Deep Learning for Inversion of Tipper Data of a Certain Railway Tunnel in Tibet Area

The traditional inversion methods for tipper rely excessively on the selection of the initial model, whose global search capability is poor. Inspired by the significant approximation advantage of deep learning for nonlinear inverse problems with big data, we design a deep learning architecture called TipInv-net for the inversion of tipper. TipInv-net takes the improved U-net as the basic framework to obtain the tipper response characteristics of the abnormal body, and then, dense skip connection is applied among the nested standard convolution modules to alleviate the gradient disappearance problem and enhance feature propagation. It’s worth noting that we construct a feature pyramid of tipper response via average pooling to obtain multi-scale receptive fields, which, thus enhancing global and detail location of abnormal body. The theoretical model test indicates that the position and attitude of geological anomaly body can be distinguished via TipInv-net, even in the presence of a certain level of noise, the inversion accuracy will not be greatly affected. TipInv-net has strong generalization. Besides, after the training process of the network, we can obtain the inversion result immediately. In order to verify the effectiveness of the method in this paper, the inversion of the field tipper data of a section of a certain railway tunnel in Tibet area is carried out.