Multispectral Coherence Analysis for Better Fault Visualization in Seismic Data

Abstract

Spectral decomposition helps the geophysicists in enhancing the data interpretation as certain geological features may get highlighted at a particular frequency. The transformation of 1-D seismic trace into the corresponding frequency components gives a better analysis of the stratigraphy in the subsurface. We propose the multispectral coherence approach to delineate the stratigraphic features such as faults. First, the data are spectrally decomposed using continuous wavelet transform as well as a recently developed synchrosqueezing wavelet transform. Once all the seismic traces are spectrally decomposed with a band of frequencies, we propose a modified spectral balancing technique that enhances the resolution of seismic data. The spectrally balanced data are then subjected to time–frequency (T-F) analysis, which results in multispectrum data of corresponding frequency of certain bandwidth. Gradient structure tensor-based coherence is applied on spectrally balanced data as well as selected frequency bands of T-F data as the stratigraphic features may get highlighted in a certain frequency band. Finally, all the coherence images are statistically fused using a weighted mean to get finer and sharper fault lines with very little noise. This proposed method helps to better visualize all the possible subtle and minor faults present in the data. Experimental results on field seismic data show that subtle and minor faults are more apparent and discernible using the proposed method.