Artificial Intelligence in Geosciences最新文献_第6页

A new integrated neurosymbolic approach for crop-yield prediction using environmental data and satellite imagery at field scale 利用环境数据和卫星图像进行作物产量预测的一种新的综合神经符号方法

Artificial Intelligence in Geosciences

Pub Date : 2025-06-01 DOI: 10.1016/j.aiig.2025.100125

Khadija Meghraoui , Teeradaj Racharak , Kenza Ait El Kadi , Saloua Bensiali , Imane Sebari

Crop-yield is a crucial metric in agriculture, essential for effective sector management and improving the overall production process. This indicator is heavily influenced by numerous environmental factors, particularly those related to soil and climate, which present a challenging task due to the complex interactions involved. In this paper, we introduce a novel integrated neurosymbolic framework that combines knowledge-based approaches with sensor data for crop-yield prediction. This framework merges predictions from vectors generated by modeling environmental factors using a newly developed ontology focused on key elements and evaluates this ontology using quantitative methods, specifically representation learning techniques, along with predictions derived from remote sensing imagery. We tested our proposed methodology on a public dataset centered on corn, aiming to predict crop-yield. Our developed smart model achieved promising results in terms of crop-yield prediction, with a root mean squared error (RMSE) of 1.72, outperforming the baseline models. The ontology-based approach achieved an RMSE of 1.73, while the remote sensing-based method yielded an RMSE of 1.77. This confirms the superior performance of our proposed approach over those using single modalities. This integrated neurosymbolic approach demonstrates that the fusion of statistical and symbolic artificial intelligence (AI) represents a significant advancement in agricultural applications. It is particularly effective for crop-yield prediction at the field scale, thus facilitating more informed decision-making in advanced agricultural practices. Additionally, it is acknowledged that results might be further improved by incorporating more detailed ontological knowledge and testing the model with higher-resolution imagery to enhance prediction accuracy.

作物产量是农业的一个关键指标，对有效的部门管理和改善整个生产过程至关重要。这一指标受到许多环境因素的严重影响，特别是与土壤和气候有关的因素，由于涉及复杂的相互作用，这是一项具有挑战性的任务。在本文中，我们介绍了一种新的集成神经符号框架，该框架将基于知识的方法与传感器数据相结合，用于作物产量预测。该框架使用新开发的专注于关键元素的本体对环境因素建模产生的向量进行预测，并使用定量方法（特别是表示学习技术）以及来自遥感图像的预测对该本体进行评估。我们在一个以玉米为中心的公共数据集上测试了我们提出的方法，旨在预测作物产量。我们开发的智能模型在作物产量预测方面取得了令人满意的结果，其均方根误差（RMSE）为1.72，优于基线模型。基于本体的方法RMSE为1.73，而基于遥感的方法RMSE为1.77。这证实了我们提出的方法优于使用单一模式的方法。这种综合神经符号方法表明，统计和符号人工智能（AI）的融合代表了农业应用的重大进步。它对田间规模的作物产量预测特别有效，从而促进在先进农业实践中做出更明智的决策。此外，我们还认识到，通过结合更详细的本体论知识和用更高分辨率的图像测试模型来提高预测精度，结果可能会进一步改善。

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引用次数: 0

Variogram modelling optimisation using genetic algorithm and machine learning linear regression: application for Sequential Gaussian Simulations mapping 变异函数建模优化使用遗传算法和机器学习线性回归：应用顺序高斯模拟映射

Artificial Intelligence in Geosciences

Pub Date : 2025-05-26 DOI: 10.1016/j.aiig.2025.100124

André William Boroh , Alpha Baster Kenfack Fokem , Martin Luther Mfenjou , Firmin Dimitry Hamat , Fritz Mbounja Besseme

The objective of this study is to develop an advanced approach to variogram modelling by integrating genetic algorithms (GA) with machine learning-based linear regression, aiming to improve the accuracy and efficiency of geostatistical analysis, particularly in mineral exploration. The study combines GA and machine learning to optimise variogram parameters, including range, sill, and nugget, by minimising the root mean square error (RMSE) and maximising the coefficient of determination (R²). The experimental variograms were computed and modelled using theoretical models, followed by optimisation via evolutionary algorithms. The method was applied to gravity data from the Ngoura-Batouri-Kette mining district in Eastern Cameroon, covering 141 data points. Sequential Gaussian Simulations (SGS) were employed for predictive mapping to validate simulated results against true values. Key findings show variograms with ranges between 24.71 km and 49.77 km, optimised RMSE and R² values of 11.21 mGal² and 0.969, respectively, after 42 generations of GA optimisation. Predictive mapping using SGS demonstrated that simulated values closely matched true values, with the simulated mean at 21.75 mGal compared to the true mean of 25.16 mGal, and variances of 465.70 mGal² and 555.28 mGal², respectively. The results confirmed spatial variability and anisotropies in the N170-N210 directions, consistent with prior studies. This work presents a novel integration of GA and machine learning for variogram modelling, offering an automated, efficient approach to parameter estimation. The methodology significantly enhances predictive geostatistical models, contributing to the advancement of mineral exploration and improving the precision and speed of decision-making in the petroleum and mining industries.

本研究的目的是通过将遗传算法（GA）与基于机器学习的线性回归相结合，开发一种先进的变异函数建模方法，旨在提高地质统计分析的准确性和效率，特别是在矿产勘探方面。该研究结合了遗传算法和机器学习，通过最小化均方根误差（RMSE）和最大化决定系数（R2）来优化变异函数参数，包括范围、基差和块金。使用理论模型对实验变差进行计算和建模，然后通过进化算法进行优化。该方法应用于喀麦隆东部Ngoura-Batouri-Kette矿区的141个数据点的重力数据。采用序贯高斯模拟（SGS）进行预测映射，根据真实值验证模拟结果。结果表明，42代遗传优化后的变异区间为24.71 ~ 49.77 km，优化后的RMSE和R2分别为11.21 mGal2和0.969。使用SGS进行预测映射表明，模拟值与真实值非常匹配，模拟平均值为21.75 mGal，而真实平均值为25.16 mGal，方差分别为465.70 mGal2和555.28 mGal2。结果证实了n170 ~ n210方向的空间变异性和各向异性，与前人的研究结果一致。这项工作提出了一种新的遗传算法和变异函数建模机器学习的集成，提供了一种自动化，有效的参数估计方法。该方法大大增强了预测地质统计模型，有助于推进矿产勘探，提高石油和采矿业决策的精度和速度。

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引用次数: 0

Soil liquefaction assessment using machine learning 利用机器学习进行土壤液化评估

Artificial Intelligence in Geosciences

Pub Date : 2025-05-20 DOI: 10.1016/j.aiig.2025.100122

Gamze Maden Muftuoglu , Kaveh Dehghanian

Liquefaction is one of the prominent factors leading to damage to soil and structures. In this study, the relationship between liquefaction potential and soil parameters is determined by applying feature importance methods to Random Forest (RF), Logistic Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) algorithms. Feature importance methods consist of permutation and Shapley Additive exPlanations (SHAP) importances along with the used model's built-in feature importance method if it exists. These suggested approaches incorporate an extensive dataset of geotechnical parameters, historical liquefaction events, and soil properties. The feature set comprises 18 parameters that are gathered from 161 field cases. Algorithms are used to determine the optimum performance feature set. Compared to other approaches, the study assesses how well these algorithms predict soil liquefaction potential. Early findings show that the algorithms perform well, demonstrating their capacity to identify non-linear connections and improve prediction accuracy. Among the feature set, σ^,_v (psf), MSF, CSR_σ, _v, FC%, V_s∗,40f _t(f ps) and N_1,60,CS are the ones that have the highest deterministic power on the result. The study's contribution is that, in the absence of extensive data for liquefaction assessment, the proposed method estimates the liquefaction potential using five parameters with promising accuracy.

液化是导致土壤和结构破坏的重要因素之一。在本研究中，通过将特征重要性方法应用于随机森林（RF）、逻辑回归（LR）、多层感知器（MLP）、支持向量机（SVM）和极端梯度增强（XGBoost）算法来确定液化势与土壤参数之间的关系。特征重要性方法包括置换和Shapley加性解释（SHAP）重要性，以及使用的模型内置的特征重要性方法（如果存在）。这些建议的方法包括岩土参数，历史液化事件和土壤特性的广泛数据集。该特性集包括从161个现场案例中收集的18个参数。采用算法确定最优性能特征集。与其他方法相比，该研究评估了这些算法预测土壤液化潜力的能力。早期的研究结果表明，这些算法表现良好，证明了它们识别非线性连接和提高预测精度的能力。在特征集中，σ,v (psf)， MSF, CSRσ, v, FC%, Vs∗,40f t（f ps）和N1,60，CS对结果具有最高的确定性。该研究的贡献在于，在缺乏液化评估的大量数据的情况下，所提出的方法使用五个参数来估计液化潜力，具有很好的准确性。

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引用次数: 0

Leveraging boosting machine learning for drilling rate of penetration (ROP) prediction based on drilling and petrophysical parameters 基于钻井和岩石物理参数，利用增强机器学习来预测钻进速度（ROP）

Artificial Intelligence in Geosciences

Pub Date : 2025-05-12 DOI: 10.1016/j.aiig.2025.100121

Raed H. Allawi , Watheq J. Al-Mudhafar , Mohammed A. Abbas , David A. Wood

Drilling optimization requires accurate drill bit rate-of-penetration (ROP) predictions. ROP decreases drilling time and costs and increases rig productivity. This study employs random forest (RF), gradient boosting modeling (GBM), extreme gradient boosting (XGBoost), and adaptive boosting (Adaboost) models to generate ROP predictions. The models use well data from a 3200-m segment across the stratigraphic column (Dibdibba to Zubair formations) of the large West Qurna oil field in Southern Iraq, penetrating 19 formations and four oil reservoirs. The reservoir sections are between 40 and 440 m thick and consist of both carbonate and clastic lithologies. The ROP predictive models were developed using 14 operational parameters: TVD, weight on bit (WOB), torque, effective circulating density (ECD), drilling rotation per minute (RPM), flow rate, standpipe pressure (SPP), bit size, total RPM, D exponent, gamma ray (GR), density, neutron, caliper, and discrete lithology distribution. Training and validation of the ROP models involves data compiled from three development wells. Applying Random subsampling, the compiled dataset was split into 85 % for training and 15 % for validation and testing. The test subgroup's measured and predicted ROP mismatch was assessed using root mean square error (RMSE) and coefficient of correlation (R²). The RF, GBM, and XGBoost models provide ROP predictions versus depth with low errors. Models with cross-validation that integrate data from three wells deliver more accurate ROP predictions than datasets from single well. The input variables' influences on ROP optimization identify the optimal value ranges for 14 operating parameters that help to increase drilling speed and reduce cost.

钻井优化需要精确的钻头钻速（ROP）预测。ROP减少了钻井时间和成本，提高了钻机生产率。本研究采用随机森林（RF）、梯度增强模型（GBM）、极端梯度增强（XGBoost）和自适应增强（Adaboost）模型来生成ROP预测。该模型使用了伊拉克南部West Qurna大型油田地层柱（Dibdibba至Zubair地层）3200米段的井数据，穿透了19个地层和4个油藏。储层剖面厚度在40 ~ 440 m之间，由碳酸盐岩和碎屑岩组成。ROP预测模型使用了14个操作参数：TVD、钻压（WOB）、扭矩、有效循环密度（ECD）、每分钟钻井转速（RPM）、流量、立管压力（SPP）、钻头尺寸、总RPM、D指数、伽马射线（GR）、密度、中子、井径器和离散岩性分布。ROP模型的训练和验证涉及三口开发井的数据。应用随机子抽样，编译的数据集被分成85%用于训练，15%用于验证和测试。采用均方根误差（RMSE）和相关系数（R2）对测试亚组测量和预测ROP失配进行评估。RF、GBM和XGBoost模型提供了相对深度的机械钻速预测，误差很小。与单井数据集相比，整合三口井数据的交叉验证模型可以提供更准确的ROP预测。输入变量对ROP优化的影响确定了14个操作参数的最佳取值范围，有助于提高钻井速度并降低成本。

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引用次数: 0

LatentPINNs: Generative physics-informed neural networks via a latent representation learning latentpinn：基于潜在表征学习的生成物理信息神经网络

Artificial Intelligence in Geosciences

Pub Date : 2025-05-09 DOI: 10.1016/j.aiig.2025.100115

Mohammad H. Taufik, Tariq Alkhalifah

Physics-informed neural networks (PINNs) are promising to replace conventional mesh-based partial differential equation (PDE) solvers by offering more accurate and flexible PDE solutions. However, PINNs are hampered by the relatively slow convergence and the need to perform additional, potentially expensive training for new PDE parameters. To solve this limitation, we introduce LatentPINN, a framework that utilizes latent representations of the PDE parameters as additional (to the coordinates) inputs into PINNs and allows for training over the distribution of these parameters. Motivated by the recent progress on generative models, we promote using latent diffusion models to learn compressed latent representations of the distribution of PDE parameters as they act as input parameters for NN functional solutions. We use a two-stage training scheme in which, in the first stage, we learn the latent representations for the distribution of PDE parameters. In the second stage, we train a physics-informed neural network over inputs given by randomly drawn samples from the coordinate space within the solution domain and samples from the learned latent representation of the PDE parameters. Considering their importance in capturing evolving interfaces and fronts in various fields, we test the approach on a class of level set equations given, for example, by the nonlinear Eikonal equation. We share results corresponding to three Eikonal parameters (velocity models) sets. The proposed method performs well on new phase velocity models without the need for any additional training.

基于物理信息的神经网络（pinn）有望通过提供更准确、更灵活的偏微分方程（PDE）解决方案，取代传统的基于网格的偏微分方程（PDE）求解器。然而，pinn的收敛速度相对较慢，并且需要对新的PDE参数进行额外的、可能昂贵的训练，这阻碍了它的发展。为了解决这个限制，我们引入了LatentPINN，这是一个框架，它利用PDE参数的潜在表示作为pinn的附加（坐标）输入，并允许在这些参数的分布上进行训练。由于生成模型的最新进展，我们提倡使用潜在扩散模型来学习PDE参数分布的压缩潜在表示，因为它们作为神经网络函数解的输入参数。我们使用了一个两阶段的训练方案，在第一阶段，我们学习PDE参数分布的潜在表示。在第二阶段，我们通过从解域内的坐标空间随机抽取的样本和从学习到的PDE参数的潜在表示中抽取的样本给出的输入训练一个物理信息的神经网络。考虑到它们在捕捉各个领域中不断变化的界面和前沿方面的重要性，我们在一类给定的水平集方程上测试了该方法，例如，由非线性Eikonal方程给出的水平集方程。我们共享了对应于三个Eikonal参数（速度模型）集的结果。该方法在新的相速度模型上表现良好，无需额外的训练。

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引用次数: 0

Identification of interlayer and connectivity analysis based on machine learning and production data: A case study from M oilfield 基于机器学习和生产数据的层间识别和连通性分析：以M油田为例

Artificial Intelligence in Geosciences

Pub Date : 2025-05-09 DOI: 10.1016/j.aiig.2025.100119

Xiaoshuai Wu , Yuanliang Zhao , Jianpeng Zhao , Shichen Shuai , Bing Yu , Junqing Rong , Hui Chen

Interlayer is an important factor affecting the distribution of remaining oil. Accurate identification of interlayer distribution is of great significance in guiding oilfield production and development. However, the traditional method of identifying interlayers has some limitations: (1) Due to the existence of overlaps in the cross plot for different categories of interlayers, it is difficult to establish a determined model to classify the type of interlayer; (2) Traditional identification methods only use two or three logging curves to identify the types of interlayers, making it difficult to fully utilize the information of the logging curves, the recognition accuracy will be greatly reduced; (3) For a large number of complex logging data, interlayer identification is time-consuming and labor-intensive. Based on the existing well area data such as logging data and core data, this paper uses machine learning method to quantitatively identify the interlayers in the single well layer of CⅢ sandstone group in the M oilfield. Through the comparison of various classifiers, it is found that the decision tree method has the best applicability and the highest accuracy in the study area. Based on single well identification of interlayers, the continuity of well interval interlayers in the study area is analyzed according to the horizontal well. Finally, the influence of the continuity of interlayers on the distribution of remaining oil is verified by the spatial distribution characteristics of interlayers combined with the production situation of the M oilfield.

层间是影响剩余油分布的重要因素。准确识别层间分布对指导油田生产和开发具有重要意义。然而，传统的中间层识别方法存在一定的局限性：(1)由于不同类别的中间层在交叉图中存在重叠，难以建立确定的模型对中间层类型进行分类；(2)传统识别方法仅利用2条或3条测井曲线识别夹层类型，难以充分利用测井曲线信息，识别精度将大大降低；(3)对于大量复杂的测井资料，层间识别费时费力。本文基于M油田CⅢ砂岩群单井层间的测井、岩心等现有井区资料，采用机器学习方法对CⅢ砂岩群单井层间进行定量识别。通过对各种分类器的比较，发现决策树方法在研究区域具有最好的适用性和最高的准确率。在单井层间识别的基础上，根据水平井分析了研究区井段层间的连续性。最后，结合M油田的生产情况，通过层间空间分布特征验证了层间连续性对剩余油分布的影响。

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引用次数: 0

Digital core reconstruction of tight carbonate rocks based on SliceGAN 基于SliceGAN的致密碳酸盐岩数字岩心重建

Artificial Intelligence in Geosciences

Pub Date : 2025-04-22 DOI: 10.1016/j.aiig.2025.100116

Ying Zhou , Taiping Zhao , Wenjing Zhang , Feiqi Teng , Xin Nie

The pore structures of the Majiagou Formation in the Ordos Basin are complex, featuring micro- and nano-scale intra-crystalline and inter-crystalline pores that significantly impact hydrocarbon storage and flow. Precisely characterizing the rock internal structures is crucial for reservoir exploration and development. However, it is difficult to accurately characterize the pore structure of rock using traditional imaging methods to meet the simulation requirements. In this context, this study focuses on high-resolution 3D digital core reconstruction using the SliceGAN model. Specifically, the Modular Automated Processing System (MAPS) image and Quantitative Evaluation of Minerals by Scanning Electron Microscopy (QEMSCAN) image were combined to divide MAPS into three categories: pore, dolomite, and calcite. Then, through the SliceGAN algorithm, the 3D digital core was reconstructed. To evaluate the reconstruction, the auto-correlation function, two-point probability function, porosity, mineral content, and specific surface area were employed. The results show that the SliceGAN can effectively capture the micro-features in the core, and the internal structure of the generated core was consistent with that of the original core. This study provided a new sight for reconstructing cores with complex pore structures and strong heterogeneity and innovatively supports tight carbonate reservoir characterization and evaluation.

鄂尔多斯盆地马家沟组孔隙结构复杂，具有微纳米级的晶内孔和晶间孔，对油气的储集和流动具有重要影响。准确表征岩石内部构造对储层勘探开发至关重要。然而，传统的成像方法难以准确表征岩石孔隙结构，难以满足模拟要求。在此背景下，本研究的重点是使用SliceGAN模型进行高分辨率3D数字岩心重建。具体而言，将模块化自动化处理系统（MAPS）图像与扫描电子显微镜矿物定量评价（QEMSCAN）图像相结合，将MAPS分为孔隙、白云石和方解石三类。然后，通过SliceGAN算法对三维数字核进行重构。利用自相关函数、两点概率函数、孔隙度、矿物含量和比表面积对重建结果进行评价。结果表明，SliceGAN能够有效捕获岩心内部的微观特征，生成的岩心内部结构与原始岩心基本一致。该研究为孔隙结构复杂、非均质性强的岩心重建提供了新的思路，为致密碳酸盐岩储层的表征和评价提供了创新的支持。

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引用次数: 0

An intelligent recognition method of deep shale gas reservoir laminaset based on laminaset clustering and R-L-M algorithm 基于层状集聚类和R-L-M算法的深层页岩气藏层状集智能识别方法

Artificial Intelligence in Geosciences

Pub Date : 2025-04-07 DOI: 10.1016/j.aiig.2025.100113

Yu Zeng , Fuqiang Lai , Haijie Zhang , Yi Jiang , Junwei Pu , Tongtong Luo , Xiaoxia Zhao

Lamina structures, as typical sedimentary features in shale formations, determine both the quality of shale reservoirs and fracturing effects. In this study, through electric imaging logging, based on core scanning photos, thin sections, and other data from the Wufeng-Longmaxi Formation shale reservoirs in the western Sichuan Block, the characteristics and classification scheme of deep shale gas reservoir laminaset were clarified. In addition, with core scale electrical images, the electrical imaging logging response characteristics of different types of laminaset were identified. Based on electrical imaging logging images, a laminaset clustering algorithm was designed to segment the laminaset and then Levenberg-Marquardt (L-M) algorithm was improved by introducing a random forest to obtain the R-L-M algorithm, which was used to extract key parameters of laminaset such as attitude, type, density, and thickness. The average accuracy, recall rate, and F1 score of laminaset recognition results of this algorithm were 14.82 % higher than those of a well-known international commercial software (T). This method was used to evaluate the Longmaxi Formation shale gas reservoir in the western Sichuan Block. The development density of clay-siliceous (organic-lean) laminaset from the Longyi 1–4 small layer to the lower Wufeng Formation firstly decreased and then increased and the minimum value was found in Longyi 1-1 small layer. In contrast, the development density of siliceous-clay laminaset (organic-rich) first increased and then gradually decreased and the maximum value was found in Longyi 1-1 small layer. The clay-siliceous laminaset (organic matters-contained) and the calcareous-clay laminaset (organic matters-contained) showed a stable developmental trend.

层状构造作为页岩层的典型沉积特征，决定着页岩储层的质量和压裂效果。本研究通过电成像测井，基于四川西部区块五峰-龙马溪地层页岩储层的岩心扫描照片、薄切片等资料，明确了深层页岩气储层层理的特征和分类方案。此外，通过岩心尺度电图像，确定了不同类型层系的电成像测井响应特征。基于电成像测井图像，设计了层丛聚类算法对层丛进行划分，然后通过引入随机森林对 Levenberg-Marquardt 算法（L-M）进行改进，得到 R-L-M 算法，用于提取层丛的姿态、类型、密度和厚度等关键参数。该算法的层集识别结果的平均准确率、召回率和 F1 分数比国际知名商业软件（T）高出 14.82%。该方法被用于评估四川西部区块龙马溪地层页岩气藏。结果表明，龙马溪地层页岩气储层的粘土-硅质（有机-鳞片）层状发育密度从龙一1-4小层到五峰地层下部先减小后增大，最小值出现在龙一1-1小层。而硅质粘土层组（富含有机质）的发育密度先增大后逐渐减小，最大值出现在龙宜 1-1 小层。粘土-硅质层组（含有机质）和石灰质-粘土层组（含有机质）呈稳定的发育趋势。

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引用次数: 0

Fast 2D forward modeling of electromagnetic propagation well logs using finite element method and data-driven deep learning 利用有限元方法和数据驱动的深度学习快速二维电磁传播测井曲线正演建模

Artificial Intelligence in Geosciences

Pub Date : 2025-03-28 DOI: 10.1016/j.aiig.2025.100112

A.M. Petrov, A.R. Leonenko, K.N. Danilovskiy, O.V. Nechaev

We propose a novel workflow for fast forward modeling of well logs in axially symmetric 2D models of the near-wellbore environment. The approach integrates the finite element method with deep residual neural networks to achieve exceptional computational efficiency and accuracy. The workflow is demonstrated through the modeling of wireline electromagnetic propagation resistivity logs, where the measured responses exhibit a highly nonlinear relationship with formation properties. The motivation for this research is the need for advanced modeling algorithms that are fast enough for use in modern quantitative interpretation tools, where thousands of simulations may be required in iterative inversion processes. The proposed algorithm achieves a remarkable enhancement in performance, being up to 3000 times faster than the finite element method alone when utilizing a GPU. While still ensuring high accuracy, this makes it well-suited for practical applications when reliable payzone assessment is needed in complex environmental scenarios. Furthermore, the algorithm's efficiency positions it as a promising tool for stochastic Bayesian inversion, facilitating reliable uncertainty quantification in subsurface property estimation.

我们提出了一种新的工作流程，用于在近井筒环境的轴对称二维模型中对测井曲线进行快速正演建模。该方法将有限元法与深度残差神经网络相结合，实现了极高的计算效率和精度。工作流程通过有线电磁传播电阻率测井建模进行了演示，测得的响应与地层属性呈现高度非线性关系。这项研究的动机是现代定量解释工具需要足够快的先进建模算法，在迭代反演过程中可能需要进行数千次模拟。所提出的算法性能显著提高，在使用 GPU 时比单独使用有限元方法快 3000 倍。在确保高精度的同时，该算法非常适合在复杂环境场景中需要进行可靠的薪区评估的实际应用。此外，该算法的高效性使其成为随机贝叶斯反演的理想工具，有助于在地下属性评估中进行可靠的不确定性量化。

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引用次数: 0

Enhancing understanding of 3D rectangular tunnel heading stability in c-φ soils with surcharge loading: A comprehensive FELA analysis using three stability factors and machine learning 加强对具有附加荷载的 c-φ 土层中三维矩形隧道顶稳定性的理解：利用三个稳定因子和机器学习进行综合 FELA 分析

Artificial Intelligence in Geosciences

Pub Date : 2025-03-14 DOI: 10.1016/j.aiig.2025.100111

Suraparb Keawsawasvong , Jim Shiau , Nhat Tan Duong , Thanachon Promwichai , Rungkhun Banyong , Van Qui Lai

This study examines the stability of three-dimensional rectangular tunnel headings in drained c-ϕ soils, incorporating surcharge effects using 3D Finite Element Limit Analysis (FELA). It focuses on the upper and lower bound solutions for three stability factors: cohesion, surcharge, and soil unit weight (Nc, Ns, and Nγ). Based on Terzaghi's principle of superposition, the analysis evaluates tunnel stability under varying parameters, such as cover-depth ratio (H/D), width-depth ratio (B/D), and friction angle (ϕ). The results align closely with previous studies, and practical design charts are provided for calculating minimum support pressures. Additionally, machine learning models (ANN and XGBoost) are used to develop accurate correlations between input parameters and stability results. A relative importance index analysis is conducted to assess the impact of these parameters. This research enhances understanding of tunnel stability and offers practical insights for tunnel design.

本研究考察了排水c- φ土壤中三维矩形隧道掘进的稳定性，采用三维有限元极限分析（FELA）结合附加效应。它侧重于三个稳定因素的上界和下界解：黏聚力、附加物和土壤单位重量（Nc、Ns和n - γ）。基于Terzaghi的叠加原理，该分析评估了不同参数下的隧道稳定性，如覆盖深度比（H/D）、宽深比（B/D）和摩擦角（ϕ）。结果与前人的研究结果一致，并提供了计算最小支撑压力的实用设计图表。此外，机器学习模型（ANN和XGBoost）用于在输入参数和稳定性结果之间建立准确的相关性。通过相对重要性指数分析来评估这些参数的影响。该研究提高了对隧道稳定性的认识，为隧道设计提供了实用的见解。

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