{"title":"利用基于双向时空神经网络的深度学习方法预测孔隙度","authors":"","doi":"10.1016/j.jappgeo.2024.105465","DOIUrl":null,"url":null,"abstract":"<div><p>Deep learning is one of the best machine learning algorithms for modeling complex mapping relationships between independent and dependent variables, and thus it can be viewed as an ideal approach to predict porosity. In this study, to overcome the deficiencies in current porosity prediction based on deep learning and improve the prediction accuracy, we proposed a deep learning model based on bidirectional temporal convolutional network (BTCN) and bidirectional long short-term memory (BLSTM) network, called bidirectional spatio-temporal neural network (BSTNN), to establish a porosity prediction model. First, the maximum information coefficient is used to analyze the correlation between well logs and porosity, which provides a basis for determining the inputs of the prediction model. Then, a hybrid network structure is constructed by using BTCN and BLSTM, in which BTCN goes to learn the bidirectional long sequence features and BLSTM goes to learn the variation trend and context information with depth, so the hybrid network structure can learn richer logging signal features. Finally, the extracted features are passed through the fully connected layer to output the porosity prediction results. Porosity prediction experiment are conducted by using the actual field data set. The results show that the proposed method has the lower prediction errors for the porosity modeling (RMSE = 0.368 and MAE = 0.260) compared to the benchmark models convolutional neural network (RMSE = 0.404 and MAE = 0.292) and long short-term memory network (RMSE = 0.418 and MAE = 0.298), which verifies the effectiveness of this prediction method.</p></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Porosity prediction using a deep learning method based on bidirectional spatio-temporal neural network\",\"authors\":\"\",\"doi\":\"10.1016/j.jappgeo.2024.105465\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Deep learning is one of the best machine learning algorithms for modeling complex mapping relationships between independent and dependent variables, and thus it can be viewed as an ideal approach to predict porosity. In this study, to overcome the deficiencies in current porosity prediction based on deep learning and improve the prediction accuracy, we proposed a deep learning model based on bidirectional temporal convolutional network (BTCN) and bidirectional long short-term memory (BLSTM) network, called bidirectional spatio-temporal neural network (BSTNN), to establish a porosity prediction model. First, the maximum information coefficient is used to analyze the correlation between well logs and porosity, which provides a basis for determining the inputs of the prediction model. Then, a hybrid network structure is constructed by using BTCN and BLSTM, in which BTCN goes to learn the bidirectional long sequence features and BLSTM goes to learn the variation trend and context information with depth, so the hybrid network structure can learn richer logging signal features. Finally, the extracted features are passed through the fully connected layer to output the porosity prediction results. Porosity prediction experiment are conducted by using the actual field data set. The results show that the proposed method has the lower prediction errors for the porosity modeling (RMSE = 0.368 and MAE = 0.260) compared to the benchmark models convolutional neural network (RMSE = 0.404 and MAE = 0.292) and long short-term memory network (RMSE = 0.418 and MAE = 0.298), which verifies the effectiveness of this prediction method.</p></div>\",\"PeriodicalId\":54882,\"journal\":{\"name\":\"Journal of Applied Geophysics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-07-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Applied Geophysics\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0926985124001812\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Geophysics","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0926985124001812","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
Porosity prediction using a deep learning method based on bidirectional spatio-temporal neural network
Deep learning is one of the best machine learning algorithms for modeling complex mapping relationships between independent and dependent variables, and thus it can be viewed as an ideal approach to predict porosity. In this study, to overcome the deficiencies in current porosity prediction based on deep learning and improve the prediction accuracy, we proposed a deep learning model based on bidirectional temporal convolutional network (BTCN) and bidirectional long short-term memory (BLSTM) network, called bidirectional spatio-temporal neural network (BSTNN), to establish a porosity prediction model. First, the maximum information coefficient is used to analyze the correlation between well logs and porosity, which provides a basis for determining the inputs of the prediction model. Then, a hybrid network structure is constructed by using BTCN and BLSTM, in which BTCN goes to learn the bidirectional long sequence features and BLSTM goes to learn the variation trend and context information with depth, so the hybrid network structure can learn richer logging signal features. Finally, the extracted features are passed through the fully connected layer to output the porosity prediction results. Porosity prediction experiment are conducted by using the actual field data set. The results show that the proposed method has the lower prediction errors for the porosity modeling (RMSE = 0.368 and MAE = 0.260) compared to the benchmark models convolutional neural network (RMSE = 0.404 and MAE = 0.292) and long short-term memory network (RMSE = 0.418 and MAE = 0.298), which verifies the effectiveness of this prediction method.
期刊介绍:
The Journal of Applied Geophysics with its key objective of responding to pertinent and timely needs, places particular emphasis on methodological developments and innovative applications of geophysical techniques for addressing environmental, engineering, and hydrological problems. Related topical research in exploration geophysics and in soil and rock physics is also covered by the Journal of Applied Geophysics.