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DEEP LEARNING-BASED CLASSIFICATION OF SEISMIC EVENTS USING WAVEFORM DATA
Keywords:
deep learning, long short term memory, convolutional neural network, seismological characterization, earthquake detectionDOI:
https://doi.org/10.17654/0974165825002Abstract
Artificial Intelligence (AI)-enhanced seismology has evolved to its tremendous capability in analyzing real-time seismic data. This supports various tasks such as earthquake detection, phase detection, epicentral location prediction, time prediction of future earthquakes, phase picking, first motion polarity, early warning systems etc. This is crucial for the management of emergency hazard response spectra and post-disaster activities.
This research study demonstrates the potential of four deep learning models, i.e., Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short Term Memory (LSTM) Network, in analyzing the real-world earthquake waveform dataset to detect an earthquake and distinguish it from the noise. The research study analyzed the deep learning models on the real-time Standard Earthquake Dataset (STEAD) primarily for the Alaska region, and it also prepared a detailed comparative analysis of the performance indicators of the deep learning models such as accuracy, precision, recall, F1-score, training loss, validation accuracy, and validation loss. The traditional statistical method namely Short-Term Average to Long Term Average (STA/LTA) triggering algorithm is also implemented for the earthquake detection in the waveform.
This study found that Long Short Term Memory (LSTM) outperformed all other models with an accuracy of 97% approx. and higher values of the other performance metrics, proving more effective in earthquake detection on the real world dataset. Therefore, LSTM networks have higher potential over other deep learning models and traditional methods in enhancing earthquake detection capabilities, thereby supporting more effective seismic data analysis and hazard response strategies.
Received: October 14, 2024
Accepted: November 7, 2024
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