Prediction of Shear Wave Velocity in Underground Layers Using SASW and Artificial Neural Networks

Alimoradi, Andisheh; Shahsavani, Hashem; Rouhani, Abolghasem Kamkar

doi:10.4236/eng.2011.33031

Cited by 9 publications

(3 citation statements)

References 2 publications

(1 reference statement)

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“…Machine learning (ML) algorithms and neural networks have an advantage in extracting relationships between various data (Thanh et al, 2024a;Thanh et al, 2024b;Ewees et al, 2024;Zhang et al, 2024), which can serve in establishing an accurate nonlinear relationship between S-wave velocity and reservoir parameters. Therefore, the prediction of S-wave velocity using logging data and neural networks has been widely employed in field data (Alimoradi et al, 2011;Maleki et al, 2014;Mehrgini et al, 2017;Feng et al, 2023). However, conventional neural networks only establish a point-to-point relationship between logging data and S-wave velocity, without considering the variation pattern of the logging curve at depth, resulting in limited accuracy of S-wave velocity prediction.…”

Section: Introductionmentioning

confidence: 99%

Shear wave velocity prediction based on 1DCNN-BiLSTM network with attention mechanism

Feng,

Liu,

Yang

et al. 2024

Front. Earth Sci.

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The Shear wave (S-wave) velocity is an essential parameter in reservoir characterization and evaluation, fluid identification, and prestack inversion. However, the cost of obtaining S-wave velocities directly from dipole acoustic logging is relatively high. At the same time, conventional data-driven S-wave velocity prediction methods exhibit several limitations, such as poor accuracy and generalization of empirical formulas, inadequate exploration of logging curve patterns of traditional fully connected neural networks, and gradient explosion and gradient vanishing problems of recurrent neural networks (RNNs). In this study, we present a reliable and low-cost deep learning (DL) approach for S-wave velocity prediction from real logging data to facilitate the solution of these problems. We designed a new network sensitive to depth sequence logging data using conventional neural networks. The new network is composed of one-dimensional (1D) convolutional, bidirectional long short-term memory (BiLSTM), attention, and fully connected layers. First, the network extracts the local features of the logging curves using a 1D convolutional layer, and then extracts the long-term sequence features of the logging curves using the BiLSTM layer, while adding an attention layer behind the BiLSTM network to further highlight the features that are more significant for S-wave velocity prediction and minimize the influence of other features to improve the accuracy of S-wave velocity prediction. Afterward, the nonlinear mapping relationship between logging data and S-wave velocity is established using several fully connected layers. We applied the new network to real field data and compared its performance with three traditional methods, including a long short-term memory (LSTM) network, a back-propagation neural network (BPNN), and an empirical formula. The performance of the four methods was quantified in terms of their coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The new network exhibited better performance and generalization ability, with R2 greater than 0.95 (0.9546, 0.9752, and 0.9680, respectively), RMSE less than 57 m/s (56.29, 23.18, and 30.17 m/s, respectively), and MAE less than 35 m/s (34.68, 16.49, and 21.47 m/s, respectively) for the three wells. The test results demonstrate the efficacy of the proposed approach, which has the potential to be widely applied in real areas where S-wave velocity logging data are not available. Furthermore, the findings of this study can help for a better understanding of the superiority of deep learning schemes and attention mechanisms for logging parameter prediction.

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Section: Introductionmentioning

confidence: 99%

Shear wave velocity prediction based on 1DCNN-BiLSTM network with attention mechanism

Feng,

Liu,

Yang

et al. 2024

Front. Earth Sci.

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“…By using this parameter, engineers can determine the stability of the rock layers and take appropriate measures to mitigate the impact of earthquakes on the system (Zheng et al, 2021). Traditional methods for predicting Vs rely on laboratory tests or field measurements, which are time-consuming and expensive (Maleki et al, 2014;Adjei et al, 2020). Moreover, these methods may not provide sufficient spatial coverage or resolution to capture local variations in soil properties.…”

Section: Introductionmentioning

confidence: 99%

Maximizing hydropower station safety against earthquake through extreme learning machine-enabled shear waves velocity prediction

Song,

Guan,

Wang

et al. 2024

Front. Environ. Sci.

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Hydropower stations are important infrastructures for generating clean energy. However, they are vulnerable to natural disasters such as earthquakes, which can cause severe damage and even lead to catastrophic failures. Therefore, it is essential to develop effective strategies for maximizing hydropower station safety against earthquakes. To evaluate the potential shear rate of surrounding rock layers, the shear wave velocity (Vs) parameter can be used as a useful tool. This parameter helps to determine the velocity at which shear waves travel through the rock layers, which can indicate their stability and susceptibility to earthquakes. This study will investigate the significance of the Vs parameter in evaluating the potential shear rate of rock layers surrounding hydropower stations and how it can be used to ensure their safety and efficiency in earthquake-prone regions. Furthermore, a novel approach is proposed in this research, which involves using extreme learning machine (ELM) technology to predict Vs and enhance the seismic safety of hydropower stations. The ELM model predicts the Vs of the soil layers around the hydropower station, a crucial factor in determining the structure’s seismic response. The predicted Vs is then used to assess seismic hazard and design appropriate safety measures. The ML-ELM model outperformed both the ELM and empirical models, with an RMSE of 0.0432 μs/ft and R2 of 0.9954, as well as fewer outlier data predictions. This approach shows promise for predicting Vs in similar environments, and future research could explore its effectiveness in other datasets and practical applications.

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“…Neural networks have great advantages in dealing with nonlinear problems, and S-wave velocity prediction is a typical nonlinear problem. In recent years, S-wave velocity prediction using well log data and back-propagation neural network (BPNN) has been widely applied in practical field areas (Eskandari et al, 2004;Alimoradi et al, 2011;Maleki et al, 2014). Each hidden layer of the recurrent neural networks (RNNs) has a feedback to a previous layer, and the subsequent behavior can be shaped by the response of the previous layer.…”

Section: Introductionmentioning

confidence: 99%

Shear wave velocity prediction based on deep neural network and theoretical rock physics modeling

Feng

Zeng²,

Xu³

et al. 2023

Front. Earth Sci.

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Shear wave velocity plays an important role in both reservoir prediction and pre-stack inversion. However, the current deep learning-based shear wave velocity prediction methods have certain limitations, including lack of training dataset, poor model generalization, and poor physical interpretability. In this study, the theoretical rock physics models are introduced into the construction of the labeled dataset for deep learning algorithms, and a forward simulation of the theoretical rock physics models is utilized to supplement the dataset that incorporates geological and geophysical knowledge. This markedly increases the physical interpretability of the deep learning algorithm. Theoretical rock physics models for two different types of reservoirs, i.e., conventional sandstone and tight sandstone reservoirs, are first established. Then, a full-sample labeled dataset is constructed using these two types of theoretical rock physics models to traverse the elasticity parameter space of the two types of reservoirs through random variation and combination of parameters in the theoretical models. Finally, based on the constructed full-sample labeled dataset, four parameters (P-wave velocity, clay content, porosity, and density) that are highly correlated with the shear wave velocity are selected and combined with a deep neural network to build a deep shear wave velocity prediction network with good generalization and robustness, which can be directly applied to field data. The errors between the predicted shear wave velocity using the deep neural network and the measured shear wave velocity data in the laboratory and the logging data in three real field work areas are less than 5%, which are much smaller than the errors predicted by both Han’s and Castagna’s empirical formula. Furthermore, the prediction accuracy and generalization performance are better than those of these two common empirical formulas. The forward simulation based on theoretical models supplements the training dataset and provides high-quality labels for machine learning. This can considerably improve the interpretability and generalization of models in real applications of a machine learning algorithm.

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Prediction of Shear Wave Velocity in Underground Layers Using SASW and Artificial Neural Networks

Cited by 9 publications

References 2 publications

Shear wave velocity prediction based on 1DCNN-BiLSTM network with attention mechanism

Shear wave velocity prediction based on 1DCNN-BiLSTM network with attention mechanism

Maximizing hydropower station safety against earthquake through extreme learning machine-enabled shear waves velocity prediction

Shear wave velocity prediction based on deep neural network and theoretical rock physics modeling

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