Preliminary Results of Automatic P-Wave Regional Earthquake Arrival Time Picking using Machine Learning with Kurtosis and Skewness as the Input Parameters

Y. H. Lumban Gaol*, R. K. Lobo, S. S. Angkasa, A. Abdullah, I. Madrinovella, S. Widyanti, A. Priyono, S. K. Suhardja, A. D. Nugraha, Z. Zulfakriza, S. Widiyantoro, M. Luqman Hakim, K. H. Palgunadi, B. Mujihardi

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)


The traditional method in determining first arrival time of earthquake dataset is time consuming process due to waveform manual inspection. Additional waveform attributes can help determine events detection. One of the widely used attribute is The Short Term Averaging/Long Term Averaging (STA/LTA) which is simply division moving average of waveform amplitude between short time and longer time. Alternatively, waveform attribute can also be built using kurtosis and skewness. The kurtosis attribute is defined as sharpness of data distribution. By definition, the maximum signal should be at or close to the P wave arrival. The skewness is typically used to show normal distribution of the data. The uniqueness of this method is that it has an ability to determine whether the first P wave arrival has positive of negative number. The skewness calculation is similar to kurtosis but it uses the power of 3 instead of 4. With the objective of generating efficient scheme to pick first time arrival, we explore use artificial neural network and a combination of kurtosis and skewness attributes. We use a collection of magnitude events with moment magnitude larger than 3 located close to Moluccas island, Indonesia. We collected all events information from the Indonesian Agency of Meteorology, Climatology and Geophysics. The process is started with manually pick all P wave arrivals using manual inspection. Next, we trained the artificial neural network with attributes numbers as inputs and arrival time we manually picked as the output. In total we used 100 regional events that has clear P wave phases. Then, we validated the results until reaching 0.99 accuracy. In the last step, we tested the once trained procedures on new waveforms contained events. Current result shows an average of 0.4s different between manual pick and trained picked from machine learning. The accuracy can be improved by applying a band pass 0.1-2 Hz filtering with an average of 0.2s.

Original languageEnglish
Article number012061
JournalIOP Conference Series: Earth and Environmental Science
Issue number1
Publication statusPublished - 1 Nov 2021
Externally publishedYes
Event3rd Southeast Asian Conference on Geophysics: Future Challenges and Opportunities in Geophysics, SEACG 2020 - Bandung, Virtual, Indonesia
Duration: 3 Nov 20205 Nov 2020


Dive into the research topics of 'Preliminary Results of Automatic P-Wave Regional Earthquake Arrival Time Picking using Machine Learning with Kurtosis and Skewness as the Input Parameters'. Together they form a unique fingerprint.

Cite this