The surface waves-based seismic exploration of soil and ground water

Serdyukov, A. S.; Yablokov, A. V.; Chernyshov, Gleb S.; Azarov, A. V.

doi:10.1088/1755-1315/53/1/012010

Cited by 8 publications

(3 citation statements)

References 4 publications

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“…Rayleigh wave exploration based on ambient noise is an important method for investigating near-surface shear wave velocity (V S ) structures. It is widely used in fundamental geological surveys [1,2], geotechnical engineering [3], geological hazard early warning [4,5], hydrogeological surveys [6], and other fields. In particular, it has incomparable advantages for urban subsurface surveying and measuring the VS of weakly consolidated soils in offshore areas because other geophysical methods are easily susceptible to noise [7] or do not allow for borehole testing to be carried out.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…We consider the inversion of surface wave dispersion curves to obtain shear‐wave velocity (

V_{S}

) profiles, which is a key step in the multichannel analysis of surface waves (MASW) (Park et al ., 1999; Socco et al ., 2010b). MASW is widely employed for noninvasive geotechnical site investigations (Strobbia et al ., 2010; Mahvelati and Coe, 2017; Serdyukov et al ., 2017; Rahman et al ., 2018) and can be applied to the estimation of statics corrections, which can increase the accuracy of deep reservoir exploration by seismic reflection data (Socco et al ., 2010a; Roohollah, 2013). Surface wave analysis is the preferred method for shallow subsurface time‐lapse four‐dimensional (4D) monitoring (Ikeda et al ., 2018), which is particularly important because of the growing demand for

{CO}_{2}

monitoring during the injection and storage of

{CO}_{2}

in carbon capture projects (White, 2009).…”

Section: Introductionmentioning

confidence: 99%

An artificial neural network approach for the inversion of surface wave dispersion curves

Yablokov

Serdyukov

Loginov

et al. 2021

Geophysical Prospecting

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We describe a new algorithm for the inversion of one‐dimensional shear‐wave velocity profiles from dispersion curves of the fundamental mode of Rayleigh surface waves. The novelties of our approach are that the layer velocities and thicknesses are set as unknowns, and an artificial neural network is proposed to solve the inverse problem. We suggest that training data should be calculated for a set of random synthetic velocity layered models, while layer thicknesses and velocities should be set to fixed intervals, with ranges estimated based on the systematic application of empirical relations between Rayleigh and S‐wave velocities to the dispersion data. Our main challenge is a total overhaul of the artificial neural network, which includes selecting the optimal artificial neural network architecture and parameters by performing a large number of numerical experiments. Our synthetic results show that the accuracy of the proposed approach outperforms that of the Monte Carlo approach. We illustrate our proposed method with West Siberia data processing obtained from an area of approximately 800 km2. From a user perspective, the main strength of our method is the computationally efficient processing of large amounts of dispersion data, which make it well suited for four‐dimensional near‐surface monitoring.

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“…Rayleigh wave measurements are highly sensitive to S‐wave velocity ( Vs ) and hence the multichannel analysis of surface waves (MASW) (Park et al ., 1999; Socco and Strobbia, 2004; Foti et al ., 2018) is widely employed for near‐surface investigations (Xia et al ., 2003; Lai, 2005; Strobbia et al ., 2010; Serdyukov et al ., 2017; Rahman et al ., 2018; Cercato et al ., 2020) and also for statics corrections in deep seismic exploration (Socco et al ., 2010). Well‐established inversion methods rely on dispersion curve inversion under the assumption of a one‐dimensional (1D) subsurface structure (Dal Moro et al ., 2007; Socco and Boiero, 2008; Maraschini and Foti, 2010; Cercato, 2011; Di Giulio et al ., 2019).…”

Section: Introductionmentioning

confidence: 99%

A hybrid residual neural network–Monte Carlo approach to invert surface wave dispersion data

Aleardi

Stucchi

2021

Near Surface Geophysics

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Surface-wave inversion is a non-linear and ill-conditioned problem usually solved through deterministic or global optimization approaches. Here, we present an alternative method based on machine learning. Under the assumption of a local onedimensional model, we train a residual neural network to predict the non-linear mapping between the full dispersion image and the model space, parameterized in terms of shear wave velocity and layer thicknesses. On the one hand, compared to standard convolutional neural networks, the residual network prevents the vanishing gradient problem when training a deep network. On the other hand, the use of the full dispersion image avoids the time-consuming and often ambiguous picking procedure and allows considering higher modes in the inversion framework. One key aspect of any machine learning inversion strategy is the definition of an appropriate training set. In this case, the models forming the training and validation examples are uniformly drawn from previously defined ranges that cover a wide range of possible near-surface layered Vs models. The reflectivity method constitutes the forward modelling operator that converts the model parameters into the observed shot gathers. The inversion also includes a Monte Carlo simulation strategy that propagates onto the model space the uncertainties related to noise in the data and the modelling error introduced by the network approximation. We first discuss synthetic inversions to assess the applicability of the proposed method and to analyse the effect of erroneous model parameterizations. The inversion results are also benchmarked with those provided by a more standard approach in which the particle swarm optimization algorithm inverts the fundamental mode only. Then, we discuss a field data application. Our tests confirm that the residual neural network inversion provides accurate model estimations and reliable uncertainty appraisals. One of the main benefits of the proposed approach is that once the network is trained it provides the near-surface shear wave velocity profile in near real-time.

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The surface waves-based seismic exploration of soil and ground water

Cited by 8 publications

References 4 publications

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

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

An artificial neural network approach for the inversion of surface wave dispersion curves

A hybrid residual neural network–Monte Carlo approach to invert surface wave dispersion data

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