Potential short-term earthquake forecasting by farm animal monitoring
Wikelski, Martin (2020), Potential short-term earthquake forecasting by farm animal monitoring, Dryad, Dataset, https://doi.org/10.5061/dryad.q2bvq83gq
Whether changes in animal behavior allow for short-term earthquake predictions has been debated for a long time. Before, during and after the 2016/2017 earthquake sequence in Italy, we deployed bio-logging tags to continuously observe the activity of farm animals (cows, dogs, sheep) close to the epicenter of the devastating magnitude M6.6 Norcia earthquake (Oct-Nov 2016) and over a subsequent longer observation period (Jan-Apr 2017). Relating 5304 (in 2016) and 12948 (in 2017) earthquakes with a wide magnitude range (0.4 ≤ M ≤ 6.6) to continuously measured animal activity, we detected how the animals collectively reacted to earthquakes. We also found consistent anticipatory activity prior to earthquakes during times when the animals were in a building (stable), but not during their time on a pasture. We detected these anticipatory patterns not only in periods with high, but also in periods of low seismic activity. Earthquake anticipation times (1-20hrs) are negatively correlated with the distance between the farm and earthquake hypocenters. Our study suggests that continuous bio-logging of animal collectives has the potential to provide statistically reliable patterns of pre-seismic activity that could yield valuable insights for short-term earthquake forecasting. Based on a-priori model parameters we provide empirical threshold values for pre-seismic animal activities to be used in real-time observation stations.
We used 3D acceleration sensors to measure the activity of animals on a farrm (cows, dogs, and sheep). For each of the three species, we computed the 15 min average of their ODBA (overall dynamic body acceleration), that is, the average acceleration and the average over all tagged animals of the respective species.
|Earthquake Data from the Nocia Region in Italy were collected from 17/01/2017 - 16/04/2017|
Data are from INGV Rome, downloaded Sept 19, 2017 (by Laura Parise & Martin Mai, KAUST)
Event specific parameters were computed by Martin Mai on Sept 20, 2017.
|Distances (in km) are calculated. with reference (= origin) to farm location: [LAT 43.0147, LON 13.0509, ELEV 0.580 km]|
Please contact Prof. Martin Wikelski for further usage help, firstname.lastname@example.org
Deutsche Forschungsgemeinschaft, Award: EXC 2117—422037984