Seismic instruments placed outside of spatially extensive hazard zones
can be used to rapidly sense a range of mass movements. However, it
remains challenging to automatically detect specific events of interest.
Benford’s law, which states that first non-zero-digit of given datasets
follow a specific probability distribution, can provide a
computationally cheap approach to identifying anomalies in large
datasets and potentially be used for event detection. Here, we select
raw seismic signals to derive the first-digit distribution. The seismic
signals generated by debris flows, landslides, lahars, and
glacier-lake-outburst floods follow Benford’s law, while those generated
by ambient noise, rockfalls, and bedload transports do not. Focusing on
debris flows, our Benford’s-law-based detector is comparable to an
existing random forest method for the Illgraben, Switzerland, but
requires only single station data and three non-dimensional parameters.
We suggest this computationally cheap, novel technique offers an
alternative for event recognition and potentially for real-time
warnings.