【摘要】：航空瞬變電磁法(ATEM)是一種以飛機(jī)為載體,電磁感應(yīng)為勘探機(jī)理的航空物探方法。由于具有勘探深度較大、可大面積勘探、能克服復(fù)雜地形等優(yōu)點(diǎn),特別適合我國(guó)的地理國(guó)情。目前,航空瞬變電磁法已經(jīng)在地質(zhì)填圖、礦產(chǎn)資源勘探和環(huán)境監(jiān)測(cè)等領(lǐng)域獲得了廣泛應(yīng)用。由于航空瞬變電磁數(shù)據(jù)屬于寬頻帶信號(hào),容易受到多種噪聲的影響,特別是航空瞬變電磁數(shù)據(jù)量較大,影響因素較多,使得對(duì)其解釋難度較大,總而言之,航空瞬變電磁信號(hào)處理與解釋方法研究仍然是當(dāng)前航空瞬變電磁法實(shí)用化的研究熱點(diǎn)和難點(diǎn)。本文嘗試引入機(jī)器學(xué)習(xí)的思想,使用支持向量機(jī)對(duì)大量地電模型及其電磁響應(yīng)進(jìn)行學(xué)習(xí),實(shí)現(xiàn)航空瞬變電磁反演,提高反演精度;此外,為了獲得高質(zhì)量航空電磁數(shù)據(jù),本文還研究了主成分分析及小波變換相結(jié)合的去噪方法以提高信噪比。具體來(lái)說(shuō),本文的主要研究?jī)?nèi)容如下:(1)主成分分析與小波變換結(jié)合的去噪方法。該方法通過(guò)主成分分析提取出航空瞬變電磁數(shù)據(jù)的主成分,然后對(duì)主成分進(jìn)行小波分析,再用主成分重構(gòu)電磁數(shù)據(jù),達(dá)到抑制噪聲的目的。經(jīng)加噪的正演模擬數(shù)據(jù)測(cè)試表明,該算法有較好的去噪能力,能夠?qū)?shù)據(jù)信噪比提高10-14dB。經(jīng)野外實(shí)測(cè)航空瞬變電磁數(shù)據(jù)測(cè)試發(fā)現(xiàn),主成分與小波變換結(jié)合能夠較好的抑制航空電磁數(shù)據(jù)在空間域和時(shí)間域上的隨機(jī)噪聲和高頻噪聲。此外,該方法在保幅去噪的同時(shí),還具有很好的穩(wěn)定性與較高的計(jì)算效率,能夠滿足野外實(shí)測(cè)數(shù)據(jù)現(xiàn)場(chǎng)處理的需求。(2)基于航空瞬變電磁原始數(shù)據(jù)的支持向量機(jī)反演。本文以航空瞬變電磁正演程序計(jì)算二層與三層地電模型的電磁響應(yīng),將其作為樣本數(shù)據(jù)集合,把樣本數(shù)據(jù)集分為兩個(gè)子集,一個(gè)子集作為支持向量機(jī)反演的訓(xùn)練樣本集,另一部分作為測(cè)試樣本集。然后用兩層地電模型的樣本集進(jìn)行支持向量機(jī)反演最佳參數(shù)組合進(jìn)行分析,找到最佳的反演參數(shù)組合。最后用該參數(shù)組合分別對(duì)二層與三層地電模型的航空瞬變電磁數(shù)據(jù)進(jìn)行支持向量機(jī)訓(xùn)練與反演。以二層地電模型為例,其反演結(jié)果的電阻率相對(duì)誤差平均值為8.06%,深度的相對(duì)誤差平均值為11.56%。(3)基于航空瞬變電磁數(shù)據(jù)主成分的支持向量機(jī)反演。由于電磁數(shù)據(jù)相鄰道間的相關(guān)系數(shù)最高達(dá)到0.9,數(shù)據(jù)的冗余信息較多,通過(guò)主成分分析提取出航空電磁數(shù)據(jù)的特征成分,采用支持向量機(jī)找到特征成分與地質(zhì)模型的屬性間的映射關(guān)系。本文以航空瞬變電磁正演程序計(jì)算二層與三層地電模型的電磁響應(yīng),采用主成分分析獲取數(shù)據(jù)主成分,將數(shù)據(jù)主成分作為樣本數(shù)據(jù)集合,把樣本數(shù)據(jù)集分為兩個(gè)子集,一個(gè)子集作為支持向量機(jī)反演的訓(xùn)練樣本集,另一部分作為測(cè)試樣本集。對(duì)二層與三層地電模型正演數(shù)據(jù)主成分的支持向量機(jī)反演實(shí)驗(yàn),發(fā)現(xiàn)其反演得到的結(jié)果與基于原始數(shù)據(jù)進(jìn)行支持向量機(jī)反演結(jié)果基本一致,其效率相較于前者也得到了一定提高。
[Abstract]:Aeronautical transient electromagnetic method (ATEM) is a kind of aerogeophysical method with aircraft as carrier and electromagnetic induction as exploration mechanism. It is especially suitable for China's geographical conditions because of its advantages of large exploration depth, large area exploration and overcoming complex topography. Aerial transient electromagnetic (TEM) data are widely used in many fields, such as measurement and so on. Because it belongs to broadband signal, it is vulnerable to many kinds of noise. Especially, the large amount of TEM data and many influencing factors make it difficult to interpret them. In a word, the research of processing and interpreting methods of aviation TEM signal is still the current aviation. In this paper, we try to introduce the idea of machine learning and use support vector machine to study a large number of geoelectric models and their electromagnetic responses, so as to realize the aeronautical transient electromagnetic inversion and improve the inversion accuracy. In addition, in order to obtain high-quality aeronautical electromagnetic data, we also study the principal component analysis and small. In particular, the main contents of this paper are as follows: (1) Principal component analysis and wavelet transform combined denoising method. This method extracts the principal component of Aeronautical transient electromagnetic data by principal component analysis, then carries out wavelet analysis on the principal component, and reconstructs the electromagnetic number by principal component analysis. The forward simulation data test shows that the algorithm has better denoising ability and can improve the signal-to-noise ratio of the data by 10-14 dB. It is found that the combination of principal component and wavelet transform can restrain the aero-electromagnetic data in the spatial and temporal domains. In addition, this method has good stability and high computational efficiency, and can meet the needs of field data processing. (2) Support Vector Machine inversion based on the original data of Aeronautical transient electromagnetic. Electromagnetic response of geoelectric model is taken as sample data set, and the sample data set is divided into two subsets, one as training sample set of SVM inversion, the other as test sample set. Finally, the combination of parameters is used to train and invert the aero-transient electromagnetic data of two-layer and three-layer geoelectric models respectively. Taking the two-layer geoelectric model as an example, the average relative error of resistivity is 8.06%, and the average relative error of depth is 11.56%. (3) Based on aero-transient electromagnetic model Because the correlation coefficient between adjacent channels of electromagnetic data is up to 0.9 and the redundant information of data is more, the characteristic components of aero-electromagnetic data are extracted by principal component analysis, and the mapping relationship between the characteristic components and the attributes of geological model is found by support vector machine. The electromagnetic response of the two-layer and three-layer geoelectric model is calculated by the magnetic forward program. The data principal component is obtained by the principal component analysis. The data principal component is taken as the sample data set. The sample data set is divided into two subsets. One subset is used as the training sample set of SVM inversion, and the other part is used as the test sample set. The SVM inversion experiment of the principal component of forward data of geoelectric model shows that the inversion result is basically consistent with the SVM inversion result based on the original data, and its efficiency is also improved compared with the former.
【學(xué)位授予單位】：成都理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：P631.326

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