Pinpointing Early Signs of Impending Slope Failures From Space

Abstract

A promised potential of spaceborne interferometric synthetic aperture radars (InSAR) is a capability for regularly monitoring ground deformation with millimeter accuracy, for timely forecasting of impending natural hazards such as landslides. The main limitation in InSAR being actually capable of unleashing this potential for hazard prediction is that key precursory ground displacements are, in the majority of cases, a very small subset of the entire big data set provided by the method over large regions. Consequently, pinpointing a single impending failure may become very difficult or impossible. We develop a data‐driven framework that can handle such imbalanced spatiotemporal data based on the concept of outlying aspects mining, to find a subset of features out of a collection of potential features, which best distinguishes the landslide source area from the others. We show that the identified feature subspace can be used to find anomalous areas across multiple spatial scales, such that Sentinel‐1 satellite monitoring points which persistently lie in these areas can accurately detect the location of the Xinmo landslide (China) almost 1 year in advance—without false alarms. In a second case study, we identify the area affected by rockfalls on Stromboli volcano, a task that is generally infeasible with traditional methods applied to InSAR data. With continuing improvements in the spatial and temporal resolution from the new generation of satellites, such as Sentinel‐1, this approach opens the door to reliable and early prediction of failure over a broad range of slope instabilities.

Multi-parametric study of an eruptive phase comprising unrest, major explosions, crater failure, pyroclastic density currents and lava flows: Stromboli volcano, 1 December 2020–30 June 2021

“…Conversely, the two explosions on 6 December 2020 and 1 March 2021 (Table 1) happened when the seismic parameters did not show particular variations and had low values (Figure 9). The output of overflows from the crater rim at the end of January was accompanied by an increasing rate of landslides occurring along the SdF (Figure 9D), as a result of the blocks detaching from the flow front and rolling down the slope, thereby triggering further landslides (Di Traglia et al, 2021;Zhou et al, 2022).…”

Landslide Regime Shift Detector (LRSD) for Landslide Early Warning Systems

“…In this context, comprehensive sampling plays a vital role in uncovering the unique dynamics of this precursory failure regime (PFR). Be it in a small-scale controlled laboratory sample or a large mountain slope, a specific dynamical signature in the form of a clustering pattern manifests in the observed motions in PFR (Tordesillas et al, 2021(Tordesillas et al, , 2018Zhou et al, 2022).…”

“…Of potential broad utility for landslide early warning is that this pattern and its spatiotemporal dynamics can be detected in the early stages of the precursory failure regime, well before the collapse in both engineered and natural slopes (Tordesillas et al, 2021(Tordesillas et al, , 2018Zhou et al, 2022). That said, numerous monitoring points are needed to identify and track kinematic clustering.…”

Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?

“…Zhou et al. (2022) follow the conventional feature engineering and prediction workflow to extract outlying motions from satellite images and achieve accurate identification of landslide location almost 1 year in advance. Granat et al.…”

“…H. perform a straightforward application of image classification to identify weather phenomena from natural images and establish a database for future weather studies. Zhou et al (2022) follow the conventional feature engineering and prediction workflow to extract outlying motions from satellite images and achieve accurate identification of landslide location almost 1 year in advance. Granat et al (2021) utilize clustering methods on global navigation satellite system data to identify major faults in California.…”