【摘要】：重力反演就是通過已知重力場的分布,使用某種手段推算出地下的密度分布,它是地球物理勘探的重要手段之一。這種方法的基本思想就是對地下待反演地質(zhì)體進(jìn)行網(wǎng)格剖分,通過不停的反演迭代直至達(dá)到精度要求,來計(jì)算出所有剖分網(wǎng)格單元的視密度。但是,這種計(jì)算方法的主要問題就是反演多解性嚴(yán)重:第一、觀測重力異常是地下的空間體積效應(yīng);第二、采集的數(shù)據(jù)是離散的而不是連續(xù)的。如何減少多解性,提高反演成像精度,成為解決重力反演的關(guān)鍵性問題。針對多解性問題,采用的是有效地提取更多有利的先驗(yàn)信息,增加反演數(shù)據(jù)體的數(shù)量來提高視密度反演成像的精度。如何有效地增加反演數(shù)據(jù)體的數(shù)據(jù),成為本文的基本出發(fā)點(diǎn),因此采用基于深度變化的正則化下延方法,得到不同深度的重力場,即三維重力數(shù)據(jù)體,然后利用三維空間數(shù)據(jù)體即不同深度的位場下延數(shù)據(jù)體進(jìn)行反演,該反演方法的數(shù)據(jù)量遠(yuǎn)遠(yuǎn)大于觀測面的數(shù)據(jù)量,然后采用黃金分割算法,并合理地加入約束進(jìn)一步減少反演的多解性問題,從而得到接近于實(shí)際地質(zhì)情況的密度分布和幾何形態(tài),提高成像精度。進(jìn)行模型試算研究該方法的可行性,采用不同的地質(zhì)模型,通過對不同下延場的選擇,比較視密度成像精度,得出最合理的下延場選取規(guī)則。在反演過程中,也要對其他不同的參數(shù)進(jìn)行比較,合理加入約束,得到最佳的反演結(jié)果,改進(jìn)反演算法,提高反演精度。通過模型試算總結(jié)出:該方法確實(shí)提高了視密度成像在縱向上的分辨率,對目標(biāo)地質(zhì)體的位置反演的更加精確,對不同深度的地質(zhì)的成像效果明顯提高了,大大提高了反演精度。應(yīng)用本研究方法對北三臺(tái)地區(qū)重力異常進(jìn)行了視密度反演,并根據(jù)視密度的三維展布進(jìn)行了火成巖解釋,反演結(jié)果與實(shí)際鉆井鉆井資料比較吻合。證明了該方法的可靠性與實(shí)用性。
[Abstract]:Gravity inversion is to calculate the density distribution of underground by some means by known distribution of gravity field. It is one of the important means of geophysical exploration. The basic idea of this method is to mesh the underground geological bodies to be retrieved and to calculate the apparent density of all grid elements by continuous inversion iteration until the accuracy requirements are met. However, the main problem of this method is that the inversion of multiple solutions is serious: first, the observed gravity anomaly is the spatial volume effect of the underground; second, the collected data are discrete rather than continuous. How to reduce the multiplicity and improve the precision of inversion becomes the key problem of gravity inversion. In order to solve the multi-solution problem, more favorable prior information is effectively extracted and the number of inversion data is increased to improve the precision of apparent density inversion. How to effectively increase the data of inversion data volume is the basic starting point of this paper, so the regularization descent method based on depth change is used to obtain the gravity field of different depth, that is, the three-dimensional gravity data volume. Then the three-dimensional spatial data volume, that is, the data volume of the potential field with different depth, is used for inversion. The amount of data of the inversion method is much larger than that of the observation surface, and then the golden segmentation algorithm is used. Furthermore, the multi-solution problem of inversion is further reduced by adding constraints reasonably, so that the density distribution and geometric shape close to the actual geological conditions can be obtained, and the imaging accuracy can be improved. The feasibility of this method is studied by means of model trial calculation. Different geological models are used to compare the precision of apparent density imaging with the selection of different drop-down fields, and the most reasonable rule for selecting the drop-off fields is obtained. In the course of inversion, it is also necessary to compare other parameters, add constraints reasonably, obtain the best inversion results, improve the inversion algorithm, and improve the inversion accuracy. Through the model test calculation, it is concluded that this method improves the resolution of the apparent density imaging in longitudinal direction, and the position inversion of the target geological body is more accurate, and the imaging effect of this method on the geology of different depths is obviously improved. The inversion precision is greatly improved. The apparent density inversion of gravity anomaly in Beisantai area is carried out by using this method, and igneous rock interpretation is carried out according to the three-dimensional distribution of apparent density. The inversion results are in good agreement with the actual drilling data. The reliability and practicability of the method are proved.
【學(xué)位授予單位】：中國石油大學(xué)(華東)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：P631.1

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