**4. Discussion**

The design of the network structure is key to improving the accuracy of a network model. We used an LSTM network prediction model that included one LSTM recurrent layer and two fully connected layers. The number of nodes in the two fully connected layers was 20 and 10, respectively. We did this because the complexity of the actual data was relatively low and because we wanted to improve calculation e fficiency. In addition to selecting parameters based on experience, the optimal network structure could also be selected by using the training dataset for repeated experiments. There are many ways to use dropout regularization in LSTM network training [30], either in the loop of LSTM or in the final fully connected layer. We chose to put dropout regularization in the fully connected layer.

The analysis of the cores in the Shenhu area showed that the gas hydrate-bearing sediments consisted of silt (70%), sand (<10%), and clays (15%–30%) [31]. Because the well logs of gas hydrate-bearing sediments were the comprehensive responses of lithology and gas hydrates, the log characteristics of gas hydrate-bearing sediments, with varying lithologies, were di fferent. Therefore, the LSTM network trained by well logs is only suitable for gas hydrate saturation predictions of gas hydrate-bearing sediments with small lithological di fferences, such as adjacent sites in the same exploration area. For sites that are further apart, or located in other exploration areas, the predictions may have large errors.
