Abstract

An automated method for detecting multiscale sporadic effects in data from ground-based neutron monitors is proposed. The method is based on the wavelet transform and neural networks of learning vector quantization type (LVQ neural networks). The choice of Daubechies wavelets and Coiflets at the data preprocessing stage is justified. An algorithm for choosing the “best” approximating wavelet basis in the class of orthogonal functions is proposed. The effectiveness of the method as applied to the detection of small-scale sporadic effects is shown experimentally. The possibility of a numerical implementation of the method for operational use is demonstrated.

中文翻译：

---

宇宙射线数据分析和偶发效应检测的自动化方法

摘要

提出了一种用于检测来自地面中子监测器的数据中的多尺度零星效应的自动化方法。该方法基于小波变换和学习矢量量化类型的神经网络（LVQ 神经网络）。在数据预处理阶段选择 Daubechies 小波和 Coiflets 是合理的。提出了一种在正交函数类中选择“最佳”近似小波基的算法。实验证明了该方法应用于检测小规模偶发效应的有效性。演示了该方法用于操作使用的数值实现的可能性。