Microseismic Event Detection Kalman Filter: Derivation of the Noise Covariance Matrix and Automated First Break Determination for Accurate Source Location Estimation

Abstract

Since 1972, Weir-Jones Engineering Consultants (WJEC) has been involved in the development and installation of microseismic monitoring systems for the mining, heavy construction and oil/gas industries. To be of practical value in an industrial environment, microseismic monitoring systems must produce information which is both reliable and timely. The most critical parameters obtained from a microseismic monitoring system are the real-time location and magnitude of the seismic events. Location and magnitude are derived using source location algorithms that typically utilize forward modeling and iterative optimal estimation techniques to determine the location of the global minimum of a predefined cost function in a three-dimensional solution space. Generally, this cost function is defined as the RMS difference between measured seismic time series information and synthetic measurements generated by assuming a velocity structure for the area under investigation (forward modeling). The seismic data typically used in the source location algorithm includes P- and S-wave arrival times, and raypath angles of incidence obtained from P-wave hodogram analysis and P-wave first break identification. In order to obtain accurate and timely source location estimates it is of paramount importance that the extraction of accurate P-wave and S-wave information from the recorded time series be automated—in this way consistent data can be made available with minimal delay. WJEC has invested considerable resources in the development of real-time digital filters to optimize extraction, and this paper outlines some of the enhancements made to existing Kalman Filter designs to facilitate the automation of P-wave first break identification.