磁总场梯度数据三维反演方法及应用

磁总场三方位梯度数据相对于总场数据包含更丰富的异常信息,将梯度数据应用于三维磁化率反演中,可以更准确地描述异常体。本文采用最小模型结构反演方法进行三维磁化率反演成像,并采用对数障碍法对磁化率的反演取值范围进行约束。通过模型试验,对磁总场异常数据及其三轴梯度数据进行单独反演、联合反演,结果表明三轴梯度联合反演结果可以更好地刻画异常体形态,更有效地分辨邻近异常体,反演物性更合乎实际。将此反演技术应用于大冶铁矿高精度航磁数据反演解释,取得了较好的应用效果。     

重磁三维可视化反演系统的设计与实现

重磁三维可视化反演解释系统一直是国内外重磁勘探领域的研究重点之一。介绍了基于Visual C++与OpenGL环境研发的重磁三维可视化反演系统,详细阐述了系统的设计思想与实现的关键技术,包括可视化技术、面向对象技术、图形拾取技术、碰撞检测技术及反演约束技术等。系统具有人机交互几何反演与最优化物性自动反演两种功能,可满足目标异常、区域模拟和盆地建模。     

Inversion of residual gravity anomalies using neural network

A new approach is presented in order to interpret residual gravity anomalies from simple geometrically shaped bodies such as horizontal cylinder, vertical cylinder, and sphere. This approach is mainly based on using modular neural network (MNN) inversion for estimating the shape factor, the depth, and the amplitude coefficient. The sigmoid function has been used as an activation function in the MNN inversion. The new approach has been tested first on synthetic data from different models using only one well-trained network. The results of this approach show that the parameter values estimated by the modular inversion are almost identical to the true parameters. Furthermore, the noise analysis has been examined where the results of the inversion produce satisfactory results up to 10% of white Gaussian noise. The reliability of this approach is demonstrated through two published real gravity field anomalies taken over a chromite deposit in Camaguey province, Cuba and over sulfide ore body, Nornada, Quebec, Canada. A comparable and acceptable agreement is obtained between the results derived by the MNN inversion method and those deduced by other interpretation methods. Furthermore, the depth obtained by the proposed technique is found to be very close to that obtained by drilling information.     

Interpretation of some magnetic bodies using neural networks inversion

Magnetic surveys have been used for mineral exploration where different data processing techniques were used to derive the parameters of causative targets. In this respect, the neural network (NN) technique was used to estimate the magnetic causative target parameters. Examples of NN inversion have been tested on synthetic examples where the NN was trained well using forward models of the vertical magnetic effect of a vertical sheet and a horizontal circular cylinder. Specifically, modular neural network (MNN) inversion has been used for the parameter estimation of the causative targets, where the sigmoid function was used as the activation function. The effect of random noise and the error estimation of the horizontal location have been analyzed. When NN is applied to real data, it estimates successfully the parameters of the causative targets such as burial depths, magnetic constants, and angle of polarization. Hilbert transform has been used to locate the source origin, which is important for the NN inversion. This approach has more advantages than the conventional data inversions in terms of its efficiency and flexibility. It also gives fast solutions. The MNN approach has been applied to the Kursk and Manjampalli anomalies, where the results were shown to be in good agreement with the other techniques published in the literature.     

概率神经网络在地震岩性反演中的应用

卧龙湖煤矿北二采区岩浆岩侵入8煤层的现象较为严重,同时该区煤层中构造煤比较发育,瓦斯富集问题较为突出。利用三维地震资料、测井曲线进行约束反演得到的波阻抗作为外部属性,并使用step-wise属性选择法确定合适数目的地震属性,利用概率神经网络技术(PNN)对该区进行孔隙度预测反演。孔隙度反演结果与波阻抗反演结果的对比表明:孔隙度较波阻抗对于识别瓦斯富集带具有更高的分辨能力;概率神经网络具有高稳定性、计算精度高等特点,可作为研究构造煤发育和瓦斯赋存的有效手段。     

直流电阻率法的深部结构分辨——三维模型的二维反演

利用三维有限差分方法进行深部三维结构的电阻率三维数值模拟,对计算得到的单极-单极和对称四极装置视电阻率剖面进行电阻率二维反演研究。结果显示:单极-单极装置在横向上只要三维异常体中心的水平间距不小于两者中心埋深之和,就可以基本将2个三维结构分辨出来;相同情况下,对称四极装置在横向上的分辨效果优于单极-单极装置;单极-单极装置在垂向上的分辨要优于对称四极装置。     

遗传算法在起伏地形下磁异常反演的应用

实际的地面磁法勘探往往是在山区进行,所测得的磁测结果也是沿地形起伏分布,这样往往会对磁异常曲线形态产生畸变,因此在起伏地形条件下,对磁测结果进行必要的反演,将会更明确地下目标地质体的位置与形态。本文首先模拟在水平条件下板状体的磁异常剖面曲线,检验算法的正确性,然后模拟在起伏地形下板状体的磁异常剖面曲线,并应用遗传算法对所得异常曲线进行反演并讨论其可行性,加入噪声并对其反演,讨论算法的抗干扰性,最后将其应用至实际找矿项目中。反演的结果表明,该方法在实际应用中具有一定参考作用。     

基于GA-BP神经网络算法的高密度电法非线性反演

高密度电法技术在煤矿地质灾害勘探中发挥着重要的作用。近年来,以BP（Backpropagation）神经网络为代表的一类非线性反演方法被广泛运用到高密度电法的反演中。针对BP神经网络方法在高密度电法反演中存在的易陷入局部极小、收敛缓慢、反演精度差等问题,将BP神经网络算法与遗传算法（Genetic Algorithm,简称GA算法）联合演算,实现高密度电法的二维非线性反演。通过典型地电模型对该方法进行验证,结果表明遗传算法能有效优化BP神经网络的权值和阈值,提高了算法的全局寻优性。     

Magnetic anomalies of two-dimensional bodies and algorithms for magnetic inversion of dykes and basement topographies

Generalized equations for the anomalies in any component of the earth’s magnetic field due to two-dimensional bodies of arbitrary magnetization are derived in terms of a new parameter, called the direction of measurement. Schemes for inverting the magnetic anomalies of arbitrarily magnetized dykes and basement topographies are then developed and the relevant computer software is presented. In both the schemes, the initial values of the parameters are calculated by the computer, so that the input merely consists of the anomalies and their distances. The differences in the observed and calculated anomalies are solved iteratively for the errors in initial values of the parameters.     

Propagation anomalies in Northwestern Italy by inversion of teleseismic residuals

Travel-time residuals of teleseismic P waves were analysed in order to elucidate the crust–upper mantle structure in Northwestern Italy, and the Western Alpine Arc. Using digital data obtained from both fixed seismograph networks operating in NW Italy (notably Liguria–Piedmont) and temporary arrays with the aid of cross-correlation techniques reliable travel-time residuals were calculated which were then inverted to obtain models of propagation anomalies. The reliability of the inversion procedure was tested using synthetic data. The model thus obtained appears to be stable and shows strong lateral heterogeneities at a litho–asthenospheric level; in particular, it confirms the high velocity contrast caused by the ‘Ivrea Body’ in the shallower layers and the presence of Alpine ‘roots’ reaching down to at least 200 km. A statistical analysis performed on the propagation times of rays crossing the resulting four-layered model reveals rms below 0.1 s.     

CSAMT一维自动迭代和人机联作方式交替反演进行拟二维反演解释

为提高野外CSAMT测深数据的反演解释效率,基于一雏全区反演,提出了CSAMT一维自动迭代与人机联作方式交替反演进行拟二维反演解释的思路。该方法在自动迭代反演中,将BOSTICK反演结果作为自动迭代反演的初始模型。这里详细介绍了该方法的反演流程,并将该方法应用于实际资料的反演,反演结果与钻探资料基本吻合,证实了该方法是有效的。     

砂土地震液化预测的人工神经网络模型

在简要分析BP算法的基础上,应用BP网络的理论与方法,选取烈度、震中距、平均粒径、不均匀系数、地下水埋深、砂层埋深、标贯击数、剪应力比等8个实测指标,建立了砂土液化预测的神经网络模型。通过实例计算与模型评价、验证了该模型的科学性、高效性并较规范法、Seed简化法等传统方法具有更高的预测精度,说明人工神经网络是解决非线性问题的有效方法之一。     

Application of a BP neural network in predicting destroyed floor depth caused by underground pressure

Predicting the destroyed floor depth caused by the mining of coal seams is of great importance in judging whether the mining of a deep coal seam can be safely performed above a confined aquifer and to prevent the inrush of water from the floor. Thirty sets of coal mining data on destroyed floor depth were selected for study. A comprehensive analysis of the factors that influence the depth of destruction of coal seam floor strata was performed and combined with the ability of a BP neural network to address dynamic nonlinear information. Then, a set of test samples was assembled and used to construct a predictive model using a BP neural network. The model was then used to predict the destroyed floor depth of the 7105 working face of the Baizhuang Coal Mine in the Feicheng coal field. To verify the effectiveness of the model, the depth of the destroyed strata comprising the coal seam floor was measured using equipment called the “Double Sided Sealed Borehole Water Injection Device.” By comparing the predictions made by the BP neural network with actual measurements, the conclusion was reached that a BP neural network model can effectively be used to predict the destroyed floor depth caused by the mining of a coal seam.     

Hartley spectral analysis of self-potential anomalies caused by a 2-D horizontal circular cylinder

The real spectral analysis of SP anomalies due to 2-D horizontal circular cylinder is carried out using Hartley transform. The Hartley transform is an alternate means of realizing spectral analysis in real domain unlike the Fourier spectral analysis. The Hartley transform yields a straightforward interpretation of SP anomalies caused by horizontal circular cylinder wherein all the parameters are derived independently as a function of frequency. A theoretical example illustrates the procedure. The effect of random noise, up to 10% of white Gaussian noise, on the interpretation scheme was also studied and found to be of negligible. The field example of the ??Sulleymonkey?? anomaly in the Ergoni copper district, Turkey exemplifies the applicability of the proposed method.     

Inversion of magnetic anomalies due to isolated thin dike-like sources using artificial neural networks

A new approach is proposed to interpret magnetic anomalies caused by isolated thin dike-like causative targets. The approach is essentially based on utilizing artificial neural network (ANN) inversion for estimating the problem parameters. Particularly, the modular neural network (MNN) is used for the inversion process in order to quantitatively interpret the magnetic anomalies. The MNN inversion has been first tested on a synthetic data with and without random white Gaussian noise. The effect of random noise has been clearly investigated where it showed that the approach provided satisfactory results. Furthermore, three field examples have been inverted in order to investigate the applicability of the proposed approach. The results showed good agreement with the techniques that have been stated in the literatures.     

应用BP神经网络诊断全液压坑道钻机故障

将BP神经网络原理应用于全液压坑道钻机现场故障诊断。根据全液压坑道钻机的故障特点,选取能够表征全液压钻机状态的量,采用归一化的方法确定样本的输入和输出;通过对网络进行训练,获得具有一定泛化能力的网络;利用Matlab软件实现此种故障诊断过程,获得了正确的诊断结果。试验表明,该法有一定的实用性,对于建立故障自动诊断系统有重要意义。     

The applicability of using NARX neural network to forecast GRACE terrestrial water storage anomalies

Natural Hazards - The Gravity Recovery and Climate Experiment (GRACE) satellite has proven adept at monitoring, characterizing, and predicting hydrological variables. This paper attempted to...     

人工神经网络专家系统在瞬变电磁法反演中的应用

目前在瞬变电磁法反演方法中, 人们使用较多的是电磁场反演拟合的方法。但往往存在以下不足:首先这些反演方法理论复杂, 不易掌握;其次它要求工作人员有丰富的工作经验, 缺乏延续性, 很难推广;再次数据处理量大, 计算复杂。本文提出一个基于人工神经网络的专家系统。它避开了具体复杂的电磁场计算, 只要经过适当的学习训练就能够解决复杂的实际问题;而且还具有学习记忆功能, 使得瞬变电磁法的反演工作具有延续性和继承性。随着专家系统的不断完善, 该方法将有广阔的发展前景。