Review of recent gamma spectrum unfolding algorithms and their application

Abstract Gamma spectrum analysis is regarded as a fast, reliable and non-destructive technology on determining the type and intensity of radionuclides. It is widely used in nuclear physics research, geological exploration, environmental assessment. Under the certain condition (detector, electronic components and measuring environment), the data obtained by different unfolding algorithms have different results. The accuracy of qualitative and quantitative analysis of radionuclides is determined by gamma spectrum unfolding technique. This paper mainly analyzes the development and application of the gamma spectrum unfolding algorithms and technologies in denoising, background subtraction and overlapping peak separation, reviews the performances, results and highlights of the typical algorithms, discusses representative examples and proposes potential research directions.

[1]  M. Morhác,et al.  Peak Clipping Algorithms for Background Estimation in Spectroscopic Data , 2008, Applied spectroscopy.

[2]  L. G. Liu,et al.  Smoothing noisy spectroscopic data with many-knot spline method , 2008 .

[3]  Mustafa Yavuz,et al.  Deconvolution of overlapping peaks from differential scanning calorimetry analysis for multi-phase NiTi alloys , 2018, Thermochimica Acta.

[4]  Lhou Maacha,et al.  A contribution of airborne magnetic, gamma ray spectrometric data in understanding the structure of the Central Jebilet Hercynian massif and implications for mining , 2017 .

[5]  Tanja M. Kneiske.  Gamma-ray background: a review , 2007 .

[6]  Haixiu Chen,et al.  Fractional-order derivative spectroscopy for resolving simulated overlapped Lorenztian peaks , 2011 .

[7]  A. Mishev,et al.  Recent gamma background measurements at high mountain altitude. , 2012, Journal of environmental radioactivity.

[8]  B. Zimmerman,et al.  Decay data for the positron emission tomography imaging radionuclide 124I: A DDEP evaluation. , 2017, Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine.

[9]  Zhang Jinzhao,et al.  High-resolution gamma spectroscopy shift-invariant wavelet de-noising , 2013 .

[10]  Dimitri Van De Ville,et al.  Polyharmonic smoothing splines and the multidimensional Wiener filtering of fractal-like signals , 2006, IEEE Transactions on Image Processing.

[11]  Douglas N Rutledge,et al.  Using ANOVA-PCA for discriminant analysis: application to the study of mid-infrared spectra of carrageenan gels as a function of concentration and temperature. , 2008, Analytica chimica acta.

[12]  Richard M. Lindstrom,et al.  A low-background gamma-ray assay laboratory for activation analysis , 1990 .

[13]  V. Sowmya,et al.  Effect of Denoising on Dimensionally Reduced Sparse Hyperspectral Unmixing , 2017 .

[14]  Zhu Meng-Hua,et al.  Least square fitting of low resolution gamma ray spectra with cubic B-spline basis functions , 2009 .

[15]  Gong Jun-jun,et al.  Disposal of Smooth γ Spectrum on Matlab Process , 2007 .

[16]  N. Kingsbury.  Image processing with complex wavelets , 1999, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[17]  Haiyang Pan,et al.  Adaptive parameterless empirical wavelet transform based time-frequency analysis method and its application to rotor rubbing fault diagnosis , 2017, Signal Process..

[18]  Meng‐Hua Zhu.  On estimating the background of remote sensing gamma-ray spectroscopic data , 2016 .

[19]  E. Larionova,et al.  Determination of overlapping peaks heights by tangent method , 2012, 2012 7th International Forum on Strategic Technology (IFOST).

[20]  S. A. Bogatov,et al.  Reconstruction of the background spectrum for processing airborne gamma survey data , 2011 .

[21]  D. R. Cousens,et al.  SNIP, A STATISTICS-SENSITIVE BACKGROUND TREATMENT FOR THE QUANTITATIVE-ANALYSIS OF PIXE SPECTRA IN GEOSCIENCE APPLICATIONS , 1988 .

[22]  A Abdel-Hafiez,et al.  Fourier transformation methods in the field of gamma spectrometry , 2006 .

[23]  R. S. Booth,et al.  A comparison of folding and unfolding techniques for determining the gamma spectrum from thermal neutron capture in aluminum , 1970 .

[24]  Chunlai Li,et al.  Background deduction of the Chang’E-1 gamma-ray spectrometer data , 2012, Chinese Journal of Geochemistry.

[25]  Ying Zheng,et al.  Peak detection of TOF-SIMS using continuous wavelet transform and curve fitting , 2018 .

[26]  Jianfeng He,et al.  A Study of Background Subtraction Method for NaI(Tl) Instrument Spectrum Based on Adaptive FWHM , 2015 .

[27]  J. Montejo-Bernardo,et al.  Natural Logarithm Derivative Method: a novel and easy methodology for finding maximums in overlapping experimental peaks. , 2009, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[28]  Robin P. Gardner,et al.  Use of an iterative convolution approach for qualitative and quantitative peak analysis in low resolution gamma-ray spectra , 2011 .

[29]  Meng‐Hua Zhu,et al.  Iterative estimation of the background in noisy spectroscopic data , 2009 .

[30]  Wei Li,et al.  Resolving overlapped spectra with curve fitting. , 2005, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[31]  Gordon R. Gilmore,et al.  Practical Gamma‐ray Spectrometry , 1995 .

[32]  Fei Li,et al.  Application of artificial neural networks to X‐ray fluorescence spectrum analysis , 2019, X-Ray Spectrometry.

[33]  Xin Lu,et al.  Deconvolution of overlapped peaks based on the exponentially modified Gaussian model in comprehensive two-dimensional gas chromatography. , 2005, Journal of chromatography. A.

[34]  Xiaowei Yang,et al.  A robust least squares support vector machine for regression and classification with noise , 2014, Neurocomputing.

[35]  M. Galera,et al.  Chemometric strategies for enhancing the chromatographic methodologies with second-order data analysis of compounds when peaks are overlapped. , 2011, Talanta.

[36]  Christos Tsabaris,et al.  Automated quantitative analysis of in-situ NaI measured spectra in the marine environment using a wavelet-based smoothing technique. , 2011, Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine.

[37]  Miroslav Morhac.  Sophisticated algorithms of analysis of spectroscopic data , 2008 .

[38]  T. J. Kennett,et al.  An automated background estimation procedure for gamma ray spectra , 1983 .

[39]  N. M. Spyrou,et al.  Characterization of LaBr3: Ce and LaCl3: Ce scintillators for gamma-ray spectroscopy , 2008 .

[40]  W. Westmeier,et al.  Background subtraction in Ge(Li) gamma-ray spectra , 1981 .

[41]  Long Bi.  A Self-Adaptive Method for the Clipping of Scatter Background of γ Spectrum , 2013 .

[42]  Yu Cao,et al.  Overlapped peaks resolution for linear sweep polarography using Gaussian-like distribution , 2013 .

[43]  Keith B. Oldham,et al.  Curve fitting to resolve overlapping voltammetric peaks: model and examples , 1995 .

[44]  Jia Jiang,et al.  Evaluation of the Bone-ligament and tendon insertions based on Raman spectrum and its PCA and CLS analysis , 2017, Scientific reports.

[45]  Charles D. Dermer.  The Extragalactic γ Ray Background , 2007 .

[46]  Miroslav Morháč,et al.  An algorithm for determination of peak regions and baseline elimination in spectroscopic data , 2009 .

[47]  M. Campi,et al.  A coverage theory for least squares , 2017 .

[48]  Kazuyuki Fujii.  Least Squares Method from the View Point of Deep Learning , 2018 .

[49]  Mats Söderström,et al.  Gamma-ray spectrometry and geological maps as tools for cadmium risk assessment in arable soils , 2013 .

[50]  Cui Huimin,et al.  Improved Threshold Denoising Method Based on Wavelet Transform , 2012 .

[51]  L Pibida,et al.  A new NIST primary standardization of 18F. , 2014, Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine.

[52]  P. Tse,et al.  A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing , 2005 .

[53]  Min Chen,et al.  A noise-driven strategy for background estimation and event detection in data streams , 2006, Signal Process..

[54]  D. Stoffer,et al.  Automatic estimation of multivariate spectra via smoothing splines , 2007 .

[55]  Xiao Gang,et al.  A Nonlinear Wavelet Method for Data Smoothing of Low-level Gamma-ray Spectra , 2004 .

[56]  András Vernes,et al.  A wavelet filtering method for cumulative gamma spectroscopy used in wear measurements. , 2017, Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine.

[57]  M. Morhác,et al.  Background elimination methods for multidimensional coincidence γ-ray spectra , 1997 .

[58]  E. Bayat,et al.  Discrete Fourier Transform Method for Discrimination of Digital Scintillation Pulses in Mixed Neutron-Gamma Fields , 2016, IEEE Transactions on Nuclear Science.

[59]  Yi-Zeng Liang,et al.  Peak alignment using wavelet pattern matching and differential evolution. , 2011, Talanta.

[60]  Chunling Qiu,et al.  An improved algorithm for peak detection in mass spectra based on continuous wavelet transform , 2016 .

[61]  Michael Wolf,et al.  Spectrum Estimation: A Unified Framework for Covariance Matrix Estimation and PCA in Large Dimensions , 2013, J. Multivar. Anal..

[62]  P. Statham.  A comparative study of techniques for quantitative analysis of the X‐ray spectra obtained with a Si(Li) detector , 1976 .

[63]  Khalid Khan,et al.  Assessment of Radionuclides, Trace Metals and Radionuclide Transfer from Soil to Food of Jhangar Valley (Pakistan) Using Gamma-Ray Spectrometry , 2010 .

[64]  S. V. Romanenko,et al.  Resolution of the overlapping peaks in the case of linear sweep anodic stripping voltammetry via curve fitting , 2004 .

[65]  Ming-Yong Pang,et al.  Reconstructing Smooth Curve from Noise Sampled Data , 2009 .

[66]  Yifang Wang,et al.  Estimation of background spectrum in a shielded HPGe detector using Monte Carlo simulations. , 2014, Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine.

[67]  Aurelian Luca,et al.  Influence of the background approximation methods on the analysis of γ-ray spectra , 2004 .

[68]  Peyman Hessari,et al.  Pseudo-spectral least squares method for linear elasticity , 2018, Comput. Math. Appl..

[69]  M. Fornasa,et al.  The nature of the Diffuse Gamma-Ray Background , 2015, 1502.02866.

[70]  J Baré,et al.  Gamma spectrum unfolding for a NaI monitor of radioactivity in aquatic systems: experimental evaluations of the minimal detectable activity. , 2011, Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine.

[71]  Liu Yuqi,et al.  Step-approximation SNIP background-elimination algorithm for HPGe gamma spectra , 2018 .

[72]  Fang Zhang,et al.  Quantification of components in overlapping peaks from capillary electrophoresis by using continues wavelet transform method. , 2008, Talanta.

[73]  Hee-Jung Im,et al.  Noise reduction in prompt gamma spectra acquired in short times , 2007 .

[74]  L. Ge,et al.  Background estimation based on Fourier Transform in the energy-dispersive X-ray fluorescence analysis , 2012 .

[75]  Liguo Zhang,et al.  Improving the Accuracy of Gamma Spectrum Analysis by Total Variation Based Adaptive Smoothing , 2013 .

[76]  Wang Qi.  The γ Spectrum Smoothing Method And Its Evaluation with NaI(TI) Detector , 2015 .

[77]  Brian Minty.  Accurate noise reduction for airborne gamma-ray spectrometry , 2003 .

[78]  Stephen J. Sangwine,et al.  The development of the quaternion wavelet transform , 2017, Signal Process..

[79]  Rudolf Scitovski,et al.  On the existence of the nonlinear weighted least squares estimate for a three-parameter Weibull distribution , 2008, Comput. Stat. Data Anal..

[80]  A. G. Belov.  A Mathematical-Statistics Approach to the Least Squares Method , 2018 .

[81]  P. Van Put,et al.  MCNP simulation and spectrum unfolding for an NaI monitor of radioactivity in aquatic systems , 2007 .

[82]  Shaoze Yan,et al.  A time-frequency analysis algorithm for ultrasonic waves generating from a debonding defect by using empirical wavelet transform , 2018 .

[83]  M. Fedoroff,et al.  Accurate gamma ray spectrum analysis , 1985 .

[84]  John Strain,et al.  Fast Fourier transforms of piecewise polynomials , 2018, J. Comput. Phys..

[85]  C. V. Hampton,et al.  Fast-Fourier-transform spectral enhancement techniques for γ-ray spectroscopy , 1994 .

[86]  Jaroslav Šolc,et al.  Subtraction of natural radiation contribution from gamma-ray spectra measured by HPGe detector. , 2018, Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine.

[87]  Bappa Mukherjee,et al.  Identification of formation interfaces by using wavelet and Fourier transforms , 2016 .

[88]  M. A. Mariscotti,et al.  A method for automatic identification of peaks in the presence of background and its application to spectrum analysis , 1967 .

[89]  Ronghui Qu,et al.  An improved method based on a new wavelet transform for overlapped peak detection on spectrum obtained by portable Raman system , 2018, Chemometrics and Intelligent Laboratory Systems.

[90]  Ming Liu,et al.  ECG signal enhancement based on improved denoising auto-encoder , 2016, Eng. Appl. Artif. Intell..

[91]  Joseph M. Dubrovkin,et al.  Evaluation of undetectable perturbations of peak parameters estimated by the least square curve fitting of analytical signal consisting of overlapping peaks , 2016 .

[92]  Xiaoming Chang,et al.  An intelligent noise reduction method for chaotic signals based on genetic algorithms and lifting wavelet transforms , 2013, Inf. Sci..

[93]  Minhao Yan,et al.  A de-noising algorithm to improve SNR of segmented gamma scanner for spectrum analysis , 2016 .

[94]  Tang Bin.  Discussion and Application of Eliminating the Background in γ-ray Spectrum by SNIP Algorithm , 2009 .

[95]  Alex B. McBratney,et al.  Understanding the utility of aerial gamma radiometrics for mapping soil properties through proximal gamma surveys , 2017 .

[96]  T Burr,et al.  Smoothing Low Resolution Gamma Spectra , 2010, IEEE Transactions on Nuclear Science.

[97]  Peter Volkovitsky,et al.  Absolute 60Co characterization based on gamma–gamma coincident detection by two NaI(Tl) detectors , 2009 .

[98]  Katsuhiko Yamaguchi,et al.  Assessment of the calibration of gamma spectrometry systems in forest environments. , 2018, Journal of environmental radioactivity.

[99]  G. Heusser,et al.  Studies of γ-ray background with a low level germanium spectrometer , 1991 .

[100]  R. Siuda,et al.  PCA-Based Analysis of X-Ray-Excited Auger Spectra from Non-ordered Ag(110) , 2003 .

[101]  Manuel Palencia,et al.  Functional transformation of Fourier-transform mid-infrared spectrum for improving spectral specificity by simple algorithm based on wavelet-like functions , 2018, Journal of advanced research.

[102]  Mustafa Secmen.  Radar target classification method with high accuracy and decision speed performance using MUSIC spectrum vectors and PCA projection , 2011 .

[103]  Zhu Meng-Hua,et al.  Automatic Estimation of Peak Regions in Gamma-Ray Spectra Measured by NaI Detector , 2008 .

[104]  Weihua Gui,et al.  State-transition-algorithm-based resolution for overlapping linear sweep voltammetric peaks with high signal ratio , 2016 .

[105]  S. Steenstrup,et al.  A simple procedure for fitting a backgound to a certain class of measured spectra , 1981 .