Seismic Multi-attribute Classification for Salt Boundary Detection - A Comparison

Accurate delineation of salt bodies is one of the major tasks of hydrocarbon exploration and production from 3D seismic surveying. With the increasing demand of high-resolution seismic interpretation, the size of 3D seismic volumes as well as the number of available seismic attributes has been rapidly rising, which adds the difficulties for interpreters to examine and interpret every vertical line and time slice in a seismic volume. Various machine learning techniques have been introduced from the field of image/video processing to help address this limitation; however, little effort has been devoted to fair comparisons between these techniques. This study implements six commonly-used classification techniques and compares their capabilities for salt-boundary detection, including logistic regression, decision tree, random forest, support vector machine, artificial neural network, and k-means clustering, through applications to the F3 seismic dataset of multiple salt bodies over the Netherlands North Sea. The good match between the detected salt boundaries and the original seismic images indicates that based on well-selected attributes, all six classification techniques are capable of providing reliable salt detection from 3D seismic data to assist structural framework modelling in the presence of salt domes.