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Modelling the Influence of Geological Structures in Paleo Rock Avalanche Failures Using Field and Remote Sensing Data

Authors :
Lingfeng He
Mirko Francioni
John Coggan
Fernando Calamita
Matthew Eyre
Source :
Remote Sensing, Vol 14, Iss 16, p 4090 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

This paper focuses on the back analysis of an ancient, catastrophic rock avalanche located in the small city of Lettopalena (Chieti, Italy). The integrated use of various investigation methods was employed for landslide analysis, including the use of traditional manual surveys and remote sensing (RS) mapping for the identification of geological structures. The outputs of the manual and RS surveys were then utilised to numerically model the landslide using a 2D distinct element method. A series of numerical simulations were undertaken to perform a sensitivity analysis to investigate the uncertainty of discontinuity properties on the slope stability analysis and provide further insight into the landslide failure mechanism. Both numerical modelling and field investigations indicate that the landslide was controlled by translational sliding along a folded bedding plane, with toe removal because of river erosion. This generated daylighting of the bedding plane, creating kinematic freedom for the landslide. The formation of lateral and rear release surfaces was influenced by the orientation of the discrete fracture network. Due to the presence of an anticline, the landslide region was constrained in the middle-lower section of the slope, where the higher inclination of the bedding plane was detected. The landslide is characterized by a step-path slip surface at the toe of the slope, which was observed both in the modelling and the field. This paper highlights the combined use of a geological model and numerical modelling to provide an improved understanding of the origin and development of rock avalanches under the influence of river erosion, anticline structures, and related faults and fractures.

Details

Language :
English
ISSN :
20724292
Volume :
14
Issue :
16
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.97be2e21b6f144d5ba92b31741341c24
Document Type :
article
Full Text :
https://doi.org/10.3390/rs14164090