Seismic fault detection with sliding windowed differential cepstrum–based coherence analysis

Abstract

Cepstral decomposition is beneficial for highlighting certain geological features within the particular quefrency bands which may be deeply buried within the wide quefrency range of the seismic data. Converting seismic traces into the corresponding cepstrum components can better analyse some characteristics of underground strata than the traditional spectral decomposition methods. We propose the sliding windowed differential cepstrum–based coherence analysis approach to delineate the fault features. First, the data are decomposed using a sliding windowed differential cepstrum, which results in multi-cepstrum data of corresponding quefrency of certain bandwidth. These different multi-cepstrum data may highlight the different stratigraphic features in a certain quefrency band. We select the first-order common quefrency volume as the featured attribute. Then, eigenstructure-based coherence is applied on the first-order common quefrency data volume to statistically obtain the fault detection result with a finer and sharper image. Synthetic data and field data examples show that the proposed method has the ability to better visualize all the possible subtle and minor faults present in the data more accurately and discernibly than the traditional coherence method. Compared with the ant-tracking method, the proposed method is more effective in revealing the major faults. It is hoped that this work will complement current fault detection methods with the addition of the cepstral-based method.