| 基于匹配追踪和遗传算法的大地电磁噪声压制 |
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| 引用本文： | 李晋,燕欢,汤井田,张贤,李广,朱会杰.基于匹配追踪和遗传算法的大地电磁噪声压制[J].地球物理学报,2018,61(7):3086-3101. |
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| 作者姓名： | 李晋 燕欢 汤井田 张贤 李广 朱会杰 |
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| 作者单位： | 1. 湖南师范大学信息科学与工程学院, 长沙 410081;2. 中南大学有色金属成矿预测与地质环境监测教育部重点实验室, 地球科学与信息物理学院, 长沙 410083;3. 陆军工程兵科研一所, 江苏 无锡 214035 |
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| 基金项目： | 国家自然科学基金（41404111），国家863计划（2014AA06A602），国家科技专项（SinoProbe-03），湖南省自然科学基金（2018JJ2258）和湖南省研究生科研创新项目（CX2017B224）联合资助. |
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| 摘 要： | 针对匹配追踪计算量大、大地电磁数据处理效率低的问题，提出基于匹配追踪和遗传算法的大地电磁噪声压制方法.首先，利用Gabor原子构建过完备原子库，并对过完备原子库集合进行划分.然后，借助遗传算法的自适应性，快速搜寻最优匹配原子及所在位置.最后，运用最优匹配原子对待处理信号进行稀疏分解，重构有用信号.通过对计算机模拟的典型强干扰和矿集区实测大地电磁数据进行分析处理，实验结果表明，相对于匹配追踪和正交匹配追踪，文中所提方法能从过完备原子库中快速、自适应地选取最优匹配原子与不同噪声干扰类型高精度的匹配，极大地提升了计算效率；大地电磁时间域序列中的大尺度强干扰被有效剔除，视电阻率曲线更为光滑、连续，低频段的数据质量得到明显改善.
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| 关 键 词： | 大地电磁 匹配追踪 遗传算法 噪声压制 |
| 收稿时间： | 2017-04-18 |
Magnetotelluric noise suppression based on matching pursuit and genetic algorithm |
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| LI Jin,YAN Huan,TANG JingTian,ZHANG Xian,LI Guang,ZHU HuiJie.Magnetotelluric noise suppression based on matching pursuit and genetic algorithm[J].Chinese Journal of Geophysics,2018,61(7):3086-3101. |
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| Authors: | LI Jin YAN Huan TANG JingTian ZHANG Xian LI Guang ZHU HuiJie |
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| Institution: | 1. College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China;2. School of Geosciences and Info-Physics, Key Laboratory of Metallogenic Prediction of Non-Ferrous Metals and Geological Environment Monitor, Ministry of Education, Central South University, Changsha 410083, China;3. The First Engineering Scientific Research Institute of Army Corps, Wuxi Jiangsu 214035, China |
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| Abstract: | In order to solve the problem that matching pursuit has large amount of calculation and magnetotelluric data processing is low efficiency, a new method for magnetotelluric noise suppression based on matching pursuit and genetic algorithm is proposed in this paper. First of all, we use Gabor atom to build an over-complete dictionary, and divide the over-complete dictionary collection. Then, we use the adaptability of genetic algorithm to search the optimal matching atom and the location. Finally, we use the optimal matching atom to sparse decompose the signal to be processed and reconstruct the useful signal. Through analysis and processing for the typical strong interference simulated by computer and the measured magnetotelluric data of ore concentration area, we get some important conclusions as follows. Compared with matching pursuit and orthogonal matching pursuit, the proposed method can select the optimal matching atoms from the over-complete dictionary quickly and adaptively. At the same time, the method ensures that the optimal matching atoms with different kinds of noise interference have high precision matching, which greatly improves the calculation efficiency. After processing by the proposed method, the large-scale strong interference of magnetotelluric data in time domain sequence is effectively removed and the apparent resistivity curve is more smooth and continuous. Moreover, the quality of magnetotelluric data for the low frequency band has been improved significantly. |
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| Keywords: | Magnetotelluric Matching pursuit Genetic algorithm Noise suppression |
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