Abstract

Abstract. In mountainous areas, rockfalls, rock avalanches, and debris flows constitute a risk to human life and property. Seismology has proven a useful tool to monitor such mass movements, while increasing data volumes and availability of real-time data streams demand new solutions for automatic signal classification. Ideally, seismic monitoring arrays have large apertures and record a significant number of mass movements to train detection algorithms. However, this is rarely the case, as a result of cost and time constraints and the rare occurrence of catastrophic mass movements. Here, we use the supervised random forest algorithm to classify windowed seismic data on a continuous data stream. We investigate algorithm performance for signal classification into noise (NO), slope failure (SF), and earthquake (EQ) classes and explore the influence of non-ideal though commonly encountered conditions: poor network coverage, imbalanced data sets, and low signal-to-noise ratios (SNRs). To this end we use data from two separate locations in the Swiss Alps: data set (i), recorded at Illgraben, contains signals of several dozen slope failures with low SNR; data set (ii), recorded at Pizzo Cengalo, contains only five slope failure events albeit with higher SNR. The low SNR of slope failure events in data set (i) leads to a classification accuracy of 70 % for SF, with the largest confusion between NO and SF. Although data set (ii) is highly imbalanced, lowering the prediction threshold for slope failures leads to a prediction accuracy of 80 % for SF, with the largest confusion between SF and EQ. Standard techniques to mitigate training data imbalance do not increase prediction accuracy. The classifier of data set (ii) is then used to train a model for the classification of 176 d of continuous seismic recordings containing four slope failure events. The model classifies eight events as slope failures, of which two are snow avalanches, and one is a rock-slope failure. The other events are local or regional earthquakes. By including earthquake detection of a permanent seismic station at 131 km distance to the test site into the decision-making process, all earthquakes falsely classified as slope failures can be excluded. Our study shows that, even for limited training data and non-optimal network geometry, machine learning algorithms applied to high-quality seismic records can be used to monitor mass movements automatically.

Highlights

  • High mountain areas are affected by climate change

  • We investigate algorithm performance for signal classification into noise (NO), slope failure (SF), and earthquake (EQ) classes and explore the influence of non-ideal though commonly encountered conditions: poor network coverage, imbalanced data sets, and low signal-to-noise ratios (SNRs)

  • We test our workflow on two data sets and explore ways to improve its performance on the imbalanced data sets

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Summary

Introduction

High mountain areas are affected by climate change. Deglaciation and thawing of permafrost has implications for rock wall stability at high elevation and, on communities down-valley (e.g., Allen and Huggel, 2013; Phillips et al, 2017; Coe et al, 2018; Hock et al, 2019). The increasing threat to mountain communities, especially in densely populated areas, demands new monitoring techniques at high temporal resolution and broad spatial coverage to improve predictability, alarm time, and post-event intervention. As a result of incomplete data and knowledge on relevant processes and triggering mechanisms, accurate prediction of rockfall events is still not possible (van Westen et al, 2006). Existing methods to monitor slope failures include point measurements (e.g., extensometers) and large-scale monitoring such as terrestrial laser scanners, interferometric radar, and video image recognition (e.g., Abellán et al, 2011). These techniques suffer from disadvantages like high operating costs, limited spatial coverage, and susceptibility to atmospheric conditions

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