# Semi-Supervised Training for (Pre-Stack) Seismic Data Analysis
## 4 Results and Discussion
### Discussion  
Obtaining acoustic impedance data from pre-stack seismic data using a DNN like our DSSL model is challenging, due to the limited availability of labeled data. However, the use of unlabeled data showed a significant improvement in the overall performance of the neural network, as seen in the learning curves in Figure 8. Furthermore, this improvement extended to the accurate prediction of absolute AI values at well locations, as confirmed by the results of the crossplots, PC, and MSE. Moreover, a comparison at the inline level for each well provided strong evidence that the DSSL method outperformed the other methods in predicting absolute AI values when working with pre-stack seismic data located at a considerable distance from the well's position. This was found to be the case for most of the wells analyzed. This suggests that the pre-training phase effectively extracted underlying features from unlabeled pre-stack seismic data, which in turn helped in fine-tuning the model, even when only a limited quantity of pre-stack labeled seismic data were available. However, it is worth mentioning that, in some instances, such as that shown in Figure 12, the predictions generated by both methods exhibited substantial similarity, which suggests that the neural network may not have needed to obtain additional hidden features from unlabeled data to model the intricate relationship between the AI and seismic data for this particular well and the selected CDPs. Using unlabeled data to improve the performance of models has been proven effective in various applications of DSSL, including lithofacies classification [9; 42]. Nevertheless, a regression model becomes imperative in scenarios where the labeling is not categorical, such as in the case of petrophysical property estimation, specifically absolute AI, as explored in this study. Furthermore, it is important to note that the prediction of other reservoir properties, such as permeability, mineralogy, and porosity, was previously undertaken by [PERSON] [25], [PERSON] [29], and [PERSON] [27], respectively. These predictions were accomplished within the framework of supervised learning. In our work, we utilized DSSL to predict absolute AI, a petrophysical property that offers crucial insights into subsurface properties and contributes to reservoir characterization [71; 72]. No other methods were found that estimated acoustic impedance using DSSL. While all DSSL methods are typically associated with classification tasks, there are no supervised methods that estimate acoustic impedance. Figure 15: (**a**) Seismic inline and the inline acoustic impedance prediction of (**b**) DSSL and (**c**) supervised method for Well-5. Therefore, we built a supervised learning version of our DSSL to have a comparison baseline for the results. In the literature focused on DSSL for lithofacies classification, it is common to use CNN architectures, along with other DNNs. However, our study took a different approach and used a LSTM recurrent neural network. When working with seismic data, we encountered significant challenges, particularly in determining the optimal balance between sequence length and the number of features per example derived from seismic gathers. The right sequence length is essential to enable the neural network to discern meaningful patterns and, concurrently, optimize training efficiency. The latter is particularly significant when working with extensive volumes of unlabeled data. We noticed a significant trend during our analysis of the appropriate number of features or traces per CDP. Specifically, we found that data scarcity tends to occur primarily at the initial offsets, rather than within the selected range. To address this issue we adopted  
Figure 16: Prediction, well and analytical AI curves comparison. a strategic approach. We deliberately retained offsets within the range, even in cases where data were absent.