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