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Self‐organized criticality in a landslide model
Author(s) -
Hergarten Stefan,
Neugebauer Horst J.
Publication year - 1998
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/98gl50419
Subject(s) - landslide , geology , self organized criticality , cellular automaton , criticality , stability (learning theory) , tectonics , fluvial , function (biology) , seismology , geomorphology , computer science , physics , algorithm , structural basin , machine learning , evolutionary biology , nuclear physics , biology
From landslide mapping it is known that the frequency of landslide occurence as a function of their magnitude can be described by a power law in many regions. In order to investigate the magnitude distribution of landslides from a theoretical point of view, we present a physically based landslide model combining aspects of slope stability and mass movement. If the long term driving processes (fluvial or tectonic) are integrated, the model shows self‐organized criticality (SOC). The results coincide with results obtained from landslide mapping, so that our model suggests that landsliding may be seen as a SOC process. In contrast to other models showing SOC that are mostly based on cellular automata, our model is based on partial differential equations. The results show that SOC is not a fashion of cellular automata, but can also occur in differential equation models.