基于深度学习的栅格测井曲线数字化研究  

Deep learning⁃based digitization of raster well logging

作　　者：周绪川[1] 游皓天 ZHOU Xu-chuan;YOU Hao-tian(School of Computer Science and Engineering,Southwest Minzu University,Chengdu 610041,China)

机构地区：[1]西南民族大学计算机科学与工程学院,四川成都610041

出　　处：《西南民族大学学报（自然科学版）》2024年第3期305-311,共7页Journal of Southwest Minzu University(Natural Science Edition)

基　　金：西南民族大学研究生创新型重点项目(ZD2022811)。

摘　　要：栅格测井曲线是记录测井数据的模拟曲线,地质专家可根据所得曲线判断出具体岩性、层位等.目前无监督计算机视觉技术解释数字曲线算法中仍然存在对人工干预有依赖,处理时间较慢且准确率不高的问题,提出了一种基于深度学习的栅格测井曲线数字化研究方法.该方法采用改进的U⁃net启发架构作为基础模型,结合栅格测井图像分辨率高的特性改变上下采样的残差块数量,并利用注意力增强的处理策略平衡在处理不同的输入和输出大小时保留其关键信号,再对栅格图像中的曲线进行语义分割减少背景干扰,增加数字化准确率.实验结果表明,与多条曲线的二值分割的真实数据相比,所提出的架构有效地对曲线进行了分类和数字化,在栅格测井图像数字化方面表现出较高的准确度和性能.Raster well⁃logging curves represent simulated data recorded during subsurface drilling operations,aiding geological experts in discerning specific lithology and strata based on the obtained curves.Presently,unsupervised computer vision tech⁃niques for interpreting digital curve algorithms still rely significantly on human intervention,exhibiting issues related to slower processing times and lower accuracy levels.This paper proposed a deep learning⁃based approach for the digitization of grid well⁃logging curves.The method leveraged an improved U⁃Net⁃inspired architecture as the foundational model,adapting the number of residual blocks during up and down⁃sampling processes to accommodate the high resolution of grid well⁃logging images.Addi⁃tionally,an attention⁃enhanced processing strategy was employed to balance the retention of critical signals when handling vari⁃ous input and output sizes.Semantic segmentation was applied to the grid images to reduce background interference and enhance digitization accuracy of curves.Experimental results demonstrated that the proposed architecture effectively classified and digi⁃tized curves compared to ground truth binary segmentation of multiple curve data,exhibiting high accuracy and performance in the digitization of raster well⁃logging images.

关 键 词：深度学习 图像分割 栅格图像 测井曲线 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]