###### Abstract  
This study proposes a deep-learning-based model to generate synthetic compressional wave velocity (Vp) from well-logging data with application to the Ulleung Basin Gas Hydrate (UBGH) in the East Sea, Republic of Korea. Because a bottom-simulating reflector (BSR) is a key indicator to define the presence of gas hydrate, this study generates the Vp for identifying the BSR by detecting the morphology of the hydrate in terms of the change in acoustic velocity. Conventional easy-to-acquire logging parameters, such as gamma-ray, neutron porosity, bulk density, and photoelectric absorption, were selected as model inputs based on a sensitivity analysis. Long short-term memory (LSTM) and an artificial neural network (ANN) were used to design an efficient learning-based predictive model with sensitivity analysis for hyperparameters. The LSTM model outperforms the ANN model by preserving the geological sequence of the well-logging data. Ten-fold cross-validation was conducted to verify the consistency of the LSTM model and yielded satisfactory results, with an average coefficient of determination greater than 0.8. These numerical results imply that generating synthetic well-logging via deep learning can accurately estimate missing well-logging data, contributing to the reservoir characterization of gas-hydrate-bearing sediments. deep learning; compressional wave velocity; well-logging; Ulleung Basin Gas Hydrate 20222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222222223222232223222232223222  
Synthesizing well-logging data in missing intervals using available petrophysical data through machine learning techniques has been investigated as a remedy, including the synthesis of acoustic logs [13; 14; 15]. [PERSON] et al. [13] adopted an artificial neural network (ANN) algorithm to estimate an acoustic log using shale volume, gamma-ray log (GR), and bulk density log (RHOB) as input variables in shale formation. [PERSON] and [PERSON] [14] generated synthetic vs. log data comparable to reference data using two algorithms: ANN-Levenberg-Marquardt and Extreme Learning Machine. [PERSON] and [PERSON] [15] used an ANN to estimate the shear velocity using Vp, porosity (\(\phi\)), RHOB, and GR. However, the conventional ANN is vulnerable in preserving the continuity of the sequential data, limiting the reliability of the prediction results [16; 17]. More advanced neural networks have been recently deployed to preserve the data sequence in generating synthetic logging data. A recurrent neural network (RNN) is an algorithm with a recurrent flow where current state information is derived from the coaction between the inputs of the current step and outputs of the previous step [18]. Using an RNN-based algorithm, such as RNN, long short-term memory (LSTM), or a gated recurrent unit, is suitable for handling well-logging data because these data exhibit sequential characteristics similar to time-series data [19]. However, an RNN has limitations in maintaining old memory due to gradient vanishing [20]. LSTM is an algorithm that can resolve the shortcomings of RNN [20]. It has the advantage of extracting information from sequential data based on the RNN algorithm, preserving the impact of distant data in the long term [20]. Accordingly, LSTM has been conducted for operating well-logging data with improved accuracy [21; 22; 23]. [PERSON] et al. [22] estimated Vp using an LSTM algorithm that inputted GR, RHOB, and \(\phi\) from UK continental shelf wellbores to estimate acoustic logs, and then evaluated its performance against Gardner's equation. [PERSON] et al. [23] generated a synthetic vs. log from GR, RHOB, compressional travel time, neutron porosity (TNPH), photoelectric absorption (PEF), and resistivity (RT) using an LSTM algorithm with an accuracy of 98.9%.