Optimizing Source Wavelets Extracted from the Chirp Sub-Bottom Profiler Using an Adaptive Filter with Machine Learning

Abstract

In this study, we extracted three source wavelets of a Chirp sub-bottom profiler (SBP), which is widely used for high-resolution marine seismic exploration, using a MATLAB-based graphical user interface tool for computational processing. To extract the source wavelet for general seismic exploration data processing techniques, including that for Chirp SBPs, we first evaluated source wavelet extraction techniques using an adaptive machine learning filter. Subsequently, we performed deterministic deconvolution by extracting the optimal source wavelet from the raw data of Chirp SBP. This source wavelet was generated by applying an adaptive filter. Various methods have been studied to solve the multivariate optimization problem of minimizing the error; in this study, a least-mean-square algorithm was selected owing to its suitability for application to geophysical time-series data. Extracting the source wavelet is a crucial part of high-resolution marine seismic wave exploration data processing. Our results highlight the effectiveness of performing deterministic deconvolution by extracting source wavelets using adaptive filters, and we believe that our method is useful for marine seismic exploration data processing.