Seismic horizon tracking using a deep convolutional neural network

Seismic horizons are essential for structural analysis, inversion, time-to-depth conversion, and seismic attribution analysis. However, seismic horizons are obtained commonly by manual tracking or a combination of manual tracking and traditional auto-tracking techniques, which is a time consuming and error-prone process. Although many conventional auto-tracking techniques have been proposed to improve the efficiency of horizon tracking, there are still many challenges to track seismic horizons with complex seismic reflection signatures. A deep convolutional neural network (CNN) method, a typical kind of deep neural networks in the field of deep learning, was proposed in this paper to solve the problems. Seismic horizon tracking is formulated as an image classification task. CNN can take full advantage of the high flexibility in network architecture and hierarchical feature extraction ability to track horizons from seismic volumes in which discontinuities, lateral variations and faults are apparent. We designed a CNN structure for horizon tracking and trained CNN models by using only 1% and 1.5% of the seismic volume to perform a single horizon and multiple horizon tracking. The root mean square error (RMSE) and the coefficient of determination (R-2) between the horizon tracked by CNN model and the manual-tracking are 1.78 and 0.998 in single horizon tracking, respectively. The average RMSE and R-2 are 2.68 and 0.992 in multiple horizon tracking, respectively. The research we have done suggests that the proposed method can track seismic horizons from 3-D seismic volumes much more accurately and efficiently than conventional methods.