Study the elastic properties and the anisotropy of rocks using different machine learning methods

Nguyen‐Sy, Tuan; To, Quy‐Dong; Vu, Minh‐Ngoc; Nguyen, Thê-Duong; Nguyen, Thoi‐Trung

doi:10.1111/1365-2478.13011

Cited by 7 publications

(5 citation statements)

References 26 publications

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“…The XGB model, an advanced tree boosting system presented by Chen and Guestrin, 35 was confirmed to be a perfect machine-learning model for learning tabular datasets. 31,[36][37][38] It uses many additive functions f k to represent the relationship between the vector of input features X i and the output target y i of ith measurement as…”

Section: The Chosen Xgboost Modelmentioning

confidence: 99%

“…35 XGBoost model is considered for the present study because it was confirmed to outperform other advanced machine-learning models for different types of tabular datasets. 31,[36][37][38] Also, the main objective of this study is not to repeat a comparison between different machine-learning tools but it focuses more on the data treatment strategy. More precisely, we aim to demonstrate that adding more low-cost and measurable features to the initial dataset would significantly improve the accuracy of the machine-learning model.…”

Section: The Chosen Xgboost Modelmentioning

confidence: 99%

“…An extensive list of machine‐learning frameworks has been developed in literature including neural network models, 32 support vector models, 33 or tree‐based models, 34 among others. The XGB model, an advanced tree boosting system presented by Chen and Guestrin, 35 was confirmed to be a perfect machine‐learning model for learning tabular datasets 31,36–38 . It uses many additive functions

f_{k}

to represent the relationship between the vector of input features

X_{i}

and the output target

{\bar{y}}_{i}

of i th measurement as

{\bar{y}}_{i} goodbreak= y_{i}^{0} goodbreak+ η \sum_{k = 1}^{M} f_{k} ((), {boldX}_{i}),

where

y_{i}^{0}

is the initial guess of

{\bar{y}}_{i}

, it is simply an average overall measurement in the training subset;

η

is the learning rate and

M

the number of boosting trees.…”

Section: Optimized Machine‐learning Model For Predicting the Creep Of...mentioning

confidence: 99%

“…XGBoost model is considered for the present study because it was confirmed to outperform other advanced machine‐learning models for different types of tabular datasets 31,36–38 . Also, the main objective of this study is not to repeat a comparison between different machine‐learning tools but it focuses more on the data treatment strategy.…”

Section: Optimized Machine‐learning Model For Predicting the Creep Of...mentioning

confidence: 99%

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Optimized machine‐learning methods for predicting the long‐term viscoelastic behavior of heterogeneous concrete mixtures

Nguyen‐Sy

Thai

2023

Structural Concrete

Self Cite

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Long‐term creep compliance is one of the most important mechanical properties for evaluating the long‐term behavior of concrete structures. This paper aims to optimize machine‐learning models to predict this viscoelastic property. The most relevant dataset available in the literature is considered, cleaned, and preprocessed to optimize the outcome. The advanced XGBoost model, which is to be the most effective shallow machine‐learning model for modeling tabular datasets, is employed in this study to maximize model accuracy. Short‐term creep compliances of a given sample at typical ages are used as input features to model the long‐term creep compliance of concrete. This approach outperforms standard machine‐learning approaches that do not include short‐term creep as an input feature. Indeed, the short‐term behavior of concrete strongly influents its long‐term one. The optimized machine model presented herein is accurate and useful for practical applications. It uses input features that are easy to obtain to predict long‐term creep compliance up to several decades, which is difficult and expensive to measure.

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Section: The Chosen Xgboost Modelmentioning

confidence: 99%

Section: The Chosen Xgboost Modelmentioning

confidence: 99%

f_{k}

to represent the relationship between the vector of input features

X_{i}

and the output target

{\bar{y}}_{i}

of i th measurement as

{\bar{y}}_{i} goodbreak= y_{i}^{0} goodbreak+ η \sum_{k = 1}^{M} f_{k} ((), {boldX}_{i}),

where

y_{i}^{0}

is the initial guess of

{\bar{y}}_{i}

, it is simply an average overall measurement in the training subset;

η

is the learning rate and

M

the number of boosting trees.…”

Section: Optimized Machine‐learning Model For Predicting the Creep Of...mentioning

confidence: 99%

Section: Optimized Machine‐learning Model For Predicting the Creep Of...mentioning

confidence: 99%

See 2 more Smart Citations

Optimized machine‐learning methods for predicting the long‐term viscoelastic behavior of heterogeneous concrete mixtures

Nguyen‐Sy

Thai

2023

Structural Concrete

Self Cite

View full text Add to dashboard Cite

show abstract

“…Because of its ability to automate analytical models using training data with minimal human intervention, machine learning (ML) approach may be a good alternative for anisotropy estimation. Nguyen-Sy et al 9 utilized three machine learning models with Artificial Neural Network, Extreme Gradient Boosting and Support Vector Machine to predict transversely anisotropy elastic stiffnesses. These models incorporated porosity, density, compressional stress, pore pressure, and burial depth, all derived from core samples, as features to train and test the ML models.…”

Section: Introductionmentioning

confidence: 99%

Seismic Anisotropy Assessment based on Machine Learning Approaches

Zhao,

Bouchaala,

Jouini

2023

Preprint

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Estimation of seismic anisotropy parameters such as Thomson’s parameters is crucial in investigating fractured and finely layered geological media. However, most of inversion methods rely on complex physical models with initial assumptions, making the estimate non-reproducible and fracture interpretation subjective. To address these issues, we have proposed three classical machine learning methods and one deep learning algorithm to estimate Thomsen's parameters, namely Support Vector Regression, Extreme Gradient Boost, Multi-layer Perceptron and one-dimensional Convolutional Neural Network. Synthetic data were generated by using earth model by using well data within a finite difference numerical program. After a thorough investigation of synthetic data, amplitudes of direct and reflected waves in time and frequency domain were selected as input features to train machine learning methods. The optimization of machine learning hyperparameters enables the accomplishment of training and testing procedures were reached with high accuracies. Subsequently, the optimized machine learning methods were used to predict Thomsen’s parameters (\({\epsilon }\) and \({\delta }\)) of a shaley formation in the zone area. The estimated \({\epsilon }\) and \({\delta }\) were compared with reference values obtained at well location by using physics-based model. The least relative errors beween reference and machine learning Thomson’s parameters are spanning from 2.92–7.14%.

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P-wave velocity anisotropy in Hamra Quartzites reservoir, Hassi Messaoud oil field in Algeria

et al. 2022

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Study the elastic properties and the anisotropy of rocks using different machine learning methods

Cited by 7 publications

References 26 publications

Optimized machine‐learning methods for predicting the long‐term viscoelastic behavior of heterogeneous concrete mixtures

Optimized machine‐learning methods for predicting the long‐term viscoelastic behavior of heterogeneous concrete mixtures

Seismic Anisotropy Assessment based on Machine Learning Approaches

P-wave velocity anisotropy in Hamra Quartzites reservoir, Hassi Messaoud oil field in Algeria

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