Data-centric strategies for deep-learning accelerated salt interpretation

Abstract Deep learning (DL) has become the most effective machine learning solution for addressing and accelerating complex problems in various fields, from computer vision and natural language processing to many more. Training well-generalized DL models requires large amounts of data which allows the model to learn the complexity of the task it is being trained to perform. Consequently, performance optimization of the deep-learning models is concentrated on complex architectures with a large number of tunable model parameters, in other words, model-centric techniques. To enable training such large models, significant effort has also gone into high-performance computing and big-data handling. However, adapting DL to tackle specialized domain-related data and problems in real-world settings presents unique challenges that model-centric techniques do not suffice to optimize. In this paper, we tackle the problem of developing DL models for seismic imaging using complex seismic data. We specifically address developing and deploying DL models for salt interpretation using seismic images. Most importantly, we discuss how looking beyond model-centric and leveraging data-centric strategies for optimization of DL model performance was crucial to significantly improve salt interpretation. This technique was also key in developing production quality, robust and generalized models.