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

Abstract

A global particle swarm algorithm utilized to assess the inverted two-dimensional horizontal thin sheet parameters from the gravity anomaly profile based on applying the second moving average method. The using of the second moving average method has more advantageous than using the Bouguer gravity anomaly because this method has a capability in eliminating the regional field up to third-order impeded in the Bouguer anomaly. This algorithm is applied to interpret the gravity anomaly profile, i.e., estimating the depth, the width, the thickness, the density contrast and the origin location of the buried structure. The efficacy and stability of this method are investigated and exposed for utilizing free-noise and noisy synthetic examples. Real gravity data associated with oil and gas and mineral exploration from three different locations around the world is interpreted. The evaluated time to discovering this appropriate clarification is very short and the estimated parameters contrast the view that the proposed method is applicable for real exploration. It can extract the inverse parameters which have a geologic and economic significance.

Magnetic anomaly interpretation for a 2D fault-like geologic structures utilizing the global particle swarm method

“…The particle swarm optimization is carefully distinguished and has been used to address a variety of geophysical problems. The development of the method was fully described in the published literature (Singh and Biswas 2016;Grandis and Maulana 2017;Essa and Munschy 2019;Essa and Géraud 2020;Essa 2021), and we do not repeat it here. Instead, we accentuation on its considerable profits in vanquishing the ill-posedness and non-uniqueness of magnetic data inversion.…”

A Review of Geophysical Modeling Based on Particle Swarm Optimization

“…PSO has been applied to both synthetic and field gravity data for 2-D (Yuan et al 2009 ; Pallero et al 2015 , 2021 ; Darisma et al 2017 ; Essa and Munschy 2019 ; Anderson et al 2020 ; Essa and Géraud 2020 ; Essa et al 2021 ) and 3-D interpretations (Pallero et al 2017 ; Jamasb et al 2019 ).…”

“…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 ). …”

“…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 );…”

Gravity inversion of 2D fault having variable density contrast using particle swarm optimization

“…Global optimization does not require any predefined initial model and can solve problems with complicated objective functions. Several authors have presented various global optimization techniques to invert potential field data: Amjadi and Naji (2013) and Kaftan (2017) used the genetic algorithm for determining the depth of buried structures; Singh and Biswas (2016), Roshan and Singh (2017), Essa and Munschy (2019) and Essa and Géraud (2020) developed particle swarm optimization (PSO) technique to determine source parameters of geological bodies having idealized geometries; Biswas (2015) and Biswas and Sharma (2017) developed very fast simulated annealing for estimation of thick sheet‐like structures and idealized geological bodies from residual gravity anomalies; Ekinci et al . (2016) and Roy et al .…”