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Denoising of gamma-ray spectrum by optimized wavelet thresholding based on modified genetic algorithm in carbon/oxygen logging

  • Guofeng Yang
  • Jiacai DaiEmail author
  • Xiangjun Liu
  • Xiaolong Wu
  • Meng Chen
  • Hao Qin
Article
  • 18 Downloads

Abstract

In order to reduce noise in gamma-ray spectrum measured by carbon/oxygen logging instrument, an improved wavelet thresholding algorithm was proposed in this paper. This algorithm established a thresholding function with an adjustable parameter, which could obtain various filtering performances by means of different parameters, and then a modified genetic algorithm combined with opposition-based learning theory was put forward to optimize the parameter and wavelet thresholds. By using Monte Carlo simulation, the objective function of the modified genetic algorithm was determined. Finally, the actual measured spectra processing results of the optimized wavelet thresholding algorithm was compared with traditional thresholding algorithms and other filtering algorithms, and the effectiveness of the proposed algorithm was verified based on signal-to-noise ratio index.

Keywords

Spectral noise reduction Wavelet thresholding Genetic algorithm Carbon/oxygen logging 

Notes

Acknowledgements

This work was supported by the Scientific Research and Technology Development Project of CNPC (No. 2016D-3802) and the author thanks for the support by the State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University.

Compliance with ethical standards

Conflict of interest

The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Supplementary material

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Supplementary material 1 (XLSX 436 kb)
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Supplementary material 4 (DOC 477 kb)

References

  1. 1.
    Wyatt DF, Jacobson LA, et al. (1994) Enhanced carbon-oxygen log interpretation using supplemental log curves. In: SPE Asia Pacific Oi&Gas conference, Society of Petroleum EngineersGoogle Scholar
  2. 2.
    Morris F, Grau J, Hemingway J, Plasek R, Gupta TD (1999) Introduction of enhanced carbon-oxygen logging for multi-well reservoir evaluation. In: SPWLA logging symposium, Society of Petrophysicists and Well-Log AnalystsGoogle Scholar
  3. 3.
    Eyvazzadeh R, Oscar K, Hajari A A, et al. (2004) Modern carbon/oxygen logging methodologies: comparing hydrocarbon saturation determination techniques. In: SPE Technical Conference & Exhibition, Society of Petroleum EngineersGoogle Scholar
  4. 4.
    Rose D, Zhou T, Beekman S, et al. (2015) In: SPWLA 40th annual logging symposium, Society of Petrophysicists and Well-Log AnalystsGoogle Scholar
  5. 5.
    Zhang F, Tian L, Liu J, Wang X (2016) Numerical simulation on scintillator detector response for determining element content in PGNAA system. J Radioanal Nucl Chem 311(2):1–6CrossRefGoogle Scholar
  6. 6.
    Ramos F, Sureshkumar S, Dickson B (2010) Denoising aerial gamma-ray surveying through non-linear dimensionality reduction. J Field Robot 24(10):849–861CrossRefGoogle Scholar
  7. 7.
    Poornachandra S (2008) Wavelet-based denoising using subband dependent threshold for ecg signals. Digit Signal Process 18(1):49–55CrossRefGoogle Scholar
  8. 8.
    Zhang Q, Aliagarossel R, Choi P (2006) Denoising of gamma-ray signals by interval-dependent thresholds of wavelet analysis. Meas Sci Technol 17(4):731CrossRefGoogle Scholar
  9. 9.
    Donoho DL, Johnstone IM, Picard KD (1995) Wavelet shrinkage: asymptopia? J R Stat Soc Ser B (Methodol) 57(2):301–369Google Scholar
  10. 10.
    Vargas RN, Veiga Paschoarelli, Cláudio Antônio (2017) Seismic trace noise reduction by wavelets and double threshold estimation. IET Signal Process 11(9):1069–1075CrossRefGoogle Scholar
  11. 11.
    Chang SG, Yu B, Vetterli M (2000) Adaptive wavelet thresholding for image denoising and compression. IEEE Trans Image Process A Publ IEEE Signal Process Soc 9(9):1532CrossRefGoogle Scholar
  12. 12.
    Zhao J, Lee JS, Xu H, Xu K, Ren ZH, Chen JC et al (2017) Scanner-dependent threshold estimation of wavelet denoising for small-animal pet. IEEE Trans Nucl Sci 64(1):705–712CrossRefGoogle Scholar
  13. 13.
    Poornachandra S (2008) Wavelet-based denoising using subband dependent threshold for ecg signals. Digital Signal Process 18(1):49–55CrossRefGoogle Scholar
  14. 14.
    Donoho DL (2002) De-noising by soft-thresholding. IEEE Trans Inf Theory 41(3):613–627CrossRefGoogle Scholar
  15. 15.
    Duan H, Ma S, Han L et al (2017) A novel denoising method for laser-induced breakdown spectroscopy: improved wavelet dual threshold function method and its application to quantitative modeling of Cu and Zn in Chinese animal manure composts. Microchem J 134:262–269CrossRefGoogle Scholar
  16. 16.
    Xu XB (2018) Single pulse threshold detection method with lifting wavelet denoising based on modified particle swarm optimization. Infrared Phys Technol 88:174–183CrossRefGoogle Scholar
  17. 17.
    Donoho DL, Johnstone IM (1995) Adapting to unknown smoothness via wavelet shrinkage. Publ Am Stat Assoc 90(432):1200–1224CrossRefGoogle Scholar
  18. 18.
    Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading, MAGoogle Scholar
  19. 19.
    Volkanovski A, Mavko B, Tome Boševski, Anton Čauševski, Marko Čepin (2017) Genetic algorithm optimisation of the maintenance scheduling of generating units in a power system. Reliab Eng Syst Saf 93(6):779–789CrossRefGoogle Scholar
  20. 20.
    Panapakidis IP, Dagoumas AS (2017) Day-ahead natural gas demand forecasting based on the combination of wavelet transform and anfis/genetic algorithm/neural network model. Energy 118:231–245CrossRefGoogle Scholar
  21. 21.
    Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: International conference on computational intelligence for modelling, control & automation, & international conference on intelligent agents, web technologies & internet commerceGoogle Scholar
  22. 22.
    Shang J, Sun Y, Li S, Liu JX, Zheng CH, Zhang J (2015) An improved opposition-based learning particle swarm optimization for the detection of snp-snp interactions. Biomed Res Int 2015:524821Google Scholar
  23. 23.
    Wang H, Wu Z, Rahnamayan S, Liu Y, Ventresca M (2011) Enhancing particle swarm optimization using generalized opposition-based learning. Inf Sci 181(20):4699–4714CrossRefGoogle Scholar
  24. 24.
    Tian L, Zhang F, Liu J, Wang X, Ti Y (2017) Monte carlo simulation of Cu, Ni and Fe grade determination in borehole by PGNAA technique. J Radioanal Nucl Chem 315(1):1–6Google Scholar
  25. 25.
    Liu J, Liu S, Zhang F, Miao B, Yuan C, Su B (2018) A method for improving the evaluation of elemental concentrations measured by geochemical well logging. J Radioanal Nucl Chem 317:1113–1121CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2019

Authors and Affiliations

  • Guofeng Yang
    • 1
  • Jiacai Dai
    • 1
    Email author
  • Xiangjun Liu
    • 1
    • 2
  • Xiaolong Wu
    • 1
  • Meng Chen
    • 1
  • Hao Qin
    • 1
  1. 1.School of Geoscience and TechnologySouthwest Petroleum UniversityChengduChina
  2. 2.State Key Laboratory of Oil and Gas Reservoir Geology and ExploitationSouthwest Petroleum UniversityChengduChina

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