【摘要】：地質勘探是地質工程前的必備過程,隨著相關技術的發(fā)展,利用前視鉆孔攝像技術獲取鉆孔孔壁圖像進行地質分析已成為地質勘探中一項非常重要的技術。當前對前視鉆孔圖像的分析處理主要存在兩方面問題,一方面,大量的鉆孔圖像分析分類工作給技術人員帶來了較大的挑戰(zhàn);另一方面,前視鉆孔圖像難以合成孔壁平面全景圖,限制了分析水平。利用數(shù)字圖像處理及模式識別相關技術代替人工完成分類以及合成孔壁全景圖,對于提高地質分析水平,拓展前視鉆孔攝像的應用范圍具有重要意義。本文以前視鉆孔圖像為研究對象,研究了前視鉆孔圖像的特征提取分類與全景圖合成技術。在前視鉆孔圖像的特征提取和分類技術方面,本文以傳統(tǒng)的特征提取方法為切入點,介紹了常用的特征提取方法,并分析了其存在的不足,重點介紹了Contourlet變換以及非下采樣Contourlet變換的基本原理及特點,以及其在自然紋理特征上的表述優(yōu)勢,通過提取非下采樣Contourlet變換子帶系數(shù)的統(tǒng)計特征結合鉆孔圖像的Hu不變矩特征,再利用方差統(tǒng)計進行特征選擇,構成特征向量,最后利用BP神經網(wǎng)絡進行分類實驗驗證,得到了比較滿意的實驗結果。在前視鉆孔圖像全景圖合成技術方面,本文分析了前視鉆孔成像與數(shù)字光學成像的基本原理及成像特點,確定了前視鉆孔圖像合成孔壁全景圖的可行性,經過圓心定位、環(huán)形區(qū)域展開和圖像匹配拼接等關鍵技術的研究,得到了良好的鉆孔孔壁平面全景圖,為后續(xù)的地質分析奠定了基礎。本文提出的算法均在Matlab平臺進行了驗證,得到了比較滿意的結果,證明了算法的可行性。
[Abstract]:Geological exploration is a necessary process before geological engineering. With the development of related technologies, it has become a very important technology in geological exploration to obtain drilling hole wall images by using forward looking drilling imaging technology for geological analysis. At present, there are two main problems in the analysis and processing of forward-looking drilling images. On the one hand, a large number of drilling image analysis and classification work has brought great challenges to technicians; on the other hand, forward-looking drilling images are difficult to synthesize hole wall panoramic images, which limits the analysis level. It is of great significance to use digital image processing and pattern recognition technology to complete classification and synthesize panoramic images of hole wall instead of manual classification, which is of great significance to improve the level of geological analysis and expand the application range of forward looking drilling camera. In this paper, the drilling image is regarded as the research object, and the feature extraction classification and panoramic image synthesis technology of forward looking drilling image are studied. In the aspect of feature extraction and classification technology of forward looking drilling image, this paper introduces the common feature extraction methods based on the traditional feature extraction method, and analyzes its shortcomings, with emphasis on the basic principle and characteristics of Contourlet transform and non-downsampling Contourlet transform, as well as its advantages in natural texture features. By extracting the statistical features of non-downsampled Contourlet transform subband coefficients combined with the Hu invariant moment features of drilling images, and then using variance statistics to select features to form feature vectors, finally, the classification experiments are verified by BP neural network, and satisfactory experimental results are obtained. In the aspect of panoramic image synthesis technology of forward-looking drilling image, the basic principle and imaging characteristics of forward-looking drilling imaging and digital optical imaging are analyzed in this paper, and the feasibility of synthesizing hole wall panoramic image from forward-looking drilling image is determined. through the research of key technologies such as center positioning, annular area expansion and image matching and stitching, a good panoramic image of hole wall is obtained, which lays a foundation for subsequent geological analysis. The algorithms proposed in this paper are verified on Matlab platform, and satisfactory results are obtained, which proves the feasibility of the algorithm.
【學位授予單位】：山東科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：P624;TP391.41

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