A Ramanujan subspace and dynamic time warping and adaptive singular value decomposition combined denoising method for low signal-to-noise ratio surface microseismic monitoring data in hydraulic fracturing

Abstract

Surface microseismic monitoring is widely used in hydraulic fracturing. Real-time monitoring data collected during fracturing can be used to perform surface-microseismic localization, which aids in assessing the effects of fracturing and provides guidance for the process. The accuracy of localization critically depends on the quality of monitoring data. However, the signal-to-noise ratio of the data is often low due to strong coherent and random noise, making denoising essential for processing surface monitoring data. To suppress noise more effectively, this paper introduces a novel denoising method that integrates the Ramanujan subspace with dynamic time warping and adaptive singular value decomposition. The new method consists of two steps: First, a Ramanujan subspace is constructed to suppress periodic noise. Then, dynamic time warping and adaptive singular value decomposition are applied to eliminate remaining coherent and random noise. The method has been evaluated using both synthetic and field data, and its performance is compared with traditional microseismic denoising techniques, including bandpass filtering and empirical mode decomposition.