Regional and residual gravity anomaly separation using the singular spectrum analysis-based low pass filtering: a case study from Nagpur, Maharashtra, India

Abstract

We present here a singular spectrum analysis (SSA)-based low pass filtering algorithm for regional and residual gravity anomaly separation. The data adaptive decomposition in SSA frequency filtering algorithm facilitates reduction of the artefacts in the filtering of the non-linear and non-stationary gravity data. Initially, the method was tested on synthetic gravity data representing combined response of regional and residual components derived from the geological structures like infinite horizontal layers, intrusive dykes, deep-seated faults and volcanic intrusive bodies and compared with fast Fourier transform (FFT) wavenumber and wavelet-based filtering techniques. The results show that SSA-based filtered output exhibits a better match with pure synthetic data than the output generated from the FFT and wavelet filtering methods. The underlying method was then applied to two parallel gravity profiles of real data from the Umred coalfield, Nagpur, Maharashtra, India. Further, the SSA-based filtered regional anomaly was modelled using Geosoft GM-SYS software to construct the model of crustal structure of the study region. In essence, the modelling results suggest the following: (i) a basin-type graben structure with variable trap and sedimentary thicknesses and (ii) deep seated faults on either sides of the basin. These results correlate fairly well with known regional geology attesting the authenticity of the regional models generated from the two gravity profiles, which also agree well with each other. We, therefore, conclude that the SSA-based filtering technique is robust for regional gravity anomaly separation and could be effectively exploited for filtering other geophysical data.