Earthquake Early Warning System (EEWS) is a warning system that provides information about the estimated S wave arrival time, which can cause significant and destructive seismic energy using the information carried by the P wave. Technological advances in analyzing data supported by big data, the interconnection between networks, and high-performance computing systems in the era of the 4.0 industrial revolution have posed challenges to process and analyze earthquake early warning using modern seismological techniques. Early identification of earthquake events is the key to time efficiency to accelerate the dissemination of information. Here, we implement deep learning for early detection and classification of the earthquake P wave and noise signals using raw historical data from 3 component BMKG single station (2014 -2020) in the subduction zone of West Sumatra. The feature selection of the waveform is only selected for earthquakes distance in the cluster close to the station centroid. Statistically, the results of training and testing show good and convergent performance. This result is a preliminary study of deep learning, which is targeted at the classification of earthquakes p wave and noise signals and its association to estimate early earthquake location using 3 component record channels.
Indonesia has high level of seismic activity, so determining magnitude of an earthquake is important in the Earthquake Early Warning System. In the Earthquake Early Warning System, the parameter magnitude must be estimated earlier, so that warnings can be disseminated before the S and surface waves arrive. In previous studies machine learning technology can be used to recognized earthquake events and extract hidden information with massive datasets. This study was a preliminary, proposed the alternative methods to calculate the earthquake magnitude as fast as possible, the data was 1s before and 3 seconds after the P wave from the 3-component single station raw seismogram historical data and developed with a classification deep neural network (DNN) model, classical machine learning random forest (RF) algorithm and the regression deep neural network (DNN). Results from the statistical analysis show that the waveform can be modelled by deep neural network (DNN) models. Classification DNN Model that we constructed reaches good pattern which final loss of 0.63. If it benchmarked to another model such as Random forest (RF), Classification DNN was a better model than RF which is determined by final loss of RF. Our recommendation related to estimate the magnitude from seismic raw modelling are better using Classification DNN with larger dataset. In our study, with relatively small dataset, modelling using RF algorithm can be another option. Another suggestion related this work was utilizing the Regression DNN, that resulting best alternative related to estimation of magnitude.
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