Matched-field geoacoustic inversion based on radial basis function neural network

Shen, Yining; Pan, Xiaoyong; Zhang, Zheng; Gerstoft, Peter

doi:10.1121/10.0002656

Cited by 28 publications

(13 citation statements)

References 38 publications

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“…There are many forms of RBF, many of which satisfy the Micchelli theorem, and its characteristic is that the quantity of hidden nodes is equal to the quantity of input samples. The following three RBFs satisfy the Micchelli theorem [ 23 , 24 ].…”

Section: Methodsmentioning

confidence: 99%

[Retracted] Macroeconomic Image Analysis and GDP Prediction Based on the Genetic Algorithm Radial Basis Function Neural Network (RBFNN‐GA)

Zhu

Meng

2021

Computational Intelligence and Neuroscience

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The prediction of gross domestic product (GDP) is a research hotspot, and its importance is self-evident. Its complex internal change mechanism also increases the difficulty of analyzing GDP data. The genetic algorithm (GA) is applied to the parameter design of the radial basis function neural network (RBFNN) based on genetic algorithm optimization (RBFNN-GA). An economic zone GDP image prediction model is proposed, which realizes the optimal design of the center vector, the base width vector of the RBFNN node function, and the weight between the hidden layer and output layer. Based on the GDP data over the years, this paper uses the RBFNN-GA prediction model to analyze and predict the GDP image and compares the image prediction results. The results show that the genetic algorithm is used to optimize RBFNN, which gives full play to the advantages of the two algorithms. The relative error of the RBFNN-GA prediction model is only 3.52%. Compared with the prediction results, the prediction accuracy is significantly higher than the ARIMA time series model and GM (1,1) model.

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Section: Methodsmentioning

confidence: 99%

[Retracted] Macroeconomic Image Analysis and GDP Prediction Based on the Genetic Algorithm Radial Basis Function Neural Network (RBFNN‐GA)

Zhu

Meng

2021

Computational Intelligence and Neuroscience

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“…The distance of the input from the central point and the degree of activation have a direct relationship. When the degree of activation is lower, the distance between the input and the central point is greater (Shen et al, 2020). This feature is called the local feature of the hidden layer node.…”

Section: Rbfnnmentioning

confidence: 99%

“…Determining the hidden layer and the output weight: The construction and training of RBFNN have a process of determining the number of neurons of the hidden layer, the center, and the width of each basis function of the hidden layer and the output weight (Shen et al, 2020). Three forms of RBF that can satisfy Micchelli's theorem are given below (Gaussian, Multiquadric, and Inverse polyquadratic function) (equations 5-7).…”

Section: Rbfnnmentioning

confidence: 99%

A new development algorithm for permeability prediction: A new milestone

Sun

Liang

2022

Front. Ecol. Evol.

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Permeability is one of the most important reservoir rock parameters in petroleum engineering, reservoir, and exploitation. This parameter causes the movement of hydrocarbon reserves in the reservoir rock. Therefore, it is an important parameter from the economic point of view because it greatly impacts the amount of extraction from the reservoir rock. In this study, the combined RBFNN-GA algorithm and 200 data sets collected from a field in the Middle East were used to predict permeability. Water saturation, porosity, and specific surface are the input variables used in this study. GA has advantages such as solving complex optimization problems of continuous functions and multi-objective problems. The advantages of RBF neural networks are that they are easy to design, strongly tolerant to input noise, and have good generalization. The RBFNN-GA model has the advantages of both algorithms. RBFNN-GA algorithm and experimental models have been compared in terms of performance accuracy. The results show that RBFNN-GA with STD = 89.8 and R-square = 0.9011 for the total data set obtained from a field in the Middle East has better accuracy and performance in predicting permeability than experimental models. Compared to other neural network methods, the RBFNN-GA model has a higher performance accuracy and is efficient for predicting other parameters. Oil researchers and engineers can use this method to predict other parameters in their studies and research.

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“…These methods typically require searching the entire parameter space and require forward fitting at each iteration, which is computationally intensive. Neural network technology is one of the rapidly developed information processing technologies [6]. The neural network itself is a nonlinear dynamic system with strong self-learning, self-adapting, self-organizing and fault-tolerant capabilities.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Application in Dispersion Curve Inversion of Seabed Geoacoustic Parameters

Zhang

Pan

2022

J. Phys.: Conf. Ser.

Self Cite

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This paper estimates seabed shear-wave velocities and the thickness of the surface sediments using Scholte wave dispersion curves extracted from data. Common surface wave dispersion curve inversion methods are divided into: local linearization methods and global optimization methods. These are model-driven, the inversion process takes a lot of time, and it is easy to get a local optimum, and the inversion results are inaccurate. Aiming at the shortcomings of the existing surface wave dispersion inversion methods, this paper introduces an inversion method based on neural network, and fits the Scholte wave dispersion curve to obtain the geoacoustic parameters of the surface sediments. Neural network inversion is data-driven, and the model is extracted from the data, which can improve the speed and accuracy of surface wave inversion. By simulating the shallow sea model, better results are obtained, and at the same time, the experimental data is used for calculation, the inversion results are close to the traditional methods, and the inversion speed is improved.

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Matched-field geoacoustic inversion based on radial basis function neural network

Cited by 28 publications

References 38 publications

[Retracted] Macroeconomic Image Analysis and GDP Prediction Based on the Genetic Algorithm Radial Basis Function Neural Network (RBFNN‐GA)

[Retracted] Macroeconomic Image Analysis and GDP Prediction Based on the Genetic Algorithm Radial Basis Function Neural Network (RBFNN‐GA)

A new development algorithm for permeability prediction: A new milestone

Neural Network Application in Dispersion Curve Inversion of Seabed Geoacoustic Parameters

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