Article
Geochemistry & Geophysics
Yang Su, Changchun Yin, Yunhe Liu, Xiuyan Ren, Bo Zhang, Changkai Qiu, Bin Xiong, Vikas Chand Baranwal
Summary: The proposed algorithm for sparse-regularized inversion based on the shearlet transform improves the resolution of 3-D frequency-domain airborne EM inversions and obtains more-focused and realistic underground structures.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Xin Huang, Colin G. Farquharson, Changchun Yin, Liangjun Yan, Xiaoyue Cao, Bo Zhang
Summary: The spectral-element (SE) method, based on the Galerkin technique, is gradually being implemented in geophysical electromagnetic (EM) 3D simulation, providing accuracy and efficiency for airborne EM forward modeling. By adapting the conventional SE method, small-scale conductivity variations and complex boundaries in realistic earth models can be better handled, allowing for the use of a coarse mesh.
Article
Geosciences, Multidisciplinary
Zhijun Huo, Zhaofa Zeng, Wenben Li, Ling Zhang
Summary: This paper introduces a 3D FAEM inversion algorithm based on the vector finite element method, which improves computational efficiency by reducing the number of variables and scale of linear equations, and utilizes a specific solver to solve multi-source forward linear equations. The effectiveness of the method is verified by applying it to a typical resistivity model with an anomalous body, and studying the influence of initial models on inversion results.
JOURNAL OF APPLIED GEOPHYSICS
(2022)
Article
Geochemistry & Geophysics
Yanfu Qi, Xiu Li, Changchun Yin, Huaiyuan Li, Zhipeng Qi, Jianmei Zhou, Yunhe Liu, Xiuyan Ren
Summary: In this article, a 3-D AEM inversion algorithm is developed for a topographic earth model. The algorithm utilizes the time-domain finite-element algorithm based on unstructured mesh and the Gauss-Newton method, improving computational efficiency and effectively considering the influence of topography on AEM inversions.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Tao Chen, Dikun Yang
Summary: The study focuses on the systematic errors in airborne and semi-airborne transient electromagnetic surveys caused by the inexact geometry of transmitter and receiver, proposing a universal approach for modeling arbitrarily complex geometries. Simulations show that considering tilt can provide more accurate estimates of near-surface conductivity in groundwater problems.
Article
Chemistry, Multidisciplinary
Ji Liu, James Garcia, Liam M. M. Leahy, Rijian Song, Daragh Mullarkey, Ban Fei, Adrian Dervan, Igor V. V. Shvets, Plamen Stamenov, Wenxin Wang, Fergal J. J. O'Brien, Jonathan N. N. Coleman, Valeria Nicolosi
Summary: Direct ink writing (DIW) is a viable 3D printing technology for custom production of functional conductive hydrogels. This study demonstrates a highly 3D printable PEDOT:PSS-based ink made from commercially accessible raw materials. The 3D-printed hydrogel exhibits high electrical conductivity, outstanding elasticity, stability in water, electromagnetic interference shielding, sensing capabilities, and biocompatibility, showing potential for implantable and tissue engineering applications. The fabrication strategy opens up new opportunities to create multifunctional hydrogels with custom features and expand the applications of hydrogel materials.
ADVANCED FUNCTIONAL MATERIALS
(2023)
Article
Environmental Sciences
Seogi Kang, Rosemary Knight, Meredith Goebel
Summary: This study developed a new approach to image the bedrock surface and the confining Corcoran Clay layer using AEM data in the Central Valley of California. The approach incorporated prior knowledge of the targets and improved imaging through multiple inversions and an interpolation process. The results showed that the AEM data provided reliable information about the bedrock surface and the Corcoran Clay layer.
WATER RESOURCES RESEARCH
(2022)
Article
Geochemistry & Geophysics
Bo Zhang, Changchun Yin, Yunhe Liu, Xiuyan Ren, Vikas C. Baranwal, Bin Xiong
Summary: In this study, a new 3D AEM inversion scheme based on the finite-element method and unstructured tetrahedral local meshes was developed. Compared to traditional methods, this scheme uses irregular tetrahedral meshes to better accommodate topography and complex underground structures. The results demonstrate that the method can accurately model AEM responses and recover subsurface main resistivity structures.
Article
Geochemistry & Geophysics
Xue Han, Changchun Yin, Yang Su, Bo Zhang, Yunhe Liu, Xiuyan Ren, Jianfu Ni, Colin G. Farquharson
Summary: In order to improve the modeling efficiency of 3-D airborne electromagnetic (AEM) inversions, this study develops an algorithm that combines hexahedral vector finite element (FE) with octree meshes. The algorithm improves flexibility for complex geology and demonstrates high accuracy and practicality through various experiments and applications.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geosciences, Multidisciplinary
Niels B. Christensen
Summary: In modern hydrogeological investigations, combining different sources of information allows for the construction of dynamic hydraulic models, leading to improved geological interpretations and better resolution of subsurface resistivity structures.
JOURNAL OF APPLIED GEOPHYSICS
(2022)
Article
Environmental Sciences
Xue Han, Jianfu Ni, Changchun Yin, Bo Zhang, Xin Huang, Jiao Zhu, Yunhe Liu, Xiuyan Ren, Yang Su
Summary: An adaptive octree meshing scheme is proposed for frequency-domain airborne electromagnetic modeling. The scheme improves efficiency and computational requirements by generating meshes more reasonably and refining them locally. The accuracy of the method is verified by comparing its results with semi-analytical solutions, and its technical advantages over the spectral-element method are demonstrated through computational cost comparisons. The feasibility of the algorithm in complex geological circumstances is also demonstrated.
Article
Geochemistry & Geophysics
Minkyu Bang, Soon Jee Seol, Joongmoo Byun
Summary: Deep neural networks (DNNs) have been used to interpret frequency-domain electromagnetic (EM) data, enabling rapid interpretation of vast amounts of information. However, the distortion caused by severe topographic changes in airborne surveys has not been addressed by DNN-based inversions, unlike conventional inversion techniques. To overcome this limitation, we propose a pseudo-1-D interpretation method that considers topographic changes using EM responses and nearby topographic information. Our trained DNN model provides reasonable interpretation results for synthetic and real AEM datasets, even in mountainous areas with rough topography.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geosciences, Multidisciplinary
Zhipeng Qi, Yingying Zhang, Xiu Li, He Li
Summary: In this article, a new S-inversion approximate interpretation approach is developed to reflect the underground electric interface from multi-source semi-airborne TEM data. This method effectively avoids random noise and highlights abnormal information in the depth.
JOURNAL OF APPLIED GEOPHYSICS
(2022)
Article
Computer Science, Information Systems
Jigen Xia, Ximei Qiao, Zhiqiang Li, Jian Wu, Xingmeng Dong, Yuwen Zhai
Summary: The semi-airborne frequency domain electromagnetic detection method is widely used in field exploration, but it suffers from low signal-to-noise ratio due to various interferences. This paper proposes an integrated denoising method based on the improved ant-colony-optimized wavelet threshold to effectively reduce different types of noise interference and improve data quality and inversion accuracy.
Article
Geochemistry & Geophysics
Changsheng Liu, Shuxu Liu, Zhuowei Li, Libin Wang, Shaoyou Kan, Jie Liang
Summary: Ground-airborne frequency-domain electromagnetic (GAFEM) is a method that can detect underground anomalies at large depth ranges in areas with complex terrain. To address the demand for real-time interpretation of GAFEM data, a rapid imaging method based on space magnetic gradient anomaly (MGA) is proposed, which enables the rapid recognition of underground electrical anomalies without heavy computational resources.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Sooyoon Kim, Soon Jee Seol, Joongmoo Byun, Seokmin Oh
Summary: This article introduces a deep-learning-based method for diffraction extraction that preserves the amplitude and phase characteristics, and can handle overlapping reflection events. By generating a training dataset through synthetic modeling and applying transfer learning, the model can be used for real seismic data.
Article
Geochemistry & Geophysics
Dowan Kim, Joongmoo Byun
Summary: Facies classification is a method of classifying rock types and pore fluids using elastic properties and machine learning techniques, linking well log and surface seismic data to improve the physical validity of the classification results.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Seokmin Oh, Joongmoo Byun
Summary: In the field of geophysics, the application of DL inversion techniques has gained widespread attention, with a focus on assessing prediction reliability and achieving this through uncertainty estimation. The introduction of uncertainty estimation based on the Bayesian framework provides a more reliable prediction for DL inversion.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Kyubo Noh, David Pardo, Carlos Torres-Verdin
Summary: This study demonstrates the applicability of real-time 2.5-D DL inversion in well geosteering for imaging the subsurface electrical conductivity distribution. By developing a DL inversion workflow and utilizing fault detection and inversion modules, we are able to detect and quantify arbitrary dipping fault planes. Furthermore, using multidimensional inversion and deep-sensing measurements improves the inversion performance.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Energy & Fuels
Choi Junhwan, Oh Seokmin, Byun Joongmoo
Summary: AVO inversion transforms seismic reflection into elastic properties, recent deep learning applications have shown excellent results, but two types of uncertainty need to be considered: aleatoric from noisy data and epistemic from lack of data. Bayesian approximation is applied to estimate impedances and uncertainties, determining the reliability of prediction results.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2022)
Article
Energy & Fuels
Yeonghwa Jo, Yonggyu Choi, Soon Jee Seol, Joongmoo Byun
Summary: The vertical resolution of seismic data is crucial for interpreting geological features at a fine scale. Spectral enhancement techniques, including machine learning algorithms, have been developed to improve resolution by reflecting target data features. Traditional methods using 1D convolutional models show low performance in areas where wavelet frequency content changes significantly, highlighting the importance of adapting to non-stationary wavelets over time.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2022)
Article
Geochemistry & Geophysics
Kyubo Noh, Dowan Kim, Joongmoo Byun
Summary: Deep learning techniques are used for geophysical seismic facies classification to avoid subjectivity. However, these algorithms are black boxes and lack interpretability. To address this issue, explainable deep learning methods have been developed and applied, using prototype-based neural networks to provide more interpretable results.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Kyubo Noh, David Pardo, Carlos Torres-Verdin
Summary: Deep learning inversion is a promising method for real-time interpretation of logging-while-drilling (LWD) resistivity measurements. We develop a method to enhance the robustness of DL inversion methods in the presence of noisy LWD resistivity measurements by generating training data sets and constructing DL architectures.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2023)
Article
Geochemistry & Geophysics
Minkyu Bang, Soon Jee Seol, Joongmoo Byun
Summary: Deep neural networks (DNNs) have been used to interpret frequency-domain electromagnetic (EM) data, enabling rapid interpretation of vast amounts of information. However, the distortion caused by severe topographic changes in airborne surveys has not been addressed by DNN-based inversions, unlike conventional inversion techniques. To overcome this limitation, we propose a pseudo-1-D interpretation method that considers topographic changes using EM responses and nearby topographic information. Our trained DNN model provides reasonable interpretation results for synthetic and real AEM datasets, even in mountainous areas with rough topography.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Dowan Kim, Yonghwan Joo, Joongmoo Byun
Summary: In this study, a new method based on the differences between multiwindow energy ratios (DERs) was proposed to detect first-break points in seismic data, addressing the challenge of accurately determining first-break points in low signal-to-noise conditions. The method was validated using two types of seismic data, showing good picking performance, higher accuracy, and fewer outliers compared to conventional methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Kyubo Noh, Carlos Torres-Verdin, David Pardo
Summary: We develop a deep-learning inversion method for interpreting 2.5-dimensional borehole resistivity measurements. The method is validated with triaxial logging-while-drilling resistivity measurements in faulted and anisotropic formations. The proposed method successfully reconstructs 2.5D resistivity distributions and geological structures, making it reliable for real-time well geosteering applications.
Article
Geochemistry & Geophysics
Jeonghun Yoo, Dowan Kim, Junhwan Choi, Joongmoo Byun
Summary: This article proposes a machine learning method based on domain adaptation for impedance inversion. By adding a seismic data reconstruction process as a constraint and adopting a pseudo-labeling strategy, the proposed model outperforms conventional machine learning models in predicting impedance. The research results demonstrate that this model can be applied to the preliminary assessment of reservoirs with no well.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)