本文選題：大地電磁 + 數(shù)據(jù)空間�。� 參考：《長江大學》2015年碩士論文

【摘要】：20世紀50年代,法國學者Cagniard和前蘇聯(lián)學者Tikhonov提出了大地電磁法(magnetotelluric, MT)。大地電磁法是利用自然界中本身存在的大地電磁場進行地球物理勘探,該方法避免高阻層屏蔽的影響、對高導層分辨能力強、橫向分辨率高、勘探深度深大數(shù)十公里、勘探范圍大、勘探費用低、野外施工簡單方便、室內(nèi)資料處理和后期地球物理解釋方法成熟等優(yōu)點。經(jīng)過半個世紀的研究和發(fā)展,目前已被廣泛應(yīng)用于油氣勘查、金屬礦勘探、非金屬礦勘探、地熱勘探、地下水和溶洞勘查等領(lǐng)域。目前來說二維解釋常常不能很好的說明存在于某些地質(zhì)復雜區(qū)域,野外采集數(shù)據(jù)所呈現(xiàn)出的重要地質(zhì)特征,因此對于常規(guī)的三維電磁反演的能力的研究是對大地電磁方法未來進一步發(fā)展的要求。目前,三維大地電磁測深數(shù)據(jù)正、反演問題,已經(jīng)是國內(nèi)外電磁感應(yīng)領(lǐng)域的主要研究方向。國外在70年代中期,就開始了三維正演的研究。隨著有限元法,有限差分法,邊界元法,積分方程法等應(yīng)用,MT二維、三維建模和反演都取得了長足的進步。隨著三維正演的進一步發(fā)展,越來越多的人投入到三維反演的研究中來,因此誕生了很多不同的反演算法,主要有非線性共軛梯度反演、快速松弛反演、共軛梯度法極大似然反演、擬線性近似反演、貝葉斯統(tǒng)計反演和人工神經(jīng)網(wǎng)絡(luò)反演等。近年來,很多人在三維大地電磁反演算法的發(fā)展做出自己的努力,使用各種合理的大范圍趨近方法(如：Mackie和Madden在1993年,]Newman和Alumbaugh在2000年,Farquharon等在2002年)。這些方法已被證明能夠合理的去恢復電導率變化,至少在某些情況下,已被理論數(shù)據(jù)例子中得到驗證。然而,該三維電磁反演問題遠未得到解決。高端工作站配置或并行計算機需求仍然阻礙三維程序在實際應(yīng)用中運行,計算機效率的改進和實際數(shù)據(jù)的真實性與準確性影響所有設(shè)想的方法。因此,三維反演算法執(zhí)行效率的提高受到人們廣泛的關(guān)注。對于常規(guī)三維反演方法,M的變大使得計算時間的變長,更重要的是計算機內(nèi)存需求的增加可能使計算機不可能運行。但是除非地球結(jié)構(gòu)有非常強的優(yōu)先限制,由于正演的結(jié)果強烈的取決于模型方案的選擇,這樣的計算結(jié)果可能會誤導我們。對于M×M階系數(shù)矩陣,而且這種方法能夠適應(yīng)更多常規(guī)的真實地質(zhì)模型。但是這種一般的迭代趨近方法只在最小三維反演結(jié)構(gòu)模型已經(jīng)有所限制的的基礎(chǔ)上進行實際計算,而且M較大時逐漸引起重視。音頻大地電磁測深(Audio-frequency magnetotelluric,AMT)法是應(yīng)用天然場源、基于平面波Cargniard視電阻率定義的頻率域電磁勘探方法。上世紀60年代初,Kennecott啟動了在音頻頻段進行大地電磁方式觀測的試驗,證明是可行的；隨后,Strangway等人應(yīng)用音頻大地電磁測深法尋找金屬硫化礦床方面做了大量工作,取得了有意義的成果。該方法儀器輕便,勘探效率高,工作頻率范圍1Hz-20kHz,勘探深度從數(shù)米至千米范圍,特別適用于深度在千米以內(nèi)的資源和工程勘查。由于觀測資料的頻率較高,對淺部特別是低阻層具有較高的分辨率。其不足之處是場源不可控,信號微弱,易受自然環(huán)境的影響,尤其是在礦山、城區(qū)附近很難開展工作。在資料解釋方面,與常規(guī)的大地電磁測深(MT)方法一樣,容易受到地形起伏和局部非均勻體造成的靜態(tài)偏移畸變影響,使得兩種極化方式的視電阻率曲線嚴重分異,給資料解釋帶來困難。隨著研究工作的深入,大地電磁資料的解釋由早期的一維反演逐步向二維和三維反演發(fā)展。1987年由Constanble等人提出的OCCAM反演算法成功用于一維MT資料反演,后來由deGroot-Hedlin等人深入研究用于二維MT資料反演。與其他算法相比,OCCAM反演算法可以通過較少的幾次迭代就得到穩(wěn)定收斂的解。二維大地電磁資料反演對計算機資源的要求不太高,反演算法也比較成熟,OCCAM算法完全達到了實用化水平。該算法基于模型空間,設(shè)模型參數(shù)的個數(shù)為M,反演需要計算MxM維靈敏度矩陣,當模型的網(wǎng)格參數(shù)M很大時計算工作量相當大,所以基于模型空間的三維MT反演不具備實用性。采用基于數(shù)據(jù)空間的反演算法可以克服上述的困難。一般來講能滿足觀測數(shù)據(jù)的參數(shù)個數(shù)N遠小于模型換個參數(shù)的個數(shù)M,基于數(shù)據(jù)空間的算法計算N維矩陣, 當NM時計算量要小很多。Siripunvaraporn和Egbert于2000年將該算法成功用于二維MT反演,并于2005年實現(xiàn)了三維反演。由于基于數(shù)據(jù)空間的算法對計算機的內(nèi)存和技術(shù)速度的要求度大大降低,所以該算法的實現(xiàn)使三維MT反演實用化成為可能。這里,我們引出一種新興的大地電磁三維反演算法,這種算法源于數(shù)據(jù)空間,同時N×N方程組將取代M×M組常規(guī)方程組。這樣的話,獨立數(shù)據(jù)N的大小將直接決定所有計算數(shù)量的大小和所需要的數(shù)組,因此三維地質(zhì)仿真模型將遠遠小于M。實際上,數(shù)據(jù)空間方法已經(jīng)被廣泛的應(yīng)用于各種地質(zhì)問題的反演(即：Parker在1994年)和其他的物理場(Egbert等在1994,Chua和bennett在2001年)。如果沒有其他特殊的限制,數(shù)據(jù)空間趨近法考慮的是反演算法而不是共軛梯度法。我們認為這類趨近方法是源于二維奧卡姆反演算法的延伸和發(fā)展。本文采用基于數(shù)據(jù)空間的三維反演算法實現(xiàn)了大陣列三維音頻大地電磁數(shù)據(jù)的反演,在CPU/GPU工作站上對一個觀測實例進行了處理和計算。反演結(jié)果表明,該算法能完成大尺度模型和大陣列觀測數(shù)據(jù)的三維反演,采用并行算法提高了反演速度,算法具有實用性。反演結(jié)果除與已知的露頭或構(gòu)造信息基本吻合外,還提供了豐富的地中電阻率參數(shù)變化和信息,避免了二維反演中靜態(tài)偏移的影響,并大大提高了對小異常的分辨能力。
[Abstract]:In 1950s, the French scholar Cagniard and the former Soviet scholar Tikhonov proposed the magnetotelluric (MT). The magnetotelluric method is geophysical exploration using the magnetotelluric field in nature itself. This method avoids the influence of the shielding of the high resistivity layer, and is strong in high resolution, high horizontal resolution and depth of exploration. After half a century of research and development, it has been widely used in oil and gas exploration, metal prospecting, non-metallic ore exploration, geothermal exploration, groundwater and karst cave exploration. At present, the two dimensional interpretation is often not a good explanation for the important geological characteristics that exist in some complex geological regions and the field data collection. Therefore, the study of the ability of the conventional three-dimensional electromagnetic inversion is the need for the further development of the magnetotelluric method. The problem is the main research direction in the field of electromagnetic induction at home and abroad. In the middle of the 70s, the three dimensional forward modeling began. With the finite element method, the finite difference method, the boundary element method, the integral equation method and so on, the MT two-dimensional, three-dimensional modeling and inversion have made great progress. With the further development of three dimensional forward, the more The more people come into the study of three-dimensional inversion, so many different inversion algorithms are born, including nonlinear conjugate gradient inversion, fast relaxation inversion, conjugate gradient method maximum likelihood inversion, quasi linear approximation inversion, Bias statistical inversion and artificial neural network inversion. In recent years, a lot of people have been in three-dimensional magnetotelluric. The development of the inversion algorithm has made its own efforts to use a variety of reasonable large range approach methods (such as Mackie and Madden in 1993,]Newman and Alumbaugh in 2000, Farquharon in 2002). These methods have been proved to be able to restore electrical conductivity in a reasonable way, at least in some cases, have been tested in theoretical data examples. However, the three dimensional electromagnetic inversion problem is far from being solved. The high end workstation configuration or parallel computer demand still hinders the operation of the three-dimensional program in the actual application, the improvement of the computer efficiency and the real and accuracy of the actual data affect all the envisaged methods. Therefore, the efficiency of the 3D inversion algorithm is improved by people. There is wide concern. For the conventional three dimensional inversion, the change in the M's ambassador has to calculate the length of the time, and more importantly, the increase in the computer's memory demand may make the computer impossible. But unless the earth's structure has a very strong priority limit, the result is strongly dependent on the selection of the model scheme, such a calculation knot. The results may mislead us. For the M x M order coefficient matrix, and this method can adapt to more conventional real geological models. But this general iterative approach is only based on the minimum three-dimensional inversion structure model which has been limited, and it is gradually paid attention to when the M is larger. The Audio-frequency magnetotelluric (AMT) method is a frequency domain electromagnetic exploration method based on the natural field source, based on the plane wave Cargniard apparent resistivity. At the beginning of the 60s of last century, Kennecott started the experiment of magnetotelluric observation at the audio frequency band, which proved to be feasible; then, Strangway et al. Applied audio magnetotelluric. A lot of work has been done in the field of sounding to find metal sulfide deposits, which have made significant achievements. This method is portable, efficient in exploration, the range of work frequency 1Hz-20kHz, the depth of exploration from several meters to kilometer, especially for resources and engineering exploration within kilometers. The low resistivity layer has a high resolution. Its inadequacy is that the source is uncontrollable, the signal is weak, and it is easily affected by the natural environment. Especially in the mine, it is difficult to work in the vicinity of the city. As for the interpretation of the data, it is like the conventional magnetotelluric sounding (MT) method, which is easily affected by the topographic fluctuation and the static migration caused by the local inhomogeneous body. The distortion effect makes the apparent resistivity curves of the two polarization ways seriously different, and brings difficulties to the interpretation of the data. With the deepening of the research work, the interpretation of magnetotelluric data from the early one-dimensional inversion to the two-dimensional and three-dimensional inversion is progressively developed by the OCCAM inversion algorithm proposed by Constanble et al. For the inversion of one dimension MT data in.1987. Later, deGroot-Hedlin and others have studied the inversion of two-dimensional MT data in depth. Compared with other algorithms, the OCCAM inversion algorithm can get the stable convergence through fewer iterations. The two-dimensional magnetotelluric data inversion is not very demanding for the computer resources, the inversion algorithm is more mature, and the OCCAM algorithm has been fully applied. The algorithm is based on the model space, the number of the model parameters is M, and the MxM dimension sensitivity matrix is calculated. When the model's grid parameter M is very large, the calculation of the 3D MT inversion based on the model space is not practical. The number of parameters that can meet the parameters of the observed data is far less than the number M of the model changing parameters, and the N dimensional matrix based on the data space algorithm is calculated. When the amount of computing in NM is less than that of.Siripunvaraporn and Egbert, the algorithm is successfully applied to the two-dimensional MT inversion in 2000, and the 3D inversion is realized in 2005. The algorithm based on data space has been realized. The requirements of the computer's memory and technical speed are greatly reduced, so the implementation of the algorithm makes it possible to apply the three-dimensional MT inversion. Here, we lead to a new three-dimensional magnetotelluric inversion algorithm. This algorithm is derived from the data space, and the N x N equations will replace the M x M group conventional equations. In this case, independent data N The size will directly determine the size of all calculated numbers and the array required, so the 3D geological simulation model will be far less than M. actually, and the data space method has been widely applied to the inversion of various geological problems (i.e., Parker in 1994) and other physical fields (Egbert and so on in 1994, Chua and Bennett in 2001). There are other special restrictions. The data space approach method considers the inversion algorithm rather than the conjugate gradient method. We think that this kind of approach is derived from the extension and development of the two-dimensional Occam inversion algorithm. In this paper, a three-dimensional inversion algorithm based on data space is used to retrieve the three dimensional audio magnetotelluric data of large array, in CPU/GPU The results show that the algorithm can complete the three-dimensional inversion of the large scale model and the large array observation data, and the parallel algorithm is used to improve the inversion speed, and the algorithm is practical. The inversion results also provide rich data in addition to the known outcrops or structural information. The variation and information of resistivity in the earth's surface can avoid the influence of static migration in two-dimensional inversion and greatly improve the resolving power of small anomalies.

【學位授予單位】：長江大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：P631.325

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