Open AccessAn Attempt to Pick Teleseismic P Wave Arrival Using Envelope and Artificial Neural Network Algorithm
Author(s) -
Renan Lobo,
Yosua Hotmaruli Lumban Gaol,
D Y Fatimah,
Ahmad Fikri Abdullah,
D A Zaky,
S K Suhardja,
Andri Dian Nugraha,
Zulfakriza Zulfakriza,
Sri Widiyantoro,
Muhammad Ramdhan,
Kadek Hendrawan Palgunadi,
Bambang Mujihardi
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/873/1/012059
Subject(s) - seismogram , envelope (radar) , amplitude , algorithm , seismic trace , computer science , artificial neural network , seismology , arrival time , reflection (computer programming) , noise (video) , event (particle physics) , hilbert transform , pattern recognition (psychology) , geology , artificial intelligence , computer vision , engineering , optics , image (mathematics) , telecommunications , physics , radar , filter (signal processing) , quantum mechanics , transport engineering , wavelet , programming language
Seismic events detection and phase picking play an essential role in earthquake studies. Typical event detection is done visually or manually on recorded seismogram by choosing a series of higher amplitude signals recorded on at least 4 stations. More sophisticated methods have been used in event detection and picking with additional attributes such as Short Time Average over Long Time Average (STA/LTA). This method is based on average number sampled at multiple predefined windows. However, STA/LTA is dependent on the window size which becomes its drawback. In this study, we explore one derivative attribute, popularly known as envelope or instantaneous amplitude. It has been extensively used in seismic reflection and refraction method. In principle, this method uses the Hilbert Transform to calculate complex seismic trace and take the magnitude of complex seismic trace as envelope amplitude that can be used to analyze P wave arrival time. We employed one of the machine learning methods, Artificial Neural Network (ANN). The ANN method works by analyzing various inputs and training them to recognize patterns in P wave arrival signals. We started our study by applying envelope attribute to synthetic data with noise addition. We found that with noisy data the envelope attribute still gives a clear signal for first-time arrival. Next, we trained 300 seismograms of teleseismic events recorded on IRIS-US networks and tested our trained program on 20 seismograms as a blind test. To compare performance between the two methods, we calculated the difference between the results of automatic picking and manual picking. The final calculation shows an average deviation of 0.355 seconds. Twenty-five percent of testing data (5 samples) has a deviation above 0.5 seconds, and 75% of the remainder (15 samples) already had a deviation under 0.5 seconds. The more significant deviations of the P wave picks are likely due to noisy signals in the data set and complex arrival signals. This study shows that the combination of envelope attribute and machine learning method is promising to distinguish teleseismic P wave arrival and automatically pick them.