Spectral recomposition for optimizing starting points in Full-Waveform Inversion

Abstract

By optimizing the wavelet fitting of the arrivals in a reflection event, it is possible to perform significant enhancements for Full-Waveform Inversion (FWI) predictions and its processing time. For this reason, approaches capable of reducing the number of iterations for FWI — without decreasing the quality of the prediction — are of interest. Once the initial guess for FWI is better estimated, it is possible to predict the velocity model within a determined accuracy with fewer iterations. We thus propose an approach which can perform this estimation, based on spectral recomposition of seismic data. We design an inversion scheme to reconstruct the seismic spectrum of wavelets of a reflection event, which subsequently allows estimating the position in time of each wavelet in a seismogram. After finding the position in time of each wavelet, we can guide the calculated wavelet to fit the corresponding observed signal, starting from a closer initial point. Our approach leads to quite accurate predictions of velocity models with fewer iterations.