Potential field data interpretation to detect the parameters of buried geometries by applying a nonlinear least-squares approach

2021 ◽  
Author(s):  
Khalid S. Essa ◽  
Eid. R. Abo-Ezz
Keyword(s):  
Least Squares ◽  
Potential Field ◽  
Field Data ◽  
Nonlinear Least Squares ◽  
Data Interpretation ◽  
Potential Field Data ◽  
Least Squares Approach

The Ischia volcanic island (Southern Italy): Inferences from potential field data interpretation

2009 ◽  
Vol 179 (1-2) ◽  
pp. 69-86 ◽  
Author(s):  
V. Paoletti ◽  
R. Di Maio ◽  
F. Cella ◽  
G. Florio ◽  
K. Motschka ◽  
...  
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Southern Italy ◽  
Data Interpretation ◽  
Volcanic Island ◽  
Potential Field Data

scholarly journals A NEW METHOD OF INTERPRETATION OF SELF‐POTENTIAL FIELD DATA

Geophysics ◽  
1948 ◽  
Vol 13 (4) ◽  
pp. 600-608 ◽  
Author(s):  
L. de Witte
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Field Work ◽  
Data Interpretation ◽  
New Method ◽  
Positive Maximum ◽  
Potential Field Data ◽  
Negative Center ◽  
Location Depth ◽  
Self Potential

In this paper a new, efficient method is worked out for the interpretation of self‐potential field data. Interpretation of location, depth and dip of the ore body is made, using a pattern of equipotential lines. The negative center and the positive maximum of the potential are found and also the so‐called “mid‐value” point. The dip α, can be determined accurately for values between 5° and 85°. The method cannot be used for vertical polarization. The depth and location can be found with relative accuracy for α>10°. The main advantage of this new method is the ease of interpretation and a greater accuracy for the high‐dip angles. It should be stressed that, for correct and accurate interpretation, the positive maximum is as important as the negative center. Therefore, it should be carefully sought during the field work, and mapped to its full extent.

An Augmented Iteratively Re-weighted and Refined Least Squares Method for lp Minimization Problem in Inverse Modeling of Potential Field Magnetic Data

2020 ◽  
Vol 1 (3) ◽  
Author(s):  
Maysam Abedi
Keyword(s):  
Magnetic Field ◽  
Magnetic Susceptibility ◽  
Least Squares ◽  
Minimization Problem ◽  
Potential Field ◽  
Field Data ◽  
Least Squares Method ◽  
Porphyry Copper ◽  
Magnetic Data ◽  
Magnetic Field Data

The presented work examines application of an Augmented Iteratively Re-weighted and Refined Least Squares method (AIRRLS) to construct a 3D magnetic susceptibility property from potential field magnetic anomalies. This algorithm replaces an lp minimization problem by a sequence of weighted linear systems in which the retrieved magnetic susceptibility model is successively converged to an optimum solution, while the regularization parameter is the stopping iteration numbers. To avoid the natural tendency of causative magnetic sources to concentrate at shallow depth, a prior depth weighting function is incorporated in the original formulation of the objective function. The speed of lp minimization problem is increased by inserting a pre-conditioner conjugate gradient method (PCCG) to solve the central system of equation in cases of large scale magnetic field data. It is assumed that there is no remanent magnetization since this study focuses on inversion of a geological structure with low magnetic susceptibility property. The method is applied on a multi-source noise-corrupted synthetic magnetic field data to demonstrate its suitability for 3D inversion, and then is applied to a real data pertaining to a geologically plausible porphyry copper unit.  The real case study located in  Semnan province of  Iran  consists  of  an arc-shaped  porphyry  andesite  covered  by  sedimentary  units  which  may  have  potential  of  mineral  occurrences, especially  porphyry copper. It is demonstrated that such structure extends down at depth, and consequently exploratory drilling is highly recommended for acquiring more pieces of information about its potential for ore-bearing mineralization.

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