We regularly run benchmark analysis of Grillo’s Earthquake Early-Warning system (EEW) against other systems in Mexico. This year we were pleased to find that our system has been performing very well and below we share the results.
The Mexican EEWs
SASMEX is created by a private company CIRES, and financed by the Mexican government since 1985. It uses seismograph sensors that cost more than $20,000 USD each including installation, and proprietary algorithms to detect earthquakes. The alerts are sent via a network of radio towers back to a handful of cities in Mexico. The SASMEX system relies on public financing and whilst it has limited coverage, it is considered to be the official alert of Mexico. It also generates revenue with its alarm devices that start at $2,000 USD.
Skyalert is a private company that has developed a sensor network using HomeSeismo sensors bought from Japan, costing several thousand USD each. When shaking is detected by a sensor, it transmits the local shaking intensity to the cloud, and push notifications are sent to the users for each separate shaking event. However, the user sometimes receives many messages depending on how many sensors reach a shaking threshold. This can be confusing for the users.
Grillo is a social enterprise with funding from USAID, AXA Foundation, the Chile government and Facebook. We have developed proprietary sensors and algorithms which are located in Mexico and Chile. This system is less than 1% the cost of SASMEX, and significantly cheaper than Skyalert. The Grillo detection system calculates what the shaking will be like for each end-user, and only alerts them if they will feel the shaking in their location.
Benchmarking the EEWs
Grillo, SASMEX and Skyalert all send real-time tweets when their network of sensors has detected an earthquake in Mexico. The twitter platform is built for low-latency mass messaging, which is ideal for EEW where large numbers of population need to receive alerts seconds or minutes before the shaking arrives to their location.
First to detect an earthquake from July 2018 - July 2019
Number of Earthquakes Detected from July 2018 - July 2019
To arrive at these scores, a year’s worth (July 2018 – July 2019) of tweets were taken from the following twitter accounts:
You can view the dataset here. To make a proper comparison, only the tweets that contain live alerts were used. Tweets that relate to post-earthquake or non-earthquake events were removed. The tweets for all EEWs were then grouped according to earthquake events. The first EEW to send an alert tweet was determined to be the fastest (labelled ‘Winner’ in the linked spreadsheet).
We also compared tweets from SASMEX with the timestamps posted in their website, and the difference was always under 1 second, which probably relates to latency involved in sending to Twitter servers.
Comparing EEW accuracy
Our team, which includes seismologists and mathematicians, has created a robust detection system that is both fast and accurate. The detection algorithms are continuously improved as they are running in the cloud.
Cost of EEW
By building sensors using the latest technologies, developing in-house our own detection system, and relying on low-cost cloud computing, Grillo’s network is significantly lower cost than the competition. This has allowed it to stay competitive with SASMEX and Skyalert who have far greater levels of funding from government and corporations.
Why Grillo Outperforms the Competition
Grillo has a world-class team of scientists and has spent years perfecting its patent-pending technology. SASMEX technology is still based on its initial concept created in the 1980s, and Skyalert relies on sensor equipment that was created by a supplier for the domestic market in Japan.
Grillo has detected hundreds of earthquakes which and also contributed sensor data to academia through its open-source initiative ‘OpenEEW’.
Our sensors leverage new Internet-of-Things technologies, and the development has been given assistance from our partners Particle, Arrow and others. We have also used the latest technologies from Amazon Web Services to create a scalable yet affordable cloud detection system that can work anywhere in the world.