期刊名称：International Journal of Advances in Engineering and Management
电子版ISSN：2395-5252
出版年度：2022
卷号：4
期号：4
页码：884-888
DOI：10.35629/5252-0404637646
语种：English
出版社：IJAEM JOURNAL
摘要：Earthquake early warning monitors when there is a sudden shaking wave which is created by an earthquake at a location. Thisallow people to take advance precaution to protect themselves from injurious or damages. Using the Earthquake science and the technology of monitoring systems to warn the people. There are three types of seismic waves those are P, S, and surface waves.these waves when interact with the surface they generate a new wave which is called as the surface wave and the velocity of the waves keeps changing with different density and if the density greater that the velocity of the wave formed. Longitudinal primary waves (pwaves) this waves same as the sound waves and travel through the solids, liquid and gas. Longitudinal secondary waves(S-waves) ishigh frequency with shorter wavelengths and travels only through solids. surface waves (L-waves) create more damages as the occurred by the displacements of the rocks. The magnitudes give vital data on alternative supply parameters, such as wave energy, fault length, seismic moment. Total characteristics of study square measure supported not solely the coaching and testing information sets however additionally qualitative information regarding earthquake prediction. Machine learning and deep learning techniques square measure applied severally to model the connection between calculated seismic information and future earthquake occurrence. This paper provides a survey report on the implementation of various techniques for Earthquake Magnitude Prediction in conjunction with their blessings and disadvantages which can facilitate within the additional development and improvement 3times better than the existing system. Ensemble Learning and Deep Learning square measure planned to predict the earthquake magnitude. Using deep learning trained convolution neural networks on several time series in data to detect automatically and extract ground deformation.
