Pysolate: A Python-Based Thresholding Tool to De-Noise or De-Signal Seismic Waveforms Based on the Continuous Wavelet Transform
Description:
Removing noise from seismic data to increase signal-to-noise ratios is one of the most important efforts in seismology. An abundance of preprocessing methods have been applied to extract signals out of noisy data as increased noise in the microseismic band is a challenge to lowering regional monitoring thresholds. Improved accuracy of shorter period (<20 second) waveform simulations is only useful if clear short period signals can be extracted from noisy waveform records. The application of a filtering method that removes microseismic noise, thus isolating the seismic signal, will be key to better utilization of improved short-period waveform simulations. To this effort, following the Langston et al. (2019) CWT application, we developed a Python-based toolset that implements the CWT-based, non-linear thresholding operations to de-noise or de-signal seismic data. We test this application using the 2020 Mw 6.5 Monte Cristo Range earthquake sequence to assess automation and portability into a processing scheme. We find that the denoiser performance varies with event size and distance from the station. As expected, smaller events are best observed on the closer stations after denoising, and larger events can be denoised on stations further from the event. Preliminary results show that denoising many events using a single noise model will be helpful in aftershock sequences, where signals arrive in rapid succession and no clear noise window can be identified. Further analysis will help us understand how far stations can be for this process to produce useful signals. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS-843563.
Session: Emerging Developments in Operational Monitoring Systems and Products [Poster]
Type: Poster
Date: 4/18/2023
Presentation Time: 08:00 AM (local time)
Presenting Author: Ana C. Aguiar
Student Presenter: No
Invited Presentation:
Authors
Ana Aguiar Presenting Author Corresponding Author aguiarmoya1@llnl.gov Lawrence Livermore National Laboratory |
Andrea Chiang chiang4@llnl.gov Lawrence Livermore National Laboratory |
Stephen Myers myers30@llnl.gov Lawrence Livermore National Laboratory |
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Pysolate: A Python-Based Thresholding Tool to De-Noise or De-Signal Seismic Waveforms Based on the Continuous Wavelet Transform
Category
General Session