A particle swarm optimization method for interpreting self-potential anomalies

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.

Combined Gravity or Self-Potential Anomaly Formula for Mineral Exploration

“…The particle swarm was suggested by [39] and has many various applications, for example, in geophysics [40][41][42]. For more detail in this approach, you find it many published literature [43,44].…”

Parameters estimation from the gravity anomaly caused by the two-dimensional horizontal thin sheet applying the global particle swarm algorithm

“…The global optimization particle swarm is developed and introduced during the last years to solve many geophysical problems (Sen and Stoffa, 2013;Singh and Biswas, 2016;Essa and Elhussein, 2018;Essa, 2019Essa, , 2020Karcıoglu and Gürer, 2019 ). The particle swarm progression is stochastic and stirred by the communal repetitive in a journey of birds for searching the foods where the birds are the models.…”

A Review of Geophysical Modeling Based on Particle Swarm Optimization

“…The main applications of PSO to the geophysical inverse problem include the interpretation of: vertical electrical sounding (VES) (Fernández-Álvarez et al 2006 ; Fernández Martínez et al 2010a ; Pekşen et al 2014 ; Cheng et al 2015 ; Pace et al 2019b ); gravity data (Yuan et al 2009 ; Pallero et al 2015 , 2017 , 2021 ; Darisma et al 2017 ; Jamasb et al 2019 ; Essa and Munschy 2019 ; Anderson et al 2020 ; Essa and Géraud 2020 ; Essa et al 2021 ); magnetic data (Liu et al 2018 ; Essa and Elhussein 2018 , 2020 ); multi-transient electromagnetic data (Olalekan and Di 2017 ); time-domain EM data (Cheng et al 2015 , 2019 ; Santilano et al 2018 ; Pace et al 2019c ; Li et al 2019 ; Amato et al 2021 ); MT data (Shaw and Srivastava 2007 ; Pace et al 2017 , 2019a , c ; Godio and Santilano 2018 ; Santilano et al 2018 ) and radio-MT data (Karcıoğlu and Gürer 2019 ); self-potential data (Santos 2010 ; Pekşen et al 2011 ; Göktürkler and Balkaya 2012 ; Essa 2019 , 2020 ) and induced polarization (Vinciguerra et al 2019 ); Rayleigh wave dispersion curve (Song et al 2012 ) and full waveform inversion (Aleardi 2019 ). …”

“…self-potential data (Santos 2010 ; Pekşen et al 2011 ; Göktürkler and Balkaya 2012 ; Essa 2019 , 2020 ) and induced polarization (Vinciguerra et al 2019 );…”