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Updated: Jun 17, 2026
07:58
Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt
Published on: August 7, 2017
Joint full waveform inversion and source localization of virtual and actual passive source seismic data.
Xujia Shang1, Liguo Han2, Pan Zhang1
1College of Geo-exploration Science and Technology, Jilin University, Changchun, 130026, China.
Scientific Reports
|June 15, 2026
Summary
This study introduces a joint inversion framework using virtual and actual passive seismic data for robust deep subsurface velocity modeling and accurate passive seismic source localization, even with limited data.
Area of Science:
- Geophysics
- Seismology
- Earth Sciences
Background:
- Full waveform inversion (FWI) of passive seismic data is promising for deep subsurface imaging.
- Reliable background velocity models and accurate source locations are critical limitations for passive seismic FWI.
- Existing methods struggle with deep illumination and precise source determination.
Purpose of the Study:
- To develop a joint inversion framework for simultaneous velocity modeling and source localization using passive seismic data.
- To overcome the limitations of requiring accurate source locations and reliable initial models in passive seismic FWI.
- To enhance deep subsurface structural information retrieval from passive seismic sources.
Main Methods:
- Utilized seismic interferometry to retrieve broadband virtual reflection responses from passive seismic data.
- Employed multiscale FWI with virtual responses for initial velocity structure recovery, independent of source locations.
- Implemented an iterative approach combining reverse-time wavefield scanning and FWI with actual passive-source data for simultaneous source localization and velocity inversion.
Main Results:
- Successfully recovered subsurface velocity structures using only passive seismic data, even with rough initial models.
- Demonstrated accurate localization of passive seismic events.
- Validated robustness across various conditions, including inhomogeneous source distribution and different signal-to-noise ratios (SNR).
Conclusions:
- The proposed joint inversion framework effectively addresses key challenges in passive seismic FWI.
- The method enables robust velocity modeling and precise source localization, improving deep subsurface imaging.
- This approach offers a significant advancement for utilizing passive seismic data in geophysical exploration.
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