Earthquake Early Magnitude Prediction Using 3D Point Cloud Based Deep Learning Model: Case of 2018 Osaka Earthquake

Earthquakes are considered to be one of the deadliest natural phenomena that exist on Earth. The destruction during an earthquake is directly related to the magnitude corresponding to a station during the earthquake. The magnitude of an earthquake is calculated from the complete time series recorded by the instrument. In recent years, the early phase of the time series has been used to measure an earthquake’s magnitude, commonly known as an earthquake early warning system. This early detection of magnitude gives sufficient time for saving lives and property damaged by the earthquake. Traditionally, simple linear relations are utilized in the EEWS. In this study, the proposed Point Cloud Magnitude Prediction (CloudMag) model utilized point cloud technology, which converts two-dimensional time series data into multi-dimensional representation in space. This representation is then utilized for earthquake magnitude prediction using a convolutional neural network architecture. This study discusses a case of the deadly Osaka earthquake that occurred on 18 June 2018 at 7:58:35 AM Japan Standard Time. The result shows that the novel deep learning approach for magnitude estimation is superior to traditional and other machine learning models.