Automatic microseismic event detection using constant false alarm rate processing in time-frequency domain

Detecting and monitoring microseismic events using surface sensors in unknown noise scenarios and low signal-to-noise ratio conditions is a challenging problem. We propose a scheme for reliable automatic detection of microseismic events based on 2D Constant False Alarm Rate (CFAR) processing in the time-frequency (TF) domain, along with an efficient 2D filtering implementation of the 2D CFAR algorithm. Detectability is improved in the TF domain where signal energy is concentrated, while noise energy spreads out. A CFAR detector applied to the TF image adapts its threshold to varying noise levels and achieves reliable detection by maintaining a specified false alarm rate. Performance of the method is evaluated using synthetic and field data. Results prove the capability of the proposed scheme to perform automatic and accurate microseismic event detection in challenging noise conditions.