Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Yavor Kamer"'
Publikováno v:
The European Physical Journal Special Topics. 230:451-471
Predictability of earthquakes has been vigorously debated in the last decades with the dominant -albeit contested -view being that earthquakes are inherently unpredictable. The absence of a framework to rigorously evaluate earthquake predictions has
Publikováno v:
The European Physical Journal Special Topics. 230:425-449
We present rigorous tests of global short-term earthquake forecasts using Epidemic Type Aftershock Sequence models with two different time kernels (one with exponentially tapered Omori kernel (ETOK) and another with linear magnitude dependent Omori k
Publikováno v:
EGUsphere
Recent advances in machine learning and pattern recognition methods have propagated into various applications in seismology. Phase picking, earthquake location, anomaly detection and classification applications have benefited also from the increased
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2176388143a8d3abb7b81c922b90ebc8
https://hdl.handle.net/20.500.11850/527422
https://hdl.handle.net/20.500.11850/527422
Nature is scary. You can be sitting at your home and next thing you know you are trapped under the ruble of your own house or sucked into a sinkhole. For millions of years we have been the figurines of this precarious scene and we have found our own
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::10b8ff0ae2a5e69d53a28d0f6a08f400
https://doi.org/10.5194/egusphere-egu21-15219
https://doi.org/10.5194/egusphere-egu21-15219
In this paper we introduce a method for fault network reconstruction based on the 3D spatial distribution of seismicity. One of the major drawbacks of statistical earthquake models is their inability to account for the highly anisotropic distribution
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d8b9361610950dbf68201a269c287409
https://doi.org/10.5194/nhess-2020-231
https://doi.org/10.5194/nhess-2020-231