## 3 Data and Methods
### Neural Network Setting  
The input of the neural network is VDL with the same size and resolution. For image-type data, the convolutional neural network has been proven to be very effective [17]. Therefore, our network settings were based on these. The network in this paper used the Tensorflow backend in Keras. The classic convolution neural network has a convolution layer, a pooling layer, and then an encryption layer. Only increasing the number of layers or convolution cores can improve the accuracy of image recognition, but it may also lead to overfitting problems. The network settings in this paper followed the recommendations of [PERSON] [18]. The maximum pooling was adopted after each convolution. After the maximum pooling, the batch standardization operation was carried out. The number of convolution layers was set to 15, and the activation function of the convolution layer adopted the Relu activation function. To prevent overfitting, the encryption layer was added with Dropout, the drop probability was 0.5, and the training batch was 16 samples. The filter is a sliding window that convolves the input data in the convolution layer to extract the features of the input data, as shown in Figure 7. The size of the filter needs to be combined with the image information to be extracted. Generally, the larger filter applies to the widely distributed information, and the smaller filter applies to the local information [19]. Considering that, when evaluating the cementing quality, it is not only necessary to observe the color, position, and waveform of the strip in the image by referring to the VDL; therefore, when extracting features, it is not only necessary to require small-scale information (strip light and shade, position), but also large-scale information (strip shape and number) in the VDL. \begin{table}
\begin{tabular}{c c c} \hline \hline
**CBL Characteristics** & **VDL Characteristic** & **Cementing Quality Results** \\ \hline Maximum & Obvious casing wave band and blank formation wave & Free casing \\ Minimum & The casing wave is dark and the formation wave is obvious & Good \\ Low & Both casing wave and formation wave are dark & Better \\ Medium & Obvious casing wave and dark formation wave & Fair \\ High, but not maximum & The casing wave band is obvious, the formation wave stripe is dark & Weak \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Marking method of cementing quality training set. To solve this problem, this paper used multiple parallel filters of different sizes for convolution, and simultaneously detected features of different sizes, as shown in Figure 8, which is the convolution network structure used in this paper. The 1 \(\times\) 1 filter looks at very localized information, whereas 5 \(\times\) 5 filters will view more global characteristics, and the overall design result of the convolution network is shown in Figure 9.  
In order to better fuse image information, after information splicing, this paper designed a follow-up convolution structure to continuously extract features from the fused information. The parameters of each layer of the convolution neural network are set up in Table 2.