## 1 Introduction
proposed a TCN-SA-BiLSTM model to study the internal correlation of features in logging datasets [26]. [PERSON] et al. introduced the theoretical rock physics model into deep learning algorithms to enhance physical interpretability [27]. Well logs are known to be sequential data generated along the depth of wells, with different types of logs representing distinct petrophysical characteristics of rocks. Therefore, the vertical data from logging can be analogized to the temporal characteristics of the subsurface, while the various types of logging correspond to the spatial characteristics. By considering both temporal and spatial characteristics simultaneously, we can obtain more comprehensive and detailed information for predicting Vs compared to conventional methods. [PERSON] et al. presented an HC-BiLSTM model that predicts Japan's future earthquake magnitudes by analyzing spatial and temporal patterns across 49 zones [28]. [PERSON] et al. proposed a model by integrating CNN, BiLSTM, and attention mechanism, to accurately predict missing well log data by leveraging spatio-temporal correlations in highly heterogeneous reservoirs [29]. [PERSON] et al. developed the MC-GAN-BiLSTM model to address missing logging data in geophysical logging [30]. [PERSON] et al. developed a CNN-BiLSTM-attention hybrid neural network model for predicting horizontal in situ stresses in complex formations [31]. [PERSON] et al. developed a hybrid network comprising 2 DCNN and GRU to establish more complex nonlinear relationships between inputs and outputs [32]. [PERSON] et al. utilized BiLSTM with attention mechanism, to predict porosity in the Tarim Oilfield by handling the nonlinear relationship between logging parameters and porosity [33]. In order to focus more on key features in the logging data, we incorporate the attention mechanism into the model that allows the model to better capture the serial relationships in the logging parameters. It is a core concept in machine learning that enables models to focus selectively on specific parts of the inputs, helping them capture relevant information and enhance performance across various tasks [34]. The attention mechanism enhances prediction by selectively focusing on critical logging features, thus capturing temporal dependencies and reducing interference. This selective emphasis helps the model maintain key relationships over time, which improves prediction accuracy and model interpretability, allowing for an understanding of the significant time intervals or features influencing outcomes. Additionally, it mitigates long-range dependency issues, making it well-suited for complex sequences such as logging data, while its adaptability enables effective generalization across different tasks, ensuring robust performance in predicting Vs velocity [35]. Traditional methods are highly reliant on the quality of logging data, and prediction results can be directly influenced by noise and missing data. There are also challenges in selecting appropriate logging data for fitting, and the presence of nonlinear relationships between different types of logging increases the uncertainty of the prediction results. To address these issues, a hybrid network is proposed in this article that combines Inception, attention mechanism, and BiLSTM. The Inception module, recognized for its ability to capture multi-scale features through parallel convolutional layers with varying kernel sizes, is a more efficient feature extractor than conventional CNN layers. This allows the proposed model to capture diverse geological patterns, improving its ability to handle complex data. Meanwhile, the attention mechanism module is integrated to highlight essential information dynamically. By selectively emphasizing important time steps, this module enhances the proposed network's sensitivity to crucial temporal features, addressing the limitations of conventional methods that often overlook these relationships.