*3.5. Results*

Figure 10 shows the training results of the LSTM recurrent neural network using site SH2. The red dotted line shows the predicted saturation of the gas hydrate of the network model, and the blue curve shows the true value input into the model. The calculation shows that the correlation coefficient between the predicted value and the true value was 0.9605, and the root mean square error was 0.0208. The LSTM recurrent neural network achieved a good training effect, so it could be used for the prediction of gas hydrate saturation at site SH7.

**Figure 10.** Training results of the LSTM recurrent neural network at site SH2.

We selected the resistivity and acoustic velocity logs of 155–167 m at site SH7, standardized the data, and input the data into the previously trained LSTM recurrent neural network to obtain the prediction of the gas hydrate saturation (Figure 11). The black curve in Figure 11 shows the predicted value, and the black asterisks show the gas hydrate saturations calculated by the chloride concentration of the pore water at site SH7. The overall change trend of the predicted value of gas hydrate saturation obtained by the LSTM recurrent neural network was reasonable, and the prediction was basically consistent with the 21 measured values of site SH7. We picked out the corresponding 21 predicted values of gas hydrate saturation, and calculated the correlation coefficient and root mean square error between the predicted value and the true value. We obtained 0.7085 and 0.1208. We therefore achieved a relatively accurate prediction of gas hydrate saturation using the LSTM recurrent neural network.

**Figure 11.** Prediction of the gas hydrate saturation at site SH7.
