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Towards optimization of boosting models for formation lithology identification

Towards optimization of boosting models for formation lithology identification
Towards optimization of boosting models for formation lithology identification
Lithology identification is an indispensable part in geological research and petroleum engineering study. In recent years, several mathematical approaches have been used to improve the accuracy of lithology classification. Based on our earlier work that assessed machine learning models on formation lithology classification, we optimize the boosting approaches to improve the classification ability of our boosting models with the data collected from the Daniudi gas field and Hangjinqi gas field. Three boosting models, namely, AdaBoost, Gradient Tree Boosting, and eXtreme Gradient Boosting, are evaluated with 5-fold cross validation. Regularization is applied to the Gradient Tree Boosting and eXtreme Gradient Boosting to avoid overfitting. After adapting the hyperparameter tuning approach on each boosting model to optimize the parameter set, we use stacking to combine the three optimized models to improve the classification accuracy. Results suggest that the optimized stacked boosting model has better performance concerning the evaluation matrix such as precision, recall, and f1 score compared with the single optimized boosting model. Confusion matrix also shows that the stacked model has better performance in distinguishing sandstone classes.
1024-123X
Xie, Yunxin
5d7e68fa-ced2-4381-9183-92a58e46ebd6
Zhu, Chenyang
67a1c085-5e0b-4dcf-8770-b99c520115fc
Lu, Yue
447d3b21-4bd8-498d-bd22-f018566b4604
Zhu, Zhengwei
90a8bf82-034a-4c94-93cf-afadf9ca7a19
Xie, Yunxin
5d7e68fa-ced2-4381-9183-92a58e46ebd6
Zhu, Chenyang
67a1c085-5e0b-4dcf-8770-b99c520115fc
Lu, Yue
447d3b21-4bd8-498d-bd22-f018566b4604
Zhu, Zhengwei
90a8bf82-034a-4c94-93cf-afadf9ca7a19

Xie, Yunxin, Zhu, Chenyang, Lu, Yue and Zhu, Zhengwei (2019) Towards optimization of boosting models for formation lithology identification. Mathematical Problems in Engineering, 2019. (doi:10.1155/2019/5309852).

Record type: Article

Abstract

Lithology identification is an indispensable part in geological research and petroleum engineering study. In recent years, several mathematical approaches have been used to improve the accuracy of lithology classification. Based on our earlier work that assessed machine learning models on formation lithology classification, we optimize the boosting approaches to improve the classification ability of our boosting models with the data collected from the Daniudi gas field and Hangjinqi gas field. Three boosting models, namely, AdaBoost, Gradient Tree Boosting, and eXtreme Gradient Boosting, are evaluated with 5-fold cross validation. Regularization is applied to the Gradient Tree Boosting and eXtreme Gradient Boosting to avoid overfitting. After adapting the hyperparameter tuning approach on each boosting model to optimize the parameter set, we use stacking to combine the three optimized models to improve the classification accuracy. Results suggest that the optimized stacked boosting model has better performance concerning the evaluation matrix such as precision, recall, and f1 score compared with the single optimized boosting model. Confusion matrix also shows that the stacked model has better performance in distinguishing sandstone classes.

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5309852 - Version of Record
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Accepted/In Press date: 24 July 2019
Published date: 14 August 2019

Identifiers

Local EPrints ID: 433514
URI: https://eprints.soton.ac.uk/id/eprint/433514
ISSN: 1024-123X
PURE UUID: 7388d31e-87e0-4236-a312-661e2278792a
ORCID for Chenyang Zhu: ORCID iD orcid.org/0000-0002-2145-0559

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Date deposited: 23 Aug 2019 16:30
Last modified: 10 Dec 2019 02:00

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