This article evaluates the performance of Grillo sensors for seismic applications such as earthquake early-warnings. Historic accelerations from the Grillo sensor network in Mexico will be compared to the UNAM Engineering Institute Accelerometric Network (RAII-UNAM) which uses observatory grade accelerometers such as
Kinemetrics Episensor with 24bit digitizers. UNAM sites are a combination
of low-noise free field sites outside of cities and basin sites within large
population centers.
The Grillo Sensor is a three component accelerometer with a 20 bit digitizer and dynamic range of +/- 2g. It is paired with a microcontroller that continuously sends samples to the cloud, as well as status updates.
The device is configured to send 125 samples for each axis per second to Grillo’s cloud, where the data is stored, processed, and visualized.
Grillo Sensors require specific installation conditions to ensure acceptable data quality:
The sensor data typically arrives to the cloud in 100ms or so, depending on the local network. Grillo uses AWS data centers situated near the physical locations of sensors, although the processing could also be done on a local server or alternative cloud provider.
Once in the cloud, the data is ingested via a scalable and reliable message broker, and then routed to different services:
Once in the cloud, Grillo can service parallel real-time ingestions with ease.
To arrive at a comparison for PGAs during the February 2018 M7.2 Pinotepa event, peak ground accelerations were taken from the UNAM database and also from the Grillo traces stored in AWS.
The maximum value for a single component was chosen for the PGA on either dataset, and plotted here. It can be observed that the Grillo sensors show similar PGAs and distance decay consistent with the UNAM sensors. Clusters of UNAM sites above the mean acceleration are within city basins (Acapulco, Oaxaca, and Puebla).
To arrive at a comparison for PGAs during the February 2018 M7.2 Pinotepa event, peak ground accelerations were taken from the UNAM database and also from the Grillo traces stored in AWS.
The maximum value for a single component was chosen for the PGA on either dataset, and plotted here. It can be observed that the Grillo sensors show similar PGAs and distance decay consistent with the UNAM sensors. Clusters of UNAM sites above the mean acceleration are within city basins (Acapulco, Oaxaca, and Puebla).
We routinely observe that shaking intensities measured by Grillo compare favorably to regional and global ground motion models (GMMs).
Shown here are 3 separate events. The scatter plots show the comparison to published GMMs (Zhao, Atkinson, McVerry, LinLee).
It can be observed that the Grillo PGAs align well with predictions from GMMs.
We note slightly high residual ground motions for the M7.2 earthquake (observed in UNAM data too). These are likely due to the unusually small source dimensions of the earthquake which denotes a high stress drop. See Li et al (2020).
Grillo also operates a network within Chile for early warning focused on Santiago. The largest event recorded thus far was an M6.2 in April 2018 at 200 km distance .
It is shown that Grillo sensors perform as well as one of Mexico’s national strong motion networks for the purpose of PGA and PVA generation.
Grillo accelerometers have enough fidelity and dynamic range to easily record all felt and damaging events at local and regional distances.
The lower cost and ease of deployment allows Grillo sensors to form dense networks to correctly capture the variability of ground motion across an urban
center.
As well as generating high quality data, Grillo also offers very low-latency transmission and processing through the cloud, resulting in near-instant and accessible data for end users.
