SEISMIC FEATURE IDENTIFICATION FOR AUTOMATED INTERPRETATION USING CONVOLUTIONAL NEURAL NETWORKS

Abstract

One of the primary aims in seismic data processing is to detect geologically important features in the seismic images. Subsurface features, like horizons, faults, unconformities, and other events contain valuable spatio-temporal data which might be of scientific importance as well as commercially significant. Therefore, it is necessary to precisely and efficiently detect such features in images. However, the usual method for extraction of features in seismic images requires human interference in various forms like visual interpretation and labelling of the pixels by an specialist. This is time-taking and costly. On the other hand, automated approaches for extraction of seismic features suffer from various pitfalls. Subsurface features are of different shapes and dimensions, and parametric modelling approaches often do not work well. Furthermore, seismic datasets are usually high-dimensional, and intricate algorithmic approaches are expensive from a computational viewpoint. Lastly, seismic images carry high amounts of noise, and this further decreases the performance of parametric approaches. In this thesis, Convolutional Networks, a supervised learning technique recently gaining attention in the field of machine learning for vision perception tasks was investigated to perform end-to-end automated seismic feature segmentation. Two popular convolutional network models namely U-NET and Seg-Net are chosen and transformed to accomplish seismic feature segmentation and the results were analyzed. Our aim was to identify complex features but as not much complex features were available in the data. The limitation in applying CNNs to these images is that the data set is not huge and we perform data augmentation on the seismic images. Various loss functions were tried with the algorithms and the final result is obtained with binary cross entropy and categorical cross entropy in the two architectures. The result from experiments are compared on the basis of different type of feature characteristics that the architectures perform.