上海国土资源2023，Vol.44Issue(4)：16-20,31,6.DOI:10.3969/j.issn.2095-1329.2023.04.004
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
摘要
Abstract
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.关键词
区域工程地质/机器学习/深度学习/序列到序列(Seq2Seq)模型/分类算法/地质分层Key words
regional engineering geological/machine learning/deep learning/Seq2Seq model/classification algorithm/geological layering分类
天文与地球科学引用本文复制引用
刘映,王寒梅..基于机器学习的区域工程地质分层思路与方法研究[J].上海国土资源,2023,44(4):16-20,31,6.基金项目
上海市科委研发公共服务平台资助项目 （）
