A two-step elastic full-waveform inversion applied to reflection seismic data for shallow hazard identification

A. Tognarelli, M. Aleardi
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引用次数: 0

Abstract

We apply a two-step elastic full-waveform inversion (FWI) to well-site survey (WSS) marine seismic data to estimate high-resolution P-wave (Vp) and S-wave (Vs) velocity models. Our approach combines a first global, genetic-algorithm optimization and a subsequent gradient-based inversion. The broad-band frequency content of the available seismic data makes it possible to extend the frequency range considered in the inversion up to 70 Hz and thus to derive a high-resolution elastic characterization of the shallowest part of the subsurface. The lack of low frequencies and the limited maximum source-to-receiver offset of the WSS acquisition, make the GA inversion particularly crucial as it provides a starting model for the gradient-based FWI that contains the large-medium wavelengths of the seismic velocity field. The following gradient-based FWI yields Vp and Vs models characterized by an improved resolution with respect to the outcomes of GA-FWI. The match between the observed and the predicted seismic data proves the reliability of our predictions.
两步弹性全波形反演方法在浅层地震灾害识别中的应用
我们将两步弹性全波形反演(FWI)应用于井场调查(WSS)海洋地震数据,以估计高分辨率的纵波(Vp)和横波(Vs)速度模型。我们的方法结合了第一个全局遗传算法优化和随后的基于梯度的反演。可用地震数据的宽带频率内容使得可以将反演中考虑的频率范围扩展到70 Hz,从而得出地下最浅部分的高分辨率弹性特征。由于WSS采集缺乏低频和有限的最大源接收机偏移量,使得遗传反演尤为重要,因为它为包含地震速度场的大中型波长的基于梯度的FWI提供了一个起始模型。以下基于梯度的FWI产生Vp和Vs模型,其特征是相对于GA-FWI的结果具有更高的分辨率。观测资料与预报资料的吻合证明了预报的可靠性。
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