###### Abstract
used the MLR and an ensemble (EN-PSO) model to predict the \(V_{s}\) values in soils [45]. In their work, the independent variables were the soil type, depth, and standard penetration resistance. They reported that both MLR and EN-PSO models predicted the \(V_{s}\) values with high accuracy. In another study, [PERSON] et al. estimated the \(V_{s}\) of soils by applying MLR and artificial neural network (ANN) [46]. It was declared that the ANN technique delivered more precise results. The MLR technique was also applied for \(V_{s}\) prediction in hydrocarbon reservoirs by some researchers [47; 48]. [PERSON] and [PERSON] evaluated the capability of MLR, multivariable polynomial regression, deep neural network (DNN), and random forest in \(V_{s}\) prediction for hydrocarbon reservoirs [48]. According to the obtained results, the random forest technique exhibited a better predictive capability than others. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \(V_{s}\) **Measurement Method** & **Advantages** & **Disadvantages** \\ \hline \multirow{3}{*}{Laboratory Core Analysis} & Provides accurate measurements. & Expensive and time-consuming. \\  & Allows detailed core sample analysis. & Limited to a small number of samples. \\  & Offers insights into rock properties. & May not replicate in situ conditions. \\ \hline Geophysical Well Logging and In Situ Measurements & Provides direct measurements. & Limited to borehole locations. \\ \cline{1-1} \cline{2-3} Empirical and Correlation-Based Methods & Suitable for real-time well logging. & Tools and data acquisition can be costly. \\ \cline{1-1} \cline{2-3} Empirical and Correlation-Based Methods & Simplicity and ease of application. & Limited accuracy, relying on correlations. \\ \cline{1-1} \cline{2-3} Theoretical and Physics-Based Models & Uses readily available well log data. & Applicability may be region-specific. \\ \cline{1-1} \cline{2-3} Theoretical and Physics-Based Models & Consider physical properties. & Complex and data-intensive. \\ \cline{1-1} \cline{2-3} Data-Driven and Machine Learning Techniques & Provides insights into rock behavior. & Requires a wide range of input parameters. \\ \cline{1-1} \cline{2-3} Seismic and Geostatistical Approaches & Handles complex data. & Needs extensive, high-quality training data. \\ \cline{1-1} \cline{2-3} \cline{2-Behnia et al. used the GEP and ANFIS techniques to extract mathematical relations for \(V_{s}\) prediction in limestone rocks in Iran [49]. In their study, the input parameters were density, porosity, and compressional wave velocity (\(V_{p}\)). Both techniques showed remarkable performance in predicting accurate \(V_{s}\) values. In another study, [PERSON] predicted the \(V_{s}\) values in soils using GEP and ANN techniques to characterize the potential of earthquakes in different sites in California, USA [50]. It was concluded that both techniques were promising for \(V_{s}\) prediction. In a similar investigation, [PERSON] et al. used the GEP technique to determine the soil types based on the \(V_{s}\) values [51]. A mathematical relationship was extracted and proposed for \(V_{s}\) estimation. Such a relationship exhibited an excellent performance for \(V_{s}\) estimation in soils. In this study, the well logging data obtained from a vertical wellbore in Aboozar limestone oilifield were used to estimate the \(V_{s}\) values. The Aboozar oilifield is situated 74 km away from Kharg Island in the Persian Gulf. The \(V_{s}\) estimation was carried out using five available empirical correlations, LR, and two AI algorithms including MLR and GEP. The main objective of the research was to compare the performance of those different techniques in \(V_{s}\) estimation. For this purpose, the \(V_{s}\) values estimated by each technique were validated and compared with the real \(V_{s}\) data. The additional target was to find a mathematical relationship capable of accurately predicting \(V_{s}\) values in the oilfield.