Lithofacies paleogeography is a data-intensive discipline that involves the interpretation and compilation of sedimentary facies.Traditional sedimentary facies analysis is a labor-intensive task with the added complex...Lithofacies paleogeography is a data-intensive discipline that involves the interpretation and compilation of sedimentary facies.Traditional sedimentary facies analysis is a labor-intensive task with the added complexity of using unstructured knowledge and unstandardized terminology.Therefore,it is very difficult for beginners or non-geology scholars who lack a systematic knowledge and experience in sedimentary facies analysis.These hurdles could be partly alleviated by having a standardized,structured,and systematic knowledge base coupled with an efficient automatic machine-assisted sedimentary facies identification system.To this end,this study constructed a knowledge system for fluvial facies and carried out knowledge representation.Components include a domain knowledge graph for types of fluvial facies(meandering,braided and other fluvial depositional environments)and their characteristic features(bedforms,grain size distribution,etc.)with visualization,a method for query and retrieval on a graph database platform,a hierarchical knowledge tree-structure,a data-mining clustering algorithm for machineanalysis of publication texts,and an algorithm model for this area of sedimentary facies reasoning.The underlying sedimentary facies identification and knowledge reasoning system is based on expert experience and synthesis of publications.For testing,17 sets literature publications data that included details of sedimentary facies data(bedforms,grain sizes,etc.)were submitted to the artificial intelligence model,then compared and validated.This testing set of automated reasoning results yielded an interpretation accuracy of about 90%relative to the published interpretations in those papers.Therefore,the model and algorithm provide an efficient and automated reasoning technology,which provides a new approach and route for the rapid and intelligent identification of other types of sedimentary facies from literature data or direct use in the field.展开更多
基金supported by the IUGS Deep-time Digital Earth(DDE)Big Science Program,National Natural Science Foundation of China(Grant No.42050104).
文摘Lithofacies paleogeography is a data-intensive discipline that involves the interpretation and compilation of sedimentary facies.Traditional sedimentary facies analysis is a labor-intensive task with the added complexity of using unstructured knowledge and unstandardized terminology.Therefore,it is very difficult for beginners or non-geology scholars who lack a systematic knowledge and experience in sedimentary facies analysis.These hurdles could be partly alleviated by having a standardized,structured,and systematic knowledge base coupled with an efficient automatic machine-assisted sedimentary facies identification system.To this end,this study constructed a knowledge system for fluvial facies and carried out knowledge representation.Components include a domain knowledge graph for types of fluvial facies(meandering,braided and other fluvial depositional environments)and their characteristic features(bedforms,grain size distribution,etc.)with visualization,a method for query and retrieval on a graph database platform,a hierarchical knowledge tree-structure,a data-mining clustering algorithm for machineanalysis of publication texts,and an algorithm model for this area of sedimentary facies reasoning.The underlying sedimentary facies identification and knowledge reasoning system is based on expert experience and synthesis of publications.For testing,17 sets literature publications data that included details of sedimentary facies data(bedforms,grain sizes,etc.)were submitted to the artificial intelligence model,then compared and validated.This testing set of automated reasoning results yielded an interpretation accuracy of about 90%relative to the published interpretations in those papers.Therefore,the model and algorithm provide an efficient and automated reasoning technology,which provides a new approach and route for the rapid and intelligent identification of other types of sedimentary facies from literature data or direct use in the field.
