## 2 Study Area and Dataset
The training dataset for this study was obtained from the Sichuan Provincial Sixth Geological Brigade, encompassing data from 22 wells in the Shiping and Lianghe areas of Southwestern Sichuan (Figure 1). Logging data from 16 wells were allocated to the training set, 3 wells to the test set, and the remaining 3 wells to the validation set. The sample statistics for the training and test datasets are illustrated in Figure 2. Majority of the training data are from the Shiping area, which is situated 100 km from the Lianghe area, allowing the Lianghe data to be used for evaluating the model's transferability. For classification purposes, we divided the rocks in this region into the following four categories: coal, sandstone, limestone, and others. The majority in others are argillaceous \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{3}{c}{**Geological Timechart**} & \multicolumn{3}{c}{**Thickness (m)**} & **Lithology** \\ \hline
**System** & **Series** & **Formation** & **Code** & & \\ \hline \multirow{4}{*}{Permanian} & \multirow{2}{*}{upper} & Changing & P\({}_{3}\)c & 32–58 & Limestone \\ \cline{3-6}  & & Longtan & P\({}_{3}\)_l_ & 86\(\sim\)112 & \
\begin{tabular}{c} Mudstone, sandy mudstone, fine sandstone, \\ and claystone interbedded with coal seams \\ \end{tabular} \\ \cline{2-6}  & Middle and & \multirow{2}{*}{Maokou, Xixia} & \multirow{2}{*}{P\({}_{2}\)m, P\({}_{2}\)q} & 196–295 & Limestone, containing flint, with a base of sandy mudstone and claystone \\ \cline{3-6}  & Lower & & & 2–11 & \\ \hline \multirow{4}{*}{Silurian} & Middle & Hanjaidian & S\({}_{2}\)h & 300–650 &
\begin{tabular}{c} Mudstone and siltstone interbedded with \\ sandstone. \\ \end{tabular} \\ \cline{2-6}  & Lower & & Shiniupeng & S\({}_{1}\)s & 180–501 & Limestone, argillaceous limestone \\ \cline{3-6}  & & Longmaxi & S\({}_{1}\)_l_ & 180–460 & Mudstone \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 2: Distribution of sample numbers in the training and test sets: Categories 0–3 correspond to other stones, coal, sandstone, and limestone, respectively. complex rock, such as argillaceous limestone, calcareous mudstone, argillaceous sandstone, and carbonaceous mudstone (Table 1). In addition, there are also small amounts of basalt and fault breccia. Therefore, the others category primarily reflects the properties of mudstone. For convenience, we can call it others in the subsequent text. Coal seams, sandstone, and limestone generally exhibit distinct logging curve characteristics and can be classified individually. In contrast, mud-bearing sandstone and limestone contain more complex information, making them challenging to differentiate using well logging curves alone; hence, they were combined into a single category. Although the different wells have varying types of well logging curves, most include normal resistivity (NR), natural gamma (GR), and gamma-gamma (GG) density logging data. Thus, we utilized these three types of logging data as inputs for our model. This approach offers the advantage of making the trained model applicable to a broader range of scenarios. Normal resistivity logging involves measuring the electrical resistivity of the formations by sending an electric current through the rock and measuring the resulting voltage. The resistivity is inversely related to the conductivity of the rock, which reflects the amount of water and the type of minerals present. The resistivity measurements can indicate the presence of hydrocarbons and the salinity of the formation water. High resistivity typically suggests low water content or the presence of hydrocarbons, while low resistivity indicates high water content or clay-rich formations. Natural gamma ray logging detects gamma rays emitted spontaneously by radioactive elements (such as clays, uranium, potassium, and thorium) in rock formations. It is often measured in picoamperes (PA) per kilogram of rock (Unit: 10 pA/kg). Thus, \"10 pA/kg\" indicates that the gamma ray intensity produces a current of 10 picoamperes per kilogram of rock.