## 6 Conclusions  
Refraction travel time tomography is not affected by the characteristics of the source and receiver, and its computational efficiency is much higher than the full waveform inversion method. Therefore, refraction travel time tomography is widely used in practical production and application. In this paper, the traditional travel time tomography method is optimized. First, we use MSFM instead of FMM as a forward algorithm. In the process of inversion calculation, dynamic selection of regularization factor and a constraint method based on a combination of the minimum perturbation constraints and smoothest constraints for the t0 difference method are proposed. By testing the synthetic data model, we proved the effectiveness and reliability of the improved travel time tomography method proposed in this paper. Through these efforts, we have come to some conclusions. The t0 difference method was selected for the model test. This method can accurately calculate the depth of the layer interface and the average velocity of each layer. It can be used as a prior constraint in the inversion process of refraction travel time tomography. Figure 17: Geological interpretation profile. The forward simulation shows that MSFM is superior to FMM in calculation accuracy, calculation efficiency, and adaptability to complex models. In the inversion, when FMM is used as a forward travel time calculation method, the reconstructed inversion results have more false anomalies, and FMM's convergence speed and error are not as good as those of MSFM. Therefore, MSFM should be selected as the forward algorithm for refraction travel time tomography. Because refraction travel time tomography is a mixed problem, regularization is needed to reduce the multiplicity of inversion solutions and improve the stability of these solutions. After synthesizing the advantages and disadvantages of several constraint methods, a composite constraint combines the minimum disturbance constraint and the smoothest constraint is proposed. By comparing the inversion results, it is found that the algorithm proposed in this paper has the least false anomalies and the best inversion effect. In the selection of regularization factors, this paper proposes a dynamic regularization factor method. In the iterative process, the large regularization factor is firstly used to outline the large-scale shape of the model, and then the regularization factor is continuously reduced to describe the disturbance of the small-scale slowness model. Finally, the algorithm proposed in this paper was applied to the measured data. The refraction exploration data collected in this paper were taken from the highway tunnel exploration project, which is located in Northeast China. The depth of the bedrock can be inferred from the inversion results and is of guiding significance in tunnel excavation. According to the actual situation, it is a better choice for tunnel excavation to avoid highly weathered low-speed zones and instead select hard and complete rock high-speed zones. This could reduce the blindness of the construction and improve the safety and reliability of tunnel engineering. At the same time, we proved that the algorithm proposed in this paper is very practical. Conceptualization, methodology, investigation and Writing, Z.J. ; Supervision, S.L. ; Formal analysis, Y.Z. ; formal analysis, S.C.; project administration, X.Z. This research was supported by the Major Projects of the National Science and Technology of China(2016 YFC0600505) and the National Natural Science Foundation of China under Grant 41574109, 41504085 and 41874136. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: