## 1 Introduction  
Well logging is a geological exploration technique that involves using specialized logging tools, which are lowered into the well during or after drilling, to measure the physical parameters of the formation near the wellbore. The data is transmitted in real-time to the surface via cable or radio transmission. The raw data obtained is processed and analyzed, converted into useful geological information, and interpreted by experts. This provides crucial data support for subsurface geological studies, oil and gas exploration, and resource evaluation. Common well-logging data include caliper, density, gamma-ray, neutron porosity, and p-wave, among others. These parameters help in understanding the geological characteristics of the area, rock types, fluid content, and the hydrocarbon potential of the subsurface formations. Shear wave velocity (Vs) is a crucial parameter for petrophysical parameter estimation of reservoir, pre-stack inversion, fluid- type identification, unconventional oil and gas development, and CO\({}_{2}\) injection [1; 2; 3; 4; 5]. However, the accurate acquisition of Vs is hindered by the high cost of dipole acoustic logging measurements and the limitations imposed by borehole conditions, leading to a lack of Vs data in various regions. Therefore, Vs prediction is essential. In the related research, various prediction methods have been proposed for estimating Vs. These prediction methods can be divided into the following three categories: (1) the empirical correlation formulas, (2) the theoretical rock physical models, and (3) artificial intelligence methods. Empirical correlation formulas were established using the relationship between existing logging data and the S-wave velocity in the specific study area, incorporating various mathematical equations [6; 7; 8; 9]. The empirical correlation formulas are simple and can be applied quickly. However, they are only suitable for relatively simple geological environments and are highly dependent on the characteristics of the study domain [10]. This limits the application of empirical correlation formulas to predict Vs in complex geological conditions. To address the limitations of empirical correlation formulas, theoretical rock physical models have been introduced to establish the relationship between elastic parameters and reservoir parameters to predict Vs. [PERSON] et al. utilized the DEM-Gassman model to achieve a more accurate prediction of Vs than that of an empirical correlation formula [11]. [PERSON] et al. introduced a method that inverts the coordination number of saturated rocks from the P-wave velocity by integrating the unified granular media model with Gassmann's equation [12]. [PERSON] et al. designed a statistical model that links logging curves with Vs using Bayesian inversion to calculate key petrophysical parameters of the Xu-White model [13]. To obtain an accurate prediction result, it is crucial to precisely determine various rock physics parameters such as mineral components, pore characteristics, and fluid distribution. Additionally, due to noise interference, there is some uncertainty in the prediction results of the theoretical rock physical models when processing field data. The prediction process using these theoretical models is complex and inefficient [14]. Artificial intelligence, especially deep learning, has developed rapidly and found widespread applications in recent years. Convolutional Neural Networks (CNNs) are a widely used model applied across various fields. CNNs can automatically and adaptively learn spatial features, establishing nonlinear relationships between inputs and outputs.