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Monitoring giant landslide detachment planes in the era of big data analytics
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SYSNO ASEP 0481928 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Monitoring giant landslide detachment planes in the era of big data analytics Author(s) Blahůt, Jan (USMH-B) RID, ORCID, SAI
Rowberry, Matthew David (USMH-B) RID, ORCID, SAI
Balek, Jan (USMH-B) ORCID
Klimeš, Jan (USMH-B) RID, ORCID, SAI
Baroň, I. (AT)
Meletlidis, S. (ES)
Martí, Xavier (USMH-B)Source Title Advancing Culture of Living with Landslides, Volume 3 Advances in Landslide Technology. - Cham : Springer, 2017 / Mikoš M. ; Arbanas Ž. ; Yin Y. ; Sassa K. - ISBN 978-3-319-53486-2 Pages s. 333-340 Number of pages 8 s. Publication form Print - P Action World Landslide Forum /4./ Event date 29.05.2017 - 02.06.2017 VEvent location Ljubljana Country SI - Slovenia Event type WRD Language eng - English Country CH - Switzerland Keywords monitoring networks ; giant landslides ; San Andrés Landslide ; El Hierro ; Canary Islands Subject RIV DB - Geology ; Mineralogy OECD category Geology R&D Projects GJ16-12227Y GA ČR - Czech Science Foundation (CSF) Institutional support USMH-B - RVO:67985891 UT WOS 000438667600038 DOI 10.1007/978-3-319-53487-9_38 Annotation A small mesh of sensors which monitor movements across detachment planes of the giant San Andrés Landslide on the northeastern lobe of El Hierro in the Canary Islands was established in 2013. In this paper we present the results obtained over a two year period spanning from October 2013 to October 2015. Our results demonstrate that the detachment planes are affected by sinistral strike slip displacements and subsidence of the depleted mass of the landslide. While these general trends are consistent the movements recorded at particular monitoring points differ in detail as one site is characterised by progressive strike slip and dip slip trends while another is characterised by movement pulses and reversals in the sense of movement. These findings contrast markedly with suggestions that the giant landslide is inactive and demonstrate that its reactivation is a possibility which cannot be dismissed categorically. Big data analytics have been used to identify interdependence between the recorded movements and a range of climatic and geophysical variables such as seismic data, tidal data, and geomagnetic data. We have found that the recorded movements correlate only weakly or moderately with climatic and seismic parameters but strongly to the horizontal and vertical intensity of the magnetic field. These findings are rather unexpected and we emphasise that special care must be taken in pushing the conclusions of a purely numerical analysis. The advantages of adopting a big data mindset led us to make significant improvements to the instrumental infrastructure in early 2016. These incremental improvements to the small mesh of sensors are driven partly by our desire to understand the kinematic behaviour of landslide itself and partly by our desire to explore the potential of big data analytics in geoscientific research. Workplace Institute of Rock Structure and Mechanics Contact Iva Švihálková, svihalkova@irsm.cas.cz, Tel.: 266 009 216 Year of Publishing 2018
Number of the records: 1