Application of Noise-Assisted Multivariate Empirical Mode Decomposition in VLF-EM Data to Identify Underground River

Abstract

Very low-frequency electromagnetic (VLF-EM) method can be used for imaging the subsurface resistivity, where this image can be used directly to determine subsurface condition. VLF-EM data are generally contaminated with unwanted noise which often leads to a mistake in the resistivity imaging result. In this study, noise-assisted multivariate empirical mode decomposition (NA-MEMD) was applied to reject the unwanted noise contained within the VLF-EM data which produced NA-MEMD-filtered VLF-EM data. The resistivity imaging resulted by filtered VLF-EM data has been used for determining the position of underground rivers over the karst area of Gunung Kidul district, Central Java province, Indonesia. The results show that the NA-MEMD-filtered VLF-EM data were more accurate in determining underground river tracks of the Suci cave areas. The overall result was supported by qualitative analyses (Fraser and K–Hjelt filters) of observed VLF-EM data as well as the NA-MEMD-filtered VLF-EM data.