High-accuracy real-time microseismic analysis platform : case study based on the super-sauze mud-based landslide
Li, J. and Stankovic, L. and Pytharouli, S. and Stankovic, V. (2020) High-accuracy real-time microseismic analysis platform : case study based on the super-sauze mud-based landslide. In: Geoconvention, 2020-08-31 - 2020-09-02.
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Abstract
Understanding the evolution of landslide and other subsurface processes via microseismic monitoring and analysis is of paramount importance in predicting or even avoiding an imminent slope failure (via an early warning system). Microseismic monitoring recordings are often continuous, noisy and consist of signals emitted by various sources. Automated analysis of landslide processes comprises detection, localization and classification of microseismic events (with magnitude <2 richter scale). Previous research has mainly focused on manually tuning signal processing methods for detecting and classifying microseismic signals based on the signal waveform and its spectrum, which is time-consuming especially for long-term monitoring and big datasets. This paper proposes an automatic analysis platform that performs event detection and classification, after suitable feature selection, in near realtime. The platform is evaluated using seismology data from the Super-Sauze mud-based landslide, which islocated in the southwestern French Alps, and features earthquake, slidequake and tremor type events.
ORCID iDs
Li, J., Stankovic, L. ORCID: https://orcid.org/0000-0002-8112-1976, Pytharouli, S. ORCID: https://orcid.org/0000-0002-2899-1518 and Stankovic, V. ORCID: https://orcid.org/0000-0002-1075-2420;-
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Item type: Conference or Workshop Item(Other) ID code: 71857 Dates: DateEvent31 August 2020Published29 February 2020AcceptedSubjects: Science > Geology Department: Faculty of Engineering > Electronic and Electrical Engineering
Faculty of Engineering > Civil and Environmental EngineeringDepositing user: Pure Administrator Date deposited: 24 Mar 2020 11:13 Last modified: 11 Nov 2024 17:01 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/71857