Research Note: Frequency domain orthogonal projection filtration of surface microseismic monitoring data

Abstract

We address the problem of increasing the signal‐to‐noise ratio during surface microseismic monitoring data processing. Interference from different seismic waves causes misleading results of microseismic event locations. Ground‐roll suppression is particularly necessary. The standard noise suppression techniques assume regular and dense acquisition geometries. Many pre‐processing noise suppression algorithms are designed for special types of noise or interference. To overcome these problems, we propose a novel general‐purpose filtration method. The goal of this method is to amplify only the seismic waves that are excited in the selected target area and suppress all other signals. We construct a linear projector onto a frequency domain data subspace, which corresponds to the seismic emission of the target area. The novel filtration method can be considered an extension of the standard frequency–wavenumber flat wave filtration method for non‐flat waves and arbitrary irregular receiver‐position geometries. To reduce the effect of the uncertainty of the velocity model, we suggest using additional active shot data (typically the perforation shots), which provide static travel time corrections for the target area. The promising prospects of the proposed method are confirmed by synthetic and semi‐synthetic data processing.

Strong noise-tolerant deep learning network for automatic microseismic events classification

“…Owing to the complexity of the underground construction environment (e.g., tunnel excavation and mining engineering), the microseismic monitoring systems are usually contaminated by different vibration sources, such as explosives, machinery, vehicles, and electronics Tang, Zhao, Li and Zhu (2018); Peng, He, Wang and Jiang (2020); Jiang, Dai, Liu and Li (2021). Various types of denoising methods have been developed to suppress the random noise with microseismic recordings, such as the Kalman filter Chen, Zhang and Eaton (2020), frequency domain filtering Azarov, Serdyukov and Gapeev (2020), template matching algorithms Mu, Lee and Chen (2017); Skoumal, Brudzinski, Currie and Levy (2014), wavelet transformation Li, Tuo, Wang and Courtois (2019), empirical and variation mode decomposition Gómez and Velis (2016); Zhang, Dong and Xu (2020), and fingerprint and similarity threshold algorithms Yoon, O'Reilly, Bergen and Beroza (2015); . These approaches have played an important role in different application fields and are still frequently applied.…”