Inversions of landslide strength as a proxy for subsurface weathering

Abstract

Distributions of landslide size are hypothesized to reflect hillslope strength, and consequently weathering patterns. However, the association of weathering and critical zone architecture with mechanical strength properties of parent rock and soil are poorly-constrained. Here we use three-dimensional stability to analyze 7330 landslides in western Oregon to infer combinations of strength - friction angles and cohesion - through analysis of both failed and reconstructed landslide terrain. Under a range of conditions, our results demonstrate that the failure envelope that relates shear strength and normal stress in landslide terrain is nonlinear owing to an exchange in strength with landslide thickness. Despite the variability in material strength at large scales, the observed gradient in proportional cohesive strength with landslide thickness may serve as a proxy for subsurface weathering. We posit that the observed relationships between strength and landslide thickness are associated with the coalescence of zones of low shear strength driven by fractures and weathering, which constitutes a first-order control on the mechanical behavior of underlying soil and rock mass.

The Geomorphic Effectiveness of Landslides

“…Hump‐shaped GE curves, such as those found for the monolithic Sharon cliff segments, Northridge, Himalaya (earthquake‐induced), and New Zealand, reflect high GE of mid‐range magnitudes (Figure 6). These inventories and their primary “dry” and relatively short triggers (i.e., seismic ground acceleration and gravitational instability from wave under‐cutting, respectively) commonly activate failures in shallow material that is at or close to the threshold of gravitational instability (Alberti et al., 2022; Harp & Jibson, 1996; Katz & Aharonov, 2006; Katz & Crouvi, 2007; Serey et al., 2019). The resiliency of deeper strata is also reflected by the steeper PDF of the largest landslide scales, implying an amplified reduction in the probability of these landslides.…”

“…Because the mechanical strength of the intact bedrock is high, deep‐seated landslides rely on preexisting fractures and discontinuities and are thus less frequent than the commonly shallow soil landslides. Being thick, bedrock landslides tend to be large in volume (Alberti et al., 2022). The size and frequency of these large landslides were found to be controlled by landscape geometry and topographic stress (Jeandet et al., 2019; Li & Moon, 2001; Medwedeff et al., 2020).…”

“…The size distribution of landslides is controlled by several factors and processes that determine the geotechnical strength of the slope. Within the soil layer, the strength is controlled by physical and chemical characteristics of the strata, such as granularity, mineral cohesion, vegetation‐root cohesion, consolidation, and pore pressure (Alberti et al., 2022; Bellugi et al., 2021; Frattini & Crosta, 2013; Iverson, 2000). When these characteristics become altered or weakened, the limiting factor over the size of landslides can be the depth of the soil layer (Katz & Aharonov, 2006; I. J. Larsen et al., 2010).…”

A Comprehensive Assessment of Submarine Landslides and Mass Wasting Processes Offshore Southern California

“…Finally, while several studies have noted the tsunami risk from Borderland slides (Borrero et al., 2004; Brothers, Andrews, et al., 2019; Brothers, Maier, et al., 2019; Fisher et al., 2005; Greene et al., 2006; Legg & Kamerling, 2003; Lee et al., 2009; K. J. Ryan et al., 2015), few have been quantitatively modeled, including the newly discovered San Nicolas slide. The slide characteristics determined in our compilation could be leveraged for future modeling studies aimed at regional geologic characterizations and as input for three‐dimensional reconstruction (e.g., Alberti et al., 2022) or machine learning workflows to better understand and quantify controls on submarine landslide processes.…”

“…J. Ryan et al, 2015), few have been quantitatively modeled, including the newly discovered San Nicolas slide. The slide characteristics determined in our compilation could be leveraged for future modeling studies aimed at regional geologic characterizations and as input for three-dimensional reconstruction (e.g., Alberti et al, 2022) or machine learning workflows to better understand and quantify controls on submarine landslide processes.…”