First Published: The Leading Edge, February 2023

Yi Hang, James Sheng, Yang He, Feng Hao, Faqi Liu, Bin Wang, Seet Li Yong, Daniel Chaikin, Adriana Ramirez and Matt Hart discuss a newly developed algorithm, Dynamic Matching FWI.

Abstract

Full-waveform inversion (FWI) is firmly established within our industry as a powerful velocity model building tool. FWI carries significant theoretical advantages over conventional velocity model building methods such as refraction and refection tomography. Specifically, by solving a nonlinear inverse problem through the wave equation, FWI is able to recover a broadband velocity model containing both high and low spatial wavenumbers, thus extending the approximation of residual moveout correction inherent in traditional velocity model building approaches. Moreover, FWI is capable of inverting information from the entire wave eld (i.e., early arrivals, reflections, refractions, and multiple energy) rather than from a subset as in conventional approaches (i.e., first break and primary reflections), thereby availing itself of more information to better constrain its model estimate. However, these theoretical benefits cannot be realized easily in practice because various complexities of real seismic data often conspire to violate algorithmic assumptions, leading to unsatisfactory results. Dynamic matching FWI (DMFWI) is a newly developed algorithm that solves an inversion problem that maximizes the cross correlation of two dynamically matched data sets — one recorded and the other synthetic. Dynamic matching of the two data sets de-emphasizes the amplitude impact, which allows the algorithm to focus on minimizing their kinematic differences rather than amplitude in the data-fitting process. The multichannel correlation makes the algorithm robust for data with low signal-to-noise ratio. Applications of DMFWI across different types of acquisition and geologic settings demonstrate that this novel FWI approach can resolve complex velocity errors and provide high-quality migrated images that exhibit a high degree of geologic plausibility. Additionally, reflectivity images can be obtained in a straightforward manner as natural byproducts through computation of the directional derivative of the inverted FWI velocity models.

Introduction

As seismic acquisition methods continue to evolve, so does the efficacy of full-waveform inversion (FWI). With the industry’s shift from narrow-azimuth (NAZ) streamers to wide-azimuth (WAZ) streamers, and most recently to ocean-bottom nodes (OBNs), associated acquisition advancements have enabled the capture of wider azimuth, longer offset information as well as richer frequency content. These improvements have enabled the FWI algorithm to produce model estimates of increasingly high quality, to the point where FWI has become a standard tool for.....

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