Next Generation Survey Adapted Synthetic Seismic Training Data with Cyclegan’s

Abstract

Summary One of the major challenges related to the use of machine learning for seismic processing and seismic interpretation is the notion of acquiring appropriate training data where we do not have the “ground truth”. Here, we present a novel approach to create survey adapted synthetic training data with cycleGANs. CycleGANs can be trained to translate between domains without paired input-output examples, and it has been applied to a wide range of applications, including collection style transfer, object transfiguration, season transfer and photo enhancement. We train a cycleGAN to translate from simple synthetic reflectivity examples (i.e. noise free and not convolved with a wavelet) to “real” or survey adapted examples, and the idea is that the survey adaption will automatically generate suitable synthetic training data for a given survey without any manual preconditioning or engineering. The approach can be used to generate training data for many different problems within seismic interpretation and seismic processing. We demonstrate the approach with a multiple attenuation case example on seismic pre-stack data.