MCE Ph.D. Thesis Seminar

Existing Earthquake Early Warning (EEW) algorithms use waveform analysis for earthquake detections, estimation of source parameters (i.e., magnitude and hypocenter location), and prediction of peak ground motions at sites near the source. The latency of warning delivery due to data collection significantly restricts the usefulness of the system, especially for users in the vicinity of the earthquake source, as the warning may not arrive before the strong shaking. This presentation discusses several methods to reduce the warning latency, while maintaining reliability and robustness, so that the warning time can be maximized for users to take appropriate actions to reduce causalities and economic losses.

Firstly, we incorporated the seismicity forecast information from Epidemic-Type Aftershock Sequence (ETAS) model into EEW as prior information, under the Bayesian probabilistic inference framework. Similar to human's decision making process, the Bayesian approach updates the probability of the estimations as more information becomes available. This allows us to reduce the required time for reliable earthquake signal detection from at least 3 seconds to 0.5 second. Furthermore, the initial error of hypocenter location estimation is reduced by 58%. The performance of the algorithm is further improved during aftershock sequences and swarm earthquakes.

Secondly, we introduce the use of multidimensional (KD tree) data structure to organize seismic database, so that the querying time can be reduced for the nearest neighbor search during earthquake source parameter estimation. The processing time of KD tree is approximately 15% of the processing time of linear exhaustive search, which allows the potential use of large seismic databases in real-time.