基于FastICA和小波阈值的联合算法对瞬变电磁信号的降噪
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摘要
对于煤矿采空区的探测,运用地球物理勘探的瞬变电磁法是行之有效的手段之一。它通过接地电极或不接地回线发射电流,在地下建立一次磁场,在关断电流的间歇,接收二次场的感应电动势的变化情况,来判断地下信息。它具有工作方便、经济快捷等优点,但同时它在工作时较易受到外在的干扰,如高压电线、无线电波以及仪器本身,使接收到的信号中含有噪声,这些噪声会导致信号失真、反映地下情况的深度变浅。
本论文就是根据瞬变电磁勘探原理及信号的衰减特征的基础上,首先探讨了影响瞬变电磁信号的噪声的来源、噪声的特点；总结了目前的学者对于瞬变电磁信号降噪提出的一些方法,如三点指数逼近非线性平滑去噪、信号的叠加、小波分析去噪等,但到目前为止没有一种精确地去噪方法。然后提出了用FastICA和小波阈值的联合算法对瞬变电磁信号降噪的方法研究；并简述了有关ICA和小波分析的理论,介绍了FastICA和小波阈值,其中FastICA是ICA中最为快速、有效的算法,而小波阈值也是小波分析的算法之一,它是通过小波的分解后的各层系数的模与某阈值系数比较,处理完后重构出新的信号。最后,用仿真实验和野外试验的方式,来证实先FastICA后小波阈值联合算法降噪的可行性,并对比了不同的信噪比对降噪效果的影响。
本文的重点就在是否能将普遍用于信噪分离的ICA和小波分析算法联合用于瞬变电磁信号降噪的观点论证上,作者通过Matlab软件进行仿真的实验,利用Matlab中的FastICA工具箱和调用小波阈值函数,严格按照FastICA的计算步骤,选用合适的小波阈值参数来进行计算,得到的图形与分别单独用FastICA、小波阈值、三点自相关的降噪方法对比,并进行相关性评价,证实联合计算的降噪效果要优于其它方法：1.虽然经过FastICA计算之后,数据的幅值发生改变,但联合算法后的曲线波形更接近理想源曲线；2.用小波阈值计算后,曲线的光滑性越好,便于图形的识别和进一步的判断。将此联合算法可以尝试在野外的实际工作中应用；3.在野外的实验工作中,完全按照现行的瞬变电磁工作方式和资料处理,只是在降噪中加入了联合算法,由此形成的V/I多测道曲线与实际地下采空区位置完全吻合,进一步证明了联合算法用于降噪的可行性。通过仿真实验和野外试验的开展,验证了该联合算法对于煤矿采空区的探测,可以提高信号的利用率和信噪比,并对提高探测的准确率具有指导意义。为瞬变电磁法在金属矿床、地下水等领域探测的资料处理中提供了借鉴。
The People use transient electromagnetic method that belongs to geophysical exploration to detect the coal goaf, it is one of the most effective methods. It emits the current through the grounding electrodes or not grounding loop line, which set up a magnetic field in the underground, and then in the intermittent of shutting off the current, secondary field signal is acquired, it has high resolution in small inhomogeneous geological object. There are convenient and economic advantages, but it was more susceptible to the disturbance of noise, such as the high voltage lines, radio waves and the instrument itself, the noise can lead to the distortion of signals, and the signals can not explore so deep in the underground as we expected.
This paper is based on the principle of using transient electromagnetic method and the characteristics of signals attenuation. Firstly probes the noise sources which polluted transient electromagnetic signals and noise characteristic. Summarizes some manners that the previous scholars brought forward for reducing the noise, such as three approximate nonlinear index noise removing, the superposition of signal, wavelet analysis, but so far an effective denoising manner has not yet appeared. And then puts forward a research method which connects FastICA with wavelet threshold to reduce the signal noise. And introduces the theory of the ICA and wavelet analysis,the main content contains the FastICA and wavelet threshold, FastICA is the most effective and fastest algorithm of ICA, wavelet threshold is also one of the wavelet analysis algorithm, comparing every layer's coefficient which was decomposed by wavelet with a certain threshold coefficient. After that, rebuild a new signal. Finally, simulation experiment and field test demonstrate the feasibility that using firstly FastICA and then wavelet threshold reduce the noise of transient electromagnetic signals.,to judge with the reducing effection of the different signal-to-noise ratio signals。
This paper focuses on the argument that whether it is possible to reduce transient electromagnetic signal noise by the union algorithm of FastICA and wavelet threshold, after all, the combination algorithm was commonly used to separate the noise from the signals. Achieving simulation experiment by means of Matlab software, make use of Matlab FastICA toolbox and call the wavelet threshold function, in strictly accordance with the FastICA calculation steps, choosing suitable wavelet threshold parameters to calculate. Obtaining some graphics contrast with separately using FastICA, wavelet threshold, the three related noise reduction method, and evaluate the relevance. confirmed that joint calculation of the noise control effect is superior to other methods:1. the amplitude of data has been changed after FastICA calculation, but the shape of curve after joint calculation was more close to ideal curve:2. After wavelet threshold calculation, the curve is more smooth,easier to recognize and further judgment. The combined algorithm is attempted to apply in the field practical work. the field experiment completely comply with the existing transient electromagnetic work methods and data processing. On multi-channel profiles finding the position of possible coal goaf is perfectly fit for the practical ones. further proved the feasibility that take advantage of combined algorithm to reduce noise. So the joint calculation for coal goaf detecting, can improve the signal utilization and SNR, and improve the detection accuracy, which provides the guiding significance. For the transient electromagnetic method in detecting metal deposits, groundwater, etc of data processing provides reference.
本论文就是根据瞬变电磁勘探原理及信号的衰减特征的基础上,首先探讨了影响瞬变电磁信号的噪声的来源、噪声的特点；总结了目前的学者对于瞬变电磁信号降噪提出的一些方法,如三点指数逼近非线性平滑去噪、信号的叠加、小波分析去噪等,但到目前为止没有一种精确地去噪方法。然后提出了用FastICA和小波阈值的联合算法对瞬变电磁信号降噪的方法研究；并简述了有关ICA和小波分析的理论,介绍了FastICA和小波阈值,其中FastICA是ICA中最为快速、有效的算法,而小波阈值也是小波分析的算法之一,它是通过小波的分解后的各层系数的模与某阈值系数比较,处理完后重构出新的信号。最后,用仿真实验和野外试验的方式,来证实先FastICA后小波阈值联合算法降噪的可行性,并对比了不同的信噪比对降噪效果的影响。
本文的重点就在是否能将普遍用于信噪分离的ICA和小波分析算法联合用于瞬变电磁信号降噪的观点论证上,作者通过Matlab软件进行仿真的实验,利用Matlab中的FastICA工具箱和调用小波阈值函数,严格按照FastICA的计算步骤,选用合适的小波阈值参数来进行计算,得到的图形与分别单独用FastICA、小波阈值、三点自相关的降噪方法对比,并进行相关性评价,证实联合计算的降噪效果要优于其它方法：1.虽然经过FastICA计算之后,数据的幅值发生改变,但联合算法后的曲线波形更接近理想源曲线；2.用小波阈值计算后,曲线的光滑性越好,便于图形的识别和进一步的判断。将此联合算法可以尝试在野外的实际工作中应用；3.在野外的实验工作中,完全按照现行的瞬变电磁工作方式和资料处理,只是在降噪中加入了联合算法,由此形成的V/I多测道曲线与实际地下采空区位置完全吻合,进一步证明了联合算法用于降噪的可行性。通过仿真实验和野外试验的开展,验证了该联合算法对于煤矿采空区的探测,可以提高信号的利用率和信噪比,并对提高探测的准确率具有指导意义。为瞬变电磁法在金属矿床、地下水等领域探测的资料处理中提供了借鉴。
The People use transient electromagnetic method that belongs to geophysical exploration to detect the coal goaf, it is one of the most effective methods. It emits the current through the grounding electrodes or not grounding loop line, which set up a magnetic field in the underground, and then in the intermittent of shutting off the current, secondary field signal is acquired, it has high resolution in small inhomogeneous geological object. There are convenient and economic advantages, but it was more susceptible to the disturbance of noise, such as the high voltage lines, radio waves and the instrument itself, the noise can lead to the distortion of signals, and the signals can not explore so deep in the underground as we expected.
This paper is based on the principle of using transient electromagnetic method and the characteristics of signals attenuation. Firstly probes the noise sources which polluted transient electromagnetic signals and noise characteristic. Summarizes some manners that the previous scholars brought forward for reducing the noise, such as three approximate nonlinear index noise removing, the superposition of signal, wavelet analysis, but so far an effective denoising manner has not yet appeared. And then puts forward a research method which connects FastICA with wavelet threshold to reduce the signal noise. And introduces the theory of the ICA and wavelet analysis,the main content contains the FastICA and wavelet threshold, FastICA is the most effective and fastest algorithm of ICA, wavelet threshold is also one of the wavelet analysis algorithm, comparing every layer's coefficient which was decomposed by wavelet with a certain threshold coefficient. After that, rebuild a new signal. Finally, simulation experiment and field test demonstrate the feasibility that using firstly FastICA and then wavelet threshold reduce the noise of transient electromagnetic signals.,to judge with the reducing effection of the different signal-to-noise ratio signals。
This paper focuses on the argument that whether it is possible to reduce transient electromagnetic signal noise by the union algorithm of FastICA and wavelet threshold, after all, the combination algorithm was commonly used to separate the noise from the signals. Achieving simulation experiment by means of Matlab software, make use of Matlab FastICA toolbox and call the wavelet threshold function, in strictly accordance with the FastICA calculation steps, choosing suitable wavelet threshold parameters to calculate. Obtaining some graphics contrast with separately using FastICA, wavelet threshold, the three related noise reduction method, and evaluate the relevance. confirmed that joint calculation of the noise control effect is superior to other methods:1. the amplitude of data has been changed after FastICA calculation, but the shape of curve after joint calculation was more close to ideal curve:2. After wavelet threshold calculation, the curve is more smooth,easier to recognize and further judgment. The combined algorithm is attempted to apply in the field practical work. the field experiment completely comply with the existing transient electromagnetic work methods and data processing. On multi-channel profiles finding the position of possible coal goaf is perfectly fit for the practical ones. further proved the feasibility that take advantage of combined algorithm to reduce noise. So the joint calculation for coal goaf detecting, can improve the signal utilization and SNR, and improve the detection accuracy, which provides the guiding significance. For the transient electromagnetic method in detecting metal deposits, groundwater, etc of data processing provides reference.
引文
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