1 Abstract

Our understanding of the nature of progressive failure of rockfall is in part limited by the temporal and spatial resolution, and coverage of monitoring data. We present a uniquely high-resolution 4D dataset that captures over 180,000 rockfall, largely < 1m3. Of these rockfall, only a small portion show evidence of pre-failure deformation. We use our data to look in detail at the nature of deformation during the week prior to failure. We see multiple phases of movement that relate to weather conditions, in addition to pulses of movement immediately prior to collapse, which we hypothesize relate to the underlying progressive failure mechanism controlling the release of the rockfall.

2 Introduction

A series of studies using repeat surveys of actively failing rock slopes have revealed that rockfalls rarely occur as instantaneous events, and are often preceded by deformation or sequences of precursors (e.g. Rosser et al. 2007). It has been suggested that the behavior of a slope prior to failure reflects, at least in part, the underlying mechanisms, including progressive failure. For example, Royán et al. (2015) identify an accelerating pattern of small-scale movements prior to a larger rockfall, which the authors attribute to a progressive mode of failure. Similarly, Rosser et al. (2013) identified an increasing rate of increasingly large rockfalls preceding subsequent rock cliff collapse. Whilst these studies offer insight, they are limited by the frequency of data collection which is normally collected over intervals longer than the duration of changes, or they rely on fortuitously capturing precursors. To address this, here we present a uniquely long-term, high-resolution (space and time) monitoring dataset, captured using repeat laser scans over 9-months.

3 Methods

Our dataset comprises c. 9,880 scans captured at < 1 hr intervals. Each scan contains c. 1.8m points at point spacing ≈ 0.05 m. The scanning system is a Riegl vz1000 with a controls system that enables sequential scans of the monitored rockface (area = 9,592 m2). Data is combined with contemporaneous weather conditions to assess controls on deformation. Data is processed using a set of algorithms developed specifically for optimizing the detection of fine-scale change in large archives of point cloud data.

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