| 基于牛顿迭代法和遗传算法的CSAMT近场校正 |
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| 引用本文： | 栾晓东,底青云,雷达.基于牛顿迭代法和遗传算法的CSAMT近场校正[J].地球物理学报,2018,61(10):4148-4159. |
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| 作者姓名： | 栾晓东 底青云 雷达 |
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| 作者单位： | 1. 中国科学院页岩气与地质工程重点实验室, 中国科学院地质与地球物理研究所, 北京 100029;2. 中国科学院地球科学研究院, 北京 100029;3. 中国科学院大学, 北京 100049 |
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| 基金项目： | 中国科学院战略性先导科技专项（A类）课题"系统集成优化及现场试验"（XDA14050100），国家重大科研装备研制项目"深部资源探测核心装备研发"-05子项目"多通道大功率电法勘探仪"（ZDYZ2012-1-05）资助. |
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| 摘 要： | 由于可控源音频大地电磁法（CSAMT）采用人工场源在大大增加信号强度的同时也带来了在近区产生非平面波效应的问题，其表现特征为在双对数坐标中卡尼亚电阻率曲线呈45°上升，即所谓的近场效应.本文首先提出了基于牛顿迭代法求解视电阻率方程的近场效应校正方法，通过对二层、三层理论模型试算验证了该方法的有效性；为进一步增强对噪声的压制能力，本文进一步引入了遗传反演的思想，将全频段误差最小作为目标函数，采用自适应正则化方法引入模型参数梯度最小作为稳定因子，由遗传算法求解得到校正后视电阻率，通过对理论模拟数据加随机噪声的校正结果，表明该方法在实现近场校正的同时能剔除噪声干扰，得到的结果与理论模拟曲线吻合度较高.通过对内蒙古曹四夭钼矿区实测CSAMT数据处理结果表明，牛顿法和遗传算法均能明显校正CSAMT因近场效应引起的假高阻异常，能更好地反映地电结构特征.
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| 关 键 词： | 牛顿迭代法 遗传算法 近场效应 CSAMT |
| 收稿时间： | 2017-04-17 |
Near-field correction of CSAMT data based on Newton iteration method and GA method |
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| LUAN XiaoDong,DI QingYun,LEI Da.Near-field correction of CSAMT data based on Newton iteration method and GA method[J].Chinese Journal of Geophysics,2018,61(10):4148-4159. |
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| Authors: | LUAN XiaoDong DI QingYun LEI Da |
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| Institution: | 1. Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China;2. Institution of Earth Science, Chinese Academy of Sciences, Beijing 100029, China;3. University of Chinese Academy of Sciences, Beijing 100049, China |
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| Abstract: | Artificial source used in CSAMT studies has greatly enhanced the high frequency signal of the audio frequency bandwidth used in subsurface prospecting. On the other hand, it causes non-planar wave, known as near-field effect in CSAMT Cagniard apparent resistivity sounding curves. This is observed as a 45° inclination in low-frequency zone on bi-logarithmic plot of subsurface apparent resistivity. Hence, a method to correct for the CSAMT near-field effect based on Newton iteration method was first established. First, we derived an equation to calculate apparent resistivity from CSAMT data and solved it using Newton method to obtain corrected apparent resistivity. The accuracy of this method was validated using two-layer and three-layer theoretical models. In order to suppress both near-field effect and noise in data concurrently, we further introduced genetic algorithm (GA) inversion to the correction of CSAMT near-field by proposing an adaptive regularization factor to minimize the objective function, which is composed of whole-frequency error and model parameter gradient. The GA method was tested with a noisy data for effective removal of the near-field effect and suppression of noise in the data. When applied to real CSAMT data from Inner Mongolia, Newton iteration method and GA method were able to correct near-field effect effectively, thus displaying the accurate subsurface geo-electric structure. |
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| Keywords: | Newton iteration method GA method Near-field effect CSAMT |
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