# A Stratigraphic Prediction Method Based on Machine Learning
## 1 Introduction  
A geost stratigraphic structure is the result of multiple factors in the course of the evolution of Earth's history, forming a complex morphology and irregular distribution. Geological bodies have spatially successive relationships, thus forming a series of strata with different lateral extensions and thicknesses. A geost stratigraphic series is spatially uncertain due to the variations in sequence and the number and thickness of the stratum layers. Within the rock-soil mass extending from the top of the bedrock (referring to lithified rock that underlies unconsolidated surface sediments, conglomerates or regolith) to the surface, only one layer or dozens of layers can be present. There can be a few layers, and each can be different. At the same time, the thickness of the strata also varies considerably, from tens of centimeters to hundreds of meters. Different geotechnical bodies have different physical, chemical, and mechanical properties, and weak stratum conditions directly threaten the safety of engineering construction and operation. A geost stratigraphic series model with high reliability is helpful to understand the geological conditions of a construction area, providing far-reaching practical guidance for site planning and selection, engineering construction, environmental assessment, cost savings, and operational risk reduction. Therefore, building a series model and accurately describing the spatial distribution of strata have become important topics in the field of geology and engineering geology. To understand the geological structure, many techniques and methods have been developed to describe, simulate, and model strata [1; 2; 3; 4; 5; 6]. With the introduction of the Glass Earth [7] concept and geological data, interdisciplinary theoretical integration and application research is being carried out. The most representative traditional method of simulating the stratum structure is three-dimensional (3D) geological modeling, such as that with the B-rep model [8], octree model [9], tri-prism model [10] and geochron concepts [11; 12; 13; 14]. However, the traditional method relies on the guidance of expert knowledge and experience in the selection of assumptions, parameters, and data interpolation methods, which are subjective and limited [15]. Assumptions about the borehole data distribution must be made, and it is difficult to effectively evaluate the stratum simulation results. Machine learning [16; 17; 18] has been widely used in various fields of geology. The machine learning method does not make too many assumptions about the data but selects a model according to the data characteristics. Then, the machine learning method divides the data into a training set and a test set and constantly adjusts the parameters to obtain better accuracy. Machine learning is more concerned with the predictive power of models [19]. In the fields of geology and engineering, there have been numerous research and application examples in different fields [20; 21; 22; 23; 24; 25]. [PERSON] et al. conducted a study on mineral exploration based on a decision tree [26]. [PERSON] et al. used radial function and neural network to evaluate potential maps in mineral exploration [27]. [PERSON] studied the relationships between chemical elements and magmatic and between the sedimentary rock lithology and sedimentary rock minerals by using a multilayer perceptron and back propagation (BP) neural network [28]. [PERSON] et al. predicted karst collapse based on the Gaussian process [29]. [PERSON] et al. carried out an inversion of reservoir parameters by combining well logging and seismic data [30]. [PERSON] combined machine learning, pattern recognition, and multivariate geostatistics to estimate the final recoverable shale gas volume [31]. [PERSON] et al. used a convolutional neural network to characterize unfavorable geological bodies and surface issues, etc. [32]. Generally, machine learning research on stratum distributions based on drilling data is in its infancy.