Deep neural networks (DNNs) can learn accurately from large quantities of labeled input data, but DNNs sometimes fail to generalize to test data sampled from different input distributions. Unsupervised Deep Domain Adaptation (DDA) proves useful when no input labels are available, and distribution shifts are observed in the target domain (TD). 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. In the present study, an improved deep neural network architecture named EarthAdaptNet (EAN) is proposed to semantically segment the seismic images. We specifically 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 introduced the CORAL (Correlation Alignment) method to the EAN to create an unsupervised deep domain adaptation network (EAN-DDA) for the classification of seismic reflections fromF3 and Penobscot. Maximum class accuracy achieved was ~99% for class 2 of Penobscot with >50% overall accuracy. Taken together, EAN-DDA has the potential to classify target domain seismic facies classes with high accuracy.

翻译：深心神经网络(DNNS)可以从大量标签输入数据中准确学习,但DNNS有时无法全面测试不同输入分布的数据样本。当没有输入标签时,无人监督的深域适应(DDA)证明是有用的,在目标域(TD)观测分布变化。在荷兰近海F3区块3D数据集的地震图像上(源域;SD)进行实验,从加拿大(目标域;TD)和Penobscot 3D调查数据中进行。SD和TD的三个具有类似反射模式的地质类别得到了考虑。此外,在目前的研究中,建议改进名为EarthAdaptNet(EneAdaptNet)的深度准确性网络结构结构,在地震图像中进行语义分析。我们特别使用一个转换的残余单位来取代解析器区块的传统拉动变变。 ECAN实现了像级的精确度 > 84% 和 ~70% 与现有结构相比,表现更好。此外,我们还采用了CORAL(Corrilationalalalal-LEMA-DRA3) 高级地震网络从深度变校正(ODRA3)到不透明地平级,从深地平面的深度变整。