Denoising teleseismic data for deep Earth studies using a supervised deep-learning auto-encoder: a case study of diffracted waves from ULVZs

Stuart Russell , Katrin Hannemann, Carl Martin, Bennet Lindhorst, Waleed Esmail, Christine Thomas

Seismic noise, particularly the microseism generated by the oceans, is a fundamental limitation on using short period (1 - 10 s) teleseismic data to study the deep Earth. Deep learning auto-encoders have proven effective at denoising seismic data in other applications. Here, we provide a demonstration of their potential for improving the quality of deep Earth seismic data, using Sdiff post-cursors generated by ultra-low velocity zones as a case study. We present a computationally efficient and accessible workflow, utilising a supervised learning process with 1D synthetics and real, global seismic noise. This workflow does not require real teleseismic signals, which are limited in number and quality, and is computationally efficient, not requiring expensive 3D spectral-element simulations. We present a series of example applications, finding the denoiser to be effective at removing noise and extracting Sdiff signal despite having not been trained with real teleseismic signals. Furthermore, we provide recommendations on how performance could be improved, including demonstrating transfer learning as an efficient and effective way to improve extraction of specific signals. Lastly we apply denoising to data sampling ULVZs, revealing observations of new short-period signals as well as sampling the Hawaiian ultra-low velocity zone using a novel source-receiver geometry from the East Pacific Rise to Japan. The potential impact of denoising deep Earth seismic data is apparent, and denoising can be implemented alongside traditional methods, such as stacking. We hope to encourage the use of deep learning denoising in future studies, particularly for exploratory screening.