Full-waveform inversion (FWI) in its classic form is a method based on minimizing the [Formula: see text] norm of the difference between the observed and simulated seismic waveforms at the receiver locations. The objective is to find a subsurface model that reproduces the full waveform including the traveltimes and amplitudes of the observed seismic data. However, the widely used [Formula: see text]-norm-based FWI faces many issues in practice. The point-wise comparison of waveforms fails when the phase difference between the compared waveforms of the predicted and observed data is larger than a half-cycle. In addition, amplitude matching is impractical considering the simplified physics that we often use to describe the medium. To avoid these known problems, we have developed a novel elastic FWI algorithm using the local-similarity attribute. It compares two traces within a predefined local time extension; thus, is not limited by the half-cycle criterion. The algorithm strives to maximize the local similarities of the predicted and observed data by stretching/squeezing the observed data. Phases instead of amplitudes of the seismic data are used in the comparison. The algorithm compares two data sets locally; thus, it performs better than the global correlation in matching multiple arrivals. Instead of picking/calculating one stationary stretching/squeezing curve, we used a weighted integral to find all possible stationary curves. We also introduced a polynomial-type weighting function, which is determined only by the predefined maximum stretching/squeezing and is guaranteed to be smoothly varying within the extension range. Compared with the previously used Gaussian or linear weighting functions, our polynomial one has fewer parameters to play around with. A modified synthetic elastic Marmousi model and the North Sea field data are used to verify the effectiveness of the developed approach and also reveal some of its limitations.
|Original language||English (US)|
|Number of pages||1|
|State||Published - Aug 23 2019|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We thank Y. Liu and Z. Wu for their helpful discussions. We also thank Y. Liu, W. Weibull, and two anonymous reviewers for the effort put into their review of this paper. We thank the King Abdullah University of Science & Technology (KAUST) for its support and specifically the seismic wave analysis group members for their valuable insights. We thank Equinor and the former Volve license partners ExxonMobil E & P Norway AS and Bayerngas Norge AS for the release of the Volve data. The views expressed in this paper are the views of the authors and do not necessarily reflect the views of Equinor and the former Volve field license partners. For computer time, this research used the resources of the Supercomputing Laboratory at KAUST in Thuwal, Saudi Arabia.