Systematic Identification of External Influences in Multi-Year Micro-Seismic Recordings Using Convolutional Neural Networks

Abstract

Abstract. Natural hazards, e.g. due to slope instabilities, are a significant risk for the population of mountainous regions.Monitoring of micro-seismic signals can be used for process analysis and risk assessment. However, these signals are subject to external influences, e.g anthropogenic or natural noise. Successful analysis depends strongly on the capability to cope with such external influences. For correct slope characterization it is thus important to be able to identify, quantify and take these influences into account. 5In long-term monitoring scenarios manual identification is infeasible due to large data quantities demanding accurate automated analysis methods. In this work we present a systematic strategy to identify multiple external influences, characterize their impact on micro-seismic analysis and develop methods for automated identification. We apply the developed strategy to a real-word, multi-sensor, multi-year micro-seismic monitoring experiment on the Matterhorn Hörnliridge (CH). We present a convolutional neural network for micro-seismic data to detect external influences originating in mountaineers, a major un-10 wanted influence, with an error rate of less than 1 %, which is 3x lower than comparable algorithms. Moreover, we present an ensemble classifier for the same task obtaining an error rate of 0.79 % and an F1 score of 0.9383 by using images and micro-seismic data. Applying the classifiers to the experiment data reveals that approximately 1/4 of events detected with an event detector are not due to seismic activity but due to anthropogenic mountaineering influences and that time periods with mountaineer activity have a 9x higher event rate. Due to these findings we argue that a systematic identification of external 15 influences, like presented in this paper, is a prerequisite for a qualitative analysis.

A decade of detailed observations (2008–2018) in steep bedrock permafrost at Matterhorn Hörnligrat (Zermatt, CH)

“…Along-side a number of technology-oriented publications have emerged that discuss sensor and wireless network design (Talzi et al, 2007;Hasler et al, 2008;Beutel et al, 2009;Keller et al, 2009b;Buchli et al, 2012;Sutton et al, 2015bSutton et al, , a, 2017a, performance analysis (Keller et al, 2012a(Keller et al, , 2011 and smart sensors (Sutton et al, 2017b;Meyer et al, 2018a). More recently focus has shifted even more to include machine learning methods to the portfolio of application specific data analysis (Meyer et al, 2017(Meyer et al, , 2018c.…”