Crosscorrelation is a classical signal-processing technique that plays an important role in exploration and earthquake geophysics. Seismic velocity estimation utilizes the crosscorrelation between observed and predicted seismic records in traveltime tomography. The crosscorrelation between two stations represents the Green’s functions retrieved from ambient noises in passive seismic interferometry. It can be used to estimate the subsurface velocity and amplitude information. The calculation of crosscorrelation usually assumes that the input data are stationary; however, the real seismic data are often non-stationary, due to the presence of multiple wave-modes and background noises. The seismic crosscorrelations often have low signal-to-noise ratio and frequently fail to provide correct information for subsequent processing. To address this problem, we develop a comprehensive technique to reduce contamination and improve the quality of crosscorrelation in the wavelet domain. The new procedure includes the forward wavelet transformation of raw records, the crosscorrelation between wavelet coefficients, single-channel image object detection, multi-channel Kalman-filter object tracking, and inverse wavelet transformation to produce the new crosscorrelation gathers. We effectively remove the unwanted components associated with contaminated wave-modes as the proposed detection and tracking algorithm can accurately extract the target wave-mode. We validate the method for three datasets: a marine streamer survey, a borehole survey, and a broadband dataset from seismology stations. We demonstrate that the proposed method can significantly improve the signal-to-noise ratio of the seismic crosscorrelations, considerably enhancing the quality of the data for subsequent advanced crosscorrelation-based seismic processing.

中文翻译：

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使用小波域卡尔曼滤波器进行地震互相关的信号检测和增强

互相关是一种经典的信号处理技术，在勘探和地震地球物理学中发挥着重要作用。地震速度估计利用走时断层扫描中观测到的和预测的地震记录之间的互相关。两个台站之间的互相关表示从被动地震干涉测量中的环境噪声中提取的格林函数。它可用于估计地下速度和幅度信息。互相关的计算通常假设输入数据是平稳的；然而，由于存在多种波模式和背景噪声，真实的地震数据往往是非平稳的。地震互相关通常具有低信噪比并且经常无法为后续处理提供正确信息。为了解决这个问题，我们开发了一种综合技术来减少污染并提高小波域中的互相关质量。新程序包括原始记录的前向小波变换、小波系数之间的互相关、单通道图像对象检测、多通道卡尔曼滤波器对象跟踪和逆小波变换以产生新的互相关集。我们有效地去除了与污染波模式相关的不需要的成分，因为所提出的检测和跟踪算法可以准确地提取目标波模式。我们验证了三个数据集的方法：海洋拖缆调查、钻孔调查和来自地震站的宽带数据集。