# Urban Geological 3D Modeling Based on Papery Borehole Log
## 2 Related Work
### Status of Research on Integration of Geological Knowledge into 3D Geological Modeling  
Over the past few decades, researchers have developed many 3D geological software and tools that are widely used by industry. The geologists have used these tools to produce many geological maps over the decades, which can assist in geological modeling.  
The quality of the 3D geological model depends on the accuracy of the data and is limited by the actual engineering project. The amount of data used for modeling is limited and the distribution is uneven. Even the geological models drawn automatically by interpolation algorithms cannot support them well. The construction of complex geological models has affected subsequent geological structure analysis. Therefore, in the actual production process, integration of geological knowledge is an essential link with the guidance of expert knowledge.  
In the early stage, the integration of geological knowledge into 3D modeling is usually based on the opinions of experts and scholars, so as to evaluate and revise the collected data and modeling data of previous samples, focusing on the experience accumulation of researchers' geological knowledge. In 2005, [PERSON] introduced the Geological Evolution Scheme (GES) standard geological descriptors from the geological composition of various continuous time geological objects. An example illustrates how GES is used to derive shared structures and geological models that can be reused by geologists throughout the workflow. This knowledge-driven idea created the model of shared earth, which is widely adopted in today's research work [7].  
Driven by geological knowledge, [PERSON] mapped and modeled Malacian groundwater based on a GIS model with a multifactor weighted linear combination of lithology, elevation and geomorphology, where each parameter and the weighted score is based on expert opinion and investigation. Not only does this fast method reduce costs, but the model can also be quickly transferred to areas with similar geological features [8]. In recent years, with the continuous development of machine learning, scholars have begun to use historical geographic data sets as training samples, combining geological knowledge and statistical knowledge, incorporating past geological knowledge and experience into the new training models. The model process flow has gradually become more intelligent. [PERSON] introduced a data-driven random forest model and geological knowledge-driven mineral vision mapping method in the gold mine case study to better describe a comprehensive lake sediment address data set [9]. [PERSON] refined and summarized the multi-criteria decision-making method, which was used in mineral mapping based on various types of geographic data sets in the past. The prospective map of porphyry copper deposits produced by this method is satisfactory and can also be applied for other similar geological conditions [10]. [PERSON] proposed a three-dimensional stochastic modeling process based on multi-point geostatistics in 2018 to address the problem of uneven data distribution during exploration, and integrated multi-source and multi-type geological data. Therefore, 3D modeling software can quickly build low-dimensional geological models without relying on complete borehole data sets [11].  
The consensus of researchers is to minimize the manual operation and modification of data by experts and combine deep learning to convert past data into intelligent rules to generate three-dimensional geological models. Therefore, this paper relies on the papery borehole data set and uses it as the basic data source to try to recognize and organize the accumulated data in the past, integrate geological knowledge and experience, reduce manual intervention as much as possible, and complete the intelligent construction process of the urban geological 3D model.