Performance Comparison of PhaseNet and Blockly Earthquake Transformer in Automatic First Arrival Picking on the Cianjur Earthquake

Fitri Afiadi, Riri Fitri Sari

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

On November 21. 2022. an earthquake with a magnitude of 5.6 struck Cianjur West Java. causing extensive damage. By giving the public early warnings. an Earthquake Early Warning System (EEWS) can assist lessen the impact of earthquakes. The seismic phase selecting system. which works to detect and identify seismic waves originating from earthquakes. is one crucial part of EEWS. In this work. the effectiveness of PhaseNet and Blockly Earthquake Transformer (BET). two seismic phase selecting techniques. is compared using Cianjur earthquake data. PhaseNet is a deep learning model that recognizes and classifies seismic waves using a convolutional neural network (CNN). A deep learning model called Blockly Earthquake Transformer employs transformer architecture to recognize and identify seismic waves. The average computational speed of PhaseNet is 8.25 seconds. while that of BET is 6.13 seconds. The results indicate that PhaseNet generates average P-probability values of 0.56 and average S probabilities of 0.53. which are higher than those of the BET. which generates average P-probability values of 0.40 and average S probabilities of 0.32. It is evident from the Cianjur earthquake data that both approaches perform well in terms of recognizing and detecting seismic waves. Compared to the BET. PhaseNet is marginally more accurate. In terms of computing time. the BET is more effective and faster.

Original languageEnglish
Title of host publication2024 6th IEEE Symposium on Computers and Informatics, ISCI 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages72-77
Number of pages6
ISBN (Electronic)9798350353853
DOIs
Publication statusPublished - 2024
Event6th IEEE Symposium on Computers and Informatics, ISCI 2024 - Kuala Lumpur, Malaysia
Duration: 10 Aug 2024 → …

Publication series

Name2024 6th IEEE Symposium on Computers and Informatics, ISCI 2024

Conference

Conference6th IEEE Symposium on Computers and Informatics, ISCI 2024
Country/TerritoryMalaysia
CityKuala Lumpur
Period10/08/24 → …

Keywords

  • Blockly Earthquake Transformer
  • Deep Learning
  • Earthquake monitoring
  • Phase Picking
  • PhaseNet

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