ML-based wavefield separation for shallow water OBN seismic data

Abstract

Summary Wavefield separation is an important processing step to remove the interfering receiver ghost in seismic data. For ocean-bottom surveys, where typically both the pressure P (hydrophone) and particle velocity Vz (geophone) is measured, PZ-summation is a commonly used wavefield separation method. Although this method delivers excellent results, the required wavefield calibration makes it user-intensive to apply and QC. Supervised machine learning (ML) can offer a robust and user-friendly alternative to the industry-standard PZ-summation method. The convolutional neural network used in the proposed approach automatically calibrates the multi-component recordings and performs the wavefield separation without any user interaction required. A comparison study is conducted where the ML method is compared to the industry-standard PZ-summation method for a controlled shallow water data experiment and an OBN field data example with water depths ranging between 25m and 40m. After an easy to execute network training phase, the method delivers excellent wavefield separation results for both the complex synthetic and the shallow water field data example.