Nano-seismic Events Detection via Discrete Wavelet Transform

The accurate, real-time detection of small local earthquakes is essential for effective seismic monitoring and for generating detailed tomographic models of seismic faults. However, the signals from these events are often weak and buried within background noise, making them difficult to detect. this study developed a P-wave arrival detection algorithm using the Discrete Wavelet Transform (DWT). We leveraged the Discrete Wavelet Transform (DWT) to analyze seismic data, a method that is highly effective at identifying the abrupt changes in spectral characteristics that signify the arrival of a P-wave. By requiring coherent signals across a network of stations, the algorithm efficiently distinguishes genuine micro and nano-events from spurious noise. When our DWT-based algorithm was applied to data from the Egyptian National Seismic Network (ENSN), it demonstrated a significant performance leap over the widely used STA/LTA algorithm. Specifically, our method identified 98% more seismic events and provided automated arrival times with a high degree of accuracy. The discrepancy between our automated picks and manual picks was minimal, ranging from only 0.08 to 0.28 seconds. These findings underscore the immense potential of integrating wavelet-based signal processing into modern seismic monitoring systems to enhance both event detectability and the precision of arrival time measurements.