Seismic Events Estimation Under Noisy Environments Using Multiple Model Particle Filter

This work considers the estimation of the seismic events using a Bayesian filtering method, a multiple model particle filtering (MMPF) framework, in particular, from the noisy seismic records. According to the nonlinearity of the seismic signals, the nonlinear dynamical system can be used for the modelling of the signal. In geological studies, it is very important to accurately estimate the events from the recorded seismic signals, under noisy environment in particular since the signal is usually acquired usually under noisy conditions resulting in the difficulty in information extraction. This work proposes the MMPF, an effective filter in a sequential Monte Carlo method, in cooperated with the seismic wavelet model for the estimation of the seismic events under the noisy environments. To evaluate the performance of the method, a set of synthetic noisy signals with different numbers of events, noise levels, was created and used in the simulations. Results illustrates that the proposed MMPF can provide the excellent seismic event estimates under noisy environment.