Seismograms contain multiple sources of seismic waves, from distinct transient signals such as earthquakes to continuous ambient seismic vibrations such as microseism. Ambient vibrations contaminate the earthquake signals, while the earthquake signals pollute the ambient noise’s statistical properties necessary for ambient-noise seismology analysis. Separating ambient noise from earthquake signals would thus benefit multiple seismological analyses. This work develops a multitask encoder–decoder network named WaveDecompNet to separate transient signals from ambient signals directly in the time domain for 3-component seismograms. We choose the active-volcanic Big Island in Hawai’i as a natural laboratory given its richness in transients (tectonic and volcanic earthquakes) and diffuse ambient noise (strong microseism). The approach takes a noisy 3-component seismogram as input and independently predicts the 3-component earthquake and noise waveforms. The model is trained on earthquake and noise waveforms from the STandford EArthquake Dataset (STEAD) and on the local noise of seismic station IU.POHA. We estimate the network’s performance by using the explained variance metric on both earthquake and noise waveforms. We explore different neural network designs for WaveDecompNet and find that the model with long-short-term memory (LSTM) performs best over other structures. Overall, we find that WaveDecompNet provides satisfactory performance down to a signal-to-noise ratio (SNR) of 0.1. The potential of the method is (1) to improve broad-band SNR of transient (earthquake) waveforms and (2) to improve local ambient noise to monitor the Earth’s structure using ambient noise signals. To test this, we apply a short-time average to a long-time average filter and improve the number of detected events. We also measure single-station cross-correlation functions of the recovered ambient noise and establish their improved coherence through time and over different frequency bands. We conclude that WaveDecompNet is a promising tool for a broad range of seismological research.
This content will become publicly available on May 28, 2025
Seismic signals, whether caused by earthquakes, other natural phenomena, or artificial noise sources, have specific characteristics in the time and frequency domains that contain crucial information reflecting their source. The analysis of seismic time series is an essential part of every seismology-oriented study program. Enabling students to work with data collected from their own campus, including signals from both anthropogenic and natural seismic sources, can provide vivid, practical examples to make abstract concepts communicated in classes more concrete and relevant. Data from research-grade broadband seismometers enable us to record time series of vibrations at a broad range of frequencies; however, these sensors are costly and are often deployed in remote places. Participation in the Raspberry Shake citizen science network enables seismology educators to record seismic signals on our own campuses and use these recordings in our classrooms and for public outreach. Yale University installed a Raspberry Shake three-component, low-cost seismometer in the Earth and Planetary Sciences department building in Summer 2022, enabling the detection of local, regional, and teleseismic earthquakes, microseismic noise, and anthropogenic noise sources from building construction, an explosive event in a steam tunnel, and general building use. Here, we discuss and illustrate the use of data from our Raspberry Shake in outreach and education activities at Yale. In particular, we highlight a series of ObsPy-based exercises that will be used in courses taught in our department, including our upper-level Introduction to Seismology course and our undergraduate classes on Natural Disasters and Forensic Geoscience.
more » « less- Award ID(s):
- 2153688
- PAR ID:
- 10528605
- Publisher / Repository:
- Seismological Society of America
- Date Published:
- Journal Name:
- Seismological Research Letters
- Volume:
- 95
- Issue:
- 4
- ISSN:
- 0895-0695
- Page Range / eLocation ID:
- 2538 to 2553
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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