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Geoscientific Instrumentation, Methods and Data Systems An interactive open-access journal of the European Geosciences Union
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Volume 6, issue 1
Geosci. Instrum. Method. Data Syst., 6, 193-198, 2017
https://doi.org/10.5194/gi-6-193-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Instrum. Method. Data Syst., 6, 193-198, 2017
https://doi.org/10.5194/gi-6-193-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 10 Apr 2017

Research article | 10 Apr 2017

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

Kunal Kishore Singh1,2 and Upendra Kumar Singh1 Kunal Kishore Singh and Upendra Kumar Singh
  • 1Department of Applied Geophysics, IIT(ISM), Dhanbad 826004, India
  • 2Geological Survey of India, Lucknow-226024, India

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.

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Short summary
Particle swarm optimization is developed to estimate the model parameters of a 2.5-D sedimentary basin. PSO have been implemented on synthetic data and two field data. An observation has been made that PSO is affected by some levels of noise, but estimated depths are close to the true depths. The PSO results are well correlated with borehole samples and provide more geological viability than Marquardt results. Despite its long computation time, it is very simple to implement.
Particle swarm optimization is developed to estimate the model parameters of a 2.5-D sedimentary...
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