Hydraulic fracturing microseismic first arrival picking method based on non-subsampled shearlet transform and higher-order-statistics

Sheng, G.Q., Tang, X.G., Xie, K. and Xiong, J., 2019. Hydraulic fracturing microseismic first arrival picking method based on non-subsampled shearlet transform and higher-order-statistics. Journal of Seismic Exploration, 28: 593-618. Fast and accurate first arrival picking is the key issue of microseismic data processing. Traditionally manual picking methods will take a lot of time and reduce the data processing efficiency, so it is difficult to meet the demand of real-time data processing for microseismic monitoring. In this paper, we proposed the S-S/L_K (Shearlet-Short time window/Long time window-Kurtosis) algorithm which combined the shearlet multiscale decomposition with higher-order-statistics (HOS). This algorithm not only keeps the advantage of non-subsampled shearlet transform in multiscale analysis, but also maintain strengths of HOS in signal abnormalities detection and Gaussian noise suppressing. The forward records and real data tests show that compared with the PAI-S/K and the STA/LTA algorithm, the proposed method can overcome the influence of noise on the P-phase picking accuracy and obtain a reliable P-phase result for microseismic monitoring.
- Ait Laasri, E.H., Akhouayri, E.-S., Agliz, D. and Atmani, A., 2014. Automatic detectionand picking of P-wave arrival in locally stationary noise using cross-correlation. Digit. Sign. Process., 26: 87-100.
- Akram, J. and Eaton, D.W., 2016. A review and appraisal of arrival-time pickingmethods for downhole microseismic data. Geophysics, 81(2): KS67-KS87.
- Allen, R.V., 1978. Automatic earthquake recognition and timing from single traces. Bull.Seismol. Soc. Am., 68; 1521-1532.
- Alvarez, I., Garcia, L., Mota, S., Cortes, G., Benitez, C. and de la Torre, A., 2013. Anautomatic P-phase picking algorithm based on adaptive multiband processing. IEEEGeosci. Remote Sens. Lett., 10: 1488-1492.http://dx.doi.org/10.1109/LGRS.2013.2260720.
- Anant, K.S. and Dowla, F.U., 1997. Wavelet transform methods for phase identificationin three-component seismograms. Bull. Seismol. Soc. Am., 87: 1598-1612.
- Anju, T.S. and Raj, N.R.N., 2016. Shearlet transform based image denoising usinghistogram thresholding. Internat. Conf. Communicat. Syst. Netw. (ComNet):162-166.
- Assous, A. and Elkington, P., 2018. Shearlets and sparse representation for micro-resistivity borehole image inpainting. Geophysics, 83(1): D17-D25.
- Baillard, C., Crawford, W.C., Ballu, V., Hibert, C. and Mangeney, A., 2014. Anautomatic Kurtosis based P-and S-phase picker designed for local seismic networks.
- Bull. Seismol. Soc. Am., 104: 394-409. http://dx.doi.org/10.1785/0120120347.
- Cao, Q., Li, B. and Fan, L., 2017. Medical image fusion based on GPU acceleratednon-subsampled shearlet transform and 2D principal component analysis. IEEE 2nd
- Internat. Conf. Sign. Image Process. (ICSIP): 203-207.doi: 10.1109/SIPROCESS.2017.8124533
- Cheng, Y., Li, Y. and Zhang, C., 2017. First arrival time picking for microseismic databased on shearlet transform. J. Geophys. Engineer., 14: 262-271.
- Dai, H. and Macbeth, C., 1997. The application of back-propagation neural network toautomatic picking seismic arrivals from single-component recordings. J. Geophys.Res.-Solid Earth, 102(B7): 15105-15113.
- Dong, L.J., Wesseloo, J., Potvin, Y. and Li, X.B., 2016. Discrimination of mine seismicevents and blasts using the Fisher classifier, naive Bayesian classifier and logisticregression. Rock Mechan. Rock Engineer., 49: 183-211.https://doi.org/10.1007/s00603-015-0733-y.
- Dong, X., Li, Y., Wu, N., Tian, Y. and Yu, P., 2018. The S-STK/LTK algorithm forarrival time picking of microseismic signals. J. Geophys. Engineer., 15.doi: 10.1088/1742-2140/aab30c.
- Duncan. P.M. and Eisner. L.. 2010. Reservoir characterization using surface micro-seismic monitoring. Geophysics, 75(3): A139-A 146.
- Earle, P.S. and Shearer, P.M., 1994. Characterization of global seismograms using anautomatic-picking algorithm. Bull. Seismol. Soc. Am., 84: 366-376.
- Easley, G., Labate, D. and Lim, W.Q., 2008. Sparse directional image representationsusing 1 e discrete shearlet transform. Appl. Computat. Harmon. Analys., 25: 25-46.
- Galiana-Merino, J.J., Rosa-Herranz, J.L. and Parolai, S., 2008. Seismic P-phase pickingusing a Kurtosis-based criterion in the stationary wavelet domain. IEEE Transact.Geosci. Remote Sens., 46: 3815-3826.http://dx.doi.org/10.1109/TGRS.2008.2002647.
- Gentili, S. and Michelini, A., 2006. Automatic picking of P- and S-phases using a neuraltree. J. Seismol., 10: 39-63. http://dx.doi.org/10.1007/s10950-006-2296-6.
- Gibbons, S.J. and Ringdal, F., 2006. The detection of low magnitude seismic eventsusing array-based waveform correlation. Geophys. J. Internat., 165: 149-166.http://dx.doi.org/10.1111/j.1365-246X.2006.02865.x
- Gou, X.T., Li, Z.M., Qin, N. and Jin, W.D., 2011. Adaptive picking of microseismicevent arrival using a power spectrum envelope. Comput. Geosci., 37: 158-164.http://dx.doi.org/10.1016/j.cageo.2010.05.022.
- Guo, K. and Labate, D., 2007. Optimally sparse multidimensional representations usingshearlets. SIAM J. Mathemat. Analys., 39: 298-318.
- Guo, K. and Labate, D., 2010. Optimally sparse 3D approximations using shearletrepresentations. Electron. Res. Announcem. Mathemat. Sci., 17: 125-137.doi: 10.3934/era.
- Hafez, A.G., Khan, M.T.A. and Kohda, T., 2009. Earthquake onset detection usingspectro- ratio on multi-threshold time-frequency sub-band. Digit. Sign Process., 19:118-126. http://dx.doi.org/10.1016/j.dsp.2008.08.003.
- Hafez, A.G., Khan, M.T.A. and Kohda, T., 2010. Clear P-wave arrival of weak eventsand automatic onset determination using wavelet filter banks. Digit. Sign. Process.,20: 715-723. http://dx.doi.org/10.1016/j.dsp.2009.10.002.
- Hafez, A.G., Rabie, M. and Kohda, T., 2013. Seismic noise study for accurate P-wavearrival detection via MODWT. Comput. Geosci., 54: 148-159.http://dx.doi.org/10.1016/j.cageo.2012.12.002.
- Hildyard, M.W., Nippress, S.E. and Rietbrock, A., 2008. Event detection and phasepicking using a time-domain estimate of predominate period Tpd. Bull. Seismol.
- Soc. Am., 98: 3025-3032. http://dx.doi.org/10.1785/0120070272.
- Hu, Y.Q., Yin, C., Pan, S., Wu, F., Li, Y. and Liu, Y., 2012. A microseismic signalrecognition technique based on improved time-varying skewness and Kurtosismethod. Geophys. Prosp. Petrol., 51: 625-632.
- Kim, D., Byun, J.M., Lee, M., Choi, J. and Kim, M.S., 2017. Fast first arrival pickingalgorithm for noisy microseismic data. Explor. Geophys., 48: 131-136.
- Ktiperkoch, L., Meier, T., Lee, J. and Friederich, W., 2010. Automated determination of
- P-phase arrival times at regional and local distances using higher order statistics.Geophys. J. Internat., 181: 1159-1170.ttp://dx.doi.org/10.1111/j.1365-246X.2010.04570.x.
- Kutyniok, G. and Labate, D., 2009. Resolution of the wavefront set using continuoushearlets. Transact. Am. Mathemat. Soc., 361: 2719-2754.
- Karamzadeh, N., Doloei, G.J. and Reza, A.M., 2013. Automatic earthquake signal onsetpicking based on the continuous wavelet transform. IEEE Transact. Geosci.emote Sens., 51: 2666-2674. http://dx.doi.org/10.1109/TGRS.2012.2213824.
- Kulesh, M., Diallo, M.S., Holschneider, M., Kurennaya, K., Kruger, F., Ohrnberger, M.and Scherbaum, E., 2007. Polarization analysis in the wavelet domain based on theadaptive covariance method. Geophys. J. Internat., 170: 667-678.http://dx.doi.org/10.1111/j.1365-246X.2007.03417.x.
- Kutyniok, G. and Labate, D. 2009. Resolution of the wavefront set using continuousshearlets. Transact. Am. Mathemat. Soc., 361: 2719-2754.
- Leonard, M. and Kennett, B.L.N., 1999. Multi-component autoregressive techniques forthe analysis of seismograms. Phys. Earth Planet. Inter., 113: 247-263.ttp://dx.doi.org/10.1016/S003 1-9201(99)00054-0.
- Leonard, M., 2000. Comparison of manual and automatic onset time picking. Bull.
- Seismol. Soc. Am., 90: 1384-1390. doi: 10.1785/0120000026.
- Li, X., Shang, X., Wang, Z., Dong, L. and Weng, L., 2016. Identifying P-phase arrivalswith noise: an improved Kurtosis method based on DWT and STA/LTA. J. Appl.Geophys., 133: 50-61.
- Liang, X., Li, Y. and Zhang, C., 2017. Noise suppression for microseismic data by non-subsampled shearlet transform based on singular value decomposition. Geophys.Prosp., 66: 894-903. doi: 10.1111/1365-2478.12576
- Liu, J.-S., Wang, Y. and Yao, Z.-X., 2013. On micro-seismic first arrival identification:A case study. Chin. J. Geophys., 56: 1660-1666.
- Liu, X.Q., Cai, Y., Zhao, R., Zhao, Y.G., Qu, B.A., Feng, Z.J. and Li, H., 2014. Anautomatic seismic signal detection method based on fourth-order statistics andapplications. Appl. Geophys., 11: 128-138.http://dx.doi.org/10.1007/s11770-014-0433-5.
- Lokajitek, T. and Klima, K., 2006. A first arrival identification system of acousticemission (AE) signals by means of a high-order statistics approach. Meas. Sci.
- Technol., 17: 2461. http://dx.doi.org/10.1088/0957-0233/17/9/013.
- Maxwell, S.C., Rutledge, J., Jones, R. and Fehler, M., 2010. Petroleum reservoircharacterization using downhole microseismic monitoring. Geophysics, 75:A129-A137.
- Maeda, N., 1985. A method for reading and checking phase times in auto-processingsystem of seismic wave data. Zisin, 38: 365-379.
- Merouane, A., Yilmaz, O. and Baysal, E., 2015. Random noise attenuation using2-Dimensional shearlet transform. Expanded Abstr., 85th Ann. Internat. SEG Mtg.,New Orleans: 4770-4774.
- Morita, Y. and Hamaguchi, H., 1984. Automatic detection of onset time of seismicwaves and its confidence interval using autoregressive model fitting. Zisin, 37:281-293.
- Moussa, O. and Khlifa, N., 2018. Video speckle noise reduction using robust diffusiontensor in shearlet domain. 4th Internat. Conf. Adv. Technolog. Sign. Image Process.(ATSIP): 1-6. doi: 10.1109/ATSIP.2018.8364523
- Nippress, S., Rietbrock, A. and Heath, A., 2010. Optimized automatic pickers:application to the ANCORP data set. Geophys. J. Internat., 181: 911-925.http://dx.doi.org/10.1111/j.1365-246X.2010.0453 1.x.
- Priya, B.L. and Jayanthi, K., 2017. Edge enhancement of liver CT images using nonsubsampled shearlet transform based multislice fusion. Internat. Conf. Wireless
- Communicat., Sign. Process. Network. (WiSPNET): 191-195.
- Ross, Z.E. and Ben-Zion, Y., 2014. Automatic picking of direct P-, S-seismic phasesand fault zone head waves. Geophys. J. Internat., 199: 368-381.http://dx.doi.org/10.1093/gji/ ggu267, doi: 10.1109/CSN.2016.7824007.
- Saragiotis, C.D., Hadjileontiadis, L.J. and Panas, S.M., 1999. A higher-orderstatistics-based phase identification of three-component seismograms in aredundant wavelet transform domain. Proc. IEEE Worksh. Higher Order Statist.:396-399.
- Saragiotis, C.D., Hadjileontiadis, L.J. and Panas, S.M., 2002. PAI-S/K: A robustautomatic seismic P-phase arrival identification scheme. IEEE Transact. Geosci.
- Remote Sens., 40: 1395-1404. http://dx.doi.org/10.1109/TGRS.2002.800438.
- Saragiotis, C.D., Hadjileontiadis, L.J., Rekanos, LT. and Panas, S.M., 2004. Automatic
- P-phase picking using maximum Kurtosis and K-statistics criteria. IEEE Geosci.
- Remote Sens. Lett., 1: 147-151. http://dx.doi-org/10.1109/LGRS.2004.828915.
- Senkaya, M. and Karsli, H., 2014. A semi-automatic approach to identify first arrivaltime: the cross correlation technique (CCT). Earth Sci. Res. J., 18: 107-113.http://dx.doi.org/10.15446/esrj.v18n2.35887.
- Shang, X., Li, X., Morales-Esteban, A. and Dong, L., 2018. An improved P-phasearrival picking method S/L-K-A with an application to the Yongshaba Mine inChina. Pure Appl. Geophys., 3: 1-19.
- Sheng. G., Li, Z. and Wang. W., 2015a. A new automatic detection method ofmicroseismic event based on CWT and HOS. Expanded Abstr., 85th Ann. Internat.SEG Mtg., New Orleans: 2625-2629.
- Sheng. G.. Li, Z. and Wang. W.. 2015b. A new automatic detection method ofmicroseismic events based on wavelet decomposition and high-order statistics.Geophys. Prosp. Petrol., 8: 388-395.
- Sleeman, R., van Eck, T., 1999. Robust automatic P-phase picking: an on-lineimplementation in the analysis of broadband seismogram recordings. Phys. Earth
- Planet. Inter., 113: 265-275. http://dx.doi.org/10.1016/S003 1-9201(99)00007-2.
- Taylor, K.M., Procopio, M.J., Young, C.J. and Meyer, F.G., 2011. Estimation of arrivaltimes from seismic waves: a manifold-based approach. Geophys. J. Internat., 185:435-452. http://dx.doi.org/10.1111/j.1365-246X.2011.04947.x.
- Tselentis, G.A., Martakis, N., Paraskevopoulos, P., Lois, A. and Sokos, E., 2012.
- Strategy for automated analysis of passive microseismic data based on S-transform,
- Otsu's thresholding, and higher order statistics. Geophysics, 77: Ks43-Ks54.http://dx.doi.org/10.1190/geo2011-0301.1.
- Van Decar, J. and Crosson, R., 1990. Determination of teleseismic relative phase arrivaltimes using multi-channel cross-correlation and least squares. Bull. Seismol. Soc.Am., 80: 150-169.
- Vidale, J.E., 1986. Complex polarization analysis of particle motion. Bull. Seismol. Soc.Am., 76: 1393-1405.
- Walden, T. and Hosken, J.W.J., 1986. The nature of the non-Gaussianity of primaryreflection coefficients and its significance for deconvolution. Geophys. Prosp.,34: 1038-1066.
- Wang, J. and Teng, T.-L., 1995. Artificial neural network-based seismic detector. Bull.Seismol. Soc. Am., 85: 308-319.
- Wang, D. and Li, Z.C., 2013. Surface wave attenuation using the Shearlet and TTtransforms. Expanded Abstr., 83rd Ann. Internat. SEG Mtg., Houston:4335-4339.
- Yung, S.K. and Ikelle, L.T., 1997. An example of seismic time picking by 3rd orderbicoherence. Geophysics, 62: 1947-1952.
- Zhang, C. and van der Baan, M., 2018. Multicomponent microseismic data denoising by3D shearlet transform. Geophysics, 83(3): A45-A51.
- Zhao, Y. and Takano, K., 1999. An artificial neural network approach for broadbandseismic phase picking. Bull. Seismol. Soc. Am., 89: 670-680.