Automatic fault tracking from 3D seismic data using the 2D Continuous Wavelet Transform combined with a Convolutional Neural Network

The aim of this work is to propose a new technique for automatic fault tracking from 3D seismic data using the 2D Continuous Wavelet Transform (CWT) method combined with artificial intelligence. Time slices of the variance attribute, derived from the 3D seismic data and chosen by the user, are analysed using the 2D CWT with the 2D Mexican Hat as an analysing wavelet, and the maxima of the modulus of the 2D CWT are mapped for the full range of scales. The ensemble of mapped maxima for the set of time slices is filtered using a Convolutional Neural Network machine. Machine training is performed with a supervised mode using the manually tracked faults as a desired output. Application to real data shows the efficiency and robustness of the proposed method, which can greatly help seismic interpreters in avoiding manual fault tracking, a difficult and time-consuming task.