Elastic geometric-mean reverse time migration for source imaging
Name
geo-2021-0516.1.pdf
Description
Published version
Size
6.5 MB
Format
Adobe PDF
Checksum (MD5)
84e12a7c8eaabfc6e53fbe8164f3a29a
Author(s)
Bai, Tong
Lyu, Bin
Li, Fangyu
Williamson, Paul
Nakata, Nori
Date Issued
June 20, 2022
Journal
Geophysics
Publisher
Society of Exploration Geophysicists
Citation
Tong Bai, Bin Lyu, Fangyu Li, Paul Williamson, Nori Nakata; Elastic geometric-mean reverse time migration for source imaging. Geophysics 2022;; 87 (4): KS135–KS146.
Version
Final published version
Abstract
Passive source location is a fundamental problem in earthquake seismology and reservoir characterization with fluid injection. It has been shown that geometric-mean reverse time migration (GmRTM) can produce source images with improved resolution and fewer migration artifacts than conventional time-reversal imaging in acoustic media. However, the acoustic assumption often requires data windowing and polarity correction when we apply it to the elastic data, which involves manual efforts and introduces uncertainties. Therefore, we have extended this crosscorrelation-based GmRTM imaging method to elastic media. Back-propagation and Helmholtz decomposition are conducted individually for each receiver wavefield, after which we apply zero-lag crosscorrelations over the decoupled P or/and S wavefields to obtain the corresponding P-, S-, and PS-source images. Considering the influences of random noise, anisotropy, and interferences from multiple sources, we test the proposed elastic GmRTM method on a synthetic example with a layered model and determine its advantages over other imaging conditions (e.g., time-reversal imaging). The method is further validated using another synthetic test based on the elastic Marmousi model and a field-data example from Oklahoma.
MIT Department
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1190/geo2021-0516.1
