Systematic Identification of Noise on Matterhorn (CH)

Abstract

Passive monitoring of ground motion can be used for geophysical process analysis and natural hazard assessment. Detecting events in microseismic signals can provide responsive insights into active geophysical processes. However, in the raw signals, microseismic events are superimposed by external influences, for example, anthropogenic or natural noise sources that distort analysis results. In order to be able to perform event-based geophysical analysis with such microseismic data records, it is imperative that negative influence factors can be systematically and efficiently identified, quantified and taken into account. Current identification methods (manual and automatic) are subject to variable quality, inconsistencies or human errors. Moreover, manual methods suffer from their inability to scale to increasing data volumes, an important property when dealing with very large data volumes as in the case of long-term monitoring.

In this work, we present a systematic strategy to identify a multitude of external influence sources, characterize and quantify their impact and develop methods for automated identification in microseismic signals. We apply the strategy developed to a real-world, multi-sensor, multi-year microseismic monitoring experiment performed at the Matterhorn Hörnligrat (Switzerland). We develop and present an approach based on convolutional neural networks for microseismic data to detect external influences originating in mountaineers, a major unwanted influence, with an error rate of less than 1 %, 3 times 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 jointly using time-lapse image and microseismic data on an annotated subset of the monitoring data. Applying these classifiers to the whole experimental dataset reveals that approximately one-fourth of events detected by an event detector without such a preprocessing step are not due to seismic activity but due to anthropogenic influences and that time periods with mountaineer activity have a 9 times higher event rate. Due to these findings, we argue that a systematic identification of external influences using a semi-automated approach and machine learning techniques as presented in this paper is a prerequisite for the qualitative and quantitative analysis of long-term monitoring experiments.

Interactive Figures

This post contains interactive plots for the paper Systematic Identification of External Influences in Multi-Year Micro-Seismic Recordings Using Convolutional Neural Networks, which is published in the Journal Earth Surface Dynamics.

Tools for interaction become available when hovering the mouse above a plot.

Figure 8

Number of image/micro-seismic data pairs in the dataset (dark blue) and in the annotated subset (light orange) displayed over the week number of the year 2016 and 2017. Note the logarithmic scale on the y-axis

Figure 9 (a)

Event count, hut occupancy and rock temperature over time for the years 2016 / 2017. The event rate from (Weber et al., 2018) is illustrated in light blue and the rate after removal of mountaineer induced events in dark blue. The strong variations in event count correlate with the presence of mountaineer, hut occupancy. Please note that due to privacy concerns the hut occupancy has a weekly granularity instead of daily as in the paper.

Figure 9 (b)

Event count, hut occupancy, total net radiation and rock temperature over time for a selected period during defreezing of the rock. Again, the event rate from (Weber et al., 2018) is illustrated in light blue and the rate after removal of mountaineer induced events in dark blue. The strong variations in event count correlate with the presence of mountaineer, hut occupancy and with the total net radiation. The impact of mountaineers is significant after July 9th and event detection analysis becomes unreliable. Please note that due to privacy concerns the hut occupancy has a weekly granularity instead of daily as in the paper.

Figure 10

Correlation of mountaineer activity and hut occupancy. The normalized number of mountaineer segments per week and the normalized number of overnight stays at the Hörnlihut per week plotted over time.