Pareto-Optimal Multi-objective Inversion of Geophysical Data

Schnaidt, Sebastian; Conway, Dennis; Krieger, Lars; Heinson, Graham

doi:10.1007/s00024-018-1784-2

Cited by 8 publications

(19 citation statements)

References 46 publications

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“…Its projection onto a surface creates the so-called Pareto Front ( PF ), which is a tradeoff surface showing which component of the objective function is mostly minimized. The PF is also useful to infer the data compatibility (Dal Moro and Pipan 2007 ; Schnaidt et al 2018 ; Pace et al 2019b ). The ruling equations of MOPSO are the same of PSO (Eqs.…”

Section: Particle Swarm Optimization: State Of the Artmentioning

confidence: 99%

“…For the sake of completeness, another MOEA adopted in geophysics is the Nondominated Sorting GA (NSGA-III) (Deb and Jain 2014). NSGA has actually been more explored than MOPSO and applied to the inversion of: Raleigh-wave dispersion curves and reflection travel times (Dal Moro and Pipan 2007 ), surface wave dispersion and horizontal-to-vertical spectral ratio (Dal Moro 2010 ), seismic and well-log data for reservoir modeling (Emami Niri and Lumley 2015 ), magnetic resonance and VES data (Akca et al 2014 ), AMT and broad-band MT data (Schnaidt et al 2018 ), and receiver functions, surface wave dispersion and MT data (Moorkamp et al 2011 ).…”

Section: Particle Swarm Optimization: State Of the Artmentioning

confidence: 99%

“…Figure 17 d zooms in the PF and shows the deviation angle between the grey-dashed ideal line and the Theil-Sen-regression blue line. Since α is higher than 45° and the PF is not symmetric, a slight conflict between TDEM and VES can be inferred (Dal Moro and Pipan 2007 ; Schnaidt et al 2018 ).

Fig.…”

Section: Performing Joint Inversion With Psomentioning

confidence: 99%

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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: Particle Swarm Optimization: State Of the Artmentioning

confidence: 99%

Section: Particle Swarm Optimization: State Of the Artmentioning

confidence: 99%

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A Review of Geophysical Modeling Based on Particle Swarm Optimization

2021

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“…If this approach is integrated into global optimization algorithms, joint inversion of geophysical data sets can be successfully carried out to obtain an optimal solution set eliminated from the aforementioned drawbacks of conventional inversion techniques without weighting and combining data misfit terms obtained from different data sets. There are a number of studies, such as Akca et al (2014), Büyük et al (2020), Dal Moro (2010), Kozlovskaya et al (2007), Paasche and Tronicke (2007), Pace et al (2019a), Schnaidt et al (2018) and Tronicke et al (2011) utilized the Pareto optimality approach integrated with global optimization methods in various joint inversion of different geophysical data sets. Despite this interest, to the authors' knowledge, no one has investigated the practicability of Pareto integrated global optimization method to the constrained inversion without the need for a regularization parameter.…”

Section: Introductionmentioning

confidence: 99%

A new method of smoothness‐constrained magnetotelluric modelling with the utility of Pareto‐optimal multi‐objective particle swarm optimization

Büyük

2024

Geophysical Prospecting

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Particle swarm optimization, one of the modern global optimization methods, is attracting widespread interest because it overcomes the difficulties of conventional inversion techniques, such as trapping at a local minimum and/or initial model dependence. The main characteristic of particle swarm optimization is the large search space of parameters, which in a sense allows the exploration of the entire objective function space if the input parameters are properly chosen. However, in the case of a high‐dimensional model space, the numerical instability of the solution may increase and lead to unrealistic models and misinterpretations due to the sampling problem of particle swarm optimization. Therefore, smoothness‐constrained regularization techniques used for the objective function or model reduction techniques are required to stabilize the solution. However, weighting and combining objective function terms is partly a subjective process, as the regularization parameter is generally chosen based on some kind of criteria of how the smoothing constraints affect the data misfits. This means that it cannot be completely predefined but needs to be adjusted during the inversion process, which begins with the response of an initial model. In this paper, a new modelling approach is proposed to obtain a smoothness‐constrained model from magnetotelluric data utilizing multi‐objective particle swarm optimization based on the Pareto optimality approach without using a regularization parameter and combining several objective function terms. The presented approach was verified on synthetic models and an application with field data set from the Çanakkale–Tuzla geothermal field in Turkey. Findings from these analyses confirm the usefulness of the method as a new approach for all constrained inversions of geophysical data without the need to combine the objective function terms weighted by a regularization parameter.

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“…A couple of recently proposed swarm intelligence-based global optimization techniques include African vultures optimization (Abdollahzadeh et al, 2021a) and artificial gorilla troops optimization (Abdollahzadeh et al, 2021b) that mimic foraging and navigation behaviour of African vultures and social intelligence of gorilla troops, respectively. Moreover, there have been several successful implementations of multi-objective global optimization algorithms to invert geophysical datasets Paasche and Tronicke, 2014;Jie and Tao, 2015;Schnaidt et al, 2018;Poormirzaee, 2018;Pace et al, 2019). Multi-objective optimization algorithms based on PSO (multi-objective particle swarm optimization) offer a high speed of convergence and have been used in the inversion of two-dimensional magnetic data (Jie and Tao, 2015) and joint inversion of seismic refraction travel times and Rayleigh wave dispersion curve (Poormirzaee, 2018).…”

Section: Introductionmentioning

confidence: 99%

Joint inversion of Rayleigh wave fundamental and higher order mode phase velocity dispersion curves using multi‐objective grey wolf optimization

Vashisth

Shekar

Srivastava

2022

Geophysical Prospecting

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Rayleigh wave dispersion curves can be inverted to retrieve subsurface seismic velocity profiles. The inverse problem is ill-posed, nonlinear and poorly conditioned, necessitating the application of global optimization methods. We present the application of the multi-objective grey wolf optimization algorithm to perform joint inversion of the phase velocity dispersion curves corresponding to the fundamental and higher order modes of Rayleigh waves to obtain shear (S-) and primary (P-) wave velocity profiles. Multi-objective grey wolf optimization is an extension of the grey wolf optimization algorithm for application to multi-objective optimization and can be adapted to solve joint inversion problems. We compare the joint inversion results obtained from the multi-objective grey wolf optimizer with those obtained from Markov chain Monte Carlo and fundamental mode inversion using the grey wolf optimizer on synthetic examples. The errors associated with phase velocity measurements are simulated by adding frequency-dependent noise, with a higher level of noise added to the phase velocities corresponding to lower frequencies as compared to the higher frequencies. In the multimode joint inversion problem, the multi-objective grey wolf optimizer gives a suite of solutions corresponding to each model parameter. The suite of S-wave and P-wave velocity profiles estimated from the multi-objective grey wolf optimizer matches closely with the true model for the synthetic case studies even in the presence of noise. However, the suite of solutions has a greater spread for the last few layers, qualitatively indicating a higher degree of uncertainty in the predicted model parameter. The uncertainty in the solution for the deeper layers is a consequence of the uncertainty in the phase velocity at lower frequencies. We demonstrate the efficacy of the algorithm on recorded data from a shallow seismic survey conducted at the Indian Institute of Technology Bombay. The results from the multi-objective grey wolf optimizer are in close agreement with those from Markov chain Monte Carlo, and the depth of investigation is found to be greater in comparison to results from refraction traveltime inversion.

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Pareto-Optimal Multi-objective Inversion of Geophysical Data

Cited by 8 publications

References 46 publications

A Review of Geophysical Modeling Based on Particle Swarm Optimization

A Review of Geophysical Modeling Based on Particle Swarm Optimization

A new method of smoothness‐constrained magnetotelluric modelling with the utility of Pareto‐optimal multi‐objective particle swarm optimization

Joint inversion of Rayleigh wave fundamental and higher order mode phase velocity dispersion curves using multi‐objective grey wolf optimization

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