Separation and Interpretation of Gravity Field Data Based on Two Dimensional Normal Space-Scale Transform (NSST2D) Algorithm: A Case Study of Kauring Airborne Gravity Test Site, Western Australia
Separation and Interpretation of Gravity Field Data Based on Two Dimensional Normal Space-Scale Transform (NSST2D) Algorithm: A Case Study of Kauring Airborne Gravity Test Site, Western Australia
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基于二维正态空间尺度变换（NSST2D）算法的重力场数据分离与解释——以西澳大利亚考林机载重力试验场为例
DOI：
10.1007/s00024-019-02131-5
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发表时间：
2019-02
影响因子：
2
通讯作者：
Li Hong Lei
中科院分区：
文献类型：
--
作者：
Liu Jin Zhao;Li Hong Lei
The gravity field signal observed on the surface of the earth is the superposition of the gravitational effects produced mainly by the mass sources at different locations and depths from the surface down to the underground. Reliable geophysical interpretation of gravity anomalies is highly dependent on the validity of the data processing methods and the rationality of the calculated results. This paper proposes a novel potential field separation method based on two-dimensional Normal Space-Scale Transform (NSST2D) algorithm to implement the residual-regional field separation. Compared with the two dimensional Discrete Wavelet Transform (DWT2D) method often appeared in the previous potential field separation studies, the NSST2D algorithm can keep the shape of the separated field from distortion by normalizing the conventional two-dimensional wavelet transform kernels. Based on gravity field of synthetic models, the separated residual-regional fields respectively derived from DWT2D with five different wavelet bases and those from NSST2D are compared. The experimental results verify the effectiveness and superiority of the proposed NSST2D algorithm. Finally, the NSST2D algorithm is also applied to the separation of the complete Bouguer gravity anomaly in the Kauring airborne gravity test site, Western Australia. The results show that the characteristics of the separated field from the corresponding mass sources with different depths maintain relatively good consistency with the feature of the three-dimensional inversion density contrast distribution.
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影响因子：
3.3
作者：
R. Pawlowski;R. O. Hansen
通讯作者：
R. Pawlowski;R. O. Hansen
DOI：
10.1016/j.cageo.2005.10.012
发表时间：
2006-08
期刊：
Comput. Geosci.
影响因子：
--
作者：
G. Cooper
通讯作者：
G. Cooper
影响因子：
3.3
作者：
W. Dean
通讯作者：
W. Dean
DOI：
10.1016/b978-0-12-466606-1.x5000-4
发表时间：
1998
期刊：
--
影响因子：
--
作者：
S. Mallat
通讯作者：
S. Mallat
影响因子：
2.3
作者：
G. Strykowski;F. Boschetti;G. Papp
通讯作者：
G. Strykowski;F. Boschetti;G. Papp