PhaseNet and EfficientNet-B0 for Phase Detection and Arrival Time Prediction

Ramakrishna, Nagalakshmi (2024) PhaseNet and EfficientNet-B0 for Phase Detection and Arrival Time Prediction. Masters thesis, Dublin, National College of Ireland.

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Abstract

Seismic phase detection and arrival time prediction are crucial for earthquake monitoring and early warning systems. This study evaluates the performance of two advanced deep learning models, PhaseNet and EfficientNet-B0, on the INSTANCE dataset for phase detection and the picking of P and S waves using spectrogram and waveform data. PhaseNet achieved a testing accuracy of 94.7%, demonstrating its effectiveness in classifying seismic events. Conversely, EfficientNet-B0 excelled in arrival time prediction, achieving a P-wave MAE of 279 ms and an S-wave MAE of 255 ms, surpassing PhaseNet in regression tasks. While PhaseNet exhibited faster training convergence, EfficientNet-B0 delivered superior accuracy and generalization. This paper highlights the complementary strengths of PhaseNet and EfficientNet-B0 in seismic phase detection and arrival time picking, contributing to advancements in seismic monitoring methodologies.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Horn, Christian UNSPECIFIED
Uncontrolled Keywords:	Seismic phase detection; P-wave; S-wave; PhaseNet; EfficientNet- B0; Deep learning; Earthquake monitoring; INSTANCE dataset
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science G Geography. Anthropology. Recreation > GE Environmental Sciences > Earth sciences > Geology > Physical geology > Sedimentation and deposition > Earth movements > Earthquakes
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	04 Sep 2025 11:24
Last Modified:	04 Sep 2025 11:24
URI:	https://norma.ncirl.ie/id/eprint/8784

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