## 4 Materials and Methods  
### Dataset Construction  
In constructing the dataset foundation for sedimentary facies recognition models, this study adopts a well logging response feature collection strategy under the framework of system tract subdivision and incorporates stratigraphic unit thickness frequency statistical methods. Based on the established sequence stratigraphic framework described earlier, well logging parameter sequences corresponding to the lowstand system tract (LST), transgressive system tract (TST), and highstand system tract (HST) were systematically collected. These parameters were stratified into four vertical thickness tiers (0-1 m, 1-2 m, 2-3 m, and >3 m) to establish feature subsets, forming a multi-scale sedimentary dynamic training dataset. First, system tract boundaries were precisely delineated through core calibration and cyclo-stratigraphic analysis. Subsequently, morphological features of five key well logging curves--natural gamma ray (GR), spontaneous potential (SP), acoustic (AC), laterolog deep resistivity (LLD), and laterolog shallow resistivity (LLS)--were extracted as well logging group images. This process yielded a standardized database containing over 2000 logging units, with LST, TST, and HST samples accounting for 42%, 26%, and 32%, respectively. After data processing, well logging facies types were extracted by classifying distinct morphological patterns. Each morphological pattern is treated as a distinct category representing specific geological significance. By correlating these patterns with regional depositional contexts, sedimentary microfacies can be inferred. For this study, five well logging curves--natural gamma ray (GR), spontaneous potential (SP), acoustic (AC), laterolog deep resistivity (LLD), and laterolog shallow resistivity (LLS)--were selected as feature parameters due to their characteristics reflecting lithology, porosity, and sedimentary structure. These curves exhibit diverse morphologies, including composite patterns formed by combining two or more basic patterns. To facilitate machine learning analysis, numerical data from Figure 6 were converted into well logging curve images, transforming the well logging information into visual representations of logging facies. Well logging curves exhibit distinct morphological patterns, but morphological classification inherently involves subjective interpretation, which inevitably introduces human errors. To ensure accurate morphological feature extraction and dataset construction, this study integrated domain expertise from geologists and implemented multi-expert judgment. From raw logging data of 131 wells, unambiguous morphological features were rigorously selected, followed by manual segmentation and calibration to generate 400+ training images of well logging facies types. The integration of system tract classifications with our image dataset yielded the final dataset in Table 2, and the first 16 samples are shown below. Figure 6: Training data sample. From left to right, the tracks display the Natural Gamma Ray (GR), Spontaneous Potential (SP), Acoustic (AC), Laterolog Deep Resistivity (LLD), and Laterolog Shallow Resistivity (LLS).