Reverse-Time Migration Using Local Nyquist Cross-Correlation Imaging Condition

doi:10.1109/TGRS.2022.3168582

Return

Reverse-Time Migration Using Local Nyquist Cross-Correlation Imaging Condition

2022-01-01

PRE

DOI：10.1109/TGRS.2022.3168582

Original

Original request for help

Save

Abstract

En 中文

Reverse-time migration (RTM) has the particular capacity for complex geological structure imaging. However, massive storage and high computational costs caused by conventional cross-correlation imaging conditions restrict the large-scale application of RTM. The excitation amplitude imaging condition (EAIC) has the cheapest imaging cost, but inadequate wavefield information causes less tolerance for noise. To alleviate these limitations, we introduce a local Nyquist cross-correlation imaging condition, which serves as a transition between the conventional cross-correlation imaging conditions and the EAIC. Instead of using the full wavefields or only the excitation amplitude to construct imaging, the local cross-correlation imaging condition (LCIC) utilizes the wavefields around the corresponding time of the maximum amplitude at each grid during the source wavefield simulation. Moreover, considering the possible oversampling situations in the LCIC, the Nyquist sampling theorem is flexibly embedded into the local cross-correlation scheme to further reduce the storage costs and improve the efficiency of RTM. Compared with the cross-correlation imaging conditions, the proposed strategy can obtain the similar results and reduce the storage requirement and time costs significantly. In the meantime, it maintains better adaptability to the complex imaging environments than the EAIC. Migration tests of synthetic and field datasets demonstrate that the local Nyquist cross-correlation scheme features good feasibility, efficiency, and practicability in RTM. As a consequence, the proposed local Nyquist cross-correlation imaging condition can effectively save storage and time costs and provide a reliable migrated image even from noisy observed data.

Keywords:

Imaging

Receivers

Mathematical models

Image reconstruction

Costs

Extrapolation

Propagation

Imaging condition

local cross correlation

Nyquist sampling

reverse time migration