基于机器学习的区域工程地质分层思路与方法研究

Research on regional engineering geological layering method based on machine learning

传统的地质分层方法通常依赖于人工解释和经验判断,存在过程繁琐、工作量大、受人为因素影响较大等缺点。本文旨在研究基于机器学习的区域工程地质分层的思路与方法,将地层分层问题转换为地质体空间单元的序列到序列预测任务及地质属性特征分类任务,以提高地质分层的准确性和效率。本文在对工程地质原始数据开展深入研究的基础上,提出采用深度学习方法对静力触探试验数据进行地质分层的训练和预测,通过分类算法对取土孔土样进行分层,并定量评价了相关算法的适用性。通过对比传统方法和机器学习方法的结果,验证了机器学习在地质分层中的优势。本研究为区域工程地质研究提供了一种新的地质分层思路,具有重要的实践意义。

Traditional geological stratification methods usually rely on manual interpretation and empirical judgment,which have shortcomings such as cumbersome processes,heavy workload,and large impact from human factors.This paper aims to study the ideas and methods of regional engineering geological stratification based on machine learning,and convert the stratigraphic stratification problem into a sequence-to-sequence prediction task of geological body spatial units and a geological attribute feature classification task,so as to improve the accuracy of geological stratification.and efficiency.Based on in-depth research on the original data of engineering geology,this paper proposes to use deep learning methods to train and predict geological stratification of static cone test data,and use classification algorithms to stratify borrow hole soil samples and quantify them.The applicability of the relevant algorithms was evaluated.By comparing the results of traditional methods and machine learning methods,the advantages of machine learning in geological stratification are verified.This study provides a new geological layering idea for regional engineering geology research,which has important practical significance.