首页> 外文期刊>Journal of Applied Geophysics >Calculation of the magnetic gradient tensor from total magnetic anomaly field based on regularized method in frequency domain
【24h】

Calculation of the magnetic gradient tensor from total magnetic anomaly field based on regularized method in frequency domain

机译:基于频域正则化方法的总磁场异常磁场梯度张量计算

获取原文
获取原文并翻译 | 示例
           

摘要

To obtain accurate magnetic gradient tensor data, a fast and robust calculation method based on regularized method in frequency domain was proposed. Using the potential field theory, the transform formula in frequency domain was deduced in order to calculate the magnetic gradient tensor from the pre-existing total magnetic anomaly data. By analyzing the filter characteristics of the Vertical vector transform operator (VVTO) and Gradient tensor transform operator (GTTO), we proved that the conventional transform process was unstable which would zoom in the high-frequency part of the data in which measuring noise locate. Due to the existing unstable problem that led to a low signal-to-noise (SNR) for the calculated result, we introduced regularized method in this paper. By selecting the optimum regularization parameters of different transform phases using the C-norm approach, the high frequency noise was restrained and the SNR was improved effectively. Numerical analysis demonstrates that most value and characteristics of the calculated data by the proposed method compare favorably with reference magnetic gradient tensor data. In addition, calculated magnetic gradient tensor components form real aeromagnetic survey provided better resolution of the magnetic sources and original profile. (C) 2016 Elsevier B.V. All rights reserved.
机译:为了获得准确的磁梯度张量数据,提出了一种基于正则化方法的频域快速鲁棒计算方法。利用势场理论,推导了频域的变换公式,以便根据预先存在的总磁异常数据计算出磁梯度张量。通过分析垂直矢量变换算子(VVTO)和梯度张量变换算子(GTTO)的滤波器特性,我们证明了常规变换过程是不稳定的,它将放大测量噪声所在数据的高频部分。由于存在不稳定的问题,导致计算结果的信噪比(SNR)较低,因此本文引入了正则化方法。通过使用C范数方法选择不同变换相位的最佳正则化参数,可以抑制高频噪声并有效改善SNR。数值分析表明,所提方法计算出的数据的大多数值和特征与参考磁梯度张量数据相比具有优势。另外,计算出的磁梯度张量分量构成了实际的航空磁测量,从而提供了更好的磁源分辨率和原始轮廓。 (C)2016 Elsevier B.V.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号