7th Asia Pacific Meeting on Near Surface Geoscience and Engineering
- Conference date: May 13-15, 2025
- Location: Xi'an, China
- Published: 13 May 2025
1 - 20 of 97 results
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Optimization Analysis of Seismic Attributes Based on Logging Parameters
More LessAuthors L. YangSummarySeismic attributes play an important role in reservoir delineation, reservoir model prediction, reservoir morphology description, and quantitative seismic facies analysis in oil and gas exploration and prediction. Due to the large number of seismic attributes under the same category, and the varying sensitivity and correlation of these seismic attributes to oil and gas prediction under the same geological conditions, researchers must select effective seismic attributes and eliminate redundant information. This article combines effectiveness analysis and Spearman coefficient analysis methods, and uses intersection analysis of 41 seismic attributes and 3 logging parameters, including oil saturation, sandstone shale percentage, and porosity, from 100 and 50 wells in a certain work area to draw conclusions on the possible correlation between seismic attributes and reservoir properties. This article found that there is a significant correlation between seismic attributes under the percentage of sandstone and mudstone. The Spearman coefficient method and the effective number method are both affected by changes in the total number of samples. Therefore, in practical work, researchers should select relatively stable sensitive seismic attributes through changes in the total number of samples, in order to reduce the impact of changes in the total number of samples on seismic attribute selection.
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Exploration of Deep Mineral Resources Using the SOTEM Method
More LessAuthors G. XueSummaryThis study introduces the Short-Offset Transient Electromagnetic (SOTEM) method for deep ore exploration, representing a significant advancement over traditional electromagnetic techniques. While methods such as Controlled Source Audio-frequency Magnetotellurics (CSAMT) and Long-offset Transient Electromagnetics (LOTEM) are widely used in mineral exploration, they are often constrained by depth limitations and resolution issues stemming from plane-wave field assumptions and signal strength challenges. The SOTEM approach employs a ground wire source with bipolar currents and utilizes shorter offsets—ranging from 0.3 to 2 times the detection depth. This innovation enhances signal strength, reduces noise interference, and minimizes averaging effects. To improve data interpretation accuracy, the adaptive regularized inversion algorithm (ARIA) is applied. A case study conducted at the Xiaoshan Mine in northern China demonstrates the practical application of SOTEM. Survey lines successfully identified high-resistivity formations and potential mineralization zones. Results indicate that SOTEM offers high detection accuracy and deep penetration capabilities, making it particularly effective in complex terrains, such as mountainous regions. Key advantages include increased signal strength, enhanced resolution, reduced transmitter power needs, and improved efficiency for deep-mining applications. The Xiaoshan case confirmed the successful detection of mineralization anomalies, highlighting the method’s suitability for deep geological exploration.
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Application of EM Method to Study the Distribution of Geological Layers in Thermal Storage and CCS
More LessSummaryIn recent years, the application of EM methods in conjunction with geological target identification and application has received considerable attention in fields such as geothermal development and CO2 storage facility construction. This article focuses on the effectiveness of time-frequency electromagnetic method (TFEM) in dynamically tracking changes in thermal reservoir distribution, as well as the application of transient electromagnetic method (TEM) in identifying target rock masses in CO2 storage facilities.
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Application of Surface Waves in the Near-surface Investigation for Oil and Gas Exploration
More LessAuthors Zhang Xiaobin, W. Peng, Geng Chun, Zhang Heng, Hu Botao and Hu XueyingSummaryThe near-surface structure plays a crucial role in the quality of seismic imaging for oil and gas exploration. To measure the velocity structure of near surface, the micro logging method is commonly used. While effective, this method faces limitations in regions where drilling is not feasible, such as urban areas, airports, railways, or gravel-rich terrains. Surface waves, which propagate along the Earth’s free surface, offer a potential alternative. We established a workflow for surface wave acquisition, processing, and interpretation using small trace intervals of less than 5 meters. This approach enables surface wave surveys to produce interpretation results comparable to those obtained from micro logging. Both theoretical models and actual drilling data demonstrate that small trace interval surface waves can match or exceed the near-surface detection capabilities of traditional micro logging.
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Near-surface Characterization Using Ambient Noise Recorded by Fiber-optic Distributed Acoustic Sensing
More LessSummaryFiber-optic Distributed Acoustic Sensing (DAS) technology has recently garnered significant attention across various fields, including engineering, oil exploration, and seismological research. Compared to traditional geophones, DAS offers the advantages of high-resolution data acquisition through dense spatial sampling and ease of deployment in challenging environments. These attributes position DAS as a promising tool for near-surface characterization in engineering geological exploration and monitoring. In this study, a DAS array was employed to capture seismic data generated by ambient noise, enabling an investigation into near-surface structures. Surface waves were extracted from the recorded subtle natural disturbances by using seismic interferometry techniques. Subsequently, the near-surface shear wave velocity profile along the DAS array was derived through inversion of surface wave dispersion curves using a genetic algorithm optimization approach. The findings highlight the efficacy of DAS in leveraging ambient noise for near-surface characterization.
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The Influence of Peri-wellbore Stress and Seepage Fields on Adjacent Faults
More LessSummaryFaults are a common type of geological structure, widely found in areas with active tectonic development. Fault zones may be accompanied by a large number of fractures, which are conducive to the formation of complex fracture networks in oil and gas development. However, faults also exhibit characteristics of low strength and susceptibility to deformation. Due to excavation and seepage effects, there are wellbore surrounding stress field and seepage stress fields near horizontal wells. To clarify the influence of the wellbore surrounding stress field and seepage stress field on the near-wellbore faults, this study established two different physical models, derived general theoretical formulas for the near-wellbore faults under the influence of the wellbore surrounding stress field and seepage field, and combined this with the basic fault activation theory based on the Mohr-Coulomb criterion. The study also defined the regional activation rate, analyzed the activation risk of near-wellbore faults under the influence of the two stress fields, and designed orthogonal experiments to study the influencing factors. This research provides certain reference significance for optimizing the selection of horizontal well locations.
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A Second-order Parallel Fast Sweeping Method for Traveltime Calculation
More LessSummaryTraveltime computation is fundamental for seismic data analysis, impacting the quality of migration images and near-surface velocity models. As exploration targets become more complex and seismic datasets expand, the demand for precise and efficient traveltime calculations has increased. This paper introduces a second-order parallel fast sweeping method (FSM) that addresses these needs by solving Eikonal equations on GPUs with enhanced accuracy and efficiency. The proposed FSM utilizes the Cuthill-McKee algorithm alongside alternating direction sweepings to achieve this. It employs a two-point stencil for second-order accuracy when two upwind points are available; otherwise, it defaults to first-order. The second-order scheme offers superior solutions over first-order methods, achieving the same level of accuracy with fewer iterations and less memory usage. Importantly, the method’s parallelization on GPU significantly accelerates computations for large 3D cases. Unlike the first-order approach, reducing grid size does not sufficiently mitigate errors, which can impact migration velocity analysis. Thus, the second-order FSM provides a robust solution for high-resolution seismic imaging and velocity model building.
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Anomalous Rapid Voltage Decay in Transient Electromagnetic Data Caused by IP Effect: A Synthetic Study
More LessAuthors J. Lu, H. El-Kaliouby, K. Panagiotis and X. WangSummaryThe induced polarization (IP) effects, associated with polarizable bodies, are increasingly observed in the transient electromagnetic (TEM) surveys, often characterized by rapid voltage decay followed by sign reversal. In a field TEM survey over a Volcanogenic Massive Sulfide (VMS) deposit, which exhibits high IP properties, an anomalous voltage decay without sign reversal was observed. To enhance data interpretation, we developed a 1D model using Cole-Cole parameters derived from spectral induced polarization (SIP) measurements of borehole core samples from the VMS area. The synthetic data exhibit rapid decay without sign reversals, consistent with the field observations. By performing both resistivity-only (RO) and IP-incorporated inversions, the results demonstrate that only the IP-incorporated inversion accurately fits the data and provides a reliable interpretation. This highlights the importance of carefully analyzing anomaly late-time voltage decay in TEM data, particularly in scenarios with moderately resistive backgrounds or deeply buried polarizable bodies. This study advocates for an approach that integrates IP effects to provide more accurate and reliable evaluations in regions with polarizable targets such as sulfides or clays.
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Interferometric Processing with Stimulated Seismic Sources for 2D/3D Near Surface Characterization with Mining Applications
More LessAuthors C. Cai, R. Tarnus, T. Allemand, T. Bardainne and H. ToubianaSummaryUnderstanding the subsurface structure within the top hundred meters is crucial for mining exploration, safety, and monitoring. This study introduces a novel hybrid seismic imaging method that integrates active sources, such as hammer strikes, accelerated weight drops and rock drops, with passive ambient noise interferometry. Through this hybrid method, we achieve high-resolution shear-wave velocity profiles and volumetric models, which are key for characterizing near-surface structures. Our method overcomes the limitations of traditional seismic techniques by significantly reducing acquisition time—from weeks for passive methods to just minutes—while avoiding the high costs and complexities associated with conventional active sources. Each type of active source brings unique frequency characteristics, enhancing depth resolution. Combining P-wave and S-wave velocity analyses provides additional insights into material hardness, further enhancing the method’s utility for diverse mining applications. Three case studies demonstrate the method’s efficiency: detecting subsurface caves, characterizing a rock valley in an open pit, and assessing bedrock hardness to ensure tailing dam safety. This hybrid approach offers a cost-effective, efficient, and adaptable solution for seismic imaging and monitoring in mining and geotechnical contexts, contributing to improved operational safety and efficiency.
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Utility Detection - A Comparative Study of Single Channel and Hand-Pushed Array GPR Systems
More LessAuthors M. Langton, A. Viberg, J. Emilsson and J. FriborgSummaryGround Penetrating Radar (GPR) has been an integral part of utility surveying since the early 2000’s and the use of GPR systems for civil engineering applications and utility mapping has been the focus of research since the early days of the development of the GPR method.
This study examines the difference between the two main developments for utility surveying – single-channel GPRs and high-density GPR arrays. Comparative data was collected in the town of Gainsborough, UK with the aim of discussing and highlighting the advantages and disadvantages for each system.
The results demonstrate the high efficiency of multi-channel array GPR, offering higher resolution results, increased data quality and faster data collection. However, single-channel GPR can be more efficient and cost-effective in hardware and data collection in areas with tight urban footpaths/sidewalks, such as city centres with numerous street furniture and obstacles.
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Research on Calculation Method of Vertical Deflection Based on Gravity Data
More LessSummaryThe vertical deflection indicates the inclination of the geoid to the earth ellipsoid, reflecting the mass distribution and migration of materials within the earth, therefore, the study of vertical deflection is an important work in geophysics. Based on Stokes boundary value theory, this paper calculates vertical deflection through gravity data. First, the gravity data of aerial survey over South Dakota, North America is processed, and the free-air gravity anomaly in the region is obtained through latitude correction and altitude correction, then the free-air gravity anomaly data is processed based on Stokes integral to determine the Geoid in this region, finally, the vertical deflection value of each measuring point in the study area is obtained through spatial difference. The calculation values were also compared with the vertical deflection of the EGM2008 model, and the comparison results indicate that the method used in this study can achieve the correct vertical deflection from airborne gravity data.
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Time Domain Induced Polarization Data Acquisition at Contaminated Sites
More LessSummaryTime domain induced polarization (TDIP) has emerged as a highly effective tool for characterizing soil and groundwater contamination. Numerous studies have focused on the objective of enhancing accuracy of TDIP results. However, the acquisition of high quality TDIP data has received less attention than it deserved. In this study, three data acquisition methods were evaluated across seven distinct sites, with a particular focus on the controlling factors that influence data quality. This study addresses the questions about how to select a reliable TDIP acquisition method. The results demonstrate that there are significant differences in the raw data obtained through different acquisition methods, with the inverted results derived from these datasets exhibiting varying discrepancy. The data quality associated with the dual cables utilizing non-polarizable electrodes layout (Dual-CL-NP) is markedly superior, thereby ensuring the reliability of the results. Furthermore, apparent resistivity and measured voltage are identified as the key factors on data quality. The threshold values for selecting the acquisition method are determined. The Dual-CL-NP method should be utilized when the averaged apparent resistivity is less than 7.9 Ω•m. Consequently, a guideline for TDIP data acquisition is proposed, which addresses the limitations associated with TDIP data quality and facilitates its advancement
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A 3D TEM Inversion Method Based on Deep Learning with Transformer and U-Net
More LessSummaryIn this abstract, we introduced a 3D TEM inversion network, UT-Net, that integrates the self-attention mechanism of Transformer into U-Net. UT-Net addresses the limitation of U-Net in capturing global features by dynamically allocating weights to different time channels. Additionally, data augmentation methods are employed to reduce the time required for generating new training datasets. Experimental results on synthetic data demonstrate that UT-Net with data augmentation achieves highly accurate inversion results in terms of the positions and boundaries of subsurface anomalies, significantly outperforming traditional U-Net. Moreover, UT-Net is also successfully applied to field data, accurately identifying shallow aquifer collapse zones. The results demonstrate that UT-Net demonstrates strong effectiveness and offers a novel method for 3D electromagnetic inversion in complex geological environments.
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A GPU-Accelerated Tensorization Implementation of the Fast Sweeping Method for Solving Eikonal Equations
More LessSummaryThe fast sweeping method (FSM) is a widely used numerical scheme for solving eikonal equations. It employs a local solver at each grid point and use Gauss-Seidel iteration to calculate the traveltime field. Due to the Gauss-Seidel iteration, the conventional programming of FSM is implemented by element-wise operations, making it difficult to leverage convenient GPU parallelization. This constraint hinders the potential for further improving the computational efficiency of the traveltime field, especially when dealing with large-scale models. In this paper, we adopt the Jacobi iteration instead and propose a tensorization implementation of FSM for factored eikonal equation with a first-order Lax-Friedrichs local solver. Particularly, it is very easy to carry out this implementation on computing device equipped with GPUs. Numerical experiments show that, although the number of iterations required for the algorithm to converge has increased a lot, tensor programming can greatly save computation time with almost no additional memory requirements.
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Deep Learning-Based Classification of Defective Tunnel Linings
More LessAuthors X. LuoSummaryThis study introduced a novel method for identifying defective tunnel lining sections through an enhanced EfficientNetV2 model, which has been optimised with EMA and transfer learning techniques to improve the distinction between highly similar scenarios. Ablation experiments demonstrated that the accuracy of defective tunnel classification has been improved to 92.9%. With the proposed method, the tunnel lining GPR measurements are gone through a classification process, in which defective radargram segments are identified and the healthy ones are excluded. This approach reduces the number of radargrams requiring interpretation, significantly lowering the workload and improving diagnostic efficiency. Additionally, it helps avoid artefacts and pseudo-noise that intepreation may introduce in healthy tunnel sections.
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Short Offset Transient Electromagnetic Method Multi-component Inversion Based on Deep Neural Network
More LessSummaryShort offset transient electromagnetic method (SOTEM) is a new electromagnetic detection method, which is suitable for mineral resources exploration. In order to optimize the data processing results, this paper introduces the CNN-BiLSTM network model for one-dimensional inversion. The response of the electric field component and the magnetic field component of the theoretical model is used as input, and the real resistivity is used as the training template for training. The observed data of the two fields can be quickly inverted by the trained network model calculation. It has been verified that the inversion method has excellent accuracy and layered structure characterization ability, good stability for low and high resistivity anomalies, and high computational efficiency.
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Three-dimensional Parallel Electrical Method Based on Cross-roadway Observation for Fine Detection of Floor-water Disaster
More LessSummaryTo enhance the accuracy of inversion imaging for detecting floor water hazards in mining faces, a detailed analysis was carried out on three geophysical methods: the single-tunnel sounding method, the double-tunnel penetration method, and a hybrid approach that integrates both techniques, referred to as comprehensive mine resistivity detection method. Building upon the comprehensive mine resistivity detection method, a novel cross-tunnel observation technique was introduced. This method incorporates cross- tunnel measurements into the resistivity imaging process, creating a comprehensive dataset that includes single-tunnel sounding method, double- tunnel penetration method, and cross-tunnel apparent resistivity data. This integrated approach significantly improves the extraction of full-potential matrix information. Numerical inversion results indicate that the incorporation of additional apparent resistivity data enhances the detection accuracy of concealed structural features beneath the coal seam floor. Furthermore, engineering applications confirm the method’s effectiveness, demonstrating its accuracy and reliability in detecting floor water hazards in coal mines.
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Surface Wave Multi-parameter Waveform Inversion Imaging Based on Partial Convolutional Siamese Network
More LessSummarySurface wave waveform inversion (SWI), recognized as a high-precision technique for shallow subsurface imaging, is extensively employed in the detection of underground structures. However, SWI faces several challenges that hinder its broader application in engineering, including the absence of low-frequency components, limited sensitivity to longitudinal waves and density parameters, and a pronounced reliance on initial models. To mitigate the Cycle-Skipping phenomenon stemming from low-frequency information loss and initial model dependency, this study introduces a novel multi-parameter waveform inversion approach for surface waves, leveraging a partial convolutional Siamese network (SIAMPCNN-SWI). This approach ingests observational and initial data into a shared-parameter partial convolutional Siamese network to extract multi-scale features, thereby enhancing the recovery of low-frequency information and reducing the dependency on initial models. Subsequently, the feature discrepancies extracted are utilized as loss functions for estimation, and automatic differentiation is harnessed for backpropagation, enabling the high-precision reconstruction of shallow subsurface structures. Numerical simulations and field data validations demonstrate that this method outperforms traditional approaches in terms of inversion accuracy, stability, and computational efficiency for multi-parameter inversion tasks. This method offers a robust solution for shallow subsurface exploration under complex geological conditions and holds significant potential for broader application.
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em3d-MT: Open-source 3D Anisotropic Forward Modeling of Radio-magnetotelluric Data Considering Displacement Currents
More LessSummaryAs exploration frequency increases, traditional models based on quasi-static assumptions fail to accurately simulate radio-magnetotelluric (RMT) responses due to the growing significance of displacement currents. While 3D numerical simulations that account for both diffusion and wave phenomena in anisotropic media remain underexplored, we developed em3d-MT, an open-source 3D RMT anisotropic forward modeling algorithm. Based on the vector finite element method, em3d-MT solves Maxwell equations considering both conduction and displacement currents. The algorithm handles arbitrary anisotropy in electrical conductivity and dielectric permittivity and includes a 3D geological modeling workflow with unstructured tetrahedral meshes to model complex geological structures. A direct solver, optimized with a two-layer parallel scheme, is used to solve the linear system, resulting in improved computational performance. em3d-MT aims at contributing to the modeling and inversion of RMT data commonly used in near-surface geophysical exploration projects. We present a detailed discussion on the principles of the em3d-MT program, its algorithmic framework, and numerical case studies.
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Azimuthal Mapping of Tracer Flow Path Using Optimized Directional In-Hole ERT
More LessAuthors Y.G. Doyoro, C. Lin and P. WuSummaryThis study introduces an optimized methodology for mapping directional contaminant flow paths by integrating rotating surface electrodes with in-hole Electrical Resistivity Tomography (ERT), supported by numerical simulations and validated through field application. Surface electrodes are rotated around a borehole at multiple azimuths while keeping in-hole electrodes stationary. Four electrode configurations—A-BMN, A-MNB, AB-MN, and AM-NB—are assessed using synthetic azimuthal apparent resistivity datasets. Directional sensitivity is evaluated for two scenarios: in-panel anomalies, where surface electrodes align with subsurface anomalies, and off-panel anomalies, where anomalies are opposite the surface electrodes. Arrays with high in-panel and low off-panel sensitivity exhibit reduced symmetrical effects and enhanced directional performance. The results show that symmetrical effects and limited directionality affect the A-BMN and A-MNB arrays, while the AB-MN array displays low accuracy and poor performance. In contrast, the AM-NB array demonstrates superior accuracy, minimal symmetrical effects, and strong directionality. Field tests validate the numerical findings, revealing significant resistivity variations at specific azimuths that align with the orientation of the injection well. Rose diagrams effectively indicate dominant flow paths and contaminant migration directions. This integrated approach offers a reliable, cost-effective solution for detecting preferential flow pathways and advancing subsurface mapping techniques.
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