No abstract

Different parameters of a fully convolutional network (FCN) are experimented to evaluate which combination predicts sound velocity models from a single configuration of seismic modeling. The evaluation is made considering some fixed parameters of the deep learning model, such as number of epochs, batch size and loss function, but with variations of the optimizer and activation function. The considered optimizers were RMSprop, Adam and Adamax, whilst the activations functions were the Rectified Linear Unit (ReLU), Leaky ReLU, Exponential Linear Unit (ELU) and Parametric ReLU (PReLU). Five metrics were used to evaluate the model during the testing stage: R2, Pearson's r, factor of two, mean absolute error and mean squared error. To the extent of these experiments, it was found that the optimizers have much more influence than the activation functions when determining the resolution of the output model. The best combination was the one using the PReLU activation function with the Adamax optimizer.

Seismic forward modeling is a computationally and data-intensive stage in the seismic processing workflow. By profiling the kernels of seismic forward modeling algorithms, was observed that they need to access a wide variety of memory locations, in addition to the computational cost of performing floating-point operations for the numerical solution of wave equations. In this context, was used the Roofline model to analyze six representative computing kernels in seismic modeling on GPU environment to indicate bottlenecks in the performance and suggest improvements of these wave equation propagators. Based on this, was implemented six viscoacoustic equations using the Devito tool. Experimental data have shown that optimizations in increasing data reuse and decreasing off-chip memory traffic can significantly improve performance.