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ESurf | Articles | Volume 7, issue 1
Earth Surf. Dynam., 7, 171–190, 2019
https://doi.org/10.5194/esurf-7-171-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Earth Surf. Dynam., 7, 171–190, 2019
https://doi.org/10.5194/esurf-7-171-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

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
Allen, R. V.: Automatic Earthquake Recognition and Timing from Single Traces, B. Seismol. Soc. Am., 68, 1521–1532, 1978. a, b, c
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
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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.
Monitoring rock slopes for a long time helps to understand the impact of climate change on the...
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