Automatic microseismic events detection using morphological multiscale top-hat transformation

Abstract

The occurrence of microseismic is not random but is related to the physical properties of the underground medium. Due to the low intensity and the influence of noise, microseismic eventually lead to poor signal-to-noise ratio. We proposed a method for automatic detection of microseismic events by adoption of multiscale top-hat transformation. The method is based on the difference between the signal and noise in the multiscale top-hat transform section and achieves the detection on a specific section. The microseismic data are decomposed into different scales by multiscale morphology top-hat transformation firstly. Then the potential microseismic events could be detected by picking up the peak value in the multiscale top-hat section, and the characteristic profile obtains the start point with a specific threshold value. Finally, the synthetic data experiences demonstrate the advantages of this method under strong and weak noisy conditions, and the filed data example also shows its reliability and adaptability.