@article{dea16,
author = {de Arcangelis Lucilla, and Godano Cataldo, and Grasso Jean Robert, and Lippiello Eugenio,},
pages = {1-91},
title = {Statistical physics approach to earthquake occurrence and forecasting},
volume = {628},
note = {Articolo in rivista},
issn = {0370-1573},
journal = {PHYSICS REPORTS},
year = {2016},
wosId = {WOS:000375510800001},
scopusId = {2-s2.0-84962009432},
abstract = {There is striking evidence that the dynamics of the Earth crust is controlled by a wide
variety of mutually dependent mechanisms acting at different spatial and temporal
scales. The interplay of these mechanisms produces instabilities in the stress field,
leading to abrupt energy releases, i.e., earthquakes. As a consequence, the evolution
towards instability before a single event is very difficult to monitor. On the other hand,
collective behavior in stress transfer and relaxation within the Earth crust leads to
emergent properties described by stable phenomenological laws for a population of many
earthquakes in size, time and space domains. This observation has stimulated a statistical
mechanics approach to earthquake occurrence, applying ideas and methods as scaling
laws, universality, fractal dimension, renormalization group, to characterize the physics
of earthquakes. In this review we first present a description of the phenomenological
laws of earthquake occurrence which represent the frame of reference for a variety
of statistical mechanical models, ranging from the spring-block to more complex fault
models. Next, we discuss the problem of seismic forecasting in the general framework
of stochastic processes, where seismic occurrence can be described as a branching
process implementing space–time-energy correlations between earthquakes. In this
context we show how correlations originate from dynamical scaling relations between
time and energy, able to account for universality and provide a unifying description
for the phenomenological power laws. Then we discuss how branching models can
be implemented to forecast the temporal evolution of the earthquake occurrence
probability and allow to discriminate among different physical mechanisms responsible
for earthquake triggering. In particular, the forecasting problem will be presented in
a rigorous mathematical framework, discussing the relevance of the processes acting
at different temporal scales for different levels of prediction. In this review we also
briefly discuss how the statistical mechanics approach can be applied to non-tectonic
earthquakes and to other natural stochastic processes, such as volcanic eruptions and solar
flares.}
}