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

机译：地球物理工程应用中的深度学习，机器学习和事物互联网：概述

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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.
机译：东地中海的地震主要是构造。地面深入60公里的地震称为浅地震。地中海的地震通常是浅的导向，他们的深度经常在0到30公里之间变化。当地壳内有一个突然的板式运动时，两块板之间的摩擦力增加可能导致地震，这对人类生活，建筑物和经济有害。随着世界上的技术发展多年来，更多信息已经访问和可靠。事物（物联网）和众包的互联网已被证明在与机器学习或深入学习结合时预测和准备未来的自然危害。机器学习不需要人类的互动，因为机器自动获取信息并实时分发，以防止具有低成本的主要或严重损坏，具有可靠的信息的功耗较低。即使电力线和移动信标，短信服务和全球定位系统仍然是功能性，即使电力线和移动信标在近期亚洲国家的近期巨大的地震中被切断。地震警报系统（SAS）是另一种防止和减少地震损害的设计。 SAS于1996年设计，有效地从地震图的实时数据。地球云使用使用MEMS（微机电系统）的地震孔。地震检塞器有加速度计和地震仪，以检测地球运动。因此，使用传感器和物联网（物联网）来监测地震并发送预警信号，以防止破坏建筑物和生命损失发挥着重要作用。

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