Publikationsdatum:
2021-09-20
Beschreibung:
Abstract
Beschreibung:
The 'Earthquake Network’ (EQN) is an app which detects earthquakes by creating an ad-hoc network of smartphones' accelerometer sensors and provides early warnings for earthquakes via the same smartphone app. Detections are not due to individual smartphone measurements but due to near-simultaneous trigger signals from clusters of smartphones running the app. Therefore detections are normally located in the closest populated regions to an earthquake's epicentre. In order to investigate the mechanisms of EQN's earthquake detection system, we searched for seismic accelerometer stations with publically available data that were close to the EQN detection locations (rather than close to the epicentre). This confirmed that EQN's detections followed strong shaking motions but that detections could follow both P-phase or S-phase rather than consistantly being sensitive to only one particular phase. It also showed that detections generally occurred between 0 - 5 seconds after the peak ground acceleration measured by the seismic station.
Analysis was conducted on 550 detections made by the EQN system between 2017-12-15 and 2020-01-31 in Chile, Italy and the USA. Strong motion accelerometer data was collected from seismic stations via the FDSN protocol. The data was calibrated, detrended and a small time shift was applied to correct for differences in distances from the epicentre between the EQN detection and the strong motion seismic station.
Calibrated waveform data was obtained for 410 EQN detections. Plots were made for each event and an analysis was carried out on the dataset to compare EQN detection times with the peak ground acceleration measured by the nearest seismic station.
The dataset consists of a zip-file containing a table of results and some summary graphs derived from it as well as a set of 410 graphs of strong motion files that are presented as image files (png-files). The graphs show the waveform data for a seismic station within 20 km of each EQN detection.
Beschreibung:
Methods
Beschreibung:
Ground motion data was retrieved from seismic networks via the FDSN protocol via the IRIS and ORFEUS institutes. This data was calibrated using station inventory files also downloaded via FDSN and filtered between 0.5 - 12 Hz. A small time shift was applied to correct for differences in distances from the epicentre between the EQN detection and the strong motion seismic station. This time shift assumed a seismc phase velocity of 8.04km/s.
Schlagwort(e):
Earthquake Network
;
earthquakes
;
strong motion
;
seismic waves
;
smartphone
;
citizen science
;
seismic surface waves
;
accelerometry
;
ground motion
;
EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 EARTHQUAKES
;
EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 EARTHQUAKES 〉 EARTHQUAKE OCCURRENCES
;
EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 EARTHQUAKES 〉 SEISMIC PROFILE 〉 SEISMIC SURFACE WAVES
;
geological process 〉 seismic activity 〉 earthquake
;
In Situ Land-based Platforms 〉 GEOPHYSICAL STATIONS/NETWORKS 〉 FDSN
;
In Situ Land-based Platforms 〉 GEOPHYSICAL STATIONS/NETWORKS 〉 IRIS-GSN
;
In Situ Land-based Platforms 〉 GEOPHYSICAL STATIONS/NETWORKS 〉 SEISMOLOGICAL STATIONS
;
monitoring 〉 seismic monitoring
;
safety 〉 safety system 〉 warning system 〉 early warning system
Materialart:
Dataset
,
Dataset
