Caractérisation quantitative des aléas rocheux de départ diffus

Abstract

L’article décrit les différentes approches permettant d’estimer la fréquence de départ des éboulements ou des blocs dans le cas d’un aléa diffus, dans l’objectif de quantifier l’aléa résultant par une simulation des trajectoires. Si l’on simule la chute des compartiments entiers, la fréquence de départ des éboulements peut être estimée à partir d’inventaires historiques, de mesures topographiques diachroniques ou d’une classification des falaises. Si l’on simule la chute de blocs indépendants, la fréquence de départ des blocs peut être estimée en passant par un modèle d’érosion de la falaise, qui donne le taux d’érosion à partir de la fréquence d’éboulements.

MATLAB Virtual Toolbox for Retrospective Rockfall Source Detection and Volume Estimation Using 3D Point Clouds: A Case Study of a Subalpine Molasse Cliff

“…Based on the volume-frequency plot, the estimated exponent value (b) was between 0.48 and 0.78, and the factors were similar to those found in the literature by [34] for seaside sandstone cliffs. This can be correlated locally high fracturation in some sandstone layers or the poor GSI of the rock cliff but suggests that the volume computation and the regression line boundaries also influenced the erosional process interpretation [37,59]. The power law shows some limits in this study site as we have temporal resolution bias and effect related to variation of thickness in sandstone layers in a too small area leading to a too large effect on sampling.…”

“…Figure 7 outlines the influence of the α value on each series of computed volumes from rockfall sources using power-law regression on each magnitude-frequency distribution. The magnitude-frequency representation is used here to To illustrate the output results of the presented procedure, and particularly the computed rockfall volumes, we used the classic rockfall magnitude-frequency representa-tion [22,31,[33][34][35][36][37]. According to [37], volumes from rockfall source areas are assumed to follow a power-law regression:…”

“…The exponent, b, depends on the geological and geomorphological context only. Thereby, recent studies showed that a and b are correlated to the GSI (Geological Strength Index) of the rock cliff [37]. In addition, the value of b indicates the proportion of small events as compared to larger events.…”

Conceptual model of fracture‐limited sea cliff erosion: Erosion of the seaward tilted flyschs of Socoa, Basque Country, France

“…Diverging exponent values may arise from a fundamental temporal bias of observation (Williams et al, 2018), where longer measurement campaign intervals lead to amalgamating the volumes from adjacent scars into a single larger rockfall. More recently, Hantz et al (2020) attempted to link exponent variations to the rock structure through Hoek and Brown's (1997) geological strength index (GSI). Fractures, which delimit blocks, influence the value of the exponent by pre‐defining detachment volume.…”

“…This derives small exponents as low as −0.3. Disintegrated massifs can generate −1.1 exponent values, and intermediate GSI indices (‘blocky’ to ‘blocky disturbed’) derive probabilistic rockfall in the intermediate range of exponents from −0.5 to −0.9 (Hantz et al, 2020). Fundamentally, rockfall probability and volume frequency scaling is linked to rock fracturing and its spatial frequency.…”