Particle Swarm Optimization (PSO) for Magnetotelluric (MT) 1D Inversion Modeling

Grandis, Hendra; Maulana, Yahya

doi:10.1088/1755-1315/62/1/012033

Cited by 11 publications

(7 citation statements)

References 11 publications

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“…Thuật toán tiến hóa được giới thiệu lần đầu tiên vào năm 1975 bởi tác giả Holland 3 . Ngày nay nó đã được phát triển theo nhiều hướng, có thể kể đến một số hướng chính như di truyền, bầy đàn (mô phỏng hành vi tìm kiếm thức ăn của đàn kiến, đàn ong…) 4 . Các thuật toán tiến hóa không đòi hỏi phải tuyến tính hóa hàm mô hình và không dùng cách tính đạo hàm trong quá trình tìm mô hình kết quả.…”

Section: Giới Thiệuunclassified

One–dimensional magnetotelluric inversion by modified differential evolution algorithm

Luu

2023

Sci. Tech. Dev. J. - Nat. Sci.

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Traditional methods of solving geophysical inverse problems often used an initial structural model and an iterative process to find solutions. During each iteration, the model was linearized and updated, then the error between the model's response and the observed data were calculated and minimized. The inverse results solved by this method were often strongly dependent on the initial model choice, and the minimum of the objective function found could be the local value but not the global minimum. Global search methods such as genetic algorithm (GA) overcame these limitations of traditional solutions. This paper presented the application of modified differential evolution (MDE) algorithm which was developed from the genetic algorithm, to one-dimension (1D) magnetotelluric inversion. The algorithm had been used to invert two 3-layer geoelectrical models. The results showed that when using a 3-layer model for inversion, the obtained parameters of the subsurface almost coincided with those of the theoretical models. In the case the number of layers used for inversion had more than three layers (four layers and five layers), the obtained results showed that there was still a good agreement between the inverted structure and the theoretical model. Therefore, the algorithm was applied to one-dimensional (1D) inversion for data of one magnetotelluric station measured at Cu Chi district, Ho Chi Minh city. The results showed that the subsurface structure down to a depth of 16 km consisted of 3 layers: the top layer had a resistivity of 119 Ω.m with a thickness of about 760 m, the middle resistive layer with a resistivity of 4353 Ω.m with about 10 km thickness, and the lower half-space with a relatively low resistivity of 41.4 Ω.m. This result was consistent with the geoelectric structure information obtained from other magnetotelluric studies carried out in the studied area, demonstrating the practical applicability of the improved differential evolution algorithm.

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Section: Giới Thiệuunclassified

One–dimensional magnetotelluric inversion by modified differential evolution algorithm

Luu

2023

Sci. Tech. Dev. J. - Nat. Sci.

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“…The earliest applications of PSO to the 1-D MT inverse problem were presented in conferences proceedings (Godio et al 2016 ; Patel et al 2016 ; Grandis and Maulana 2017 ) and published in full articles (Shaw and Srivastava 2007 ; Godio and Santilano 2018 ; Santilano et al 2018 ). At the same time, research focused on the solution of the 1-D MT inverse problem by adopting the Markov Chain Monte Carlo (MCMC) method (Grandis et al 1999 ; Mandolesi et al 2018 ; Xiang et al 2018 ; Conway et al 2018 ) and the SA algorithm (Dosso and Oldenburg 1991 ).…”

Section: Pso Of Electromagnetic Datamentioning

confidence: 99%

A Review of Geophysical Modeling Based on Particle Swarm Optimization

2021

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This paper reviews the application of the algorithm particle swarm optimization (PSO) to perform stochastic inverse modeling of geophysical data. The main features of PSO are summarized, and the most important contributions in several geophysical fields are analyzed. The aim is to indicate the fundamental steps of the evolution of PSO methodologies that have been adopted to model the Earth’s subsurface and then to undertake a critical evaluation of their benefits and limitations. Original works have been selected from the existing geophysical literature to illustrate successful PSO applied to the interpretation of electromagnetic (magnetotelluric and time-domain) data, gravimetric and magnetic data, self-potential, direct current and seismic data. These case studies are critically described and compared. In addition, joint optimization of multiple geophysical data sets by means of multi-objective PSO is presented to highlight the advantage of using a single solver that deploys Pareto optimality to handle different data sets without conflicting solutions. Finally, we propose best practices for the implementation of a customized algorithm from scratch to perform stochastic inverse modeling of any kind of geophysical data sets for the benefit of PSO practitioners or inexperienced researchers.

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“…In recent years, the particle swarm optimization (PSO) method has been successfully applied to the electromagnetic method in both underground and above ground environments. Based on the global convergence of the algorithm, the particle swarm optimization inversion of electromagnetic data is developed, which is mainly used for the qualitative interpretation of electromagnetic [21,53]. In order to make better use of the swarm theory of particle swarm optimization, some scholars proposed to add constraints and carry out joint data inversion, and achieved good results [54], [55].…”

Section: A Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Development of the Inversion Method for Transient Electromagnetic Data

Xue

Hai

et al. 2020

IEEE Access

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Transient electromagnetic method (TEM) is a geophysical tool to obtain resistivity distribution in the subsurface. Combining with the resistivity property of typical rocks, the TEM method can make inferences of the geological maps underground. The inversion method is the main technique to extract resistivity form the recorded TEM data. There are various inversion methods that have been applied to TEM data, each of which favors different model structures. It is essential to choose the optimal inversion algorithm for a TEM survey in a given geological setting. Thus, this paper presents a systematic summary of recent developments of inversion methods for TEM data. We first summarize the basic concept of the TEM inversion theory. Then, the recent developments TEM inverse method are divided into deterministic inversion and stochastic inversion. For the deterministic method, we present the development of constrained inversion and joint inversion. For the stochastic method, we analyze the particle swarm optimization, Bayesian inversion, and TEM pseudo-seismic imaging. Thereafter, we prospect the future research direction of the TEM method. INDEX TERMS transient electromagnetic method; inverse problem; progress; particle swarm optimization; Bayesian inversion

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Particle Swarm Optimization (PSO) for Magnetotelluric (MT) 1D Inversion Modeling

Cited by 11 publications

References 11 publications

One–dimensional magnetotelluric inversion by modified differential evolution algorithm

One–dimensional magnetotelluric inversion by modified differential evolution algorithm

A Review of Geophysical Modeling Based on Particle Swarm Optimization

Development of the Inversion Method for Transient Electromagnetic Data

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