Time-Frequency methods for seismic denoising

Seismic data recorded by surface arrays are often contaminated by unwanted noise. In many conventional seismic methods, the reliability of the seismic data and the accuracy of parameter extraction, such as onset time, polarity, and amplitude, are directly affected by the background noise level. As a result, the accuracy of the event location and other attributes derived from seismic traces are also influenced by the noise content. Therefore, there is a great need for developing suitable procedures that improve signal-to-noise ratios allowing for robust seismic processing. In this presentation, I introduce four different methods for the automatic denoising of seismic data. These methods are based on the time-frequency thresholding approach. The efficiency and performance of the thresholding-based method for seismic data have been improved significantly. Proposed methods are automatic and data-driven in the sense that all the filter parameters for denoising are dynamically adjusted to the characteristics of the signal and noise. These algorithms are applied to single-channel data analysis and do not require large arrays of seismometers or coherency of arrivals across an array. Hence, they can be applied to every type of seismic data and can be combined with other array-based methods. Results show these methods can improve the detection of small-magnitude events and the accuracy of arrival time picking.

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Mousavi, S. M., & Langston, C. A. (2016). Hybrid seismic denoising using higher‐order statistics and improved wavelet block thresholding. Bulletin of the Seismological Society of America, 106(4), 1380-1393.

Mousavi, S. M., & Langston, C. A. (2016). Hybrid seismic denoising using higher‐order statistics and improved wavelet block thresholding. Bulletin of the Seismological Society of America, 106(4), 1380-1393.

Mousavi, S. M., & Langston, C. A. (2016). Adaptive noise estimation and suppression for improving microseismic event detection. Journal of Applied Geophysics, 132, 116-124.

Langston, C. A., & Mousavi, S. M. (2019). Separating signal from noise and from other signals using nonlinear thresholding and scale‐time windowing of continuous wavelet transforms. Bulletin of the Seismological Society of America, 109(5), 1691-1700.

Zhu, W., Mousavi, S. M., & Beroza, G. C. (2019). Seismic signal denoising and decomposition using deep neural networks. IEEE Transactions on Geoscience and Remote Sensing, 57(11), 9476-9488.