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Abstract

Summary

Particle swarm optimization (PSO) is an optimization technique inspired by the social behavior of individuals in nature (swarms). In this study, we have been developed and applied the technique to the gravity anomaly data. PSO algorithm was tested on syntethic data with 10% and 20% noise, and without noise. The syntethic gravity data was generated with finite rectangular prims that have density contrast with background. The inversion shows that calculated gravity anomaly and observed gravity anomaly are matched well. In fact, the challenge in PSO inversion is to determine the suitable value of the controlling parameters (i.e. inetia weight, cognitive parameter, and social parameter). But trial experiment shows that with the exact controlling parameter value, we could get a good inversion result that close to the model.

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/content/papers/10.3997/2214-4609.201702117
2017-09-03
2024-04-19

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References

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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201702117
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2D Gravity Inversion Using Particle Swarm Optimization Method
Conference Proceedings 2017, 1 (2017); https://doi.org/10.3997/2214-4609.201702117
/content/papers/10.3997/2214-4609.201702117
/content/papers/10.3997/2214-4609.201702117
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