Data assimilaiton, a procedure in which observed data is combined with prior knowledge, is widely used in geophysical systems and especially popular in atmospheric and oceanic models. In this study a Monte Carlo based data assimilation method referred as a bootstrap Particle Filter (PF) and a time lag sampling technique are combined together to perform Sequential Data Assimilation (SDA) of borehole observation into a Seismo-Thermo-Mechanical model (STM). The aim of this study is to estimate the state of faults in subduction zones. The STM, a strongly non-linear model, is taken to be a perfect model and serves as a source for both observed data and model realizations termed "particles" or ensemble members. The ensemble is being generated by drawing particles out of a seismic cycle with a constant time lag. Results demonstrate that assimilation strongly depends on the choice of time lag since, small time lags provided with better results. Changing the time lag for sampling leads to a trade off between ensemble spread and resolution due to presence of trends in some of the observed state variables. Although the sampling technique in its current setup is computationally efficient, it was found to be insuficient in representing the model errors. Comparison between the current study of the PF and recent work involving the Ensemble Kalman Filter (EnKF) suggests that the success of the EnKF is related to its error covariance matrix correlating the various state variables. Based on the results and comparison to the EnKF improvements and possible next steps are discussed.