Deep learning, machine learning and internet of things in geophysical engineering applications: An overview

The earthquakes in Eastern Mediterranean are mostly tectonic. The earthquakes that are 60 km deep in the ground are called Shallow earthquakes. The earthquakes in the eastern Mediterranean are generally shallow orientated, and their depth often varies between 0 and 30 km. When there is a sudden plate movement within the earth's crust, increasing friction between two plates can result in earthquakes, which are harmful to human lives, buildings and the economy. With the technological developments in the world over the years, more information has been accessible and reliable. The Internet of things (IoT) and crowdsourcing have proven to be effective in predicting and preparing for the future natural hazards when combined with Machine Learning or Deep Learning. Machine learning does not require human interaction, as the machines automatically gets information and distributes in real-time to prevent major or severe damages, which are low-cost, has lower power consumption with reliable information. Short Message Service and Global Positioning System are still functional, even though powerlines and mobile beacons, were cut during recent massive earthquakes in some countries of Asia. The Seismic Alert System (SAS) is another design to prevent and reduce earthquake damages. The SAS was designed in 1996, to be effective with real time data from seismograms. The Earth cloud uses geophones which uses MEMS (micro electro-mechanical system). Geophones have an accelerometer and seismometers to detect the earth movement. Therefore, usage of sensors and the Internet of Things (IoT) to monitor earthquakes and send early warning signals to prevent destruction of buildings and loss of life play a significant role.

Automated summary

Earthquakes in the Eastern Mediterranean are mostly tectonic, with depths generally varying between 0 and 30 km. As technology has advanced, the use of IoT and machine learning have proven effective in predicting and preparing for future natural disasters.