Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Stratigraphic lithology identification based on no-dig mud property detection system and weakly-supervised learning
In view of the lack of geological investigation information data in no-dig and the difficulty in distinguishing the lithology of tunneling stratum, a typical no-dig formation lithology identification method based on support vector machines(SVM)algorithm of no-dig mud property data is proposed.Combined with the field application of the self-designed no-dig mud property detection system, the training samples of rheological parameters, density and other sensitive mud parameters were obtained.The obtained mud parameters training samples were learned by SVM algorithm, and the mud parameters sample space was constructed.The kernel function was used to map to the high-dimensional space for classification, a classification model is established for the classification of typical no-dig strata in Shanghai.The model is applied to the no-dig engineering in Shanghai to verify its effectiveness.The results show that the method can quickly identify the drilling stratigraphic lithology under the condition of no-dig real-time drilling, and the recognition accuracy is as high as 96%.The research provide important geological information for drilling tool selection and mud design in no-dig reaming stage by collecting the mud property parameters of the guide section and identifying the formation lithology of the no-dig advance section.
Stratigraphic lithology identification based on no-dig mud property detection system and weakly-supervised learning
In view of the lack of geological investigation information data in no-dig and the difficulty in distinguishing the lithology of tunneling stratum, a typical no-dig formation lithology identification method based on support vector machines(SVM)algorithm of no-dig mud property data is proposed.Combined with the field application of the self-designed no-dig mud property detection system, the training samples of rheological parameters, density and other sensitive mud parameters were obtained.The obtained mud parameters training samples were learned by SVM algorithm, and the mud parameters sample space was constructed.The kernel function was used to map to the high-dimensional space for classification, a classification model is established for the classification of typical no-dig strata in Shanghai.The model is applied to the no-dig engineering in Shanghai to verify its effectiveness.The results show that the method can quickly identify the drilling stratigraphic lithology under the condition of no-dig real-time drilling, and the recognition accuracy is as high as 96%.The research provide important geological information for drilling tool selection and mud design in no-dig reaming stage by collecting the mud property parameters of the guide section and identifying the formation lithology of the no-dig advance section.
Stratigraphic lithology identification based on no-dig mud property detection system and weakly-supervised learning
Han Xu (Autor:in) / Danyi Cheng (Autor:in) / Yonghua Xu (Autor:in) / Kongxuan Yao (Autor:in) / Feng Qiu (Autor:in) / Xiaoming Wu (Autor:in) / Penghao Lin (Autor:in)
2021
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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