## 4 Materials and Methods
### Methodology  
#### 4.2.1 Convolutional Neural Networks  
Convolutional Neural Networks (CNNs) represent a paradigm shift in image classification, offering distinct advantages over traditional machine learning methods such as neural networks [3; 25]. Their strength lies in discriminative feature learning, enabling noise resilience through pixel-space pattern preservation. Unlike conventional approaches that treat well logging curves as 1D data volumes for processing with Deep Neural Networks (DNNs) or 1D-CNNs, this study innovatively reinterprets log curve sets as 2D images [15; 26]. Well logging curves reflect corresponding sedimentary microfacies under different depositional environments. By characterizing the variation patterns of well logging curves (amplitude characteristics, morphological features, variation trends) and integrating geological interpretations (sedimentary structures, paleocurrent directions), log facies can be systematically identified [27; 28]. These log facies are further calibrated using geological data (e.g., core analysis). However, 1D data analysis, limited to numerical values, lacks the capacity to evaluate spatial correlations with neighboring samples, rendering it incapable of capturing macroscopic geological features such as textures, laminations, thin interbeds, thin overburdens, and top-bottom relationships [29; 30]. Consequently, representing data as 2D images enables the capture of subtle geological information through enhanced spatial context visualization. By leveraging 2D-CNN architectures as shown in Figure 7, we construct an intelligent sedimentary facies recognition model that integrates system tract classifications as auxiliary inputs. The model's input dimensionality combines pixel-space features (spatial dimensions) with system tract categories, forming a novel N\({}^{2}\) + 1 dimensional dataset where N represents the logarithmic curve picture pixel resolution. \(\bullet\) Data preprocessing  
Dataset images were acquired with uniform image resolution. Our dataset was partitioned into training (70%) and testing (30%) subsets. To preserve geological details, preprocessing and dynamic augmentation were implemented to enhance data diversity while maintaining stratigraphic fidelity for model training. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample ID**} & \multicolumn{6}{c}{**Pictures**} \\ \cline{2-7}  & GR & SP & AC & LLD & LLS & \multirow{2}{*}{**Thickness**} & \multirow{2}{*}{**System Tract**} & \multirow{2}{*}{**Microfacies**} \\  & 30-150 & 20-120 & 500-0 & 0.1-100 & 0.1-100 & & \\  & API & mV & \(\mu\)s/m & \(\Omega\cdot\)m(Log) & \(\Omega\cdot\)m(Log) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._Due to stratigraphic thickness variations causing significant disparities in pixel dimensions across images, excessive thickness leads to severe stretching of thin-layer images (resulting in distortion), while insufficient thickness compresses thick layers, causing substantial loss of geological information. To address this, we implemented a thickness-based classification strategy. Based on statistical analysis of our training samples, the dataset was categorized into four stratigraphic thickness classes: >3 m, 2-3 m, 1-2 m, and <1 m.  
To resolve multi-scale representation challenges in geological bodies, this study proposed a thickness-adaptive input sizing algorithm based on seismic reflection thickness. Thickness metadata parsed from filenames automatically classified strata into four granularity levels (0-1 m, 1-2 m, 2-3 m, >3 m), corresponding to input resolutions of 64 \(\times\) 64, 128 \(\times\) 128, 192 \(\times\) 192, and 256 \(\times\) 256, respectively. This design captured fine-scale laminae with small resolutions and preserved macro-scale structures with large resolutions, achieving resampling via bilinear interpolation combined with centered cropping via zero-padding.