Nuclear Science and Techniques ›› 2019, Vol. 42 ›› Issue (1): 10201-010201.doi: 10.11889/j.0253-3219.2019.hjs.42.010201

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A method for the identification of ore-caused anomalies information in the airborne γ-ray spectrum based on fractal filtering by layers

Chao XIONG1,Kun SUN2,Liangquan GE2,Qingxian ZHANG2,Yi GU2,Qing ZHAO1()   

  1. 1. School of Resources and Environment, University of Electronic Science and Technology, Chengdu 611731, China
    2. The College of Nuclear Techonology and Autornation Engineering, Chengdu University of Techonglogy, Chengdu 610059, China
  • Received:2018-09-26 Revised:2018-11-05 Online:2019-01-10 Published:2019-01-25
  • Contact: Qing ZHAO E-mail:zhaoq@uestc.edu.cn
  • Supported by:
    Supported by National Natural Science Foundation of China (No.41774147, No.41774190), National Key Research and Development Plan (No.2017YFC0602105), Scientific Research Fund of Sichuan Provincial Education Department (No.16ZA0085)

Abstract: Background

Identification method on ore-caused anomalies information is one of the core technologies of airborne γ-ray spectrum data post-processing. However, the current ore-caused anomalies information identification methods in airborne γ-ray spectrum are generally based on statistical analysis while the frequency scales are ignored.

Purpose

This study aims to propose an anomaly identification method on the frequency scale to discover the structural features of complex systems and identify anomalies.

Methods

Based on multi-fractal theory, the power spectrum of the nuclides specific activity data of the airborne γ-ray spectrum under frequency domain was analyzed. The cut-off frequency of the power spectrum by the multi-fractal theory was quantitatively calculated. The 1:50 000 airborne γ-ray spectrum trial production flight data of a certain area in Inner Mongolia (the surveying area contains proved metal ore occurrences) to verify the method, and the original data was filtered by layers to determine and identify the optimal frequency band of the ore-caused anomalies information.

Results

Through filtering the power spectrum data of original signals in the method of fractal filtering by layers, the signals of the ore-caused anomalies information were found in each fractal frequency band. The results show that background noises in the frequency band off 2~f 3(ie 0.308 01~0.406 15 Hz) can be effectively separated, and ore-caused anomalies of known ore occurrences can be identified and band-like false anomalies caused by time domain batches can be removed.

Conclusion

Due to the false anomaly signals caused by time domain batches only exists in the middle and low frequency bands, the band-pass filtering of medium-high frequency band in fractal filtering by layers proposed in this paper can be used to separate and remove them.

Key words: Airborne γ-ray spectrum, Filtering by layers, Multi-fractal, Anomalies identification

CLC Number: 

  • TL84