Anisotropic Elastic-Waveform Inversion with Compressive Sensing for Sparse Seismic Data

Abstract:

Elastic-waveform inversion (EWI) is a promising tool to reconstruct subsurface P- and S-wave velocity models with multicomponent seismic data. However, the existence of velocity anisotropy caused by aligned fractures introduces great challenge in reconstructing accurate medium properties. The number of elastic parameters for anisotropic media is much higher than that of elastic media, making anisotropic elastic-waveform inversion even more nonlinear and non-unique than isotropic elastic-waveform inversion. In addition, seismic data for many applications such as geothermal exploration and monitoring for enhanced geothermal systems are often acquired using sparsely distributed sources and receivers. We develop an anisotropic elastic-waveform inversion method with a compressive sensing technique for characterizing fracture zones using sparse seismic data. Rather than inverting for a reference velocity and Thomsen’s anisotropic parameters that are based on weak anisotropy assumption, we directly invert for subsurface elasticity parameters with multicomponent seismic data. We employ a compressive sensing technique to handle sparsely acquired seismic data. Furthermore, we formulate the conventional least-squares-based elastic-waveform inversion problem into two minimization subproblems to improve the robustness and the convergence rate of the inverse problem. We demonstrate the effectiveness of our new anisotropic elastic-waveform inversion with compressive sensing using synthetic and field sparse seismic data.