Galactic Cosmic-Ray Background Deduction Method Based on Empirical Mode Decomposition*

Abstract

The inversion of the relative content and spatial distribution characteristics of radioactive elements on the lunar surface, as inferred from gamma-ray spectrometer data, holds substantial importance for forecasting lunar surface compositions. During the inversion process of gamma-ray spectrometer data, galactic cosmic rays (GCRs) have engendered disruptive “stripe noise” in the distribution map of radioactive elements. This phenomenon significantly hampers the interpretation of data and the extraction of lunar surface information. The proposed approach adeptly separates the influence of GCR from the counting rate distribution map of the lunar surface by employing the empirical mode decomposition method. It achieves the deduction of GCR background from the Chang’e-2 gamma-ray spectrometer data with precision. Compared to conventional GCR background deduction methods employed by predecessors, this model does not need to process a large amount of original data repeatedly. Moreover, it achieves an accurate deduction of the GCR background without intricate formulaic derivations. The procedural simplicity and reduced time investment make this approach significantly superior.