Cristiano Fidani - Academia.edu (original) (raw)

Papers by Cristiano Fidani

Journal of geography and cartography, Nov 27, 2023

Frontiers in Earth Science, Jul 17, 2023

A correlation between low seismic activity and CO2 measurements variations was observed at the Ga... more A correlation between low seismic activity and CO2 measurements variations was observed at the Gallicano thermomineral spring, Tuscany, Italy, where an automatic monitoring multiparametric geochemical station is operative since 2003 (Pierotti et al., 2015). The above-mentioned correlation reported a time delay of about 2 days of small earthquakes with respect to CO2 anomalies. Starting from this correlation a conditional probability of earthquake occurrence given the CO2 anomaly detection was calculated, with a probability gain near 4 (Pierotti et al., 2022). A statistical correlation was also calculated between rain events and CO2 anomalies which was observed for rain vents ahead CO2 anomalies of one days. This permitted to distinguish CO2 anomalies due to meteorological versus tectonic activities. Following this distinction, and subtracting the rain contribution to the CO2 variations, a new correlation was observed between small earthquakes and CO2 anomalies which confirmed the past results whit a better performance. The new correlation peak is better defined and concentrated in the time lag of 2 days. The p-values of both earthquake and rain to CO2 correlations were calculated. The correspondent probability gain in an earthquake forecasting experiment, taking into account the rain events, increased from less than 4 to 4.5. Fidani, C. (2021). West Pacific Earthquake Forecasting Using NOAA Electron Bursts With Independent L-Shells and Ground-Based Magnetic Correlations. Front. Earth Sci. 9:673105.Pierotti, L., Botti, F., D’Intinosante, V., Facca, G., Gherardi, F. (2015). Anomalous CO2 content in the Gallicano thermo-mineral spring (Serchio Valley, Italy) before the 21 June 2013, Alpi Apuane earthquake (M= 5.2). Physics and Chemistry of the Earth, Parts A/B/C, 85, 131-140.Pierotti, L., Fidani C., Facca, G., Gherardi, F. (2022). Local earthquake conditional probability based on long term CO2 measurements. In 40st GNGTS National Conference, Trieste, 27 - 29 June 2022.

The Astrophysical Journal, Jul 27, 2023

GRB 221009A is a long gamma-ray burst among the most energetic and nearest (z = 0.151) detected s... more GRB 221009A is a long gamma-ray burst among the most energetic and nearest (z = 0.151) detected so far. The energy fluence of the burst was so large to cause ionization of the upper layers of Earth’s atmosphere and also observable signals in satellite-borne particle detectors. Electron signals, with the same GRB time development, can arise from the interaction of energetic photons with the particle detector and support structures. This effect was previously reported for the HEPP-L on board the China Seismo-Electromagnetic Satellite. We searched for the same effect on the particle detectors on board five POES and MetOp satellites. Electron signals in coincidence with the gamma-ray emission of the burst were found in three satellites, which were well illuminated by the GRB. The properties of the found electron signals are reported and discussed.

M., High-energy electron detection onboard DEMETER: The IDP spectrometer, description and first r... more M., High-energy electron detection onboard DEMETER: The IDP spectrometer, description and first results on the inner belt, Planetary and Space Science, 54, 502-511, 2006. Sgrigna, V., Carota, L., Conti, L., et al., Correlations between earthquakes and anomalous particle bursts from SAMPEX/PET satellite observations, Journal of Atmospheric and Solar-Terrestrial Physics, 67, 1448-1462, 2005. Sidiropoulos, N. F., Anognostopoulos, G., and Rigas, V., Comparative study on earthquake and ground based transmitter induced radiation belt electron precipitation at middle latitudes, Nat. Hazards Earth Syst. Sci., 11, 1901-1913, 2011. Vassiliadis, D., Klimas, A. J., Kanekal, S. G., Baker, D. N., and Weigel, R. S., Long-term-average, solar cycle, and seasonal response of magnetospheric energetic electrons to the solar wind speed, Journal of Geophysical Research, 107, A11, 1382, 2002.

Three earthquakes of comparable magnitude and in different tectonic contexts occurred on 15 June ... more Three earthquakes of comparable magnitude and in different tectonic contexts occurred on 15 June 2019 (M7.2) in New Zealand (Kermadec Islands), on 6 July 2019 (M7.1) in California (Ridgecrest) and on 21 May 2021 (M7.3) in China (Maduo) (dates in UT). We applied a multiparameter - multilayer approach to lithospheric, atmospheric and ionospheric data, the latter taken from CSES and Swarm satellites, before the mentioned large earthquakes to detect potential pre-earthquake anomalies. In all case studies, we note the following: a) similar precursor times of occurrences, confirming the Rikitake law for which the larger the earthquake magnitude the longer the anticipation time of the precursor and b) a clear acceleration of the possible precursory anomalies before each mainshock, as typical of critical systems approaching a critical state. We propose an interpretative model to take into account the chain of detected phenomena.

Astroparticle, Particle, Space Physics, Radiation Interaction, Detectors and Medical Physics Applications, 2012

Applied Sciences

A correlation between low L-shell 30–100 keV electrons precipitating into the atmosphere and M ≥ ... more A correlation between low L-shell 30–100 keV electrons precipitating into the atmosphere and M ≥ 6 earthquakes in West Pacific was presented in past works where ionospheric events anticipated earthquakes by 1.5–3.5 h. This was a statistical result obtained from the Medium Energy Protons Electrons Detector on board the NOAA-15 satellite, which was analyzed for 16.5 years. The present analysis, utilizing the same database, translated into adiabatic coordinates during geomagnetic quiet periods, lead to another significant correlation regarding East Pacific strong earthquakes. This new correlation is still observed between high energy precipitating electrons detected by the NOAA-15 0° telescope and M ≥ 6 events of another very dangerous seismic region of the Pacific ring of fire. The particle precipitation that contributed to this correlation was characterized by electron L-shell, pitch-angle, possible disturbance altitudes, and geographical locations. This correlation occurred circa 57...

An M7.3 seismic event occurred on the Kermadec Islands (New Zealand) on June 15,... more An M7.3 seismic event occurred on the Kermadec Islands (New Zealand) on June 15, 2019. It was investigated by the high-energy electron detectors of the NOAA and METOP satellites since its destructive energy could interact with the ionosphere and the Inner Van Allen Belts. Moreover, the Tonga subduction area was affected on November 11, 2022, by a strong superficial M7.3 earthquake, near the Hunga Tonga-Hunga Ha&#8217;apai volcano, whose last eruption at the beginning of 2022 represented an exceptional lithosphere-ionosphere coupling. For all the events, particle precipitation phenomena were observed. Concerning the earthquakes, the electron bursts were measured from a few hours to some days before the events while for the eruption the electron bursts were observed subsequent to the paroxysmal phase. Since the subduction area and its neighbouring regions are intensely active, we are searching for a possible connection between ionospheric events and these tectonic events to forecast the consequent natural hazards. After the recent discovery of the connections between electron bursts and successive earthquakes in the Western and Eastern Pacific (Fidani, 2021; https://doi.org/10.3389/feart.2021.673105; Fidani. 2022; https://doi.org/10.3390/app122010528), we focus on the statistical correlation between Southern Pacific earthquakes and high-energy electrons. Due to this statistical correlation, we are able to find a conditional probability of a strong earthquake given an ionospheric observation to mitigate the associated risk.

The Swarm three-satellite mission by ESA was initially designed with its origina... more The Swarm three-satellite mission by ESA was initially designed with its original configuration to monitor and study the geomagnetic field and the state of the ionosphere and magnetosphere. For the first time, in 2017, the Swarm satellites detected some pre- and post-earthquake magnetic field anomalies on occasion of the 2015 Nepal M7.8 earthquake. Interestingly, the cumulative number of satellite anomalies and the cumulative number of earthquakes behaved similarly with the so-called S-shape, providing an empirical proof on the lithospheric origin of the satellite anomalies (De Santis et al., 2017; doi:10.1016/j.epsl.2016.12.037). Following the same approach, other promising results were obtained for 12 case studies in the range of 6.1-8.3 earthquake magnitude, in the framework of the SAFE (SwArm For Earthquake study) project funded by ESA (De Santis et al., 2019a; doi:10.3390/atmos10070371). In 2019, almost five years of Swarm magnetic field and electron density data were analysed with a Superposed Epoch and Space approach and correlated with major worldwide M5.5+ earthquakes (De Santis et al. 2019b; doi:10.1038/s41598-019-56599-1). The analysis verified a significant correlation between satellite anomalies and earthquakes above any reasonable doubt, after a statistical comparison with random simulations of anomalies. The work also confirmed the Rikitake (1987) law, initially proposed for ground-based data: the larger the magnitude of the impending earthquake, the longer the precursory time of anomaly occurrence in ionosphere from satellite. A more recent investigation (Marchetti et al. 2022; doi:10.3390/rs1411264) over a longer time series of data, i.e. 8 years, confirmed the same results. Furthermore, we demonstrated in several case studies (e.g., Akhoondzadeh et al. 2019; doi: 10.1016/j.asr.2019.03.020; De Santis et al. 2020; doi:10.3389/feart.2020.540398) that the integration of Swarm satellite data with other kinds of measurements from ground, atmosphere and space (e.g., CSES-01 satellite data) reveals a chain of processes before the mainshocks of many seismic sequences.&#160;

Statistical analyses of NOAA POES data have recently evidenced electron burst lo... more Statistical analyses of NOAA POES data have recently evidenced electron burst losses 1.5-3.5 h before strong earthquakes in the West Pacific and 55-59 h before strong earthquakes in East Pacific. The conditional probability of a strong seismic event after an ionospheric loss event was calculated depicting possible scenarios in both areas. It presented a geohazard risk reduction initiative that can gain valuable preparation time by adopting a probabilistic short-term warning a few hours prior, especially for tsunamis in those dangerous areas. As electron losses were detected in the same region both for West and East Pacific earthquakes, the probability of a strong event in the West Pacific would be first considered and vanish in less than 4 h. Then, after considering the seismic activity, a statistical evaluation of a disastrous event for the East Pacific coast is generated, so defining a time-dependent increase in conditional probability.

AGU Fall Meeting Abstracts, Dec 1, 2015

Géomorphologie : relief, processus, environnement

Remote Sensing

On 20 December 2021, after six quiet years, the Hunga Tonga–Hunga Ha’apai volcano erupted abruptl... more On 20 December 2021, after six quiet years, the Hunga Tonga–Hunga Ha’apai volcano erupted abruptly. Then, on 15 January 2022, the largest eruption produced a plume well registered from satellites and destroyed the volcanic cone previously formed in 2015, connecting the two islands. We applied a multi-parametric and multi-layer study to investigate all the possible pre-eruption signals and effects of this volcanic activity in the lithosphere, atmosphere, and ionosphere. We focused our attention on: (a) seismological features considering the eruption in terms of an earthquake with equivalent energy released in the lithosphere; (b) atmospheric parameters, such as skin and air temperature, outgoing longwave radiation (OLR), cloud cover, relative humidity from climatological datasets; (c) varying magnetic field and electron density observed by ground magnetometers and satellites, even if the event was in the recovery phase of an intense geomagnetic storm. We found different precursors of...

&lt;p&gt;Since late 2002, a network of six automatic monitoring stations is opera... more &lt;p&gt;Since late 2002, a network of six automatic monitoring stations is operating in Tuscany, Central Italy, to investigate possible geochemical precursors of earthquakes. The network is operated by the Institute of Geosciences and Earth Resources&amp;#160;(IGG), of&amp;#160;the National Research Council of Italy&amp;#160;(CNR), in collaboration and with the financial support of the Government of the Tuscany Region. The areas of highest seismic risk of the region, Garfagnana, Lunigiana, Mugello, Upper Tiber Valley and Mt. Amiata, are currently investigated. The monitoring stations are equipped with multi-parametric sensors to measure temperature, &lt;em&gt;pH&lt;/em&gt;, electric conductivity, redox potential, dissolved CO&lt;sub&gt;2&lt;/sub&gt; and CH&lt;sub&gt;4&lt;/sub&gt; concentrations in spring waters. The elaboration of long-term time series allowed for an accurate definition of the geochemical background, and for the recognition of a number of geochemical anomalies in concomitance with the most energetic seismic events occurred during the monitoring period (Pierotti et al., 2017).&lt;/p&gt;&lt;p&gt;In an attempt to further exploit data from the geochemical network of Tuscany in a seismic risk reduction perspective, here we present a new statistical analysis that focuses on the possible correlation between low to moderate seismic events and variations in the chemical-physical parameters detected by the monitoring network. This approach relies on the estimate of a conditional probability for the forecast of earthquakes from the correlation coefficient between seismic events and signals variations (Fidani, 2021).&lt;/p&gt;&lt;p&gt;Seismic events (&lt;em&gt;EQ&lt;/em&gt;) are classified according to a magnitude threshold, &lt;em&gt;M&lt;sub&gt;o&lt;/sub&gt;&lt;/em&gt;. We set &lt;em&gt;EQ&lt;/em&gt; = 0, if no seismic events were observed with &lt;em&gt;M &lt; M&lt;sub&gt;o&lt;/sub&gt;&lt;/em&gt;, and &lt;em&gt;EQ&lt;/em&gt; = 1, if at least a seismic event was observed with &lt;em&gt;M &gt; M&lt;sub&gt;o&lt;/sub&gt;&lt;/em&gt;. Chemical-physical (&lt;em&gt;CP&lt;/em&gt;) events were defined based on their appropriate amplitudes threshold &lt;em&gt;A&lt;sub&gt;o&lt;/sub&gt;&lt;/em&gt;, being &lt;em&gt;CP&lt;/em&gt; = 0 if the amplitude &lt;em&gt;A &lt; A&lt;sub&gt;o&lt;/sub&gt;&lt;/em&gt;, and &lt;em&gt;CP&lt;/em&gt; = 1 if &lt;em&gt;A &gt; A&lt;sub&gt;o&lt;/sub&gt;&lt;/em&gt;. Digital time series were elaborated from data collected over the last 10 years, where &lt;em&gt;EQs&lt;/em&gt; were declustered and &lt;em&gt;CPs&lt;/em&gt; detrended for external influences. The couples of events with the same time differences &lt;em&gt;T&lt;sub&gt;EQ&lt;/sub&gt; &amp;#8211; T&lt;sub&gt;CP&lt;/sub&gt;&lt;/em&gt;, between &lt;em&gt;EQs&lt;/em&gt; and &lt;em&gt;CPs&lt;/em&gt;, were summed in a histogram. Then, a Pearson statistical correlation coefficient &lt;em&gt;corr(EQ,CP)&lt;/em&gt; was obtained starting from the covariance definition.&lt;/p&gt;&lt;p&gt;A conditional probability for &lt;em&gt;EQ&lt;/em&gt; forecasting is estimated starting from the correlation coefficient in an attempt to use data from &lt;em&gt;CP&lt;/em&gt; network of Tuscany in a seismic risk reduction framework. The approach consists in an evaluation of &lt;em&gt;EQ&lt;/em&gt; probability in a defined area, given a &lt;em&gt;CP&lt;/em&gt; detection by the station in the same area. The conditional probability &lt;em&gt;P(EQCP)&lt;/em&gt;, when a correlation between &lt;em&gt;EQs&lt;/em&gt; and &lt;em&gt;CPs&lt;/em&gt; exists and time difference is that evidenced by the correlation, is increased by a term proportional to the correlation coefficient as&lt;/p&gt;&lt;p&gt;&lt;img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.9e880d2041e161891512461/sdaolpUECMynit/22UGE&amp;app=m&amp;a=0&amp;c=d3b4ea5ab3bdd75edb9db103b582241c&amp;ct=x&amp;pn=gnp.elif&amp;d=1" alt=""&gt;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;with respect to the unconditioned probability &lt;em&gt;P(EQ)&lt;/em&gt; when a &lt;em&gt;CP&lt;/em&gt; event is detected, where &lt;em&gt;P(CP)&lt;/em&gt; is the unconditioned probability of…

Frontiers in Earth Science, 2020