data

Analyzing a magnitude 7.2 earthquake in Mexico using Python

A quick overview of OpenEEW

OpenEEW is an initiative by Grillo to share our data, sensor technology and detection algorithms that form part of our earthquake early-warning (EEW) systems in Mexico and Chile.

As I discussed in OpenEEW: how we plan to democratize earthquake early-warning systems across borders, we hope that this initiative will lead to many more people around the world benefiting from the life-saving alerts that EEW systems provide.

On 11 April 2019, the first step in this journey was taken with the publication of our entire archive of unprocessed accelerometer data as an AWS Public Dataset. Anyone can now access this data, which dates back to 1 December 2017, and we are very excited to see what kind of analyses and machine learning models people develop.

Exploring OpenEEW data with Python

As discussed in our article The Earthquake Bell, the concept of such an EEW system was first proposed in 1868 for the San Francisco area by Dr JD Cooper, but it was not until 100 years later that they started to become a reality.

Following the devastating 1985 earthquake in Mexico City, an EEW system was developed for Mexico City that began issuing public alerts in 1993, making it the first EEW system in the world to do so. Since then it has been expanded to cover areas of central and southern Mexico.

Mexico’s EEW system was inspired by Japan, which has pioneered the development of EEW systems since the 1960s when its national rail company began developing an earthquake alarm as a way to reduce the risk of derailment during earthquakes. Despite this, Japan’s nationwide EEW system only began issuing public alerts in 2007. A notable success came on 11 March 2011 when the system successfully provided alerts for the magnitude 9 earthquake that struck off the coast of Tohoku.

Besides Japan, the only other country with a nationwide EEW system is Taiwan, which began issuing alerts to key agencies in 2014 and then to the general public in 2016.

An EEW system is currently being developed for the west coast of the USA, with the first alerts being available for the LA area since the start of 2019 by means of a smartphone app.

Lowering the cost with the Internet of Things

Despite the potential of EEW systems to save lives and reduce the damage caused by earthquakes, the majority of seismic regions in the world do not have one. With a price tag ranging from tens to hundreds of millions of dollars, if not more, an EEW system like those described above is a luxury that many earthquake-prone countries cannot afford.

However, recent advances in Internet of Things (IoT) and cloud technology have opened up the possibility to develop lower-cost and rapidly-deployable alternatives that can significantly expand the reach of EEW systems around the world.

Our mission at Grillo is to turn this possibility into a reality. During the past two years, we have developed our own IoT- and cloud-based EEW systems in Mexico and Chile that have been issuing alerts since March 2018. Our sensors, which are small in size and easy to install, are constantly sending unprocessed accelerometer data to the cloud, where detection algorithms check for earthquakes and send notifications via our smartphone app and Twitter.

The reduced cost of our sensors compared to traditional seismic sensors, their ease of deployment and our use of the cloud mean we are able to set up EEW systems in new regions quickly. Indeed, Grillo’s EEW system in Chile emitted its first alerts within weeks of its sensors arriving there.

Democratizing alerts with OpenEEW

Our experience so far has confirmed the potential of an IoT-based approach to EEW systems to bring life-saving alerts to new regions around the world. To expedite this process, Grillo will soon be launching OpenEEW, an initiative to share our data, sensor technology and detection algorithms.

The first step will be to open up our entire archive of unprocessed accelerometer data, including a magnitude 7.2 earthquake, enabling people to develop their own detection algorithms using cutting-edge machine learning models.

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