Automatic classification of endogenous landslide seismicity using the Random Forest supervised classifier. Issue 1 (5th January 2017)

• Record Type: Journal Article
• Authors: Provost, F.; Hibert, C.; Malet, J.‐P.
• Is Part Of: Geophysical research letters. Volume 44:Issue 1(2017)
• Page Start: 113
• Page End: 120
• Publication Date: 2017-01-05
• Subjects: landslide seismology -- classification -- random forest -- machine learning; Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1002/2016GL070709
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: 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. Key Points: Landslide seismic sources are automatically classified using the Random Forest supervised classifier with 71 attributes The sensitivity of the classification is up to 93% in the case of four different classes of seismic events The large amount of attributes enables the method to be implemented easily and automatically in many contexts