Abstract

Onshore seismic exploration analyzes seismic wave propagation in elastic media, which includes the conversion between P- and S-waves. The development of multi-wave and multi-component seismic exploration methods provides data that enable onshore elastic wave full-waveform inversion. However, most data sets of onshore exploration are single component obtained from the particle-motion response from the vertical geophone. When the aiming area has a low-velocity zone, the ray path of reflected wave that propagates to the detector is nearly perpendicular to the ground surface, so that we call it P-wave data. In this paper, we focus on multi-parameter waveform inversion using P-wave reflection seismic data. Although only P-wave data are received, it still contains the converted P-wave information, and the converted P-wave energy gradually increases as the offset increases. As seismic acquisition technology, observation systems and science develop, the folds and acquisition offset increase significantly, and the seismic data contain important converted P-wave information. In this paper, the first-order elastic velocity–stress equation is decomposed to obtain the scalar-P-wave equation from which the S-wave velocity is included firstly. Then we present the theoretical framework for onshore multi-parameter full-waveform inversion using P-wave data. In order to explore the inversion potential of the P-wave data (extracting the S-wave velocity from the converted P-wave information) and accuracy and stability of the P- and S-wave velocity inverted by our method, we carry out numerical tests via different inversion strategies, by using the P-wave data regarded as containing converted P-wave information, and get successful results.

Highlights

  • Full-waveform inversion (FWI) is a quantitative data-fitting procedure which minimizes the residuals between the simulated and the observed seismic data to obtain high-resolution subsurface physical parameters such as P-wave velocity, density, impedance, or anisotropic parameters (Virieux and Operto 2009). Lailly (1983) and Tarantola (1984) first proposed the least-squares misfit functional FWI framework and built the gradient of the misfit function by cross-correlating the incident wavefield emitted from the source and the back-propagated residual wavefields. Pratt and Worthington (1990) extended FWI to the frequency domain and pointed out that multi-scale inversion can improve the effectiveness and stability of the inversion

  • We propose a multi-parameter full-waveform inversion method using the P-wave data with high-quality converted P-wave information

  • The scalar-P-wave equation obtained by decoupling the first-order velocity–stress equation contains only P-wave information, unlike the conventional acoustic and elastic wave equations

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Summary

Introduction

Full-waveform inversion (FWI) is a quantitative data-fitting procedure which minimizes the residuals between the simulated and the observed seismic data to obtain high-resolution subsurface physical parameters such as P-wave velocity, density, impedance, or anisotropic parameters (Virieux and Operto 2009). Lailly (1983) and Tarantola (1984) first proposed the least-squares misfit functional FWI framework and built the gradient of the misfit function by cross-correlating the incident wavefield emitted from the source and the back-propagated residual wavefields. Pratt and Worthington (1990) extended FWI to the frequency domain and pointed out that multi-scale inversion can improve the effectiveness and stability of the inversion. Full-waveform inversion (FWI) is a quantitative data-fitting procedure which minimizes the residuals between the simulated and the observed seismic data to obtain high-resolution subsurface physical parameters such as P-wave velocity, density, impedance, or anisotropic parameters (Virieux and Operto 2009). Pratt and Worthington (1990) extended FWI to the frequency domain and pointed out that multi-scale inversion can improve the effectiveness and stability of the inversion. FWI has been successfully applied for both simulated and real seismic data (Shipp and Singh 2002; Sears et al 2008; Sirgue et al 2010). For multi-component seismic data, elastic wave fullwaveform inversion (EFWI) is more suitable for high-precision reconstruction of subsurface elastic parameters than acoustic full-wave inversion (AFWI) (Brossier et al 2009).

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