摘要
为实现快速、精确、自动化、智能化的海底浅地层层界提取,克服传统浅地层层界在复杂海洋环境下提取时的低效、模糊、主观性等缺点,提出一种基于图像信息熵约束的浅地层层界划分方法.首先,将浅剖图像分割为不同区块;然后,在不同区块计算信息熵,并结合钻孔数据,建立信息熵与显著性参数关系模型;最后,据此模型对整个浅剖图像进行层界划分.研究表明,该方法克服了现有方法的不足,实现了浅地层剖面层界的自适应、准确划分,试验中取得了与钻孔层界深度、厚度同量级的精度.由此可知采用图像信息熵约束进行层界提取,可以实现浅地层层界提取的自动化与智能化.
To address the issue of the sub-bottom profile layer extraction in complex circumstance, this paper proposes a new demarcating method based on constraint of image information entropy. Firstly, the image of sub-bottom is divided into different blocks; then, the information entropy in each block is calculated and a relation model of information entropy and significant parameters are established according to drilling data;finally, the whole sub-bottom profiling is demarcated according to the model. It is revealed that this method has overcome the shortcomings of existing methods, realized the self-adapting and exacted demarcation of sub-bottom layers. The experiment has gained the same accuracy as the depth and thickness of layers got by drilling data.
出处
《哈尔滨工业大学学报》
EI
CAS
CSCD
北大核心
2017年第8期165-170,共6页
Journal of Harbin Institute of Technology
基金
国家自然科学基金(41376109
41176068
41576107)
关键词
浅地层剖面图像
浅地层层界及提取
二维熵
中误差系数
自适应层界提取
sub-bottom profiling
sub-bottom layer and its extraction
2-D entropy
coefficient of mean square error
self-adaption extraction
