發(fā)布時(shí)間：2018-07-02 23:55

  本文選題：淺地層剖面圖像 + 淺地層層界及提取　； 參考：《哈爾濱工業(yè)大學(xué)學(xué)報(bào)》2017年08期

【摘要】：為實(shí)現(xiàn)快速、精確、自動(dòng)化、智能化的海底淺地層層界提取,克服傳統(tǒng)淺地層層界在復(fù)雜海洋環(huán)境下提取時(shí)的低效、模糊、主觀性等缺點(diǎn),提出一種基于圖像信息熵約束的淺地層層界劃分方法.首先,將淺剖圖像分割為不同區(qū)塊;然后,在不同區(qū)塊計(jì)算信息熵,并結(jié)合鉆孔數(shù)據(jù),建立信息熵與顯著性參數(shù)關(guān)系模型;最后,據(jù)此模型對(duì)整個(gè)淺剖圖像進(jìn)行層界劃分.研究表明,該方法克服了現(xiàn)有方法的不足,實(shí)現(xiàn)了淺地層剖面層界的自適應(yīng)、準(zhǔn)確劃分,試驗(yàn)中取得了與鉆孔層界深度、厚度同量級(jí)的精度.由此可知采用圖像信息熵約束進(jìn)行層界提取,可以實(shí)現(xiàn)淺地層層界提取的自動(dòng)化與智能化.
[Abstract]:In order to realize the fast, accurate, automatic and intelligent extraction of the shallow layer boundaries of the sea floor, and overcome the shortcomings of the traditional shallow layer boundaries extraction in complex marine environment, such as inefficiency, fuzziness, subjectivity, etc. A method of dividing shallow layer boundaries based on entropy constraint of image information is proposed. Firstly, the shallow-cut image is divided into different blocks; then, the information entropy is calculated in different blocks and the relationship between information entropy and salient parameters is established by combining the borehole data. Finally, the whole shallow profile image is divided into layers according to the model. The research shows that the method overcomes the shortcomings of the existing methods and realizes the self-adaptation and accurate division of the stratigraphic boundary of the shallow strata. The accuracy of the experiment is the same as the depth and thickness of the borehole boundary. It can be concluded that using the restriction of image information entropy to extract the layer boundary can realize the automation and intelligence of the shallow layer boundary extraction.
【作者單位】： 武漢大學(xué)測(cè)繪學(xué)院;武漢大學(xué)動(dòng)力與機(jī)械學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(41376109,41176068,41576107)
【分類號(hào)】：P229;TP391.41

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