Semantic Segmentation for Geophysical Data

Xiaojin Tan and Eldad Haber The University of British Columbia, Vancouver, BC, Canada

Segmentation of geophysical data is the process of dividing a geophysical image into multiple geological units. This process is typically done manually by experts, it is time consuming and inefficient. In recent years, machine learning techniques such as Convolutional Neural Networks (CNNs) have been used for semantic segmentation. Semantic segmentation is the process that associates each pixel in a natural image with a labeled class. When attempting to use similar technology to automatically segment geophysical data there are a number of challenges to consider, in particular, data inconsistency, scarcity and complexity. To overcome these challenges, we develop a new process that we call geophysical semantic segmentation (GSS). This process addresses the pre-processing of geophysical data in order to enable learning, the enrichment of the data set (data augmentation) by using a geo-statistical technique, referred to as Multiple-Point Simulations (MPS) and finally, the training of such a data set based on a new neural network architecture called inverse Convolution Neural Networks (iCNN) that is specifically developed to identify patterns. As demonstrated by the results on a field magnetic data set, this approach shows its competitiveness with human segmentation and indicates promising results.