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
Department of Applied Geophysics, Indian School of Mines, Dhanbad-826004, India
Received: 23 Mar 2016 – Accepted for review: 24 Aug 2016 – Discussion started: 25 Aug 2016
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.
Singh, K. K. and Singh, U. K.: Application of particle swarm optimization for gravity inversion of 2.5-D sedimentary basins using variable density contrast, Geosci. Instrum. Method. Data Syst. Discuss., doi:10.5194/gi-2016-10, in review, 2016.