###### Abstract
Because compressional and shear logs are essential variables in assessing the presence of gas hydrate, studies have used these acoustic logs as inputs to estimate the petrophysical features of gas hydrate fields. [PERSON] and [PERSON] [24] estimated pore-space gas hydrate saturation using sonic velocities in gas-hydrate-bearing sands based on their relationships. [PERSON] et al. [25] qualitatively evaluated gas hydrate saturation from the Vp and Vs of the Alaska North Slope Hydrate 01 wellbore using effective medium theory [26] and the [PERSON] and [PERSON] [27] approach. [PERSON] et al. [28] predicted Vs using LSTM with GR, RT, RHOB, \(\phi\), and Vp as input variables for a gas hydrate field in the Gulf of Mexico. They claimed that the LSTM model with a coefficient of determination (\(R^{2}\)) above 0.85 was superior to the least-squared fitting model. In their work, the input variables were selected based on the input-output correlation analysis using the Pearson, Kendall, Spearman, and maximum correlations instead of conventional well-log variables (e.g., GR, RT, and RHOB). However, acoustic logs are often not acquired, thus limiting accurate geomechanical interpretation and modeling [29]. Therefore, the capability to synthesize acoustic logs such as Vp is necessary to predict missing data, correct poor-quality data, manage reservoir uncertainty, and improve the interpretation of seismic attenuation coupled with well-logging data [30]. [PERSON] and [PERSON] [31] estimated the solution of the seismic acoustic wave velocity based on the machine learning algorithm coupled with physics governing laws. Furthermore, more hydrate-field case studies are needed to demonstrate the synthesis capability considering that the number of hydrate fields is much smaller than the number of conventional hydrocarbon reservoirs worldwide. The Ulleung Basin Gas Hydrate (UBGH) is being developed after BSR was detected in the southwestern part of the Ulleung Basin in 1996 [32]. Since 1996, UBGH-related projects and studies have been conducted by several research institutions, such as the Korea Institute of Geoscience and Mineral Resources, Korea Gas Corporation, and Korea National Oil Corporation [32]. At two UBGH wellbores (UBGH1-9 and UBGH1-10), [PERSON] et al. [33] identified the BSR and estimated gas hydrate saturation using RT and Vp. [PERSON] and [PERSON] [34] calculated gas hydrate saturation using the modified Biot-Gassmann Theory. [PERSON] et al. [35] evaluated the feasibility of gas production in the UBGH by estimating gas production at the UBGH2-6 wellbore. [PERSON] et al. [36] conducted numerical simulations to estimate gas hydrate production at the UBGH through a cyclic depressurization method, which varies the depressurization stages based on bottom hole pressure changes. [PERSON] et al. [37] quantified the mineral composition of gas-hydrate-bearing sediments in the UBGH using machine learning techniques such as a convolutional neural network (CNN), RNN, ANN, and random forest (RF). Furthermore, the gas-hydrate-bearing zone in the UBGH is less consolidated; thus, its porosity range is larger than that of conventional reservoirs [27]. We intend to proceed with more case studies using hydrate field data to enhance UBGH development. This study aims to generate synthetic Vp in distant wellbores using an LSTM model from easy-to-acquire well-logging variables (e.g., GR, RHOB, and RT), with application to the Ulleung Basin in the East Sea, Republic of Korea. This paper focuses on synthetic Vp generation due to the absence of Vs at the well-logging data of the UBGH field. First, conventional well-logging features are chosen as the input variables to design a learning-based predictive model. Second, ANN and LSTM models are designed based on the selected features. These models are trained to recognize geological relationships among the input and output well-logging features. Third, the performance of the models (ANN and LSTM) is compared to identify a suitable approach to handle well-logging data.