Supervised Learning Approach for Multi-Component Deghosting with a Focus on Shallow Water Data

Abstract

In recent years, the use of machine learning (ML) has shown more and more successful applications, initially mainly in seismic interpretation (classification) but recently also in seismic processing challenges (regression) like denoising, interpolation and demultiple. In this paper, a supervised machine learning algorithm is proposed for receiver deghosting on multi-component data. A neural network is trained to map input pairs of hydrophone and geophone data into the deghosted up-going wavefield. This data-driven approach could offer a user-friendly alternative to existing deghosting methods, like the PZ-summation approach that is commonly used for multi-component streamer and ocean-bottom acquisitions. Although the PZ-summation approach offers an analytical solution to the deghosting problem, it requires some pre-processing steps that make it more time-consuming and user-intensive to run and QC. The advantage of the proposed ML approach is that it can be directly applied to multi-component data without any parameterization. However, the success of the supervised method strongly depends on whether a neural network can be trained easily with minimal user-interaction and can be applied to various types of (field) data without adapting the network for each individual case. To investigate if this is possible for the supervised multi-component receiver deghosting method, the selected neural network is trained on easy to model 1.5D data examples and applied to test data with increasing complexity. Results are very encouraging and evaluated to be at least on par in comparison to the existing PZ-summation deghosting method.