Date : 2024-11-25 DOI: 10.1016/j.cageo.2024.105787

Wenzhi Lan , Yunhe Tao , Bin Liang , Rui Zhu , Yazhai Wei , Bo Shen

Shear wave velocity (VS) is one of the fundamental geophysical parameters essential for pre-stack seismic inversion, rock mechanics evaluation, and in-situ stress assessment. However, due to the high cost of acquiring VS log data, it is impossible to carry out this logging project in all wells. Thus, it is extremely necessary to develop an efficient and reliable VS prediction method. Deep learning methods have distinct advantages in data inversion, but different neural network have their own characteristics. A single-structured neural network has inevitable limitations in VS prediction, making it challenging to effectively capture the nonlinear mapping relationships of multiple parameters. Therefore, an integrated VS prediction model was proposed based on analyzing the applicability of classical neural networks. This new model, denoted as Bo-MA-CNN-BiLSTM, combines a Bayesian-optimized and multi-head attention mechanism (Bo-MA) with a convolutional neural network (CNN) and a bidirectional long short-term memory network (BiLSTM). It can effectively capture spatio-temporal data reflecting geophysical characteristics from log data, and the integration of the multi-head attention mechanism enhances the rational allocation of weights for log data. Bayesian optimization is utilized to determine the values of hyperparameters, overcoming the subjectivity and empiricism associated with manual selection. Actual data processing demonstrates that the new model achieves higher accuracy in predicting VS than applying CNN, LSTM, BiLSTM, and CNN-LSTM individually. The application results of well log data not involved in training indicate that, compared to other classical models, this new model exhibits optimal evaluation metrics. Especially for strongly heterogeneous formations, the predicted results demonstrate significant superiority, verifying the generalization ability and robustness of the proposed model.

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引用次数: 0

Multivariate simulation using a locally varying coregionalization model 利用局部变化的核心区域化模型进行多变量模拟

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

Computers & Geosciences

Pub Date : 2024-11-24 DOI: 10.1016/j.cageo.2024.105781

Álvaro I. Riquelme, Julian M. Ortiz

Understanding the response of materials in downstream processes of mining operations relies heavily on proper multivariate spatial modeling of relevant properties of such materials. Ore recovery and the behavior of tailings and waste are examples where capturing the mineralogical composition is a key component: in the first case, to ensure reliable revenues, and in the second one, to avoid environmental risks involved in their disposal. However, multivariate spatial modeling can be difficult when variables exhibit intricate relationships, such as non-linear correlation, heteroscedastic behavior, or spatial trends. This work demonstrates that the complex multivariate behavior among variables can be reproduced by disaggregating the global non-linear behavior through the spatial domain and looking instead at the local correlations between Gaussianized variables. Local linear dependencies are first inferred from a local neighborhood and then interpolated through the domain using Riemannian geometry tools that allow us to handle correlation matrices and their spatial interpolation. By employing a non-stationary modification of the linear model of coregionalization, it is possible to independently simulate variables and then combine them as a linear mixture that locally varies according to the inferred correlation, reproducing the global multivariate behavior seen on input variables. A real case study is presented, showing the reproduction of the reference multivariate distributions, as well as direct and cross semi-variograms.

了解材料在采矿作业下游过程中的反应，在很大程度上依赖于对这些材料的相关特性进行适当的多变量空间建模。以矿石回收和尾矿及废料的行为为例，掌握矿物成分是关键的一环：前者是为了确保可靠的收益，后者是为了避免处理过程中的环境风险。然而，当变量表现出错综复杂的关系（如非线性相关性、异方差行为或空间趋势）时，多变量空间建模就会变得困难。这项研究表明，通过空间域分解全局非线性行为，转而研究高斯化变量之间的局部相关性，可以再现变量之间复杂的多变量行为。首先从局部邻域推断出局部线性相关关系，然后利用黎曼几何工具对整个域进行插值，从而处理相关矩阵及其空间插值。通过对核心区域化线性模型进行非稳态修改，可以独立模拟变量，然后将它们组合成线性混合物，该混合物根据推断的相关性在局部发生变化，从而再现输入变量的全局多元行为。本文介绍了一个实际案例研究，显示了参考多元分布以及直接和交叉半变量图的再现。

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引用次数: 0

Optimized AI-MPM: Application of PSO for tuning the hyperparameters of SVM and RF algorithms 优化的 AI-MPM：应用 PSO 调整 SVM 和 RF 算法的超参数

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

Computers & Geosciences

Pub Date : 2024-11-20 DOI: 10.1016/j.cageo.2024.105785

Mehrdad Daviran , Abbas Maghsoudi , Reza Ghezelbash

Modern computational techniques, particularly Support Vector Machines (SVM) and Random Forest (RF) models, are revolutionizing predictive mineral prospectivity mapping. These advanced systems excel at identifying prime resource locations but require meticulous fine-tuning of their internal settings to achieve peak performance. Careful calibration of these configurations during the learning phase significantly enhances their ability to detect promising deposits. The main goal of this study is to introduce a hybrid model called PSO -SVM and PSO-RF, which aim to combine particle swarm optimization (PSO) with SVM (with RBF kernel) and RF models. This hybrid model automatically adjusts the optimized hyperparameters of SVM and RF, resulting in highly accurate predictions and a wide range of applicability. The PSO algorithm has been applied to fine-tune two main parameters (C and λ) for SVM-RBF and three main parameters (N_T, N_S, and d) for RF, creating efficient models for both. The proposed hybrid model as well as the traditional versions of SVM and RF models, were tested using a geo-spatial dataset related to Cu mineralization in Kerman belt, SE Iran. Forecasting algorithms were developed by integrating diverse datasets: multi-element concentrations from stream samples, bedrock and fault line maps, indicators of hot fluid interaction, aeromagnetic survey results, coordinates of previously identified copper-rich igneous intrusions, and verified ore body positions as reference points. The models' performance was evaluated using four validation methods: Multi-round data partitioning (K-fold), error classification tables (confusion matrix), true-positive vs. false-positive graphical analysis ((ROC) curve), and P-A plot were used to assess algorithms and models performance. Tests revealed that the PSO-SVM surpassed all competitors. Impressively, this fine-tuned classifier identified prime target zones in merely one-seventh of the region (14%), yet these areas encompassed nearly all verified resource sites (97%).

现代计算技术，尤其是支持向量机（SVM）和随机森林（RF）模型，正在彻底改变矿产远景预测绘图。这些先进的系统在确定主要资源位置方面表现出色，但需要对其内部设置进行细致的微调才能达到最佳性能。在学习阶段对这些配置进行仔细校准，可大大提高其探测有潜力矿藏的能力。本研究的主要目标是引入一种名为 PSO -SVM 和 PSO-RF 的混合模型，旨在将粒子群优化（PSO）与 SVM（带 RBF 内核）和 RF 模型相结合。这种混合模型可自动调整 SVM 和 RF 的优化超参数，从而实现高精度预测和广泛的适用性。PSO 算法被用于微调 SVM-RBF 的两个主要参数（C 和 λ）和 RF 的三个主要参数（NT、NS 和 d），为两者创建了高效模型。我们使用与伊朗东南部克尔曼矿带铜矿化相关的地理空间数据集对所提出的混合模型以及传统版本的 SVM 和 RF 模型进行了测试。预测算法是通过整合多种数据集而开发的：溪流样本中的多元素浓度、基岩和断层线图、热流体相互作用指标、航磁勘测结果、先前确定的富铜火成岩侵入体坐标以及作为参考点的已验证矿体位置。使用四种验证方法对模型的性能进行了评估：使用多轮数据分区（K-fold）、误差分类表（混淆矩阵）、真阳性与假阳性图形分析（（ROC）曲线）和 P-A 图来评估算法和模型的性能。测试表明，PSO-SVM 超越了所有竞争对手。令人印象深刻的是，这种经过微调的分类器仅在七分之一的区域（14%）识别出了主要目标区，但这些区域几乎涵盖了所有经过验证的资源点（97%）。

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引用次数: 0

Automatic variogram calculation and modeling 自动变异图计算和建模

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

Computers & Geosciences

Pub Date : 2024-11-20 DOI: 10.1016/j.cageo.2024.105774

Luis Davila Saavedra , Clayton V. Deutsch

The variogram is one of the most used tools in geostatistics. It represents a key step for the results of estimation and simulation. This paper presents a methodology of the experimental variogram points calculation and subsequent modeling, including some practical considerations. The proposed methodology infers the variogram parameters directly from the dataset to require minimum user input. Autovar is a program that implements the described methodology, giving an initial variogram model for disseminated and tabular deposits.

变异图是地质统计学中最常用的工具之一。它是估算和模拟结果的关键步骤。本文介绍了实验变异图点计算和后续建模的方法，包括一些实际考虑因素。所提出的方法可直接从数据集中推导出变异图参数，从而将用户输入量降至最低。Autovar 是一个实现所述方法的程序，它给出了散布式和表格式矿床的初始变分法模型。

引用次数: 0

SaltFormer: A hybrid CNN-Transformer network for automatic salt dome detection SaltFormer：用于自动检测盐穹的混合 CNN-Transformer 网络

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

Computers & Geosciences

Pub Date : 2024-11-19 DOI: 10.1016/j.cageo.2024.105772

Yang Li , Suping Peng , Dengke He

Salt dome interpretation of seismic data is a crucial task in the exploration and development of oil and gas. Conventional techniques, such as multi-attribute analysis, are laborious, time-consuming, and susceptible to subjective biases in their results. To achieve a more automated and precise identification of salt dome, we developed a hybrid network for salt dome detection. In order to optimally exploit both local and global features, a hierarchical Vision Transformer is employed as an encoder for feature extraction. Concurrently, the concurrent spatial and channel squeeze & excitation attention module is utilized to improve detection accuracy in the decoder. Furthermore, we leveraged the complementarity of information between multiple tasks to enhance the model’s generalization performance. Using the competition data from the Kaggle platform provided by TGS-NOPEC Geophysics Company, automatic segmentation of salt domes was completed with a detection accuracy of 85.20%. A series of experiments were conducted using state-of-the-art models and the SaltFormer model, which was found to have higher detection accuracy compared to other networks. Finally, the test conducted with seismic field data from the Netherlands offshore F3 block in the North Sea demonstrate that the novel method is highly effective in detecting salt domes in seismic data.

地震数据的盐穹解译是勘探和开发油气的一项重要任务。多属性分析等传统技术费力、费时，而且结果容易出现主观偏差。为了实现更加自动化和精确的盐穹顶识别，我们开发了一种用于盐穹顶检测的混合网络。为了充分利用局部和全局特征，我们采用了分层视觉变换器作为特征提取的编码器。同时，利用并发空间和信道挤压& 激励注意模块来提高解码器的检测精度。此外，我们还利用多个任务之间的信息互补性来提高模型的泛化性能。利用 TGS-NOPEC 地球物理公司提供的 Kaggle 平台竞赛数据，完成了盐穹顶的自动分割，检测准确率达到 85.20%。使用最先进的模型和 SaltFormer 模型进行了一系列实验，发现与其他网络相比，SaltFormer 的检测准确率更高。最后，利用荷兰北海近海 F3 区块的地震现场数据进行的测试表明，这种新方法在检测地震数据中的盐穹顶方面非常有效。

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引用次数: 0

MagTFs: A tool for estimating multiple magnetic transfer functions to constrain Earth’s electrical conductivity structure 磁传递函数：估算多重磁传递函数的工具，用于约束地球的导电结构

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

Computers & Geosciences

Pub Date : 2024-11-19 DOI: 10.1016/j.cageo.2024.105769

Zhengyong Ren , Zijun Zuo , Hongbo Yao , Chaojian Chen , Linan Xu , Jingtian Tang , Keke Zhang

Time-varying magnetic signals measured by geomagnetic observatories and satellites carry information about the Earth’s deep electrical conductivity structure and external current sources in the ionosphere and magnetosphere. Estimating magnetic transfer functions (TFs), which reflect the Earth’s internal conductivity structure, is a primary task in interpreting geomagnetic data from observatories and satellites. However, available TFs estimation tools either focus on a single source (ionosphere currents or magnetosphere currents) or are not publicly accessible. Therefore, we developed a flexible TFs estimation tool, named MagTFs, to achieve robust and precise estimation of magnetic TFs from the time series of magnetic field data acquired through land or satellite-based observations. This tool can handle magnetic data originating from time-varying currents in both the ionosphere and magnetosphere. We tested its performance on four kinds of data sets, and the good agreements with published results underscore the tool’s maturity and versatility in accurately estimating multi-source TFs. As a contribution to the scientific community, we have released MagTFs as an open-source tool, facilitating broader utilization and collaborative advancements.

地磁观测站和卫星测量到的时变磁信号携带着有关地球深层导电结构以及电离层和磁层中外部电流源的信息。估算反映地球内部传导结构的磁传递函数（TFs）是解释来自观测站和卫星的地磁数据的首要任务。然而，现有的磁传递函数估算工具要么只关注单一来源（电离层电流或磁层电流），要么无法公开获取。因此，我们开发了一种灵活的 TFs 估算工具，命名为 MagTFs，以便从陆基或卫星观测获得的磁场数据时间序列中稳健而精确地估算磁场 TFs。该工具可处理来自电离层和磁层中时变电流的磁数据。我们在四种数据集上测试了该工具的性能，结果与已发表的结果吻合良好，凸显了该工具在准确估算多源 TF 方面的成熟性和多功能性。作为对科学界的贡献，我们已将 MagTFs 作为开源工具发布，以促进更广泛的利用和合作进步。

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引用次数: 0

An identification for channel mislabel of strong motion records based on Siamese neural network 基于连体神经网络的强运动记录信道误标识别方法

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

Computers & Geosciences

Pub Date : 2024-11-19 DOI: 10.1016/j.cageo.2024.105780

Baofeng Zhou , Bo Liu , Xiaomin Wang , Yefei Ren , Maosheng Gong

Strong motion records are first-hand data for studying the seismic response of sites or engineering structures, and their objectivity is crucial for the credibility of the results in earthquake engineering and engineering seismology. However, domestic and international earthquake data may be mislabeled between horizontal and vertical channels. This issue is typically addressed by manually comparing the similarity between the three components of strong motion records, which is inherently subjective and inefficient in identification. To achieve the intelligent recognition of massive records, this study used 14,983 sets of ground motion records with significant differences between horizontal and vertical components from the NGA-West2 database. A Siamese neural network preliminarily distinguished the similarity between the acceleration waveform and the three components of the Fourier amplitude spectrum (FAS) of ground motion records. Combined with manual identification, an efficient and accurate method for identifying vertical components in ground motion records was proposed, and applied to verify the channel directions of the strong motion records in Strong Motion Network in China. It was found that 308 sets of records from 170 stations were suspected of mislabeling vertical and horizontal components. This advancement significantly enhances the objectivity of strong motion records. This proposed method holds potential for remote maintenance of strong motion stations, verifying the channels of strong motion instruments, and mitigating the negative impact of channel confusion on research results.

强震记录是研究场地或工程结构地震反应的第一手资料，其客观性对地震工程和工程地震学研究结果的可信度至关重要。然而，国内外的地震数据可能会在水平道和垂直道之间出现标注错误。解决这一问题的方法通常是人工比较强震记录三个部分的相似度，这种方法本身主观性强，识别效率低。为了实现海量记录的智能识别，本研究使用了来自 NGA-West2 数据库的 14,983 组水平和垂直分量差异显著的地面运动记录。暹罗神经网络初步区分了加速度波形与地动记录的傅里叶振幅谱（FAS）三个分量之间的相似性。结合人工识别，提出了一种高效、准确的地动记录垂直分量识别方法，并应用于中国强震网强震记录通道方向的验证。结果发现，170 个台站的 308 组记录存在误标垂直和水平分量的嫌疑。这一进步大大提高了强运动记录的客观性。该方法可用于强运动台站的远程维护，验证强运动仪器的信道，减少信道混乱对研究结果的负面影响。

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引用次数: 0

ReUNet: Efficient deep learning for precise ore segmentation in mineral processing ReUNet：用于矿物加工中精确矿石分割的高效深度学习

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

Computers & Geosciences

Pub Date : 2024-11-19 DOI: 10.1016/j.cageo.2024.105773

Chanjuan Wang , Huilan Luo , Jiyuan Wang , Daniel Groom

Efficient ore segmentation plays a pivotal role in advancing mineral processing technologies. With the rise of computer vision, deep learning models like UNet have increasingly outperformed traditional methods in automatic segmentation tasks. Despite these advancements, the substantial computational demands of such models have hindered their widespread adoption in practical production environments. To overcome this limitation, we developed ReUNet, a lightweight and efficient model tailored for mineral image segmentation. ReUNet optimizes computational efficiency by selectively focusing on critical spatial and channel information, boasting only 1.7 million parameters and 24.88 GFLOPS. It delivers superior segmentation performance across three public datasets (CuV1, FeMV1, and Pellets) and achieves the most accurate average particle size estimation, closely matching the true values. Our findings underscore ReUNet’s potential as a highly effective tool for mineral image analysis, offering both precision and efficiency in processing mineral images.

高效的矿石分割在推动矿物加工技术发展方面发挥着举足轻重的作用。随着计算机视觉技术的兴起，UNet 等深度学习模型在自动分割任务中的表现越来越优于传统方法。尽管取得了这些进步，但此类模型的大量计算需求阻碍了它们在实际生产环境中的广泛应用。为了克服这一限制，我们开发了 ReUNet，一种专为矿物图像分割定制的轻量级高效模型。ReUNet 通过选择性地关注关键的空间和通道信息来优化计算效率，仅有 170 万个参数和 24.88 GFLOPS。它在三个公共数据集（CuV1、FeMV1 和 Pellets）中提供了卓越的分割性能，并实现了最准确的平均粒度估计，与真实值非常接近。我们的研究结果凸显了 ReUNet 作为矿物图像分析高效工具的潜力，它在处理矿物图像方面既精确又高效。

引用次数: 0