Open AccessPreliminary Results of Automatic P-Wave Regional Earthquake Arrival Time Picking Using Machine Learning with STA/LTA As the Input Parameters
Author(s) -
Yosua Hotmaruli Lumban Gaol,
Renan Lobo,
S S Angkasa,
Ahmad Fikri Abdullah,
I Madrinovella,
S Widyanti,
Awali Priyono,
S K Suhardja,
Andri Dian Nugraha,
Zulfakriza Zulfakriza,
Sri Widiyantoro,
Moad Hakim,
Kadek Hendrawan Palgunadi
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/873/1/012060
Subject(s) - waveform , computer science , arrival time , noise (video) , artificial neural network , filter (signal processing) , signal (programming language) , algorithm , artificial intelligence , amplitude , event (particle physics) , pattern recognition (psychology) , speech recognition , telecommunications , engineering , computer vision , physics , transport engineering , image (mathematics) , programming language , radar , quantum mechanics
The Short Term Averaging/Long Term Averaging (STA/LTA) has been widely used to detect earthquake arrival time. The method simply calculates the ratio of moving average of the waveform amplitude at short and long-time windows. However, although STA/LTA signals can distinguish between real events and noise, we still recognize some lack of accuracies in first P wave arrival pickings. In this study, we attempt to implement one machine learning method popularly, Artificial Neural Network (ANN) that employ input, hidden and output layer similar as human brain works. Note that in this study, we also try to add input parameters with another derivative signal attributes such as Recursive STA/LTA and Carl STA/LTA. The processing step started by collecting event waveforms from the Agency of Meteorology, Climatology and Geophysics. We chose regional events with moment magnitude higher than 3 in the Maluku region Indonesia. Next, we apply all STA/LTA attributes to the input waveforms. We also tested our STA/LTA with synthetic data and additional noise. Further step, we manually picked the arrival of P wave events and used this as the output for ANN. In total, we used 100 events for arrival data training in P wave phases. In the validation process, an accuracy of more than 0.98 can be obtained after 200 iterations. Final outputs showed, that in average, the difference between manual picking and automatic picking from ANN is 0.45 s. We are able to increase the accuracy by band pass filter (0.1 – 3 Hz) all signal and improve the mean into 0.15s difference between manual picking and ANN picks.