An enhanced correlation identification algorithm and its application on spread spectrum induced polarization data

Abstract

Abstract. In spread spectrum induced polarization (SSIP) data processing, attenuation of background noise from the observed data is the essential step that improves the signal-to-noise ratio (SNR) of SSIP data. The traditional correlation identification (TCI) algorithm has been proposed to improve the SNR of these data. However, signal processing in background noise is still a challenging problem. We propose an enhanced correlation identification (ECI) algorithm to attenuate the background noise. In this algorithm, the cross-correlation matching method is helpful for the extraction of useful components of the raw SSIP data and suppression of background noise. Then the formula of the TCI algorithm is used for identifying the frequency response of the observation system. Even when the signal to noise ratio (SNR) is −37.5 dB, this ECI algorithm can still be able to keep 3.0 % relative error. Experiments on both synthetic and real SSIP data show that the ECI algorithm can not only suppress the background noise but also better preserves the valid information of the raw SSIP data to display the actual location and shape of adjacent high resistivity anomalies, which can improve subsequent steps in SSIP data processing and imaging.