Statistically-based approach for monitoring of micro-seismic events

Abstract

We consider the problem of optimal statistical estimation of micro-seismic source parameters using multichannel data from surface arrays of seismometers affected by a strong seismic noise. The problem is treated as a statistical task of parameter estimation for a general type multidimensional linear model with random or completely unknown input time functions. The maximum-likelihood generic estimators are derived and their relationship with the well-known seismic emission tomography (SET) algorithm is established. The proposed estimation algorithms perform processing of multichannel discrete observations in the frequency domain and can be implemented in on-line mode. Using the method of successive independent trials (Monte-Carlo), we demonstrate that a proposed statistical estimation algorithm provides much higher accuracy of the micro-seismic event source location in real noise conditions than the widely used SET algorithm and, as a consequence, is more reliable in practical applications.