SeisLabel: An AI-Assisted Annotation Tool for Seismic Data Labeling

Tiash Ghosh, Ratul Kishore Saha, Mamata Jenamani, Aurobinda Routray, Sanjai Kumar Singh, Arpita Mondal

Abstract: In recent years, there has been significant progress in utilizing neural networks and deep learning methods for enhancing the delineation of seismic faults. However, the scarcity of labeled data has posed a challenge in training such networks, leading to a reliance on synthetic samples. Consequently, the task of annotation has become a crucial component within machine learning frameworks. Data labeling not only consumes considerable time but also necessitates a high level of precision. To address the above limitations, an Artificial Intelligence-powered interactive annotation tool has been developed. This tool aims to minimize the immense human effort involved in labeling data by offering an efficient and accurate solution. By leveraging the power of artificial intelligence, the tool enables faster and more precise annotation. The effectiveness and reliability of the proposed tool are affirmed through the observed enhancements in segmentation quality and the average speedup achieved in the annotation process.