A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning

The earthquake early warning system uses a highspeed computer network to transmit information about earthquakes to the population center prior to the arrival of destructive seismic waves. Traditional EEW seismometric methods do not accurately identify large earthquakes due to their sensitivity to the speed of ground movement. Precision GPS stations, on the other hand, are ineffective in identifying average earthquakes due to their tendency to generate noisy data. An early warning system is primarily required to set off an alarm so that critical facilities can be evacuated or closed, rather than determining the exact parameters of the earthquake. Therefore, the early warning system must be carried out independently and the government and other authorities must immediately publish accurate information on earthquakes. The properties required for early warning systems can be summarized as follows: Fully automatic: As the time frame is limited, the facility must be controlled directly without human judgment. Fast and Reliable: Since there is limited time to respond to the movement of an earthquake, this type of system must be fast and reliable. Small and Inexpensive -For easy installation, the system must be small and inexpensive. Independence -In order to issue fail-safe alarms, the system must be independent of other systems. Easy to connect network: In order to provide the earthquake information, the system must be easy to connect to the network. Accuracy is better: The accuracy of the information is not such a serious problem for the alarm. In this document, I use machine learning methods to address the most pressing challenges facing EEW systems. Several data sources are integrated in real time to cover the entire spectrum of potentially damaging earthquakes (medium and large). Our solution is based on two types of complementary sensors (GPS stations and seismometers).