The influence of environmental microseismicity on detection and interpretation of small-magnitude events in a polar glacier setting

Abstract

<jats:title>Abstract</jats:title> <jats:p>Glacial environments exhibit temporally variable microseismicity. To investigate how microseismicity influences event detection, we implement two noise-adaptive digital power detectors to process seismic data from Taylor Glacier, Antarctica. We add scaled icequake waveforms to the original data stream, run detectors on the hybrid data stream to estimate reliable detection magnitudes and compare analytical magnitudes predicted from an ice crack source model. We find that detection capability is influenced by environmental microseismicity for seismic events with source size comparable to thermal penetration depths. When event counts and minimum detectable event sizes change in the same direction (i.e. increase in event counts and minimum detectable event size), we interpret measured seismicity changes as ‘true’ seismicity changes rather than as changes in detection. Generally, one detector (two degree of freedom (2dof)) outperforms the other: it identifies more events, a more prominent summertime diurnal signal and maintains a higher detection capability. We conclude that real physical processes are responsible for the summertime diurnal inter-detector difference. One detector (3dof) identifies this process as environmental microseismicity; the other detector (2dof) identifies it as elevated waveform activity. Our analysis provides an example for minimizing detection biases and estimating source sizes when interpreting temporal seismicity patterns to better infer glacial seismogenic processes.</jats:p>

The Detection of Seismicity on Icy Ocean Worlds by Single‐Station and Small‐Aperture Seismometer Arrays

“…The InSight mission to Mars are able to detect more events during the evening hours than during the day when winds are increased (Giardini et al., 2020). Previous terrestrial studies on glaciers have also shown that microseismicity can affect the detection of other events (C. G. Carr et al., 2020). C. G. Carr et al.…”

“…C. G. Carr et al. (2020) shows how modifications to the standard STA/LTA algorithm by using a noise‐adaptive detector can improve detection and reduce bias in detection statistics.…”

“…This may be preferred if a goal of the mission are to investigate larger scale structure or global‐scale activity rather than a specific feature. On short‐term missions in which sensor or telemetry lifetimes may last only last a few weeks, an automated detection algorithm will be crucial for efficient data return where high sampling is needed to resolve events (e.g., local seismicity or HF and VF style events). More sophisticated automated detection algorithms (e.g., C. G. Carr et al., 2020) that account for diurnal changes in noise would further improve detections. Operator searches are expected to find the most events, but requires large data volumes and interactivity with the data on the station.…”

Wintertime Brine Discharge at the Surface of a Cold Polar Glacier and the Unexpected Absence of Associated Seismicity

“…The L‐22 seismometers have a natural or corner frequency of 2 Hz, and for our installation the Nyquist frequency was 100 Hz (sample rate: 200 Hz). The Rayleigh detector used in this study as well as the STA/LTA detectors from our previous study (Carr et al., 2020) implement (2.5, 35) Hz bandpass filters. This filter band is consistent with other Rayleigh‐wave icequake studies (e.g., Carmichael et al., 2015; Hudson et al., 2020; Linder et al., 2019).…”

Hypothesis Tests on Rayleigh Wave Radiation Pattern Shapes: A Theoretical Assessment for Source Screening (Independent Review: LANL Source Physics LCP) [Slides]

“…The term ρA u(ξ 0 ) β 2 represents the elastic energy released by volumetric crack growth. Such sources are common in seismogenic hydrofracture events within glaciers (Carr et al, 2020;Taylor-Offord et al, 2019) and ice sheets (Carmichael et al, 2015).…”