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Particle precipitation prior to large earthquakes of both the Sumatra and Philippine Regions: A statistical analysis
Accepted Manuscript Particle precipitation prior to large earthquakes of both the Sumatra and Philippine Regions: A statistical analysis Cristiano Fidani PII: DOI: Reference:
S1367-9120(15)00336-3 http://dx.doi.org/10.1016/j.jseaes.2015.06.010 JAES 2414
To appear in:
Journal of Asian Earth Sciences
Received Date: Revised Date: Accepted Date:
25 July 2014 18 February 2015 2 June 2015
Please cite this article as: Fidani, C., Particle precipitation prior to large earthquakes of both the Sumatra and Philippine Regions: A statistical analysis, Journal of Asian Earth Sciences (2015), doi: http://dx.doi.org/10.1016/ j.jseaes.2015.06.010
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Particle precipitation prior to large earthquakes of both the Sumatra and Philippine Regions: A statistical analysis
Cristiano Fidani1,2 [1]{Osservatorio Sismico “Andrea Bina”, Borgo XX Giugno 74, 06121 Perugia, Italy} [2]{Central Italy Electromagnetic Network, Via Fosso del Passo 6, 63847 San Procolo, Fermo, Italy} Correspondence to: Cristiano Fidani ([email protected])
Abstract A study of statistical correlation between low L-shell electrons precipitating into the atmosphere and strong earthquakes is presented. More than 11 years of the Medium Energy Protons Electrons Detector data from the NOAA-15 Sun-synchronous polar orbiting satellite were analysed. Electron fluxes were analysed using a set of adiabatic coordinates. From this, significant electron counting rate fluctuations were evidenced during geomagnetic quiet periods. Electron counting rates were compared to earthquakes by defining a seismic event L-shell obtained radially projecting the epicentre geographical positions to a given altitude towards the zenith. Counting rates were grouped in every satellite semi-orbit together with strong seismic events and these were chosen with the L-shell coordinates close to each other. NOAA-15 electron data from July 1998 to December 2011 were compared for nearly 1,800 earthquakes with magnitudes larger than or equal to 6, occurring worldwide. When considering 30 - 100 keV precipitating electrons detected by the vertical NOAA-15 telescope and earthquake epicentre projections at altitudes greater that 1,300 km, a significant correlation appeared where a 2 - 3 1
hour electron precipitation was detected prior to large events in the Sumatra and Philippine Regions. This was in physical agreement with different correlation times obtained from past studies that considered particles with greater energies. The Discussion below of satellite orbits and detectors is useful for future satellite missions for earthquake mitigation.
Introduction The process driving a seismic rupture may cause local surface deformation fields, rock dislocations, charged particle generation and motion, electrical conductivity changes, gas emission, fluid diffusion and electrokinetic, piezomagnetic and piezoelectric effects (Varotsos 2001). Charge carriers could be activated in dry rocks mainly by increasing external stress (Freund 2000). These phenomena have been considered as the main sources of the so-called seismo-electromagnetic emissions consisting of broadband electromagnetic (EM) fields observed at the Earth’s surface and in the near-Earth space, the neutral and ionized atmosphere, and the magnetosphere. Low Earth Orbit (LEO) satellite observations provide information on ionospheric and magnetospheric perturbations possibly caused by EM waves and, in particular, radiation belt particle precipitations (Aleksandrin et al. 2003), infrared emissions (Yurur 2006), temperature and density variations of the ions and electrons of the ionospheric plasma (Sarkar et al. 2007), and electric and magnetic field fluctuations (Bhattacharya et al. 2009). A LEO satellite, at an altitude of 200–2000 km, provides, in principle, a platform of observation extending over the entire affected region (Parrot 1995). Whereas, terrestrial observations of non-seismic phenomena linked to strong earthquakes (EQs) produce local perturbations, which can be strongly affected by several land and atmospheric variables, including anthropogenic disturbances, so not expressing the EQ preparation. 2
LEO environment has been studied from space using charged particle detectors to principally analyse solar cycle (Reeves et al., 2011). Auroral phenomena and cosmic ray (Baker et al., 2004; Adriani et al., 2009) were also investigated. Ionospheric disturbances linked to seismic activity were first observed around the time of the great Alaskan EQ (Davies and Backer, 1965) on March 28, 1964. Satellites measurements (Larkina et al., 1983) have confirmed the potential importance of this research, which provided medium and far field viewing points of lithosphere phenomena with respect to the EQ influence size. Several of the particle detectors used in solar studies have been used to investigate electron precipitation in connection with strong EQs (Sgrigna et al., 2005; Rothkaehl et al., 2006). They concerned the more stable inner (L < 2) Van Allen Belts (VAB) with respect to the outer radiation belts which strongly depend on geomagnetic activity (Millan and Thorne, 2007). These studies were based on data coming from satellite missions lasting only a few months or collected a few months with equal attitude data, therein providing weak evidence for correlations (Alexandrin et al., 2003; Sgrigna et al., 2005). These flux variations have been interpreted as pitch angle diffusions induced by Coulomb scattering (Walt and Farley, 1976) and resonant wave-particle interactions (Abel and Thorne, 1998). However, the theoretical explanation for the flux variations are still being elaborated upon (Albert and Bortnik, 2009). The National Oceanographic and Atmospheric Administration (NOAA) and the National Aeronautics and Space Administration (NASA) have jointly developed a series of Polar Operational Environmental Satellites (POES). These Advanced TIROSN (ATN) spacecrafts are named after the prototype satellites, TIROS-N (Television Infrared Observation Satellites), which have flown since 1978 (Davis, 2007). The system consists of pairs of satellites, which ensure that every part of the Earth is regularly observed at least twice 3
every 12 hours from about 800 km altitude. Starting with the NOAA-15 satellite in 1998, the satellites have an upgraded version of the Space Environment Monitor (SEM-2). The SEM-2 contains two sets of instruments that monitor the energetic charged particle environment near the Earth. These detect and monitor the flux of energetic ions and electrons in the atmosphere and the particle radiation environment at the altitude of the satellite. The NOAA particle database had already been studied by means of particle bursts in connection with global seismic activity during quiet solar periods (Fidani et al., 2008). Exceptional increasings of particle fluxes were discovered in connection with the largest quakes that struck the Indonesian region over the last two decades. Contiguous particle bursts were studied in order to distinguish correlations with seismic activity from seasonal variations of particle flux and solar activity, and EQ clustering was initially included to study the types and causes of false correlations (Fidani et al., 2010). Their auto-correlations were also investigated (Fidani et al., 2012). The particles registered by NOAA satellite fall into a lower range of energy, compared to some previously observed particle precipitations associated with EQs (Galperin et al., 1992; Boskova et al., 1994). Abnormal bursts of energetic electrons preceding EQs for the first time have already been presented having the same NOAA energy range (Ruzhin et al., 1996; 1997 and 1998). In these works simultaneous data of energetic electrons at 40 keV–1.2 MeV and magnetic and electric components in the ELF-VLF bands (0.1–16 kHz) were measured on board of INTERCOSMOS 19 over the epicentre of the prone EQ. The NOAA particle database contains from 30 keV to a few MeV electron and proton counting rates (CRs) (Evans and Greer, 2004), which is also similar to DEMETER IDP (Sauvad et al., 2006). A criterion to select electron bursts has been applied on high energy electron flux data at 70 keV–2.34 MeV recorded by IDP on DEMETER (Zhang et al., 2013). The calculated time 4
differences between CRs by IDP and EQs were longer than those of previously published works, and increases in energetic electron fluxes were observed a few days before EQs (Anagnostopoulos
et
al.,
2010),
accompanied
by
broadband
kHz
emissions
(Anagnostopoulos et al., 2012; Zhang et al., 2012) and compared to the VLF transmitter contributions (Sidiropoulos et al., 2011; Sauvad et al., 2014). Furthermore, a statistical similarity between high energy charged particle fluxes detected by DEMETER and EQs has also been reported (Wang et al., 2014). Being so, complete correlation analysis was applied to NOAA database to verify a non-casual association of particle precipitation with large seismic events. This study has started successfully with NOAA-15 database only, and needs of further development to be applied with other NOAA databases which satellites cover slightly different orbits.
NOAA particle data and its preparation for the analysis The NOAA-15 has been operational since July 1998, on a near Sun-synchronous circular orbit (7:30 - 19:30 LT) with a period of 102’, 98.5° inclination and an altitude of about 800 km (Davis, 2007). Over this operating time, the satellite LT was constant for only two years; gradually drifting, reaching 5:30 - 17:30 LT in 2010. This study was based on the CR measurements provided by the particle telescopes, which are part of the Space Environment Monitor (SEM-2) (Evans and Greer, 2004). The Medium Energy Proton and Electron Detector (MEPED) is composed of eight solid-state detectors measuring proton and electron fluxes from 30 keV to 200 MeV (Evans and Greer, 2004) which include the radiation belt populations, energetic solar particle events and the low energy portion of the galactic cosmic-ray population. The eight detectors consist of two proton telescopes which monitor the flux in five energy bands in the range 30 keV to 6.9 MeV, two electron 5
telescopes which monitor the flux in three energy bands in the range 30 keV to 2.5 MeV, and four omnidirectional detectors sensitive to proton energies above 16 MeV. One telescope views at an angle of 9° with respect to the local zenith. The second telescope views in the orthogonal direction. The small solid state electrons and protons detectors have nominal geometric acceptances of 0.1 cm2sr and opening angle apertures of ±15°, which have been recently confirmed by Monte Carlo simulations (Yando et al., 2011). The simulations also supported the relevance of opposite charge contamination effects. The 0° and 90° telescope electron energy bands have been analysed, both in the three original channels from 30, 100 and 300 to 2,700 keV, as well as three differential channels: 30 to 100 keV, 100 to 300 keV and 300 to 2,700 keV. Proton contamination was excluded only from the lower energy range, taking into account both observations (Asikanen and Mursula, 2008) and simulations (Yando et al., 2011). Nonetheless, the fluxes were corrected for proton contamination (Rodger et al., 2010) by using software downloaded from the Virtual Radiation Belt Observatory (http://virbo.org/POES#Processing). MEPED energy bands of the 0° and 90° proton telescopes and omnidirectional electron and proton detectors were corrected and analysed in a similar way. Detector energy band increased the detection energy intervals of a few 10 keV due to degradation (Evans et al., 2008). Inner radiation belt particle flux increases by over one order of magnitude were always observed during the main phase of magnetic storms (Tadokoro et al., 2007), which occur in rapid response (< 1 day) of the inner radiation belts to the solar wind velocity peak (Vassiliadis, 2008). Sudden Ionospheric Disturbances (SID) were considered possible sources of particle flux perturbations as in past discussions (Sgrigna et al., 2005). They are produced in the ionosphere by enhanced solar radiation during solar flares, which include VLF-LF effects (Deshpande et al., 1972), and cosmic rays (Inan et al., 2007b). EM whistler 6
waves injected by lightning discharges can scatter the energetic electrons and cause them to precipitate out of the radiation belts (Abel and Thorne, 1998). A seasonal dependence of energetic particle precipitation in relation to lightning activity was observed (Gemelos et al., 2009). In order to include the geomagnetic, meteorological and extraterrestrial influences on the particle fluctuations, the CR data were associated to daily averages of the geomagnetic Ap index and SID (http://www.aavso.org/solar-sids), as well as three hour averages of the Ap index (ftp://ftp.ngdc.noaa.gov/STP/GEOMAGNETICDATA/APST AR/apindex). The CR exclusions from the correlation analysis occurred when geomagnetic indexes overcame thresholds, which were calculated by annual and 11-years Sun particle modulations (Fidani et al., 2012). As CR fluctuations originating in the magnetosphere occur in sub-storm activity also, the goodness of the selected quiet geomagnetic days was verified requiring Dst variations (http://wdc.kugi.kyoto-u.ac.jp/dst_final/index.html) to be less than 30 nT. From July 1, 1998 to December 31, 2009, data were downloaded from NOAA (http://www.ngdc.noaa.gov/stp/satellite/poes/dataaccess.html) and examined to exclude uncorrected instrument operations through their corresponding flags. The USGS catalogue (http://earthquake.usgs.gov/earthquakes/search/) was used to retrieve EQ informations. From July 1998 to December 2009, about 2 10 4 earthquakes with M ≥ 5 occurred worldwide. The earthquake list has been adjusted to eliminate foreshocks and aftershocks.
Significant CR fluctuations Data processing started by building a map of daily CRs in the geomagnetic invariant space (Walt, 1994). In fact, CRs are strongly variable along the satellite orbit, while CR contribution from the same geomagnetic invariant space intervals are more regular in 7
stable periods. Adiabatic invariants allow for the determination of different geographical positions where particles are expected to possess the same dynamics and where CR deviations can be studied (McIlwain, 1966); thereby permitting to search for fluctuations. Particle dynamics were described by geomagnetic field at mirror points Bm and the magnetic shell parameter L. The parametrization of Bm = B/cos2α with respect to the geomagnetic field B and the pitch angles α at each other position were introduced, so to represent CR in a 4-dimensional matrix (t;L;α;B) including time. The pitch angle is equal to the difference between the particle telescope and geomagnetic field directions. However, the SEM-2 detectors have a finite aperture of 30° so that α can be defined only very approximately. The pitch angle intervals Δα were chosen to be 12 equal intervals of 15°. The introduction of B parameter was useful because it allowed to control the strong CR spatial variability when it entered the South Atlantic Anomaly (SAA) (Asikanen and Mursula, 2008). B covered the range 16 μT to 47 μT along the satellite orbit and was divided into nine nonidentical intervals: shorter where CR was greater, to better control the CR spatial variations (Fidani et al., 2008). The initially considered ΔB intervals were: 16.0-17.5 μT, 17.5-19.0 μT, 19.0-20.5 μT, 20.5-22.0 μT, 22.0-25.0 μT, 25.0-28.0 μT, 28.0-32.5 μT, 32.5-37.0 μT and 37.047.0 μT. Geomagnetic B-field, L-shell and α-pitch values had already been provided by NOAA (Evans and Greer, 2004). However, B-field and L-shell were re-evaluated on the NOAA-15 orbit utilising the latest International Geomagnetic Reference Field (IGRF-11) model (http://www.ngdc.noaa.gov/IAGA/vmod/igrf.html) (Finlay et a., 2010a), to include recent model corrections of past years as well as height variations along the satellite orbits. The IGRF-11 errors for 2010 are expected to be slightly larger than those of previous years; approximately 10 nT (Finlay et al., 2010a). Regarding the predictive model, retrospective 8
analysis of previous predictions has shown that errors of up to 20 nT yr -1 are likely (Maus et al., 2005; Finlay et al., 2010b). In this work, data from the inner VAB, restricting the analysis at 1.0 ≤ L ≤ 2.2, was only used. It was verified that the errors induced by a B uncertainty of 20 nT on the calculated L-shells was always less than 0.05 in the considered subspace. For this, an L-shell step of ΔL = 0.1, defining 12 equal intervals, was utilized. NOAA-15 CR records data every two seconds and, due to the small area particle detectors, CR is usually very low outside the SAA or far from geomagnetic poles. Thus, a matrix was created by accumulating eight-second CR histograms, considering the sum of four consecutive measurements. The distributions of the CRs followed a Poisson pattern. The B, L and α ranges covered in 8 sec were verified to be less than 200 nT, 0.04 and 1.1 degrees, respectively. These were all shorter than the ΔB, ΔL and Δα intervals considered here. Being that the 8 sec B range was greater than B uncertainty, the CR contribution coming from the adjacent B intervals was checked and it was always less than 10%. Observing the CR fluctuations, the increases in CRs concerned not only single isolated 8 sec events, but also multiple consecutive events lasting several minutes of satellite orbit. However, given that CRs were likely to belong to different adiabatic intervals in multiple events, the significance of the CR fluctuations was tested on single 8 sec events, by comparing them to Poisson fluctuations from the same adiabatic regions. To obtain less than 1% probability that the CR fluctuations xs were of a statistical origin, the condition P(xs) < 0.01 had to be satisfied by calculating the number of sigma nσ (Fidani et al., 2008). The x was considered to be a significant CR fluctuation with probability greater than 99% if x > xs, corresponding to the same adiabatic intervals. Distributions at all regions were analysed and their averages calculated in the adiabatic intervals. The time interval used to calculate the averages had to be long enough in order to consider a minimum number of samples, or increasing invariant 9
space dimension must be chosen. The small detector acceptance of NOAA satellites required long time and large adiabatic intervals to obtain sufficient statistics. As a strong response of inner VAB to the solar activity was observed in a daily time interval (Vassiliadis et al., 2002), CR averages xm = xm(Day;ΔL;Δα;ΔB) were calculated for each day sub matrix histogram. In order to obtain a more accurate reading of the particle dynamics, a small cell dimensions of adiabatic invariants should be preferred. Being so, an interpolated average xmi(Day;L;Δα;B) was used to map continuously L and B from the centres of their intervals. In this way, cell sizes were not reduced and daily averages were accepted only for cells having at least 20 satellite passages/day. As CRs strongly increase near the SAA, a cubic non-linear algorithm was used to better interpolate xm. Starting from averages, and variances, it was possible to verify if NOAA MEPED detected any significant CR fluctuations along the entire satellite orbit. In fact, each x value of CR was compared with the interpolated average at its invariant position; and then the associated Poisson probability was calculated in order to select non-Poissonian fluctuations. CRs are neither stable inside the external VAB because of the influence of variable solar wind nor inside the internal VAB as interactions with EM waves influence particle fluxes (Datlowe, 2006). The possible influence of earthquakes on particle fluxes, detected by NOAA satellites, was easier to study in a more stable low CR regime region. Low CRs are detected outside VABs and CR fluctuations are due to particles leaving the trapping conditions from the inner VAB, i.e. precipitating particles. Being so, poles and SAA regions data were excluded from the analysis. Based on particle flux stability, a L-shell maximum limit of 2.2 was chosen to exclude data in and around the poles, while a minimum value of of 20.5 μT for the geomagnetic field was chosen to exclude data in the SAA region. These
10
limitations excluded the areas south and north above 50° latitude and a birdhead-shaped area extending over the South Atlantic Ocean and South America, see Figure 1. Precipitating particles are those which interact with atmosphere and are absorbed. They follow geomagnetic lines in their spiral motions, having the lowest altitudes along the geomagnetic line equal to the bouncing altitude h. Since VAB particles move also along the L-shells by longitudinal drift, and the geomagnetic axis is departed from the terrestrial axis, they experience variable bouncing altitudes along their drift motion. When h become lower than 100 km, while particle drifting, the particle will interact with the atmosphere and will be absorbed. Precipitating particles can be then determined calculating their minimum mirror point altitudes, hmin, and verifying that it will be under 100 km along the drift period. The UNILIB library was used to calculate mirror points of detected particles (Krungasky, 2003). The hmin for the electrons of not excluded areas in Figure 1 was less than 100 km for 95% cases, and it was less than 200 km for all cases. At these altitudes the absorption length for electrons having energies between 30 keV and 3 MeV assures that particles will be absorbed in the residual atmosphere. Precipitating electrons were concentrated in a fairly small region creating a dovetail-shape, located up until 80 degrees longitude from the westward edge of SAA. This dovetail shape is green coloured in Figure 1. The electron flux distribution concentrated westwards of SAA means that bouncing points are far above the satellite, just below VAB, at the opposite longitudes of SAA; being so, no CR can be detected. When lower bouncing points begin to cross the NOAA altitude as longitudes approach SAA, some CR can be detected. Maximum CR of non-trapped electrons can be detected immediately west of SAA, where the maximum number of electrons have bouncing altitudes under the satellite orbit and above the atmosphere. These bouncing altitudes fall below 100 km in the SAA. The process can be represented by coloured areas in Figure 2. 11
The space-time structure of the detected electrons consist of series of consecutive CRs corresponding to time intervals lasting up to a few minutes along the orbit. There are rarely two series of consecutive selected CRs, due to the crossing between satellite trajectory and the dovetail shape during a satellite semi-orbit. In fact, the space time structure usually is made up of one or more non-consecutive detected CRs. Moreover, the distribution of events made up of consecutive CRs which are considered as one, produces non-Poissonian correlation distributions with earthquakes (Fidani et al., 2010). This type of distribution defined not completely independent correlation events. Given this, it was chosen to define a Particle Perturbation (PP) event as the detection of at least a significant CR in the entire satellite semi-orbit. The duration of a satellite semi orbit is about 51 minutes but, as L-Shell was limited at L ≤ 2.2, a semi orbit lasted about 20 minutes. PP are different from particle bursts as the latter were defined as short-term increases of particle fluxes. PP can be made up of several particle bursts. Due to the fact that CR selection had effective L ≤ 1.4, the maximum time length of PP was about 12 minutes. PP times were defined as the average detection times of the selected CRs during the semi orbit of the satellite.
The correlation between particle precipitations and EQs L-shell parameter was considered to define a volume where a physical interaction, whatever it is, can connect the seismic and ionospheric activities. L-shells relative to the significant CRs, referred to as LCR, were compared with some L-shell values, referred to as LEQ, assigned to each EQ. To define LEQ, the EQ geographical positions were projected along the vertical at fixed altitudes, so to evidence some positions in the ionosphere. The corresponding L-shells of ionosphere positions were calculated by the same geomagnetic
12
software used for the particle adiabatic calculus. The subsets of EQs and CRs meeting the condition ΔL = |LEQ − LCR | ≤ 0.1,
(1)
were further analysed. Expression (1) links EQs with all precipitating CRs belonging to well defined L-shell, even if the satellite detects them at different latitudes and longitudes. Being each PP defined as a set of significant CRs detected along the same satellite semi orbit near the equator, and the time difference between the CRs in a PP up to 12 minutes, the CR Lshells will always be not very different. Because of distant CRs are detected at magnetic conjugated points of such semi-orbits, condition (1) is generally satisfied for all CRs of each PP and the entire PP can be compared to an EQ with ΔL = |LEQ − LPP | ≤ 0.1.
(2)
Correlation was defined by filling an histogram with the differences TEQ-TPP, between the EQ time TEQ and the PP time TPP, only for those seismic events and particle precipitations which satisfied (2). This process was performed at different altitudes of EQ epicentre projections, by calculating LEQ at each altitude from -600 km up to 3200 km in increments of 100 km. Negative depths were also considered to verify if some kind of interaction could be plausible between EQ occurring in the SAA and any geomagnetic line. To reduce the effects of solar activity, low values of Ap indexes were chosen for three and 24 hour intervals when particle precipitation in the inner VAB did not occur (Fidani et al., 2012). These defined the maximum thresholds to exclude the corresponding intervals of data. Furthermore, PP data were considered “Sun influenced” when SID occurred within the same day; therefore, such days were excluded from the correlation. In past works (Fidani et al., 2010) the Ap daily threshold was fixed at 18, while the three hour Ap indexes were considered with a threshold fixed at 13. Finally, due to long period Sun influences which 13
had already been reported in Fidani works, annual and eleven year modulations of the thresholds associated with the two integer Ap indexes, were also applied to exclude data with geomagnetic indexes greater than the functions: Apm = fm + gm sin(Dy)+[km - lm sin(Dy)] cos(Ds),
(3)
where m = 1 for three hour Ap and m = 2 for 24 hour Ap, Dy = y (year-y0) and Ds = s (days0). The complete NOAA-15 electron CR database, from the beginning of July 1998 to December 2009, was processed and correlated to EQ events during the same period by applying the rule (2). The correlation histogram was filled without any reference to the EQ magnitudes or PP intensities. Being so, time bins were filled with unit weights. Time intervals ranging from a few minutes up to few days were tested to verify the stability of the TEQ-TPP histogram. A stable correlation histogram was obtained for time intervals around one hour, in agreement with previous studies (Sgrigna et al., 2005). A set of parameters was used to study the correlation histogram which included four geomagnetic indexes: 24 hour Ap, three hour Ap, SID and Dst. Three ionospheric particle parameters ΔL, CR pitch angle α plus mirror altitude h, and EQ parameters of magnitude and depth were also used to study the correlation histogram. Sun index modulations were applied with the aims of decreasing solar influence. The optimal modulations were obtained from (3) with coefficients: y = 0.37, s = 0.0172, y0 = 1996, s0 = 27, f1 = 14.8, f2 = 11.1, g1 = 1.3, g2 = 0.8, k1 = 7.5, k2 = 2.1, l1 = 1.3 and l2 = 0.1. The threshold functions are shown in Figure 3. Even if this modulation corresponds to very small geomagnetic indexes, it was checked that the obtained correlation between EQs and PPs could not be due to sub-storm events by verifying that Dst variations > -27 nT, during the 20 hours before the time of CRs contributing to the correlation. 14
The analysis was repeated for all the EQ altitude projections. Several correlation peaks appeared from the first electron energy channels corresponding to 30 keV - 2.7 MeV or differential channels corresponding to 30 keV - 100 keV detected by the vertical telescope. Whereas, correlation peaks did not appear from other electron energy channels utilising vertical telescope or from any electron energy channels utilising horizontal or omnidirectional telescopes. Furthermore, no correlation peaks up until now have appeared from similar NOAA proton analyses. The same algorithm was applied to the particle databases of all other polar NOAA satellites, but no correlation peaks were observed from the first electron energy channels, nor from any other energy channels or particle types. The orbit of NOAA-15 characterised by sunrise/sunset times was the only one different from the other NOAA POES orbits, which were all characterised by day/night times. To analyse all the NOAA satellite databases, a further step in the analysis should be made in the algorithm to select anomalous CRs from such databases which take in account the different particle flux regimes observed during day or night. Two correlation plots of differential channel 30 keV - 100 keV electrons are shown in Figure 4, when the EQ epicentre projections were at 1800 km and 2000 km altitudes. The plot ranges ±1 days in time difference between EQs and semi orbit PPs; the average values calculated on ±3 days are 16 and 13.5 correlation events respectively, with a standard deviations of 4.0 and 3.7 events. All the correlation distributions at any altitude were Poissonians. Significant correlation peaks appear between 2 and 3 hours of positive time difference, which means that the PP time is observed before the corresponding EQ. The peak starts to be significant when considering EQ projections above 1300 km altitudes, see Figure 5, where red and orange areas in Figure 2 coherently shows mirror altitudes above 1300 km at EQ longitudes. The correlation was maximised by using EQ magnitude M ≥ 6 15
only. Pitch angle restrictions required that the particles were precipitating, meaning that their values were mostly in the loss cone. Specifically, particles in these PPs concentrated in intervals around 65° and 135°. Results in Figure 4 were obtained neglecting pitch angles far from these values, with 30° ≤ α ≤ 80° and 120° ≤ α ≤ 160°. The EQ depths lower than 200 km were considered, that is they had to be close to the surface. The correlation peaks were about 4.5 standard deviations above the average values. An increase in the statistical significance of the correlation peaks was observed compared to altitude, see Figure 5. This began to exceed 3 standard deviations at 1300 km and reached a maximum at about 2700 km, where the number of standard deviations was close to 5.5. However, the number of correlated events started to decrease for EQ altitude projections above 1200 km and became very low for altitudes greater than 2400 km. Figure 5 continuous black line shown the standard deviations of 2 – 3 hour correlation peaks, the dashed line shown the averages, and continuous red line shown the number of standard deviations necessary to have a correlation peak significance of at least 99%. Both correlation calculus using a randomized space and time distributions of EQs were also checked by using same EQ times, and the same EQ epicentres, respectively. In all the randomised cases, the previously obtained correlation peaks completely disappeared.
Discussion and suggestions for future missions Epicentre locations whose EQs were correlated with PPs, were those in both the Indonesian and Philippine Regions, with few in South America, see Figure 6. Great EQs in Japan were not correlated with PPs in this study, thus decreasing of electron precipitation above the future epicentres a few hours before the occurrence of the seismic events obtained by a different analysis (Anagnastopoulos et al., 2012) was not observed. EQs occurred 16
principally in the sea, which was in contrast with previous results (Sgrigna et al., 2005) and in agreement with past EM results (Nemec et al., 2009). The epicentre positions of all these EQs were located in the positions where the more frequent strong seismic events occurred during the studied period. These EQ positions were also causally physically linked with electron perturbations occurring several hours before the main shocks which were located west of detection positions. In fact, when perturbations of electron trapped conditions occurred at EQ projection altitudes, they could have changed both electron pitch angles and bouncing altitudes at locations westwards of SAA. Furthermore, when considering similar energy range to NOAA MEPED (Ruzhin et al., 1996; 1997, 1998) it was obtained that the shape correlated bursts were registered 2 - 2.5 hours before the seismic event. Whereas, when considering detectors sensitive to much greater energies of previous studies it was obtained that correlated bursts were registered 4 - 5 hours before EQs. This latter observation suggests that the drift velocity of detected electrons will be generally lower than previous cases. Its corresponding electron drift periods were calculated using the expression: Td=
1.05 1 . E ( MeV ) L 1+ 0.43sin α eq
(4)
For 30 keV and 100 keV, the results from (4) were 21 hours and 6 hours, respectively. These electrons belonged to the low L-shell range of 1.15 < L < 1.35, as in past works. The electrons which produced the correlation met satellite vertical detectors in the drift loss cone near the SAA about 2 - 3 hours before the EQ time, after having drifted eastwards. This means that the perturbations had to have started much earlier. In fact, it was calculated that an earlier time between 2 - 7 hours was necessary for electrons to cover about 120° which divided EQ epicentre longitudes of Sumatra and The Philippines from the detection area;
17
with the electrons moving eastwards at drift velocity calculated by (4). Being so, if the perturbations which caused electron precipitations from inner VABs occurred above the EQ epicentres in the ionosphere, they anticipated the EQ times by 4 - 10 hours. This was in agreement with past experiments, showing that the simple expression (4) produced drift periods of a few minutes for MeV electrons, where correlations were calculated to be 4 - 5 hours. Therefore, larger early time correlations obtained in past studies could have been caused by the same type of perturbations on VAB MeV electrons which drifted faster. These electrons reached satellite particle detectors a few minutes after the perturbations occurred. The relationship of ELF-VLF electric field intensity and its spectra with energetic electrons flux density measured on board of INTERCOSMOS 19 were investigated, and the correlation coefficient between the electron flow and electric component of the EM field bursts were 0.60-0.72 (Rutzhin et al., 1996; 1997). It was the first reliable evidence of sufficiently close connection of the VLF wave with the radiation belt particles occurring before strong earthquakes. The Authors concluded that stable pitch-angle distribution of these particles could be disturbed as a result of it’s scattering on the electric field fluctuations over future earthquake epicentre. A possible process to link the Earth surface to space in vertical direction was presented (Pulinets et al., 2007) and could explain the observed significant CRs fluctuations at NOAA altitudes. It can be argued from the above considerations that MeV electrons are better indicators for an impending earthquake in the Sumatra and The Philippine regions, as PP are detected much earlier than PP relative to keV electrons. To this regard, a weak result was obtained suggesting a correlation between MeV electrons and EQs, as past works concerned only few months of data. However, this result suggests that a satellite missions dedicated to EQ early 18
warnings should be equipped with MeV electron detectors. Moreover, the acceptance area of electron detectors for a EQ dedicated mission would need to be at least two order greater than NOAA acceptance areas, as the electron flux was two order minor at MeV energies (Freden, 1969). Satellite altitudes and orbits were also determinant in increasing PPs detection. Figure 1 shows that the active area, where NOAA satellite detected PPs, formed a dove-tail pattern westwards of SAA. This area covered about 70° in latitude and about 150° in longitude. Being so, an equatorial orbit should be preferred for a particle detector to maximise the time permanence in the active area, having a gain of about 2 with respect to NOAA POES. However, it should be remembered that a positive correlation result was obtained only in the NOAA-15 database analysis, which was operational at sunrise/sunset times and not for other NOAA satellites, which have day/night orbits. An equatorial orbit for a particle detector is subject to day/night exposure also, thus electron CRs should be analysed using an algorithm taking into account day/night flux variations. Finally, Figure 2 shows that the higher the satellite altitude the greater the longitude extensions will be where electron mirror points are under the satellite. Consequently, this higher altitude increases the active area. Furthermore, an increase in area positions, where the satellite is inside the inner VAB, determined by B = 20.5 μT, also occur with altitude. Comparing the altitudes of constant B = 20.5 μT for low L-shells (Venkatesan, 1965) with the altitude of mirror points, it is possible to suggest that the best detection altitude is between 900 – 1000 km, as it guarantees the greatest longitude extension of the active area outside the inner VABs.
Conclusions
19
By using more than 11 years of NOAA particle data, a statistically significant correlation between extended perturbations of electron CRs and large EQs was observed. The precipitating low L-shell electrons phenomena occurred 2 - 3 hours before main shocks. This result supports the hypothesis that there exists a link between ionospheric and surface seismic activities, when EQ depths are less than 200 km (Sgrigna et al. 2005, Nemec et al. 2009). This result is further bolstered by the fact that different satellite data sets were used in past studies. Contrary to past particle precipitation results, this correlation was observed for both EQs in the sea and in the mainland. Based on the drift period (4), and hypothesising an interaction region above the epicentre of Sumatra and The Philippine regions, the times of EQs - ionosphere interactions occurred 4 -10 hours before the EQ times. Future missions would need detectors to be sensible to MeV electrons with acceptance areas two orders greater than NOAA detectors. These detectors would have to be vertical with an aperture of more than 30° to maximise acceptance in every trajectory; even if, better direction and energy resolutions of every particle would be useful to trace their past trajectories. The satellite orbit which maximises the area of precipitating electron detection is an equatorial orbit with altitudes between 900 and 1000 km. Further studies are needed to apply the above correlation analysis to the other satellites, taking into account the day/night effect on particle fluxes.
Acknowledgements I would like to thank the “Consulta delle Fondazioni delle Casse di Risparmio Umbre” for supporting this study. Also, I would like to express my thanks to Craig J. Rodger and Janet Green from NOAA for their useful codes to subtract the proton contamination of electron 20
channels. Additionally, I would like to express my gratitude to M. Kruglanski for the library to calculate bouncing altitudes and anonymous Referees for the useful suggestions. The publication fee is supported by the iSTEP project NSC102-2628-M-008-001.
21
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Figure 1. The geographical area considered in this analysis is evidenced by bright colours while high flux areas are colour matted. The wave-shaped limits of polar areas are due to the geomagnetic field inclination compared to the earth axis while the birdhead-shaped area in the centre represents the interior of inner VAB at NOAA altitude. Colours indicate the average annual electron flux detected at 30-100 keV, 0° NOAA-15 on board telescope.
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Figure 2. The altitudes of mirror points for electrons at L = 1.2 are indicated by continuous and dashed lines compared to NOAA-15 and atmosphere altitudes. Green coloured areas indicate altitude/longitude regions where there are bouncing points of electrons that are detectable by NOAA-15 satellite. The yellow to red areas are the more frequent possible interaction areas between EQs and PPs. The SAA crossing the mirror points is delimited by thick vertical lines, continuous for northern hemisphere and dashed for southern hemisphere.
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Figure 3. Geomagnetic thresholds calculated by (3) to reduce the Sun effect on electron burst activity; the three hour Ap threshold is shown on the left and 24 hour Ap threshold is shown on the right.
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Figure 4. Correlation time histograms between EQs and PPs at 1800 km, up, and 2000 km, down, of EQ epicentre projections; the significances of 2 – 3 hours correlations are 4.4 and 4.5 standard deviations for up and down plots respectively.
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Figure 5. The number of standard deviations (Nsd) above the correlation average (Ave) of 2 - 3 hour correlation peak is indicated by a continuous line compared to EQ epicentre projection altitudes; the dashed line indicates the average correlation; the standard deviation threshold to obtain 99% probability is lower than Nsd for EQ projection altitudes greater than 1300 km, as indicated by the red line. The 5.5 standard deviation limit was reached for EQ epicentre projection altitudes of about 2700 km, even if this limit was obtained with a very low number of events.
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Figure 6. Epicentres of EQs that contributed to the 2 - 3 hour correlation are reported here; with a major concentration in the Indonesian and The Philippine regions.
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Highlights
NOAA particle data analysis demonstrated a correlation with strong earthquakes Drift loss cone electrons from inner belts detected 2–3 hours before quake times The correlation regards shallow earthquakes of Sumatra and Philippine regions 30–100 keV electrons disturbed above 1300 km altitudes 4–10 hours before earthquakes
37
S1367-9120(15)00336-3 http://dx.doi.org/10.1016/j.jseaes.2015.06.010 JAES 2414
To appear in:
Journal of Asian Earth Sciences
Received Date: Revised Date: Accepted Date:
25 July 2014 18 February 2015 2 June 2015
Please cite this article as: Fidani, C., Particle precipitation prior to large earthquakes of both the Sumatra and Philippine Regions: A statistical analysis, Journal of Asian Earth Sciences (2015), doi: http://dx.doi.org/10.1016/ j.jseaes.2015.06.010
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Particle precipitation prior to large earthquakes of both the Sumatra and Philippine Regions: A statistical analysis
Cristiano Fidani1,2 [1]{Osservatorio Sismico “Andrea Bina”, Borgo XX Giugno 74, 06121 Perugia, Italy} [2]{Central Italy Electromagnetic Network, Via Fosso del Passo 6, 63847 San Procolo, Fermo, Italy} Correspondence to: Cristiano Fidani ([email protected])
Abstract A study of statistical correlation between low L-shell electrons precipitating into the atmosphere and strong earthquakes is presented. More than 11 years of the Medium Energy Protons Electrons Detector data from the NOAA-15 Sun-synchronous polar orbiting satellite were analysed. Electron fluxes were analysed using a set of adiabatic coordinates. From this, significant electron counting rate fluctuations were evidenced during geomagnetic quiet periods. Electron counting rates were compared to earthquakes by defining a seismic event L-shell obtained radially projecting the epicentre geographical positions to a given altitude towards the zenith. Counting rates were grouped in every satellite semi-orbit together with strong seismic events and these were chosen with the L-shell coordinates close to each other. NOAA-15 electron data from July 1998 to December 2011 were compared for nearly 1,800 earthquakes with magnitudes larger than or equal to 6, occurring worldwide. When considering 30 - 100 keV precipitating electrons detected by the vertical NOAA-15 telescope and earthquake epicentre projections at altitudes greater that 1,300 km, a significant correlation appeared where a 2 - 3 1
hour electron precipitation was detected prior to large events in the Sumatra and Philippine Regions. This was in physical agreement with different correlation times obtained from past studies that considered particles with greater energies. The Discussion below of satellite orbits and detectors is useful for future satellite missions for earthquake mitigation.
Introduction The process driving a seismic rupture may cause local surface deformation fields, rock dislocations, charged particle generation and motion, electrical conductivity changes, gas emission, fluid diffusion and electrokinetic, piezomagnetic and piezoelectric effects (Varotsos 2001). Charge carriers could be activated in dry rocks mainly by increasing external stress (Freund 2000). These phenomena have been considered as the main sources of the so-called seismo-electromagnetic emissions consisting of broadband electromagnetic (EM) fields observed at the Earth’s surface and in the near-Earth space, the neutral and ionized atmosphere, and the magnetosphere. Low Earth Orbit (LEO) satellite observations provide information on ionospheric and magnetospheric perturbations possibly caused by EM waves and, in particular, radiation belt particle precipitations (Aleksandrin et al. 2003), infrared emissions (Yurur 2006), temperature and density variations of the ions and electrons of the ionospheric plasma (Sarkar et al. 2007), and electric and magnetic field fluctuations (Bhattacharya et al. 2009). A LEO satellite, at an altitude of 200–2000 km, provides, in principle, a platform of observation extending over the entire affected region (Parrot 1995). Whereas, terrestrial observations of non-seismic phenomena linked to strong earthquakes (EQs) produce local perturbations, which can be strongly affected by several land and atmospheric variables, including anthropogenic disturbances, so not expressing the EQ preparation. 2
LEO environment has been studied from space using charged particle detectors to principally analyse solar cycle (Reeves et al., 2011). Auroral phenomena and cosmic ray (Baker et al., 2004; Adriani et al., 2009) were also investigated. Ionospheric disturbances linked to seismic activity were first observed around the time of the great Alaskan EQ (Davies and Backer, 1965) on March 28, 1964. Satellites measurements (Larkina et al., 1983) have confirmed the potential importance of this research, which provided medium and far field viewing points of lithosphere phenomena with respect to the EQ influence size. Several of the particle detectors used in solar studies have been used to investigate electron precipitation in connection with strong EQs (Sgrigna et al., 2005; Rothkaehl et al., 2006). They concerned the more stable inner (L < 2) Van Allen Belts (VAB) with respect to the outer radiation belts which strongly depend on geomagnetic activity (Millan and Thorne, 2007). These studies were based on data coming from satellite missions lasting only a few months or collected a few months with equal attitude data, therein providing weak evidence for correlations (Alexandrin et al., 2003; Sgrigna et al., 2005). These flux variations have been interpreted as pitch angle diffusions induced by Coulomb scattering (Walt and Farley, 1976) and resonant wave-particle interactions (Abel and Thorne, 1998). However, the theoretical explanation for the flux variations are still being elaborated upon (Albert and Bortnik, 2009). The National Oceanographic and Atmospheric Administration (NOAA) and the National Aeronautics and Space Administration (NASA) have jointly developed a series of Polar Operational Environmental Satellites (POES). These Advanced TIROSN (ATN) spacecrafts are named after the prototype satellites, TIROS-N (Television Infrared Observation Satellites), which have flown since 1978 (Davis, 2007). The system consists of pairs of satellites, which ensure that every part of the Earth is regularly observed at least twice 3
every 12 hours from about 800 km altitude. Starting with the NOAA-15 satellite in 1998, the satellites have an upgraded version of the Space Environment Monitor (SEM-2). The SEM-2 contains two sets of instruments that monitor the energetic charged particle environment near the Earth. These detect and monitor the flux of energetic ions and electrons in the atmosphere and the particle radiation environment at the altitude of the satellite. The NOAA particle database had already been studied by means of particle bursts in connection with global seismic activity during quiet solar periods (Fidani et al., 2008). Exceptional increasings of particle fluxes were discovered in connection with the largest quakes that struck the Indonesian region over the last two decades. Contiguous particle bursts were studied in order to distinguish correlations with seismic activity from seasonal variations of particle flux and solar activity, and EQ clustering was initially included to study the types and causes of false correlations (Fidani et al., 2010). Their auto-correlations were also investigated (Fidani et al., 2012). The particles registered by NOAA satellite fall into a lower range of energy, compared to some previously observed particle precipitations associated with EQs (Galperin et al., 1992; Boskova et al., 1994). Abnormal bursts of energetic electrons preceding EQs for the first time have already been presented having the same NOAA energy range (Ruzhin et al., 1996; 1997 and 1998). In these works simultaneous data of energetic electrons at 40 keV–1.2 MeV and magnetic and electric components in the ELF-VLF bands (0.1–16 kHz) were measured on board of INTERCOSMOS 19 over the epicentre of the prone EQ. The NOAA particle database contains from 30 keV to a few MeV electron and proton counting rates (CRs) (Evans and Greer, 2004), which is also similar to DEMETER IDP (Sauvad et al., 2006). A criterion to select electron bursts has been applied on high energy electron flux data at 70 keV–2.34 MeV recorded by IDP on DEMETER (Zhang et al., 2013). The calculated time 4
differences between CRs by IDP and EQs were longer than those of previously published works, and increases in energetic electron fluxes were observed a few days before EQs (Anagnostopoulos
et
al.,
2010),
accompanied
by
broadband
kHz
emissions
(Anagnostopoulos et al., 2012; Zhang et al., 2012) and compared to the VLF transmitter contributions (Sidiropoulos et al., 2011; Sauvad et al., 2014). Furthermore, a statistical similarity between high energy charged particle fluxes detected by DEMETER and EQs has also been reported (Wang et al., 2014). Being so, complete correlation analysis was applied to NOAA database to verify a non-casual association of particle precipitation with large seismic events. This study has started successfully with NOAA-15 database only, and needs of further development to be applied with other NOAA databases which satellites cover slightly different orbits.
NOAA particle data and its preparation for the analysis The NOAA-15 has been operational since July 1998, on a near Sun-synchronous circular orbit (7:30 - 19:30 LT) with a period of 102’, 98.5° inclination and an altitude of about 800 km (Davis, 2007). Over this operating time, the satellite LT was constant for only two years; gradually drifting, reaching 5:30 - 17:30 LT in 2010. This study was based on the CR measurements provided by the particle telescopes, which are part of the Space Environment Monitor (SEM-2) (Evans and Greer, 2004). The Medium Energy Proton and Electron Detector (MEPED) is composed of eight solid-state detectors measuring proton and electron fluxes from 30 keV to 200 MeV (Evans and Greer, 2004) which include the radiation belt populations, energetic solar particle events and the low energy portion of the galactic cosmic-ray population. The eight detectors consist of two proton telescopes which monitor the flux in five energy bands in the range 30 keV to 6.9 MeV, two electron 5
telescopes which monitor the flux in three energy bands in the range 30 keV to 2.5 MeV, and four omnidirectional detectors sensitive to proton energies above 16 MeV. One telescope views at an angle of 9° with respect to the local zenith. The second telescope views in the orthogonal direction. The small solid state electrons and protons detectors have nominal geometric acceptances of 0.1 cm2sr and opening angle apertures of ±15°, which have been recently confirmed by Monte Carlo simulations (Yando et al., 2011). The simulations also supported the relevance of opposite charge contamination effects. The 0° and 90° telescope electron energy bands have been analysed, both in the three original channels from 30, 100 and 300 to 2,700 keV, as well as three differential channels: 30 to 100 keV, 100 to 300 keV and 300 to 2,700 keV. Proton contamination was excluded only from the lower energy range, taking into account both observations (Asikanen and Mursula, 2008) and simulations (Yando et al., 2011). Nonetheless, the fluxes were corrected for proton contamination (Rodger et al., 2010) by using software downloaded from the Virtual Radiation Belt Observatory (http://virbo.org/POES#Processing). MEPED energy bands of the 0° and 90° proton telescopes and omnidirectional electron and proton detectors were corrected and analysed in a similar way. Detector energy band increased the detection energy intervals of a few 10 keV due to degradation (Evans et al., 2008). Inner radiation belt particle flux increases by over one order of magnitude were always observed during the main phase of magnetic storms (Tadokoro et al., 2007), which occur in rapid response (< 1 day) of the inner radiation belts to the solar wind velocity peak (Vassiliadis, 2008). Sudden Ionospheric Disturbances (SID) were considered possible sources of particle flux perturbations as in past discussions (Sgrigna et al., 2005). They are produced in the ionosphere by enhanced solar radiation during solar flares, which include VLF-LF effects (Deshpande et al., 1972), and cosmic rays (Inan et al., 2007b). EM whistler 6
waves injected by lightning discharges can scatter the energetic electrons and cause them to precipitate out of the radiation belts (Abel and Thorne, 1998). A seasonal dependence of energetic particle precipitation in relation to lightning activity was observed (Gemelos et al., 2009). In order to include the geomagnetic, meteorological and extraterrestrial influences on the particle fluctuations, the CR data were associated to daily averages of the geomagnetic Ap index and SID (http://www.aavso.org/solar-sids), as well as three hour averages of the Ap index (ftp://ftp.ngdc.noaa.gov/STP/GEOMAGNETICDATA/APST AR/apindex). The CR exclusions from the correlation analysis occurred when geomagnetic indexes overcame thresholds, which were calculated by annual and 11-years Sun particle modulations (Fidani et al., 2012). As CR fluctuations originating in the magnetosphere occur in sub-storm activity also, the goodness of the selected quiet geomagnetic days was verified requiring Dst variations (http://wdc.kugi.kyoto-u.ac.jp/dst_final/index.html) to be less than 30 nT. From July 1, 1998 to December 31, 2009, data were downloaded from NOAA (http://www.ngdc.noaa.gov/stp/satellite/poes/dataaccess.html) and examined to exclude uncorrected instrument operations through their corresponding flags. The USGS catalogue (http://earthquake.usgs.gov/earthquakes/search/) was used to retrieve EQ informations. From July 1998 to December 2009, about 2 10 4 earthquakes with M ≥ 5 occurred worldwide. The earthquake list has been adjusted to eliminate foreshocks and aftershocks.
Significant CR fluctuations Data processing started by building a map of daily CRs in the geomagnetic invariant space (Walt, 1994). In fact, CRs are strongly variable along the satellite orbit, while CR contribution from the same geomagnetic invariant space intervals are more regular in 7
stable periods. Adiabatic invariants allow for the determination of different geographical positions where particles are expected to possess the same dynamics and where CR deviations can be studied (McIlwain, 1966); thereby permitting to search for fluctuations. Particle dynamics were described by geomagnetic field at mirror points Bm and the magnetic shell parameter L. The parametrization of Bm = B/cos2α with respect to the geomagnetic field B and the pitch angles α at each other position were introduced, so to represent CR in a 4-dimensional matrix (t;L;α;B) including time. The pitch angle is equal to the difference between the particle telescope and geomagnetic field directions. However, the SEM-2 detectors have a finite aperture of 30° so that α can be defined only very approximately. The pitch angle intervals Δα were chosen to be 12 equal intervals of 15°. The introduction of B parameter was useful because it allowed to control the strong CR spatial variability when it entered the South Atlantic Anomaly (SAA) (Asikanen and Mursula, 2008). B covered the range 16 μT to 47 μT along the satellite orbit and was divided into nine nonidentical intervals: shorter where CR was greater, to better control the CR spatial variations (Fidani et al., 2008). The initially considered ΔB intervals were: 16.0-17.5 μT, 17.5-19.0 μT, 19.0-20.5 μT, 20.5-22.0 μT, 22.0-25.0 μT, 25.0-28.0 μT, 28.0-32.5 μT, 32.5-37.0 μT and 37.047.0 μT. Geomagnetic B-field, L-shell and α-pitch values had already been provided by NOAA (Evans and Greer, 2004). However, B-field and L-shell were re-evaluated on the NOAA-15 orbit utilising the latest International Geomagnetic Reference Field (IGRF-11) model (http://www.ngdc.noaa.gov/IAGA/vmod/igrf.html) (Finlay et a., 2010a), to include recent model corrections of past years as well as height variations along the satellite orbits. The IGRF-11 errors for 2010 are expected to be slightly larger than those of previous years; approximately 10 nT (Finlay et al., 2010a). Regarding the predictive model, retrospective 8
analysis of previous predictions has shown that errors of up to 20 nT yr -1 are likely (Maus et al., 2005; Finlay et al., 2010b). In this work, data from the inner VAB, restricting the analysis at 1.0 ≤ L ≤ 2.2, was only used. It was verified that the errors induced by a B uncertainty of 20 nT on the calculated L-shells was always less than 0.05 in the considered subspace. For this, an L-shell step of ΔL = 0.1, defining 12 equal intervals, was utilized. NOAA-15 CR records data every two seconds and, due to the small area particle detectors, CR is usually very low outside the SAA or far from geomagnetic poles. Thus, a matrix was created by accumulating eight-second CR histograms, considering the sum of four consecutive measurements. The distributions of the CRs followed a Poisson pattern. The B, L and α ranges covered in 8 sec were verified to be less than 200 nT, 0.04 and 1.1 degrees, respectively. These were all shorter than the ΔB, ΔL and Δα intervals considered here. Being that the 8 sec B range was greater than B uncertainty, the CR contribution coming from the adjacent B intervals was checked and it was always less than 10%. Observing the CR fluctuations, the increases in CRs concerned not only single isolated 8 sec events, but also multiple consecutive events lasting several minutes of satellite orbit. However, given that CRs were likely to belong to different adiabatic intervals in multiple events, the significance of the CR fluctuations was tested on single 8 sec events, by comparing them to Poisson fluctuations from the same adiabatic regions. To obtain less than 1% probability that the CR fluctuations xs were of a statistical origin, the condition P(xs) < 0.01 had to be satisfied by calculating the number of sigma nσ (Fidani et al., 2008). The x was considered to be a significant CR fluctuation with probability greater than 99% if x > xs, corresponding to the same adiabatic intervals. Distributions at all regions were analysed and their averages calculated in the adiabatic intervals. The time interval used to calculate the averages had to be long enough in order to consider a minimum number of samples, or increasing invariant 9
space dimension must be chosen. The small detector acceptance of NOAA satellites required long time and large adiabatic intervals to obtain sufficient statistics. As a strong response of inner VAB to the solar activity was observed in a daily time interval (Vassiliadis et al., 2002), CR averages xm = xm(Day;ΔL;Δα;ΔB) were calculated for each day sub matrix histogram. In order to obtain a more accurate reading of the particle dynamics, a small cell dimensions of adiabatic invariants should be preferred. Being so, an interpolated average xmi(Day;L;Δα;B) was used to map continuously L and B from the centres of their intervals. In this way, cell sizes were not reduced and daily averages were accepted only for cells having at least 20 satellite passages/day. As CRs strongly increase near the SAA, a cubic non-linear algorithm was used to better interpolate xm. Starting from averages, and variances, it was possible to verify if NOAA MEPED detected any significant CR fluctuations along the entire satellite orbit. In fact, each x value of CR was compared with the interpolated average at its invariant position; and then the associated Poisson probability was calculated in order to select non-Poissonian fluctuations. CRs are neither stable inside the external VAB because of the influence of variable solar wind nor inside the internal VAB as interactions with EM waves influence particle fluxes (Datlowe, 2006). The possible influence of earthquakes on particle fluxes, detected by NOAA satellites, was easier to study in a more stable low CR regime region. Low CRs are detected outside VABs and CR fluctuations are due to particles leaving the trapping conditions from the inner VAB, i.e. precipitating particles. Being so, poles and SAA regions data were excluded from the analysis. Based on particle flux stability, a L-shell maximum limit of 2.2 was chosen to exclude data in and around the poles, while a minimum value of of 20.5 μT for the geomagnetic field was chosen to exclude data in the SAA region. These
10
limitations excluded the areas south and north above 50° latitude and a birdhead-shaped area extending over the South Atlantic Ocean and South America, see Figure 1. Precipitating particles are those which interact with atmosphere and are absorbed. They follow geomagnetic lines in their spiral motions, having the lowest altitudes along the geomagnetic line equal to the bouncing altitude h. Since VAB particles move also along the L-shells by longitudinal drift, and the geomagnetic axis is departed from the terrestrial axis, they experience variable bouncing altitudes along their drift motion. When h become lower than 100 km, while particle drifting, the particle will interact with the atmosphere and will be absorbed. Precipitating particles can be then determined calculating their minimum mirror point altitudes, hmin, and verifying that it will be under 100 km along the drift period. The UNILIB library was used to calculate mirror points of detected particles (Krungasky, 2003). The hmin for the electrons of not excluded areas in Figure 1 was less than 100 km for 95% cases, and it was less than 200 km for all cases. At these altitudes the absorption length for electrons having energies between 30 keV and 3 MeV assures that particles will be absorbed in the residual atmosphere. Precipitating electrons were concentrated in a fairly small region creating a dovetail-shape, located up until 80 degrees longitude from the westward edge of SAA. This dovetail shape is green coloured in Figure 1. The electron flux distribution concentrated westwards of SAA means that bouncing points are far above the satellite, just below VAB, at the opposite longitudes of SAA; being so, no CR can be detected. When lower bouncing points begin to cross the NOAA altitude as longitudes approach SAA, some CR can be detected. Maximum CR of non-trapped electrons can be detected immediately west of SAA, where the maximum number of electrons have bouncing altitudes under the satellite orbit and above the atmosphere. These bouncing altitudes fall below 100 km in the SAA. The process can be represented by coloured areas in Figure 2. 11
The space-time structure of the detected electrons consist of series of consecutive CRs corresponding to time intervals lasting up to a few minutes along the orbit. There are rarely two series of consecutive selected CRs, due to the crossing between satellite trajectory and the dovetail shape during a satellite semi-orbit. In fact, the space time structure usually is made up of one or more non-consecutive detected CRs. Moreover, the distribution of events made up of consecutive CRs which are considered as one, produces non-Poissonian correlation distributions with earthquakes (Fidani et al., 2010). This type of distribution defined not completely independent correlation events. Given this, it was chosen to define a Particle Perturbation (PP) event as the detection of at least a significant CR in the entire satellite semi-orbit. The duration of a satellite semi orbit is about 51 minutes but, as L-Shell was limited at L ≤ 2.2, a semi orbit lasted about 20 minutes. PP are different from particle bursts as the latter were defined as short-term increases of particle fluxes. PP can be made up of several particle bursts. Due to the fact that CR selection had effective L ≤ 1.4, the maximum time length of PP was about 12 minutes. PP times were defined as the average detection times of the selected CRs during the semi orbit of the satellite.
The correlation between particle precipitations and EQs L-shell parameter was considered to define a volume where a physical interaction, whatever it is, can connect the seismic and ionospheric activities. L-shells relative to the significant CRs, referred to as LCR, were compared with some L-shell values, referred to as LEQ, assigned to each EQ. To define LEQ, the EQ geographical positions were projected along the vertical at fixed altitudes, so to evidence some positions in the ionosphere. The corresponding L-shells of ionosphere positions were calculated by the same geomagnetic
12
software used for the particle adiabatic calculus. The subsets of EQs and CRs meeting the condition ΔL = |LEQ − LCR | ≤ 0.1,
(1)
were further analysed. Expression (1) links EQs with all precipitating CRs belonging to well defined L-shell, even if the satellite detects them at different latitudes and longitudes. Being each PP defined as a set of significant CRs detected along the same satellite semi orbit near the equator, and the time difference between the CRs in a PP up to 12 minutes, the CR Lshells will always be not very different. Because of distant CRs are detected at magnetic conjugated points of such semi-orbits, condition (1) is generally satisfied for all CRs of each PP and the entire PP can be compared to an EQ with ΔL = |LEQ − LPP | ≤ 0.1.
(2)
Correlation was defined by filling an histogram with the differences TEQ-TPP, between the EQ time TEQ and the PP time TPP, only for those seismic events and particle precipitations which satisfied (2). This process was performed at different altitudes of EQ epicentre projections, by calculating LEQ at each altitude from -600 km up to 3200 km in increments of 100 km. Negative depths were also considered to verify if some kind of interaction could be plausible between EQ occurring in the SAA and any geomagnetic line. To reduce the effects of solar activity, low values of Ap indexes were chosen for three and 24 hour intervals when particle precipitation in the inner VAB did not occur (Fidani et al., 2012). These defined the maximum thresholds to exclude the corresponding intervals of data. Furthermore, PP data were considered “Sun influenced” when SID occurred within the same day; therefore, such days were excluded from the correlation. In past works (Fidani et al., 2010) the Ap daily threshold was fixed at 18, while the three hour Ap indexes were considered with a threshold fixed at 13. Finally, due to long period Sun influences which 13
had already been reported in Fidani works, annual and eleven year modulations of the thresholds associated with the two integer Ap indexes, were also applied to exclude data with geomagnetic indexes greater than the functions: Apm = fm + gm sin(Dy)+[km - lm sin(Dy)] cos(Ds),
(3)
where m = 1 for three hour Ap and m = 2 for 24 hour Ap, Dy = y (year-y0) and Ds = s (days0). The complete NOAA-15 electron CR database, from the beginning of July 1998 to December 2009, was processed and correlated to EQ events during the same period by applying the rule (2). The correlation histogram was filled without any reference to the EQ magnitudes or PP intensities. Being so, time bins were filled with unit weights. Time intervals ranging from a few minutes up to few days were tested to verify the stability of the TEQ-TPP histogram. A stable correlation histogram was obtained for time intervals around one hour, in agreement with previous studies (Sgrigna et al., 2005). A set of parameters was used to study the correlation histogram which included four geomagnetic indexes: 24 hour Ap, three hour Ap, SID and Dst. Three ionospheric particle parameters ΔL, CR pitch angle α plus mirror altitude h, and EQ parameters of magnitude and depth were also used to study the correlation histogram. Sun index modulations were applied with the aims of decreasing solar influence. The optimal modulations were obtained from (3) with coefficients: y = 0.37, s = 0.0172, y0 = 1996, s0 = 27, f1 = 14.8, f2 = 11.1, g1 = 1.3, g2 = 0.8, k1 = 7.5, k2 = 2.1, l1 = 1.3 and l2 = 0.1. The threshold functions are shown in Figure 3. Even if this modulation corresponds to very small geomagnetic indexes, it was checked that the obtained correlation between EQs and PPs could not be due to sub-storm events by verifying that Dst variations > -27 nT, during the 20 hours before the time of CRs contributing to the correlation. 14
The analysis was repeated for all the EQ altitude projections. Several correlation peaks appeared from the first electron energy channels corresponding to 30 keV - 2.7 MeV or differential channels corresponding to 30 keV - 100 keV detected by the vertical telescope. Whereas, correlation peaks did not appear from other electron energy channels utilising vertical telescope or from any electron energy channels utilising horizontal or omnidirectional telescopes. Furthermore, no correlation peaks up until now have appeared from similar NOAA proton analyses. The same algorithm was applied to the particle databases of all other polar NOAA satellites, but no correlation peaks were observed from the first electron energy channels, nor from any other energy channels or particle types. The orbit of NOAA-15 characterised by sunrise/sunset times was the only one different from the other NOAA POES orbits, which were all characterised by day/night times. To analyse all the NOAA satellite databases, a further step in the analysis should be made in the algorithm to select anomalous CRs from such databases which take in account the different particle flux regimes observed during day or night. Two correlation plots of differential channel 30 keV - 100 keV electrons are shown in Figure 4, when the EQ epicentre projections were at 1800 km and 2000 km altitudes. The plot ranges ±1 days in time difference between EQs and semi orbit PPs; the average values calculated on ±3 days are 16 and 13.5 correlation events respectively, with a standard deviations of 4.0 and 3.7 events. All the correlation distributions at any altitude were Poissonians. Significant correlation peaks appear between 2 and 3 hours of positive time difference, which means that the PP time is observed before the corresponding EQ. The peak starts to be significant when considering EQ projections above 1300 km altitudes, see Figure 5, where red and orange areas in Figure 2 coherently shows mirror altitudes above 1300 km at EQ longitudes. The correlation was maximised by using EQ magnitude M ≥ 6 15
only. Pitch angle restrictions required that the particles were precipitating, meaning that their values were mostly in the loss cone. Specifically, particles in these PPs concentrated in intervals around 65° and 135°. Results in Figure 4 were obtained neglecting pitch angles far from these values, with 30° ≤ α ≤ 80° and 120° ≤ α ≤ 160°. The EQ depths lower than 200 km were considered, that is they had to be close to the surface. The correlation peaks were about 4.5 standard deviations above the average values. An increase in the statistical significance of the correlation peaks was observed compared to altitude, see Figure 5. This began to exceed 3 standard deviations at 1300 km and reached a maximum at about 2700 km, where the number of standard deviations was close to 5.5. However, the number of correlated events started to decrease for EQ altitude projections above 1200 km and became very low for altitudes greater than 2400 km. Figure 5 continuous black line shown the standard deviations of 2 – 3 hour correlation peaks, the dashed line shown the averages, and continuous red line shown the number of standard deviations necessary to have a correlation peak significance of at least 99%. Both correlation calculus using a randomized space and time distributions of EQs were also checked by using same EQ times, and the same EQ epicentres, respectively. In all the randomised cases, the previously obtained correlation peaks completely disappeared.
Discussion and suggestions for future missions Epicentre locations whose EQs were correlated with PPs, were those in both the Indonesian and Philippine Regions, with few in South America, see Figure 6. Great EQs in Japan were not correlated with PPs in this study, thus decreasing of electron precipitation above the future epicentres a few hours before the occurrence of the seismic events obtained by a different analysis (Anagnastopoulos et al., 2012) was not observed. EQs occurred 16
principally in the sea, which was in contrast with previous results (Sgrigna et al., 2005) and in agreement with past EM results (Nemec et al., 2009). The epicentre positions of all these EQs were located in the positions where the more frequent strong seismic events occurred during the studied period. These EQ positions were also causally physically linked with electron perturbations occurring several hours before the main shocks which were located west of detection positions. In fact, when perturbations of electron trapped conditions occurred at EQ projection altitudes, they could have changed both electron pitch angles and bouncing altitudes at locations westwards of SAA. Furthermore, when considering similar energy range to NOAA MEPED (Ruzhin et al., 1996; 1997, 1998) it was obtained that the shape correlated bursts were registered 2 - 2.5 hours before the seismic event. Whereas, when considering detectors sensitive to much greater energies of previous studies it was obtained that correlated bursts were registered 4 - 5 hours before EQs. This latter observation suggests that the drift velocity of detected electrons will be generally lower than previous cases. Its corresponding electron drift periods were calculated using the expression: Td=
1.05 1 . E ( MeV ) L 1+ 0.43sin α eq
(4)
For 30 keV and 100 keV, the results from (4) were 21 hours and 6 hours, respectively. These electrons belonged to the low L-shell range of 1.15 < L < 1.35, as in past works. The electrons which produced the correlation met satellite vertical detectors in the drift loss cone near the SAA about 2 - 3 hours before the EQ time, after having drifted eastwards. This means that the perturbations had to have started much earlier. In fact, it was calculated that an earlier time between 2 - 7 hours was necessary for electrons to cover about 120° which divided EQ epicentre longitudes of Sumatra and The Philippines from the detection area;
17
with the electrons moving eastwards at drift velocity calculated by (4). Being so, if the perturbations which caused electron precipitations from inner VABs occurred above the EQ epicentres in the ionosphere, they anticipated the EQ times by 4 - 10 hours. This was in agreement with past experiments, showing that the simple expression (4) produced drift periods of a few minutes for MeV electrons, where correlations were calculated to be 4 - 5 hours. Therefore, larger early time correlations obtained in past studies could have been caused by the same type of perturbations on VAB MeV electrons which drifted faster. These electrons reached satellite particle detectors a few minutes after the perturbations occurred. The relationship of ELF-VLF electric field intensity and its spectra with energetic electrons flux density measured on board of INTERCOSMOS 19 were investigated, and the correlation coefficient between the electron flow and electric component of the EM field bursts were 0.60-0.72 (Rutzhin et al., 1996; 1997). It was the first reliable evidence of sufficiently close connection of the VLF wave with the radiation belt particles occurring before strong earthquakes. The Authors concluded that stable pitch-angle distribution of these particles could be disturbed as a result of it’s scattering on the electric field fluctuations over future earthquake epicentre. A possible process to link the Earth surface to space in vertical direction was presented (Pulinets et al., 2007) and could explain the observed significant CRs fluctuations at NOAA altitudes. It can be argued from the above considerations that MeV electrons are better indicators for an impending earthquake in the Sumatra and The Philippine regions, as PP are detected much earlier than PP relative to keV electrons. To this regard, a weak result was obtained suggesting a correlation between MeV electrons and EQs, as past works concerned only few months of data. However, this result suggests that a satellite missions dedicated to EQ early 18
warnings should be equipped with MeV electron detectors. Moreover, the acceptance area of electron detectors for a EQ dedicated mission would need to be at least two order greater than NOAA acceptance areas, as the electron flux was two order minor at MeV energies (Freden, 1969). Satellite altitudes and orbits were also determinant in increasing PPs detection. Figure 1 shows that the active area, where NOAA satellite detected PPs, formed a dove-tail pattern westwards of SAA. This area covered about 70° in latitude and about 150° in longitude. Being so, an equatorial orbit should be preferred for a particle detector to maximise the time permanence in the active area, having a gain of about 2 with respect to NOAA POES. However, it should be remembered that a positive correlation result was obtained only in the NOAA-15 database analysis, which was operational at sunrise/sunset times and not for other NOAA satellites, which have day/night orbits. An equatorial orbit for a particle detector is subject to day/night exposure also, thus electron CRs should be analysed using an algorithm taking into account day/night flux variations. Finally, Figure 2 shows that the higher the satellite altitude the greater the longitude extensions will be where electron mirror points are under the satellite. Consequently, this higher altitude increases the active area. Furthermore, an increase in area positions, where the satellite is inside the inner VAB, determined by B = 20.5 μT, also occur with altitude. Comparing the altitudes of constant B = 20.5 μT for low L-shells (Venkatesan, 1965) with the altitude of mirror points, it is possible to suggest that the best detection altitude is between 900 – 1000 km, as it guarantees the greatest longitude extension of the active area outside the inner VABs.
Conclusions
19
By using more than 11 years of NOAA particle data, a statistically significant correlation between extended perturbations of electron CRs and large EQs was observed. The precipitating low L-shell electrons phenomena occurred 2 - 3 hours before main shocks. This result supports the hypothesis that there exists a link between ionospheric and surface seismic activities, when EQ depths are less than 200 km (Sgrigna et al. 2005, Nemec et al. 2009). This result is further bolstered by the fact that different satellite data sets were used in past studies. Contrary to past particle precipitation results, this correlation was observed for both EQs in the sea and in the mainland. Based on the drift period (4), and hypothesising an interaction region above the epicentre of Sumatra and The Philippine regions, the times of EQs - ionosphere interactions occurred 4 -10 hours before the EQ times. Future missions would need detectors to be sensible to MeV electrons with acceptance areas two orders greater than NOAA detectors. These detectors would have to be vertical with an aperture of more than 30° to maximise acceptance in every trajectory; even if, better direction and energy resolutions of every particle would be useful to trace their past trajectories. The satellite orbit which maximises the area of precipitating electron detection is an equatorial orbit with altitudes between 900 and 1000 km. Further studies are needed to apply the above correlation analysis to the other satellites, taking into account the day/night effect on particle fluxes.
Acknowledgements I would like to thank the “Consulta delle Fondazioni delle Casse di Risparmio Umbre” for supporting this study. Also, I would like to express my thanks to Craig J. Rodger and Janet Green from NOAA for their useful codes to subtract the proton contamination of electron 20
channels. Additionally, I would like to express my gratitude to M. Kruglanski for the library to calculate bouncing altitudes and anonymous Referees for the useful suggestions. The publication fee is supported by the iSTEP project NSC102-2628-M-008-001.
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Figure 1. The geographical area considered in this analysis is evidenced by bright colours while high flux areas are colour matted. The wave-shaped limits of polar areas are due to the geomagnetic field inclination compared to the earth axis while the birdhead-shaped area in the centre represents the interior of inner VAB at NOAA altitude. Colours indicate the average annual electron flux detected at 30-100 keV, 0° NOAA-15 on board telescope.
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Figure 2. The altitudes of mirror points for electrons at L = 1.2 are indicated by continuous and dashed lines compared to NOAA-15 and atmosphere altitudes. Green coloured areas indicate altitude/longitude regions where there are bouncing points of electrons that are detectable by NOAA-15 satellite. The yellow to red areas are the more frequent possible interaction areas between EQs and PPs. The SAA crossing the mirror points is delimited by thick vertical lines, continuous for northern hemisphere and dashed for southern hemisphere.
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Figure 3. Geomagnetic thresholds calculated by (3) to reduce the Sun effect on electron burst activity; the three hour Ap threshold is shown on the left and 24 hour Ap threshold is shown on the right.
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Figure 4. Correlation time histograms between EQs and PPs at 1800 km, up, and 2000 km, down, of EQ epicentre projections; the significances of 2 – 3 hours correlations are 4.4 and 4.5 standard deviations for up and down plots respectively.
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Figure 5. The number of standard deviations (Nsd) above the correlation average (Ave) of 2 - 3 hour correlation peak is indicated by a continuous line compared to EQ epicentre projection altitudes; the dashed line indicates the average correlation; the standard deviation threshold to obtain 99% probability is lower than Nsd for EQ projection altitudes greater than 1300 km, as indicated by the red line. The 5.5 standard deviation limit was reached for EQ epicentre projection altitudes of about 2700 km, even if this limit was obtained with a very low number of events.
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Figure 6. Epicentres of EQs that contributed to the 2 - 3 hour correlation are reported here; with a major concentration in the Indonesian and The Philippine regions.
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Highlights
NOAA particle data analysis demonstrated a correlation with strong earthquakes Drift loss cone electrons from inner belts detected 2–3 hours before quake times The correlation regards shallow earthquakes of Sumatra and Philippine regions 30–100 keV electrons disturbed above 1300 km altitudes 4–10 hours before earthquakes
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