UNDERGROUND MODELING WITH LINIER INVERSION WEIGHTED UNDERDETERMINED GRAVITY WITH RECTANGULAR TALWANI GRAVITY METHOD USING FORWARD MODELING SYNTHETIC DATA
The Gravity Method is a geophysical exploration method that uses measurable variations in gravity in the field. In subsurface model variables and gravity anomaly data, it is closely related to forward modeling and inversion. Forward modeling is a technique of predicting data from a specified mode...
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id-itb.:501462020-09-22T19:32:30ZUNDERGROUND MODELING WITH LINIER INVERSION WEIGHTED UNDERDETERMINED GRAVITY WITH RECTANGULAR TALWANI GRAVITY METHOD USING FORWARD MODELING SYNTHETIC DATA Wardana, Giri Indonesia Final Project forward modeling, gravity method, weighted underdetermined linear inversion, rectangular talwani INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/50146 The Gravity Method is a geophysical exploration method that uses measurable variations in gravity in the field. In subsurface model variables and gravity anomaly data, it is closely related to forward modeling and inversion. Forward modeling is a technique of predicting data from a specified model, while inversion is a technique of predicting a model on the surface with specified data. Rectangular talwani is a method that plays an important role in running forward modeling programs as well as inversions to measure ground surface gravity anomalies. This study aims to determine the suitability of synthetic data with data inversion. The inversion used is weighted underdetermined linear inversion, then the inversion model is modeled forward again to be compared with the synthetic data. From the research, the accuracy of the values obtained from the linear inversion of the underdetermined tobobot is compared with the previous research using the underdetermined linear inversion. text |
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The Gravity Method is a geophysical exploration method that uses measurable
variations in gravity in the field. In subsurface model variables and gravity
anomaly data, it is closely related to forward modeling and inversion. Forward
modeling is a technique of predicting data from a specified model, while inversion
is a technique of predicting a model on the surface with specified data.
Rectangular talwani is a method that plays an important role in running forward
modeling programs as well as inversions to measure ground surface gravity
anomalies. This study aims to determine the suitability of synthetic data with data
inversion. The inversion used is weighted underdetermined linear inversion, then
the inversion model is modeled forward again to be compared with the synthetic
data. From the research, the accuracy of the values obtained from the linear
inversion of the underdetermined tobobot is compared with the previous research
using the underdetermined linear inversion. |
format |
Final Project |
author |
Wardana, Giri |
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Wardana, Giri UNDERGROUND MODELING WITH LINIER INVERSION WEIGHTED UNDERDETERMINED GRAVITY WITH RECTANGULAR TALWANI GRAVITY METHOD USING FORWARD MODELING SYNTHETIC DATA |
author_facet |
Wardana, Giri |
author_sort |
Wardana, Giri |
title |
UNDERGROUND MODELING WITH LINIER INVERSION WEIGHTED UNDERDETERMINED GRAVITY WITH RECTANGULAR TALWANI GRAVITY METHOD USING FORWARD MODELING SYNTHETIC DATA |
title_short |
UNDERGROUND MODELING WITH LINIER INVERSION WEIGHTED UNDERDETERMINED GRAVITY WITH RECTANGULAR TALWANI GRAVITY METHOD USING FORWARD MODELING SYNTHETIC DATA |
title_full |
UNDERGROUND MODELING WITH LINIER INVERSION WEIGHTED UNDERDETERMINED GRAVITY WITH RECTANGULAR TALWANI GRAVITY METHOD USING FORWARD MODELING SYNTHETIC DATA |
title_fullStr |
UNDERGROUND MODELING WITH LINIER INVERSION WEIGHTED UNDERDETERMINED GRAVITY WITH RECTANGULAR TALWANI GRAVITY METHOD USING FORWARD MODELING SYNTHETIC DATA |
title_full_unstemmed |
UNDERGROUND MODELING WITH LINIER INVERSION WEIGHTED UNDERDETERMINED GRAVITY WITH RECTANGULAR TALWANI GRAVITY METHOD USING FORWARD MODELING SYNTHETIC DATA |
title_sort |
underground modeling with linier inversion weighted underdetermined gravity with rectangular talwani gravity method using forward modeling synthetic data |
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https://digilib.itb.ac.id/gdl/view/50146 |
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