Bagging-based Positive–Unlabeled Data Learning Algorithm with Base Learners Random Forest and XGBoost for 3D Exploration Targeting in the Kalatongke District, Xinjiang, China

Gao, Meng; Wang, Gongwen; Yang, Wangdong; Zhang, Zhiqiang; Cai, Dongyu; Xu, Yunchou; Yang, Shuren

doi:10.1007/s11053-023-10170-y

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Cited by 5 publications

References 61 publications

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3D Au Targeting using Machine Learning with Different Sample Combination and Return-Risk Analysis in the Sanshandao-Cangshang District, Shandong Province, China

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Wang,

Carranza

et al. 2023

Nat Resour Res

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3D Au Targeting using Machine Learning with Different Sample Combination and Return-Risk Analysis in the Sanshandao-Cangshang District, Shandong Province, China

Gao,

Wang,

Carranza

et al. 2023

Nat Resour Res

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Quantitative prediction methods and applications of digital ore deposit models

Xiao,

Li,

Fan

et al. 2024

Ore Geology Reviews

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Porosity prediction through well logging data: A combined approach of convolutional neural network and transformer model (CNN-transformer)

Sun,

Pang,

Zhang

et al. 2024

Physics of Fluids

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Porosity, as a key parameter to describe the properties of rock reservoirs, is essential for evaluating the permeability and fluid migration performance of underground rocks. In order to overcome the limitations of traditional logging porosity interpretation methods in the face of geological complexity and nonlinear relationships, this study introduces a CNN (convolutional neural network)-transformer model, which aims to improve the accuracy and generalization ability of logging porosity prediction. CNNs have excellent spatial feature capture capabilities. The convolution operation of CNNs can effectively learn the mapping relationship of local features, so as to better capture the local correlation in the well log. Transformer models are able to effectively capture complex sequence relationships between different depths or time points. This enables the model to better integrate information from different depths or times, and improve the porosity prediction accuracy. We trained the model on the well log dataset to ensure that it has good generalization ability. In addition, we comprehensively compare the performance of the CNN-transformer model with other traditional machine learning models to verify its superiority in logging porosity prediction. Through the analysis of experimental results, the CNN-transformer model shows good superiority in the task of logging porosity prediction. The introduction of this model will bring a new perspective to the development of logging technology and provide a more efficient and accurate tool for the field of geoscience.

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Bagging-based Positive–Unlabeled Data Learning Algorithm with Base Learners Random Forest and XGBoost for 3D Exploration Targeting in the Kalatongke District, Xinjiang, China

Cited by 5 publications

References 61 publications

3D Au Targeting using Machine Learning with Different Sample Combination and Return-Risk Analysis in the Sanshandao-Cangshang District, Shandong Province, China

3D Au Targeting using Machine Learning with Different Sample Combination and Return-Risk Analysis in the Sanshandao-Cangshang District, Shandong Province, China

Quantitative prediction methods and applications of digital ore deposit models

Porosity prediction through well logging data: A combined approach of convolutional neural network and transformer model (CNN-transformer)

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