Simultaneous Inversion of Layered Velocity and Density Profiles Using Direct Waveform Inversion (DWI): 1D Case

Frontiers in Earth Science

Earth Science

Hua-Wei Zhou¹  Yingcai Zheng¹  Zhonghan Liu² 

[1] Department of Earth and Atmospheric Sciences, The University of Houston, Houston, TX, United States;null;

关键词: full waveform inversion (FWI);    velocity model building;    density inversion;    waveform inversion;    multi-parameter inversion;    modeling;   

DOI  :  10.3389/feart.2021.800312

 received in 2021-10-22, accepted in 2021-12-03,  发布年份 2022

来源: Frontiers

【 摘 要 】

To better interpret the subsurface structures and characterize the reservoir, a depth model quantifying P-wave velocity together with additional rock’s physical parameters such as density, the S-wave velocity, and anisotropy is always preferred by geologists and engineers. Tradeoffs among different parameters can bring extra challenges to the seismic inversion process. In this study, we propose and test the Direct Waveform Inversion (DWI) scheme to simultaneously invert for 1D layered velocity and density profiles, using reflection seismic waveforms recorded on the surface. The recorded data includes primary reflections and interbed multiples. DWI is implemented in the time-space domain then followed by a wavefield extrapolation to downward continue the source and receiver. By explicitly enforcing the wavefield time-space causality, DWI can recursively determine the subsurface seismic structure in a local layer-by-layer fashion for both sharp interfaces and the properties of the layers, from shallow to deep depths. DWI is different from the layer stripping methods in the frequency domain. By not requiring a global initial model, DWI also avoids many nonlinear optimization problems, such as the local minima or the need for an accurate initial model in most waveform inversion schemes. Two numerical tests show the validity of this DWI scheme serving as a new strategy for multi-parameter seismic inversion.

【 授权许可】

Unknown   
Copyright © 2022 Liu, Zheng and Zhou.