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Title: Elastic-Waveform Inversion with Compressive Sensing for Sparse Seismic Data

Accurate velocity models of compressional- and shear-waves are essential for geothermal reservoir characterization and microseismic imaging. Elastic-waveform inversion of multi-component seismic data can provide high-resolution inversion results of subsurface geophysical properties. However, the method requires seismic data acquired using dense source and receiver arrays. In practice, seismic sources and/or geophones are often sparsely distributed on the surface and/or in a borehole, such as 3D vertical seismic profiling (VSP) surveys. We develop a novel elastic-waveform inversion method with compressive sensing for inversion of sparse seismic data. We employ an alternating-minimization algorithm to solve the optimization problem of our new waveform inversion method. We validate our new method using synthetic VSP data for a geophysical model built using geologic features found at the Raft River enhanced-geothermal-system (EGS) field. We apply our method to synthetic VSP data with a sparse source array and compare the results with those obtained with a dense source array. Our numerical results demonstrate that the velocity models produced with our new method using a sparse source array are almost as accurate as those obtained using a dense source array.
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  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Geophysics Group
Publication Date:
OSTI Identifier:
Report Number(s):
LA-UR--15-20434; SGP-TR--204
DOE Contract Number:
Resource Type:
Technical Report
Resource Relation:
Conference: 14. Workshop on Geothermal Reservoir Engineering, Stanford, CA (United States), 26-28 Jan 2015
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Geothermal Technologies Office (EE-4G)
Country of Publication:
United States
58 GEOSCIENCES Planetary Sciences; elastic-waveform inversion; compressional and shear waves; compressive sensing; vertical seismic profiling