Near real-time characterization of spatio-temporal precursory evolution of a rockslide from radar data: integrating statistical and machine learning with dynamics of granular failure

Das, Sourav, and Tordesillas, Antoinette (2019) Near real-time characterization of spatio-temporal precursory evolution of a rockslide from radar data: integrating statistical and machine learning with dynamics of granular failure. Remote Sensing, 11 (23). 2777.

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Abstract

This study builds on fundamental knowledge of granular failure dynamics to develop a statistical and machine learning approach for characterization of a landslide. We demonstrate our approach for a rockslide using surface displacement data from a ground based radar monitoring system. The algorithm has three key components: (i) identification of a regime change point t0 marking the departure from statistical invariance of the global velocity field, (ii) characterization of the clustering pattern formed by the velocity time series at t0 , and (iii) classification of velocity patterns for t>t0 to deliver a measure of risk of failure from t0 and estimates of the time of emergent and imminent risk of failure. Unlike the prevailing approach of analysing time series data from one or a few chosen locations, we make full use of data from all monitored points on the slope (here 1803). We do not make a priori assumptions on the monitored domain and base our characterization of the complex spatial patterns and associated dynamics only from the data. Our approach is informed by recent developments in the physics and micromechanics of failure in granular media and is configured to accommodate additional data on landslide triggers and other determinants of landslide risk readily.

Item ID: 61057
Item Type: Article (Research - C1)
ISSN: 2072-4292
Copyright Information: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Date Deposited: 26 Nov 2019 04:26
FoR Codes: 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 50%
04 EARTH SCIENCES > 0406 Physical Geography and Environmental Geoscience > 040604 Natural Hazards @ 50%
SEO Codes: 96 ENVIRONMENT > 9610 Natural Hazards > 961007 Natural Hazards in Mining Environments @ 100%
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