1887
Volume 73, Issue 8
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

Acoustic emission (AE) is elastic waves generated spontaneously from the creation of micro‐cracks. AE waveforms share significant similarities with microseismic signals and serve as an effective tool for improving the understanding of fracture processes during hydraulic fracturing. AE events typically have small magnitude with low amplitude. To detect weak AE events, it is always necessary to set a larger gain control, but this increases the risk of large amplitude waveform being clipped beyond the saturation level of the A/D converter. Amplitude‐clipped AE events are usually considered unusable and must be excluded from the estimation of source properties such as focal mechanisms. We introduce an extension of compressed sensing methods to reconstruct the clipped waveform and further use them to perform the moment tensor inversions and decomposition. This method assumes that the AE events are band‐limited and the clipped segment of the waveform shares the same frequency content as the unclipped segment. Compared to conventional techniques, the proposed method can effectively reconstruct the clipped waveforms with clipping level less than 0.7, ensuring reliable moment tensor inversions and decomposition. The reconstruction method reduces the risk of confounding reasoning or misinterpretation caused by waveform distortion and provides a more reliable basis for the physical interpretation of AE properties.

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2025-10-25
2025-11-12
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References

  1. Aker, E., D.Kühn, V.Vavryčuk, M.Soldal, and V.Oye. 2014. “Experimental Investigation of Acoustic Emissions and Their Moment Tensors in Rock During Failure.” International Journal of Rock Mechanics and Mining Sciences70: 286–295. https://doi.org/10.1016/j.ijrmms.2014.05.003.
    [Google Scholar]
  2. Aki, K., and P.Richards. 2003. Quantitative Seismology. W. H. Freeman.
    [Google Scholar]
  3. Azizzadeh Mehmandost Olya, B., and R.Mohebian. 2023. “Q‐Factor Estimation From Vertical Seismic Profiling (vsp) With Deep Learning Algorithm, Cudnnlstm.” Journal of Seismic Exploration32: 89–104.
    [Google Scholar]
  4. Candès, E. J., M. B.Wakin, and S. P.Boyd. 2008. “Enhancing Sparsity by Reweighted ℓ 1 Minimization.” Journal of Fourier Analysis and Applications14, no. 5–6: 877–905. https://doi.org/10.1007/s00041‐008‐9045‐x.
    [Google Scholar]
  5. Collins, D. S., W. S.Pettitt, and R. P.Young. 2002. “High‐Resolution Mechanics of a Microearthquake Sequence.” Pure and Applied Geophysics159, no. 1: 197–219. https://doi.org/10.1007/pl00001251.
    [Google Scholar]
  6. Corbetta, A., V.Menkovski, R.Benzi, and F.Toschi. 2021. “Deep Learning Velocity Signals Allow Quantifying Turbulence Intensity.” Science Advances7, no. 12: eaba7281. https://doi.org/10.1126/sciadv.aba7281.
    [Google Scholar]
  7. Eaton, D. W., N.Igonin, and A.Poulin, et al. 2018. “Induced Seismicity Characterization During Hydraulic‐Fracture Monitoring With a Shallow‐Wellbore Geophone Array and Broadband Sensors.” Seismological Research Letters89, no. 5: 1641–1651. https://doi.org/10.1785/0220180055.
    [Google Scholar]
  8. Gao, L., and Y.Pan. 2016. “Acquisition and Processing Pitfall With Clipped Traces in Surface‐Wave Analysis.” Journal of Applied Geophysics125: 1–6. https://doi.org/10.1016/j.jappgeo.2015.12.004.
    [Google Scholar]
  9. Gholami, A.2014. “Non‐Convex Compressed Sensing With Frequency Mask for Seismic Data Reconstruction and Denoising.” Geophysical Prospecting62, no. 6: 1389–1405.
    [Google Scholar]
  10. Hudson, J. A., R. G.Pearce, and R. M.Rogers. 1989. “Source Type Plot for Inversion of the Moment Tensor.” Journal of Geophysical Research94, no. B1: 765–774. https://doi.org/10.1029/JB094iB01p00765.
    [Google Scholar]
  11. Jiang, Y., and X.Xing. 2024. “Seismic Communication Data Processing Based on Compressed Sensing Algorithm.” Geophysical Prospecting72, no. 5: 1698–1709.
    [Google Scholar]
  12. Karabulut, H., and M.Bouchon. 2007. “Spatial Variability and Non‐Linearity of Strong Ground Motion near a Fault.” Geophysical Journal International170, no. 1: 262–274. https://doi.org/10.1111/j.1365‐246X.2007.03406.x.
    [Google Scholar]
  13. King, M., W.Pettitt, J.Haycox, and R.Young. 2012. “Acoustic Emissions Associated With the Formation of Fracture Sets in Sandstone Under Polyaxial Stress Conditions.” Geophysical Prospecting60, no. 1: 93–102.
    [Google Scholar]
  14. Knopoff, L., and M. J.Randall. 1970. “The Compensated Linear‐Vector Dipole: A Possible Mechanism for Deep Earthquakes.” Journal of Geophysical Research75, no. 26: 4957–4963. https://doi.org/10.1029/JB075i026p04957.
    [Google Scholar]
  15. Kondrashov, D., R.Denton, Y. Y.Shprits, and H. J.Singer. 2014. “Reconstruction of Gaps in the Past History of Solar Wind Parameters.” Geophysical Research Letters41, no. 8: 2702–2707. https://doi.org/10.1002/2014gl059741.
    [Google Scholar]
  16. Larki, E., B.Jaffarbabaei, and B.Soleimani, et al. 2024. “A New Insight to Access Carbonate Reservoir Quality Using Quality Factor and Velocity Deviation Log.” Acta Geophysica72, no. 5: 3159–3178.
    [Google Scholar]
  17. Lei, X.2024. “Fluid‐Driven Fault Nucleation, Rupture Processes, and Permeability Evolution in Oshima Granite—Preliminary Results and Acoustic Emission Datasets.” Geohazard Mechanics2, no. 3: 164–180. https://doi.org/10.1016/j.ghm.2024.04.003.
    [Google Scholar]
  18. Lei, X., and S.Ma. 2014. “Laboratory Acoustic Emission Study for Earthquake Generation Process.” Earthquake Science27, no. 6: 627–646. https://doi.org/10.1007/s11589‐014‐0103‐y.
    [Google Scholar]
  19. Ma, J.2009. “A Single‐Pixel Imaging System for Remote Sensing by Two‐Step Iterative Curvelet Thresholding.” IEEE Geoscience and Remote Sensing Letters6, no. 4: 676–680. https://doi.org/10.1109/lgrs.2009.2023249.
    [Google Scholar]
  20. Ma, Z., Y.Wang, and Y.Zheng. 2022. “In Situ Dynamic X‐Ray Imaging of Fluid‐Rock Interactions inside Tight Sandstone During Hydraulic Fracturing: Fluid Flow Process and Fracture Network Growth.” Journal of Petroleum Science and Engineering214: 110490. https://doi.org/10.1016/j.petrol.2022.110490.
    [Google Scholar]
  21. Muñoz‐Ibáñez, A., J.Delgado‐Martín, and E.Grande‐García. 2019. “Acoustic Emission Processes Occurring During High‐Pressure Sand Compaction.” Geophysical Prospecting67: 761–783.
    [Google Scholar]
  22. Munteanu, C., C.Negrea, M.Echim, and K.Mursula. 2016. “Effect of Data Gaps: Comparison of Different Spectral Analysis Methods.” Annales Geophysicae34, no. 4: 437–449. https://doi.org/10.5194/angeo‐34‐437‐2016.
    [Google Scholar]
  23. Okafor, A. C., N.Singh, and N.Singh. 2007. “Acoustic Emission Detection and Prediction of Fatigue Crack Propagation in Composite Patch Repairs Using Neural Networks.” AIP Conference Proceedings894: 1532–1539. https://doi.org/10.1063/1.2718147.
    [Google Scholar]
  24. Olofsson, T.2005. “Deconvolution and Model‐Based Restoration of Clipped Ultrasonic Signals.” IEEE Transactions on Instrumentation and Measurement54, no. 3: 1235–1240. https://doi.org/10.1109/tim.2005.847222.
    [Google Scholar]
  25. Ringler, A. T., L. S.Gee, B.Marshall, C. R.Hutt, and T.Storm. 2012. “Data Quality of Seismic Records From the Tohoku, Japan, Earthquake as Recorded Across the Albuquerque Seismological Laboratory Networks.” Seismological Research Letters83, no. 3: 575–584. https://doi.org/10.1785/gssrl.83.3.575.
    [Google Scholar]
  26. Sloan, S. D., D. W.Steeples, and P. E.Malin. 2008. “Acquisition and Processing Pitfall Associated With Clipping Near‐Surface Seismic Reflection Traces.” Geophysics73, no. 1: W1–W5. https://doi.org/10.1190/1.2807051.
    [Google Scholar]
  27. Thompson, B. D., R. P.Young, and D. A.Lockner. 2005. “Observations of Premonitory Acoustic Emission and Slip Nucleation During a Stick Slip Experiment in Smooth Faulted Westerly Granite.” Geophysical Research Letters32, no. 10: 10304. https://doi.org/10.1029/2005gl022750.
    [Google Scholar]
  28. Wang, B.2016. “An Efficient POCS Interpolation Method in the Frequency‐Space Domain.” IEEE Geoscience and Remote Sensing Letters13, no. 9: 1384–1387. https://doi.org/10.1109/lgrs.2016.2589260.
    [Google Scholar]
  29. Wang, Y., L.Zhu, F.Shi, et al. 2017. “A Laboratory Nanoseismological Study on Deep‐Focus Earthquake Micromechanics.” Science Advances3, no. 7: e1601896. https://doi.org/10.1126/sciadv.1601896.
    [Google Scholar]
  30. Yang, W., and Y.Ben‐Zion. 2010. “An Algorithm for Detecting Clipped Waveforms and Suggested Correction Procedures.” Seismological Research Letters81, no. 1: 53–62. https://doi.org/10.1785/gssrl.81.1.53.
    [Google Scholar]
  31. Zhang, J., J.Hao, and X.Zhao, et al. 2016. “Restoration of Clipped Seismic Waveforms Using Projection Onto Convex Sets Method.” Scientific Reports6: 39056. https://doi.org/10.1038/srep39056.
    [Google Scholar]
  32. Zu, S., H.Zhou, Y.Chen, X.Pan, S.Gan, and D.Zhang. 2016. “Interpolating Big Gaps Using Inversion With Slope Constraint.” IEEE Geoscience and Remote Sensing Letters13, no. 9: 1369–1373. https://doi.org/10.1109/lgrs.2016.2587301.
    [Google Scholar]
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  • Article Type: Research Article
Keyword(s): inverse problem; passive method; rock physics; signal processing

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