Intelligent interpolation by Monte Carlo machine learning

Acquisition technology advances, as well as the exploration of geologically complex areas are pushing the quantity of data to be analyzed into the ‘big data’ era. In our related work, it was shown that a machine learning method based on support vector regression (SVR) for seismic data intelligent interpolation can fully utilize large data as training data, and can eliminate certain pre-assumptions in the existing methods, such as linear events, sparsity or low rank. However, immense training sets not only encompass with high redundancy but also result in considerable computational costs, especially for high- dimensional seismic data. In this paper, we propose a criterion based on Monte Carlo for the intelligent reduction of training sets. For seismic data, pixel values in each local patch can be regarded as a set of statistical data and a variance value for the patch can be computed. A high variance means that there are events centered its corresponding patch or the pixel values in the patch range obvious...

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