THERMAL ANOMALIES OF OUTGOING LONG WAVE RADIATION (OLR) SIGNALS

In this study is presented information on the thermal anomalies (OLR) collected by the satellites during the earthquakes and from the past two years without earthquakes for the relevant geographic locations. It is known that the masses of the tectonic plates are subjected to enormous pressure and critical stresses are generated whereby positively charged particles “p-holes” are emitted. When these reach the ground, they ionize the molecules of the air and infrared rays are emitted. It is known as OLR. The satellite sensors at tens of kilometers catch the infrared radiation and keep track of it as a reflection from the Earth's surface with wavelength of 10-13 m.

NOMENCLATURE

W_a (t) – The average OLR value per day for the two years without collision is [W/m^2 ]; W_M^d (t) – The momentary OLR value in the course of the year with an earthquake is [W/m^2 ]; 〖W_amax W〗_amin - Maximum and minimum value of the variation W_a (t) in [W/m^2 ]; W_(AI )- Average integral value of the W_a (t) over the period considered in [W/m^2 ]; W_(AA )- Average algebraic value of the W_a (t) in[W/m^2 ]; W_AI^d- Average integral value of the W_M^d (t) over the years with earthquakes in [W/m^2 ]; W_Mmax^d W_Mmin^d - Average algebraic value of the W_M^d (t) in [W/m^2 ]; t_1 t_2 - Times in which W_(AI )≡ W_M^d (t) in [s]; t_3 - The time in which the earthquake occurs in [days]; ∆ E_max- Maximum energy limit of the OLR in [kWh/m^2 ]  ; ∆t= (t_3-t_2 )The time after which the earthquake occurs in [days].

ENERGY ASSESSMENT OF THE OLR SIGNALS

On the Figures1a and 1b are shown examples of variations of OLR signals. One of the figures represents variations of OLR signal without any seismic phenomena for a two- year long period for the specific place on Earth with geographical coordinates – Latitude and Longitude. The other figure represents the OLR signal for the same place of the Earth with the same geographical coordinates, but for a time period of one year with occurrence of big seismic phenomena. The minimum and maximum values are as follows:W_amin, W_amax, W_Mmin^d, W_Mmax^d and the average integral values are:

W_(AI )=  1/T ∫1_0^T▒W_a  (t)  dt   and   W_AI^d  =  1/T ∫1_0^T▒〖W_M^d (t)  dt 〗                (1)

These are exhibited in the two figures. Extensive analysis (hundred occurred earthquakes with M>6) shows that the difference between the average integral OLR signal values and the arithmetical average values is less than 5%. For this reason could be assumed that:

W_(AI )≈  1/2 (W_amin+ W_amax )   and   W_AI^d  ≈   1/2 (W_Mmin^d+ W_Mmax^d )              (2)

The variation of the energy of the OLR signal in the time interval h=t_(1 )- t_2 is shown in the Figure 1c, where the variation ∆E_max is most significant. The points A and B match aligned values of:

W_M^d 〖(t_1 )  ≡W_(AI ) hence   W_M^d (t_1 )≡W_M^d (t_2 )  ≡W〗_(AI )               (3)

The largest amount of change of energy ∆ E_maxin a year with an earthquake is determined by the expression:

∆ E_max=  ∫1_(t_1)^(t_2)▒〖W_M^d (t)  dt 〗-W_AI (t_2 〖-t〗_1 )[kWh/m^2 ]            (4)

The extent of variation of the radiation during the period of two years without any cataclysms isδ_N [-]:

δ_(N )=  (W_amax- W_amin)/W_AI   ≈ 2  (W_amax- W_amin)/(W_amax+ W_amin )                  (5)

and the extent of variation of the radiation during the period with cataclysms isδ_d [-]:

δ_(d )=  (W_Mmax^d  - W_(Mmin )^d)/(W_AI^d )  ≈ 2  (W_Mmax^d  - W_(Mmin )^d)/(W_Mmax^d+ W_(Mmin )^d )                  (6)

which are additional criteria for earthquake forecast. On the Figures1b and 1c, with star is marked the earthquake occurrence at time point t_3.

RESULTS

The Table 1 shows the proposed by the authors numerical energy indicators for forecasting of strong earthquakes – main results of the study presented by the maximum values of energy change ∆E_max [kWh/m^2 ]and time in days after ∆E_max occurrence as well as the variation δ_(N  ) before and the variationδ_(d  )during the disasters [1-3].

1.      Venelin J, Philipoff P,Ivanov A, Muсoz M, Raikova G(2013)Spectral properties of quadruple symmetric real functions.Appl MathComput 221:344-350.

2.      2. Jivkov V, Natarajan V, Paneva A, PhilipoffP(2017) Forecasting of strong earthquakes M>6 according to energy approach.J Earth SciClim Change8: 433.

3.      3.Natarajan V, Philipoff P(2018)Observation of surface and atmospheric parametersusing ‘‘NOAA 18’’ satellite: A study on earthquakesof Sumatra and Nicobar is regions for the year 2014(M>6.0).Springer Science+Business Media B.V.