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

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.

Author(s):  
Kha Van Tran ◽  
Trung Nhu Nguyen

Summary Downward continuation is a very useful technique in the interpretation of potential field data. It would enhance the short wavelength of the gravity anomalies or accentuate the details of the source distribution. Taylor series expansion method has been proposed to be one of the best downward continued methods. However, the method using high-order vertical derivatives leads to low accuracy and instability results in many cases. In this paper, we propose a new method using a combination of Taylor series expansion and upward continuation for computing vertical derivatives. This method has been tested on the gravitational anomaly of infinite horizontal cylinder in both cases with and without random noise for higher accurate and stable than Hilbert transform method and Laplace equation method, especially in the case of noise input data. This vertical derivative method is applied successfully to calculate the downward continuation according to Taylor series expansion method. The downward continuation is also tested on both complex synthetic models and real data in the East Vietnam Sea (South China Sea). The results reveal that by calculating this new vertical derivative, the downward continuation method gave higher accurate and stable than the previous downward continuation methods.


Geophysics ◽  
2021 ◽  
pp. 1-46
Author(s):  
Tao Chen ◽  
Dikun Yang

Data interpolation is critical in the analysis of geophysical data when some data is missing or inaccessible. We propose to interpolate irregular or missing potential field data using the relation between adjacent data points inspired by the Taylor series expansion (TSE). The TSE method first finds the derivatives of a given point near the query point using data from neighboring points, and then uses the Taylor series to obtain the value at the query point. The TSE method works by extracting local features represented as derivatives from the original data for interpolation in the area of data vacancy. Compared with other interpolation methods, the TSE provides a complete description of potential field data. Specifically, the remainder in TSE can measure local fitting errors and help obtain accurate results. Implementation of the TSE method involves two critical parameters – the order of the Taylor series and the number of neighbors used in the calculation of derivatives. We have found that the first parameter must be carefully chosen to balance between the accuracy and numerical stability when data contains noise. The second parameter can help us build an over-determined system for improved robustness against noise. Methods of selecting neighbors around the given point using an azimuthally uniform distribution or the nearest-distance principle are also presented. The proposed approach is first illustrated by a synthetic gravity dataset from a single survey line, then is generalized to the case over a survey grid. In both numerical experiments, the TSE method has demonstrated an improved interpolation accuracy in comparison with the minimum curvature method. Finally we apply the TSE method to a ground gravity dataset from the Abitibi Greenstone Belt, Canada, and an airborne gravity dataset from the Vinton Dome, Louisiana, USA.


2013 ◽  
Vol 98 ◽  
pp. 205-211 ◽  
Author(s):  
Guoqing Ma ◽  
Cai Liu ◽  
Danian Huang ◽  
Lili Li

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

2003 ◽  
Vol 34 (4) ◽  
pp. 249-256 ◽  
Author(s):  
Herve Trompat ◽  
Fabio Boschetti ◽  
Peter Hornby

2016 ◽  
Vol 126 ◽  
pp. 74-86 ◽  
Author(s):  
Yile Zhang ◽  
Yau Shu Wong ◽  
Yuanfang Lin

Author(s):  
Shaole An ◽  
Kefa Zhou ◽  
Jinlin Wang ◽  
Wenqiang Xu ◽  
Bingqiang Yuan ◽  
...  

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