Extracting low SNR events with the Hough Transform from sparse array data
Abstract
Low frequency acoustic, i.e., infrasound waves, are measured by sparse arrays of microbarometers. Recorded data are processed by automatic detection algorithms based on array processing techniques such as time-domain beamforming and f-k analysis. These algorithms use a signal-to-noise ratio (SNR) value as a detection criterion. In the case of high background noise or in the presence of multiple coinciding signals, the event's SNR decreases and can be missed by the automatic processing. In seismology, detecting low SNR events with geophone arrays is a well known problem. Whether it is in global earthquake monitoring or reservoir microseismic activity characterization, detecting low SNR events is needed to better understand the sources or the medium of propagation. We propose the use of an image processing technique as a post-processing step in the automatic detection of low SNR events. In particular, we consider the use of the Hough Transform (HT) technique to detect straight lines in beamforming results, i.e., back azimuth (BA) timeseries. The presence of such lines, due to similar BA values, can be indicative of a low SNR event. A statistical framework is developed for the HT parametrization, which includes defining a threshold value for detection as well as evaluating the false alarm rate. The method is tested on both synthetic data and five years of recorded infrasound from glaciers. It is shown that the automatic detection capability is increased by detecting low SNR events while keeping a low false alarm rate.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFM.S51C0346A
- Keywords:
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- 0902 Computational methods: seismic;
- EXPLORATION GEOPHYSICSDE: 0925 Magnetic and electrical methods;
- EXPLORATION GEOPHYSICSDE: 7255 Surface waves and free oscillations;
- SEISMOLOGYDE: 7290 Computational seismology;
- SEISMOLOGY