磁异常欧拉反褶积方法的改进及在福建永定大排矿区的应用

发布时间：2018-04-03 15:50

本文选题：欧拉反褶积　切入点：构造指数　出处：《中国地质大学(北京)》2017年硕士论文

【摘要】：欧拉反褶积方法是磁力勘探常用的一种估计磁性场源体分布的解释方法,近年来被广泛应用并取得了良好效果,能够自动或半自动地估算场源体位置,只需要使用较少的参数来约束,对场源体的磁性的参数不做要求,对初始选择地质模型的精度要求不高,能快速有效地反演出地下场源体的位置、边界、深度,适用于大面积的网格的磁异常数据的计算和解释。然而,经过多年来的解释应用表明,该方法还受到人工选择参数的影响。主要表现在,计算窗口尺寸的选择和构造指数的选择,参数选择不当,往往会使估算结果容易反散。因此,对大区域中不同的地质体采取固定窗口和构造指数,反演结果会不够精确。针对这些问题,本文引入两种均衡边界识别滤波器。其中,TDX边界识别滤波器的表达式是总水平梯度和垂直梯度的反三角函数,Theta边界识别滤波器的表达式为磁异常水平变化与垂直变化的比值函数。将TDX和Theta两种边界识别的函数分别与欧拉反褶积方法相结合。对常规的欧拉方程进行数学变换,分别证明并建立以TDX导数和Theta导数为系数的欧拉线性方程,消除了常规欧拉方程组中的构造指数,再利用最小二乘法对两种改进的欧拉反演方程进行求解,达到进行位场异常反演的目的。实际应用表明这两种改进的欧拉反演方法可以在不需要提供构造指数的情况下反演出地质体的边界和深度。本文利用四种磁异常模型体来验证这两种改进方法的有效性。每种模型体分为不加噪声、添加噪声和斜磁化三种情况。两种反演方法的反演结果均表明无论是面对哪种模型体状况,都能很好的反演出模型体的位置参数。对福建省永定县大排矿区的实测航空磁测数据进行实际应用研究。通过对福建省永定县地区的地球物理勘测资料的收集整理,分析该区域磁力异常分布特征,分别利用TDX欧拉反褶积方法和Theta欧拉反褶积方法分析该区航空磁测勘探数据,并根据反演结果推断了该测区的断裂分布特征。
[Abstract]:Euler deconvolution method is a commonly used interpretation method for estimating the distribution of magnetic field source body in magnetic exploration. It has been widely used in recent years and has achieved good results. It can estimate the position of field source body automatically or semi-automatically.Only a few parameters are used to restrict the magnetic parameters of the field source body, and the accuracy of the initial selection of the geological model is not high, and the location, boundary, depth of the underground field source body can be quickly and effectively reversed.It is suitable for the calculation and interpretation of magnetic anomaly data for large area grids.However, after many years of interpretation and application, it is shown that the method is also affected by manual selection of parameters.The main performance is that the selection of calculation window size and construction index and the improper selection of parameters often make the estimation result easy to be reversed.Therefore, for different geological bodies in large regions, the inversion results will not be accurate by using fixed windows and structural indices.To solve these problems, two equalization boundary recognition filters are introduced in this paper.The expression of TDX boundary recognition filter is the inverse trigonometric function of total horizontal gradient and vertical gradient. The expression of Theta boundary recognition filter is a ratio function of horizontal and vertical variation of magnetic anomalies.The two boundary recognition functions of TDX and Theta are combined with Euler deconvolution method respectively.The Euler linear equations with the coefficients of TDX derivative and Theta derivative are proved and established by mathematical transformation of the conventional Euler equations. The constructive exponents of the conventional Euler equations are eliminated.Then two improved Euler inversion equations are solved by least square method to achieve the purpose of inversion of potential field anomalies.The practical application shows that the two improved Euler inversion methods can invert the boundary and depth of geological bodies without providing structural index.In this paper, four kinds of magnetic anomaly models are used to verify the effectiveness of these two improved methods.Each model body can be divided into three cases: no noise, added noise and oblique magnetization.The inversion results of the two inversion methods show that no matter which model body is faced with, the position parameters of the model body can be well inversed.The practical application of aeromagnetic data measured in Dapai Mining area of Yongding County, Fujian Province is studied.Based on the collection and arrangement of geophysical survey data in Yongding County, Fujian Province, the distribution characteristics of magnetic anomalies in this area are analyzed, and the aeromagnetic exploration data in this area are analyzed by using TDX Euler deconvolution method and Theta Euler deconvolution method, respectively.Based on the inversion results, the characteristics of fault distribution in this area are inferred.
【学位授予单位】：中国地质大学(北京)
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：P631.2

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