Denoising of Seismic Waveform Data and its Impact on the Analysis of North Korean Nuclear Tests

In the past years numerous machine learning based applications have been introduced to the field of seismology. These applications for example address issues such as earthquake detection, event classification, feature extraction and waveform data analysis.

In this study we focus on the denoising of waveform data by separating the seismic signal from different noise sources. Machine learning models are able to recognize noise patterns and can effectively suppress unwanted noise, enhancing the quality of the waveform signals. A deep learning based denoising autoencoder algorithm is tested on regional and teleseismic seismological and hydroacoustic datasets, which are compiled from the International Monitoring System of the Comprehensive Nuclear-Test-Ban Treaty Organisation. We focus on seismic and hydroacoustic stations which can be relevant to investigate North Korean nuclear tests.

We investigate the performance of different denoising autoencoder models, for short- and long waveform periods, trained on the complete station network as well as on individual stations. We investigate if the denoised waveform signals are useful for seismic source analysis and if the denoised waveforms can reliably be used in further analysis steps, such as the comparison of computed array beams, seismic phase picking and or amplitude estimation.

The declared North Korean nuclear tests are a suitable benchmark test set, as they have been extensively researched and their source type and location can be assumed known. Further the verification of the source type is of particular interest for potential nuclear tests under international law.