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Geosciences
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Geophysical Inversion
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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 1023

Special Issue Editors

Prof. Dr. Yangkang Chen

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals
Dr. Ziyi Yin

E-Mail Website
Guest Editor
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
Dr. Xiaolong Wei

E-Mail Website
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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Geosciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

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Published Papers (1 paper)

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Research

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 307
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)
►▼ Show Figures

Figure 1

Figure 1
<p>The location of the Gansu Qilian Mountain active seismic source and Xiliushui fault zone. (<b>a</b>) In the monitoring area (blue box), the red star represents the location of the airgun and reservoir. In the topographic map of the window, red line represents Xiliushui fault. (<b>b</b>) A linear dense array with a length of 500 m across the Xiliushui fault; the fault (red line) is outcropped on the surface. (<b>c</b>) The strike and the dip of the fault are SE and SW from surface geology survey, respectively. The aerial view of reservoir and station location in the study area is shown in <a href="#geosciences-15-00016-f0A1" class="html-fig">Figure A1</a>.</p> Full article ">Figure 2
<p>(<b>a</b>–<b>c</b>) The three-component observation waveforms (blue) of airgun seismic source excitation recorded at Station_N24 (24 m); the red traces are the stacked waveforms. (<b>d</b>) The frequency spectrum of station near the FZ recorded at Station_S20 (−20 m), N24 (24 m), and N60 (60 m).</p> Full article ">Figure 3
<p>(<b>a</b>) The Z-component (blue line) and R-component (red line) observation waveforms in the bandpass 8–12 Hz of airgun seismic source excitation recorded at Station_S90 (−90 m), S60 (−60 m), S30 (−30 m), N00 (fault trace), N15 (15 m), N30 (30 m), N60 (60 m), N90 (90 m). (<b>b</b>) Particle motion of the waveform in the different color-shaded window in (<b>a</b>). The big point represents the end point of the particle motion.</p> Full article ">Figure 4
<p>The multi-band (0–20 Hz) filtering (<b>a</b>–<b>c</b>) and corresponding amplitude envelope (<b>d</b>–<b>f</b>) of observation waveforms at representative station_S60, N24 and N60, where the raw wave represents observation stacked waveform from R-component, and the red dotted line indicates the arrival time of surface wave.</p> Full article ">Figure 5
<p>The filter-stacked (<b>a</b>,<b>b</b>) observation waveforms in the Z- and R-component stations across the fault. The dotted line represents the arrival time of P-body wave (black dotted line) and surface wave (purple dotted line), and red dotted rectangle boxes represent the fault zone waveform observed by three-components stations close to the fault zone.</p> Full article ">Figure 6
<p>(<b>a</b>) Fault zone trapped waves (FZTWs) following surface wave arrivals (along the line of the array) after being rotated clockwise to the fault-parallel component (45°). (<b>b</b>) Distributions of normalized peak ground velocities (PGVs; red dots) and root mean square (RMS) amplitudes (blue stars) of the surface waveforms shown in (<b>a</b>). The black curve represents the likelihood of FZTWs (i.e., the normalized multiplication of PGV and RMS values) and is used to identify FZTWs. The green bar outlines the stations with FZTWs.</p> Full article ">Figure 7
<p>The spectral ratio results along the entire array obtained from horizontal component. The white dashed zone indicates high spectral ratio value.</p> Full article ">Figure 8
<p>The cross-correlation method calculates the P-wave and surface wave, generated in R-component by airgun excitation. (<b>a</b>) represents the P-wave arrival time and (<b>b</b>) represents the surface wave arrival time. The yellow and blue curves represent the results obtained using different reference stations. Dotted line represents fitting regional velocity. P-wave velocity is 4.67 km/s and surface wave is 2.8 km/s as the regional maximal seismic wave velocity, which is the model velocity at the bottom. The green bar outlines the stations in fault zone. Arrival time (<b>c</b>) and period characteristics (<b>d</b>) of surface wave in R-component (<a href="#geosciences-15-00016-f005" class="html-fig">Figure 5</a>b) of all stations across the fault zone. The green bar outlines the stations in fault zone.</p> Full article ">Figure 9
<p>A workflow showing the key steps in the seismic forward modeling method used in the study: forward modeling, CC value calculation, and velocity analysis. (<b>a</b>) Fault model in depth and width. (<b>b</b>) Snapshot of the wave propagation and the shot gathered from the same source. (<b>c</b>) Synthetic waveform figure. (<b>d</b>) The waveform of the source wavelet: Ricker with a frequency of 10 Hz.</p> Full article ">Figure 10
<p>The misfit 2D maps of the dip angle and central location (horizontal shift value relative to the surface fault trace) of the FZ at the optimal values of velocity reduction ratio, width and depth. Trade-off between the dip angle and the fault zone central location can be observed.</p> Full article ">Figure 11
<p>(<b>a</b>) P-wave arrival times curve of observations (green) and predictions (red) near the FZ and their modification value after detrending. (<b>b</b>) Delay time curve of observation and prediction after detrending and equal scaling in (<b>a</b>). (<b>c</b>) Observed P-wave incoming angle (blue dots) and P-wave incoming angle (green dots) of fault zone velocity model in (<b>a</b>).</p> Full article ">Figure A1
<p>The location of the Gansu Qilian Mountain active seismic source (<b>a</b>), and the aerial view of reservoir and topography in the study area. (<b>a</b>) Regional geotectonic map; the green rectangle is the research area in <a href="#geosciences-15-00016-f001" class="html-fig">Figure 1</a>. HYF is Qilian-Haiyuan Fault; ATF is Altyn-Tagh Fault; KF is Kunlun Fault; XHF is Xianshuihe Fault. (<b>b</b>) The red circle represents airgun source, the red points represent stations in the array (white line).</p> Full article ">Figure A2
<p>Flash snapshot in basic forward model. The red and blue represent wavefield; green represents array zone. (<b>a</b>–<b>f</b>) is the wave field snapshot at different source times.</p> Full article ">Figure A3
<p>P-wave arrival times curve and predictions of different models near the FZ. Adjusting dip of FZ (±5°) based on optimal FZ’s parameter model. (<b>a</b>) dip: 35° + 5°, detrend NCC value of travel delay time is 0.8; (<b>b</b>) dip: 35°− 5°, detrend NCC value of travel delay time is 0.73.</p> Full article ">Figure A4
<p>P-wave arrival times curve and predictions of different models near the FZ. Adjusting location of FZ core (±5 m) based on optimal FZ’s parameter model. (<b>a</b>) FZ core location: 25 m + 5 m, detrend NCC value of travel delay time is 0.58; (<b>b</b>) dip: 25 m − 5 m, detrend NCC value of travel delay time is 0.8.</p> Full article ">Figure A5
<p>P-wave arrival times curve and predictions of different models near the FZ. Adjusting depth of FZ (±5 m) based on optimal FZ’s parameter model. (<b>a</b>) FZ’s depth: 165 m + 5 m, detrend NCC value of travel delay time is 0.76; (<b>b</b>) FZ’s depth: 165 m− 5 m, detrend NCC value of travel delay time is 0.77.</p> Full article ">Figure A6
<p>P-wave arrival times curve and predictions of different models near the FZ. Adjusting velocity reduction of FZ (45%) based on optimal FZ parameter model. (<b>a</b>) FZ velocity reduction to 45% + 5%; detrend in NCC value of travel delay time is 0.75; (<b>b</b>) FZ velocity reduction to 45% − 5%; detrend in NCC value of travel delay time is 0.77.</p> Full article ">
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