[{"title":"( 26 个子文件 5.12MB ) 人工测井: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。学习,深度学习和其他相关方法的岩性识别和相关测井数据的回归-源码","children":[{"title":"AI-in-well-logging-master","children":[{"title":"catboost美国油田_延安油田_Catboost U.S. Oilfield_Yan'an Oilfield.ipynb <span style='color:#111;'> 1004.37KB </span>","children":null,"spread":false},{"title":"LSTM_simple_RNN_Bi_LSTM实现PE回归_regression_analysis.ipynb <span style='color:#111;'> 182.63KB </span>","children":null,"spread":false},{"title":"延安油田_岩性分类.ipynb <span style='color:#111;'> 139.23KB </span>","children":null,"spread":false},{"title":"<Concise code>SVM Random Forests GBDT XGBoost facies classification.ipynb <span style='color:#111;'> 115.13KB </span>","children":null,"spread":false},{"title":"Facies_Classification_SVM.ipynb <span style='color:#111;'> 978.94KB </span>","children":null,"spread":false},{"title":"美国油田GMM_smote_knn_gbdt,随机森林,xgboost.ipynb <span style='color:#111;'> 826.98KB </span>","children":null,"spread":false},{"title":"延安油田,KNN,gbdt,随机森林,xgboost.ipynb <span style='color:#111;'> 257.99KB </span>","children":null,"spread":false},{"title":"延安油田标签传播算法半监督_Yan'an Oilfield Label Propagation Algorithm Semi-supervised.ipynb <span style='color:#111;'> 39.45KB </span>","children":null,"spread":false},{"title":"resnet_无步长_延安油田.ipynb <span style='color:#111;'> 40.11KB </span>","children":null,"spread":false},{"title":"采用PCA_KPCA_LDA_做数据降维以美国油田为例.ipynb <span style='color:#111;'> 235.83KB </span>","children":null,"spread":false},{"title":"电阻率线性回归.ipynb <span style='color:#111;'> 67.27KB </span>","children":null,"spread":false},{"title":"RNN_DNN岩性分类Lithology classification.ipynb <span style='color:#111;'> 103.68KB </span>","children":null,"spread":false},{"title":"延安油田SMOTE,KNN,gbdt,随机森林,xgboost.ipynb <span style='color:#111;'> 261.85KB </span>","children":null,"spread":false},{"title":"神经网络分类.ipynb <span style='color:#111;'> 114.30KB </span>","children":null,"spread":false},{"title":"超参数选择.ipynb <span style='color:#111;'> 36.46KB </span>","children":null,"spread":false},{"title":"测井曲线画图总结.ipynb <span style='color:#111;'> 657.22KB </span>","children":null,"spread":false},{"title":"美国油田朴素贝叶斯.ipynb <span style='color:#111;'> 1.16MB </span>","children":null,"spread":false},{"title":"使用matplotlib_可视化_csv测井数据实现ui控制的测井曲线生成.ipynb <span style='color:#111;'> 375.43KB </span>","children":null,"spread":false},{"title":"延安油田决策树.ipynb <span style='color:#111;'> 752.56KB </span>","children":null,"spread":false},{"title":"Conv1d_PE回归_regression_analysis.ipynb <span style='color:#111;'> 59.45KB </span>","children":null,"spread":false},{"title":"“02_Map_View_ipynb”的副本.ipynb <span style='color:#111;'> 51.32KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 813B </span>","children":null,"spread":false},{"title":"采用PCA_KPCA_LDA不采用归一化_做数据降维以美国油田为例.ipynb <span style='color:#111;'> 196.83KB </span>","children":null,"spread":false},{"title":"岩性分类_SVM.ipynb <span style='color:#111;'> 1.15MB </span>","children":null,"spread":false},{"title":"catboost美国油田_延安油田_gpu.ipynb <span style='color:#111;'> 1003.50KB </span>","children":null,"spread":false},{"title":"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 <span style='color:#111;'> 28.96KB </span>","children":null,"spread":false}],"spread":false}],"spread":true}]