Journal of Applied Geophysics最新文献_第2页

A new method for constructing dynamic-static conversion models for rock mechanics based on acoustic wave propagation characteristics 基于声波传播特性的岩石力学动静转换模型的新方法

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2025-12-29 DOI: 10.1016/j.jappgeo.2025.106083

Sheng Wang , Kaizhou Xu , Haixue Wang , Xiaofei Fu , Chengxuan Ren

To overcome the limitations of conventional dynamic-static parameter conversion methods characterized by low accuracy and destructive core dependency, this study presents a novel non-destructive approach for high-precision dynamic-static mechanical parameter conversion based on acoustic wave propagation characteristics. Considering that the acoustic wave wavelength is much smaller than the measurement scale of the rock body, a stratified mechanical model for heterogeneous rocks was constructed. The bulk density and reciprocal of wave velocity for each sub-sample layer of the rock sample are set to follow normal distributions, with the variance and expected values of these distributions determined by experimental measurements of wave velocity and bulk density in macroscopic rock samples. Under these constraints, target values of layer parameters are determined through a statistical search algorithm, establishing the dynamic-static mechanical parameter conversion model. Taking Bozhong Oilfield reservoir cores as an example, mechanical and acoustic experiments demonstrate that static parameters obtained with the new method show close agreement with test values, yielding average deviations of 0.048 for Young's modulus and 0.066 for Poisson's ratio. Compared with conventional methods, the dynamic-static conversion relationship developed through this new method proves more effective and accurate in estimating stratum static mechanical parameters when applied to well logging interpretation data. The new method enhances core utilization while maintaining accuracy, offering a cost-efficient solution for reservoir mechanical characterization.

针对传统动-静参数转换方法精度低、依赖破坏性核心的局限性，提出了一种基于声波传播特性的高精度动-静力学参数转换的非破坏性方法。考虑到声波波长远小于岩体的测量尺度，建立了非均质岩石的分层力学模型。设置岩样各子样层的容重和波速倒数服从正态分布，通过实验测量宏观岩样的波速和容重来确定这些分布的方差和期望值。在这些约束条件下，通过统计搜索算法确定层参数目标值，建立动-静态力学参数转换模型。以渤中油田储层岩心为例，力学和声学实验表明，新方法得到的静态参数与试验值吻合较好，杨氏模量和泊松比的平均偏差分别为0.048和0.066。与常规方法相比，将该方法建立的动静转换关系应用于测井解释资料中，对地层静态力学参数的估计更加有效和准确。新方法提高了岩心的利用率，同时保持了精度，为油藏力学表征提供了一种经济高效的解决方案。

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引用次数: 0

Induced polarization effects in transient electromagnetic data: A case study from the Hawiah volcanogenic massive sulfide deposit in Saudi Arabia 瞬变电磁数据中的诱导极化效应：以沙特阿拉伯Hawiah火山块状硫化物矿床为例

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2025-12-27 DOI: 10.1016/j.jappgeo.2025.106084

Hesham El-Kaliouby , Juntao Lu , Panagiotis Kirmizakis , Abid Khogali , Tim Eatwell , Tomos Bryan , Pantelis Soupios

In exploring volcanogenic massive sulfide (VMS) deposits, the transient electromagnetic (TEM) method is highly effective due to its sensitivity to metallic conductors such as sulfide minerals. A recent TEM survey over the Hawiah VMS deposit in Saudi Arabia utilized ten stations to investigate the distribution of VMS targets. Several stations exhibited anomalous fast decay curves attributed to induced polarization (IP) effects, a common characteristic of VMS minerals. Spectral IP (SIP) measurements of local VMS core samples indicated large IP effects, aligning with results from a previous time domain IP (TDIP) survey in the area. IP effects can distort TEM measurements, causing as fast decay followed by sign reversals, complicating interpretation when using conventional resistivity-only (RO) workflows. These distortions can result in unreliable interpretations, especially in regions with complex subsurface conditions. In this paper, we present a synthetic model analysis based on geological setting information, demonstrating that in scenarios with moderately resistive backgrounds and deeply buried high-polarizable bodies, TEM curves can exhibit anomalous fast decay in the middle to late time, aligning with the observed field data. Thus, this behavior serves as a key indicator of IP effects in the survey area. Through RO and IP-incorporated inversions, along with an uncertainty analysis of the resulting resistivity models, our findings show that the data is better fitted using an IP-incorporated inversion approach. This highlights the importance of analyzing anomalous decay in TEM data and supports adopting refined methodologies that account for IP effects. Such approaches are crucial for achieving accurate and reliable evaluations in areas with highly conductive and polarizable materials, like VMS deposits.

瞬变电磁（TEM）方法对金属导体（如硫化物矿物）的敏感性使其在火山成因块状硫化物矿床勘探中具有很高的效率。最近对沙特阿拉伯Hawiah VMS矿床进行的TEM调查使用了10个站点来调查VMS目标的分布。一些站点显示出异常的快速衰减曲线，这归因于诱导极化（IP）效应，这是VMS矿物的共同特征。对当地VMS岩心样本的频谱IP （SIP）测量表明，存在较大的IP效应，这与之前在该地区进行的时域IP （TDIP）调查结果一致。IP效应会扭曲TEM测量结果，导致快速衰减，然后是符号反转，这使得使用传统的纯电阻率（RO）工作流程进行解释变得复杂。这些扭曲可能导致不可靠的解释，特别是在地下条件复杂的地区。基于地质背景信息的综合模型分析表明，在中等电阻背景和深埋高极化体的情况下，瞬变电磁法曲线在中后期表现出异常的快速衰减，与现场观测数据一致。因此，这种行为可以作为调查区域IP效应的关键指标。通过RO和ip结合反演，以及对所得电阻率模型的不确定性分析，我们的研究结果表明，使用ip结合反演方法可以更好地拟合数据。这突出了分析TEM数据中异常衰减的重要性，并支持采用精细的方法来解释激电效应。这种方法对于在具有高导电性和极化材料的地区（如VMS沉积物）实现准确可靠的评估至关重要。

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引用次数: 0

Deep learning seismic impedance inversion with CVAE-generated labels and bidirectional self-supervised learning 基于cvae生成标签和双向自监督学习的深度学习地震阻抗反演

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2025-12-27 DOI: 10.1016/j.jappgeo.2025.106074

Heng Su, Junxing Cao, Linsen Zhao, Pengfei Jian

In recent years, deep learning has gained increasing attention in the field of geophysics due to its powerful feature extraction and nonlinear modeling capabilities, providing innovative solutions to complex subsurface characterization problems. However, its widespread application remains constrained by two major challenges: limited generalization ability and a heavy reliance on large-scale, high-quality labeled data. Although seismic and well-log data are relatively abundant, accurately paired labeled data remain scarce, which largely limits the application of deep learning in the field of geophysics. To address this problem, we propose a novel label-generation approach based on Conditional Variational Autoencoders (CVAEs), and introduce a novel training framework that integrates supervised and self-supervised learning within a bidirectional learning model. Experiments conducted on both synthetic and real post-stack seismic datasets for impedance inversion demonstrate that the proposed method significantly outperforms baseline method in terms of accuracy and generalization. These results highlight the practicality and robustness of our approach, offering a promising solution to the long-standing challenge of data scarcity in geophysical applications.

近年来，深度学习以其强大的特征提取和非线性建模能力在地球物理领域受到越来越多的关注，为复杂的地下表征问题提供了创新的解决方案。然而，它的广泛应用仍然受到两个主要挑战的制约：有限的泛化能力和对大规模、高质量标记数据的严重依赖。虽然地震和测井数据相对丰富，但准确配对标记的数据仍然很少，这在很大程度上限制了深度学习在地球物理领域的应用。为了解决这个问题，我们提出了一种新的基于条件变分自编码器（CVAEs）的标签生成方法，并引入了一种新的训练框架，该框架在双向学习模型中集成了监督学习和自监督学习。在合成和真实叠后地震数据集上进行的阻抗反演实验表明，该方法在精度和泛化方面明显优于基线方法。这些结果突出了我们方法的实用性和稳健性，为地球物理应用中数据稀缺的长期挑战提供了一个有希望的解决方案。

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引用次数: 0

Error calibration for cross-shaped magnetic gradient tensor system based on the improved differential evolution algorithm 基于改进差分进化算法的十字形磁梯度张量系统误差标定

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2025-12-26 DOI: 10.1016/j.jappgeo.2025.106080

Chenxu Dong, Zhuoxuan Li, Yuguo Li, Xuezhen Ding

Errors arising from sensor manufacturing and misalignment during installation have been shown to have a significant impact on the measurement accuracy of magnetic gradient tensor systems. This, in turn, can result in a reduction in detection performance in practical applications such as magnetic anomaly detection. This study proposes a high-precision error calibration method for a cross-shaped magnetic gradient tensor system. This method uses the Success-History based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE) algorithm, an enhanced variant of the Differential Evolution (DE) algorithm, to estimate these error parameters. A unified optimization model has been developed that incorporates multiple sensor errors into a single calibration framework, thereby enabling one-step error correction. Simulation experiments are conducted under both conditions of absence of noise and conditions of presence of noise. In conditions of absence of noise, the discrepancy between the calibrated and theoretical total magnetic field is of the order of

10^{- 6}

nT, with all magnetic gradient tensor components and invariants tending toward zero. In field experiments, the maximum deviation between the calibrated total magnetic field and the actual geomagnetic field is 1.92 nT, and the maximum improvement ratio of tensor components reaches 979. The findings from both simulation and field trials have been shown to demonstrate that the proposed algorithm successfully attains high calibration accuracy and computational efficiency, thus providing a pragmatic approach for sensor error correction.

由于传感器的制造和安装过程中的不对准引起的误差对磁梯度张量系统的测量精度有很大的影响。这反过来又会导致磁异常检测等实际应用中检测性能的降低。提出了一种十字形磁梯度张量系统的高精度误差标定方法。该方法采用基于成功历史的线性种群大小缩减自适应差分进化（L-SHADE）算法，该算法是差分进化（DE）算法的一种增强变体，用于估计这些误差参数。已经开发了一个统一的优化模型，该模型将多个传感器误差合并到单个校准框架中，从而实现一步误差校正。在无噪声和有噪声两种情况下进行了仿真实验。在无噪声条件下，标定总磁场与理论总磁场之间的差异约为10−6 nT，所有磁梯度张量分量和不变量都趋于零。在现场实验中，标定总磁场与实际地磁场的最大偏差为1.92 nT，张量分量的最大改进比达到979。仿真和现场试验结果表明，该算法成功地实现了较高的校准精度和计算效率，为传感器误差校正提供了一种实用的方法。

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引用次数: 0

Microseismic identification and effectiveness assessment of hydraulic-fracturing–induced roof cutting using an STFT–CNN framework 基于STFT-CNN框架的水力压裂顶板切割微震识别与有效性评估

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2025-12-26 DOI: 10.1016/j.jappgeo.2025.106078

Haowei Tian , Zhizhong Jiang , Zhenqian Ma , Zhijie Wen , Shaojie Zuo , Yu Liu , Jinhui Li , Wenjian Wang , Mingying Wang , Youchi Jin

During underground mining of solid mineral deposits, hard roofs can induce large-scale strata pressure disasters, posing severe threats to mine safety. Hydraulic-fracturing-based roof-cutting technology offers a means of weakening hard roofs by severing key structural connections, thereby altering the roof structure and reducing the likelihood of roof-related accidents. However, limitations remain in accurately characterizing fracture propagation and evaluating the effectiveness of hydraulic fracturing roof cutting. In this study, a coal mine in southwestern China was selected as the engineering site. By integrating hydraulic-fracturing roof cutting with microseismic monitoring, a hydraulic-fracturing microseismic event recognition model based on the Short-Time Fourier Transform (STFT) and Convolutional Neural Networks (CNN) was developed. Time-frequency analysis revealed that different microseismic signal types exhibit distinct dominant frequency ranges, with hydraulic-fracturing signals concentrated at 130–200 Hz. The proposed STFT–CNN model achieved a recognition accuracy exceeding 92 %. Microseismic source-location and kernel density analyses indicated that hydraulic-fracturing-induced fractures propagated symmetrically along the borehole axis, with an effective influence range of approximately ±5 m horizontally and ± 10 m vertically, and some fractures extending downward into the coal seam. Analysis of microseismic energy evolution showed abrupt energy surges when the working face advanced 1–2 m past the roof-cutting boreholes, with more than three high-magnitude events typically occurring about 3 m behind the working face. Overall, the field results demonstrate that hydraulic fracturing effectively weakened the hard roof and successfully facilitated controlled roof collapse, thereby enhancing the safety of underground coal extraction.

在固体矿床地下开采过程中，硬顶板会诱发大规模的地压灾害，对矿山安全构成严重威胁。基于水力压裂的顶板切割技术提供了一种通过切断关键结构连接来削弱硬顶板的方法，从而改变顶板结构，降低与顶板有关的事故发生的可能性。然而，在准确表征裂缝扩展和评估水力压裂顶板切割效果方面仍然存在局限性。本研究选择西南某煤矿作为工程场地。将水力压裂顶板切割与微地震监测相结合，建立了一种基于短时傅里叶变换（STFT）和卷积神经网络（CNN）的水力压裂微地震事件识别模型。时频分析表明，不同微震信号类型具有不同的主导频率范围，水力压裂信号集中在130 ~ 200 Hz。所提出的STFT-CNN模型的识别准确率超过92%。微震震源定位和核密度分析表明，水力压裂裂缝沿井轴线对称扩展，有效影响范围约为水平方向±5 m，垂直方向±10 m，部分裂缝向下延伸至煤层。微震能量演化分析表明，工作面向切顶钻孔推进1 ~ 2 m时能量突变，工作面后方约3 m处通常发生3次以上的高震级事件。综上所述，现场结果表明，水力压裂有效地削弱了硬顶板，成功地促进了顶板的可控坍塌，从而提高了井下采煤的安全性。

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引用次数: 0

Acoustic emission responses and failure characteristics of rocks with varying rockburst tendencies under uniaxial loading 单轴加载下不同岩爆倾向岩石的声发射响应及破坏特征

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2025-12-26 DOI: 10.1016/j.jappgeo.2025.106082

Xiaojun Wang , He Zhang , Xiao Feng , Qinglin Chen , Shirong Cao , Haowen Jiang , Jian liu

Understanding damage mechanisms in rocks exhibiting different rockburst tendencies is critical for monitoring rock damage at varied risk levels via acoustic emission (AE) technology. This study conducted rockburst tendency evaluation and uniaxial compression AE tests on limestone, granite, and red sandstone. Metallographic imaging and scanning electron microscopy (SEM) tests were also performed on fracture surfaces. AE responses and failure characteristics of rocks with different rockburst tendencies were analyzed. The parameter r (RA/AF) served as a rock damage index, with its Coefficient of Variation (CV) and AE b-value calculated. The results show that AE ring-down counts have a period of decline during the unstable crack propagation stage. Limestone and granite with rockburst tendencies fail under tension-shear coupling with mutual transitions between failure modes. The CV(r) shows a gradual decrease, stabilization, and significant increase. Red sandstone without rockburst tendencies mainly undergoes tensile action, and its CV(r) remains stable after decreasing. Compared with the traditional b-value, the CV(r) more effectively identifies failure progression, and its abrupt surge serves as a precursor for failure in rocks possessing rockburst tendency.

了解具有不同岩爆倾向的岩石的损伤机制对于利用声发射技术监测不同危险水平的岩石损伤至关重要。研究对石灰岩、花岗岩和红砂岩进行了岩爆倾向评价和单轴压缩声发射试验。对断口表面进行了金相成像和扫描电镜（SEM）测试。分析了不同岩爆倾向岩石的声发射响应和破坏特征。参数r （RA/AF）作为岩石损伤指标，计算其变异系数（CV）和AE b值。结果表明，在不稳定裂纹扩展阶段，声发射衰响次数呈下降趋势。具有岩爆倾向的灰岩和花岗岩在拉剪耦合作用下破坏，破坏模式相互转换。CV(r)呈逐渐下降、稳定和显著上升的趋势。无冲击地压倾向的红砂岩主要受拉伸作用，其CV(r)减小后保持稳定。与传统的b值相比，CV(r)能更有效地识别破坏进程，其突变波动是具有岩爆倾向的岩石破坏的前兆。

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引用次数: 0

Geophysical survey methods (GPR and ERT) to find architectural remains from the 17th century at the Fort of San Diego in Acapulco, Mexico. A case study. 地球物理测量方法（GPR和ERT）在墨西哥阿卡普尔科的圣地亚哥堡发现了17世纪的建筑遗迹。案例研究。

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2025-12-25 DOI: 10.1016/j.jappgeo.2025.106079

J. Ortega-Ramirez , M. Bano , J.L. Salas-Corrales , R. Junco Sánchez , L.A. Villa-Alvarado

Fort San Diego in Acapulco, Mexico, is an iconic monument, deeply linked to the history of the continent and a vital source of cultural identity for its community and future generations. Given its immense value as a cultural asset, it is essential to understand its architectural evolution, especially as historical records indicate significant alterations due to seismic activity and changes of use over time.

The article presents a geophysical study with the objective of locating and documenting the hidden architectural remains of the fort constructed in the 17th century. Given the paucity of documentation on the fort's modifications, we used non-destructive methods such as georadar (GPR) and electrical resistivity tomography (ERT). Both techniques identified a large anomaly measuring 3 by 6 m beneath the surface of the fort. This anomaly, characterized by multiple GPR diffractions and high electrical resistivity values, was then validated by a small archaeological excavation. The excavation confirmed that the anomaly corresponded to an ancient architectural foundation, visible from a depth of 30 cm down to at least 2.0 m. We hypothesize that this structure represents the remains of a drawbridge that served as the main entrance to the fort before the devastating earthquake of 1776, supporting the theory that the main gate was located on the opposite side to the current one. The study highlights the effectiveness and versatility of geophysical methods as essential tools for the investigation and conservation of cultural heritage, revealing crucial details about the hidden history of the fort.

位于墨西哥阿卡普尔科的圣地亚哥堡是一座标志性的纪念碑，与美洲大陆的历史紧密相连，是其社区和子孙后代文化认同的重要来源。鉴于其作为文化资产的巨大价值，了解其建筑演变是至关重要的，特别是历史记录表明，由于地震活动和使用的变化，随着时间的推移发生了重大变化。本文介绍了一项地球物理研究，目的是定位和记录17世纪建造的堡垒的隐藏建筑遗迹。鉴于缺乏关于堡垒改造的文件，我们使用了非破坏性方法，如地质雷达（GPR）和电阻率层析成像（ERT）。两种技术都发现了一个巨大的异常，在堡垒表面下3米乘6米。该异常具有多个GPR衍射和高电阻率值的特征，随后通过小型考古挖掘进行了验证。挖掘证实，这个异常对应于一个古老的建筑基础，从30厘米到至少2.0米的深度都可以看到。我们假设这个结构代表了1776年毁灭性地震之前作为堡垒主要入口的吊桥的遗迹，支持了主要大门位于当前大门对面的理论。这项研究突出了地球物理方法作为调查和保护文化遗产的重要工具的有效性和多功能性，揭示了有关堡垒隐藏历史的重要细节。

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引用次数: 0

A data-driven approach for identification of coal-related lithofacies using single and meta-learner ensemble classifiers from well-log data: A case study from Sohagpur coal field, India 利用测井数据中的单学习器和元学习器集成分类器识别煤相关岩相的数据驱动方法：以印度Sohagpur煤田为例

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2025-12-25 DOI: 10.1016/j.jappgeo.2025.106073

Rupam Roy , Dip Kumar Singha , Sayan Ghosh , Laraib Abbas , Debjeet Mondal

This study focuses on building a set of classification models based on single Machine Learning (ML) classifiers, followed by utilization of the trained single classifiers to construct optimized homogeneous, heterogeneous, and stacked ensemble learners to predict coal, carbonaceous shale, and non-coal lithofacies solely based on a set of high resolution conventional well-log data. A total of 6 lithofacies were considered as classes set as the target variable, along with 5 variables as input features obtained from well-log data from 3 wells (well-1, well-2, and well-3) to construct the training data. A couple of wells (well-4, 5) were utilized as blind testing wells to evaluate all 12 classification models, one of them (well-4) having a true litholog. A high inherent imbalance was observed in the class distribution for both the training as well as blind testing datasets. The imbalance issue was resolved by utilizing the class weight parameter assigned to certain classifiers and randomly removing the dominant (the sandstone) class through a random under-sampling operation in Python. The trained single classifiers, despite having overall good performance on blind datasets, were poor at identifying the coal, sandy shale, shaly coal, and carbshale. However, the trained heterogeneous ensemble is proven to be the best classifier among the 12, both class-wise and in terms of overall accuracy (more than 90 %), and the stacked ensemble learner is the second best, as far as coal, carbshale, and shaly coal are concerned. In the case of the homogeneous ensemble learners, the class-wise prediction performance has improved considerably, and all the homogeneous ensemble learners have performed better than their respective single classifier counterparts. This study successfully verified the usefulness of stacked, homogeneous, and heterogeneous ensemble meta learners over the single classifier models, for coal, non-coal, and carbonaceous lithofacies identification, both class-wise and in an overall manner.

本研究的重点是建立一套基于单个机器学习（ML）分类器的分类模型，然后利用训练好的单个分类器构建优化的同质、异质和堆叠集成学习器，仅基于一组高分辨率常规测井数据预测煤、碳质页岩和非煤岩相。将6个岩相作为类集作为目标变量，将5个变量作为从3口井（井1、井2、井3）测井数据中获得的输入特征，构建训练数据。几口井（井4和井5）被用作盲测井，以评估所有12种分类模型，其中一口井（井4）具有真实的岩性。在训练和盲测数据集的类分布中观察到高度固有的不平衡。通过利用分配给某些分类器的类权重参数，并通过Python中的随机欠采样操作随机删除主导（砂岩）类，解决了不平衡问题。尽管训练的单一分类器在盲数据集上具有良好的总体性能，但在识别煤、砂质页岩、页岩煤和碳页岩方面表现不佳。然而，经过训练的异构集成被证明是12个分类器中最好的分类器，无论是在类别方面还是在总体准确率方面（超过90%），而堆叠集成学习器在煤、碳页岩和页岩煤方面排名第二。在同质集成学习器的情况下，类预测性能有了很大的提高，并且所有同质集成学习器都比它们各自的单分类器表现得更好。该研究成功地验证了堆叠、均匀和异构集成元学习器在单一分类器模型上对煤、非煤和碳质岩相识别的有用性，无论是分类还是整体方式。

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引用次数: 0

Horizontal fracture prediction in shale gas reservoirs based on a generalization-enhanced framework integrating rock physics-driven data augmentation and CNN 基于集成岩石物理驱动数据增强和CNN的广义增强框架的页岩气藏水平裂缝预测

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2025-12-24 DOI: 10.1016/j.jappgeo.2025.106075

Xiaodong Zhang, Zhiqi Guo, Cai Liu

Fracture detection is essential for characterizing shale gas reservoirs. Although amplitude variation with azimuth methods are widely applied to predict vertical fractures, identifying horizontal fractures remains challenging due to their complex seismic responses, which differ from azimuthal anisotropy signatures. Quantitative seismic interpretation that integrates rock physics modeling with deep learning provides a promising framework for horizontal fracture prediction. However, the representativeness of available data poses a key limitation in areas with sparse borehole control, constraining the generalization capability of predictive models. A generalization-enhanced framework that combines rock physics-driven data augmentation with convolutional neural networks (CNN) is proposed to address this limitation. A shale-specific rock physics model for horizontal fractures is first established, followed by a model-based inversion scheme to estimate horizontal fracture density from well logs. The estimated fracture densities are then statistically expanded as random variables to generate augmented datasets that simulate spatial variability beyond borehole control. Corresponding elastic properties are computed using the rock physics model, forming physics-constrained datasets for CNN training. Cross-validation results demonstrate that the proposed data augmentation strategy reduces the root-mean-square error (RMSE) of horizontal fracture density estimation by approximately 14 %. Field application further confirms that the augmented model improves consistency with log-derived fracture densities and mitigates spurious anomalies compared with the non-augmented approach. The proposed framework thus provides a physics-guided and data-augmented methodology for robust prediction of horizontal fracture density, offering enhanced fracture characterization in shale gas reservoirs.

裂缝检测是页岩气储层表征的关键。尽管基于方位角的振幅变化方法被广泛应用于预测垂直裂缝，但由于水平裂缝的地震响应复杂，与方位角各向异性特征不同，因此识别水平裂缝仍然具有挑战性。将岩石物理建模与深度学习相结合的定量地震解释为水平裂缝预测提供了一个很有前途的框架。然而，在井眼控制稀疏的地区，可用数据的代表性是一个关键的限制，限制了预测模型的泛化能力。为了解决这一限制，提出了一种将岩石物理驱动的数据增强与卷积神经网络（CNN）相结合的泛化增强框架。首先建立了针对页岩的水平裂缝物理模型，然后采用基于模型的反演方案，根据测井曲线估算水平裂缝密度。然后将估计的裂缝密度作为随机变量进行统计扩展，以生成增强数据集，模拟井眼控制之外的空间变异性。使用岩石物理模型计算相应的弹性特性，形成物理约束的数据集用于CNN训练。交叉验证结果表明，所提出的数据增强策略将水平裂缝密度估计的均方根误差（RMSE）降低了约14%。现场应用进一步证实，与非增强模型相比，增强模型提高了与测井裂缝密度的一致性，并减轻了虚假异常。因此，所提出的框架为水平裂缝密度的可靠预测提供了一种物理指导和数据增强的方法，从而增强了页岩气藏的裂缝表征。

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引用次数: 0

Three-dimensional anisotropic modelling of magnetotelluric data to determine the boundary between cap rock and reservoir formation: A case study of the Sarab field, Iran 利用大地电磁数据的三维各向异性建模来确定盖层与储层之间的边界：以伊朗Sarab油田为例

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2025-12-23 DOI: 10.1016/j.jappgeo.2025.106077

Mohammad Filbandi Kashkouli , Matthew J. Comeau , Milad Farshad , Abolghasem Kamkar-Rouhani

Reservoirs of interest for resource exploration, including geothermal and hydrocarbon reservoirs, commonly have an impermeable cap, which traps fluids below. Identifying this boundary is important for resource development. The cap rock for hydrocarbon reservoirs in southwest Iran contains evaporites and thus some geophysical exploration methods, specifically seismic reflection, have faced problems recovering subsurface information in this environment. As an alternative, we generate an electrical resistivity model from magnetotelluric (MT) data. Furthermore, we consider three-dimensional triaxial electrical anisotropy, which is rarely done. The study objectives are to a) define and map the boundary between the cap rock and the principal reservoir, b) characterize geological and tectonic formations in the area, and c) analyze the tectonic factors influencing the evolution of the region. A total of 359 MT measurements were acquired across the Sarab field in an array consisting of five profiles separated by >2000 m with a measurement spacing of >200 m. Transient electromagnetic (TEM) measurements were co-located with the MT measurements at 181 locations and used to correct for static shifts. Isotropic and anisotropic inversions of the MT data were performed, using all impedance tensor elements. The anisotropic electrical resistivity model exhibits both a significantly better alignment with the depths of geological formations known from drilling data and a better fit to the data. Therefore, the boundary between the primary cap rock and principal reservoir, the Gachsaran and Asmari formations, is defined and mapped across the survey area. In addition, major tectonic and fault-related features in the region are identified.

资源勘探感兴趣的储层，包括地热和油气储层，通常有一个不渗透的盖层，将流体困在下面。确定这一边界对资源开发非常重要。伊朗西南部的油气藏盖层含有蒸发岩，因此一些地球物理勘探方法，特别是地震反射，在这种环境下恢复地下信息面临问题。作为一种替代方法，我们从大地电磁（MT）数据中生成电阻率模型。此外，我们考虑了三维三轴电各向异性，这是很少做的。研究的目的是：a)确定盖层与主储层之间的边界并绘制图；b)描述该地区的地质和构造层；c)分析影响该地区演化的构造因素。整个Sarab油田共获得了359吨的测量数据，这些数据由5条剖面组成，间隔为2000米，测量间距为200米。瞬变电磁（TEM）测量与MT测量在181个位置同时进行，并用于校正静态位移。利用所有阻抗张量元素对大地电磁学数据进行各向同性和各向异性反演。各向异性电阻率模型与钻探资料中已知的地质地层深度具有更好的一致性，并且与数据具有更好的拟合性。因此，确定了主要盖层与主要储层（Gachsaran和Asmari组）之间的边界，并绘制了整个测量区域。此外，还确定了区内主要的构造和断裂特征。

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