3 Dimensional Inverse Modeling of Gravity Data Using IRLS (Iteratively Reweighted Least Squares) Algorithm
In this final project, 3 dimensional inversion modeling on gravity survey data using IRLS (Iteratively Reweighted Least Squares) algorithm has been done so that an anomalous model of gravity below the surface is obtained. IRLS algorithm is commonly used to solve 1-norm problems. The result...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/23556 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | In this final project, 3 dimensional inversion modeling on gravity survey data using IRLS (Iteratively Reweighted Least Squares) algorithm has been done so that an anomalous model of gravity below the surface is obtained. IRLS algorithm is commonly used to solve 1-norm problems. The result of solving the 1-norm problem with the inversion process, resulting in a solution that is more resistant to noise and not affected by the outlier in the data. The subsurface model is structured by an anomalous body in the form of a 3 dimensional rectangular block, where the geometry and position of each body of the block anomaly are known. So the density of each body of the block anomaly in the subsurface model is a model parameter that should be estimated using the inversion scheme. Furthermore, in the inversion scheme, validation is done by using synthetic data whose model parameter values are known. The anomaly model obtained by using the inversion scheme, then compared with the actual anomaly model. Finally, the inversion scheme is performed on the residual anomaly data in which the data is generated from the data separation process using the wavelength filtering method in CBA (Complete Bouguer Anomaly) data. <br />
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