Seismic Signal Denoising and Decomposition Using Deep Neural Networks

Abstract

Denoising and filtering are widely used in routine seismic-data-processing to improve the signal-to-noise ratio (SNR) of recorded signals and by doing so to improve subsequent analyses. In this paper we develop a new denoising/decomposition method, DeepDenoiser, based on a deep neural network. This network is able to learn simultaneously a sparse representation of data in the time-frequency domain and a non-linear function that maps this representation into masks that decompose input data into a signal of interest and noise (defined as any nonseismic signal). We show that DeepDenoiser achieves impressive denoising of seismic signals even when the signal and noise share a common frequency band. Our method properly handles a variety of colored noise and non-earthquake signals. DeepDenoiser can significantly improve the SNR with minimal changes in the waveform shape of interest, even in presence of high noise levels. We demonstrate the effect of our method on improving earthquake detection. There are clear applications of DeepDenoiser to seismic imaging, micro-seismic monitoring, and preprocessing of ambient noise data. We also note that potential applications of our approach are not limited to these applications or even to earthquake data, and that our approach can be adapted to diverse signals and applications in other settings.

A Machine‐Learning Approach for Earthquake Magnitude Estimation

“…Performing seismic denoising (e.g. Langston & Mousavi, ; Mousavi & Langston, ; Mousavi & Langston, ; Zhu et al, ) during the preprocessing step can be one potential solution for this problem. Building a deeper network can also make the network less sensitive to the noise level; however, this requires more training data to prevent overfitting.…”

“…Our goal in this study was to develop a method for a fast and reliable estimation of earthquake magnitude directly from raw seismograms recorded on a single station. This is part of a larger project aiming to develop a full deep‐learning pipeline (Zhu et al (); Mousavi et al (); Zhu and Beroza (); Mousavi et al, , Mousavi et al, ]) for earthquake signal processing and monitoring.…”

Extracting Dispersion Curves From Ambient Noise Correlations Using Deep Learning

“…In recent years, deep learning has become state-of-the-art in numerous areas of artificial intelligence, which has quickly translated into major advances within seismology. Such applications include detection and picking of seismic waves [4], [5], signal denoising [6], and phase association [7]. These problems can all be cast as supervised learning objectives and benefit from the wealth of labeled datasets that exist in the seismological community.…”

Microseismic Signal Denoising and Separation Based on Fully Convolutional Encoder–Decoder Network

“…Further, the combination of traditional methods and the neural network will be explored. direction of future research [29]. The increase in training data can continuously separate the microseismic signal and noise perfectly, which will be the goal of the future research.…”

“…However, for new complex noise or microseismic signal samples, the method may not achieve the current performance, which needs validation in further research. The current estimation of the microseismic signal is based on the output of masks, and thus non-mask prediction will be the direction of future research [29]. The increase in training data can continuously separate the microseismic signal and noise perfectly, which will be the goal of the future research.…”