Computer based algorithms to analyze gravity anomalies for subsurface structures have gained momentum in the search of natural resources. The enormous progress since then, however, led to the development of new interpretational techniques with increasing accuracy to analyze the gravity anomalies. The fact that variable density models yield more reliable interpretations has paved the way for developing new analytical tools to analyze gravity anomalies. In this book, the parabolic density function which unambiguously describes the density-depth dependence of sedimentary rocks is used to design new algorithms and relevant GUI based JAVA programs to analyze the gravity anomalies of subsurface geological structures. Although the terms modeling and inversion are used more or less synonymously to refer to various interpretation strategies of gravity anomalies, criteria has been formulated and followed to design modeling and inversion strategies of gravity anomalies. Accordingly, automatic inversion algorithms coupled with relevant computer codes to analyze the gravity anomalies due to 2-D and 2.5-D fault structures described with both planar and non-planar fault planes are presented. Automatic techniques based on modeling and inversion principles to analyze the gravity anomalies due to 2-D and 2.5-D sedimentary basins even when the profile of interpretation fails to bisects the strike length of the target are presented with related software. Automatic modeling and inversion techniques for the analysis of measured gravity anomalies due to 3-D sedimentary basins are presented. Also new is the automatic determination of regional gravity background in case of inversion algorithms. The highlight of the book is that, in each case, the robustness is demonstrated with both synthetic and real field gravity anomalies. Thus this book is very useful to academicians, researchers and field geophysicists. To the best of my knowledge no comprehensive book is available to address the issues described above and hence this volume would certainly attract the market.