Articles | Volume 7, issue 1
Earth Surf. Dynam., 7, 171–190, 2019
https://doi.org/10.5194/esurf-7-171-2019

Special issue: From process to signal – advancing environmental...

Earth Surf. Dynam., 7, 171–190, 2019
https://doi.org/10.5194/esurf-7-171-2019
Research article
04 Feb 2019
Research article | 04 Feb 2019

Systematic identification of external influences in multi-year microseismic recordings using convolutional neural networks

Matthias Meyer et al.

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Cited articles

Aguiar, A. C. and Beroza, G. C.: PageRank for Earthquakes, Seismol. Res. Lett., 85, 344–350, https://doi.org/10.1785/0220130162, 2014. a
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Amitrano, D., Grasso, J. R., and Senfaute, G.: Seismic Precursory Patterns before a Cliff Collapse and Critical Point Phenomena, Geophys. Res. Lett., 32, L08314, https://doi.org/10.1029/2004GL022270, 2005. a
Amitrano, D., Arattano, M., Chiarle, M., Mortara, G., Occhiena, C., Pirulli, M., and Scavia, C.: Microseismic activity analysis for the study of the rupture mechanisms in unstable rock masses, Nat. Hazards Earth Syst. Sci., 10, 831–841, https://doi.org/10.5194/nhess-10-831-2010, 2010. a, b
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Monitoring rock slopes for a long time helps to understand the impact of climate change on the alpine environment. Measurements of seismic signals are often affected by external influences, e.g., unwanted anthropogenic noise. In the presented work, these influences are automatically identified and removed to enable proper geoscientific analysis. The methods presented are based on machine learning and intentionally kept generic so that they can be equally applied in other (more generic) settings.