基于小波变换的航空γ能谱异常信息线单元校正方法

孙坤; 熊超; 葛良全; 张庆贤; 何庆驹; 王猛; 谷懿

doi:10.11889/j.0253-3219.2018.hjs.41.100501

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基于小波变换的航空γ能谱异常信息线单元校正方法

核物理、交叉学科研究 | 更新时间：2021-05-19

- 基于小波变换的航空γ能谱异常信息线单元校正方法
- Line unit correction method of airborne gamma-ray spectrum anomaly information based on wavelet transform
- 核技术 2018年41卷第10期文章编号：100501
- 作者机构：
  
  1.成都理工大学核技术与自动化工程学院成都 610059
  2.电子科技大学资源与环境学院成都 611731
- 作者简介：
  
  [ "孙坤, 男, 1993年出生, 2016年毕业于东华理工大学, 现为硕士研究生, 从事航空伽马能谱数据处理研究", "SUN Kun, male, born in 1993, graduated from East China University of Science and Technology in 2016, master student, focusing on data processing of airborne gamma spectrometry" ]
  葛良全, E-mail:glq@cdut.edu.cn GE Liangquan, E-mail:glq@cdut.edu.cn
- 基金信息：
  
  国家自然科学基金(41774147);国家重点研发计划(2017YFC0602105);National Natural Science Foundation of China(41774190);四川省教育厅科研项目(16ZA0085)
- DOI：10.11889/j.0253-3219.2018.hjs.41.100501
  中图分类号： TL84
- 收稿日期：2018-06-20，
  
  修回日期：2018-07-11，
  
  纸质出版日期：2018-10-10
- 稿件说明：
移动端阅览
孙坤, 熊超, 葛良全, 等. 基于小波变换的航空γ能谱异常信息线单元校正方法[J]. 核技术, 2018,41(10):100501

Kun SUN, Chao XIONG, Liangquan GE, et al. Line unit correction method of airborne gamma-ray spectrum anomaly information based on wavelet transform[J]. Nuclear techniques, 2018, 41(10): 100501
孙坤, 熊超, 葛良全, 等. 基于小波变换的航空γ能谱异常信息线单元校正方法[J]. 核技术, 2018,41(10):100501 DOI： 10.11889/j.0253-3219.2018.hjs.41.100501.

Kun SUN, Chao XIONG, Liangquan GE, et al. Line unit correction method of airborne gamma-ray spectrum anomaly information based on wavelet transform[J]. Nuclear techniques, 2018, 41(10): 100501 DOI： 10.11889/j.0253-3219.2018.hjs.41.100501.

摘要

针对航空γ能谱测量工作时矿致异常信息在时域批次性干扰下难以有效提取的问题，结合小波变换理论与测线标准差变异系数理论，提出一种新的线单元校正方法。该方法利用小波变换擅长处理非稳态采样数据信号的特性，采用小波变换对线单元数据进行多尺度分解，通过软阈值滤除近似信号，有效去除含有时域批次性干扰的非地质背景信息，并对表征异常的细节信息进行重构，实现对时域批次性干扰的线单元校正。通过对实测数据的校正处理验证，结合航空伽马能谱地面异常查证结果及矿区地质简图，表明该方法削弱时域批次性效果明显，缩小了异常范围，提高了矿致异常点识别准确性，且异常形态与地面查证结果一致，客观还原测区的放射性核素分布情况。

Abstract

Background

Due to different measuring time

the measured data may fluctuate abnormally. Therefore

it is not easy to extract anomalous information caused by ores effectively in airborne gamma spectrometry.

Purpose

The aim was to reduce the impact caused by different measurement time

low background field

highlight anomalies

and help in guiding for metallogenic prognosis.

Methods

Since the wavelet transform is good at dealing with unsteady sampled data signals

this method used wavelet transform to perform multi-scale decomposition on the data of each measurement line unit. According to the coefficient of variation

the wavelet basis function was selected to transform the measured data

and the approximate signal was filtered by soft threshold to reconstruct the detail signal.

Results

After the approximate signals were filtered by soft thresholds

the non-geological background information containing data influenced by different measurement time can be effectively removed

and the detail information can be reconstructed to reduce the impact caused by different measurement time.

Conclusion

Through verifying the corrected measured data

combined with the ground anomaly inspection result of airborne gamma spectrum and the geological map of the mining area

the result shows that this method can significantly weaken the error caused by time domain batches

narrow the anomaly ranges

and improve the recognition accuracy of ore-causing anomaly points. Moreover

the shape of anomalies is consistent with the ground anomaly inspection result and the distribution of radionuclides can be restored in the survey area.

关键词

Keywords

references

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