ORCID Profile
0000-0003-3136-4838
Current Organisations
University of Melbourne
,
Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences
,
University of Peradeniya
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Publisher: Cambridge University Press (CUP)
Date: 08-10-2020
DOI: 10.1017/JFM.2020.662
Publisher: Cambridge University Press (CUP)
Date: 25-03-2021
DOI: 10.1017/JFM.2021.138
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-8127
Abstract: Rapid mass movements are a major threat in populated landscapes, as they can cause significant loss of life and damage civil infrastructure. Previous work has shown that using environmental seismology methods to monitor such mass movements and establish monitoring systems offers advantages over existing approaches. The first important step in developing an early warning system for rapid mass movements based on seismic signals is automatically detecting events of interest. Though the approach, such as short-term average to long-term average ratio (STA/LTA) and machine learning model, was introduced to detect events (e.g., debris flow and rockfall), it is still challenging to calibrate input parameters and migrate existing methods to other catchments. Detection of debris flows, for instance, is similar to anomaly detection if we consider the seismic stations recording background signals as an overwhelming majority condition.& Benford's law describes the probability distribution of the first non-zero digits in numerical datasets, which provides a functional, computationally cheap approach to anomaly detection, such as fraud detection in financial data or earthquake detection in seismic signals. In this study, seismic signals generated by rapid mass movements were collected to check the agreement of the distribution of the first digit with Benford's law. Subsequently, we develop a computationally efficient and non-site-specific model to detect events based on Benford's law using debris flows from the Illgraben, a Swiss torrent, as an ex le. Our results show that seismic signals generated by high-energy mass movements, such as debris flows, landslides, and lahars, follow Benford's law, while those generated by rockfall and background signals do not. Furthermore, our detector performance in picking debris-flow events is comparable to a published random forest and seismic network-based approach. Our method can be applied at other sites to detect debris-flow events without additional calibration and offers the potential for real-time warnings.
Publisher: Authorea, Inc.
Date: 20-07-2023
DOI: 10.22541/ESSOAR.168987133.36073109/V1
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.
Location: Germany
Location: Switzerland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Nilanka Ekanayake.