SEED® 2019

BCE’s proprietary signal enhancement and event detection (SEED®) software allows for real-time signal enhancement, event detection, event dominant frequency estimation and ambient noise characterization. SEED® implements a state-of-the-art Bayesian recursive estimation technique which incorporates Kalman filtering, particle filtering, Rao-Blackwellized particle filtering and hidden Markov model (HMM) filtering.

  • State-of-the-art Bayesian Recursive Estimation
  • Real-Time Signal Enhancement
  • Real-Time Event Detection
  • Real-Time Event Frequency Estimation
  • Real-Time Ambient Noise Characterization

Figure 1. (a) Strip chart display of raw time series with the associated SEED® event detection, corresponding error and dominant frequency estimates and estimated noise parameters of variance and time constant. (b)-(d) SEED® strip chart estimates of the amplitude modulating term (AMT), short and long term average ratio (STA/LTA) and event dominant frequency.


1) Baziw, E. (2007). Application of Bayesian Recursive Estimation for Seismic Signal Processing. Ph.D. Thesis, Dept. of Earth and Ocean Sciences, University of British Columbia, 2006.

2) Baziw, E. (2005), "Real Time Seismic Signal Enhancement Utilizing a Hybrid Rao Blackwellised Particle Filter and Hidden Markov Model Filter", IEEE Geosci. Remote Sensing Letters, vol. 2, no. 4, pp. 418- 422, Oct.

3) Baziw, E., Nedilko, B, and Weir Jones, I (2004), "Microseismic Event Detection Kalman Filter: Derivation of the Noise Covariance Matrix and Automated First Break Determination for Accurate Source Location Estimation" , Pure appl. geophys. vol. 161, no. 2, pp. 303 329.

4) Baziw, E., and Weir Jones, I (2002), "Application of Kalman Filtering Techniques for Microseismic Event Detection", Pure appl. geophys., vol. 159, pp. 449 471.