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.
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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.