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Open source initiative aims to accelerate deployment of earthquake early-warning systems

Linux Foundation is collaborating with IBM and Grillo on the OpenEEW project to help reduce the costs of systems, accelerate earthquake preparedness around the world and save lives.

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One of the earthquake early warning systems being deployed in Oaxaca, Mexico
One of the earthquake early warning systems being deployed in Oaxaca, Mexico

The Linux Foundation, in collaboration with IBM and Grillo, has announced an initiative to help accelerate the standardisation and deployment of earthquake early-warning detection systems (EEWs) around the world.

 

Grillo is open sourcing ‘OpenEEW’, its IoT-based earthquake early-warning platform comprised of integrated capabilities to sense, detect, and analyse earthquakes as well as alert communities. OpenEEW was developed by Grillo with support from IBM, USAID, the Clinton Foundation, and Arrow Electronics.

 

Public alerts

 

EEW systems provide public alerts in countries including Mexico, Japan, South Korea, and Taiwan, but nearly three billion people globally live with the threat of an earthquake and don’t have access to nation-wide systems, which can cost upwards of $1bn, the organisations report.

 

OpenEEW bids to help reduce the costs of EEW systems, accelerate their deployments for earthquake preparedness around the world, and has the potential to save many lives.

 

“For years we have seen that EEWs have only been possible with very significant governmental financing, due to the cost of dedicated infrastructure and development of algorithms,” said Andres Meira, founder, Grillo.

“We expect that OpenEEW will reduce these barriers and work towards a future where everyone who lives in seismically-active areas can feel safe.”

 

The OpenEEW project includes several core IoT components: sensor hardware and firmware that can rapidly detect and transmit ground motion; real-time detection systems that can be deployed on various platforms from a Kubernetes cluster to a Raspberry Pi; and applications that allow users to receive alerts on hardware devices, wearables, or mobile apps as quickly as possible.

“The open source community can enable rapid development and deployment of these critical systems across the world”

The open source community aims to help advance earthquake technology by contributing to OpenEEW’s three integrated technology capabilities: deploying sensors, detecting earthquakes, and sending alerts.

 

“The OpenEEW project represents the very best in technology and in open source,” said Mike Dolan, senior vice president and general manager of projects at the Linux Foundation.

 

The Grillo sensor that can rapidly detect and transmit ground motion
The Grillo sensor that can rapidly detect and transmit ground motion

“We’re pleased to be able to host and support such an important project and community at the Linux Foundation. The open source community can enable rapid development and deployment of these critical systems across the world.”

IBM has deployed a set of six of Grillo’s earthquake sensor hardware and is conducting tests in Puerto Rico, complementing Grillo’s tools with a new Node-RED dashboard to visualize readings. IBM is also extending a Docker software version of the detection component that can be deployed to Kubernetes and Red Hat OpenShift on the IBM Cloud.

 

Earthquake characterisation

 

Grillo sensors have generated more than one terabyte of data since 2017 in Mexico, Chile, Puerto Rico and Costa Rica, including information from large earthquakes of magnitudes 6.0 and 7.0. Researchers from Harvard University and the University of Oregon are already working with this data, which will enable new machine learning earthquake characterisation and detection methods.

The primary aim of the project is to encourage a variety of individuals – makers, data scientists, entrepreneurs, seismologists – to build EEWs in places like Nepal, New Zealand, Ecuador, and other seismic regions.

 

This community may also contribute to OpenEEW by advancing the sensor hardware design, improving detection and characterisation of earthquakes through machine learning, and creating new methods for delivering alerts to citizens.

 

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