A Generic Shear Wave Velocity Profiling Model for Use in Ground Motion Simulation

Tang, Yajuan; Xiang, Xinmei; Sun, Jing; Zhang, Yongshan

doi:10.3390/geosciences10100408

Cited by 7 publications

(2 citation statements)

References 51 publications

(65 reference statements)

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“…The 10 th layer is assumed to be a uniform half space of infinite thickness. Then the P-wave velocity (Tang et al, 2020) and the density (Ikeda et al, 2017) of each layer can be expressed by the S-wave velocity as:…”

Section: Sample Set Constructionmentioning

confidence: 99%

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: Sample Set Constructionmentioning

confidence: 99%

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

Luo

2023

Preprint

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“…Although their influence on the Rayleigh wave velocity is much smaller than that of V s , accurate values are also important for calculating dispersion data. Much previous research provided the empirical relationships among V p , V s and ρ [27,28], which apply to different lithology and depth ranges. In our study, we use the approximate relationship given by Tang et al to calculate the V p .…”

Section: Generate Velocity Modelsmentioning

confidence: 99%

Inversion of Rayleigh Wave Dispersion Curve Extracting from Ambient Noise Based on DNN Architecture

Meng,

Chen,

Sha

et al. 2023

Applied Sciences

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The inversion of the Rayleigh wave dispersion curve is a crucial step in obtaining the shear wave velocity (VS) of near-surface structures. Due to the characteristics of being ill-posed and nonlinear, the existing inversion methods presented low efficiency and ambiguity. To address these challenges, we describe a six-layer deep neural network algorithm for the inversion of 1D VS from dispersion curves of the fundamental mode Rayleigh surface waves. Our method encompasses several key advancements: (1) we use a finer layer to construct the 1-D VS model of the subsurface, which can describe a more complex near-surface geology structure; (2) considering the ergodicity and orderliness of strata evolution, the constrained Markov Chain was employed to reconstruct the complex velocity model; (3) we build a practical and complete dispersion curve inversion process. Our model tested the performance using a random synthetic dataset and the influence of different factors, including the number of training samples, learning rate, and the selection of optimal artificial neural network architecture. Finally, the field test dispersion data were used to further verify the method’s effectiveness. Our synthetic dataset proved the diversity and rationality of the random VS model. The results of training and predicting showed higher accuracy and could speed the inversion process (only ~15 s), and we proved the important effect of different factors. The outcomes derived from the application of this technique to the measured dispersion data in the Yellow River Delta exhibit a strong correlation with the outcomes obtained from the integration of the very fast simulated annealing method and the downhill simplex method, as well as the statistically derived shear wave velocity data of the sedimentary layers in the Yellow River Delta. From a long-term perspective, our method can provide an alternative for deriving VS models for complex near-surface structures.

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