本文選題：全張量重力梯度 + 加強重力��； 參考：《吉林大學(xué)》2015年博士論文

【摘要】：地球重力場是地球的固有物理特征，可以反映地球內(nèi)部的物質(zhì)分布、運動和變化的規(guī)律。重力測量是對重力場變化規(guī)律的直接反映。隨著測量手段的多樣化（地面測量、海洋測量、航空測量和衛(wèi)星測量）和測量數(shù)據(jù)精度的提高，人們不僅可以直接測量地球重力場，而且可以對重力梯度場進行測量。重力梯度張量數(shù)據(jù)相對于傳統(tǒng)的重力測量數(shù)據(jù)有著更高的頻率信息，能更加準確、細致的研究地球淺部構(gòu)造、礦產(chǎn)資源分布等信息。為了更加準確的處理和解釋全張量重力梯度數(shù)據(jù)，需要從全張量重力梯度儀器設(shè)計原理出發(fā)，分析儀器產(chǎn)生噪聲的原因，進而更好的從測量數(shù)據(jù)中提取反應(yīng)地下異常體的真實信號進行解釋。 首先，本文利用理論模型對比分析了重力梯度測量相對于重力測量的優(yōu)勢，為文章的選題提供依據(jù)。重力梯度張量數(shù)據(jù)的獲取，可以通過實際測量或計算得到。然而，計算得到的重力梯度張量數(shù)據(jù)最多能包含與測量的重力數(shù)據(jù)相同的信息成分，而且在計算過程中還會造成信息缺失，并不會增加信息成分。因此，重力梯度張量數(shù)據(jù)的獲取通常需要通過重力梯度儀器測量得到。通過對比分析，重力梯度異常能反映相對波長短的高頻信號，重力異常數(shù)據(jù)能反映相對波長長的低頻信號。因此，聯(lián)合梯度張量數(shù)據(jù)的高頻信號成分和重力異常的低頻信號成分可以得到加強的重力異常數(shù)據(jù)，該數(shù)據(jù)能同時保留重力異常數(shù)據(jù)及其梯度張量數(shù)據(jù)的所有信息，拓寬重力異常數(shù)據(jù)的頻寬范圍。這一過程主要通過余弦平方濾波完成。 目前，國內(nèi)外多家研究機構(gòu)正在研究多種不同類型的重力梯度儀，但是，投入生產(chǎn)使用的只有基于旋轉(zhuǎn)加速度計的重力梯度儀。因此，本文針對用于航空移動平臺高精度全張量重力梯度測量系統(tǒng)，深入研究了全張量梯度儀12個加速度計按3個不同旋轉(zhuǎn)軸圓盤形成差分組合的結(jié)構(gòu)。在確認該結(jié)構(gòu)具有有效抑制運動共模加速度減少外界環(huán)境干擾實現(xiàn)高精度探測優(yōu)點的同時，著重分析了重力梯度儀測量誤差的來源和影響。研究表明，主要影響包括儀器固有隨機噪聲和外界確定性噪聲。為定量描述影響程度，筆者推導(dǎo)了在航空動態(tài)環(huán)境下的測量方程，并分離出加速度計的性能不匹配、平臺不穩(wěn)定、圓盤轉(zhuǎn)速不穩(wěn)定3個主要固有因素，從時間域和頻率域角度定量分析固有影響因素的噪聲水平。試驗分析表明，通過利用Simulink仿真系統(tǒng)可以獲得固有因素產(chǎn)生的噪聲水平，并提出抑制方案。針對搭載環(huán)境測量誤差，筆者還分析了實測飛行中姿態(tài)和質(zhì)量的改變對重力梯度測量值造成的環(huán)境影響，提出了基于點質(zhì)量源的自身梯度校正方法。經(jīng)固有噪聲和自身梯度校正后的各個梯度數(shù)據(jù)中還存在大量隨機噪聲，但是其噪聲水平各不相同。本文利用奇偶測線網(wǎng)格化方法對各個梯度張量數(shù)據(jù)的噪聲進行了定量估計，為后續(xù)噪聲去除提供依據(jù)。 全張量重力梯度張量數(shù)據(jù)相對于重力數(shù)據(jù)含有更高頻的信號成分，能更好的描繪小的異常特征。然而，全張量重力梯度儀測量值的噪聲成分也為高頻。因此，從高頻信號成分中分離出噪聲將是處理重力梯度張量數(shù)據(jù)的一個挑戰(zhàn)。通過聯(lián)合全張量重力梯度儀測量的多個信息成分進行噪聲處理能更好的壓制隨機干擾。本文利用重力梯度信號滿足的拉普拉斯方程約束條件，推導(dǎo)了重力梯度張量的笛卡爾方程和引力位的級數(shù)解表達式，然后利用級數(shù)解去擬合測量的重力梯度張量值，擬合的部分認為是真實的梯度信號，未擬合的部分認為是噪聲。在擬合過程中引入數(shù)據(jù)噪聲加權(quán)矩陣和數(shù)據(jù)能量加權(quán)矩陣進行最優(yōu)線性反演求解拉普拉斯方程級數(shù)解的系數(shù)，然后利用求得的系數(shù)進行正演計算得到真實的梯度信號。全張量重力梯度數(shù)據(jù)經(jīng)噪聲濾波后，只包含實際地下重力信息，能更加準確的進行數(shù)據(jù)的解釋。重力梯度數(shù)據(jù)的解釋工作通常需要從數(shù)據(jù)中獲取有關(guān)場源的水平位置和深度范圍。水平位置的確定通常利用邊界識別方法；深度范圍參數(shù)的確定需要深度計算方法來完成。 邊界識別方法在重力梯度數(shù)據(jù)解釋中占有重要角色，它能準確且快速地確定地質(zhì)體邊界位置、構(gòu)造水平位置而被廣泛關(guān)注。一些傳統(tǒng)的方法有的不能同時顯示不同埋藏深度地質(zhì)體的邊界信息，有的在深部地質(zhì)體邊界位置確定中誤差較大，且對細節(jié)信息的提取能力不足。而且，已有的方法基本上都是針對重力異常數(shù)據(jù)，專門針對重力梯度張量數(shù)據(jù)的方法還比較少。本文針對全張量重力梯度數(shù)據(jù)信息量大、信號頻率高，能更好的描述小的異常特征等特點，提出了改進的水平解析信號方法、加強水平方向總水平導(dǎo)數(shù)方法和改進的結(jié)構(gòu)張量算法進行全張量重力梯度數(shù)據(jù)的邊界解釋。在解釋過程中，本文針對各個方法進行相應(yīng)的歸一化處理，使其能均衡不同埋藏深度的異常體的邊界結(jié)果的振幅強度。通過模型試驗和實際測量的全張量重力梯度數(shù)據(jù)驗證了這些方法的可靠性、實用性和抗噪能力，并與一些傳統(tǒng)的邊界識別方法進行對比，證明了這些方法的優(yōu)點。 重力異常及重力梯度異常場源深度計算的方法經(jīng)歷了漫長的發(fā)展歷史，形成了針對不同的場源類型和地質(zhì)研究對象的不同深度計算方法。快速成像方法是近幾年的一個發(fā)展熱點，它能快速得到地下異常體分布狀況，避免傳統(tǒng)方法耗時長，內(nèi)存消耗大等缺點。本文利用極大值深度計算方法DEXP進行地下異常體的埋藏深度成像，從而進行異常體的深度估計。然而，，傳統(tǒng)的DEXP方法需要事先指定異常體的構(gòu)造指數(shù)，通常是根據(jù)異常形態(tài)對其進行假設(shè)，但這會對成像結(jié)果帶來誤差。因此，本文又利用不同階的垂向?qū)?shù)的比值進行DEXP變換，有效的去除了構(gòu)造指數(shù)的影響。該方法能在不知道構(gòu)造指數(shù)的情況下對異常體進行深度成像，從而得到地質(zhì)體的深度。利用估計的深度值在尺度函數(shù)上的對應(yīng)值可以對地質(zhì)體的構(gòu)造指數(shù)進行估計。利用該方法對模型數(shù)據(jù)和實際測量的文頓巖丘的重力梯度數(shù)據(jù)進行分析，取得了準確的深度和構(gòu)造指數(shù)估計。
[Abstract]:The earth's gravity field is an inherent physical feature of the earth, which can reflect the distribution of material, movement and change of the earth's interior. Gravity measurement is a direct reflection of the law of the variation of the gravity field. With the diversification of the measuring means (ground measurement, oceanographic survey, aeronautical measurement and satellite measurement) and the accuracy of measurement data, people can not only be able to improve the accuracy of the measurement data. The gravity gradient field can be measured directly and the gravity gradient field can be measured. The gravity gradient tensor data has higher frequency information relative to the traditional gravimetric data. It can be more accurate and detailed to study the shallow structure of the earth and the distribution of mineral resources. In order to more accurately deal with and explain the full tensor gravity gradient. According to the design principle of the full tensor gravity gradient instrument, the cause of the noise generated by the instrument is analyzed, and then the real signal of the underground abnormal body is extracted from the measured data.
First, in this paper, the advantages of gravity gradient measurement relative to gravity measurement are compared and analyzed by the theoretical model. The acquisition of the gravity gradient tensor data can be obtained by actual measurement or calculation. However, the calculated gravity gradient tensor data can contain most of the same data as the measured gravity data. Therefore, the acquisition of gravity gradient tensor data is usually measured by gravity gradient instruments. By contrast, gravity gradient anomalies can reflect high frequency signals with short relative wavelengths, and gravity anomaly data can reflect the length of relative wavelengths. Therefore, the high frequency signal components of the combined gradient tensor data and the low frequency signal components of the gravity anomaly can obtain enhanced gravity anomaly data. This data can simultaneously retain all the information of the gravity anomaly data and its gradient tensor data, widening the bandwidth of the gravity anomaly data. This process is mainly through the cosine square. The filter is completed.
At present, many research institutions at home and abroad are studying a variety of different types of gravity gradiometer. However, only the gravity gradiometer based on the rotation accelerometer is used in production. Therefore, this paper has studied 12 accelerometers by full tensor gradiometer for the high precision full tensor gravity gradient measurement system used in the aviation Mobile platform. 3 disks of different rotating axes form a structure of difference combinations. The source and influence of the measurement error of gravity gradiometer are emphatically analyzed in the confirmation that the structure has the advantages of effective suppression of the movement common mode acceleration and the reduction of external environment interference. The main influence of the structure is that the main influence includes the inherent random noise and the outside accuracy of the instrument. Qualitative noise. In order to quantitatively describe the degree of influence, the author derives the measurement equation under the air dynamic environment, and separates the 3 main inherent factors of the accelerometer's performance mismatch, the instability of the platform and the instability of the disc speed, and the analysis of the noise level which has the influence factors from the time domain and the frequency domain. The noise level generated by inherent factors can be obtained by using the Simulink simulation system, and the suppression scheme is proposed. In view of the environmental measurement error carrying the environment, the author also analyzes the environmental influence caused by the change of attitude and mass to the measured value of gravity gradient in the measured flight, and proposes a self gradient correction method based on the point mass source. There are a lot of random noises in each gradient data after sound and self gradient correction, but their noise levels are different. This paper quantificationally estimates the noise of each gradient tensor data by using the odd even grid method to provide the basis for the subsequent noise removal.
The full tensor gravity gradient tensor data contains a more high-frequency signal component relative to the gravity data, which can better describe the small anomaly characteristics. However, the noise component of the total tensor gravity gradiometer is also high frequency. Therefore, the separation of noise from the high frequency signal component will be a challenge to deal with the gravity gradient tensor data. The multiple information components measured by the full tensor gravity gradiometer can better suppress random interference. This paper derives the expression of the Cartesian equation of gravity gradient tensor and the series of gravitational potential, and then uses the series solution to fit the measured gravity with the constraints of the Laplasse equation satisfied by the gravity gradient signal. The value of the gradient tensor is considered to be a real gradient signal, and the part of the non fitting is considered to be noise. In the fitting process, the data noise weighting matrix and the data energy weighting matrix are introduced to the optimal linear inversion to solve the coefficients of the series solution of the Laplasse equation, and then the calculated coefficients are calculated to be true. Gradient signal. After noise filtering, full tensor gravity gradient data only contains actual underground gravity information, it can be more accurate to explain the data. The interpretation work of gravity gradient data usually needs to obtain the horizontal and depth range of the related field sources from the data. The determination of range parameters requires deep calculation.
The boundary recognition method plays an important role in the interpretation of gravity gradient data. It can accurately and quickly determine the location of the geological body boundary and construct the horizontal position. Some traditional methods can not display the boundary information of different buried depth geological bodies at the same time, and there are some errors in the determination of the boundary position of the deep geological body. In addition, the existing methods are basically aimed at gravity anomaly data, and the methods specially aimed at gravity gradient tensor data are relatively few. In this paper, the characteristics of the full tensor gravity gradient data are large, the signal frequency is high, and the small abnormal characteristics can be described better. The improved water is proposed. The horizontal analytic signal method, the horizontal directional total horizontal derivative method and the improved structural tensor algorithm are used to explain the boundary of the full tensor gravity gradient data. In the process of interpretation, this paper deals with the corresponding normalization of each method so that it can balance the amplitude intensity of the boundary result of the abnormal body with different buried depth. The full tensor gravity gradient data of the model test and the actual measurement verify the reliability, practicability and anti noise ability of these methods, and compare with some traditional method of boundary recognition, which prove the advantages of these methods.
The method of calculating gravity anomaly and gravity gradient anomaly source depth has experienced a long history, forming different depth calculation methods for different field source types and geological research objects. Fast imaging method is a hot spot of development in recent years. It can quickly get the distribution of ground anomaly bodies and avoid the time-consuming of traditional methods. This paper uses the maximum depth calculation method DEXP to carry out the buried depth imaging of the underground abnormal body by using the maximum value depth calculation method, so as to estimate the depth of the abnormal body. However, the traditional DEXP method needs to specify the structure index of the abnormal body in advance, usually based on the abnormal form, but this will bring the imaging result. Therefore, this paper makes use of the ratio of the vertical derivative of different order to carry out DEXP transformation, effectively removing the influence of the tectonic index. This method can make the depth imaging of the abnormal body without knowing the structure index, so that the depth of the geological body can be obtained. The structure index of the body is estimated. The method is used to analyze the gravity gradient data of the model data and the actual measured venturi mound, and the accurate depth and structural index are estimated.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2015
【分類號】：P631.14

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