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Reverse time migration: A prospect of seismic imaging methodology

Research paper by Hua-Wei Zhou, Hao Hu; Zhihui Zou; Yukai Wo; Oong Youn

Indexed on: 28 Feb '18Published on: 26 Feb '18Published in: Revista de la Sociedad Española del Dolor



Abstract

Publication date: Available online 24 February 2018 Source:Earth-Science Reviews Author(s): Hua-Wei Zhou, Hao Hu, Zhihui Zou, Yukai Wo, Oong Youn Reverse time migration (RTM) is a seismic imaging method to map the subsurface reflectivity using recorded seismic waveforms. The practice in exploration seismology has long established a two-fold approach of seismic imaging: Using velocity modeling building to establish the long-wavelength reference velocity models, and using seismic migration to map the short-wavelength reflectivity structures. Among various seismic migration methods for different situations, RTM is the only method that is capable to use all seismic wave types that can be computed numerically. Being initiated in early 1980's, RTM seeks an image of the subsurface reflectivity as the best match in an image space between the extrapolation of time-reversed waveform data and the prediction based on estimated velocity model and source parameters. Judging the image quality in the same space of forming the images is more advantageous than the approaches of modeling and inversion which seek the solution in the model space but judge its fitness in data space. Considering that most seismic migration applications today still use primary reflection as the only signal, the capability of RTM to use all computable wave types is unique and helpful reducing the imaging artifacts due to mistaking non-primary waves as primary reflections. Hence, we refer to those RTM algorithms using only primary reflections as the first-generation RTM methods, and the RTM algorithms making a full use of primary reflections, multiple reflections and other non-primary waveform data as the second-generation RTM methods. This paper reviews the development history of the RTM along with its major challenges, current solutions, and future perspectives.