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.
PENGARUH KUNJUNGAN WISATAWAN DAN RATA-RATA PENGELUARAN WISATAWAN TERHADAP PAD DAN PERTUMBUHAN EKONOMI PROVINSI BALI
