Ð Microseismic monitoring systems are generally installed in areas of induced seismicity caused by human activity. Induced seismicity results from changes in the state of stress which may occur as a result of excavation within the rock mass in mining (i.e., rockbursts), and changes in hydrostatic pressures and rock temperatures (e.g., during¯uid injection or extraction) in oil exploitation, dam construction or¯uid disposal. Microseismic monitoring systems determine event locations and important source parameters such as attenuation, seismic moment, source radius, static stress drop, peak particle velocity and seismic energy. An essential part of the operation of a microseismic monitoring system is the reliable detection of microseismic events. In the absence of reliable, automated picking techniques, operators rely upon manual picking. This is time-consuming, costly and, in the presence of background noise, very prone to error. The techniques described in this paper not only permit the reliable identi®cation of events in cluttered signal environments they have also enabled the authors to develop reliable automated event picking procedures. This opens the way to use microseismic monitoring as a cost-eective production/operations procedure. It has been the experience of the authors that in certain noisy environments, the seismic monitoring system may trigger on and subsequently acquire substantial quantities of erroneous data, due to the high energy content of the ambient noise. Digital ®ltering techniques need to be applied on the microseismic data so that the ambient noise is removed and event detection simpli®ed. The monitoring of seismic acoustic emissions is a continuous, real-time process and it is desirable to implement digital ®lters which can also be designed in the time domain and in real-time such as the Kalman Filter. This paper presents a real-time Kalman Filter which removes the statistically describable background noise from the recorded seismic traces.

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.

No abstract

This letter outlines a novel and robust algorithm for identifying seismic events within low signal-to-noise ratio (SNR) passive seismic data in real time. Since the event detection problem is a continuous, real-time process which has nonlinear mathematical representations, a Rao-Blackwellized particle filter (RBPF) is utilized. In this algorithm, a jump Markov linear Gaussian system (JMLGS) is defined where changes (i.e., jumps) in the state-space system and measurement equations are due to the occurrences and losses of events within the measurement noise. The RBPF obtains optimal estimates of the possible seismic events by individually weighting and subsequently summing a bank of Kalman filters (KFs). These KFs are specified and updated by samples drawn from a Markov chain distribution which defines the probability of the individual dynamical systems which compose the JMLGS. In addition, a hidden Markov model filter is utilized within the RBPF filter formulation so that real-time estimates of the phase of the seismic event can be obtained. The filter is demonstrated to provide up to an 80-fold improvement in the SNR when processing simulated seismic data with Gauss-Markov measurement noise.Index Terms-Acoustic signal detection, hidden Markov model (HMM), jump processes, Rao-Blackwellized particle filter (RBPF).