Receiver Function analysis of noise reduced OBS data recorded at the ultra-slow spreading Knipovich Ridge

Ultra-slow spreading ridges are characterized by huge volcanic complexes which are separated by up to 150 km long amagmatic segments. The mechanisms controlling the ultra-slow spreading ridges are not yet fully understood. With the aim to better understand the spreading mechanisms and the flow of the magma beneath the volcanic complexes an ocean-bottom array has been installed along a segment of the ultra-slow spreading Knipovich Ridge in the Greenland sea. The array consists of 23 LOBSTER-type ocean bottom seismometers (OBS) from the DEPAS pool and 5 LOBSTERs from the Institute of Geophysics of the Polish Academy of Sciences. We aim to constrain the crustal and mantle structure beneath the segment of the Knipovich Ridge by using receiver functions calculated from teleseismic events.

Seismic data, recorded on the ocean bottom, are highly contaminated by different noise sources, which are dominating at frequencies below 1 Hz. During the experiment the DEPAS-LOBSTERs were equipped with a MCS recorder and a Güralp CMG-40T seismometer (changed now to 6D6 recorder and Trillium Compact seismometer). This characteristic design introduces electronic noise at selected stations at frequencies below 0.2 Hz. Recently head-buoy-strumming has been identified as additional noise source at frequencies above 0.5 Hz during tidal currents. Hence, most teleseismic signals are masked by the high noise level, especially on the horizontal components. However, a good signal to noise ratio on both, the vertical and horizontal components is crucial for seismological analysis, especially the receiver function method. Applying the HPS noise reduction algorithm on OBS data, as shown by Zali et al (submitted in 2021), allows to separate percussive or transient signals, such as the teleseismic earthquake from more harmonic and monochromatic signals, such as most of the noise generated at the ocean bottom.

The results of the HPS noise reduction algorithm processing of selected KNIPAS station data show a significantly reduced noise level below 1 Hz on all seismogram components, especially on the horizontals. Here, the signal-to-noise ratio increased by up to 3.2-3.7 (average by 1.4-1.6). The increased signal-to-noise ratio on the noise reduced data allows for more reliable receiver function results and their interpretation. Here, we show the reduced noise level on the OBS data and compare the receiver function results calculated from original data with the results from noise-reduced data.