Estimation of Gravity Parameters Related to Simple Geometrical Structures by Developing an Approach Based on Deconvolution and Linear Optimization Techniques

An easy and practical method for interpreting residual gravity anomalies due to simple geometrically shaped models such as cylinders and spheres has been proposed in this paper. This proposed method is based on both the deconvolution technique and the simplex algorithm for linear optimization to mos...

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Bibliographic Details
Published in:Pure and applied geophysics 2015-10, Vol.172 (10), p.2891-2899
Main Authors: Asfahani, J., Tlas, M.
Format: Article
Language:English
Subjects:
Acceptability
Cylinders
Deconvolution
Earth and Environmental Science
Earth Sciences
Estimating techniques
Gaussian
Geometry
Geophysics
Geophysics/Geodesy
Gravitation
Gravity
Gravity anomalies
Linear equations
Mathematical models
Noise levels
Optimization
Optimization techniques
Parameter estimation
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Summary:An easy and practical method for interpreting residual gravity anomalies due to simple geometrically shaped models such as cylinders and spheres has been proposed in this paper. This proposed method is based on both the deconvolution technique and the simplex algorithm for linear optimization to most effectively estimate the model parameters, e.g., the depth from the surface to the center of a buried structure (sphere or horizontal cylinder) or the depth from the surface to the top of a buried object (vertical cylinder), and the amplitude coefficient from the residual gravity anomaly profile. The method was tested on synthetic data sets corrupted by different white Gaussian random noise levels to demonstrate the capability and reliability of the method. The results acquired show that the estimated parameter values derived by this proposed method are close to the assumed true parameter values. The validity of this method is also demonstrated using real field residual gravity anomalies from Cuba and Sweden. Comparable and acceptable agreement is shown between the results derived by this method and those derived from real field data.
ISSN:0033-4553
1420-9136
DOI:10.1007/s00024-015-1068-z