基于小波变换的地球化学数据场分析
发布时间：2018-09-09 10:02
【摘要】：地球化学数据处理是勘查地球化学找矿中很重要的一部分，它主要是对采集的化探数据进行处理，包括数据加工、数据分析、数据可视化以及数据的解释问题。在地球化学数据处理工作中，需要完成的阶段性目标有：区分研究区域的背景和异常，根据确定的背景异常来确定与成矿有关联的元素的组合规律以及各元素间的空间变化规律等。 小波变换是近些年化探数据处理的一个新的分支，它能够有效的从化探数据中提取有用的信息,通过伸缩和平移等对化探数据进行多尺度细化分析，这就为地球化学数据的非线性分析提供了理论依据。 论文将小波变换工具应用于地球化学数据处理。首先对于研究区域中采集到的化探元素数据做地球化学降噪处理，在保持原有数据有效性的同时，减少了地球化学数据中某些误差产生的影响。所用的方法是小波阈值去噪法。针对阈值去噪过程中，最主要的几个参数：最优小波基，阈值，阈值函数，分解层数做了探讨分析，得出适合于本次试验的阈值去噪参数，并表明小波变换工具在处理地球化学数据时能够通过时频分析，将小波分解后的细节信息更加突出，小波阈值降噪能够很好的去除化探数据中的噪声。通过对阈值函数的选择对比发现，改进阈值函数在处理地球化学数据时有一定的局限性，并不总是优越于传统的阈值函数，在处理地球化学数据时，有必要根据实际情况选择合适的阈值函数。其次对去除噪声后的地球化学数据做地球化学场数据分析，采用不同的背景来区分异常，得出不同背景下各个元素的异常等值线图并作分析，表明不同背景值下元素的异常区域不同，选择不同背景值有利于弱异常分布情况的客观反映与提取，，同时使强异常区域更为突出。最后利用不同背景下的元素异常信息，确定与成矿有关的共生组合元素，利用与成矿有关的元素异常叠加图形成的交集区域，结合地质资料信息，初步推测异常区域可能存在的矿产资源。 总之,本文选取小波分析的方法来对地球化学数据进行分析处理以及基于不同背景下的数据场分析，旨在为地球化学数据处理与异常分析以及矿产资源预测提供一种新的技术方法。
[Abstract]:Geochemical data processing is an important part of exploration geochemical prospecting. It mainly deals with collected geochemical data, including data processing, data analysis, data visualization and data interpretation. In the process of geochemical data processing, the objectives to be accomplished are to distinguish the background and anomaly of the study area. According to the determined background anomaly, the combination law of the elements associated with the mineralization and the law of spatial variation among the elements are determined. Wavelet transform is a new branch of geochemical data processing in recent years. It can effectively extract useful information from geochemical data and analyze geochemical data by scaling and translation. This provides a theoretical basis for nonlinear analysis of geochemical data. In this paper, wavelet transform tool is applied to geochemical data processing. Firstly, geochemical noise reduction is done for geochemical exploration element data collected in the study area, which not only maintains the validity of the original data, but also reduces the influence of some errors in the geochemical data. The wavelet threshold denoising method is used. In the process of threshold denoising, the most important parameters, such as the optimal wavelet basis, the threshold function, the number of decomposition layers, are discussed and analyzed, and the threshold denoising parameters suitable for this experiment are obtained. It is shown that wavelet transform tool can deal with geochemical data through time-frequency analysis, and the detail information after wavelet decomposition is more prominent. Wavelet threshold de-noising can remove the noise in geochemical data very well. By comparing the selection of threshold function, it is found that the improved threshold function has some limitations in dealing with geochemical data and is not always superior to the traditional threshold function. It is necessary to select an appropriate threshold function according to the actual situation. Secondly, the geochemical data after noise removal are analyzed by geochemical field data. Different backgrounds are used to distinguish anomalies, and the anomalous isoline maps of each element in different backgrounds are obtained and analyzed. The results show that the anomaly regions of elements are different under different background values. The selection of different background values is beneficial to the objective reflection and extraction of the distribution of weak anomalies and makes the strong anomaly regions more prominent. Finally, by using the element anomaly information under different background, the symbiotic assemblage elements related to metallogenesis are determined, and the intersection area formed by the superposition map of the elements related to mineralization is used to combine the geological data information. The possible mineral resources in anomalous regions are preliminarily speculated. In a word, wavelet analysis method is selected to analyze and process geochemical data and data field analysis based on different background. This paper aims to provide a new technical method for geochemical data processing, anomaly analysis and mineral resource prediction.
【学位授予单位】：内蒙古科技大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：P632
本文编号：2232064
[Abstract]:Geochemical data processing is an important part of exploration geochemical prospecting. It mainly deals with collected geochemical data, including data processing, data analysis, data visualization and data interpretation. In the process of geochemical data processing, the objectives to be accomplished are to distinguish the background and anomaly of the study area. According to the determined background anomaly, the combination law of the elements associated with the mineralization and the law of spatial variation among the elements are determined. Wavelet transform is a new branch of geochemical data processing in recent years. It can effectively extract useful information from geochemical data and analyze geochemical data by scaling and translation. This provides a theoretical basis for nonlinear analysis of geochemical data. In this paper, wavelet transform tool is applied to geochemical data processing. Firstly, geochemical noise reduction is done for geochemical exploration element data collected in the study area, which not only maintains the validity of the original data, but also reduces the influence of some errors in the geochemical data. The wavelet threshold denoising method is used. In the process of threshold denoising, the most important parameters, such as the optimal wavelet basis, the threshold function, the number of decomposition layers, are discussed and analyzed, and the threshold denoising parameters suitable for this experiment are obtained. It is shown that wavelet transform tool can deal with geochemical data through time-frequency analysis, and the detail information after wavelet decomposition is more prominent. Wavelet threshold de-noising can remove the noise in geochemical data very well. By comparing the selection of threshold function, it is found that the improved threshold function has some limitations in dealing with geochemical data and is not always superior to the traditional threshold function. It is necessary to select an appropriate threshold function according to the actual situation. Secondly, the geochemical data after noise removal are analyzed by geochemical field data. Different backgrounds are used to distinguish anomalies, and the anomalous isoline maps of each element in different backgrounds are obtained and analyzed. The results show that the anomaly regions of elements are different under different background values. The selection of different background values is beneficial to the objective reflection and extraction of the distribution of weak anomalies and makes the strong anomaly regions more prominent. Finally, by using the element anomaly information under different background, the symbiotic assemblage elements related to metallogenesis are determined, and the intersection area formed by the superposition map of the elements related to mineralization is used to combine the geological data information. The possible mineral resources in anomalous regions are preliminarily speculated. In a word, wavelet analysis method is selected to analyze and process geochemical data and data field analysis based on different background. This paper aims to provide a new technical method for geochemical data processing, anomaly analysis and mineral resource prediction.
【学位授予单位】：内蒙古科技大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：P632
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