An efficient local operator-based Q-compensated reverse time migration algorithm with multistage optimization

doi:10.1190/GEO2017-0026.1

Return

An efficient local operator-based Q-compensated reverse time migration algorithm with multistage optimization

2018-05-01

PRE

DOI：10.1190/GEO2017-0026.1

Original

Original request for help

Save

Abstract

En 中文

Most existing Q-compensated reverse time migration (Q-RTM) algorithms are based on pseudospectral methods. Because of the global nature of pseudospectral operators, these methods are not ideal for efficient parallelization, implying that they may suffer from high computational cost and inefficient memory usage for large-scale industrial problems. In this work, we reported a novel Q-RTM algorithm - the multistage optimized Q-RTM method. This Q-RTM algorithm uses a finite-difference method to compensate the amplitude and the phase simultaneously by uniquely combining two techniques: (1) a negative tau method for amplitude compensation and (2) a multistage dispersion optimization technique for phase correction. To prevent high-frequency noise from growing exponentially and ruining the imaging results, we apply a finite impulse response low-pass filter using the Kaiser window. The theoretical analyses and numerical experiments demonstrate that this Q-RTM algorithm precisely recovers the decayed amplitude and corrects the distorted phase caused by seismic attenuation effects, and hence produces higher resolution subsurface images with the correct structural depth information. This new method performs best in the frequency range of 10-70 Hz. Compared with pseudospectral Q-RTM methods, this Q-RTM approach offers nearly identical imaging quality. Based on local numerical differential operators, this Q-RTM method is very suitable for parallel computing and graphic processing unit implementation, an important feature for large 3D seismic surveys.

Keywords:

PERFECTLY MATCHED LAYER

WAVE-PROPAGATION

DOMAIN DECOMPOSITION

FOURIER-TRANSFORM

DEPTH MIGRATION

CONSTANT-Q

ATTENUATION

EQUATION

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.