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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, 71–79, 2017
https://doi.org/10.5194/gi-6-71-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Instrum. Method. Data Syst., 6, 71–79, 2017
https://doi.org/10.5194/gi-6-71-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 06 Feb 2017

Research article | 06 Feb 2017

Inversion of residual gravity anomalies using tuned PSO

Ravi Roshan and Upendra Kumar Singh Ravi Roshan and Upendra Kumar Singh
  • Department of Applied Geophysics, IIT(ISM), Dhanbad – 826 004, India

Abstract. Many kinds of particle swarm optimization (PSO) techniques are now available and various efforts have been made to solve linear and non-linear problems as well as one-dimensional and multi-dimensional problems of geophysical data. Particle swarm optimization is a metaheuristic optimization method that requires intelligent guesswork and a suitable selection of controlling parameters (i.e. inertia weight and acceleration coefficient) for better convergence at global minima. The proposed technique, tuned PSO, is an improved technique of PSO, in which efforts have been made to choose the controlling parameters, and these parameters have been selected after analysing the responses of various possible exercises using synthetic gravity anomalies over various geological sources. The applicability and efficacy of the proposed method is tested and validated using synthetic gravity anomalies over various source geometries. Finally, tuned PSO is applied over field residual gravity anomalies of two different geological terrains to find the model parameters, namely amplitude coefficient factor (A), shape factor (q) and depth (z). The analysed results have been compared with published results obtained by different methods that show a significantly excellent agreement with real model parameters. The results also show that the proposed approach is not only superior to the other methods but also that the strategy has enhanced the exploration capability of the proposed method. Thus tuned PSO is an efficient and more robust technique to achieve an optimal solution with minimal error.

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Short summary
A new technique, tuned PSO, is developed for optimal convergence, avoiding traps of local minima and computing the model parameters: amplitude coefficient factor, shape factor and depth. A number of exercises were done to select the PSO learning parameters. The applicability and efficacy of the proposed method is implemented on synthetic and field gravity anomalies. The analysed results were compared with published results by other methods that show a significant agreement with the real model.
A new technique, tuned PSO, is developed for optimal convergence, avoiding traps of local minima...
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