# Deep Learning Logging Sedimentary Microfacies via Improved U-Net
## 1 Introduction
Although these traditional neural networks can explore complex nonlinear mapping relationships between logging data, they only construct mapping relationships between logging depth sampling points and sampling points, ignoring the local curve structure and morphological characteristics of logging data with changes in formation depth. Sedimentary microfacies reflect the progressive sedimentation process of geological periods. Through well logging, the characteristics of rock strata such as resistivity and radioactivity are obtained and the corresponding changes in the values of these characteristics at the longitudinal depth constitute the logging curves. Logging phase labeling is obtained according to the morphological characteristics of the logging curve, then the sedimentary microfacies information can be obtained by analyzing the changes in the lithologic combination, composition, particle size, mud content, porosity and other characteristics of the corresponding sediment at the longitudinal depth. As a response to the progressive sedimentation process, the local curve structure and morphological characteristics of logging curves with depth changes are key to identifying sedimentary microfacies. By constructing a logging dataset as a two-dimensional image with depth as the vertical axis and different types of logging parameters (such as longitudinal and transverse wave velocities and density) as the horizontal axis [16], the complex rules of sedimentary microfacies in logging curves can be excavated by employing deep learning methods on the strength of image semantic segmentation. Fully Convolutional Networks (FCNs) are one of the most commonly used deep learning architectures for image semantic segmentation. Using FCNs for the identification of sedimentary microfacies in logging curves can not only consider the local or overall curve structure and morphological characteristics of logging curves with depth changes, but also perform layering, feature extraction and classification simultaneously. The U-net network is a network model developed on the basis of the FCN structure. Compared with other network models, its major feature is that it fuses the up-sampled output with the corresponding down-sampled output by means of \"skipconnection\", so that the network can spread the context information to the layer with higher resolution, and the segmentation effect is often very good. Its unique network architecture and data augmentation method have alleviated to some extent the requirement for a large number of sample inputs in deep learning, which is beneficial for solving the problem that logging data samples are insufficient due to the limitation of drilling costs and cannot be applied to fully convolutional neural networks. The U-net network presents great advantages in processing image segmentation tasks of small datasets, and has been widely applied in remote sensing image analysis [17], medical image segmentation [18; 19] and other tasks. Unlike the analysis of medical images and remote sensing images, identifying sedimentary microfacies based on logging curves only requires image segmentation in the depth direction. The traditional sedimentary microfacies identification methods often ignore the change in the morphological characteristics of the logging curve in the depth direction. The shape of the logging curve is \"vectorial\" because of the time sequence of sedimentation. Rotation, translation and inversion of the logging curve image will destroy the meaning of the sedimentary relationship represented by the curve itself. Therefore, deep learning network algorithms applied to sedimentary microfacies division in logging data need to maintain scale invariance of feature extraction while discarding rotation invariance and flip invariance of feature extraction. We propose a U-net framework-based deep learning logging sedimentary microfacies identification method based on the ideas of deep learning and image semantic segmentation.