The Failure Forecast Method applied to the GPS and seismic data
collected in the Campi Flegrei caldera (Italy) in 2011-2020.
Abstract
Episodes of slow uplift and subsidence of the ground, called bradyseism,
characterize the recent dynamics of the Campi Flegrei caldera (Italy).
In the last decades two major bradyseismic crises occurred, in 1969/1972
and in 1982/1984, with a ground uplift of 1.70 m and 1.85 m,
respectively. Thousands of earthquakes, with a maximum magnitude of 4.2,
caused the partial evacuation of the town of Pozzuoli in October 1983.
This was followed by about 20 years of overall subsidence, about 1 m in
total, until 2005. After 2005 the Campi Flegrei caldera has been rising
again, with a slower rate, and a total maximum vertical displacement in
the central area of ca. 70 cm. The two signals of ground deformation and
background seismicity have been found to share similar accelerating
trends. The failure forecast method can provide a first assessment of
failure time on present‐day unrest signals at Campi Flegrei caldera
based on the monitoring data collected in [2011, 2020] and under the
assumption to extrapolate such a trend into the future. In this study,
we apply a probabilistic approach that enhances the well‐established
method by incorporating stochastic perturbations in the linearized
equations. The stochastic formulation enables the processing of
decade‐long time windows of data, including the effects of variable
dynamics that characterize the unrest. We provide temporal forecasts
with uncertainty quantification, potentially indicative of eruption
dates. The basis of the failure forecast method is a fundamental law for
failing materials: ẇ-α ẅ = A, where ẇ is the rate of
the precursor signal, and α, A are model parameters that we fit on the
data. The solution when α >1 is a power law of exponent
1/(1 − α) diverging at time Tf , called failure time. In
our case study, Tf is the time when the accelerating
signals collected at Campi Flegrei would diverge if we extrapolate their
trend. The interpretation of Tf as the onset of a
volcanic eruption is speculative. It is important to note that future
variations of monitoring data could either slow down the increase so far
observed, or suddenly further increase it leading to shorter failure
times than those here reported. Data from observations at all locations
in the region were also aggregated to reinforce the computations of
Tf reducing the impact of observation errors.