De-noising receiver function data using the unsupervised deep learning approach

Abstract

Summary The converted wave data (P-to-s or S-to-p), traditionally termed as receiver functions, are often contaminated with noise of different origin that may lead to the erroneous identification of phases and thus influence the interpretations. Here we utilize an unsupervised deep learning approach called Patchunet to de-noise the converted wave data. We divide the input data into several patches, which are input to the encoder and decoder network to extract some meaningful features. The method de-noises an image patch-by-patch and utilizes the redundant information on similar patches to obtain the final de-noised results. The method is first tested on a suite of synthetic data contaminated with various amount of Gaussian and realistic noise and then on the observed data from three permanent seismic stations: HYB (Hyderabad, India), LBTB (Lobatse, Botswana, South Africa), COR (Corvallis, Oregon, USA). The method works very well even when the signal-to-noise ratio is poor or with the presence of spike noise and deconvolution artifacts. The field data demonstrate the effectiveness of the method for attenuating the random noise especially for the mantle phases, which show significant improvements over conventional receiver function based images.

Recent advances in earthquake seismology using machine learning

“…The denoising of seismic waveform records is related to the issue discussed in this section. Several studies have been conducted to extract the target signal from originals containing waves from various sources or separate signals and noise (Zhu et al 2019b;Tibi et al 2021Tibi et al , 2022Dalai et al 2021;Novoselov et al 2022;Yin et al 2022a;Xu et al 2022;Wang and Zhang 2023). These efforts will lead to the effective use of observation data unanalyzed due to a low signal-to-noise ratio.…”

Crustal Imaging with Noisy Teleseismic Receiver Functions Using Sparse Radon Transform

“…Zhang et al, 2022;Chen et al, 2022;Q. Zhang et al, 2021;Chen et al, 2019;Dalai et al, 2019), rank-reduction techniques (Dokht et al, 2016;Rubio et al, 2020), and machine-learning frameworks (F. Dalai et al, 2021).…”

A multi-task deep learning scheme using receiver functions to study crustal tectonics and its application to the middle-southern segment of Tanlu Fault Zone and adjacencies

“…Over the last few years, machine learning has been massively adapted to assist the investigation of deep earth structures. Nevertheless, the previous combining study of machine learning and RFs focused mainly on denoising (Dalai et al, 2022) and auto-picking (Gan et al, 2021;, rather than delineating structural information of the subsurface. Recently, researchers have begun to realize the potential of combining the two methods.…”