Downward Continuation of Potential Field Data Based on Chebyshev–Padé Approximation Function

2017 ◽  
Vol 175 (1) ◽  
pp. 275-286 ◽  
Author(s):  
Wenna Zhou ◽  
Jiyan Li ◽  
Yuan Yuan
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Padé Approximation ◽  
Downward Continuation ◽  
Pade Approximation ◽  
Potential Field Data ◽  
Approximation Function

A stable iterative downward continuation of potential field data

2013 ◽  
Vol 98 ◽  
pp. 205-211 ◽  
Author(s):  
Guoqing Ma ◽  
Cai Liu ◽  
Danian Huang ◽  
Lili Li
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Downward Continuation ◽  
Potential Field Data

An improved regularized downward continuation of potential field data

2014 ◽  
Vol 106 ◽  
pp. 114-118 ◽  
Author(s):  
Xiaoniu Zeng ◽  
Daizhi Liu ◽  
Xihai Li ◽  
Dingxin Chen ◽  
Chao Niu
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Downward Continuation ◽  
Potential Field Data

scholarly journals Improved Downward Continuation of Potential Field Data

2003 ◽  
Vol 34 (4) ◽  
pp. 249-256 ◽  
Author(s):  
Herve Trompat ◽  
Fabio Boschetti ◽  
Peter Hornby
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Downward Continuation ◽  
Potential Field Data

BTTB–RRCG method for downward continuation of potential field data

2016 ◽  
Vol 126 ◽  
pp. 74-86 ◽  
Author(s):  
Yile Zhang ◽  
Yau Shu Wong ◽  
Yuanfang Lin
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Downward Continuation ◽  
Potential Field Data

A Stable Downward Continuation of Potential Field Data: A Case of Study of the Kalatag Polymetallic District, NW China

2021 ◽  
Author(s):  
Shaole An ◽  
Kefa Zhou ◽  
Jinlin Wang ◽  
Wenqiang Xu ◽  
Bingqiang Yuan ◽  
...  
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Downward Continuation ◽  
Potential Field Data ◽  
Nw China

An adaptive iterative method for downward continuation of potential-field data from a horizontal plane

Geophysics ◽  
2013 ◽  
Vol 78 (4) ◽  
pp. J43-J52 ◽  
Author(s):  
Xiaoniu Zeng ◽  
Xihai Li ◽  
Juan Su ◽  
Daizhi Liu ◽  
Hongxing Zou
Keyword(s):  
Iterative Method ◽  
Tikhonov Regularization ◽  
Potential Field ◽  
Horizontal Plane ◽  
Field Data ◽  
Regularization Parameter ◽  
Downward Continuation ◽  
Tikhonov Regularization Method ◽  
Potential Field Data ◽  
Wavenumber Domain

We have developed an improved adaptive iterative method based on the nonstationary iterative Tikhonov regularization method for performing a downward continuation of the potential-field data from a horizontal plane. Our method uses the Tikhonov regularization result as initial value and has an incremental geometric choice of the regularization parameter. We compared our method with previous methods (Tikhonov regularization, Landweber iteration, and integral-iteration method). The downward-continuation performance of these methods in spatial and wavenumber domains were compared with the aspects of their iterative schemes, filter functions, and downward-continuation operators. Applications to synthetic gravity and real aeromagnetic data showed that our iterative method yields a better downward continuation of the data than other methods. Our method shows fast computation times and a stable convergence. In addition, the [Formula: see text]-curve criterion for choosing the regularization parameter is expressed here in the wavenumber domain and used to speed up computations and to adapt the wavenumber-domain iterative method.

A Constrained Scheme for High Precision Downward Continuation of Potential Field Data

2018 ◽  
Vol 175 (10) ◽  
pp. 3511-3523 ◽  
Author(s):  
Jun Wang ◽  
Xiaohong Meng ◽  
Zhiwen Zhou
Keyword(s):  
High Precision ◽  
Potential Field ◽  
Field Data ◽  
Downward Continuation ◽  
Potential Field Data

An improved and stable downward continuation of potential field data: The truncated Taylor series iterative downward continuation method

Geophysics ◽  
2013 ◽  
Vol 78 (5) ◽  
pp. J75-J86 ◽  
Author(s):  
HengLei Zhang ◽  
Dhananjay Ravat ◽  
XiangYun Hu
Keyword(s):  
Taylor Series ◽  
Potential Field ◽  
Field Data ◽  
Memory Performance ◽  
Continuation Method ◽  
Taylor Series Expansion ◽  
Downward Continuation ◽  
Continuation Methods ◽  
Potential Field Data ◽  
The Difference

We present a stable downward continuation strategy based on combining the ideas of the Taylor series expansion and the iterative downward continuation methods in a single method with better downward continuation and/or computer time/memory performance for potential field data containing noise. In the new truncated Taylor series iterative downward continuation (TTSIDC) method, a correction is made on the continuing plane by downward continuing the difference between the observed and the calculated field. The process is iteratively repeated until the difference meets the convergence conditions. It is tested on synthetic and field data and compared to other downward continuation methods. The proposed method yields sharper images and estimates more accurate amplitudes than most of the existing methods, especially for downward continuation over larger distances. The TTSIDC method also gives comparable results to the method of downward continuation using the least-squares inversion (DCLSI); however, the DCLSI method’s requirements of computer memory and time are substantially greater than our TTSIDC method, rendering the DCLSI method impractical for data sets of routine size on desktop computers commonly available today.

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