Jonathan Atuquaye Quaye, Kwame Sarkodie, Zaixing Jiang, Chenlin Hu, Joshua Agbanu, Stephen Adjei, Baiqiang Li
{"title":"应用 SVC、k-NN 和 LDA 机器学习算法改进生物扰动预测:以中国苏北盆地为例","authors":"Jonathan Atuquaye Quaye, Kwame Sarkodie, Zaixing Jiang, Chenlin Hu, Joshua Agbanu, Stephen Adjei, Baiqiang Li","doi":"10.1007/s12145-024-01450-z","DOIUrl":null,"url":null,"abstract":"<p>Three supervised machine learning (ML) classification algorithms: Support Vector Classifier (SVC), K- Nearest Neighbour (K-NN), and Linear Discriminant Analysis (LDA) classification algorithms are combined with seventy-six (76) data points of nine (9) core sample datasets retrieved from five (5) selected wells in oilfields of the Subei Basin to delineate bioturbation. Application of feature selection via p-score and f-scoring reduced the number of relevant features to 7 out of the 12 considered. Each classifier underwent model training and testing allocating 80% of the data for training and the remaining 20% for testing. Under the model training, optimization of hyperparameters of the SVC (C, Gamma and Kernel) and K-NN (K value) was performed via the grid search to understand the best form of the decision boundaries that provides optimal accuracy of prediction of Bioturbation. Results aided the selection of optimized SVC hyperparameters such as a linear kernel, C-1000 and Gamma parameter—0.10 that provided a training accuracy of 96.17%. The optimized KNN classifier was obtained based on the K = 5 nearest neighbour to obtain a training accuracy of 73.28%. The training accuracy of the LDA classifier was 67.36% which made it the worst-performing classifier in this work. Further cross-validation based on a fivefold stratification was performed on each classifier to ascertain model generalization and stability for the prediction of unseen test data. Results of the test performance of each classifier indicated that the SVC was the best predictor of the bioturbation index at 92.86% accuracy, followed by the K-NN model at 90.48%, and then the LDA classifier which gave the lowest test accuracy at 76.2%. The results of this work indicate that bioturbation can be predicted via ML methods which is a more efficient and effective means of rock characterization compared to conventional methods used in the oil and gas industry.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of SVC, k-NN, and LDA machine learning algorithms for improved prediction of Bioturbation: Example from the Subei Basin, China\",\"authors\":\"Jonathan Atuquaye Quaye, Kwame Sarkodie, Zaixing Jiang, Chenlin Hu, Joshua Agbanu, Stephen Adjei, Baiqiang Li\",\"doi\":\"10.1007/s12145-024-01450-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Three supervised machine learning (ML) classification algorithms: Support Vector Classifier (SVC), K- Nearest Neighbour (K-NN), and Linear Discriminant Analysis (LDA) classification algorithms are combined with seventy-six (76) data points of nine (9) core sample datasets retrieved from five (5) selected wells in oilfields of the Subei Basin to delineate bioturbation. Application of feature selection via p-score and f-scoring reduced the number of relevant features to 7 out of the 12 considered. Each classifier underwent model training and testing allocating 80% of the data for training and the remaining 20% for testing. Under the model training, optimization of hyperparameters of the SVC (C, Gamma and Kernel) and K-NN (K value) was performed via the grid search to understand the best form of the decision boundaries that provides optimal accuracy of prediction of Bioturbation. Results aided the selection of optimized SVC hyperparameters such as a linear kernel, C-1000 and Gamma parameter—0.10 that provided a training accuracy of 96.17%. The optimized KNN classifier was obtained based on the K = 5 nearest neighbour to obtain a training accuracy of 73.28%. The training accuracy of the LDA classifier was 67.36% which made it the worst-performing classifier in this work. Further cross-validation based on a fivefold stratification was performed on each classifier to ascertain model generalization and stability for the prediction of unseen test data. Results of the test performance of each classifier indicated that the SVC was the best predictor of the bioturbation index at 92.86% accuracy, followed by the K-NN model at 90.48%, and then the LDA classifier which gave the lowest test accuracy at 76.2%. The results of this work indicate that bioturbation can be predicted via ML methods which is a more efficient and effective means of rock characterization compared to conventional methods used in the oil and gas industry.</p>\",\"PeriodicalId\":49318,\"journal\":{\"name\":\"Earth Science Informatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Earth Science Informatics\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1007/s12145-024-01450-z\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earth Science Informatics","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s12145-024-01450-z","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Application of SVC, k-NN, and LDA machine learning algorithms for improved prediction of Bioturbation: Example from the Subei Basin, China
Three supervised machine learning (ML) classification algorithms: Support Vector Classifier (SVC), K- Nearest Neighbour (K-NN), and Linear Discriminant Analysis (LDA) classification algorithms are combined with seventy-six (76) data points of nine (9) core sample datasets retrieved from five (5) selected wells in oilfields of the Subei Basin to delineate bioturbation. Application of feature selection via p-score and f-scoring reduced the number of relevant features to 7 out of the 12 considered. Each classifier underwent model training and testing allocating 80% of the data for training and the remaining 20% for testing. Under the model training, optimization of hyperparameters of the SVC (C, Gamma and Kernel) and K-NN (K value) was performed via the grid search to understand the best form of the decision boundaries that provides optimal accuracy of prediction of Bioturbation. Results aided the selection of optimized SVC hyperparameters such as a linear kernel, C-1000 and Gamma parameter—0.10 that provided a training accuracy of 96.17%. The optimized KNN classifier was obtained based on the K = 5 nearest neighbour to obtain a training accuracy of 73.28%. The training accuracy of the LDA classifier was 67.36% which made it the worst-performing classifier in this work. Further cross-validation based on a fivefold stratification was performed on each classifier to ascertain model generalization and stability for the prediction of unseen test data. Results of the test performance of each classifier indicated that the SVC was the best predictor of the bioturbation index at 92.86% accuracy, followed by the K-NN model at 90.48%, and then the LDA classifier which gave the lowest test accuracy at 76.2%. The results of this work indicate that bioturbation can be predicted via ML methods which is a more efficient and effective means of rock characterization compared to conventional methods used in the oil and gas industry.
期刊介绍:
The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.