Blind noise estimation and denoising filter for recovery of microquake signals

ABSTRACT In this study, a data-driven linear filtering method is proposed to recover microseismic signals from noisy data/observations. The proposed method is based on the statistics of the background noise and the observation, which are directly extracted from the recorded data, obviating prior statistical knowledge of the microseismic source signal. The proposed method does not depend on any specific underlying noise statistics; therefore, it works for any type of noise, e.g. uncorrelated (random/white Gaussian), temporally correlated and spatially correlated noises. Consequently, the proposed method is suitable for microquake data sets that are recorded in contrastive noise environments. A mathematical analysis is presented to interpret the proposed method in two different ways. Furthermore, a number of practical concerns are discussed and their corresponding solutions are introduced. Finally, the proposed scheme is evaluated using both field and synthetic data sets and the experimental results show a reasonable and robust performance.

[1]  Microseismic event detection: Comparing P-wave migration with P- and S-wave cross-correlation , 2014 .

[2]  Frederik Tilmann,et al.  Application of multichannel Wiener filters to the suppression of ambient seismic noise in passive seismic arrays , 2008 .

[3]  Lijun Zhu,et al.  Microseismic events enhancement and detection in sensor arrays using autocorrelation‐based filtering , 2016, ArXiv.

[4]  Richard G. Baraniuk,et al.  Improved wavelet denoising via empirical Wiener filtering , 1997, Optics & Photonics.

[5]  B. Khadhraoui,et al.  Multicomponent Time-Frequency Noise Attenuation of Microseismic Data , 2013 .

[6]  Yusuf Bayrak,et al.  Ground-roll attenuation based on Wiener filtering and benefits of time-frequency imaging , 2008 .

[7]  Azzedine Zerguine,et al.  Detection and Denoising of Microseismic Events Using Time–Frequency Representation and Tensor Decomposition , 2018, IEEE Access.

[8]  Wenkai Lu,et al.  Adaptive noise attenuation of seismic images based on singular value decomposition and texture direction detection , 2006 .

[9]  Zhenhua He,et al.  Seismic data denoising based on mixed time-frequency methods , 2011 .

[10]  Peter M. Duncan.  Microseismic Monitoring for Unconventional Resource Development , 2012 .

[11]  J. Wang,et al.  Application of frequency-dependent multichannel Wiener filters to detect events in 2D three-component seismometer arrays , 2009 .

[12]  Yunfeng Zhang,et al.  Particle filter based noise removal method for acoustic emission signals , 2012 .

[13]  Leo Eisner,et al.  Microseismicity: Beyond dots in a box — Introduction , 2011 .

[14]  J. Díaz,et al.  On the Ability of the Benford’s Law to Detect Earthquakes and Discriminate Seismic Signals , 2015 .

[15]  Detection and analysis of microseismic events using a Matched Filtering Algorithm (MFA) , 2016 .

[16]  Philipos C. Loizou,et al.  A noise-estimation algorithm for highly non-stationary environments , 2006, Speech Commun..

[17]  Taghi Shirzad,et al.  Extracting Seismic Body and Rayleigh Waves from the Ambient Seismic Noise Using the rms‐Stacking Method , 2015 .

[18]  Chao Zhang,et al.  Seismic random noise attenuation by time-frequency peak filtering based on joint time-frequency distribution , 2013 .

[19]  J. Verdon.  Microseismic Monitoring and Geomechanical Modelling of CO2 Storage in Subsurface Reservoirs , 2012 .

[20]  D. P. Mandic,et al.  Multivariate empirical mode decomposition , 2010, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[21]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[22]  P. Jaiswal,et al.  Seismic denoising using the redundant lifting scheme , 2016 .

[23]  Gabriel Rilling,et al.  Bivariate Empirical Mode Decomposition , 2007, IEEE Signal Processing Letters.

[24]  Nelson Christensen,et al.  Wiener filtering with a seismic underground array at the Sanford Underground Research Facility , 2014, 1403.7756.

[25]  E. Baziw,et al.  Application of Kalman Filtering Techniques for Microseismic Event Detection , 2002 .

[26]  Erick Baziw.  Real-time seismic signal enhancement utilizing a hybrid Rao-Blackwellized particle filter and hidden Markov model filter , 2005, IEEE Geoscience and Remote Sensing Letters.

[27]  S. Mostafa Mousavi,et al.  Hybrid Seismic Denoising Using Higher-Order Statistics and Improved Wavelet Block Thresholding , 2016 .

[28]  James H. McClellan,et al.  Iterative interferometry-based method for picking microseismic events , 2017, 1703.03154.

[29]  James H. McClellan,et al.  Enhancement of Microseismic Events Using Tensor Decomposition and Time-frequency Representation , 2017 .

[30]  Paul Anderson,et al.  It's a matter of size: Magnitude and moment estimates for microseismic data , 2010 .

[31]  S. Mostafa Mousavi,et al.  Automatic noise-removal/signal-removal based on general cross-validation thresholding in synchrosqueezed domain and its application on earthquake data , 2017 .

[32]  S. Mostafa Mousavi,et al.  Automatic microseismic denoising and onset detection using the synchrosqueezed continuous wavelet transform , 2016 .

[33]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  Yue Li,et al.  Seismic random noise attenuation based on adaptive time–frequency peak filtering , 2015 .

[35]  Yoones Vaezi,et al.  Analysis of instrument self-noise and microseismic event detection using power spectral density estimates , 2014 .

[36]  Azzedine Zerguine,et al.  Observation-Driven Method Based on IIR Wiener Filter for Microseismic Data Denoising , 2018, Pure and Applied Geophysics.

[37]  Hite,et al.  Application of multichannel Wiener filters to the suppression of ambient seismic noise in passive seismic arrays , 2009 .

[38]  Entao Liu,et al.  Array Processing in Microseismic Monitoring: Detection, Enhancement, and Localization of Induced Seismicity , 2018, IEEE Signal Processing Magazine.

[39]  S. Mostafa Mousavi,et al.  Adaptive noise estimation and suppression for improving microseismic event detection , 2016 .