Acoustic and microseismic characterization in steep bedrock permafrost on Matterhorn (CH)
Understanding of processes and factors influencing slope stability is essential for assessing the stability of potentially hazardous slopes. Passive monitoring of acoustic emissions and microseismology provides subsurface information on fracturing (timing and identification of the mechanism) and there...
| Main Authors: | , , , , |
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| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Wiley-Blackwell Publishing, Inc.
2018
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| Subjects: | |
| Online Access: | https://www.zora.uzh.ch/id/eprint/152613/ https://www.zora.uzh.ch/id/eprint/152613/1/2018_Weber_et_al-2018-Journal_of_Geophysical_Research%253A_Earth_Surface.pdf https://doi.org/10.5167/uzh-152613 https://doi.org/10.1029/2018JF004615 |
| Summary: | Understanding of processes and factors influencing slope stability is essential for assessing the stability of potentially hazardous slopes. Passive monitoring of acoustic emissions and microseismology provides subsurface information on fracturing (timing and identification of the mechanism) and therefore complement surface displacement data. This study investigates for the first time acoustic and microseismic signals generated in steep, fractured bedrock permafrost, covering the broad frequency range of 1 − 105Hz. The analysis of artificial forcing experiments using a rebound hammer in a controlled setting led to two major findings: First, statistically insignificant cross correlation between signals indicates that waveforms change strongly with propagation distance. Second, a signficant amplification is found in the frequency band 33–67 Hz. This finding is strongly supported by evidence from artificial rockfall events and more importantly by naturally occurring fracture events identified in fracture displacement data. Thus, filtering this frequency band enables enhanced detection of microseismic events relevant for slope stability assessment. The analysis of 2-year time series in this frequency band further suggests that the detected energy rate baseline of all automatically triggered events using the STA/LTA algorithm is not sensitive to temperature forcing, an observation of primary importance for long-term data collection, analysis, and interpretation. The event detection in the established frequency band is not only improved but also not affected by the short- and long-term temperature changes in such a rapidly changing environment. |
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