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Source-Independent Amplitude-Semblance Full-Waveform Inversion using a Hybrid Time-and Frequency-Domain Approach

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摘要 Full-waveform inversion is a promising tool to produce accurate and high-resolution subsurface models.Conventional full-waveform inversion requires an accu-rate estimation of the source wavelet,and its computational cost is high.We develop a novel source-independent full-waveform inversion method using a hybrid time-and frequency-domain scheme to avoid the requirement of source wavelet estimation and to reduce the computational cost.We employ an amplitude-semblance objective function to not only effectively remove the source wavelet effect on full-waveform inver-sion,but also to eliminate the impact of the inconsistency of source wavelets among different shot gathers on full-waveform inversion.To reduce the high computational cost of full-waveform inversion in the time domain,we implement our new algorithm using a hybrid time-and frequency-domain approach.The forward and backward wave propagation operations are conducted in the time domain,while the frequency-domain wavefields are obtained during modeling using the discrete-time Fourier trans-form.The inversion process is conducted in the frequency domain for selected frequen-cies.We verify our method using synthetic seismic data for the Marmousi model.The results demonstrate that our novel source-independent full-waveform inversion pro-duces accurate velocity models even if the source signature is incorrect.In addition,our method can significantly reduce the computational time using the hybrid time-and frequency-domain approach compared to the conventional full-waveform inversion in the time domain.
机构地区 Geophysics Group
出处 《Communications in Computational Physics》 SCIE 2020年第6期328-341,共14页 计算物理通讯(英文)
基金 supported by the U.S.Department of Energy(DOE)through the Los Alamos National Laboratory(LANL),which is operated by Triad National Security,LLC,for the National Nuclear Security Administration(NNSA)of U.S.DOE under Contract No.89233218CNA000001 provided by the LANL Institutional Computing Program,which is supported by the U.S.DOE NNSA under Contract No.89233218CNA000001.
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