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. These datasets compare sets of detections with the earthquake parameters published by seismic institutes in order to analyse the performance of the EQN network.
One dataset contains 550 detections made by EQN between 2017-12-15 and 2020-01-31 in Chile, USA and Italy. Wherever possible, each detection was associated with an earthquake from the parameter catalogue of each country's seismic institute (CSN for Chile, USGS for USA and INGV for Italy). Associations were carried out automatically but also checked manually.
The other dataset contains 134 detections from around the world that could be associated to earthquakes with magnitude ≥ M5 or magnitude ≥ M4.5 in Italy and the USA. There are 68 detections that are common to the first dataset. All detections were associated to parameters from the the USGS earthquake parameter catalogue for consistency.
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.
Here we report the raw data of the physical properties of carbonate samples collected along the Monte Maggio normal Fault (MMF), a regional structure (length ~10 km and displacement ~500 m) located within the active system of the Apennines (Italy). In particular, we report results coming from large cores (100 mm in diameter and up to 20 cm long) drilled perpendicular to the fault plane made of Calcare Massiccio (massive limestone) and Bugarone fm (limestone with 8.3 % of clay). From these large cores, we obtained smaller cores, 38 mm in diameter both parallel and perpendicular to the fault plane, that have been used for experiments. We have divided the rock samples in four categories following the fault architecture. The four structural domains of the fault are:1) the hangingwall (HW) made of Bugarone fm that is still preserved in some portions of the fault, 2) a Cemented Cataclasite (CC) and 3) a Fault Breccia (FB) that characterize the cataclastic damage zones and 4) the correspondent undeformed protolith of the footwall block made of Calcare Massiccio. Raw data reported here are those used for drawing Figures 5, 6, 8 and 9 of the paper “Physical and transport property variations within carbonate- bearing fault zones: Insights from the Monte Maggio Fault (central Italy)”, http://doi.org/10.1002/ 2017GC007097 by Trippetta et al.
Dataset_Fig05.txt reports P- and S-wave velocities (in km/s) of the described samples at pressure from 0.1 MPa (ambient pressure) up to 100 MPa at ambient temperature in dry conditions and the corresponding Vp/Vs ratio. Experiments have been performed by using the permeameter at the HP-HT Laboratory of experimental Volcanology and Geophysics at INGV (Rome).Dataset_Fig06.txt reports permeability data (in m^2) on the same type of samples of fig05 for the same range of confining pressure at ambient temperature. Pore pressure values athletes each confining pressure step are indicated in the file. Data have been again acquired with the permeameter.Dataset_Fig08.txt reports P-wave velocity data (in km/s) vs depth (in m), recorded on the portion that crossed the Calare Massiccio fm of three boreholes drilled in the Apennines: Varoni 1, Monte Civitello 1 and Daniel1. Data have been obtained by digitalizing each pdf file of the boreholes mentioned above, that are available at http://unmig.sviluppoeconomico.gov.it/videpi/videpi.asp. Once digitalized, respect to the original pdf file, velocity data have been simply converted from um/f to km/s.Dataset_Fig09.txt reports values of the maximum, minimum and average values of Critical fault nucleation length (in m) at each corresponding depth (in m) and applied confining pressure (in MPa). Critical nucleation lengths have been calculated by using the equations described in the text of the Trippetta et al paper and by using the elastic parameters calculated from data reported here. Data on earthquakes-depth distribution of the 2009 L'Aquila sequence can be found on Chiaraluce et al. (2011).