A Least-squares Minimization Approach to Depth Determination from Magnetic Data

2003 ◽  
Vol 160 (7) ◽  
pp. 1259-1271 ◽  
Author(s):  
E. M. Abdelrahman ◽  
H. M. El-Arby ◽  
T. M. El-Arby ◽  
K. S. Essa
Keyword(s):  
Least Squares ◽  
Magnetic Data ◽  
Depth Determination ◽  
Minimization Approach ◽  
Least Squares Minimization

A new least-squares minimization approach to depth and shape determination from magnetic data

2007 ◽  
Vol 55 (3) ◽  
pp. 433-446 ◽  
Author(s):  
El-Sayed M. Abdelrahman ◽  
Eid. R. Abo-Ezz ◽  
Khalid S. Essa ◽  
T.M. El-Araby ◽  
Khaled S. Soliman
Keyword(s):  
Least Squares ◽  
Magnetic Data ◽  
Shape Determination ◽  
Minimization Approach ◽  
Least Squares Minimization

scholarly journals A least-squares minimization approach to interpret gravity anomalies caused by a 2D thick, vertically faulted slab

2019 ◽  
Vol 49 (3) ◽  
pp. 229-247
Author(s):  
El-Sayed Abdelrahman ◽  
Mohamed Gobashy
Keyword(s):  
Least Squares ◽  
Gravity Data ◽  
Gravity Anomalies ◽  
Central Valley ◽  
Real Data ◽  
Horizontal Gradient ◽  
Vertical Fault ◽  
Independent Observations ◽  
Minimization Approach ◽  
Least Squares Minimization

Abstract We present a least-squares minimization approach to estimate simultaneously the depth to and thickness of a buried 2D thick, vertically faulted slab from gravity data using the sample spacing – curves method or simply s-curves method. The method also provides an estimate for the horizontal location of the fault and a least-squares estimate for the density contrast of the slab relative to the host. The method involves using a 2D thick vertical fault model convolved with the same finite difference second horizontal gradient filter as applied to the gravity data. The synthetic examples (noise-free and noise affected) are presented to illustrate our method. The test on the real data (Central Valley of Chile) and the obtained results were consistent with the available independent observations and the broader geological aspects of this region.

A least‐squares minimization approach to shape determination from gravity data

Geophysics ◽  
1995 ◽  
Vol 60 (2) ◽  
pp. 589-590 ◽  
Author(s):  
El‐Sayed M. Abdelrahman ◽  
Sharafeldin M. Sharafeldin
Keyword(s):  
Least Squares ◽  
Gravity Anomaly ◽  
Shape Factor ◽  
Gravity Data ◽  
Correlation Factor ◽  
Walsh Transform ◽  
Residual Gravity ◽  
Residual Gravity Anomaly ◽  
Minimization Approach ◽  
Least Squares Minimization

The gravity anomaly expression produced by most geologic structures can be represented by a continuous function of both shape (shape‐factor) and depth‐related variables with an amplitude coefficient related to mass (Abdelrahman and El‐Araby, 1993). Few methods have been developed to determine the shape of the buried geologic structure from residual gravity anomaly profiles. These methods include a Walsh transform approach (Shaw and Agarwal, 1990) and the employment of a correlation factor between successive least‐squares residuals (Abdelrahman and El‐Araby 1993). In the present note, a least‐squares minimization approach to shape‐factor determination from a residual gravity anomaly profile is presented. The problem of the shape‐factor determination is transformed into the problem of finding a solution of a nonlinear equation of the form f(q) = 0.

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.

Sign in / Sign up

Export Citation Format

Share Document