Preprint - manuscript submitted to Geophysical Research Letters.
ISSN/ISBN: Not available at this time. DOI: Not available at this time.
Abstract: Seismic instruments placed outside of spatially extensive hazard zones can be used to rapidly sense a range of mass movements. However, it remains challenging to automatically detect specific events of interest. Benford's law, which states that first non-zero-digit of given datasets follow a specific probability distribution, can provide a computationally cheap approach to identifying anomalies in large datasets and potentially be used for event detection. Here, we select raw seismic signals to derive the first-digit distribution. The seismic signals generated by debris flows, landslides, lahars, and glacier-lake-outburst floods follow Benford's law, while those generated by ambient noise, rockfalls, and bedload transports do not. Focusing on debris flows, our Benford's-law-based detector is comparable to an existing random forest method for the Illgraben, Switzerland, but requires only single station data and three non-dimensional parameters. We suggest this computationally cheap, novel technique offers an alternative for event recognition and potentially for real-time warnings.
Bibtex:
@misc{,
author = {Qi Zhou and Hui Tang and Jens Martin Turowski and Jean Braun and Michael Dietze and Fabian Walter and Ci-Jian Yang and Sophie Lagarde},
title = {Benford’s law as mass movement detector in seismic signals},
year = {2023},
url = { https://d197for5662m48.cloudfront.net/documents/publicationstatus/144049/preprint_pdf/d86bd9a940b5d3a9dcd5c51a2c666a98.pdf},
}
Reference Type: Preprint
Subject Area(s): Natural Sciences