## 1 Introduction  
Seismic data is essential to reservoir characterization studies for providing optimal estimation of elastic rock properties of the subsurface. Seismic forward modeling is used to transform elastic rock properties into seismic reflection amplitudes. The transformation involves a convolution between reflectivity series, i.e., a product of impedance contrast,with a band-limited wavelet [1; 2]. Seismic inversion deals with estimating elastic properties from seismic reflection amplitudes that are achieved by solving an inverse problem [3]. Two common approaches in seismic inversion fall generally onto deterministic and stochastic-probabilistic techniques. The generalized linear inversion is the most common deterministic seismic inversion technique [4]. This technique requires the existence of an accurate initial estimate of elastic properties models, which will be later refined iteratively during the inversion process based on the similarity between the measured seismic data. The output of the inversion process consists of only single elastic properties models [5; 6]. On the other hand, stochastic inversion attempts to search for all statistically acceptable earth elastic models that can generate synthetic seismic data that fit well with the input seismic data. The outcomes of stochastic-based inversion comprise the best-fit properties model represented from the average of all the high correlation elastic properties models [7]. The deterministic method is reasonably practical for operational work, as it only uses simple linearized algorithms and assumptions. However, the method may be less applicable when dealing with a difficult geological setting, such as the complex elastic channelize system in the Malay basin, because it may introduce non-linearity and noises, which will violate the simple assumption of linearity between seismic amplitude and elastic properties used in the convolutional model. The method also can only partially address the problem with a non-unique solution, as it has difficulty finding an accurate prior model, contributing to high uncertainty in properties estimation. An alternative way of dealing with non-linearity and non-unique solutions is by applying stochastic-probabilistic inversion. However, stochastic inversion requires large numbers of reliable well data to work efficiently. The technique also demands high computational power and time consumption. In the Malay basin, we often deal with limited numbers of quantitative interpretation (QI) compliance well data to conduct seismic inversion. This makes the method less practical for extensive and rapid production works to solve large-scale nonlinear inverse problems [8]. Deep learning-based predictive methods have recently been applied to solve the seismic inverse problem. [PERSON] and [PERSON] [8] investigated a unique approach of inverting the reflection coefficient from seismic tracing by utilizing a deep learning method trained on synthetic data. They showed that the machine learning-based inversion can provide inverted reflectivity section with a higher resolution and can better resolve geological thin beds compared to conventional least-squares-based reflectivity inversion. However, the synthetic generation data strategy allows only to predict reflectivity and can only be used with limited resolution to predict elastic properties. The approach also demands sufficient training data with the relevant hyper-parameter optimization to obtain good quality predictive network models. Attempts to estimate elastic properties using a machine learning technique have also been proposed by [PERSON] et al. [9]. Their work used a supervised decision-tree-based Gradient Boosting with knowledge-driven feature augmentation strategy to construct a predictive network. Similarly, [PERSON] et al. [10] also presented a different type of deep learning architecture, namely a temporal convolutional network (TCN), to construct a direct supervised predictive network for elastic properties estimation.