  Deep neural networks (DNNs) can learn accurately from large quantities of
labeled input data, but often fail to do so when labelled data are scarce. DNNs
sometimes fail to generalize ontest data sampled from different input
distributions. Unsupervised Deep Domain Adaptation (DDA)techniques have been
proven useful when no labels are available, and when distribution shifts are
observed in the target domain (TD). In the present study, experiments are
performed on seismic images of the F3 block 3D dataset from offshore
Netherlands (source domain; SD) and Penobscot 3D survey data from Canada
(target domain; TD). Three geological classes from SD and TD that have similar
reflection patterns are considered. A deep neural network architecture named
EarthAdaptNet (EAN) is proposed to semantically segment the seismic images when
few classes have data scarcity, and we use a transposed residual unit to
replace the traditional dilated convolution in the decoder block. The EAN
achieved a pixel-level accuracy >84% and an accuracy of ~70% for the minority
classes, showing improved performance compared to existing architectures. In
addition, we introduce the CORAL (Correlation Alignment) method to the EAN to
create an unsupervised deep domain adaptation network (EAN-DDA) for the
classification of seismic reflections from F3 and Penobscot, to demonstrate
possible approaches when labelled data are unavailable. Maximum class accuracy
achieved was ~99% for class 2 of Penobscot, with an overall accuracy>50%. Taken
together, the EAN-DDA has the potential to classify target domain seismic
facies classes with high accuracy.
