A new method of automatic processing of seismic waves: waveform modeling by using Hidden Markov Model
Abstract
Development of a method of automatic processing of seismic waves is needed since there are limitations to manually picking out earthquake events from seismograms. However, there is no practical method to automatically detect arrival times of P and S waves in seismograms. One typical example of previously proposed methods is automatic detection by using AR model (e.g. Kitagawa et al., 2004). This method seems not to be effective for seismograms contaminated with spike noise, because it cannot distinguish non-stationary signals generated by earthquakes from those generated by noise. The difficulty of distinguishing the signals is caused by the fact that the automatic detection system has a lack of information on time series variation of seismic waves. We expect that an automatic detection system that includes the information on seismic waves is more effective for seismograms contaminated with noise. So we try to adapt Hidden Markov Model (HMM) to construct seismic wave models and establish a new automatic detection method. HMM has been widely used in many fields such as voice recognition (e.g. Bishop, 2006). With the use of HMM, P- or S-waveform models that include envelops can be constructed directly and semi-automatically from lots of observed waveform data of P or S waves. These waveform models are expected to become more robust if the quantity of observation data increases. We have constructed seismic wave models based on HMM from seismograms observed in Ashio, Japan. By using these models, we have tried automatic detection of arrival times of earthquake events in Ashio. Results show that automatic detection based on HMM is more effective for seismograms contaminated with noise than that based on AR model.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2012
- Bibcode:
- 2012AGUFM.S43A2472K
- Keywords:
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- 0520 COMPUTATIONAL GEOPHYSICS / Data analysis: algorithms and implementation;
- 0545 COMPUTATIONAL GEOPHYSICS / Modeling;
- 7290 SEISMOLOGY / Computational seismology