A fuzzy entropy, Contourlet based automatic fault detection

Abstract

Seismic fault interpretation has great importance in characterising subsurface geology. However, it is in general a manual and time-consuming task, thus, adaptive methods to achieve good fault detection results would be valuable. In this paper, we present a new method using combination of Contourlet transform and fuzzy entropy definition to adaptively detect and extract faults from seismic data. Contourlet is a multiscale and multidirectional filter that has high directionality which decomposes an image to various subscales. Our proposed scheme has 3 phases: first, employing Contourlet to pull out fault information using multidirectional and multiscale property of contourlet (feature extraction), second, using differentiation to boost fault information and calculating the correlation coefficient between the input image and subscales, fault information can be isolated from reflectors adaptively (feature selection) and third, applying a multi-level thresholding approach built on fuzzy partition of the histogram and entropy theory to classify image pixels into fault and non-fault (classification). The adaptive hybrid technique was applied to one synthetic and two real datasets containing fault data, reflectors and random noise. According to the results and their assessments, the proposed scheme has desirably located fault features in the data. We also examined the effect of random noise (Signal to Noise Ratio (SNR) = 2) on our adaptive algorithm which showed the success of our designed technique in the presence of noise.