Geophysical Inversion

A special issue of Geosciences (ISSN 2076-3263). This special issue belongs to the section "Geophysics".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 1766

Special Issue Editors


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Guest Editor
Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin, Austin, TX, USA
Interests: simultaneous sources deblending and imaging; high-dimensional seismic reconstruction; microseismic processing, imaging, and inversion; reservoir seismics; regional- to global-scale seismic imaging; deep learning in seismics
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School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
Interests: inverse problems; scientific machine learning; Bayesian inference; uncertainty quantification; numerical modeling and simulation; geophysical imaging, inversion, and monitoring

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Guest Editor
Department of Earth and Planetary Sciences, Stanford University, California, CA, USA
Interests: geophysical separate and joint inversions; uncertainty quantification; geoscience data integration; deep learning for geosciences

Special Issue Information

Dear Colleagues,

Geophysical inverse problems are ubiquitous in many areas of geoscience, including geophysical imaging, exploration, and monitoring. Solving these inverse problems is often challenging because the unknown Earth parameters of interest are highly dimensional, and their observations are indirect and corrupted by noise, while the creation of parameter-to-observable maps are computationally expensive and suffer from non-trivial null-spaces. To this end, robust and uncertainty-aware inversion becomes important in real-world applications to extract the full value from such observations. 

This Special Issue welcomes the submission of manuscripts that present recent methodologies, workflows, case studies, and real-data examples that discuss the state of the art in geophysical inversion, including, but not limited to, the following:

  • Innovations in modeling, simulation, and optimization via computational algorithms;
  • Case histories highlighting challenges and solutions in geophysical applications;
  • Novel inversion methods based on scientific machine learning and generative artificial intelligence;
  • Low-cost and scalable uncertainty quantification and Bayesian inference techniques;
  • Applications for energy transitions, such as geological carbon/hydrogen storage and geothermal exploration.

Prof. Dr. Yangkang Chen
Dr. Ziyi Yin
Dr. Xiaolong Wei
Guest Editors

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Keywords

  • inverse problems
  • uncertainty quantification
  • imaging and inversion
  • scientific machine learning
  • energy transition

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Published Papers (2 papers)

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Research

22 pages, 54340 KiB  
Article
Exploring Copper Resources: A Geophysical and Geological Approach in the South Riogrande Shield, RS, Brazil
by Marieli Machado Zago and Maximilian Fries
Geosciences 2025, 15(2), 38; https://doi.org/10.3390/geosciences15020038 - 24 Jan 2025
Viewed by 477
Abstract
The search for mineral resources presents an enduring challenge as these demands consistently surge, and the utilization of geophysics is undeniably intertwined with the pursuit of novel prospects. Technological advancements over recent decades have facilitated access to 2D and 3D visualization software, enabling [...] Read more.
The search for mineral resources presents an enduring challenge as these demands consistently surge, and the utilization of geophysics is undeniably intertwined with the pursuit of novel prospects. Technological advancements over recent decades have facilitated access to 2D and 3D visualization software, enabling robust data integrations. Consequently, interpreters possess the latitude to harness their ingenuity and technical acumen in conducting multifarious analyses. Mineral exploration in greenfield areas, a particularly challenging endeavor, often commences with regional surveys and circumscribed information about the terrain. Notwithstanding limited preliminary data, the judicious deployment of filtering, modeling, and inversion techniques with geophysical data holds sway in catalyzing discoveries. This study, with its comprehensive amalgamation of diverse copper occurrence indicators and the novel procedural framework it establishes for processing and integrating airborne gamma-ray spectrometry and magnetometry geophysical and geological data, exemplifies the complexity and depth of our field. Elaborate litho-geophysical profiles, linked with data concerning mineral occurrences and geochemistry, pinpoint potential copper deposits in the area. This multidisciplinary approach and inversion mode provide detailed insights into probable mineralized body continuity and regional structural frameworks, offering valuable guidance for future regional mineral exploration efforts. Full article
(This article belongs to the Special Issue Geophysical Inversion)
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25 pages, 16213 KiB  
Article
Imaging Shallow Velocity Structure of an Inactive Fault by Airgun Seismic Source: A Case Study of Xiliushui Fault in Qiliang Mountain
by Manzhong Qin, Baichen Wu, Yi Wang, Xueyi Shang, Yuansheng Zhang, Xuzhou Liu, Xiao Guo, Rui Zou, Yahong Wang and Dianfeng Sun
Geosciences 2025, 15(1), 16; https://doi.org/10.3390/geosciences15010016 - 7 Jan 2025
Viewed by 463
Abstract
We observed high-quality waves from a repeatable airgun seismic source recorded by a linear ultra-dense seismic array across the Xiliushui fault zone, one of the inactive faults in the Qilian Mountain, on the northeastern margin of the Tibetan Plateau, China. We used Snell’s [...] Read more.
We observed high-quality waves from a repeatable airgun seismic source recorded by a linear ultra-dense seismic array across the Xiliushui fault zone, one of the inactive faults in the Qilian Mountain, on the northeastern margin of the Tibetan Plateau, China. We used Snell’s law of seismic ray propagation to determine a simplified ambient velocity model. Based on the flexible and precise spectral element method, we computed broadband synthetic seismograms for a shallow low-velocity fault zone (FZ) to model the direct P-wave travel time delay and incident angle of the wavefield near the FZ. The FZ extent range and boundaries were inverted by apparent travel time delays and amplification patterns across the fault. According to prior information on the properties of the direct P-waves, we could constrain the inverse modeling and conduct a grid search for the fault parameters. The velocity reduction between the FZ and host rock, along with the dip angle of the FZ, were also constrained by the P-wave travel time delay systematic analysis and incoming angle of the P-waves. We found that the Xiliushui fault has a 70~80 m-wide low-velocity fault damage zone in which the P-wave velocity is reduced to ~40% with respect to the host rock. The fault damage zone dips ~35°southwest and extends to ~165 m in depth. The repeatability and environment protection characteristics of the airgun seismic survey and the economic benefits of a limited number of instruments setting are prominent. Full article
(This article belongs to the Special Issue Geophysical Inversion)
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