Change-point analysis of geophysical time-series: application to landslide displacement rate (Séchilienne rock avalanche, France)

Abstract

The rank-sum multiple change-point method is a robust statistical procedure designed to search for the optimal number and the location of change points in an arbitrary continue or discrete sequence of values. As such, this procedure can be used to analyse time-series data. Twelve years of robust data sets for the Séchilienne (French Alps) rockslide show a continuous increase in average displacement rate from 50 to 280 mm per month, in the 2004-2014 period, followed by a strong decrease back to 50 mm per month in the 2014-2015 period. When possible kinematic phases are tentatively suggested in previous studies, its solely rely on the basis of empirical threshold values. In this paper, we analyse how the use of a statistical algorithm for change-point detection helps to better understand time phases in landslide kinematics. First, we test the efficiency of the statistical algorithm on geophysical benchmark data, these data sets (stream flows and Northern Hemisphere temperatures) being already analysed by independent statistical tools. Second, we apply the method to 12-yr daily time-series of the Séchilienne landslide, for rainfall and displacement data, from 2003 December to 2015 December, in order to quantitatively extract changes in landslide kinematics. We find two strong significant discontinuities in the weekly cumulated rainfall values: an average rainfall rate increase is resolved in 2012 April and a decrease in 2014 August. Four robust changes are highlighted in the displacement time-series (

Seismic signatures and site characterization of an intermittent stream in dry and flood conditions: an implication for soil losses and landslide triggering

“…Similarly, to validate RMS results, change-point analysis (CPA), an approach for detecting the numerical value of change in two-time series, was adopted [54] . There are few case studies on the application of CPA in seismology as ambient noise interferometry [55], landslide monitoring [56] , and seismic monitoring of COVID-19 [54] . An open-access python rupture library was used for the analysis [57] .…”