Realistic synthetic data generation using neural style transfer: Application to automatic fault interpretation

Abstract

The robust and automatic detection of faults within seismic images remains a challenging issue within the field of seismic interpretation. Recently, supervised deep learning-based approaches using synthetics have shown much promise towards accomplishing a robust and automatic fault detection algorithm. However, training only on synthetics often results in noisy and low-quality predictions due to the massive gap in data features that can exist between real and synthetic images. In order to overcome this issue, we propose a Neural Style Transfer workflow for incorporating real image features into synthetic images prior to training. With our workflow, we demonstrate that the updated synthetic images after Neural Style Transfer have similar characteristics to a real 2D seismic image from the Gulf of Mexico and are more suitable to be used as training images than the original synthetic images. To verify the effectiveness of our workflow, we train a model on the synthetic data generated from our workflow and a model from the original synthetic images and compare the results of automatic fault interpretation on a 2D image from the Gulf of Mexico. Our comparisons show that the model trained on the updated images provides less noisy and more accurate predictions than the model trained on the original images.