Journal of Applied Geophysics最新文献_第2页

Higher-order total-variation regularization methods for 3D inversion of gravity data 重力数据三维反演的高阶全变分正则化方法

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.2026.106123

Mohammad Rezaie

Inverting gravity data is an important geophysical method for imaging underground structures, but it naturally suffers from non-uniqueness. To produce solutions that make geological sense, some form of regularization is necessary. This paper presents an improved methodology for 3D gravity data inversion using higher-order total variation regularization techniques. Traditional first-order TV regularization has proved effective for blocky geological structures with sharp contrasts but often fails when the subsurface features are complex. We propose second and third-order TV regularization schemes that have the strengths of classical TV but provide much greater flexibility in handling more general geological contexts. The optimization problem including these higher-order regularization terms is solved by the reweighted regularized conjugate gradient algorithm, carefully addressing the parameters selection and focusing strategies. We demonstrate the effectiveness of our approach through synthetic examples, including a dipping dyke model and a multiple blocks scenario, where the higher-order TV methods give better performance for the reconstruction of subsurface structures compared to the minimum norm and first-order TV regularization. Then, the methodology was further validated using field data from the Thunderbird V-Ti-Fe deposit, Ontario, Canada, where the inversion of airborne gravity gradiometer data successfully revealed subsurface density distributions well-matched by the known geological and drilling data. The results illustrate that the higher-order TV regularization produces a more focused and geologically plausible model, especially in resolving complex structures at different levels. Current advances in inversion methodology are offering superior performance for applications in mineral exploration and geological studies.

反演重力数据是地下构造成像的重要物探方法，但其存在非唯一性的缺陷。为了产生具有地质学意义的解决方案，某种形式的正则化是必要的。提出了一种利用高阶全变分正则化技术进行三维重力数据反演的改进方法。传统的一阶电视正则化方法对对比强烈的块状地质构造是有效的，但在地下特征复杂的情况下往往失效。我们提出了二阶和三阶电视正则化方案，它们具有经典电视的优点，但在处理更一般的地质背景时提供了更大的灵活性。采用重加权正则化共轭梯度算法求解包含高阶正则化项的优化问题，仔细处理参数选择和聚焦策略。我们通过综合实例证明了该方法的有效性，包括倾斜岩脉模型和多区块场景，其中高阶电视方法与最小范数和一阶电视正则化相比，在地下结构重建方面具有更好的性能。然后，使用加拿大安大略省雷鸟V-Ti-Fe矿床的现场数据进一步验证了该方法，在那里，航空重力梯度数据的反演成功地揭示了地下密度分布，与已知的地质和钻井数据非常匹配。结果表明，高阶电视正则化产生了一个更集中和地质上更合理的模型，特别是在不同层次上解决复杂构造。目前反演方法的发展为矿产勘探和地质研究提供了优越的性能。

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引用次数: 0

Geophysical survey of the medieval Castle in Sławków, Poland: Insights from ERT and EM 波兰Sławków中世纪城堡的地球物理调查：来自ERT和EM的见解

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2026-03-01 Epub Date: 2026-01-19 DOI: 10.1016/j.jappgeo.2026.106118

Maciej J. Mendecki , Rafał Warchulski , Mateusz Kicza

This study presents Electrical Resistivity Tomography (ERT) and Electromagnetic Induction (EMI) measurements conducted in two areas: the Kraków Bishops' Castle (area A1) and the Municipal Park (area A2) in Sławków. ERT data are displayed as cross-sections, while EMI data are mapped. A reference resistivity of 350 Ωm was established for natural geological substrates. Anomalies exceeding this threshold suggest anthropogenic origins, including remnants of the Bishops' Castle. In A1, ERT profiles ERT1–ERT3 revealed high-resistivity anomalies linked to rock fragments, possible tunnels, and castle walls; shallower ones (<2 m) were interpreted cautiously due to natural effects or artifacts. In A2, ERT4–ERT7 profiles indicated embankments, rock fragments, and inferred defensive structures. EMI confirmed anomalies: two subsurface features inside the castle near NE and SW walls (potential metallic objects or a well).

Extended verification analyzed ERT statistical analysis (RMS, χ², residual analysis, observed vs. interpreted scatter plots), Depth of Investigation Index (DOI), and for EMI data analysis (spatial data analysis, variograms, EMI-derived resistivity, in-phase difference maps, and EMI data cross-validation), emphasizing careful interpretation under complex geological-anthropogenic conditions. The study refines archaeological geophysics practices, optimizing techniques for varied materials and site histories.

本研究介绍了在两个地区进行的电阻率断层扫描（ERT）和电磁感应（EMI）测量：Sławków的Kraków主教城堡（A1区）和市政公园（A2区）。ERT数据显示为横截面，而EMI数据显示为映射。建立了自然地质基底的参考电阻率为350 Ωm。超过这个阈值的异常表明人类活动的起源，包括主教城堡的遗迹。在A1区，ERT剖面ERT1-ERT3显示了与岩石碎片、可能的隧道和城堡墙壁相关的高电阻率异常；由于自然影响或人为因素，较浅的（2米）被谨慎地解释。在A2中，ERT4-ERT7剖面显示了堤防、岩石碎片和推断的防御结构。电磁干扰证实了异常：城堡内靠近东北和西南墙壁的两个地下特征（潜在的金属物体或井）。扩展验证分析了ERT统计分析（均方根、χ2、残差分析、观测与解释散点图）、调查深度指数（DOI）和电磁干扰数据分析（空间数据分析、变异图、电磁干扰衍生电阻率、同相差图和电磁干扰数据交叉验证），强调了在复杂地质-人为条件下的仔细解释。该研究改进了考古地球物理实践，优化了不同材料和遗址历史的技术。

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引用次数: 0

Landslide susceptibility assessment in a reservoir area using integrated models based on time-series InSAR 基于时间序列InSAR综合模型的库区滑坡易感性评价

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.2026.106115

Xiaoliang Xu , Yu He , Huifang Liu , Quan Shi , Xinlong Yao , Kaiyu Tang

Landslide disasters pose a serious threat to human life and property, and landslide susceptibility assessment (LSA) is a core technical approach for landslide risk prevention and control. Conventional LSA methods face challenges in efficiently extracting features and accurately classifying multi source data, and they often lack temporal responsiveness. This study proposes a multi model fusion LSA framework that integrates interferometric synthetic aperture radar (InSAR) data. The framework combines convolutional neural networks with tree based models, incorporates dynamic surface deformation data derived from InSAR inversion, and conducts joint modeling using 14 environmental factors covering topography, geology, hydrology, and human activities. In addition, the SHAP method is employed to provide an interpretable analysis of the model decision mechanisms. The results indicate that elevation, distance to rivers, slope, and surface deformation rate are the key driving factors for landslide occurrence. Among the six comparative models, the XGBoost–VGG fusion model achieves the best performance, with overall results significantly superior to other single and fusion models. Although incorporating the surface deformation factor slightly reduces the overall performance of the models, it substantially enhances their temporal responsiveness. The proposed timeliness oriented, model fusion based LSA approach provides scientific support for landslide risk assessment and demonstrates the practical engineering value of coupling model fusion techniques with dynamic surface deformation data in LSA applications.

滑坡灾害对人类生命财产造成严重威胁，滑坡易感性评价是滑坡风险防控的核心技术手段。传统的LSA方法在高效提取特征和准确分类多源数据方面面临挑战，而且往往缺乏时间响应性。本文提出了一种融合干涉合成孔径雷达（InSAR）数据的多模型融合LSA框架。该框架将卷积神经网络与基于树的模型相结合，结合InSAR反演的地表动态变形数据，结合地形、地质、水文和人类活动等14个环境因子进行联合建模。此外，采用SHAP方法对模型决策机制进行了可解释性分析。结果表明，高程、与河流的距离、坡度和地表变形速率是滑坡发生的关键驱动因素。在6个比较模型中，XGBoost-VGG融合模型的性能最好，整体效果明显优于其他单一模型和融合模型。虽然纳入地表变形因子会略微降低模型的整体性能，但它大大提高了模型的时间响应性。提出的基于时效性、模型融合的LSA方法为滑坡风险评估提供了科学支持，并展示了模型融合技术与动态地表变形数据耦合在LSA应用中的实际工程价值。

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引用次数: 0

Meta-transfer learning for efficient initialization of neural network wavefield solutions 神经网络波场解有效初始化的元迁移学习

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.2026.106116

Zhijun Cheng , Xiang Wang , Guojun Mao , Weijian Mao , Shijun Cheng

Meta-learning-based physics-informed neural networks (Meta-PINN) show significant advantages in solving seismic wave equations across multi-velocity models, where model-agnostic meta-learning (MAML) algorithm is used to learn a shared initialization. The learned initialization helps physics-informed neural networks (PINNs) rapidly adapt to new seismic velocity models. However, the dual-loop optimization mechanism of MAML significantly increases meta-training cost. To address this issue, we introduce the idea of transfer learning into the meta-learning algorithm to reduce the computational burden during the meta-training stage. Specifically, we optimize the meta-model by performing fast gradient updates for a single velocity model on the support set, and then employing a parameter averaging strategy across multiple velocity models on the query set, and the resulting initialization is used for regular training of the new velocity model. Experimental results on diverse velocity models demonstrate that, compared to the conventional Meta-PINN, our method can provide a slightly faster convergence speed and, also, significantly reduce the meta-training time.

基于元学习的物理信息神经网络（Meta-PINN）在求解跨多速度模型的地震波方程方面具有显著优势，其中模型不可知元学习（MAML）算法用于学习共享初始化。学习的初始化有助于物理信息神经网络（pinn）快速适应新的地震速度模型。然而，MAML的双环优化机制显著增加了元训练成本。为了解决这个问题，我们在元学习算法中引入迁移学习的思想，以减少元训练阶段的计算负担。具体来说，我们通过在支持集上对单个速度模型进行快速梯度更新来优化元模型，然后在查询集上采用跨多个速度模型的参数平均策略，并将结果初始化用于新速度模型的常规训练。在不同速度模型上的实验结果表明，与传统的Meta-PINN相比，我们的方法可以提供略快的收敛速度，并显著减少元训练时间。

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引用次数: 0

Real-time wave velocity evolution and thermal damage development in hollow-sphere granite: Insights from progressive heating experiments 空心球体花岗岩的实时波速演化和热损伤发展：来自渐进加热实验的见解

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.2026.106105

Zixin Huang , Qiaomu Luo , Longjun Dong , Longbin Yang , Shenglan Li , Xuewei Li

Thermal effects influence rock stability in geothermal extraction and deep engineering. Wave velocity, as a key physical indicator, provides essential insights into these thermal-induced structural and mechanical changes. This study focused on the behavior and evolution of wave velocity in granite under high-temperature conditions, which is essential for assessing the stability of deep geothermal areas. A novel experimental setup was developed using an internal point heat source to simulate deep geothermal conditions while synchronously recording active ultrasonic pulse signals and temperature data. The analysis includes the anisotropy, temperature sensitivity, attenuation behavior, and spatiotemporal evolution of wave velocity. The results reveal that wave velocity exhibits pronounced anisotropy within the granite, intensifying with increasing temperature. Wave velocity shows a fluctuating decay pattern, with a stable-accelerated-stable trend in cycles of approximately 10 °C before 80 °C. The attenuation was most considerable at the initial heating phase and beyond 80 °C. Using a piecewise linear decay model, we identified critical transition points in the attenuation behavior, marked by a notable change in decay rate before and after the points. Tomographic imaging visualizes the spatial-temporal evolution of the wave velocity field, highlighting localized thermal damage and progressive crack development. The findings provide insights into early instability warnings in deep geological environments and offer theoretical and technical support for the safe extraction of deep mineral and geothermal resources.

在地热开采和深部工程中，热效应影响岩石的稳定性。波速作为一个关键的物理指标，提供了对这些热引起的结构和机械变化的基本见解。研究花岗岩在高温条件下的波速变化特征及其演化规律，对深部地热稳定性评价具有重要意义。建立了一种新型实验装置，利用内部点热源模拟深层地热条件，同时同步记录主动超声脉冲信号和温度数据。分析了波速的各向异性、温度敏感性、衰减特性和时空演化。结果表明，波速在花岗岩内部表现出明显的各向异性，并随着温度的升高而增强。波速呈现波动衰减模式，在80°C之前的大约10°C周期中具有稳定-加速-稳定的趋势。在初始加热阶段和80℃以上，衰减最为显著。使用分段线性衰减模型，我们确定了衰减行为中的关键过渡点，标志着衰减率在点前后的显着变化。层析成像可视化了波速场的时空演变，突出了局部热损伤和渐进裂纹的发展。研究结果为深部地质环境的早期不稳定预警提供了依据，为深部矿产和地热资源的安全开采提供了理论和技术支持。

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引用次数: 0

Strain rate and confining pressure effects in micro-nano carbon fiber-reinforced grout: an SHPB impact study 应变速率和围压对微纳碳纤维增强浆液的影响：SHPB影响研究

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2026-03-01 Epub Date: 2026-01-17 DOI: 10.1016/j.jappgeo.2026.106111

Changxing Zhu, Duo Li, Dazhi Wu, Jiaxin Huo

To mitigate disasters such as sand inrush and water gushing potentially induced by sand layer geology in underground engineering, carbon fiber-reinforced grouting materials demonstrate promising potential for remediation. However, in practical engineering, static and dynamic loads often act on surrounding rock in combination, and existing research on this aspect remains limited. To address this, this study employs micro-nano carbon fibers to modify ultrafine cement-based grouting material. Laboratory grouting reinforcement tests were conducted on graded sand layers. Using a Split Hopkinson Pressure Bar (SHPB) equipped with an active confining pressure device, systematic dynamic compression tests were performed under various impact velocities (corresponding to strain rates of 49 to 92 s⁻¹) and confining pressures (0 to 8 MPa). The results indicate that the peak stress of the specimens increases with the strain rate, exhibiting a significant strain rate effect. Under different impact velocities and confining pressures, the peak stress of fiber-containing specimens was significantly higher than that of plain specimens, confirming the enhancing and toughening effect of carbon fibers. When the confining pressure increased to 8 MPa, the peak stress of fiber-containing specimens was approximately 1.56 times higher than that under unconfined conditions, and the failure mode transitioned from tensile splitting to crushing failure. Microscopically, carbon fibers effectively inhibit crack propagation and enhance energy absorption capacity through “micro-reinforcement” and three-dimensional network bridging, with their primary failure modes being fiber debonding or fracture. The strain rate effect of the specimens originates from the combined action of microscopic damage evolution and inertial lateral confinement. The confining pressure enhancement mechanism primarily lies in suppressing brittle crack propagation, driving the material towards a triaxial stress state and inducing ductile hardening. This research reveals the dynamic response mechanism of carbon fiber-reinforced grouted bodies under coupled static-dynamic loading, providing a material basis and theoretical foundation for the reinforcement design in sand layer strata.

为了减轻地下工程中砂层地质可能引起的涌沙、涌水等灾害，碳纤维增强注浆材料具有良好的修复潜力。然而，在实际工程中，往往是静、动荷载共同作用于围岩，现有的研究还很有限。为此，本研究采用微纳碳纤维对超细水泥基灌浆材料进行改性。对分级砂层进行了室内注浆加固试验。采用装有主动围压装置的分离式霍普金森压杆（SHPB），在不同的冲击速度（对应的应变速率为49 ~ 92 s−1）和围压（0 ~ 8 MPa）下进行了系统的动态压缩试验。结果表明：试样的峰值应力随应变速率的增大而增大，表现出明显的应变速率效应；在不同的冲击速度和围压下，含纤维试样的峰值应力显著高于普通试样，证实了碳纤维的增强增韧作用。当围压增加到8 MPa时，含纤维试件的峰值应力约为无侧限条件下的1.56倍，破坏模式由拉伸劈裂过渡到破碎破坏。微观上，碳纤维通过“微补强”和三维网络桥接等方式有效抑制裂纹扩展，增强吸能能力，其主要破坏模式为纤维脱粘或断裂。试样的应变速率效应源于细观损伤演化和惯性侧向约束的共同作用。围压增强机制主要在于抑制脆性裂纹扩展，使材料进入三轴应力状态，诱发韧性硬化。本研究揭示了碳纤维加固注浆体在动静耦合荷载作用下的动力响应机制，为砂层地层的加固设计提供了物质依据和理论依据。

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引用次数: 0

Patch selection-based dual attention unsupervised deep learning model for suppressing random and erratic noise in seismic data 基于Patch选择的双注意无监督深度学习模型抑制地震数据中的随机和非稳定噪声

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2026-03-01 Epub Date: 2026-01-19 DOI: 10.1016/j.jappgeo.2026.106107

Zixiang Zhou , Guochang Liu , Min Bai , Zhaoyang Ma , Zhiyong Wang , Yannan Wang

Seismic data denoising is a challenging task in complex noise environments, especially in unsupervised learning settings where labeled data is unavailable. Existing unsupervised learning methods, such as Deep Image Prior, effectively remove noise but still face issues related to network structure stability during training, which limits their accuracy. To further improve denoising performance, this paper proposes a patch selection-based dual attention deep learning model (PS-DADL) designed to suppress random and erratic noise in seismic data. First, we adopt a patch-based processing approach, selecting patches with high information content for training based on variance calculations, which improves the model's training efficiency. Then, we design a deep neural network that extracts features and recovers denoised signals through an encoder-decoder structure. Additionally, a dual attention module is introduced. This module adaptively aggregates dependencies within the data through spatial and channel attention mechanisms, enhancing feature representation and boosting the model's adaptability in complex noise environments. Experimental results show that PS-DADL, within the unsupervised learning framework, improves seismic data quality and demonstrates strong robustness, outperforming several baseline unsupervised learning methods.

在复杂的噪声环境中，地震数据去噪是一项具有挑战性的任务，特别是在没有标记数据的无监督学习环境中。现有的无监督学习方法，如Deep Image Prior，可以有效地去除噪声，但在训练过程中仍然面临与网络结构稳定性相关的问题，这限制了它们的准确性。为了进一步提高去噪性能，本文提出了一种基于patch选择的双注意深度学习模型（PS-DADL），用于抑制地震数据中的随机和非稳定噪声。首先，我们采用基于patch的处理方法，通过方差计算选择信息量高的patch进行训练，提高了模型的训练效率。然后，我们设计了一个深度神经网络，通过编码器-解码器结构提取特征并恢复去噪信号。此外，还介绍了双注意模块。该模块通过空间和通道注意机制自适应地聚合数据中的依赖关系，增强特征表示，提高模型在复杂噪声环境中的适应性。实验结果表明，PS-DADL在无监督学习框架下提高了地震数据质量，具有较强的鲁棒性，优于几种基线无监督学习方法。

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引用次数: 0

NMR study on the changes of water content characteristics and pore structure evolution during melting of coal frozen with liquid nitrogen 液氮冻结煤熔融过程中含水量特征变化及孔隙结构演化的核磁共振研究

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.2026.106124

Lei Qin , Hui Wang , Haifei Lin , Pengfei Liu , Shiyin Lv , Jiawei Li

The content of unfrozen water in frozen coal affects the permeability of coal at low temperature, and the study of the ice-water phase change during the freezing and thawing process of the coal body is the key to study the liquid nitrogen fracturing and seepage enhancement technology. In this paper, we take Hengyi bituminous coal as the research object, and study the pore structure evolution and unfrozen water distribution changes during the thawing process based on nuclear magnetic resonance technique for high water content and low water content coal samples at different freezing times. The results show that the water space ratio growth of coal samples during thawing can be divided into three stages; liquid nitrogen freeze-thaw coal sample can significantly promote the development of large pores and large pore throats, and the difference of initial water content only has a significant effect on the development of large pores and large pore throats. Pore diameter is positively correlated with pore ice melting-point, and in the frozen coal sample, the unfrozen water at the initial melting stage mainly exists in the small water space. Freezing process of low-temperature liquid nitrogen on the coal mass has been freeze-swelling and freeze-shrinking effect, different freezing time will affect the combined effect of freeze-swelling and freeze-shrinking effect, resulting in the variability of pore throat and pore space development under different freezing time.

冻结煤中未冻水的含量影响着煤在低温下的渗透性，研究煤体冻融过程中冰-水相变是研究液氮压裂增渗技术的关键。本文以恒一烟煤为研究对象，基于核磁共振技术研究了不同冻结时间高含水率和低含水率煤样在解冻过程中的孔隙结构演化和未冻水分布变化。结果表明：煤样在解冻过程中水空比的增长可分为三个阶段；液氮冻融煤样能显著促进大孔隙和大孔喉发育，初始含水率差异仅对大孔隙和大孔喉发育有显著影响。孔隙直径与孔隙冰熔点呈正相关，在冻结煤样中，初始融化阶段的未冻水主要存在于小的水空间中。低温液氮对煤体的冻结过程具有冻胀和冻缩效应，不同的冻结时间会影响冻胀和冻缩效应的联合作用，导致不同冻结时间下孔隙喉和孔隙空间发育的变异性。

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引用次数: 0

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

Stratification effects of acoustic emission signal propagation in stratified rocks: Results from laboratory research 层状岩石中声发射信号传播的分层效应：实验室研究结果

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2026-03-01 Epub Date: 2026-01-03 DOI: 10.1016/j.jappgeo.2025.106085

Youbang Lai , Zhipeng Li , Peng Liang , Wenxue Deng , Yang Liu

To reveal the propagation characteristics of acoustic emission (AE) signals in layered rock and their implications for monitoring and interpretation, AE propagation tests were conducted in two directions: in-layer (parallel-to-bedding) and cross-layer (perpendicular-to-bedding). The effects of bedding on AE wave velocity, waveform, spectrum, and time–frequency characteristics were systematically analysed. Results revealed significant anisotropy: the cross-layer wave velocity was approximately 10.61% lower than the in-layer velocity. Propagation through bedding caused pronounced attenuation in amplitude, energy, and ringing count, accompanied by a significant increase in rise time, indicating waveform distortion and dispersion. The dominant frequency decreased from about 120 kHz to 50 kHz, showing strong high-frequency attenuation and energy transfer toward lower frequencies. Frequency-dependent attenuation was most pronounced in the 250–500 kHz range, moderate in 125–250 kHz, and weak below 125 kHz. The attenuation bandwidth for cross-layer propagation (62.5–500 kHz) was broader than that for in-layer propagation (125–500 kHz). These findings demonstrate that bedding interfaces play a critical role in controlling AE signal behaviour, providing a theoretical and experimental basis for improving AE source interpretation and dynamic hazard monitoring in layered rock masses.

为了揭示声发射信号在层状岩石中的传播特征及其对监测和解释的意义，在层内（与层理平行）和层间（与层理垂直）两个方向上进行了声发射传播试验。系统分析了层理对声发射波速、波形、波谱和时频特性的影响。结果表明：层间波速比层内波速低约10.61%；通过层理传播会导致振幅、能量和振铃数的明显衰减，并伴随着上升时间的显著增加，表明波形失真和色散。主导频率从120 kHz左右下降到50 kHz，表现出较强的高频衰减和向低频的能量转移。频率相关衰减在250-500 kHz范围内最明显，在125 - 250 kHz范围内中等，在125 kHz以下较弱。层间传播（62.5 ~ 500 kHz）的衰减带宽比层内传播（125 ~ 500 kHz）的衰减带宽宽。研究结果表明，层理界面在控制声发射信号行为中起着至关重要的作用，为提高层状岩体声发射震源解释和动态灾害监测水平提供了理论和实验依据。

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