What can machine learning do for seismic data processing? An interpolation application

Abstract

Machine learning (ML) systems can automatically mine data sets for hidden features or relationships. Recently, ML methods have become increasingly used within many scientific fields. We have evaluated common applications of ML, and then we developed a novel method based on the classic ML method of support vector regression (SVR) for reconstructing seismic data from under-sampled or missing traces. First, the SVR method mines a continuous regression hyperplane from training data that indicates the hidden relationship between input data with missing traces and output completed data, and then it interpolates missing seismic traces for other input data by using the learned hyperplane. The key idea of our new ML method is significantly different from that of many previous interpolation methods. Our method depends on the characteristics of the training data, rather than the assumptions of linear events, sparsity, or low rank. Therefore, it can break out the previous assumptions or constraints and show universality to different data sets. In addition, our method dramatically reduces the manual workload; for example, it allows users to avoid selecting the window size parameters, as is required for methods based on the assumption of linear events. The ML method facilitates intelligent interpolation between data sets with similar geomorphological structures, which can significantly reduce costs in engineering applications. Furthermore, we combine a sparse transform called the data-driven tight frame (so-called compressed learning) with the SVR method to improve the training performance, in which the training is implemented in a sparse coefficient domain rather than in the data domain. Numerical experiments show the competitive performance of our method in comparison with the traditional [Formula: see text]-[Formula: see text] interpolation method.