# Automated Geological Features Detection in 3D Seismic Data Using Semi-Supervised Learning
## 5 Applications
### The A Field Real Seismic Example  
In addition to the synthetic example in the previous part, we further use 3D real seismic data acquired from the A Field in the Malay Basin to verify the generalization of the proposed workflow. We extracted three seismic attributes based on amplitude as the starting point. As shown in Figure 19, these three attributes improve the delineation of channel features, which represent the sand distribution on seismic data, as indicated by the red-coded high value. The sand distribution indicated that the channel-filled deposits were made up of point bar, abandoned channel, crevasse splays, and flood plain [48]. The basic statistics information of the attributes is detailed in Table 6.  
As in the previous example, these attributes are assigned to be the input of the KernelPCA algorithm to transform into a single principal component. The result can be shown in Figure 20. The dimensionality reduction technique helps visualize the meandering of channel features and quantify the channel features. This output can be a good input to the clustering process.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Attribute** & **Count** & **Mean** & **Std** & **Min** & **25\%** & **50\%** & **75\%** & **Max** \\ \hline RMS & 31,457,280 & 13047.69 & 6902.03 & 0.00 & 8269.11 & 11,525.32 & 16,103.23 & 70,431  
Figure 19: The extracted attributes for channel delineation including (**a**) RMS Amplitude, (**b**) envelope, and (**c**) sweetness.  
By using these two unsupervised learning techniques i.e., KernelPCA and K-means clustering, we can generate the high resolution of channelized features. Figure 21 shows the classified values, clustered into two geological features, i.e., a (binary) channel and non-channel. This ground truth data are also confirmed by the manual interpretation based on the previous results (Figure 4).  
We generated 384 small patch data pairs and assigned 300 data pairs for the training and the rest for the validation. Using 100 epochs to train the model, we achieved 0.84 _loU_ score on training data and 0.89 _loU_ score on validation data (Figure 22). The training process takes 32 min in total using the GPU.  
A good result is also obtained by the confusion matrix evaluation. The model obtained 0.98 accuracy score for training and 0.97 for the validation (Figure 23). Tables 7 and 8 shows  
Figure 21: The ground truth data, generated from the clustering result.  
Figure 20: The result of dimensionality reduction using KernelPCA.  
Figure 25 shows the prediction result on the seismic section; the top part is a time-slice in the middle from the inline, and the bottom part on crossline. The channel interpretation is usually performed after time-slice section. The prediction of the CNN model is enhanced and clearly detected the meandering of the channel and channel point bar features. It can also detect the channel features in inline and crossline, where it has only small features.  
The main advantage to train the model on 3D data is that the model can have spatial and geomorphology information from the object that it wants to detect. Hence, the model gives clear channel body interpretation on the 3D visualization, as shown on the Figure 26.  
Figure 24: The 3D real seismic data, acquired from the A Field, Malay Basin.  
Figure 26: The predicted channel features on 3D data.  
Figure 25: The predicted result of channel features on (**a**) time-slice, (**b**) inline, and (**c**) crossline seismic section.