Estimation of water table from self‐potential data using particle swarm optimization (PSO)

2008 ◽  
Author(s):  
V. Naudet ◽  
J. L. Fernández‐Martínez ◽  
E. García‐Gonzalo ◽  
J. P. Fernández‐Álvarez
Keyword(s):  
Particle Swarm Optimization ◽  
Water Table ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Self Potential

Inversion of self-potential anomalies caused by simple polarized bodies based on particle swarm optimization

2021 ◽  
Vol 28 (6) ◽  
pp. 1797-1812
Author(s):  
Yi-jian Luo ◽  
Yi-an Cui ◽  
Jing Xie ◽  
He-shun-zi Lu ◽  
Jian-xin Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Self Potential

Application of particle swarm optimization on self-potential data

2011 ◽  
Vol 75 (2) ◽  
pp. 305-318 ◽  
Author(s):  
Ertan Pekşen ◽  
Türker Yas ◽  
A. Yekta Kayman ◽  
Coşkun Özkan
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Self Potential

scholarly journals A particle swarm optimization method for interpreting self-potential anomalies

2019 ◽  
Vol 16 (2) ◽  
pp. 463-477 ◽  
Author(s):  
Khalid S Essa
Keyword(s):  
Particle Swarm Optimization ◽  
Moving Average ◽  
Source Parameters ◽  
Particle Swarm ◽  
Optimization Method ◽  
Polarization Angle ◽  
Swarm Optimization ◽  
The Usa ◽  
Residual Response ◽  
Self Potential

Abstract This paper describes the use of the particle swarm optimization (PSO) method for interpreting observed self-potential anomalies measured along a profile. First, the technique applies the second moving average to the observed self-potential data in order to eradicate the possible influence of the regional anomaly (up to the third-order polynomial effect) via the filter of consecutive window lengths (s-values) and to calculate the residual anomaly. Following that, the PSO method is applied to the residual response to infer the source parameters: amplitude coefficient (K), depth (z), polarization angle (θ) and the shape factor (q) of the underlying buried target. The technique has been applied to three different theoretical and two field examples from the USA and Turkey. Comparisons have shown that the source parameters retrieved from the technique described here are in good agreement with the available geologic and geophysical information.

scholarly journals Particle swarm optimization applied to solving and appraising the streaming-potential inverse problem

Geophysics ◽  
2010 ◽  
Vol 75 (4) ◽  
pp. WA3-WA15 ◽  
Author(s):  
Juan Luis Fernández-Martínez ◽  
Esperanza García-Gonzalo ◽  
Véronique Naudet
Keyword(s):  
Particle Swarm Optimization ◽  
Water Table ◽  
Streaming Potential ◽  
Particle Swarm ◽  
Ground Surface ◽  
Model Space ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Good Convergence ◽  
Streaming Potentials

Water flow in the subsoil generates electrical currents measurable at the ground surface with the self-potential (SP) method. These measured potentials, which result from hydroelectric coupling, are called streaming potentials and are well correlated with the geometry of the water table. The particle swarm algorithm can be used to estimate the water-table elevation from SP data measured at the ground surface. The basic idea behind particle swarm optimization (PSO) is that each model searches the model space according to its misfit history and the misfit of the other models (particles) of the swarm. PSO is a simple, robust, and versatile algorithm with a very good convergence rate (typically before 3000 forward runs), and it can explore a large model space without being time consuming. Based on samples gathered in a low-misfit area, we have computed a fast approximation of the posterior distribution of the water table, the electrokinetic coupling constant, and the reference hydraulic head. Although PSO is a stochastic search technique, our convergence results, based on the stability of particle trajectories, specify clear criteria to tune PSO parameters.

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