Journal of Applied Geophysics最新文献_第4页

Characterization and Applications of Favorable Coal–Rock Architectures Based on Seismic Facies Boundaries: The Ordos Basin 基于地震相边界的煤岩有利构型表征及应用：鄂尔多斯盆地

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.2025.106092

ZeLei Jiang , Xuri Huang , Dong Zhang , YuCong Huang , Yong Wu

The Ordos Basin is one of the most resource-rich and critical regions for deep coalbed methane gas within China, in which the efficient development of this methane gas is key to increasing reserves and boosting production. The gas-bearing content of coal–rock systems is largely controlled by their internal architectural configurations. Seismic detection plays a critical role in attempts to convert coalbed methane gas resources into recoverable reserves and increase production capacity. However, unconventional self-sourced and trapped coal–rock gas reservoirs exhibit distinct geological features. Coalbeds are generally characterized by limited thicknesses, complex capping lithologies, and laterally heterogeneous architectures. These complexities hinder a clear understanding of their architectural patterns and seismic response signatures, resulting in underdeveloped seismic detection methods. To address these challenges and achieve high-resolution characterizations of favorable coal–rock architectures, we here focus on a representative area in the Yulin region of the Ordos Basin. By integrating basic geological coal–rock types with gas-enriched architectural features, favorable coal–rock architectures in the study area were classified into three distinct types: dual-layer limestone–coal, integrated mudstone–coal, and integrated sandstone–coal. The geophysical response characteristics of these architectures were then identified using seismic forward modeling of favorable architectural models. After selecting sensitive seismic attributes, a neural network-based multi-attribute clustering method was applied to characterize the spatial distribution of favorable coal–rock facies architectures. In addition, image-processing edge detection techniques were used to delineate the lateral boundaries of each type of architecture. Herein, an innovative methodology is proposed for seismic- and well-data integration to achieve the fine-scale characterization of favorable coal–rock architectures under facies-type and architectural boundaries. Our findings provide both theoretical insights and technical guidance for the efficient exploration and development of coalbed methane gas in the Ordos Basin.

鄂尔多斯盆地是中国深层煤层气资源最丰富、最关键的地区之一，有效开发是增加储量和提高产量的关键。煤岩系统的含气性在很大程度上受其内部构造形态的控制。地震探测在煤层气资源转化为可采储量和提高产能方面起着至关重要的作用。然而，非常规自源圈闭煤岩气藏具有明显的地质特征。煤层一般具有厚度有限、盖层岩性复杂、横向构造不均匀等特点。这些复杂性阻碍了对其建筑模式和地震响应特征的清晰理解，导致地震探测方法的不发达。为了应对这些挑战并实现有利煤岩构型的高分辨率表征，我们在此以鄂尔多斯盆地榆林地区的一个代表性区域为研究对象。通过对煤岩基本地质类型与富气构造特征的综合分析，将研究区有利煤岩构造划分为双层灰岩-煤、泥岩-煤一体化和砂岩-煤一体化3种类型。然后利用有利建筑模型的地震正演模拟识别这些建筑的地球物理响应特征。在选取敏感地震属性后，采用基于神经网络的多属性聚类方法表征煤岩相有利构型的空间分布。此外，使用图像处理边缘检测技术来描绘每种建筑类型的横向边界。在此基础上，提出了一种创新的地震和井数据集成方法，以实现相型和建筑边界下有利煤岩构型的精细表征。研究结果为鄂尔多斯盆地煤层气高效勘探开发提供了理论依据和技术指导。

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引用次数: 0

Target-oriented full waveform inversion based on optimal transport theory 基于最优输运理论的目标定向全波形反演

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2026-03-01 Epub Date: 2026-01-22 DOI: 10.1016/j.jappgeo.2026.106120

Tianjing Shen , Xiaochun Chen , Kai Chen , Yukai Wo , Xuri Huang

Target-oriented full waveform inversion (TOFWI) provides an efficient strategy for high-resolution imaging in local regions of interest, but its effectiveness is often limited by two main challenges: the need for redatuming of the acquisition system and the sensitivity of conventional

L_{2}

-norm-based TOFWI (

L_{2}

-TOFWI) to initial models and data quality. In this study, we propose an optimal transport (OT)-based TOFWI framework to overcome these limitations. First, we apply the Marchenko redatuming method to retrieve virtual reflection responses that isolate the target-zone wavefield. Then, we incorporate OT-based misfit functions, including graph-space OT (GSOT) and Kantorovich-relaxed OT (KROT), to enhance the convexity of the inversion landscape and improve robustness against noise and velocity-model inaccuracies. KROT introduces entropy regularization via the Sinkhorn algorithm, leading to smooth transport plans and improved numerical stability, whereas GSOT relies on a discrete assignment formulation that yields sparse transport plans and higher sensitivity to local waveform variations. Numerical experiments demonstrate that the proposed OT-TOFWI framework delivers more accurate and stable reconstructions than conventional

L_{2}

-TOFWI, particularly under conditions of significant initial-model errors and low signal-to-noise ratio. Furthermore, comparisons with global full waveform inversion highlight that OT-TOFWI achieves better resolution in deeper structures with lower computational cost. These results confirm that integrating Marchenko redatuming with OT-based misfit functions provides a promising pathway for reliable target-oriented seismic imaging in complex geological settings.

面向目标的全波形反演（TOFWI）为局部感兴趣区域的高分辨率成像提供了一种有效的策略，但其有效性通常受到两个主要挑战的限制：需要重新恢复采集系统以及传统的基于l2规范的TOFWI （L2-TOFWI）对初始模型和数据质量的敏感性。在这项研究中，我们提出了一个基于最佳传输（OT）的TOFWI框架来克服这些限制。首先，我们应用Marchenko重数据方法来检索分离目标区波场的虚拟反射响应。然后，我们结合了基于逆拟合函数的逆拟合函数，包括图空间逆拟合函数（GSOT）和kantorovich -relax逆拟合函数（KROT），以增强反演景观的凹凸性，并提高对噪声和速度模型不准确性的鲁棒性。KROT通过Sinkhorn算法引入熵正则化，导致平滑的传输计划和改进的数值稳定性，而GSOT依赖于离散分配公式，产生稀疏的传输计划和对局部波形变化的更高灵敏度。数值实验表明，与传统的L2-TOFWI相比，本文提出的OT-TOFWI框架提供了更精确和稳定的重建，特别是在初始模型误差较大和信噪比较低的情况下。此外，与全局全波形反演的比较表明，OT-TOFWI在更深的结构中获得了更好的分辨率，且计算成本更低。这些结果证实，将Marchenko重建与基于ot的失配函数相结合，为复杂地质环境下可靠的目标导向地震成像提供了一条有希望的途径。

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引用次数: 0

Automated microseismic classification in deep coal seams: Application to stress redistribution and fault reactivation in the Dongtan coal mine 深部煤层微震自动分类：在东滩煤矿应力重分布和断层活化中的应用

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2026-03-01 Epub Date: 2026-01-07 DOI: 10.1016/j.jappgeo.2026.106093

Jinhai Liu , Rui Xu , Kai Zhan , Jiajun Chen , Guangming Li , Chao Kong

Understanding how stress redistribution and structural reactivation evolve during deep coal mining is essential for assessing seismic hazards. In this study, we develop an automated microseismic classification workflow that integrates PhaseNet-based P-wave picking, residual-guided multi-window trimming, short-time Fourier transform (STFT) spectrogram generation and a dynamic-attention convolutional neural network to identify mining-induced and tectonic events in real time. The workflow is first trained and validated on labelled microseismic waveforms, achieving 93% overall accuracy on a five-class test set (blast, microseismic, earthquake, noise and others). We then deploy it on five high-SNR stations (WDZ4–WDZ8) at the 6306 working face of the Dongtan Coal Mine, where it captures the progressive transition from blast-dominated to tectonic-dominated microseismicity as mining advances into faulted zones. This trend, interpreted together with independent geological mapping and published focal-mechanism and stress-inversion results, indicates enhanced stress transfer and structural activation within the surrounding strata. Overall, the results demonstrate that intelligent seismic classification can quantitatively track the coupling between mining activities and geological structures, providing a practical tool for stress monitoring and early warning in deep coal seams.

了解深部煤矿开采过程中应力重分布和构造活化的演化过程是评估地震危险性的关键。在这项研究中，我们开发了一种自动化微地震分类工作流程，该工作流程集成了基于phasenet的p波拾取、残差引导的多窗口修剪、短时傅立叶变换（STFT）频谱图生成和动态注意力卷积神经网络，以实时识别采矿和构造事件。该工作流程首先在标记的微地震波形上进行训练和验证，在五类测试集（爆炸、微地震、地震、噪声和其他）上达到93%的总体准确率。然后，我们将它部署在东滩煤矿6306工作面的五个高信噪比站（WDZ4-WDZ8）上，在那里它捕捉到了随着采矿进入断裂带，从爆破为主到构造为主的微震活动的渐进转变。结合独立的地质填图和已发表的震源机制和应力反演结果，这一趋势表明，围岩内应力传递和构造活化增强。结果表明，智能地震分类可以定量跟踪采矿活动与地质构造之间的耦合关系，为深部煤层应力监测和预警提供实用工具。

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引用次数: 0

Effect of data filtering on source mechanisms inverted from surface microseismic monitoring array 数据滤波对地面微震监测阵列反演震源机制的影响

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2026-02-01 Epub Date: 2025-12-18 DOI: 10.1016/j.jappgeo.2025.106070

Toufik Chtouki , Matej Petružálek , Frantisek Staněk , Leo Eisner , Zuzana Jechumtálová , Naveed Iqbal , Umair bin Waheed

The source mechanisms of induced microseismic events help understanding underground operations and mitigating hazards associated with induced seismicity. However, the uncertainty in the inverted source mechanisms is not well understood. In this study, we examine the impact of digital filters applied to dense surface monitoring data on the inverted source mechanisms derived from P-wave amplitudes. Ten filters, designed and used to increase signal to noise ratio, were tested. Filtering strongly affects both the shear and non-shear components of the full moment tensor. The differences in shear component orientation can exceed 20° in Kagan angle for some filters, despite the excellent coverage provided by the monitoring network. By constraining the inversion to pure shear mechanisms, the orientation was more stable. The smallest errors were observed with bandpass, interferometry, wavelet (with a well-chosen wavelet), and Wiener filters. On the other hand, the SVD and AGC filters resulted in largest changes in source mechanisms. Our results show that data filtering can lead to significant errors in the source mechanisms, which could potentially be misinterpreted if used to infer stress or other reservoir parameters.

诱发微地震事件的源机制有助于了解地下作业和减轻与诱发地震活动相关的危害。然而，倒置源机制的不确定性尚未得到很好的理解。在这项研究中，我们研究了应用于密集地表监测数据的数字滤波器对由p波振幅推导出的反向震源机制的影响。测试了设计并用于提高信噪比的10种滤波器。滤波对全矩张量的剪切和非剪切分量都有强烈的影响。尽管监测网络提供了良好的覆盖，但某些过滤器的剪切分量方向差异可超过20°的卡根角。通过将反演约束为纯剪切机制，取向更加稳定。用带通、干涉测量、小波（选择合适的小波）和维纳滤波器观察到的误差最小。另一方面，SVD和AGC滤波器导致源机制的最大变化。我们的研究结果表明，数据过滤可能导致源机制出现重大错误，如果用于推断应力或其他储层参数，可能会被误解。

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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 : 2026-02-01 Epub 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

Deblending of simultaneous source seismic data in common shot domain based on multi-output U-shaped net transformer 基于多输出u型网变压器的共炮域同步震源数据解混

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2026-02-01 Epub Date: 2025-11-08 DOI: 10.1016/j.jappgeo.2025.106017

Binghui Zhao , Liguo Han , Laiyu Lu , Xiaomiao Tan

In seismic data acquisition, the simultaneous source technique has been widely used by virtue of its high acquisition efficiency. After collecting a large amount of simultaneous source data, the simultaneous source data needs to be deblended. Nevertheless，the highly coherent and intricate entanglement of aliased signals with desired signals poses a significant hurdle for effective shot deblending. Conventional deblending methods require determining the specific excitation time of each shot, and based on this, performing operations such as pseudo deblending, channel set conversion, and denoising. This not only requires high accuracy of the excitation time, but also is a complicated operation that requires denoising each shot separately, which is computationally huge. We designed a multi-output U-shaped Net Transformer (UNetr)based on the principles of imaging. By utilizing a transformer, which is more sensitive to positional information, as an encoder, this network can distinguish the waveform characteristics of different single shots and separate the blended data directly in the common shot channel set. After testing, the method is more capable for coherent signals and more effective for deblending of overlapping shots. Without relying on time coding, the method skips the complex processing flow. The processing efficiency is improved and the deblending effect is significant.

在地震数据采集中，同震源技术以其较高的采集效率得到了广泛的应用。在采集到大量的同时源数据后，需要对同时源数据进行解混。然而，混叠信号与期望信号的高度相干和复杂纠缠对有效的镜头去混构成了重大障碍。传统的去混方法需要确定每个镜头的具体激励时间，并在此基础上进行伪去混、信道集转换、去噪等操作。这不仅要求激发时间的精度高，而且是一个复杂的操作，需要对每个镜头分别去噪，计算量巨大。基于成像原理设计了一种多输出u型网络变压器（UNetr）。该网络利用对位置信息更敏感的变压器作为编码器，可以区分不同单镜头的波形特征，并在共镜头通道集中直接分离混合数据。经过测试，该方法对相干信号的处理能力更强，对重叠镜头的去混效果更好。该方法不依赖于时间编码，跳过了复杂的处理流程。提高了加工效率，脱混效果显著。

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引用次数: 0

Research on ERT advanced detection imaging of goaf floor in coal mining face based on random forest algorithm 基于随机森林算法的采煤工作面采空区底板ERT超前探测成像研究

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2026-02-01 Epub Date: 2025-12-03 DOI: 10.1016/j.jappgeo.2025.106053

Pengyu Wang, Xiaofeng Yi, Shumin Wang

Water inrush of goaf floor is one of the most important factors threatening the safety production of coal mines, which often causes great economic losses and casualties. After the goaf floor is filled with water, the apparent resistivity value decreases significantly. Therefore, the electrical resistivity tomography (ERT), which is sensitive to low-resistivity anomalous bodies such as water, has a unique advantage in the detection of water in goaf floor. At present, the main method for advanced detection of goaf floor is ERT three-point-source method, but this method can only realize one-dimensional positioning of the water-bearing body in goaf floor, which is easy to misjudge the location of the water-bearing body in practical application. To solve this problem, the random forest algorithm is used to process the advanced detection data, and then the apparent resistivity contour map of the goaf floor is predicted, which simplifies the measurement process and realizes two-dimensional positioning of the water-bearing body in goaf floor. Its effectiveness has been proved by the verification experiments, and the prediction accuracy reaches 98.86 %. This method is used to detect the goaf floor in Ji 17–33,200 coal mining face, and the location of the suspected water-bearing body has been determined.

采空区底板突水是威胁煤矿安全生产的重要因素之一，经常造成巨大的经济损失和人员伤亡。采空区底板充水后，视电阻率值明显降低。因此，电阻率层析成像（ERT）对水等低阻异常体敏感，在采空区底板水探测中具有独特的优势。目前采空区底板超前探测的主要方法是ERT三点源法，但该方法只能实现采空区底板含水体的一维定位，在实际应用中容易误判含水体的位置。针对这一问题，采用随机森林算法对超前探测数据进行处理，进而预测采空区底板视电阻率等值线图，简化了测量过程，实现了采空区底板含水体的二维定位。通过验证实验证明了该方法的有效性，预测精度达到98.86%。利用该方法对冀17-33,200采煤工作面采空区底板进行了探测，确定了疑似含水体的位置。

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引用次数: 0

Automatic first arrival picking for low signal-to-noise ratio data based on supervirtual interferometry and deep learning 基于超虚拟干涉和深度学习的低信噪比数据自动初到拾取

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2026-02-01 Epub Date: 2025-12-05 DOI: 10.1016/j.jappgeo.2025.106060

Xuefeng Gao , Weiping Cao , Ranran Yang , Xuri Huang , Wensheng Duan , Zhongbo Xu

First arrival picking is an important step in seismic data processing, as its accuracy and efficiency directly impact the quality and turnaround time of near-surface velocity models and even the overall seismic processing result. This step can be very challenging for seismic data acquired in regions with complex near-surface structures, such as foothills and desert, where seismic data exhibit low signal-to-noise ratios (SNR) and first arrival picking is critical for effective subsurface exploration. To address these challenges, we propose an automated first arrival picking method that integrates supervirtual interferometry (SVI) with deep learning (DL) to achieve robust picking under low-SNR conditions. Our two-stage framework first employs SVI to enhance the first arrival signals in low-SNR seismic traces, thereby recovering the first arrival signals in low-SNR regions. Subsequently, to correct the impact of the pre-arrival artifacts introduced by SVI, an improved U-Net neural network architecture is properly trained with labels containing these pre-arrival artifacts to achieve accurate first arrival picking for SVI output. Tests on synthetic seismic traces and field low-SNR data from complex near-surface geologic condition demonstrate that this method achieves reliable results under low SNR conditions without human intervention, and verify this approach as a viable tool for automatic picking of first arrival times for low SNR seismic data.

初到拾取是地震资料处理的重要步骤，其精度和效率直接影响到近地表速度模型的质量和周转时间，甚至影响到整个地震处理结果。对于在山麓和沙漠等具有复杂近地表结构的地区获取地震数据来说，这一步骤非常具有挑战性，因为这些地区的地震数据信噪比（SNR）较低，首次到达拾取对于有效的地下勘探至关重要。为了解决这些挑战，我们提出了一种将超虚拟干涉测量（SVI）与深度学习（DL）相结合的自动初到拾取方法，以实现低信噪比条件下的鲁棒拾取。我们的两阶段框架首先使用SVI增强低信噪比地震道的初到信号，从而恢复低信噪比区域的初到信号。随后，为了纠正SVI引入的预到达伪影的影响，使用包含这些预到达伪影的标签对改进的U-Net神经网络架构进行了适当的训练，以实现SVI输出的准确的首次到达拾取。对复杂近地表地质条件下的合成地震道和现场低信噪比数据的实验表明，该方法在无人为干预的低信噪比条件下获得了可靠的结果，验证了该方法是低信噪比地震资料首到时间自动提取的可行工具。

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引用次数: 0

Rock physics modeling and seismic responses of deep carbonate rocks in Tahe oilfield 塔河油田深部碳酸盐岩岩石物理模拟与地震响应

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2026-02-01 Epub Date: 2025-12-18 DOI: 10.1016/j.jappgeo.2025.106072

Chenglong Wu , Hemin Yuan , Xin Zhang

Controlled by multi-stage tectonic-karst processes and high-temperature high-pressure (HTHP) environment, the deep carbonate reservoir in Tahe oilfield has complex seismic responses. To characterize these intricate seismic responses, we integrate a comprehensive rock physics modeling workflow with seismic forward modeling to bridge micro-scale elastic properties and macro-scale seismic signatures. We constructed a dual-porosity rock model by combining the differential effective medium (DEM) model and Gassmann equation. Critically, we incorporated temperature-pressure-salinity corrections for fluid properties and modeled the effects of pressure and temperature on the rock frame. The model was then used to generate reservoir parameters for seismic forward modeling. The modeling results demonstrated that the Gassmann equation outperformed the DEM model, and P-wave velocity prediction was improved by adding HTHP corrections and salinity. The seismic forward modeling results revealed that porosity and pore structure are the dominant controls on seismic features, with fluid type being minor unless gas is present. This study quantitatively characterized the seismic rock physics properties of the deep carbonates in the Tahe Oilfield, providing a robust method for accurately predicting velocities and seismic responses of carbonates in similar geological settings.

塔河油田深部碳酸盐岩储层受多期构造岩溶作用和高温高压环境控制，地震反应复杂。为了描述这些复杂的地震反应，我们将综合岩石物理建模工作流程与地震正演建模相结合，以桥接微观尺度弹性特性和宏观尺度地震特征。将微分有效介质（DEM）模型与Gassmann方程相结合，建立了双孔隙度岩石模型。关键的是，我们结合了流体性质的温度-压力-盐度校正，并模拟了压力和温度对岩石框架的影响。然后利用该模型生成储层参数进行地震正演模拟。模拟结果表明，Gassmann方程优于DEM模型，并通过加入HTHP校正和盐度改善了纵波速度预测。地震正演模拟结果表明，孔隙度和孔隙结构是控制地震特征的主要因素，除非存在气体，否则流体类型较小。该研究定量表征了塔河油田深部碳酸盐岩的地震岩石物理性质，为类似地质环境下碳酸盐岩速度和地震响应的准确预测提供了一种可靠的方法。

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引用次数: 0

2.75-D Global joint inversion of gravity and magnetic anomalies with appraisal of model reconstruction uncertainty 2.75-D全球重磁异常联合反演及模型重建不确定性评价

IF 2.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Applied Geophysics

Pub Date : 2026-02-01 Epub Date: 2025-11-27 DOI: 10.1016/j.jappgeo.2025.106036

Yunus Levent Ekinci , Hanbing Ai , Çağlayan Balkaya , Arka Roy

Inversion procedures are fundamental tools for reconstructing causative sources of gravity and magnetic anomalies. While 2-D polygonal and 3-D polyhedral models can represent irregularly shaped bodies, a more flexible framework is needed to bridge the gap between computational simplicity and geometric realism. To address this, we propose a 2.75-D global joint inversion scheme based on the nature-inspired Hunger Games Search (HGS) metaheuristic algorithm. Synthetic tests involving modal and sensitivity analyses were carried out to identify potential difficulties and uncertainties in the considered problem, revealing that the global optimizer must efficiently balance the critical trade-off between global exploration and local exploitation. The proposed scheme was applied to synthetic anomalies, with and without noise, and benchmarked against the probabilistic Hamiltonian Monte Carlo algorithm. Two field datasets were then analyzed, and the results were interpreted considering existing geological and geophysical knowledge. Post-inversion analyses confirmed the reliability of the estimated models, while compact inversion and correlation imaging techniques supported the HGS outcomes. Notably, joint inversion consistently improved convergence and reduced estimation errors compared to individual inversions by exploiting the complementary sensitivities of gravity and magnetic data. The 2.75-D approach enhances geometric flexibility while maintaining a parsimonious parameterization. HGS is an efficient and robust optimizer for joint inversion of gravity and magnetic anomalies, capable of producing geologically plausible models with rapid convergence and minimal uncertainty.

反演程序是重建重磁异常成因的基本工具。虽然二维多边形和三维多面体模型可以表示不规则形状的物体，但需要一个更灵活的框架来弥合计算简单性和几何真实感之间的差距。为了解决这个问题，我们提出了一种基于自然启发的饥饿游戏搜索（HGS）元启发式算法的2.75维全球联合反演方案。采用模态分析和敏感性分析进行综合测试，以识别所考虑问题中的潜在困难和不确定性，揭示全局优化器必须有效地平衡全局勘探和局部开采之间的关键权衡。将该方法应用于有噪声和无噪声的综合异常，并以概率哈密顿蒙特卡罗算法为基准进行了测试。然后分析了两个现场数据集，并根据现有的地质和地球物理知识对结果进行了解释。反演后分析证实了估计模型的可靠性，而紧凑的反演和相关成像技术支持了HGS的结果。值得注意的是，通过利用重磁数据的互补灵敏度，联合反演与单独反演相比，始终提高了收敛性，减少了估计误差。2.75-D方法增强了几何灵活性，同时保持了简洁的参数化。HGS是一种高效、稳健的重磁联合反演优化器，能够生成地质上合理的模型，具有快速收敛和最小的不确定性。

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