DOE OSTI · code-164114
WaveDenoiser
Abstract
We developed a robust deep learning model to effectively reduce background noise in the time domain from seismic waveforms. The deep learning model processes a 57-second three-component seismogram to predict and generate a denoised seismogram. The training was conducted using the benchmark STEAD dataset, which comprises globally distributed earthquake signals recorded at local distances ranging from 0 to 350 kilometers.
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Chai, Chengping [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (0000000267926014), Derek, Rose [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)], Stewart, Scott [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)], Martindale, Nathan [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)], Adams, Mark [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)]. 2025-09-19. WaveDenoiser. https://doi.org/10.11578/dc.20250915.4
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