| 基于灰色判别准则和有理函数滤波的伪随机电磁数据去噪 |
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| 引用本文： | 陈超健,蒋奇云,莫丹,李广,周峰.基于灰色判别准则和有理函数滤波的伪随机电磁数据去噪[J].地球物理学报,2019,62(10):3854-3865. |
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| 作者姓名： | 陈超健 蒋奇云 莫丹 李广 周峰 |
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| 作者单位： | 1. 中南大学地球科学与信息物理学院, 长沙 410083;2. 有色金属成矿预测与地质环境监测教育部重点实验室(中南大学), 长沙 410083;3. 核资源与环境国家重点实验室, 东华理工大学, 南昌 330013 |
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| 基金项目： | 国家高技术研究发展计划（863计划）（2014AA06A602），国家重点研发计划（2018YFC0603202），国家自然科学基金（41904076）和国家自然科学基金国家重大科研仪器设备研制专项（41227803）联合资助. |
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| 摘 要： | 为压制伪随机多频电磁信号中的强干扰、提高数据质量，本文提出一种基于灰色判别准则和有理函数滤波的数据处理方法.首先通过灰色判别准则剔除各个频点频谱数据中的明显异常值，然后进行有理函数滤波得到充分接近真实值的圆滑数据曲线，进而约束数据的二次优化处理，剔除残余噪声的影响.为验证本文方法的处理效果，在实测无明显噪声数据中加入几种不同类型的强噪声，然后用本文方法进行处理.仿真处理结果表明，本文方法能高精度逼近原始数据，处理后数据误差低达8.09%.最后，将本文方法应用于重庆某工区实测伪随机多频电磁数据处理.结果表明，本文方法可以有效压制干扰，在频谱数据个数少、干扰幅值大（高达有效信号幅值的几个数量级）的情况下，仍可有效压制强干扰.处理后的数据相对误差显著下降，视电阻率曲线形态平滑，达到提高信噪比，改善实测数据质量的目的.
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| 关 键 词： | 伪随机多频电磁 强干扰压制 灰色判别准则 有理函数滤波 |
| 收稿时间： | 2018-07-05 |
De-noising pseudo-random electromagnetic data using gray judgment criterion and rational function filtering |
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| CHEN ChaoJian,JIANG QiYun,MO Dan,LI Guang,ZHOU Feng.De-noising pseudo-random electromagnetic data using gray judgment criterion and rational function filtering[J].Chinese Journal of Geophysics,2019,62(10):3854-3865. |
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| Authors: | CHEN ChaoJian JIANG QiYun MO Dan LI Guang ZHOU Feng |
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| Institution: | 1. School of Geosciences and Info-Physics, Central South University, Changsha 410083, China;2. Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring(Central South University), Changsha 410083, China;3. State Key Laboratory of Nuclear Resources and Environment, East China University of Technology, Nanchang 330013, China |
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| Abstract: | We present an adaptive data processing approach mainly based on gray judgment criterion and rational function filtering to suppress strong interference and improve data quality in pseudo-rand multi-frequency electromagnetic method. Firstly, the data with large noise, whose value is bigger or smaller than normal data, can be removed by the gray judgment criterion. Then, the rational function filtering method is applied to filter the remaining data. Furthermore, to eliminate residual noise influence, a smooth curve which closes to the true data is applied to constrain quadratic optimization. Finally, the so-called "pure anomalies" are obtained. To verify our proposed data processing method, firstly the measured data without obvious noises are tested. Different kinds of strong interferences are added into the measured data and our method is adopted to process these signals. Results show that our method can hold down the effects originating from strong noises well, with the relative error being only 8.09%. In addition, we apply the proposed de-noising approach to process the field data with noises acquired in Chongqing. After processing, the relative error of the data decreases, apparent resistivity curve becomes smooth, as well as the SNR increases, which imply that that our method can be well used to eliminate noises and improve the quality of the measured data. |
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| Keywords: | Pseudo-rand multi-frequency electromagnetic Strong interference suppression Gray judgment criterion Rational function filtering |
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