Receiver grouping strategies for a hybrid version of geometric-mean reverse-time migration
Name
segam2021-3594116.1.pdf
Description
Published version
Size
3.1 MB
Format
Adobe PDF
Checksum (MD5)
1b385e1eff00103e86e8991bea350848
Author(s)
Bai, Tong
Lyu, Bin
Gao, Fuchun
Williamson, Paul
Nakata, Nori
Date Issued
September 1, 2021
Journal
First International Meeting for Applied Geoscience & Energy
Publisher
Society of Exploration Geophysicists
Citation
Tong Bai, Bin Lyu, Fuchun Gao, Paul Williamson, Nori Nakata; September 1, 2021. "Receiver grouping strategies for a hybrid version of geometric-mean reverse-time migration." Proceedings of the First International Meeting for Applied Geoscience & Energy. First International Meeting for Applied Geoscience & Energy. (pp. pp. 2046-2050). ASME.
Version
Final published version
Abstract
Geometric-mean Reverse-time migration (GmRTM), a powerful crosscorrelation-based imaging method, generates higherresolution source images and is more robust to noise compared with conventional time-reversal imaging. The price to pay is the requirements of more accurate velocity models and much higher computational costs. A hybrid strategy can retain the advantages of both, and is often more practical than GmRTM. Yet, designing appropriate grouping strategy is not trivial. Here, we use numerical examples to test the performances of two grouping strategies (adjacent and scattered) with various group numbers. In addition to the spatial resolution, robustness to random noise is another important assessment criterion, for which we consider two distribution patterns (concentrated and scattered) of traces contaminated with strong random noise. We also evaluated their effectiveness to visualize event(s) which might not be well recorded by all receivers. Our numerical examples illustrate the respective advantages of the two grouping strategies.
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/segam2021-3594116.1
