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.
Online Predictive Maintenance Monitoring Adopting Convolutional Neural Networks
