Seismic noise suppression: array stations, waveform cross-correlation, and noise stochastization

TL;DR

This paper reviews advanced seismic noise suppression techniques, including array processing, waveform cross-correlation, and noise stochastization, demonstrating their effectiveness in enhancing seismic signal detection.

Contribution

It introduces a novel combination of waveform cross-correlation with noise stochastization, improving detection thresholds in seismic monitoring.

Findings

Array processing reduces detection thresholds by exploiting destructive interference.

Waveform cross-correlation enhances signal-to-noise ratio for similar seismic signals.

Noise stochastization further improves cross-correlation performance when combined with array techniques.

Abstract

Seismic noise with an amplitude higher than that of the sought signal is a challenge for detection. Several techniques have been developed to suppress the ambient noise and to reduce the detection threshold in order to find signals with the lowest possible amplitudes produced by events with the magnitudes significant for scientific research and technical applications. Seismic arrays were introduced in the late 1950s as a method for improving underground test monitoring, potentially reducing detection thresholds by fivefold or more by exploiting destructive interference effects of a quasi-random noise. The beamforming method is the backbone of data processing at the International Data Centre (IDC) with more than 30 array stations of the International Monitoring System (IMS) installed around the globe. The matched filter method allows for the suppression of noise incoherent to the sought signal. It employs waveform cross-correlation (WCC) with templates based on actual and simulated seismic signals to improve the signal-to-noise ratio estimates for similar signals. The performance of this method is significantly enhanced when it is applied to a seismic array. A novel technique combined with WCC, is the noise stochastization or the addition of scaled random noise to the actual data before calculating the cross-correlation coefficient. The stochastic component can easily be generated by a computer program. Alternatively, a regular signal propagating at an angle of around 90{\deg} to the plane of the sought signal can play a role of stochastic component at array stations. We demonstrate the separate and joint effects of these noise reduction techniques on the WCC performance, when applied to filtered data from selected IMS arrays and various waveform templates of historical events available at the IDC.