Spectral analysis of ground thermal image temperatures: what we are learning at Solfatara volcano (Italy)

Abstract

The Solfatara volcano in the Campi Flegrei caldera (Italy), is monitored by different, permanent ground networks handled by INGV (Istituto Nazionale di Geofisica e Vulcanologia), including thermal infrared cameras (TIRNet). The TIRNet network is composed by five stations equipped with FLIR A645SC or A655SC thermal cameras acquiring at nightime infrared scenes of portions of the Solfatara area characterized by significant thermal anomalies. The dataset processed in this work consists of daily maximum temperatures time-series from 25 April 2014 to 31 May 2019, acquired by three TIRNet stations (SF1 and SF2 inside Solfatara crater, and PIS near Pisciarelli boiling mud pool), and also consists of atmospheric pressure and air temperature time-series. Data pre-processing was carried out in order to remove the seasonal components and the influence of the Earth tides to the selected time-series. By using the STL algorithm (Seasonal Decomposition of Time Series by Loess), the time-series were decomposed into three components (seasonal, trend and remainder) to find seasonality and remove it. Then, a harmonic analysis was performed on the de-seasonalized signals in order to identify and remove the long-period tidal constituents (mainly fortnightly and monthly). Finally, Power Spectral Density was calculated by FFT Matlab algorithm, after applying an acausal Butterworth filter, focusing on the [15–120] d band, to check if characteristic periodicities exist for each site. The reliability and significance of the spectral peaks were proved by statistical and empirical methods. We found that most of the residual periodicities are ascribable to ambient factors, while 18.16 d for Pisciarelli site and 88.71 d for Solfatara have a possible endogenous origin.