Full waveform inversion without low frequencies: A synthetic study

Abstract

Full waveform inversion (FWI) is an optimization procedure which minimizes the waveform difference between the observed and calculated seismic data. The method, through an iterative forward modeling and inversion based on the acoustic or elastic wave equation, has recently gained wide interest with specific application in exploration and producing. FWI is wellknown for its ability to obtain high resolution and accurate subsurface models in areas with complex geological structures related to the presence of salt bodies encountered in deep water exploration. However, conventional FWI algorithms require seismic data that contains low frequencies (<5Hz). Lack of low frequencies results in unreliable velocity models for seismic imaging. In this paper, we aim to relax the low-frequency requirement of FWI and propose a technique which employs principles from sparse deconvolution in order to recover the missing low frequencies. Through a series of synthetic examples, it is shown that our proposed methodology is able to preserve and utilize the traveltime information of the original data; hence, it allows convergence of the FWI. The major advantage of this method is that it is fully automatic, involving no residual moveout or traveltime difference analysis.