Microseismic data denoising using a 3C group sparsity constrained time-frequency transform

Rodriguez, Ismael Vera; Bonar, David; Sacchi, Mauricio D.

doi:10.1190/geo2011-0260.1

Cited by 90 publications

(50 citation statements)

References 19 publications

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“…In actual situations, seismic signals recorded by geophones are usually contaminated by noise, which may cause picking errors (Rodriguez et al 2012;Song et al 2010;Tan et al 2014). Compared with borehole observations, the picking errors of P-wave arrivals are relatively large at the surface.…”

Section: The Sensitivity To Picking Errorsmentioning

confidence: 99%

Velocity calibration for microseismic event location using surface data

et al. 2016

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Because surface-based monitoring of hydraulic fracturing is not restricted by borehole geometry or the difficulties in maintaining subsurface equipment, it is becoming an increasingly common part of microseismic monitoring. The ability to determine an accurate velocity model for the monitored area directly affects the accuracy of microseismic event locations. However, velocity model calibration for location with surface instruments is difficult for several reasons: well log measurements are often inaccurate or incomplete, yielding intractable models; origin times of perforation shots are not always accurate; and the non-uniqueness of velocity models obtained by inversion becomes especially problematic when only perforation shots are used. In this paper, we propose a new approach to overcome these limitations. We establish an initial velocity model from well logging data, and then use the root mean square (RMS) error of double-difference arrival times as a proxy measure for the misfit between the well log velocity model and the true velocity structure of the medium. Double-difference RMS errors are reduced by using a very fast simulated annealing for model perturbance, and a sample set of double-difference RMS errors is then selected to determine an empirical threshold. This threshold value is set near the minimum RMS of the selected samples, and an appropriate number of travel times within the threshold range are chosen. The corresponding velocity models are then used to relocate the perforation-shot. We use the velocity model with the smallest relative location errors as the basis for microseismic location. Numerical analysis with exact input velocity models shows that although large differences exist between the calculated and true velocity models, perforation shots can still be located to their actual positions with the proposed technique; the location inaccuracy of the perforation is \2 m. Further tests on field data demonstrate the validity of this technique.

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Section: The Sensitivity To Picking Errorsmentioning

confidence: 99%

Velocity calibration for microseismic event location using surface data

et al. 2016

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“…Wavelet transform and thresholding criterion in the timefrequency domain [5] was designed to enhance the amplitude of microseismic data. Rodriguez proposed an algorithm based on constrained time-frequency transform [6] for 3C microseismic data. Lately, a denoising technique based on the τ − p transform [7] in surface microseismic data was implemented.…”

Section: Introductionmentioning

confidence: 99%

SNR Enhancement for Downhole Microseismic Data Using CSST

Zhao

Zhang

2016

IEEE Geosci. Remote Sensing Lett.

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Noise contamination is a significant issue in microseismic data processing due to the low magnitude of highfrequency downhole microseismic signals induced during fluid injection. In this letter, a noncoherent noise attenuation technique based on cycle spinning shearlet transform (CSST) is presented. The CSST algorithm is implemented in three steps. In the first step, we forcibly shift signals so that their features change positions and orientations and then transform the noisy data into shearlet domain to obtain coefficients of different scales and directions. In the second stage, we apply hard thresholding to the resulting coefficients of individual component. Finally, we transform them back into the original domain and averagely superimpose the filtering results to preserve the amplitudes of the signals. The resulting methodology is tested on the synthetic and field datasets that were recorded with a vertical array of receivers. The experimental results show that the proposed CSST algorithm has better performance than the conventional threshold-based shearlet transform denoising method in terms of both high-frequency signal preservation and noise attenuation.Index Terms-Cycle spinning, high-frequency preservation, microseismicity, random noise attenuation, shearlet transform.

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“…Noise in a signal is impossible to eliminate completely because of the superposition of the redundant noise frequency and the effective wave frequency [18,19]. Regarding frequency superposition, two challenges must be faced.…”

Section: Introductionmentioning

confidence: 99%

A Synthetic Solution for Identification and Extraction of the Effective Microseismic Wave Component Using Decomposition on Time, Frequency, and Wavelet Coefficient Domains

Zhang

Meng

Liu

et al. 2017

Shock and Vibration

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To reduce noise components from original microseismic waves, a comprehensive fine signal processing approach using the integrated decomposition analysis of the wave duration, frequency spectrum, and wavelet coefficient domain was developed and implemented. Distribution regularities of the wave component and redundant noise on the frequency spectrum and the wavelet coefficient domain were first expounded. The frequency threshold and wavelet coefficient threshold were determined for the identification and extraction of the effective wave component. The frequency components between the reconstructed microseismic wave and the original measuring signal were compared. The noise elimination effect via the scale-changed domain decomposition was evaluated. Interaction between the frequency threshold and the wavelet coefficient threshold in the time domain was discussed. The findings reveal that tri-domain decomposition analysis achieves the precise identification and extraction of the effective microseismic wave component and improves the reliability of waves by eliminating the redundant noise. The frequency threshold and the wavelet coefficient threshold on a specific time window are two critical parameters that determine the degree of precision for the identification of the extracted wave component. This research involves development of the proposed integrated domain decomposition method and provides a diverse view on the fine processing of the microseismic signal.

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Microseismic data denoising using a 3C group sparsity constrained time-frequency transform

Cited by 90 publications

References 19 publications

Velocity calibration for microseismic event location using surface data

Velocity calibration for microseismic event location using surface data

SNR Enhancement for Downhole Microseismic Data Using CSST

A Synthetic Solution for Identification and Extraction of the Effective Microseismic Wave Component Using Decomposition on Time, Frequency, and Wavelet Coefficient Domains

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