Abstract
Early tsunami and earthquake warning systems need a good Automatic First Arrival Picking (AFAP) subsystem to determine the earthquake arrival time. This subsystem has a time-domain earthquake signal as the input and the arrival time of the primary earthquake wave (P-Wave) as the output. There are several methods of AFAP that are widely used nowadays, one of them is Short Term Average/Long Term Average (STA/LTA) fused with the AR-AIC method. Even though this method is real-time, its performance is still relatively low. With similar characteristics between the seismic signals and image data, utilising deep learning on AFAP can further increase its performance. The seismogram channels can be seen as the image height, and the signal at a certain window can be seen as the image width. Unfortunately, these image data will be considered an imbalanced dataset. In this research, deep learning with time domain and frequency domain are proposed as inputs with the Synthetic Minority Oversampling Technique (SMOTE) method. Deep learning is used because of its ability to generalise well on a huge dataset, while SMOTE is used to overcome the imbalanced dataset problem. With this proposed system, the accuracy is 99.3%, the Root Mean Square Error (RMSE) is 0.202 seconds, and the maximum execution time is 0.17 seconds with a periodic time of 0.4 seconds. With these results, the AFAP system has good results for estimating the first arrival earthquake time.
Highlights
An earthquake is a disastrous phenomenon that has a hazardous impact on the environment
The system itself consists of several subsystems. This system will receive a continuous seismic wave and will produce an output of the first time the primary earthquake wave (P-Wave) arrived as soon as the system detected it. This system is constructed based on the main principle of the CNN-based Phase-Identification Classifier (CPIC) research system [7]
It can be concluded that the system had 99.3% accuracy, 99.3% recall, 98.9% precision, and Root Mean Square Error (RMSE) at 0.202 seconds
Summary
With the pre-processing data, additional features, and changes in the architecture, this research aims to create an AFAP system with an accuracy of 95% and RMSE lower than 0.3 seconds. The purpose of this research is to develop a system that works better than the traditional method
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