Radon transform-based microseismic event detection and signal-to-noise ratio enhancement

• Published in: Journal of applied geophysics Vol. 113; pp. 51 - 63
• Main Authors: Sabbione, Juan I., Sacchi, Mauricio D., Velis, Danilo R.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2015
• Subjects: Adaptive filtering; Adaptive filters; Algorithms; Denoising; Mathematical analysis; Microseismic; Microseisms; Radon; Radon transform; Seismic phenomena; Signal to noise ratio; Transforms; Adaptive filtering; Radon transform; Microseismic; Denoising
• ISSN: 0926-9851; 1879-1859; 1879-1859
• DOI: 10.1016/j.jappgeo.2014.12.008

We present an adaptive filtering method to denoise downhole microseismic data. The methodology uses the apex-shifted parabolic Radon transform. The algorithm is implemented in two steps. In the first step we apply the apex-shifted parabolic Radon transform to the normalized root mean square envelope of the microseismic data to detect the presence of an event. The Radon coefficients are efficiently calculated by restricting the integration paths of the Radon operator. In a second stage, a new (preconditioned) Radon transform is applied to individual components to enhance the recorded signal. The denoising is posed as an inverse problem preconditioned by the Radon coefficients obtained in the previous step. The algorithm was tested with synthetic and field datasets that were recorded with a vertical array of receivers. The method performs rapidly due to the parabolic approximation making it suitable for real-time monitoring. The P– and S–wave direct arrivals are properly denoised for high to moderate signal-to-noise ratio records. •We present an adaptive filtering method to denoise downhole microseismic data.•The methodology uses the apex-shifted parabolic Radon transform.•The algorithm detects the presence of an event automatically and then enhances the recorded signal.•We use synthetic and field datasets recorded with a vertical array of receivers to test the algorithm.•The method performs rapidly and efficiently making it suitable for real-time monitoring.