势场数据的经验模态分解：以航空重力数据为例(含外文出处)

来源：wenku7.com 资料编号：WK78754 资料等级：★★★★★ %E8%B5%84%E6%96%99%E7%BC%96%E5%8F%B7%EF%BC%9AWK78754

以下是资料介绍,如需要完整的请充值下载。
1.无需注册登录,支付后按照提示操作即可获取该资料.
2.资料以网页介绍的为准,下载后不会有水印.资料仅供学习参考之用. 密保惠帮助

资料介绍

势场数据的经验模态分解：以航空重力数据为例(含外文出处)(中文1400字,英文1200字)
外文出处：SEG 72th Annual Meeting Expanded
Abstract, 2005: 704-706
附件： 1.外文资料翻译译文；2.外文原文。
摘要：
在这篇论文中，我们介绍一种新的方法来作为基于傅立叶小波变换技术的替代品用于处理势场数据。这种方法被称做经验模态分解（EMD），它是由美国航空暨太空总署太空飞行中心的Dr. Norden E. Huang提出的。这种经验模态分解不同于傅立叶小波变换，它主要用于处理非线形非平稳的信号。
因此，我们设计了一项工作，用在加拿大Alberta丘陵地区的Turner 山谷上空的航空重力数据来测试这一处理势场数据的方法。

第二部分：外文资料原文

Empirical Mode Decomposition (EMD) of potential field data: airborne gravity data as an example
Hassan H. Hassan, GEDCO

Summary:
In this paper, we introduce a newly developed method to process potential field data as an alternative to Fourier and wavelet based techniques. This new method is called Empirical Mode Decomposition (EMD) and was developed by Dr. Norden E. Huang at the NASA Goddard Space Flight Center (Huang et al. 1998). The EMD method is different from Fourier and wavelet transforms because it handles nonlinear and nonstationary signals.
The Fourier transform (FFT) is designed to work with linear and stationary signals. The wavelet transform, on the other hand, is well-suited to handle non-stationary data but poor at processing nonlinear data. Since potential field data are in general nonlinear and non-stationary in nature we expect limitations in processing the data using FFT or wavelet methods.This work is therefore designed to test this new
technique on potential field data using airborne gravity over the Turner Valley area in the foothills of Alberta, Canada.