.[1] Seismic data analysis is a powerful tool for remote characterization of rock slope failures. Here we develop quantitative estimates of fundamental rockslide properties (e.g., volume) based solely on data from an existing regional seismic network. We assembled a data set of twenty known rockslides in the central Alps (with volumes between 1,000 and 2,000,000 m 3 ) and analyzed their corresponding seismograms. Common signal characteristics include emergent onsets, slowly decaying tails, and a triangular spectrogram shape. The main component of seismic energy is contained in frequencies below ∼3-4 Hz, while higher-frequency signals may be caused by block impacts. Location estimates were generated using automatic arrival time picks and resulted in a mean location error of 10.9 km. A linear relationship for the detection limit of a rockslide as a function of volume was identified for our seismic station network. To estimate rockslide volume, runout distance, drop height, potential energy, and Fahrböschung (angle of reach), we extracted five simple metrics from each seismogram: signal duration, peak value of the ground velocity envelope, velocity envelope area, risetime, and average ground velocity. Using multivariate linear regression, the combination of duration, peak envelope velocity, and envelope area best estimated event parameters, with r 2 values ranging between 0.8 and 0.88. Three new rockslides were then used to validate our method, and volume, runout, drop height, and potential energy were estimated within the correct order of magnitude. When provided with a suitable data set of rockslide events, our method can be easily adapted to other regions and seismic networks.

The purpose of the present experiment was to investigate the integration of sequences of visual and tactile events. Subjects were presented with sequences of visual flashes and tactile taps simultaneously and instructed to count either the flashes (Session 1) or the taps (Session 2). The number of flashes could differ from the number of taps by +/-1. For both sessions, the perceived number of events was significantly influenced by the number of events presented in the task-irrelevant modality. Touch had a stronger influence on vision than vision on touch. Interestingly, touch was the more reliable of the two modalities-less variable estimates when presented alone. For both sessions, the perceptual estimates were less variable when stimuli were presented in both modalities than when the task-relevant modality was presented alone. These results indicate that even when one signal is explicitly task irrelevant, sensory information tends to be automatically integrated across modalities. They also suggest that the relative weight of each sensory channel in the integration process depends on its relative reliability. The results are described using a Bayesian probabilistic model for multimodal integration that accounts for the coupling between the sensory estimates.

Data from continuously recording permanent seismic networks can contain information about rockslide occurrence and timing complementary to eyewitness observations and thus aid in construction of robust event catalogs. However, detecting infrequent rockslide signals within large volumes of continuous seismic waveform data remains challenging and often requires demanding manual intervention. We adapted an automatic classification method using hidden Markov models to detect rockslide signals in seismic data from two stations in central Switzerland. We first processed 21 known rockslides, with event volumes spanning 3 orders of magnitude and station event distances varying by 1 order of magnitude, which resulted in 13 and 19 successfully classified events at the two stations. Retraining the models to incorporate seismic noise from the day of the event improved the respective results to 16 and 19 successful classifications. The missed events generally had low signal-to-noise ratio and small to medium volumes. We then processed nearly 14 years of continuous seismic data from the same two stations to detect previously unknown events. After postprocessing, we classified 30 new events as rockslides, of which we could verify three through independent observation. In particular, the largest new event, with estimated volume of 500,000 m 3 , was not generally known within the Swiss landslide community, highlighting the importance of regional seismic data analysis even in densely populated mountainous regions. Our method can be easily implemented as part of existing earthquake monitoring systems, and with an average event detection rate of about two per month, manual verification would not significantly increase operational workload.

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