## 3 Methodology
### Deep Learning Network Training  
The proposed architecture consists of UNET and RESNET-18 networks for feature extraction, followed by a series of 5 dense layers for classification and dimensionality reduction, as shown in Figure 2. For the feature extraction part, the input dimension is 1 \(\times\) 32 \(\times\) 32. The first block permits the input into down-sampling trend and comprises one convolutional layer with 64 channel features, and 7 \(\times\) 7 filter kernel and strides 2, followed by batch normalization and rectified linear unit (ReLU) activation function. This produces an output dimension of 64 \(\times\) 16 \(\times\) 16. The second block consists of a max pooling layer with 3 \(\times\) 3 filter kernel and strides 2, followed by 4 convolutional layers where each layer comprises of 64 channel features, and 3 \(\times\) 3 filter kernel and strides 1, along with batch normalization and ReLU activation in which the output dimension is reduced to 64 \(\times\) 8 \(\times\) 8. The third block contains 4 convolutional layers; the first layer comprises 128 channel features, and 3 \(\times\) 3 filter kernel and strides 2, along with Batch normalization and ReLU activation. The output from the second layer is concatenated with another convolutional layer of 128 channel features, 1 \(\times\) 1 filter kernel and strides 2, along with Batch normalization, that act as identity mapping to establish a skip connection. The output dimension from this block is further reduced to 128 \(\times\) 4 \(\times\) 4. The same process from the third block is repeated for fourth and fifth blocks, which further reduces the output dimension while increasing the channel features to 256 \(\times\) 2 \(\times\) 2 and 512 \(\times\) 1 \(\times\) 1, respectively. Similarly, the decoder section also consists of 5 residual blocks. Each block permits the input into up-sampling trend and poses a similar number of convolutional layers as the respective encoder block with the same number of channel features and filter kernels, along with batch normalization and ReLU activation, but the strides remain 1 since we expand the output dimension. After passing each decoder block, the number of feature maps are reduced to half and appended by the feature maps' output from the corresponding encoder block, which acts as an identity mapping to establish skip connections among sections. The output dimension of the decoder section is similar to the input dimension i.e., 1 \(\times\) 32 \(\times\) 32. The training was done in 2 steps, as shown in Figure 3. The first step is called rock physics driven training that trains the network to apprehend the behavior between seismic amplitude characteristics and various rock physics combination possibilities [13; 14]. The input, X, is the synthetic seismic gathers in 32 sample mini sequences and the output, Y, is the pseudo-elastic properties logs of density, P, and S-Impedance at a mid-point of the mini-sequences. The second step, on the other hand, is called weak supervision training. The weak supervision idea was adopted from tensor flow in transfer learning and fine-tuning tutorials to ascertain that the weakly supervised network undertakes the transfer learning process to recognize the unique character of actual seismic data, i.e., amplitude noise and scaling, attenuation, phase rotation, etc., which is based on the rock physics combination of actual logs at the reference wells. Here, the input, X, is the actual seismic and the output, Y, is the real elastic properties logs taken from 3 actual reference wells. Prior to the network training, both input X and output Y were scaled using a min-max scaler and simple normalization respectively, to ensure faster convergence during network training optimization. During training, we used the learning rate of 0.0001 with a cosine scheduler criteria for a more efficient convergence.