Viability of long-short term memory neural networks for seismic refraction first break detection – a preliminary study

Abstract

Summary Seismic data processing and analysis focuses on identifying the arrival of seismic waves or ‘first-breaks’. The identification of the arrival of first breaks is complicated by the variance of recording quality typically found across the dataset. In an exploration setting, models need to be developed and refined multiple times. Picking these first breaks then becomes time consuming, limiting the interpreter to processing their dataset rather than considering the implications of their model. Machine Learning as a field continues to respond to many data centric issues within geoscience. However, the field as a whole continues to grapple with balancing the power of these new techniques against operator expertise and skill. This paper presents a methodology to identify the first break in seismic refraction data using a Long-Short Term Memory (LSTM) network, which is a recurrent network architecture. I propose one way to delineate between different groups of traces that the operator would intuitively pick differently, by using dynamic time warping to generate a distance matrix of the seismic traces for clustering. This clustering of trace types allows for a more targeted selection of training samples. I conclude with a proposed framework for the integration of operator skill with machine learning speed and repeatability.