An Adaptive Spectral Subtraction Algorithm to Remove Persistent Cultural Noise

Seismic stations and seismic arrays suffer from unwanted seismic noise as a result of inevi-table population growth and development. This encroachment of noise degrades stations' performance. Moving stations to a quieter location breaks the continuity of historical records and can be logistically complicated. This is especially true for seismic arrays that requires a larger footprint. In this study, we examine the feasibility and merit of an adap-tive denoising algorithm to reduce the impact of persistent anthropogenic noise. We build our algorithm on spectral subtraction techniques that have been commonly applied to speech and audio traces and develop a noise-suppression technique that is tailored for seismic data. Using the continuous wavelet transform, we subtract estimates of the noise in the frequency domain. We evaluate this algorithm on synthetic data, consisting of a set of carefully selected events on a low-noise array environment in Alaska. Then we apply this technique to a seismic array in Turkey known to suffer from persistent anthropogenic noise. Our results on individual seismic traces demonstrate that the noise-suppression technique is quite successful at improving the signal-to-noise ratio of key seismic phases. The strengths of this approach include its intuitiveness, its ability to adapt to changes in the background noise, and the ability to reduce noise while preserving the phase of the original signal-a prerequisite for use in array analysis. When the denoised traces are used for array analysis we do not find the noise suppression to be as effective as it is on indi-vidual traces. We explore a number of reasons why this performance is less than desired. Despite the results of our particular implementation, we demonstrate that the larger fam-ily of spectral subtraction techniques offer considerable adaptability and deserve more attention in the seismic community.