Engineering Papers⌕ Search

Engineering topics

Hammond, Patrick

Publications and source records attributed to Hammond, Patrick.

6 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Deep Learning Denoiser

SAND2022-14744 O The Deep Learning Denoiser uses a trained deep convolutional neural network (CNN) model to decompose an input waveform into a signal and noise waveform. In the approach, the CNN provides a signal mask and a noise mask for an input signal. The Short-Time Fourier Transform (STFT) of the estimated signal is obtained by multiplying the signal mask with the STFT of the input signal. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Koch, Clinton↗
DOE OSTI2021

Auto-Curation of Seismic Event Data for Signal Denoising

Denoising contaminated seismic signals for later processing is a fundamental problem in seismic signals analysis. Neural network approaches have shown success denoising local signals when trained on short-time Fourier transform spectrograms. One challenge of this approach is the onerous process of hand-labeling event signals for training. By leveraging the SCALODEEP seismic event detector, we develop an automated set of techniques for labeling event data. Despite region specific challenges, training the neural network denoiser on machine curated events shows comparable performance to the neural network trained on hand curated events. We showcase our technique with two experiments, one using Utah regional data and one using regional data from the Korean peninsula.

58 GEOSCIENCES↗
DOE OSTI2021

Evaluating Scalograms for Seismic Event Denoising

Denoising contaminated seismic signals for later processing is a fundamental problem in seismic signals analysis. The most straightforward denoising approach, using spectral filtering, is not effective when noise and seismic signal occupy the same frequency range. Neural network approaches have shown success denoising local signal when trained on short-time Fourier transform spectrograms (Zhu et al 2018; Tibi et al 2021). Scalograms, a wavelet-based transform, achieved ~15% better reconstruction as measured by dynamic time warping on a seismic waveform test set than spectrograms, suggesting their use as an alternative for denoising. We train a deep neural network on a scalogram dataset derived from waveforms recorded by the University of Utah Seismograph Stations network. We find that initial results are no better than a spectrogram approach, with additional overhead imposed by the significantly larger size of scalograms. A robust exploration of neural network hyperparameters and network architecture was not performed, which could be done in follow on work.

58 GEOSCIENCES↗