Application of Machine Learning Methods for Signal Processing in Piecewise-Polynomial Bases

Hakimjon Zaynidinov, Javohir Nurmurodov, Sirojiddin Qobilov
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Abstract

This article is devoted to digital processing of radiation signals in the field of geophysics based on spectrum analysis. With the help of these signals, it was studied whether it is possible to determine the layers of underground mineral wealth. A machine learning method was proposed to determine the layer where the ores are located. Haar’s piecewise-quadratic basis was chosen as the mathematical model of the machine learning method due to the small number of calculations. The purpose of choosing this model is that in the digital processing of signals, the number of near-zero values of spectral coefficients is large, and these values can be discarded as signal noise. This process helps us reduce the amount of data. As a result of comparing the values of spectral coefficients that are not close to zero, it gives an effective result in determining the location of the ore layer.
机器学习方法在分段多项式基信号处理中的应用
本文研究了地球物理领域中基于频谱分析的辐射信号的数字化处理。在这些信号的帮助下,研究人员研究了是否有可能确定地下矿产财富的层次。提出了一种机器学习方法来确定矿石所在层。由于计算量少,选择Haar的分段二次基作为机器学习方法的数学模型。选择该模型的目的是在信号的数字处理中,谱系数的近零值数量较多,这些值可以作为信号噪声丢弃。这个过程帮助我们减少了数据量。通过对不接近于零的谱系数值的比较,给出了确定矿层位置的有效结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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