Iterative Least-Squares Migration without Cycle Skipping
W. DaI, X. Cheng, K Jiao and D. Vigh
Event name: First EAGE/SBGf Workshop on Least-Squares Migration
Session: Theory and Implementation II
Publication date: 27 November 2018
Info: Extended abstract, PDF ( 2.6Mb )
Price: € 20
Least-squares reverse-time migration (LSRTM) has been shown to improve image quality over conventional RTM by enhancing the resolution, balancing illumination, and suppressing migration artefacts. However, it is also known to be sensitive to velocity errors. In the presence of velocity errors, predicted data show different moveouts from the observed data, which will hinge LSRTM convergence and yield sub-optimal results. To mitigate velocity errors, we propose to apply dynamic time warping (DTW) to the observed data and shift them towards the predicted data to improve data matching and subsequently images. In this paper, we show 2 synthetic examples and 1 real data example to demonstrate the advantages of dynamic time warping. Our observations show that dynamic time warping helps with event focusing, corrects phase distortion, improves event amplitudes, and thus improves event continuity.