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1.
三维地质建模是实现深部矿产勘查突破的重要途径,通过对控矿地质体建模,可以直观刻画控矿要素之间关系,实现矿集区“透明化”,进行深部找矿及预测.本文利用赣东北矿集区及外围1：5万重力数据和1：5万航磁数据,开展位场分离获取用于重磁反演的异常数据,再分析物性和岩性之间的关系,完成人机交互重磁反演,最后通过剖面建立朱溪矿区及邻区的三维地质模型.在三维模型建立的基础上,利用BP神经网络,选用随机单元作为样本训练,对朱溪矿区进行成矿预测并获得以下主要认识：(1)获得了赣东北矿集区朱溪外围的三维地质-地球物理模型,获取了地下5 km深度的岩体、地层的三维空间形态;(2)利用BP神经网络对矿集区进行成矿预测,得到了矿集区不同位置的成矿有利度分布图,成矿有利单元与前人划分的成矿远景区大致相符,为赣东北矿区继续找矿提供了依据,也为类似矿集区寻找深部金属资源提供了思路和技术． 相似文献
2.
本文应用特征分析综合信息矿产资源评价的原理,利用现有地质、物化探、遥感资料对香花岭地区的矿化程度进行深入分析,以汇水盆地为统计单元并通过应用数量化理论Ⅲ分别从26个统计单元中筛选出21个单元作为模型单元,从47个变量中筛选出41个变量,建立多矿种和单矿种特征分析定量模型4个,对该区不同的单元矿化程度进行定量评价和预测研究.定量地给出预测该区的Ⅰ级成矿远景区（中型矿区）,Ⅱ级成矿远景区（小型矿区）,Ⅲ级成矿远景区（矿点）的关联度和找矿标准. 相似文献
3.
本文介绍了朱家冲矿区地质背景和成矿地质、地球物理特征,根据现有地质物探资料,并与铜陵地区对比,对朱家冲地区成矿条件、赋矿层位、找矿空间及靶区进行了分析,预测朱家冲地区层控矽卡岩型铜矿找矿具有一定潜力。 相似文献
4.
《地球物理学进展》2015,(5)
本文详细阐释应用逻辑信息法进行多金属矿床矿化规模的定量评价和预测原理,并对建立逻辑信息法预测模型方法、步骤进行讨论.然后以汇水盆地划分出香花岭地区多金属矿床中26个地质体(矿床、矿点)作为统计单元,经通判研究选出与这些地质体矿化规模有关的47个标志(变量).并从25个统计单元中筛选出21个单元作为模型单元,同时根据逻辑信息法选择变异序列的原则,从47个变量中筛选出41个变量,建立2个多矿种的逻辑信息法定量模型,对该区不同的单元矿化程度进行定量评价和预测研究.定量地预测该区的Ⅰ级成矿远景区(小型矿区)1个,Ⅱ级成矿远景区(中型矿区)6个,Ⅲ级成矿远景区(矿点)3个,为确定该区的成矿靶区提供了可靠的依据. 相似文献
5.
储备矿产地对保障资源稳定供应和国民经济可持续发展具有重要的战略意义,现有定性划分储备矿产地方法难以提供可靠的量化依据.为了辅助我国正在开展的战略性矿产资源储备工作,本文在已有定性评价原则的基础上,综合考虑矿产资源有利地段的基础地质、自然地理、交通、经济、地质灾害、化探异常等多元数据,提出了基于模糊证据权的定量化储备矿产地分析模型与评价方法,构建了储备矿产地评价指标体系,以金矿最小预测区、火山岩、构造(断裂)等作为主要证据层进行计算,评价适宜作为储备矿产地的最佳区域.以云南省金矿为例进行验证,分别使用模糊证据权模型和层次分析模型计算有利度.共圈定出祥云人头箐金矿、鹤庆北衙金矿等9处有利储备矿产地作为候选,与已知成矿规律吻合度高.两种方法定量化评价结果与排序情况总体基本一致,其中模糊证据权法的局部效果多处优于层次分析法结果,证明了本文所提方法的正确性与科学性,可为本省或其他省份各矿种的矿产地储备评价提供技术支持. 相似文献
6.
7.
随着人类对资源的需求的增加,传统的矿产勘探空间已经越来越小,必须拓展新的找矿空间,近年来,全球资源能源勘查已经转向大陆深部和覆盖层之下.中国西部广泛分布的荒漠戈壁区,找矿勘查工作程度相对较低,有着巨大的找矿空间,但由于没有露头地表地质观察无法获取成矿信息,地质理论预测的不确定性大幅增加,制约了隐伏矿预测和定位的效果,亟待加强对荒漠覆盖区隐伏矿勘查技术的研究,探索有效的技术方法体系.本文选择准噶尔盆地东缘琼河坝地区为例,开展荒漠戈壁覆盖区找矿预测实践.首先以高精度地面大比例尺重力和磁力资料为基础,采用多尺度边缘检测技术划分断裂构造.然后结合重磁三维反演和多尺度边缘检测技术,开展了隐伏岩体的三维形态识别.在此基础上,结合区域化探和地质资料,预测了 7处找矿靶区.对其中的拉伊克勒克靶区进行了大比例尺地球物理和钻探查证,从预查到详查,通过地球物理技术的创新组合,在地表没有矿化线索的荒漠戈壁之下,新发现和评价拉伊克勒克大型铜多金属矿,实现琼河坝地区荒漠戈壁覆盖区找矿突破.结果证明我们提出的荒漠覆盖区隐伏矿预测与定位技术,在类似景观区具有一定的借鉴和示范意义.拉伊克勒克大型铜多金属矿的发现,也说明在新疆、内蒙等戈壁荒漠覆盖区找矿工作大有可为. 相似文献
8.
矿产资源勘查与评价中的某些地质异常信息常常具有非线性的动力学特性,利用人工神经网络良好的非线性特征可在矿产预测领域发挥一定优势.本文探讨了Hopfield神经网络的原理,利用Lyapunov函数的正交化权值设计,可以保证Hopfield网络能量递减并收敛至稳定平衡点,实现联想记忆和分类的功能.本次研究以新疆东天山地区岩浆型铜镍硫化物矿床为例,建立预测模型,提取预测要素,构造网络的平衡点,利用Hopfield神经网络对该类矿产远景区进行级别分类并输出,根据分类结果并综合区域地质和物化探信息在东天山地区划分出了两个铜镍矿的重点勘查区.对比证据权法的分类结果,两者A级远景区部分接近或者重合,并符合一定地质意义,可为矿产预测人员提供最优决策.将非线性的神经网络方法和传统的线性方法进行结合,对比研究,相互验证,可在一定程度上提高矿产预测的精确度和可信度. 相似文献
9.
《地球物理学进展》2020,(3)
铀成矿信息提取和识别是当前铀矿地质找矿工作的研究热点之一.本文利用地面伽玛能谱钾测量数据,采用差量法对铀成矿信息进行提取,结果显示,黄梅尖地区钾差量正值域与负值域呈不均匀面状展布,且具有跨越不同侵入期次岩石单元现象,钾差量亏损场与铀矿床(点)的空间位置显示较好的对应关系,反映了铀成矿作用与钾差量亏损场具有成因联系.结合野外地质调查、显微岩石学和元素地球化学对比研究表明,钾差量亏损场的形成是成矿流体与围岩作用导致水云母、钠长石、绿泥石交代钾长石的结果,是一个铀元素富集、钾质含量流失的过程;依据钾差量亏损场可大致圈定与铀成矿密切相关的"褪色"蚀变作用的空间分布范围,进一步突显了岩体内带铀矿找矿的有利信息,缩小了找矿靶区;初步划分白虎山地段和黄龙桥-4340矿点一带两处铀成矿有利地段,对下一步岩体内带铀矿找矿工作具有重要的指示作用. 相似文献
10.
马扎拉金锑矿是西藏藏南地区特提斯喜马拉雅成矿带上典型的受构造控制的蚀变岩型矿床.为了查明工作区控矿构造的空间展布以及为找矿评价提供依据,在前人总结的区域成矿模式的指导下,以物性为桥梁,将成矿模式转化为地质-地球物理模型,并以此模型为指导思想,综合分析了工作区地质资料和面积性的地球物理和地球化学资料,圈定了找矿有利的靶区,然后在靶区开展大比例尺的音频大地电磁测深和联合剖面测量工作.对音频大地电磁反演解译的断层进行了评价,推测与容矿相关的构造主要为F7、F12、Ft1、Ft2、Ft3.最后通过钻探对Ft1进行验证,成功发现了矿化破碎带.从而证明了综合信息找矿的合理性,为区内相似地质背景下的找矿工作提供了成功的经验. 相似文献
11.
A neural network is employed to select earthquake waves in a time history approach for structural dynamics. The neural network is a preferable alternative to an expert system because knowledge can easily be renewed. It involves a back propagation model having three layers (one input, one hidden and one output layer) and is used to avoid inappropriate earthquake input prior to practical numerical computations. Knowledge to categorize the earthquake waves is acquired through network training with earthquake response spectra and structural responses. The trained network is tested by categorizing the responses of three types of unknown structures caused by 50 previously recorded earthquakes. Comparisons are made with analogous data from the traditional site dominant period method. Results demonstrate that, unlike the latter method, a neural network is generally more successful as the number of training patterns increases. 相似文献
12.
现代地裂缝在世界许多国家普遍存在 ,已成为当今世界范围内的主要地质灾害之一。本文在详尽分析了山西榆次地裂缝的各个致灾因子的基础上 ,利用GIS技术建立了地质学意义上的专题层 ;然后采用人工神经网络技术构建出了地裂缝灾害活动性的评价模型 ,并建立了地裂缝活动性的评价系统 ,对榆次地裂缝进行了灾害活动性评价 ,为榆次市城建和国土规划等部门的正确决策提供了重要的科学依据 相似文献
13.
A hybrid neural network model for typhoon-rainfall forecasting 总被引：2，自引：0，他引：2
A hybrid neural network model is proposed in this paper to forecast the typhoon rainfall. Two different types of artificial neural networks, the self-organizing map (SOM) and the multilayer perceptron network (MLPN), are combined to develop the proposed model. In the proposed model, a data analysis technique is developed based on the SOM, which can perform cluster analysis and discrimination analysis in one step. The MLPN is used as the nonlinear regression technique to construct the relationship between the input and output data. First, the input data are analyzed using a SOM-based data analysis technique. Through the SOM-based data analysis technique, input data with different properties are first divided into distinct clusters, which can help the multivariate nonlinear regression of each cluster. Additionally, the topological relationships among data are discovered from which more insight into the typhoon-rainfall process can be revealed. Then, for each cluster, the individual relationship between the input and output data is constructed by a specific MLPN. For evaluating the forecasting performance of the proposed model, an application is conducted. The proposed model is applied to the Tanshui River Basin to forecast the typhoon rainfall. The results show that the proposed model can forecast more precisely than the model developed by the conventional neural network approach. 相似文献
14.
控制路基沉降是公路工程中的一个关键技术问题,而路基沉降与其影响因素之间存在着线性、非线性关系。当输入自变量较多时,用传统神经网络建模容易出现过拟合现象,导致网络模型预测精度较低。针对此问题,本文用遗传算法对神经网络模型的权值和阈值进行优化,同时讨论遗传参数的设定对输出结果的影响。通过对成南高速的实测数据进行仿真,试验结果表明:优化后的BP神经网络具有较高的预测精度,预测效果明显优于传统神经网络模型的输出结果,该预测方法可作为高速公路路基长期沉降预测的一种有效辅助手段。 相似文献
15.
Laura Carmine Elsa Aristodemou Christopher Pain Ann Muggeridge Cassiano de Oliveira 《Geophysical Prospecting》2008,56(1):115-140
The purpose of this work was to investigate a new and fast inversion methodology for the prediction of subsurface formation properties such as porosity, salinity and oil saturation, using time‐dependent nuclear well logging data. Although the ultimate aim is to apply the technique to real‐field data, an initial investigation as described in this paper, was first required; this has been carried out using simulation results from the time‐dependent radiation transport problem within a borehole. Simulated neutron and γ‐ray fluxes at two sodium iodide (NaI) detectors, one near and one far from a pulsed neutron source emitting at ～14 MeV, were used for the investigation. A total of 67 energy groups from the BUGLE96 cross section library together with 567 property combinations were employed for the original flux response generation, achieved by solving numerically the time‐dependent Boltzmann radiation transport equation in its even parity form. Material property combinations (scenarios) and their correspondent teaching outputs (flux response at detectors) are used to train the Artificial Neural Networks (ANNs) and test data is used to assess the accuracy of the ANNs. The trained networks are then used to produce a surrogate model of the expensive, in terms of computational time and resources, forward model with which a simple inversion method is applied to calculate material properties from the time evolution of flux responses at the two detectors. The inversion technique uses a fast surrogate model comprising 8026 artificial neural networks, which consist of an input layer with three input units (neurons) for porosity, salinity and oil saturation; and two hidden layers and one output neuron representing the scalar photon or neutron flux prediction at the detector. This is the first time this technique has been applied to invert pulsed neutron logging tool information and the results produced are very promising. The next step in the procedure is to apply the methodology to real data. 相似文献
16.
The spectral characteristics of mangroves on the Beihai Coast of Guangxi, P. R. China are acquired on the basis of spectral data from field measurements. Following this, the 3‐layer reverse‐conversing neural networks (NN) classification technology is used to analyze the Landsat TM5 image obtained on January 8, 2003. It is detailed enough to facilitate the introduction of the algorithm principle and trains project of the neural network. Neural network algorithms have characteristics including large‐scale data handling and distributing information storage. This research firstly analyzes the necessity and complexity of this translation system, and then introduces the strong points of the neural network. Processing mangrove landscape characteristics by using neural network is an important innovation, with great theoretical and practical significance. This kind of neural network can greatly improve the classification accuracy. The spatial resolution of Landsat TM5 is high enough to facilitate the research, and the false color composite from 3‐, 4‐, and 5‐bands has a clear boundary and provides a significant quantity of information and effective images. On the basis of a field survey, the exported layers are defined as mangrove, vegetation, bare land, wetlands and shrimp pool. TM satellite images are applied to false color composites by using 3‐, 4‐, and 5‐bands, and then a supervised classification model is used to classify the image. The processing method of hyper‐spectrum remote sensing allows the spectral characteristics of the mangrove to be determined, and integrates the result with the NN classification for the false color composite by using 3‐, 4‐, and 5‐bands. The network model consists of three layers, i. e., the input layer, the hidden layer, and the output layer. The input layer number of classification is defined as 3, and the hidden layers are defined as 5 according to the function operation. The control threshold is 0.9. The training ratio is 0.2. The maximum permit error is 0.08. The classification precision reaches 86.86%. This is higher than the precision of maximal parallel classification (50.79%) and the spectrum angle classification (75.39%). The results include the uniformity ratio (1.7789), the assembly ratio (0.6854), the dominance ratio (–1.5850), and the fragmentation ratio (0.0325). 相似文献
17.
当前震后建筑经济损失评估模型得到的震后建筑经济损失评估精确度、效率低,针对单一神经网络易产生局部极值等问题,对神经网络方法进行了改进,提出LM-BP神经网络在震后建筑损失评估模型中的应用。输入样本要素为影响震后建筑经济损失的5项因素,输出样本是震后建筑经济损失评估结果,在此基础上采用LM-BP神经网络将训练转化成最小二乘问题,结合LM算法重新定义隐含层节点数量,构建基于LM-BP的神经网络震后经济损失评估模型,采用该模型获取最优震后建筑经济损失评估结果。仿真实验结果表明,所设计的评估模型最小评估误差为0.1%,相比同类模型具有高精确度的优势,是一种可靠的震后建筑经济损失评估模型。 相似文献
18.
We developed a mineral classification technique of electron probe microanalyzer (EPMA) maps in order to reveal the mineral textures and compositions of volcanic rocks. In the case of lithologies such as basalt that include several kinds of minerals, X-ray intensities of several elements derived from EPMA must be considered simultaneously to determine the mineral map. In this research, we used a Kohonen self-organizing map (SOM) to classify minerals in the thin-sections from several X-ray intensity maps. The SOM is a type of artificial neural network that is trained using unsupervised training to produce a two-dimensional representation of multi-dimensional input data. The classified mineral maps of in situ oceanic basalts of the Juan de Fuca Plate allowed us to quantify mineralogical and textural differences among the marginal and central parts of the pillow basalts and the massive flow basalt. One advantage of mineral classification using a SOM is that relatively many minerals can be estimated from limited input elements. By applying our method to altered basalt which contains multiple minerals, we successfully classify eight minerals in thin-section. 相似文献
19.
The aim of this work is to introduce the application of the fuzzy ordered weighted averaging method as a straightforward knowledge‐driven approach to explore porphyry copper deposits in an airborne prospect. In this paper, the proposed method is applied to airborne geophysical (potassium radiometry, magnetometry, and frequency‐domain electromagnetic) data, geological layers (fault and host rock zones), and various extracted alteration layers from remote sensing images. The central Iranian volcanic–sedimentary belt in Kerman province of Iran that is located within the Urumieh–Dokhtar (Sahand–Bazman) magmatic arc is chosen for this study. This region has high potential of mineral occurrences, especially porphyry copper, containing some active world‐class copper mines such as Sarcheshmeh. Two evidential layers, including the downward continued map and the analytic signal of such filtered magnetic data, are generated to be used as geophysical plausible traces of porphyry copper occurrences. The low values of the resistivity layer acquired from airborne frequency‐domain electromagnetic data are also used as an electrical criterion in this study. Four remote sensing evidential layers, including argillic, phyllic, propylitic, and hydroxyl alterations, are extracted from Advanced Spaceborne Thermal Emission and Reflection Radiometer images in order to map the altered areas associated with porphyry copper deposits. The Enhanced Thematic Mapper plus images are used to map iron oxide layer. Since potassium alteration is the mainstay of copper alteration, the airborne potassium radiometry data are used. Here, the fuzzy ordered weighted averaging method uses a wide range of decision strategies in order to generate numerous mineral potential/prospectivity maps. The final mineral potential map based upon desired geo‐data set indicates adequately matching of high‐potential zones with previous working mines and copper deposits. 相似文献
20.
Syam S. De Barin K. De Goutami Chattopadhyay Suman Paul Dilip K. Haldar Dipak K. Chakrabarty 《Acta Geophysica》2011,59(2):361-376
Autoregressive neural network (AR-NN) models of various orders have been generated in this work for the daily total ozone
(TO) time series over Kolkata (22.56°N, 88.5°E). Artificial neural network in the form of multilayer perceptron (MLP) is implemented
in order to generate the AR-NN models of orders varying from 1 to 13. An extensive variable selection method through multiple
linear regression (MLR) is implemented while developing the AR-NNs. The MLPs are characterized by sigmoid non-linearity. The
optimum size of the hidden layer is identified in each model and prediction are produced by validating it over the test cases
using the coefficient of determination (R
2) and Willmott’s index (WI). It is observed that AR-NN model of order 7 having 6 nodes in the hidden layer has maximum prediction
capacity. It is further observed that any increase in the orders of AR-NN leads to less accurate prediction. 相似文献