# Advanced Elastic and Reservoir Properties Prediction through Generative Adversarial Network
## 2 Related Work  
**AI-Velocity Model. ** The application of neural networks for velocity model building dates back to the 1990s. [PERSON], [PERSON] et al. [12] used a neural network to predict 1D velocity from shot gathers. Similar work has recently been published [13] that maps the 1D vertical velocity profile from data cubes of the neighboring common midpoint (CMP) gathers. The work applied advanced DL architecture, the Visual Geometry Group (VGG) network, and is able to accommodate lateral heterogeneity in the model, a problem faced by [14] in generalizing the neural network training, by creating four different sets of geologically inspired geometric training models with and without background velocity gradient. The authors managed to produce plausible results with only one neural net (NN) and one fully connected (FC) layer as the network architecture. Earlier work of [15] has also shown the feasibility of the UNET architecture to approximate the non-linear mapping into the velocity model from multi-shot gathers. The authors also suggested that a generative adversarial network (GAN) be used in future testing. Another example is by [16] in a paper on DL tomography in which the authors applied three dense layers to learn the tomography operator from the seismic data. Based on the example here, most of the model-building techniques focus only on building the velocity model [13; 14; 16; 17] and, in certain cases, the density model [18]. **AI-Rock Physics. ** The application of machine learning or deep learning in rock physics has gained attention in recent years. One of the early applications used two simple 1D convolutional layers in solving the seismic inversion problem of predicting the elastic model of the subsurface from recorded seismic data [19]. The authors generated a numerical training set of P-impedance realizations with the corresponding seismic response and were able to have a good generalized network. In another example, convolutional neural networks (CNN) have been used to predict reservoir properties directly in the depth domain given the time domain pre-stack seismic data [20]. The feasibility of the method was shown by comparing two CNN networks, namely PetroNet (end-to-end CNN) and ElasticNet-ElasticPetroNet (cascaded CNN). In addition to deriving the reservoir and elastic properties directly from the seismic data, the neural net was also tested in predicting the reservoir properties from the elastic properties [21]. Several inputs of different elastic properties were passed together into a three-layer neural net to produce the reservoir properties. **Generative Adversarial Networks. ** Deep learning is a subset of machine learning, and machine learning itself is a subset of artificial intelligence. Generative adversarial networks, or GAN [22], is a two-network architecture that consists of a generator that produces the translated image and a discriminator that tries to classify its input whether it is generated (fake) or coming from the label set (real). GAN has become one of the best deep learning networks due to its ability to produce remarkable photorealistic results in many computer vision tasks such as image generation [22], image transformation or translation [22; 23], styling [24], and super-resolution [25; 26]. Furthermore, many GAN networks were also applied to medical image segmentation [27; 28; 29; 30; 31; 32; 33]. Despite the success of GAN in both domains, the application of GAN in geosciences is limited. Some of the applications in geosciences are compressive sensing [34], facies classification [35], and rock-type inference [36; 37]. In this paper, we modified a generative deep learning (GAN) method called Pix2 Pix proposed by [PERSON], [PERSON] et al. [22] and applied it to synthetic post-stack seismic data. We verified the feasibility of the method in predicting or mapping the reservoir and elastic properties directly. First, the input and target data were prepared as required by the DL network.