# Deep Learning Logging Sedimentary Microfacies via Improved U-Net
## 1 Introduction  
The study of sedimentary microfacies is beneficial for revealing the properties of reservoirs and providing practical guidance for oil and gas exploration. As core acquisition is difficult and expensive, because specialized equipment and technology are required, as well as a series of experiments and analyses. In addition, the sampling process may encounter some difficulties, such as difficulty drilling into the rock, core fracture and other problems, which will also increase the cost. So far, logging data are the only geological data that can realize continuous, high-precision and high-resolution formation analysis and interpretation of the whole well interval. The values of different types of logging curves vary with the sedimentary characteristics of the rock mass, such as source, lithology, grain size and structure, and these geological characteristics will be reflected in the morphological characteristics of the logging curves. Therefore, well logging data have become an important source of information for sedimentary microfacies analysis. The amplitude, shape and other features of logging data can be utilized to identify related lithofacies and sedimentary environments. Traditionally, geologists have relied on subjective interpretations of logging curves for sedimentary microfacies identification [1, 2]. For example, [PERSON] et al. [3] used the morphological change trend of a natural gamma logging curve to analyze the sedimentary microfacies of the Taiwan Hill fan delta. This method is highly subjective, inaccurate, time-consuming and laborious, and consumes a great deal of humanand material resources. To define sedimentary microfacies division criteria more accurately and conveniently, identification of sedimentary microfacies has gradually transitioned from traditional qualitative identification to quantitative identification. Quantitative analysis is more efficient and accurate than qualitative analysis, as it applies complex mathematical operations to sedimentary facies in logging data and constructs specific parameters as the basis for judging sedimentary microfacies. Numerous scholars have mathematically described variations in logging curve characteristics and constructed specific parameter indicators as the basis for identifying sedimentary microfacies [4]. For example, [PERSON] [5] used the analysis of variance to automatically stratify the curve initially, then carried out fine stratification through further calculations, and then obtained parameter values related to log curve morphology in each layer as the basis for distinguishing sedimentary microfacies. Bayesian criteria [5; 6], linear discriminant analysis [6], fuzzy logic [7], K-nearest neighbor algorithms (KNN) [8] and support vector machines (SVM) [9; 10], as well as other mathematical statistics or machine learning methods, have been successively applied to sedimentary microfacies identification based on logging curves. These methods involve the pre-layering of logging curves and manual selection of criteria and parameters to extract curve features. This process is complex and subjective, demanding a high level of expertise from layering personnel. The volume of well logging data is large and has multi-source heterogeneity [11]. The use of logging curves for identifying sedimentary microfacies in the logging process faces difficulties such as ambiguity and uncertainty. In recent years, deep learning composed of neural networks that can autonomously learn and extract features between data has provided a new way to identify sedimentary microfacies in logging. The deep neural network autonomously learns curve features, which maximizes the characteristics of the logging data themselves. Networks such as SOM [12; 13] and ANN [14; 15] have already been preliminarily applied in the identification of sedimentary microfacies. These methods still require the logging curves to be layered in advance, so that the single-layer logging data and feature parameters can be input in vector form.