scholarly journals Potential field continuation: a comparative analysis of three different types of software

1998 ◽  
Vol 41 (3) ◽  
Author(s):  
M. Ciminale ◽  
M. Loddo

DARING.F is a new Fortran77 computer program which has been developed to perform the continuation of potential field data between arbitrary surfaces. The implemented equivalent source algorithm inverts a system of linear equations by using sparse matrix. A comparative analysis between the performance of this software and that of two computer programs (named UPWARD.F and UPNEW.F) previously written by the same authors is carried out. As a result of this analysis, some useful and important suggestions on how to obtain the highest level of accuracy of transformed data in various situations (i.e. large matrices of data, close surfaces) are given.

Geophysics ◽  
1998 ◽  
Vol 63 (1) ◽  
pp. 104-108 ◽  
Author(s):  
Bruno Meurers ◽  
Roland Pail

Xia et al. (1993) offer an excellent method for potential‐field continuation between irregular surfaces by applying the equivalent source technique. This method has proven to be the fastest and most stable procedure for solving the problem of reducing potential‐field data to a constant datum (e.g., Pail, 1995) as long as no sources exist between observation surface and the equivalent stratum. The authors suggest using special equations for the continuation of magnetic fields. Theoretically this is correct, but neither necessary nor well suited, because of the characteristics of the operator for magnetic fields applied in the wavenumber domain.


Geophysics ◽  
1993 ◽  
Vol 58 (4) ◽  
pp. 515-523 ◽  
Author(s):  
Jianghai Xia ◽  
Donald R. Sprowl ◽  
Dana Adkins‐Heljeson

The equivalent source concept is used in the wavenumber domain to correct distortions in potential‐field data caused by topographic relief. The equivalent source distribution on a horizontal surface is determined iteratively through forward calculation of the anomaly on the topographic surface. Convergence of the solution is stable and rapid. The accuracy of the Fourier‐based approach is demonstrated by two synthetic examples. For the gravity example, the rms error between the corrected anomaly and the desired anomaly is 0.01 mGal, which is less than 0.5 percent of the maximum synthetic anomaly. For the magnetic example, the rms error is 0.7 nT, which is less than 1 percent of the maximum synthetic anomaly. The efficiency of the approach is shown by application to the gravity and aeromagnetic grids for Kansas. For gravity data, with a maximum elevation change of 500 m reducing to a horizontal datum results in a maximum correction in gravity anomaly amplitude of up to 2.6 mGal. For aeromagnetic data, the method results in a maximum horizontal shift of anomalies of 470 m with a maximum correction in aeromagnetic anomaly amplitudes up to 270 nT.


2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Luan Thanh Pham ◽  
Ozkan Kafadar ◽  
Erdinc Oksum ◽  
Ahmed M. Eldosouky

Geophysics ◽  
2014 ◽  
Vol 79 (1) ◽  
pp. IM1-IM9 ◽  
Author(s):  
Nathan Leon Foks ◽  
Richard Krahenbuhl ◽  
Yaoguo Li

Compressive inversion uses computational algorithms that decrease the time and storage needs of a traditional inverse problem. Most compression approaches focus on the model domain, and very few, other than traditional downsampling focus on the data domain for potential-field applications. To further the compression in the data domain, a direct and practical approach to the adaptive downsampling of potential-field data for large inversion problems has been developed. The approach is formulated to significantly reduce the quantity of data in relatively smooth or quiet regions of the data set, while preserving the signal anomalies that contain the relevant target information. Two major benefits arise from this form of compressive inversion. First, because the approach compresses the problem in the data domain, it can be applied immediately without the addition of, or modification to, existing inversion software. Second, as most industry software use some form of model or sensitivity compression, the addition of this adaptive data sampling creates a complete compressive inversion methodology whereby the reduction of computational cost is achieved simultaneously in the model and data domains. We applied the method to a synthetic magnetic data set and two large field magnetic data sets; however, the method is also applicable to other data types. Our results showed that the relevant model information is maintained after inversion despite using 1%–5% of the data.


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