正则化方法在比值类位场边缘识别方法中的研究

doi:10.11720/wtyht.2019.1112

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Abstract

The ratio methods are one sort of edge recognition methods by using potential field, which contains the Normalized Standard Deviation method (NSTD), Tilt Angle method (Ta) and Theta Map (cosθ). If the denominator of ratio method closes or even equals to zero in the process of calculation, the result obtaining from ratio methods is unstable and may even bear little resemblance to the true geology. In order to relief this problem, a regularization factor, which is greater than zero, is added in the denominator of the ratio methods’ formula, which not only enhances the numerical stability of the ratio methods but also improves the accuracy of some ratio edge recognition methods. The stability, accuracy and effectiveness of the new method is verified by testing synthetic models and calculating real data. Also, the introduction of the regularization factor also can improve the effect of recognizing edge by the second-derivative edge recognition methods, which is based on the ratio methods, such as the Total Horizontal Derivative of the Tilt Angle (Ta-THDR). The idea of regularization can not only solve the numerical calculation problem of the ratio methods of edge recognition for potential field, but also solve the numerical calculation problem of all ratio methods.

Key words： edge recognition ratio methods regularization method stability accuracy

Received: 03 December 2018 Published: 15 April 2019

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	Tao HE
	Wan-Yin WANG
	Jin-Ming HUANG
	Ming-Hua ZHANG
	Min YANG

Cite this article:

Tao HE,Wan-Yin WANG,Jin-Ming HUANG, et al. The research of the regularization method in the ratio methods of edge recognition by potential field[J]. Geophysical and Geochemical Exploration, 2019, 43(2): 308-319.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2019.1112 OR https://www.wutanyuhuatan.com/EN/Y2019/V43/I2/308

The parameters of models

Gravity anomaly of model A

The ratio methods edge recognition results comparison map based on model A
a—Ta(α=0);b—R-Ta(α=0.2);c—R-Ta(α=0.5);d—cosθ(α=0);e—R-cosθ(α=0.2);f—R-cosθ(α=0.5);g—NSTD(α=0);h—R-NSTD(α=0.2);i—R-NSTD(α=0.5)

The Ta-THDR method edge recognition results comparison map based on model A
a—Ta-THDR(α=0);b—R-Ta-THDR(α=0.2);c—R-Ta-THDR(α=0.5)

Gravity anomaly of model B

The ratio methods edge recognition results comparison map based on model B
a—Ta;b—R-Ta;c—cosθ;d—R-cosθ;e—NSTD;f—R-NSTD

The Ta-THDR method edge recognition results comparison map based on model B
a—Ta-THDR;b—R-Ta-THDR

Gravity anomaly of model C

The ratio methods edge recognition results comparison map based on model C
a—Ta;b—R-Ta;c—cosθ;d—R-cosθ;e—NSTD;f—R-NSTD

The Ta-THDR method edge recognition results comparison map based on model C
a—Ta-THDR;b—R-Ta-THDR

Magnetic anomaly and RTP magnetic anomaly map
a—magnetic anomaly map;b—RTP magnetic anomaly map

The ratio methods edge recognition results comparison map based on model D
a—Ta;b—R-Ta;c—cosθ;d—R-cosθ;e—NSTD;f—R-NSTD

The Ta-THDR method edge recognition results comparison map based on model D
a—Ta-THDR;b—R-Ta-THDR

Gravity anomaly of model E

The ratio methods edge recognition results comparison map based on model E
a—Ta;b—R-Ta;c—cosθ;d—R-cosθ;e—NSTD;f—R-NSTD

The Ta-THDR method edge recognition results comparison map based on model E
a—Ta-THDR;b—R-Ta-THDR

Real gravity anomaly map

The ratio methods edge recognition results comparison map based on real dataa—Ta;b—R-Ta;c—cosθ;d—R-cosθ;e—NSTD;f—R-NSTD

The Ta-THDR method edge recognition results comparison map based on real dataa—Ta-THDR;b—R-Ta-THDR

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