Automating long-term glacier dynamics monitoring using single-station seismological observations and fuzzy logic classification: a case study from Spitsbergen

Automating long-term glacier dynamics monitoring using single-station seismological observations and fuzzy logic classification: a case study from Spitsbergen

Blog Article

Retreating glaciers are a consequence of a warming climate.Thus, numerous monitoring campaigns are being carried out to increase understanding of this on-going process.One phenomenon related to rosy teacup dogwood dynamic glacial changes is glacier-induced seismicity; however, weak seismic events are difficult to record due to the sparse seismological network in arctic areas.We have developed an automatic procedure capable of detecting glacier-induced seismic events using records from a single permanent seismological station.

To distinguish between glacial and non-glacial signals, we developed a fuzzy logic algorithm based on the signal frequency and energy flow analysis.We studied the long-term changes in glacier-induced seismicity in Hornsund (southern Spitsbergen) and in Kongsfjorden (western Spitsbergen).We found that the number of detected glacial-origin events in the Hornsund dataset over the years 2013-14 has doubled.In the Kongsfjorden dataset, we observed a steady increase in the number of glacier-induced events with each year.

We also observed that the seasonal event distribution correlates best with 1 month lagged temperatures, and that extreme rain events can intensify seismic emissions.Our study demonstrates the possibility of using long-term seismological observations from a single permanent station to skull bride and groom automatically monitor the dynamic activity of nearby glaciers and retrieve its characteristic features.