Improving the seismic image in reverse time migration by analyzing of waveﬁelds and post processing the zero lag cross correlation imaging condition
Author: Juan Guillermo Paniagua Castrillón.
It is widely known that deep neural networks can be diﬃcult to train in practice, since in order to obtain state-of-the-art results we need a great amount of data and computing power. However, we can overcome this issue either using autoencoders as way to “pre-train” deep neural networks or following a “transfer learning” approach. In particular, here we carried out several experiments to study how both approaches can beneﬁt the training of deep networks.
The earth is at least a visco elastic medium, in which absorption losses give rise to attenuation and dispersion effects. The elastic wave equation is framed in terms of tensor operators acting on vector quantities. It is also true that a proper treatment of anisotropy fundamentally demands an elastic viewpoint, even when only P-waves (quasi-P waves) are contemplated. Different representations for the same physical law can lead to diﬀerent computational techniques in solving the same problem, which can produce different and new numerical results, so this new but accurate representation should lead us to new results and descriptions of the phenomena.