S-wave velocity profiling from refraction microtremor Rayleigh wave dispersion curves via PSO inversion algorithm

Poormirzaee, Rashed

doi:10.1007/s12517-016-2701-6

Cited by 14 publications

(9 citation statements)

References 25 publications

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“…According to the literatures [28,38] and experiments, the parameter for the IHKA is set as N ξ = 10, α = 0.9, β = 5 and Bsi ze = Nd 5 + 0.5 . The parameter for the PSO is set as c 1 = 2.8, c 2 = 1.3 and w = 0.729 [33]. The parameter for the MPPSO is set as ph = 2, pcf = 5, g = 2, sllu = [1, min (10, N d)] and VC =10 [1].…”

Section: Resultsmentioning

confidence: 99%

“…Particularly deeply explored is the field of Planning and Scheduling, from service activities [42] to production systems [37]. The authors implement Artificial Intelligence algorithms to benchmark examples [28], as well as to realworld examples [33]. The high efficiency of modern Artificial Intelligence methods are reflected in the implementation of the Kalman Filter approach for the purpose of multiprocessor evolutionary computation and obtaining estimated solutions [21].…”

Section: Literature Reviewmentioning

confidence: 99%

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Use of a Simulation Environment and Metaheuristic Algorithm for Human Resource Management in a Cyber-Physical System

Zhang

Buchmeister

Liu

et al. 2019

Springer Series in Advanced Manufacturing

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At the time of Industry 4.0 and the emergence of collaborative workplaces based on the cooperation of robots (machines) and humans, the number of human workplaces in the Industry 4.0 production system is crucial. In this chapter, we present the use of the evolutionary computation methods that use the input data of a real production system and transfer it through the five-stage Cyber-Physical System architecture into the simulation environment in order to determine the optimal number of workers. By using these methods, we confirm the hypothesis of the importance of correctly determining the number of workers in the manufacturing process in Industry 4.0. Number of workers' determination has a key influence on the product flow time, machine utilization and cost-effectiveness of a production system. Research results show the importance and effectiveness of combining evolutionary computation methods and simulation modelling for the purpose of implementing the advanced approaches of Industry 4.0. The demonstrated approach of combining evolutionary computing, simulation environments and methods of Industry 4.0 can be used from mass customization to mass production systems for the purpose of single-criteria or multi-criteria optimization.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Use of a Simulation Environment and Metaheuristic Algorithm for Human Resource Management in a Cyber-Physical System

Zhang

Buchmeister

Liu

et al. 2019

Springer Series in Advanced Manufacturing

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“…In this work, we develop a new Rayleigh wave dispersion equation (27) (or (28)), from which the dispersion curve can be easily computed. Based on this new forward model, we use the structure of a mixture density network to develop a new 9 If the noises drawn from N(0, 0.2% 2 ) are added to the test data, FW-MDN's test performance becomes 18.7%, which also implies non-robustness to noises.…”

Section: Discussionmentioning

confidence: 99%

“…Hence, for a given S-wave velocity VS , one can efficiently compute the phase velocity C through the procedure (27). It should be noted that although we do not obtain the closed form of function f , the value of C can be numerically determined by solving a nonlinear equation (25).…”

Section: Free Surfacementioning

confidence: 99%

“…Therefore, this type of methods often fails in the real world application. The heuristic global-optimization methods, such as the annealing simulation algorithm [30], the particle-swarm global optimization method [27], the genetic algorithm [9,20,36], and others [39,2,29,25], are usually derivative-free methods and the implementation is simple. Hence, during last decades, they have been widely used in the practice.…”

Section: Introductionmentioning

confidence: 99%

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A new mathematical model for dispersion of Rayleigh wave and a machine learning based inversion solver

Yang,

Xu,

Zhang

2021

Preprint

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In this work, by introducing the seismic impedance tensor we propose a new Rayleigh wave dispersion function in a homogeneous and layered medium of the Earth, which provides an efficient way to compute the dispersion curve -a relation between the frequencies and the phase velocities. With this newly established forward model, based on the Mixture Density Networks (MDN) we develop a machine learning based inversion approach, named as FW-MDN, for the problem of estimating the S-wave velocity from the dispersion curves. The method FW-MDN deals with the non-uniqueness issue encountered in studies that invert dispersion curves for crust and upper mantle models and attains a satisfactory performance on the dataset with various noise structure. Numerical simulations are performed to show that the FW-MDN possesses the characteristics of easy calculation, efficient computation, and high precision for the model characterization 1 .

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Multi‐mode non‐linear inversion of Rayleigh wave dispersion curves with grey wolf optimization and cuckoo search algorithm

Che,

Shen,

et al. 2024

Near Surface Geophysics

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Dispersion curve inversion is one of the core contents of Rayleigh wave data processing. However, the dispersion curve inversion has the characteristics of multi‐parameter, multi‐extremum as well as nonlinearity. In the face of Rayleigh wave data processing under complex seismic‐geological conditions, it is difficult to reconstruct an underground structure quickly and accurately apply a single global‐searching non‐linear inversion algorithm. For this reason, we proposed a strategy to invert multi‐order mode Rayleigh wave dispersion curves by combining with grey wolf optimization (GWO) and cuckoo search (CS) algorithms. On the basis of introducing the mechanism of iterative chaotic map with infinite collapses (ICMIC) and the strategy of dimension learning–based hunting (DLH), an improved GWO was developed that was called IDGWO (ICMIC and DLH GWO). After searching the near‐optimal region through IDGWO, the CS with a variable step‐size Lévy flight search mechanism was switched adaptively to complete the final inversion. The correctness of our method was verified by the multi‐order mode dispersion curve inversion of a six‐layer high‐velocity interlayer model. Then it was further applied to the processing of real seismic datasets. The research results show that our method fully utilizes the advantages of each of the two global‐searching non‐linear algorithms after integrating IDGWO and CS, while effectively balancing the ability between global search and local exploitation, further improving the convergence speed and inversion accuracy and having good anti‐noise performance.

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S-wave velocity profiling from refraction microtremor Rayleigh wave dispersion curves via PSO inversion algorithm

Cited by 14 publications

References 25 publications

Use of a Simulation Environment and Metaheuristic Algorithm for Human Resource Management in a Cyber-Physical System

Use of a Simulation Environment and Metaheuristic Algorithm for Human Resource Management in a Cyber-Physical System

A new mathematical model for dispersion of Rayleigh wave and a machine learning based inversion solver

Multi‐mode non‐linear inversion of Rayleigh wave dispersion curves with grey wolf optimization and cuckoo search algorithm

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