Unpaired training: Optimize the seismic data denoising model without paired training data

Abstract

With the development of seismic exploration technology, distributed acoustic sensing (DAS) has recently received attention in geophysics. However, owing to the complexity of the layout techniques in the DAS systems, and the unknown or harsh exploration environment, seismic data acquired by this technique usually contain the noise of diverse components, which increases the difficulty in subsequent data analysis and interpretation. This study has (1) trained a deep learning model that effectively suppressed noise with augmented noise data sets to obtain a high signal-to-noise ratio in the DAS vertical seismic profile (VSP) records, (2) introduced an attention module to enhance the extraction and recognition of signal features to recover effective signals under substantial noise interference, and (3) introduced adversarial loss and cycle-consistent loss to replace the commonly used L1 norm or L2 norm to train the network. The obtained hybrid training set containing unpaired synthetic and unpaired field data sets for model pretraining and fine tuning effectively improves the denoising performance of the seismic field data. In summary, this study develops an unpaired training-based DAS seismic data denoising method that transformed noisy DAS VSP data into noise-free data. By analyzing the noise suppression results of other methods, including qualitative and quantitative analyses, we demonstrate that our method successfully suppressed multiple types of noise in the DAS VSP data. The study has indicated clear and continuous signals in the denoising results and improved the denoising performance on the seismic field data.