Open AccessThe Study of Automatic Picking of P and S Wave Arrival and Identification of Earthquake Sequence Pattern using Scalogram in Obspy (Python)
Author(s) -
Sri Kiswanti,
Indriati Retno Palupi,
Wiji Raharjo,
Faricha Yuna Arwa,
Nela Elisa Dwiyanti
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 - 10.1088/1755-1315/873/1/012014
Subject(s) - hypocenter , aftershock , seismology , foreshock , geology , continuous wavelet transform , sequence (biology) , wavelet , arrival time , identification (biology) , seismogram , computer science , earthquake location , data mining , pattern recognition (psychology) , algorithm , wavelet transform , artificial intelligence , induced seismicity , discrete wavelet transform , engineering , botany , biology , transport engineering , genetics
Initial identification on an earthquake record (seismogram) is something that needs to be done precisely and accurately. Moreover, the discovery of a series of unexpected successive earthquake events has caused unpreparedness for the community and related agencies in tackling these events. Determining the arrival time of the P and S waves becomes an important parameter to finding the location of the earthquake source (hypocenter) as well as further information related to the earthquake event. However, manual steps that are currently often used are considered to be less effective, because it requires a lot of time in the process. Continuous Wavelet Transform (CWT) analysis can be a solution for this problem. With further CWT analysis in the form of a scalogram, can help to determine the arrival time of P and S waves automatically (automatic picking) becomes simpler. In addition, further CWT analysis can also be utilized to help identify the sequence of earthquake events (foreshock, mainshock, aftershock) through the resulting scalogram pattern.