Noise attenuation in sparse-surface microseismic datasets

Variations on a noise attenuation workflow for sparse surface networks are described, implemented and tested. The flow makes use of the data redundancy afforded by super-stations, groups of closely spaced 3C sensors, which permits the application of a Multi-Channel Weiner Filter (MCWF) as well as non-linear stacking operators such as semblance weighted stacks to the records. Using a dataset recorded on 3 super stations, we examine the Signal to Noise Ratio (SNR) of arrivals from 71 events observed during 6 hours of hydraulic fracturing operations in order to test the efficacy of different variations in the workflow. In particular, we test the impact of the use of multi-component sensors on the noise cancelling properties of the MCWF, finding that including the the multi-component nature of data in the MCWF formulation provides 0.5-1 dB of additional SNR enhancement. We also examine the effect of using linear and non-linear stacking operators such as semblance weighted stacks both with and without prior coherent noise attenuation. The optimal variation on the workflow utilizes both a multi-component MCWF and nonlinear stacking operators, it improves arrivals SNR by 7.2 dB relative to the original records. This in turn translates into a significant decrease in the detectable magnitude of events, and a corresponding increase in the number of events a given survey can expect to detect, thus allowing more detailed structure to be revealed by the microseismic cloud. Presentation Date: Monday, October 15, 2018 Start Time: 1:50:00 PM Location: 208A (Anaheim Convention Center) Presentation Type: Oral