GitHub - sunyingjian/AI-in-well-logging: 人工智能在石油测井上的应用包括采用机器学习，深度学习等相关方法进行岩性识别与相关测井曲线的回归。The application of artificial intelligence in well logging includes the use of machine learning, deep learning and other related methods for lithology identification and regression of related well logging data.

这是我的科研实验备份文件夹

如果觉得该项目对你有帮助，请帮我点个star 谢谢！

以上实验的实验数据均在名为numpy-的存储库中https://github.com/sunyingjian/numpy-

有任何问题欢迎在Discussions发帖或发起issue与我交流

推荐采用Google colab 运行实验，方便快捷 https://colab.research.google.com/

This is my scientific research experiment backup folder

If you think this project is helpful to you, please give me a star. Thank you!

The experimental data of the above experiments are all in a repository named numpy-https://github.com/sunyingjian/numpy-

If you have any questions, please open an issue to communicate with me, or send an email to [email protected]

Google colab is recommended to run experiments, which is convenient and quick https://colab.research.google.com/

Name		Name	Last commit message	Last commit date
Latest commit History 65 Commits
<Concise code>SVM Random Forests GBDT XGBoost facies classification.ipynb		<Concise code>SVM Random Forests GBDT XGBoost facies classification.ipynb
Conv1d_PE回归_regression_analysis.ipynb		Conv1d_PE回归_regression_analysis.ipynb
Facies_Classification_SVM.ipynb		Facies_Classification_SVM.ipynb
Informer原始代码.ipynb		Informer原始代码.ipynb
LICENSE		LICENSE
LSTM_simple_RNN_Bi_LSTM实现PE回归_regression_analysis.ipynb		LSTM_simple_RNN_Bi_LSTM实现PE回归_regression_analysis.ipynb
README.md		README.md
RNN_DNN岩性分类Lithology classification.ipynb		RNN_DNN岩性分类Lithology classification.ipynb
SECURITY.md		SECURITY.md
catboost美国油田_延安油田_Catboost U.S. Oilfield_Yan'an Oilfield.ipynb		catboost美国油田_延安油田_Catboost U.S. Oilfield_Yan'an Oilfield.ipynb
catboost美国油田_延安油田_gpu.ipynb		catboost美国油田_延安油田_gpu.ipynb
resnet_无步长_延安油田.ipynb		resnet_无步长_延安油田.ipynb
“02_Map_View_ipynb”的副本.ipynb		“02_Map_View_ipynb”的副本.ipynb
使用matplotlib_可视化_csv测井数据实现ui控制的测井曲线生成.ipynb		使用matplotlib_可视化_csv测井数据实现ui控制的测井曲线生成.ipynb
岩性分类_SVM.ipynb		岩性分类_SVM.ipynb
延安油田,KNN,gbdt,随机森林,xgboost.ipynb		延安油田,KNN,gbdt,随机森林,xgboost.ipynb
延安油田SMOTE,KNN,gbdt,随机森林,xgboost.ipynb		延安油田SMOTE,KNN,gbdt,随机森林,xgboost.ipynb
延安油田_岩性分类.ipynb		延安油田_岩性分类.ipynb
延安油田决策树.ipynb		延安油田决策树.ipynb
延安油田标签传播算法半监督_Yan'an Oilfield Label Propagation Algorithm Semi-supervised.ipynb		延安油田标签传播算法半监督_Yan'an Oilfield Label Propagation Algorithm Semi-supervised.ipynb
测井曲线画图总结.ipynb		测井曲线画图总结.ipynb
电阻率线性回归.ipynb		电阻率线性回归.ipynb
神经网络分类.ipynb		神经网络分类.ipynb
美国油田GMM_smote_knn_gbdt,随机森林，xgboost.ipynb		美国油田GMM_smote_knn_gbdt,随机森林，xgboost.ipynb
美国油田朴素贝叶斯.ipynb		美国油田朴素贝叶斯.ipynb
超参数选择.ipynb		超参数选择.ipynb
采用PCA_KPCA_LDA_做数据降维以美国油田为例.ipynb		采用PCA_KPCA_LDA_做数据降维以美国油田为例.ipynb
采用PCA_KPCA_LDA不采用归一化_做数据降维以美国油田为例.ipynb		采用PCA_KPCA_LDA不采用归一化_做数据降维以美国油田为例.ipynb
采用多元线性,岭回归,SVR,GBDT实现孔隙度回归_Use multiple linear, ridge regression, SVR, GBDT to achieve porosity regression.ipynb		采用多元线性,岭回归,SVR,GBDT实现孔隙度回归_Use multiple linear, ridge regression, SVR, GBDT to achieve porosity regression.ipynb

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Repository files navigation

About

Releases

Packages

Languages

License

sunyingjian/AI-in-well-logging

Folders and files

Latest commit

History

Repository files navigation

About

Resources

License

Security policy

Stars

Watchers

Forks

Releases

Packages 0

Languages

Packages