Characterizing the complexity of microseismic signals at slow-moving clay-rich debris slides: the Super-Sauze (southeastern France) and Pechgraben (Upper Austria) case studies

Abstract

Abstract. Soil and debris slides are prone to rapid and dramatic reactivation. Deformation within the instability is accommodated by sliding, whereby weak seismic energies are released through material deformation. Thus, passive microseismic monitoring provides information that relates to the slope dynamics. In this study, passive microseismic data acquired at Super-Sauze (southeastern France) and Pechgraben (Upper Austria) slow-moving clay-rich debris slides (“clayey landslides”) are investigated. Observations are benchmarked against previous similar case studies to provide a comprehensive and homogenized typology of microseismic signals at clayey landslides. A thorough knowledge of the various microseismic signals generated by slope deformation is crucial for the future development of automatic detection systems to be implemented in landslide early-warning systems. Detected signals range from short-duration (< 2 s) quake-like signals to a wide variety of longer-duration tremor-like radiations (> 2 s – several min). The complexity of seismic velocity structures, the low quantity and low quality of available signal onsets and non-optimal seismic network geometry severely impedes the source location procedure; thus, rendering source processes characterization challenging. Therefore, we constrain sources' locations using the prominent waveform amplitude attenuation pattern characteristic of near-source area (< about 50 m) landslide-induced microseismic events. A local magnitude scale for clayey landslides (ML−LS) is empirically calibrated using calibration shots and hammer blow data. The derived ML−LS returns daily landslide-induced microseismicity rates that positively correlate with higher average daily displacement rates. However, high temporal and spatial resolution analyses of the landslide dynamics and hydrology are required to better decipher the potential relations linking landslide-induced microseismic signals to landslide deformation.