MOPSO: a new computing algorithm for joint inversion of Rayleigh wave dispersion curve and refraction traveltimes

Poormirzaee, Rashed

doi:10.1071/eg16044

Cited by 9 publications

(4 citation statements)

References 18 publications

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“…Thirdly, joint inversion can be considered as an important approach to improve the inversion accuracy. For instance, Rayleigh wave exploration can be combined with other exploration methods such as electrical sounding (Senkaya & Karsli, 2016) and ground penetrating radar (Qin et al, 2020); Rayleigh wave inversion can be combined with Love wave inversion (Wittkamp et al, 2019), P-wave inversion (Boiero & Socco, 2014;Poormirzaee, 2018), and ellipticity inversion (Ai et al, 2021;Gouveia et al, 2016). Joint inversion can greatly improve the reliability of inversion, while the defect that will increase the workload is also significant.…”

Section: Introductionmentioning

confidence: 99%

“…The second category is the completely nonlinear global optimization algorithm, including the global optimization algorithm based on random sampling in the solution space and the random search algorithm based on meta heuristic. The former is represented by Monte Carlo method Sun et al, 2022;Yang & Yuen, 2021), while the latter is represented by particle swarm optimization (PSO) (Ai et al, 2021;Poormirzaee, 2016Poormirzaee, , 2018, cuckoo search algorithm (Poormirzaee & Fister Jr, 2021), genetic algorithm (Qin et al, 2020;Zeng et al, 2011), firefly algorithm (Zhou et al, 2014), artificial neural network (Jian et al, 2011;Yablokov et al, 2021) and simulated annealing (Calderón-Macías & Luke, 2007;Chong et al, 2015;Lu et al, 2016;Pei et al, 2007). The gradient-based liner algorithm is characterized by rigorous derivative derivation, but depends on the selection of initial solution and the calculation of partial derivative matrix.…”

Section: Introductionmentioning

confidence: 99%

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Inversion of Rayleigh Wave Dispersion Curves Via BP Neural Network and PSO

Luo

2023

Preprint

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Rayleigh wave is widely applied in engineering exploration and geotectonic research. While how to reconstruct the corresponding geological information via Rayleigh wave is the critical process and difficulty. This paper presents an inversion method of Rayleigh wave dispersion curves based on BP neural network and PSO. In this work, a sample set that referring to the actual stratum distribution is firstly generated. Then, BP neural network is adopted to train the nonlinear mapping relationship between the dispersion curves and the shear wave velocity of each stratum. The trained BP neural network can quickly output a predicted value with rationality but poor precision, which can be utilized as the initial model of PSO inversion. PSO will then be adopted to further optimize the inversion result on the basis of BP neural network prediction. The combination of BP neural network and PSO aims at overcoming the defects of BP neural network that unable to carry out continual optimization and the slow optimization of PSO in the absence of reasonable initial solution. Finally, the effectiveness of the proposed algorithm is verified by a series of synthetic models and an active-source Rayleigh wave experiment carried out in a new railway project from Baotou, Inner Mongolia to Yinchuan, Ningxia.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inversion of Rayleigh Wave Dispersion Curves Via BP Neural Network and PSO

Luo

2023

Preprint

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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%

“…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). Multi-objective particle swarm optimizer has also been used to carry out joint inversion of time-domain electromagnetic and vertical electrical sounding data (Pace et al, 2019).…”

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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Model-Based Inversion of Rayleigh Wave Dispersion Curves Via Linear and Nonlinear Methods

Poormirzaee

Fister

2021

Pure Appl. Geophys.

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MOPSO: a new computing algorithm for joint inversion of Rayleigh wave dispersion curve and refraction traveltimes

Cited by 9 publications

References 18 publications

Inversion of Rayleigh Wave Dispersion Curves Via BP Neural Network and PSO

Inversion of Rayleigh Wave Dispersion Curves Via BP Neural Network and PSO

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

Model-Based Inversion of Rayleigh Wave Dispersion Curves Via Linear and Nonlinear Methods

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