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

机译：增强的相关识别算法及其在扩频诱导极化数据中的应用

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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 time-domain spectral induced polarization based on pseudorandom sequence (TSIP) 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 frequency-domain IP (FDIP) method is used for extracting the frequency response of the observation system. Experiments on both synthetic and real SSIP data show that the ECI algorithm will not only suppress the background noise but also better preserve 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.
机译：在扩频诱导的偏振（SSIP）数据处理中，从观察到的数据的背景噪声的衰减是提高SSIP数据的信噪比（SNR）的基本步骤。已经提出了基于伪随机序列（TSIP）算法的时域光谱感应极化来改善这些数据的SNR。然而，背景噪声中的信号处理仍然是一个具有挑战性的问题。我们提出了增强的相关识别（ECI）算法来衰减背景噪声。在该算法中，互相关匹配方法有助于提取原始SSIP数据的有用组件和背景噪声的抑制。然后，频率域IP（FDIP）方法用于提取观察系统的频率响应。合成和实际SSIP数据的实验表明，ECI算法不仅抑制了背景噪声，还可以更好地保留原始SSIP数据的有效信息，以显示相邻的高电阻率异常的实际位置和形状，可以改善随后改善SSIP数据处理和成像中的步骤。

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