Application of Particle Swarm Optimization for Gravity Inversion of 2.5-D Sedimentary Basins using Variable Density Contrast Kunal Kishore Singh and Upendra Kumar Singh

2017 ◽  
Author(s):  
Anonymous
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Sedimentary Basins ◽  
Variable Density ◽  
Density Contrast ◽  
Gravity Inversion ◽  
Swarm Optimization

scholarly journals Application of particle swarm optimization for gravity inversion of 2.5-D sedimentary basins using variable density contrast

2017 ◽  
Vol 6 (1) ◽  
pp. 193-198 ◽  
Author(s):  
Kunal Kishore Singh ◽  
Upendra Kumar Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Gravity Anomalies ◽  
Sedimentary Basins ◽  
Pso Algorithm ◽  
Density Contrast ◽  
Gravity Inversion ◽  
Western Anatolia ◽  
Swarm Optimization

Abstract. Particle swarm optimization (PSO) is a global optimization technique that works similarly to swarms of birds searching for food. A MATLAB code in the PSO algorithm has been developed to estimate the depth to the bottom of a 2.5-D sedimentary basin and coefficients of regional background from observed gravity anomalies. The density contrast within the source is assumed to vary parabolically with depth. Initially, the PSO algorithm is applied on synthetic data with and without some Gaussian noise, and its validity is tested by calculating the depth of the Gediz Graben, western Anatolia, and the Godavari sub-basin, India. The Gediz Graben consists of Neogen sediments, and the metamorphic complex forms the basement of the graben. A thick uninterrupted sequence of Permian–Triassic and partly Jurassic and Cretaceous sediments forms the Godavari sub-basin. The PSO results are better correlated with results obtained by the Marquardt method and borehole information.

scholarly journals Application of particle swarm optimization for gravity inversion of 2.5-D sedimentary basins using variable density contrast

2016 ◽  
Author(s):  
Kunal Kishore Singh ◽  
Upendra Kumar Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Gravity Anomalies ◽  
Sedimentary Basins ◽  
Pso Algorithm ◽  
Density Contrast ◽  
Gravity Inversion ◽  
Western Anatolia ◽  
Swarm Optimization

Abstract. Particle swarm optimization (PSO) is a global optimization technique that works similarly to swarms of birds searching for food. A Matlab code in PSO algorithm is developed to estimate the depth to the bottom of a 2.5-D sedimentary basin and coefficients of regional background from observed gravity anomalies. The density contrast within the source is assumed to be varying parabolically with depth. Initially, the PSO algorithm is applied on synthetic data with and without some Gaussian noise and its validity is tested by calculating the depth of the Gediz Graben, Western Anatolia and Godavari sub-basin, India. The Gediz Graben consists of Neogen sediments and the metamorphic complex forms the basement of the Graben. A thick uninterrupted sequence of Permian-Triassic and partly Jurassic and Cretaceous sediments forms the Godavari sub-basin. The PSO results are better than the results obtained by Marquardt method and the results are well correlated with borehole information.

Sign in / Sign up

Export Citation Format

Share Document