Near-surface velocity analysis for single-sensor data: An integrated workflow using surface waves,  AI,  and structure-regularized inversion

Alyousuf, Taqi; Colombo, Daniele; Rovetta, Diego; Sandoval-Curiel, Ernesto

doi:10.1190/segam2018-2994696.1

Cited by 13 publications

(6 citation statements)

References 8 publications

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“…It is expensive to set up an optimization space that includes all the hyperparameter combinations. Referring to deep learningbased surface wave exploration research (Alyousuf et al, 2018;Zhang et al, 2020;Aleardi and Stucchi, 2021;Dai et al, 2021;Fu et al, 2021;Luo et al, 2022), this study sets up four hidden layer blocks. The layers of each hidden layer block are between (0, 2), and the neurons are between (16,256).…”

Section: Tuning the Dnn Hyperparameters Using Bayesian Optimization A...mentioning

confidence: 99%

“…Tschannen et al (2022) accelerated the compilation speed in wavelet extraction through deep learning, which restrains the iterative adjustment parameters and potential noise. Similarly, some researchers in surface wave exploration have applied deep learning to extract dispersion curves (Alyousuf et al, 2018;Zhang et al, 2020;Dai et al, 2021) and invert velocity structures (Aleardi and Stucchi, 2021;Fu et al, 2021;Luo et al, 2022). Alyousuf et al (2018) used a fully connected network to extract the fundamental-mode dispersion curve.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, some researchers in surface wave exploration have applied deep learning to extract dispersion curves (Alyousuf et al, 2018;Zhang et al, 2020;Dai et al, 2021) and invert velocity structures (Aleardi and Stucchi, 2021;Fu et al, 2021;Luo et al, 2022). Alyousuf et al (2018) used a fully connected network to extract the fundamental-mode dispersion curve. Zhang et al (2020) presented a convolution network to automatically extract dispersion curves.…”

Section: Introductionmentioning

confidence: 99%

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Rapid construction of Rayleigh wave dispersion curve based on deep learning

2023

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Introduction: The dispersion curve of the Rayleigh-wave phase velocity (VR) is widely utilized to determine site shear-wave velocity (Vs) structures from a distance of a few metres to hundreds of metres, even on a ten-kilometre crustal scale. However, the traditional theoretical-analytical methods for calculating VRs of a wide frequency range are time-consuming because numerous extensive matrix multiplications, transfer matrix iterations and the root searching of the secular dispersion equation are involved. It is very difficult to model site structures with many layers and apply them to a population-based inversion algorithm for which many populations of multilayers forward modelling and many generations of iterations are essential.Method: In this study, we propose a deep learning method for constructing the VR dispersion curve in a horizontally layered site with great efficiency. A deep neural network (DNN) based on the fully connected dense neural network is designed and trained to directly learn the relationships between Vs structures and dispersion curves. First, the training and validation sets are generated randomly according to a truncated Gaussian distribution, in which the mean and variance of the Vs models are statistically analysed from different regions’ empirical relationships between soil Vs and its depth. To be the supervised dataset, the corresponding VRs are calculated by the generalized reflection-transmission (R/T) coefficient method. Then, the Bayesian optimization (BO) is designed and trained to seek the optimal architecture of the deep neural network, such as the number of neurons and hidden layers and their combinations. Once the network is trained, the dispersion curve of VR can be constructed instantaneously without building and solving the secular equation.Results and Discussion: The results show that the DNN-BO achieves a coefficient of determination (R2) and MAE for the training and validation sets of 0.98 and 8.30 and 0.97 and 8.94, respectively, which suggests that the rapid method has satisfactory generalizability and stability. The DNN-BO method accelerates the dispersion curve calculation by at least 400 times, and there is almost no increase in computation expense with an increase in soil layers.

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Section: Tuning the Dnn Hyperparameters Using Bayesian Optimization A...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Rapid construction of Rayleigh wave dispersion curve based on deep learning

2023

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“…For the supervised learning methods, Alyousuf et al. (2018) proposed to pick fundamental‐mode dispersion curves with a multiple layer neural network. Li et al.…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al (2021) develop an unsupervised automatic picking scheme based on the Gaussian mix model clustering algorithm. For the supervised learning methods, Alyousuf et al (2018) proposed to pick fundamental-mode dispersion curves with a multiple layer neural network. Li et al (2019) studied the separation of multi-mode surface waves by supervised machine learning.…”

mentioning

confidence: 99%

Automatic Extraction of Surface Wave Dispersion Curves Using Unsupervised Learning and High‐Resolution Tau‐p Transform

Yao,

Cao,

Huang

et al. 2023

Earth and Space Science

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Dispersion curves for surface wave recordings are required input for many surface wave inversion methods to image subsurface shear wave velocity distribution, while the conventional extraction of dispersion curves requires significant amount of human interaction. This step impedes efficiency enhancement of surface wave analysis and its automation. In this paper, we present an unsupervised learning scheme to achieve efficient automatic picking of dispersion curves of the fundamental mode for surface wave gathers. This scheme is composed of four major steps: computing a frequency velocity(f‐v) spectrum for the surface wave gather using a high‐resolution Tau‐p transform improved by the iteratively shrinkage Thresholding algorithm (ISTA) algorithm, generating clusters points along the dispersion energy in the f‐v spectrum via a weighted Kmeans algorithm, filtering these cluster points by principal component analysis (PCA) and Local Outlier Factor (LOF) algorithms to remove the erroneous clusters, and fitting the remaining clusters to form the dispersion curve. Tests with synthetic and field noisy surface wave recordings demonstrated the effectiveness of this approach and its potential in automatic processing of noisy surface wave data sets.

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Coupling strategies in multiparameter geophysical joint inversion

Colombo

Rovetta

2018

Geophysical Journal International

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SUMMARY A big potential lies in the quantitative integration of multiple geophysical measurements for what concerns more unique and robust inversion results, complementary sensitivity to geological features and enhanced resolution. The mechanisms that enable such integration typically rely on statistics where the multiphysics parameter values are related to each other through regression functions (rock physics) or through the shape of the parameter spatial distributions (structure). Such coupling operators are jointly minimized with the data misfit to obtain coupled parameter distributions. We explore the application of various coupling mechanisms to synthetic and real data comprising seismic and electromagnetic measurements acquired in complex geological conditions such as salt geology and complex near surface in desert environment. Two types of structure operators, consisting of the standard cross-gradient and a newly introduced summative gradient, together with rock-physics operators are tested and combined for velocity model reconstruction of salt overburden in a marine environment and in a complex near surface case. Results of seismic-EM joint minimization suggest that the summative gradient operator with the introduction of the sign of the gradient correlation provides a strong coupling mechanism that can become advantageous in the presence of noise-affected seismic data. The rock-physics coupling mechanism is extremely strong and its effectiveness depends primarily on the reliability of the rock-physics relation. A Bayesian approach for the rock-physics operator is introduced to balance the related uncertainties and it is successfully tested in a joint minimization scheme. The combined use of rock-physics and structure coupling operators provides the best results in synthetic and real data applications. The use and combination of various regularization operators, as described, provide a formidable toolbox for solving a wide variety of ill-posed and non-unique geophysical inverse problems.

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Near-surface velocity analysis for single-sensor data: An integrated workflow using surface waves, AI, and structure-regularized inversion

Cited by 13 publications

References 8 publications

Rapid construction of Rayleigh wave dispersion curve based on deep learning

Rapid construction of Rayleigh wave dispersion curve based on deep learning

Automatic Extraction of Surface Wave Dispersion Curves Using Unsupervised Learning and High‐Resolution Tau‐p Transform

Coupling strategies in multiparameter geophysical joint inversion

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