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Accurate locations and moment tensor information are of paramount importance for the correct derivation of fracture positions and source mechanisms (Eisner et al., 2011, Kendall et al., 2011, Leaney, 2008, Vera Rodriguez et al., 2012). Noise-suppression has become an important challenge to precondition microseismic data for the estimation of the event location and inversion of the seismic moment tensor. Downhole acquisition configurations involve arrays of three-component geophones buried in vertical or deviated boreholes close to the injection well (Maxwell and Rutledge, 2010). This paper focuses on downhole geometries and on the problem of detecting and enhancing microseismic events. Microseismic acquisition projects can be divided according to two different scenarios: surface and downhole monitoring. Furthermore, inadequate array coverage and imprecise knowledge of subsurface P– and S–wave velocity models complicate the detection and location of microseismic events (Eisner et al., 2009). Therefore, microseismic data are generally acquired in low signal-to-noise ( S/ N) environments. The microseismicity induced by the hydraulic fracturing is characterized by small magnitude micro-earthquakes (Maxwell and Urbancic, 2001). This gives rise to a broad set of geophysical applications designed to monitor the reservoir dynamics while controlling the injection process. Low permeability reservoirs require fluid injection in order to fracture the bedrock and favor hydrocarbon extraction. The P– and S–wave direct arrivals are properly denoised for high to moderate signal-to-noise ratio records. The method performs rapidly due to the parabolic approximation making it suitable for real-time monitoring.
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The algorithm was tested with synthetic and field datasets that were recorded with a vertical array of receivers. The denoising is posed as an inverse problem preconditioned by the Radon coefficients obtained in the previous step. In a second stage, a new (preconditioned) Radon transform is applied to individual components to enhance the recorded signal. The Radon coefficients are efficiently calculated by restricting the integration paths of the Radon operator. 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 algorithm is implemented in two steps. The methodology uses the apex-shifted parabolic Radon transform. We present an adaptive filtering method to denoise downhole microseismic data.