Quantitative interpretation of multiple self-potential anomaly sources by a global optimization approach

Maio, Rosa Di; Piegari, E.; Rani, Payal; Carbonari, Rolando; Vitagliano, Eleonora; Milano, L.

doi:10.1016/j.jappgeo.2019.02.004

Cited by 26 publications

(5 citation statements)

References 55 publications

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“…Focusing on the interpretation of SP signals, the most recent updates cover mining exploration and monitoring. A global optimization method based on a Genetic-Price hybrid Algorithm (GPA) has been proposed for identifying the source parameters of SP anomalies (Di Maio et al 2019 ). The approach leads to the interpretation of simple polarized structures, such as spheres, vertical or horizontal cylinders and inclined sheets.…”

Section: Pso Of Other Geophysical 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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Section: Pso Of Other Geophysical Datamentioning

confidence: 99%

A Review of Geophysical Modeling Based on Particle Swarm Optimization

2021

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“…Table 1 presents the synthetic model parameters, the parameter model bounds for the search spaces and the inversion results from the IMSOS algorithm. Recently, MHAs have succeeded in recovering the model parameters of multiple SP anomaly sources, including BHA [9], PSO [25], the Genetic Prices Algorithm (GPA) [27], Very Fast Simulated Annealing (VFSA) [28], the Flower Pollination Algorithm (FPA) [10] and the Whale Optimization Algorithm (WOA) [29]. GPA and VFSA need many more forward modeling evaluations for global optimization compared to the other algorithms [9,10,25,29].…”

Section: Self-potential Datamentioning

confidence: 99%

Improved Modified Symbiosis Organisms Search (IMSOS): A New and Adaptive Approach for Determining Model Parameters from Geoelectrical Data

Sungkono

Grandis

2021

J. Eng. Technol. Sci.

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Symbiotic Organisms Search (SOS) is a global optimization algorithm inspired by the natural synergy between the organisms in an ecosystem. The interactive behavior among organisms in nature simulated in SOS consists of mutualism, commensalism, and parasitism strategies to find the global optimum solution in the search space. The SOS algorithm does not require a tuning parameter, which is usually used to balance explorative and exploitative search by providing posterior sampling of the model parameters. This paper proposes an improvement of the Modified SOS (MSOS) algorithm, called IMSOS, to enhance exploitation along with exploration strategies via a modified parasitism vector. This improves the search efficiency in finding the global minimum of two multimodal testing functions. Furthermore, the algorithm is proposed for solving inversion problems in geophysics. The performance of IMSOS was tested on the inversion of synthetic and field data sets from self-potential (SP) and vertical electrical sounding (VES) measurements. The IMSOS results were comparable to those of other global optimization algorithms, including the Particle Swarm Optimization, the Differential Evolution and the Black Holes Algorithms. IMSOS accurately determined the model parameters and their uncertainties. It can be adapted and can potentially be used to solve the inversion of other geophysical data as well.

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“…However, the global optimization algorithms can overcome the defects of the gradient algorithms, because they have the capability of jumping out of the local optimum and need not calculate the gradient. Common global optimization algorithms for SP inversion include simulated annealing (Biswas & Sharma, 2014, 2017), neural network (El‐Kaliouby & Al‐Garni, 2009; Li et al., 2019), particle swarm optimization (PSO) (Essa, 2019, 2020; Luo et al., 2021), whale algorithm (Abdelazeem et al., 2019; Gobashy et al., 2020) and genetic algorithm (Di‐Maio et al., 2019; Rani et al., 2019). Among these algorithms, PSO has the advantage of balancing exploration and exploitation effectively, that is, it can guarantee both the global search capability and the convergence speed.…”

Section: Introductionmentioning

confidence: 99%

Inversion of self‐potential source based on particle swarm optimization

Luo

Cui

et al. 2022

Geophysical Prospecting

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Particle swarm optimization is widely applied in self-potential inversion, but most of these applications are limited to simple polarized bodies like inclined sheets and spheres. In this paper, two variants of particle swarm optimization are formed by introducing the resistivity constraint matrix and the damping factor and are then applied to twodimensional self-potential source inversion. The two variants are combined to balance the exploration and exploitation capabilities in different situations, and the rationality of the parameter selection is validated through the stability analysis. To ensure both compactness and smoothness of the inversion results, we adopt an objective function based on L1-L2 norm regularization. Finally, we verify the effectiveness of the proposed algorithm through synthetic examples, the Cu-Fe model sandbox experiment and a field example. By imposing certain constraints, particle swarm optimization also shows great potential in high-dimensional self-potential source inversion, instead of being limited to the inversion of a few parameters.

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Quantitative interpretation of multiple self-potential anomaly sources by a global optimization approach

Cited by 26 publications

References 55 publications

A Review of Geophysical Modeling Based on Particle Swarm Optimization

A Review of Geophysical Modeling Based on Particle Swarm Optimization

Improved Modified Symbiosis Organisms Search (IMSOS): A New and Adaptive Approach for Determining Model Parameters from Geoelectrical Data

Inversion of self‐potential source based on particle swarm optimization

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