Project Abstract
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W developed a deep-learning-based seismic denoising algorithm – UrbanDenoiser – to suppress the strong cultural noise in seismic recordings inherent to urban environments. The algorithm is trained using a waveform data set that combines noise sources from the urban Long Beach dense array and high signal-to-noise ratio earthquake signals extracted from the rural San Jacinto dense array, and is based on the framework of DeepDenoiser. We apply UrbanDenoiser to denoise the Long Beach dense array data and seismograms recorded by isolated stations from regional seismic network and find that seismic noise levels are strongly suppressed relative to seismic signals, so that the seismic signals can be recovered even from noisy seismic data with signal-to-noise ratio (SNR) around one. The seismic detection/location results based on denoised data preserve real earthquake events and exclude large amplitude non-earthquake sources. To explore whether or not widespread seismicity in the upper mantle beneath Los Angeles is real, we perform back-projection imaging/location on the denoised continuous data. We do not find widespread earthquakes below 20 km, but observe seismicity distributed beneath the surface fault trace of Newport-Inglewood fault at 0 – 5 km, which becomes more diffuse at 5 – 15 km, before concentrating again near the fault trace at 15 – 20 km depth. This suggests a fault model featuring shallow creep, intermediate locking, and localized stress concentration at the base of the seismogenic zone. |
