Elastic Wavefield Reconstruction Inversion With Source Estimation

Elastic full-waveform inversion (EFWI) can retrieve multiple subsurface elastic parameters beyond the capabilities of the simple acoustic assumption. Compared to acoustic FWI (AFWI), EFWI faces complexities in dealing with multiple parameters and high nonlinearity in elastic inversion. Wavefield reconstruction inversion (WRI) was proposed to mitigate the cycle skipping and improve the computational efficiency of AFWI. WRI uses the wave equation as a regularization term for the objective of data fitting. By controlling the weight factor for the regularization term, the accuracy of the wave equation is relaxed and the data fitting term is enhanced. Thus, the cycle-skipping issue is reduced. WRI requires wavefield reconstruction in the calculation of the model gradient, which is the key step in WRI. The success of this wavefield reconstruction step highly depends on the accuracy of the source wavelet. In this paper, we propose an elastic WRI (EWRI) with source estimation (SE) method for multiple elastic parameters inversion. In the proposed method, we first reconstruct multicomponent wavefields (vertical and horizontal displacements) and estimate the source wavelet, simultaneously. Then, we formulate the elastic waveform inversion problem into a linear inversion system to mitigate the nonlinearity in EFWI. Applications on synthetic data generated from a modified Overthrust model and a Section of the Sigsbee2A model show the effectiveness of the proposed method in inverting P- and S-wave velocity models with an unknown source wavelet.