Estimation of Magnitude and Epicentral Distance From Seismic Waves Using Deeper CRNN

Estimating earthquake parameters is an essential process for an earthquake analysis system. In particular, the magnitude and epicentral distance of an earthquake are the most basic parameters in earthquake analysis. To estimate these, the existing approaches require long waveform data from multiple stations. In this letter, we propose a novel estimation method based on multitasking deep learning and a convolutional recurrent neural network (CRNN) using only a single station. We also use the stream maximum of the input waveform to accurately estimate the earthquake magnitude. Based on the evaluation using the Stanford Earthquake dataset (STEAD) and the Kiban Kyoshin Network (KiK-net) dataset, we verify the high performance of the proposed method.

Automated summary

Estimating earthquake parameters is crucial for an earthquake analysis system. This letter proposes a novel estimation method using multitasking deep learning and a convolutional recurrent neural network (CRNN) with only a single station. The method accurately estimates earthquake magnitude using the stream maximum of the input waveform. The high performance of the proposed method is verified through evaluation using the Stanford Earthquake dataset (STEAD) and the Kiban Kyoshin Network (KiK-net) dataset.