Automatic fault instance segmentation based on mask propagation neural network

Abstract

Fault interpretation plays a critical role in understanding the crustal development and exploring the subsurface reservoirs such as gas and oil. Recently, significant advances have been made towards fault semantic segmentation using deep learning. However, few studies employ deep learning in fault instance segmentation. We introduce mask propagation neural network for fault instance segmentation. Our study focuses on the description of the differences and relationships between each fault profile and the consistency of fault instance segmentations with adjacent profiles. Our method refers to the reference-guided mask propagation network, which is firstly used in video object segmentation: taking the seismic profiles as video frames while the seismic data volume as a video sequence along the inline direction we can achieve fault instance segmentation based on the mask propagation method. As a multi-level convolutional neural network, the mask propagation network receives a small number of user-defined tags as the guidance and outputs the fault instance segmentation on 3D seismic data, which can facilitate the fault reconstruction workflow. Compared with the traditional deep learning method, the introduced mask propagation neural network can complete the fault instance segmentation work under the premise of ensuring the accuracy of fault detection. • We demonstrate the results of fault instance segmentation in seismic profiles on the first try. • We show fault reconstruction based on fault instance segmentation. • The effectiveness of our method is verified by experiments.