TY - JOUR
T1 - 3-D S Wave Imaging via Robust Neural Network Interpolation of 2-D Profiles From Wave-Equation Dispersion Inversion of Seismic Ambient Noise
AU - Chen, Yuqing
AU - Saygin, Erdinc
N1 - Funding Information:
This research was fully funded by the Deep Earth Imaging Future Science Platform, CSIRO. This work was supported by resources provided by the Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia. We would like to thank the Chevron, Australia for their assistance with the raw data. The authors thank Dr. Caroline Johnson for reviewing the manuscript. They would like to thank Dr. Ben Clenell for discussions. They also appreciate the time and efforts of the editor - Prof. Michael Bostock, associate editor and two anonymous reviewers in reviewing this manuscript.
Funding Information:
This research was fully funded by the Deep Earth Imaging Future Science Platform, CSIRO. This work was supported by resources provided by the Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia. We would like to thank the Chevron, Australia for their assistance with the raw data. The authors thank Dr. Caroline Johnson for reviewing the manuscript. They would like to thank Dr. Ben Clenell for discussions. They also appreciate the time and efforts of the editor ‐ Prof. Michael Bostock, associate editor and two anonymous reviewers in reviewing this manuscript.
Publisher Copyright:
© 2022 Commonwealth Scientific and Industrial Research Organisation.
PY - 2022/12
Y1 - 2022/12
N2 - Ambient noise seismic data are widely used by geophysicists to explore subsurface properties at crustal and exploration scales. Two-step dispersion inversion schema is the dominant method used to invert the surface wave data generated by the cross-correlation of ambient noise signals. However, the two-step methods have a 1-D layered model assumption, which does not account for the complex wave propagation. To overcome this limitation, we employ a 2-D wave-equation dispersion (WD) inversion method which reconstructs the subsurface shear (S) velocity model in one step, and elastic wave-equation modeling is used to simulate the subsurface wave propagation. In the WD method, the optimal S velocity model is obtained by minimizing the dispersion curve differences between the observed and predicted surface wave data, which makes WD method less prone to getting stuck to local minima. In our study, the observed Scholte waves are generated by cross-correlating continuous ambient noise signals recorded by each ocean bottom node (OBN) in the 3-D Gorgon OBN survey, Western Australia. For every two OBN lines, the WD method is used to retrieve the 2-D S velocity structure beneath the first line. We then use a robust neural network (NN)-based method to interpolate the inverted 2-D velocity slices to a continuous 3-D velocity model and also generate a corresponding uncertainty model. Moreover, we compared the predicted dispersion curves and waveforms to the observed data, and a robust waveform and dispersion match are observed across all of the Gorgon OBN lines.
AB - Ambient noise seismic data are widely used by geophysicists to explore subsurface properties at crustal and exploration scales. Two-step dispersion inversion schema is the dominant method used to invert the surface wave data generated by the cross-correlation of ambient noise signals. However, the two-step methods have a 1-D layered model assumption, which does not account for the complex wave propagation. To overcome this limitation, we employ a 2-D wave-equation dispersion (WD) inversion method which reconstructs the subsurface shear (S) velocity model in one step, and elastic wave-equation modeling is used to simulate the subsurface wave propagation. In the WD method, the optimal S velocity model is obtained by minimizing the dispersion curve differences between the observed and predicted surface wave data, which makes WD method less prone to getting stuck to local minima. In our study, the observed Scholte waves are generated by cross-correlating continuous ambient noise signals recorded by each ocean bottom node (OBN) in the 3-D Gorgon OBN survey, Western Australia. For every two OBN lines, the WD method is used to retrieve the 2-D S velocity structure beneath the first line. We then use a robust neural network (NN)-based method to interpolate the inverted 2-D velocity slices to a continuous 3-D velocity model and also generate a corresponding uncertainty model. Moreover, we compared the predicted dispersion curves and waveforms to the observed data, and a robust waveform and dispersion match are observed across all of the Gorgon OBN lines.
UR - http://www.scopus.com/inward/record.url?scp=85145190459&partnerID=8YFLogxK
U2 - 10.1029/2022JB024663
DO - 10.1029/2022JB024663
M3 - Article
AN - SCOPUS:85145190459
VL - 127
JO - Journal of Geophysical Research: Solid Earth
JF - Journal of Geophysical Research: Solid Earth
SN - 2169-9313
IS - 12
M1 - e2022JB024663
ER -