Journal of Applied Geophysics最新文献_第3页

Geophysical insights into copper deposits at Mina Seival, Caçapava do Sul, Brazil: 3D magnetic inversions and euler deconvolution 巴西南卡帕拉帕瓦Mina Seival铜矿的地球物理研究：三维磁反演和欧拉反褶积

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2026-03-01 Epub Date: 2026-01-14 DOI: 10.1016/j.jappgeo.2026.106096

Marieli Machado Zago , Maximilian Fries

Copper deposits are critical resources for modern industries, particularly in the transition toward clean energy technologies, electric vehicles, and digital infrastructure. In southern Brazil, the Lavras do Sul–Caçapava do Sul region represents a metallogenic province that has been extensively studied since the nineteenth century, hosting significant copper and gold occurrences. These deposits are commonly associated with volcanic rocks of the Hilário Formation, which play a central role in the regional mineralization processes. Although structural controls and hydrothermal alteration patterns have been previously documented, the three-dimensional geometry and connectivity of mineralized zones at depth remain insufficiently constrained. This study investigates the geophysical signature of copper mineralization within the Hilário Formation using 3D inversion of aeromagnetic data integrated with structural and geological information. Magnetic enhancement techniques such as the Tilt-angle derivative, Analytic Signal (AS), and Euler Deconvolution were applied to improve the detection of subsurface structures and magnetic sources. Additionally, Magnetization Vector Inversion (MVI) was employed to refine the delineation of magnetic bodies associated with mineralization. The integrated analysis revealed NE- and NW-trending fault systems as the dominant structural frameworks influencing copper mineralization. Magnetic lows near the surface, interpreted as hydrothermal alteration zones, were found overlying deeper magnetic highs related to magnetite-rich and potentially sulfide-bearing zones. The combined application of Euler Deconvolution and MVI produced consistent results that correlate well with known geological features, improving subsurface interpretation and reducing uncertainty in the modeling of mineralized bodies. Overall, the results demonstrate the effectiveness of integrating advanced geophysical techniques with geological and structural datasets for copper exploration. The proposed workflow enhances interpretive confidence, supports target delineation, and provides a robust framework for future exploration in the region.

铜矿是现代工业的关键资源，尤其是在向清洁能源技术、电动汽车和数字基础设施转型的过程中。在巴西南部，lalavas do Sul - carapava do Sul地区是一个成矿省，自19世纪以来，人们对该地区进行了广泛的研究，发现了大量的铜和金矿床。这些矿床通常与Hilário组火山岩伴生，在区域成矿过程中起中心作用。尽管构造控制和热液蚀变模式已经被记录下来，但深部矿化带的三维几何形状和连通性仍然没有得到充分的限制。利用航磁数据三维反演，结合构造和地质信息，研究了Hilário组内铜成矿的地球物理特征。利用倾斜导数、解析信号（as）和欧拉反褶积等磁增强技术改进地下结构和磁源的探测。此外，利用磁化矢量反演（MVI）对矿化相关磁体进行了精细圈定。综合分析表明，NE向断裂和nw向断裂是影响铜矿化的主要构造格架。地表附近的磁低被解释为热液蚀变带，其上覆的磁高与富磁铁矿和潜在含硫化物带有关。欧拉反褶积和MVI的结合应用产生了与已知地质特征相关性良好的一致结果，提高了地下解释，减少了矿化体建模的不确定性。综上所述，研究结果证明了先进地球物理技术与地质构造数据相结合在铜矿勘查中的有效性。提出的工作流程提高了解释的可信度，支持目标描述，并为该地区未来的勘探提供了一个强大的框架。

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引用次数: 0

Interferometric synthetic aperture sonar for high-resolution seafloor mapping and imaging in contrasting geomorphological and benthic settings 干涉式合成孔径声纳在对比地貌和底栖环境中的高分辨率海底测绘和成像

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2026-03-01 Epub Date: 2026-01-01 DOI: 10.1016/j.jappgeo.2025.106087

Caroline Gini , John W. Jamieson , Craig J. Brown , Brian Carroll , Richard Charron , David Shea , Katleen Robert

Seafloor exploration for geological or biological studies ideally requires high resolution survey data over a large area. However, there is a trade-off between resolution and coverage using conventional acoustic imaging and mapping techniques. The recent development of interferometric synthetic aperture sonar (InSAS), which provides high resolution imagery and bathymetry (3 and 25 cm/pixel, respectively) over large areas of seafloor, and has primarily been used for military and commercial purposes, opens the door for new applications for geological mapping, and seafloor classification and monitoring. For these applications, the processing steps, survey parameters and requirements for InSAS surveying, compared to conventionally used techniques such as multibeam echosounders and side-scan sonars, are not well defined. In this study, we describe and discuss the results of InSAS surveys in two contrasting geological and benthic settings: a relatively flat continental shelf, and a topographically complex mid-ocean ridge. Features of sizes down to 6 cm were identified on the imagery, including lava flow crust lineations, bedrock sedimentary bedding, gravels, and discarded rope. We found that seafloor features <1 m high were better imaged than taller features, such as hydrothermal vents or faults. We test and quantify survey parameters necessary to optimize data quality for effective use for scientific applications. Our results indicate that seafloor bathymetry is the most important consideration to maximize likelihood of data generation success and data quality when planning InSAS surveys.

地质或生物研究的海底勘探理想地需要大面积的高分辨率调查数据。然而，在使用常规声学成像和制图技术的分辨率和覆盖范围之间存在权衡。干涉合成孔径声纳（InSAS）的最新发展，在海底大面积提供高分辨率图像和测深（分别为3和25厘米/像素），主要用于军事和商业目的，为地质测绘和海底分类和监测的新应用打开了大门。对于这些应用，与传统技术（如多波束回声测深仪和侧扫声纳）相比，InSAS测量的处理步骤、测量参数和要求并没有很好地定义。在本研究中，我们描述并讨论了InSAS在两种不同地质和底栖环境下的调查结果：相对平坦的大陆架和地形复杂的洋中脊。在图像上识别出小至6厘米的特征，包括熔岩流地壳线、基岩沉积层理、砾石和丢弃的绳索。我们发现，1米高的海底特征比更高的特征（如热液喷口或断层）成像更好。我们测试和量化必要的调查参数，以优化数据质量，有效地用于科学应用。我们的研究结果表明，在规划InSAS调查时，海底测深是最大限度地提高数据生成成功率和数据质量的最重要考虑因素。

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引用次数: 0

Research on multi-physics field collaborative detection methods for concealed fire zones: Based on variable-temperature magnetic-dielectric-resistive characteristics 隐火区多物理场协同探测方法研究——基于变温磁介电阻特性

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2026-03-01 Epub Date: 2026-01-08 DOI: 10.1016/j.jappgeo.2025.106091

Xiaokun Zhao , Jun Ge , Wencai Wang , An Zhang , Yuhua Bai , Yong Liu , Weixia Fu

Accurate detection of hidden coal fire areas is crucial for early warning and disaster mitigation, yet single-physics methods often have limitations in scope and applicability. This study proposes a multi-physics cooperative detection approach based on temperature variations, integrating magnetic, dielectric, and resistivity measurements, and verifies it on a large-scale physical simulation platform. The results show that as the fire temperature increases (500–700 °C), the magnetic susceptibility of coal and rocks significantly enhances, raising the magnetic anomaly intensity from 1100nT to 1600nT, effectively delineating fire boundaries. Variable-temperature dielectric measurements reveal a three-stage evolution pattern, with burned-out zones exhibiting reduced permittivity, causing polarity inversion of ground-penetrating radar (GPR) reflections, which can be effectively identified in the 20–24 ns time window. High-density resistivity surveys indicate a distinct transition from high-resistivity anomalies (∼10^5Ω·m) at ambient conditions to low-resistivity anomalies (50 - 200 Ω·m) at elevated temperatures, with inversion results consistent with forward modeling. The integration of magnetic, dielectric, and resistivity methods demonstrates strong complementarity in boundary delineation, void detection, and spatial inversion, ultimately achieving precise localization of fire centers. This study establishes a cooperative multi-physics detection framework for hidden coal fires, providing a new technical approach for integrated detection, disaster early warning, and fire control design, with potential applicability in geothermal monitoring and hydrocarbon leakage detection.

准确探测煤火隐伏区对早期预警和减灾至关重要，但单一物理方法在范围和适用性方面往往存在局限性。本研究提出了一种基于温度变化的多物理场协同探测方法，将磁、介电和电阻率测量相结合，并在大型物理模拟平台上进行了验证。结果表明：随着火区温度的升高（500 ~ 700℃），煤岩磁化率显著增强，磁异常强度由1100nT提高到1600nT，有效圈定了火区边界；变温介质测量揭示了一个三阶段的演化模式，烧毁区呈现出降低的介电常数，导致探地雷达（GPR）反射的极性反转，这可以在20-24 ns时间窗内有效识别。高密度电阻率测量表明，从环境条件下的高电阻率异常（~ 10^5Ω·m）到高温下的低电阻率异常（50 - 200 Ω·m）有明显的转变，反演结果与正演模拟一致。磁法、介电法和电阻法相结合，在边界圈定、空洞探测和空间反演等方面具有很强的互补性，最终实现了火点的精确定位。本研究建立了煤隐火多物理场协同探测框架，为综合探测、灾害预警和消防设计提供了新的技术途径，在地热监测和油气泄漏探测中具有潜在的适用性。

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引用次数: 0

Analysis of deep structures and key factors for oil and gas accumulation in the Erjiacun Depression of South Poyang Basin based on magnetotelluric sounding imaging 基于大地电磁测深成像的南阳盆地二家村凹陷深部构造及油气成藏关键因素分析

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2026-03-01 Epub Date: 2026-01-02 DOI: 10.1016/j.jappgeo.2025.106088

Ronghua Xu , Juzhi Deng , Fasheng Lou , Yang Gao , Zequn Wen , Hui Yu

South Poyang Basin is an important hydrocarbon exploration block in the Lower Yangtze region. The Upper Paleozoic strata are widely distributed with active hydrocarbon anomalies. However, industrial hydrocarbon flow has yet to be achieved. The damage caused by shallow structural adjustments and the lack of clarity in deep geological structures are considered significant factors constraining breakthroughs in oil and gas exploration. This study performed a 2D nonlinear conjugate gradient inversion on 30 magnetotelluric (MT) data points from the north-south opposing thrust area in the Erjia Depression of South Poyang Basin and successfully constructed a detailed resistivity model extending to a depth of 6 km. Then the hydrocarbon accumulation patterns and favorable exploration directions were analyzed by combining imaging results with 2D seismic profiles and previous borehole logs. ‌The results reveal that the Erjia Depression has undergone multiple tectonic events, forming a typical imbricate thrust system. Within this system, the F₂ structure is characterized by low-angle thrusting from NW to SE, extending approximately 12.5 km along the NE direction, with its formation period preliminarily constrained to the Indosinian. A stable median apparent resistivity anomaly beneath the thrust nappe suggests the footwall of the nappe is less influenced by structural disruption and the potential presence of an in-situ stratum dating from the Late Permian to Middle Carboniferous (P₃-C₂). Within this sequence, the Middle Permian carbonate rocks possess key elements for hydrocarbon accumulation: high-quality source rocks, favorable reservoir properties, and ideal burial conditions.The results can provide crucial constraints on the electrical structure and introduce a new technical approach for the deep hydrocarbon exploration in Southern Poyang Basin.

南阳盆地是下扬子地区重要的油气勘探区块。上古生界地层分布广泛，油气异常活跃。然而，工业烃类流动尚未实现。浅层构造调整造成的破坏和深部地质构造不清晰被认为是制约油气勘探取得突破的重要因素。对南阳盆地二家坳陷南北逆冲区30个大地电磁资料进行二维非线性共轭梯度反演，成功建立了延伸至6 km深度的详细电阻率模型。结合成像结果、二维地震剖面和以往测井资料，分析了油气成藏模式和有利勘探方向。研究结果表明，二家坳陷经历了多次构造事件，形成了典型的叠瓦状逆冲构造体系。在该体系内，F2构造以NW - SE低角度逆冲为特征，沿NE方向延伸约12.5 km，其形成期初步限定在印支期。逆冲推覆体下方稳定的中位视电阻率异常表明推覆体下盘受构造断裂的影响较小，可能存在晚二叠世至中石炭世（P3-C2）的原位地层。在该层序中，中二叠统碳酸盐岩具有高质量的烃源岩、有利的储层性质和理想的埋藏条件，是油气成藏的关键要素。研究结果可为鄱阳盆地南部深层油气勘探提供重要的电性构造约束，并为该区深部油气勘探提供新的技术途径。

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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 : 2026-03-01 Epub 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

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 : 2026-03-01 Epub 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

Triple-duty distributed acoustic sensing in urban environments: Concurrent subsurface imaging, pipeline diagnostics, and traffic surveillance 城市环境中的三任务分布式声学传感：并发地下成像、管道诊断和交通监控

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2026-03-01 Epub Date: 2026-01-20 DOI: 10.1016/j.jappgeo.2026.106117

Ao Song , Aichun Liu , Zhixiang Li , Guanzhong Liu , Aipeng Guo , Junfeng Jiang

This study presents an innovative application of Distributed Acoustic Sensing (DAS) by repurposing urban sewer pipelines into a large-scale sensing network through the deployment of fiber-optic cables. This approach facilitates three major applications: subsurface imaging, pipeline blockage detection, and urban traffic monitoring. Using passive seismic interferometry on ambient noise signals acquired via the in-pipe fiber, we reconstructed high-resolution shear-wave velocity profiles of the shallow urban subsurface. Combined analysis of field data and numerical simulations identified characteristic patterns associated with pipeline blockages in cross-correlations (CCs), which were validated through closed-circuit television (CCTV) inspections. For traffic monitoring, vehicle-induced vibrations were processed using seismic attribute analysis and a U-Net convolutional neural network, enabling precise vehicle trajectory identification and speed estimation based on Hilbert instantaneous amplitude attributes. The results demonstrate that the proposed DAS-based method offers a non-invasive, cost-effective, and scalable solution for integrated urban monitoring, providing a sustainable alternative to traditional point-based sensing and enabling continuous, large-scale infrastructure assessment in densely populated areas.

本研究提出了分布式声学传感（DAS）的创新应用，通过部署光纤电缆，将城市下水道管道改造成大规模传感网络。这种方法促进了三个主要应用：地下成像、管道堵塞检测和城市交通监控。利用被动地震干涉测量技术对管道内光纤采集的环境噪声信号进行处理，重建了城市浅层地下的高分辨率横波速度剖面。现场数据分析和数值模拟相结合，确定了相互关联（cc）管道堵塞的特征模式，并通过闭路电视（CCTV）检查进行了验证。在交通监控方面，利用地震属性分析和U-Net卷积神经网络对车辆引起的振动进行处理，实现基于Hilbert瞬时振幅属性的精确车辆轨迹识别和速度估计。结果表明，本文提出的基于das的方法为城市综合监测提供了一种无创、经济、可扩展的解决方案，为传统的基于点的传感提供了一种可持续的替代方案，并能够在人口密集地区进行连续的大规模基础设施评估。

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引用次数: 0

High-resolution elastic full-waveform inversion using dual-channel CNN and Kolmogorov–Arnold network 基于双通道CNN和Kolmogorov-Arnold网络的高分辨率弹性全波形反演

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2026-03-01 Epub Date: 2026-01-12 DOI: 10.1016/j.jappgeo.2026.106095

Faxuan Wu, Yang Li, Zhenwu Fu, Bo Han, Yong Chen

Elastic full-waveform inversion (EFWI) can provide high-resolution subsurface structures and physical properties by iteratively matching observed and synthetic data. However, the success of EFWI relies on the availability of a good initial model and high signal-to-noise ratio observed data with sufficient low-frequency information, both of which are often challenging to obtain in practical applications. In addition, the coupling of different parameters degrades the inversion result. Recently, inversion methods based on physics-informed deep neural networks (DNN) have proven effective in mitigating the issue of multiple local minima caused by inaccurate initial models, missing low-frequency information, and noisy seismic data. However, existing DNN-based approaches commonly rely on fixed activation functions (e.g., rectified linear unit). In addition, their capacity to represent high-frequency components – namely, fine-scale structural details – is inherently limited due to spectral bias. These limitations may, in turn, impede their broader applicability. To mitigate this issue, we propose a model reparameterized EFWI method based on a dual-channel convolutional neural network (CNN) and Kolmogorov–Arnold networks (KAN) to enhance the reconstruction of fine-scale structural details. Specifically, our network incorporates KAN into the U-Net architecture, where CNN and KAN operate in dual channels to efficiently capture nonlinear relationships in the data. The hybrid network maps an initial model to the subsurface parameter model, with the output of the network serving as input for partial differential equations (PDEs) to generate synthetic data. Various numerical examples are conducted to investigate the performance of the inversion method, including its ability to mitigate the parameter crosstalk issue, the effect of noise and missing low-frequency information, and the influence of different initial models and network inputs. The numerical results demonstrate that, by combining CNN’s fixed activation functions with KAN’s inherently learnable activations, our method – despite a modest increase in computational cost – outperforms both EFWI and CNN-based reparameterized EFWI in reconstruction accuracy and convergence efficiency.

弹性全波形反演（EFWI）可以通过迭代匹配观测数据和合成数据来提供高分辨率的地下结构和物理性质。然而，EFWI的成功依赖于良好的初始模型和具有足够低频信息的高信噪比观测数据的可用性，这两者在实际应用中往往难以获得。此外，不同参数的耦合会降低反演结果。最近，基于物理信息的深度神经网络（DNN）的反演方法被证明可以有效地缓解由初始模型不准确、低频信息缺失和地震数据噪声引起的多个局部最小值问题。然而，现有的基于dnn的方法通常依赖于固定的激活函数（例如，整流线性单元）。此外，由于频谱偏倚，它们表示高频成分（即精细尺度结构细节）的能力本身就受到限制。这些限制可能反过来阻碍其更广泛的适用性。为了解决这个问题，我们提出了一种基于双通道卷积神经网络（CNN）和Kolmogorov-Arnold网络（KAN）的模型重参数化EFWI方法，以增强精细尺度结构细节的重建。具体来说，我们的网络将KAN整合到U-Net架构中，其中CNN和KAN在双通道中运行，以有效捕获数据中的非线性关系。混合网络将初始模型映射到地下参数模型，网络的输出作为偏微分方程（pde）的输入，以生成合成数据。通过各种数值算例研究了该反演方法的性能，包括其缓解参数串扰问题的能力，噪声和低频信息缺失的影响，以及不同初始模型和网络输入的影响。数值结果表明，通过将CNN的固定激活函数与KAN的固有可学习激活相结合，我们的方法在重建精度和收敛效率方面优于EFWI和基于CNN的重参数化EFWI，尽管计算成本有所增加。

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引用次数: 0

Petrophysics and mass balance integration for alteration detection in orogenic gold exploration 造山带金矿蚀变探测中的岩石物理与物质平衡集成

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2026-03-01 Epub Date: 2026-01-24 DOI: 10.1016/j.jappgeo.2026.106114

Yasaman Nemati , J. Christian Dupuis , Benoit Quesnel , Bernard Giroux , Richard Smith , Georges Beaudoin

This study explores the integration of petrophysical and geochemical data to characterize hydrothermal alteration in mafic and ultramafic rocks, with a focus on the Augmitto-Bouzan orogenic gold deposit in the Abitibi Greenstone Belt. Employing established mass balance techniques using zirconium (Zr) as the immobile reference element, we quantified element mobility during alteration processes. Due to the absence of original komatiite protolith data, we compiled geochemical compositions from published sources across the Superior Province and applied bootstrapping to derive a representative baseline for mass balance calculations, which were then correlated with petrophysical logs from multiple boreholes to identify modification signatures. The analysis reveals robust correlations: potassium enrichment aligns with elevated gamma-ray responses, carbonate alteration is marked by increased CaO and reduced density, and magnetic susceptibility decreases correspond to Fe-Mg depletion and sulfide mineralization. These observations demonstrate systematic links between geochemical changes and petrophysical data, showing that petrophysical logs can serve as high-resolution, cost-effective proxies for alteration mapping, offering a scalable framework for exploration targeting in orogenic gold systems hosted in ultramafic rocks. The methodology bridges traditional geochemistry and in-situ measurements of physical properties, improving subsurface characterization and supporting the development of data-driven exploration tools while advancing our understanding of processes associated with gold mineralization.

本文以阿比提比绿岩带的Augmitto-Bouzan造山带金矿为研究对象，结合岩石物理和地球化学资料，探讨了基性岩和超基性岩热液蚀变特征。采用已建立的质量平衡技术，以锆（Zr）作为固定参考元素，我们量化了蚀变过程中的元素迁移率。由于缺乏原始的科马地岩原岩数据，我们从苏必利尔省的公开资料中收集了地球化学成分，并应用bootstrapping获得了一个具有代表性的质量平衡计算基线，然后将其与多个井眼的岩石物理测井相关联，以识别变质特征。分析表明，钾的富集与伽马射线响应的升高有关，碳酸盐蚀变的特征是CaO的增加和密度的降低，磁化率的降低与Fe-Mg的耗尽和硫化物矿化有关。这些观测结果证明了地球化学变化与岩石物理数据之间的系统联系，表明岩石物理测井可以作为高分辨率、高成本效益的蚀变填图代理，为超基性岩石中的造山带金系统的勘探目标提供了可扩展的框架。该方法将传统的地球化学和物理性质的原位测量相结合，改善了地下特征，并支持数据驱动勘探工具的开发，同时提高了我们对金矿化过程的理解。

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引用次数: 0

A proposed brittleness index model based on shale multicomponent digital cores 基于页岩多组分数字岩心的脆性指数模型

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2026-03-01 Epub Date: 2026-01-21 DOI: 10.1016/j.jappgeo.2026.106110

Haitao Wang , Fuqiang Lai , Min Wang , Zhaohui Huang , Zhangxiong Zhu , Yuejiao Liu , Xianfeng Tan , Huaimin Dong , Ye Yang

The exploitation of organic shale reservoirs depends on the brittleness affected by lamination, fracture, porosity, and minerals. However, the existing brittleness index for petroleum only considers mineral types and requires comprehensive characterization. Therefore, a multicomponent model was constructed by inserting a planar fracture, planar lamination, and gas saturation model into a multimineral model using the Markov-chain Monte Carlo method. The elastic parameters were calculated using a finite element method, and the sensitivity of the parameters to the brittleness index defined by the Young's modulus and Poisson's ratio was determined using Morris screening and analytical hierarchy process methods. The investigation indicated that the development of brittle minerals and fracture porosity increased the brittleness index and, Young's modulus and decreased the Poisson's ratio, whereas the fracture width decreased the brittleness index, Gas saturation increases the brittleness index. A new comprehensive brittleness index was proposed in which the sensitivity decreased from the dip angle, azimuthal angle of fracture, dip angle of lamination, width and length of fracture, mineral types, width, and azimuthal angle of lamination to gas saturation. The proposed brittleness model was applied to a shale reservoir and is helpful in further identifying zones for hydraulic fracturing.

有机页岩储层的开发取决于层理、裂缝、孔隙度和矿物对其脆性的影响。然而，现有的石油脆性指标只考虑矿物类型，需要综合表征。为此，采用马尔可夫链蒙特卡罗方法，将平面裂缝、平面层合和含气饱和度模型插入到多矿物模型中，构建了多组分模型。采用有限元法计算弹性参数，采用Morris筛选法和层次分析法确定弹性参数对杨氏模量和泊松比定义的脆性指数的敏感性。研究表明，脆性矿物和裂缝孔隙度的发育使脆性指数和杨氏模量增大，泊松比减小，裂缝宽度减小脆性指数，含气饱和度增大脆性指数。提出了一种新的综合脆性指标，其敏感性从倾角、裂缝方位角、层状倾角、裂缝宽度和长度、矿物类型、层状宽度和方位角对含气饱和度的敏感性依次递减。将所提出的脆性模型应用于页岩储层，有助于进一步确定水力压裂区域。

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引用次数: 0