Automated platform for microseismic signal analysis : denoising, detection and classification in slope stability studies

Li, Jiangfeng and Stankovic, Lina and Pytharouli, Stella and Stankovic, Vladimir (2021) Automated platform for microseismic signal analysis : denoising, detection and classification in slope stability studies. IEEE Transactions on Geoscience and Remote Sensing, 59 (9). 7996 - 8006. ISSN 0196-2892 (https://doi.org/10.1109/TGRS.2020.3032664)

[thumbnail of Li-etal-IEEE-TGRS-2020-Automated-platform-for-microseismic-signal-analysis]
Preview
 Text. Filename: Li_etal_IEEE_TGRS_2020_Automated_platform_for_microseismic_signal_analysis.pdf
Accepted Author Manuscript
Download (438kB)| Preview

Abstract

Microseismic monitoring has been increasingly used in the past two decades to illuminate (sub)surface processes such as landslides, due to its ability to record small seismic waves generated by soil movement and/or brittle behaviour of rock. Understanding the evolution of landslide processes is of paramount importance in predicting or even avoiding an imminent failure. Microseismic monitoring recordings are often continuous, noisy and consist of signals emitted by various sources. Manually detecting and distinguishing the signals emitted by an unstable slope is challenging. Research on automated end-to-end denoising, detection, and classification of microseismic events, as an early warning system, is still in its infancy. To this effect, our work is focused on jointly evaluating and developing suitable approaches for signal denoising, accurate event detection, non site-specific feature construction, feature selection and event classification. We propose an automated end-to-end system that can process big data sets of continuous seismic recordings fast and demonstrate applicability and robustness to a wide range of events (distant and local earthquakes, slidequakes, anthropogenic noise etc.). Algorithmic contributions lie in novel signal processing and analysis methods with fewer tunable parameters than the state of the art, evaluated on two field datasets and benchmarked against the state of the art.

ORCID iDs

Li, Jiangfeng, Stankovic, Lina ORCID logoORCID: https://orcid.org/0000-0002-8112-1976, Pytharouli, Stella ORCID logoORCID: https://orcid.org/0000-0002-2899-1518 and Stankovic, Vladimir ORCID logoORCID: https://orcid.org/0000-0002-1075-2420;
  • Item type:Article
    ID code:74339
    Dates:
    Date
    Event
    September 2021
    Published
    3 November 2020
    Published Online
    15 October 2020
    Accepted
    Notes:© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Science > Geology
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Faculty of Engineering > Civil and Environmental Engineering
    Depositing user: Pure Administrator
    Date deposited:22 Oct 2020 10:16
    Last modified:11 Nov 2024 12:52
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/74339
CORE (COnnecting REpositories)