期刊
GEOPHYSICAL RESEARCH LETTERS
卷 44, 期 1, 页码 113-120出版社
AMER GEOPHYSICAL UNION
DOI: 10.1002/2016GL070709
关键词
landslide seismology; classification; random forest; machine learning
资金
- French National Research Agency (ANR)
- Open Partial Agreement Major Hazards of Council of Europe
The deformation of slow-moving landslides developed in clays induces endogenous seismicity of mostly low-magnitude events (M-L<1). Long seismic records and complete catalogs are needed to identify the type of seismic sources and understand their mechanisms. Manual classification of long records is time-consuming and may be highly subjective. We propose an automatic classification method based on the computation of 71 seismic attributes and the use of a supervised classifier. No attribute was selected a priori in order to create a generic multi-class classification method applicable to many landslide contexts. The method can be applied directly on the results of a simple detector. We developed the approach on the seismic network of eight sensors of the Super-Sauze clay-rich landslide (South French Alps) for the detection of four types of seismic sources. The automatic algorithm retrieves 93% of sensitivity in comparison to a manually interpreted catalog considered as reference.
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