Prediction system and method

• US 7,277,797 B1
• Filed: 03/29/2006
• Issued: 10/02/2007
• Est. Priority Date: 03/29/2005
• Status: Active Grant

• Current Assignee: Andre V. Smirnov, Douglas A. Rekenthaler, Viatcheslav E. Kunitsyn
• Original Assignee: Andre V. Smirnov, Douglas A. Rekenthaler, Viatcheslav E. Kunitsyn
• Inventors: Rekenthaler, Douglas A., Smirnov, Andre V., Kunitsyn, Viatcheslav E.
• Primary Examiner(s): McElheny, Jr.; Donald E
• Application Number:
  US11/391,329
• Time in Patent Office: 552 Days
• Field of Search: 702/15, 702/2, 702/3, 702/4
• US Class Current: 702/15
• CPC Class Codes: G01V 1/01 Measuring or predicting earthquakes

Abstract

Method and apparatus to predict the onset of earthquakes, one to three days prior to the event. Predictions of latitude, longitude, and time of occurrence of an incipient earthquake, by monitoring and exploiting unique changes in the ionosphere and atmosphere, may be obtained. The methodology, focused on radio-tomography of the ionosphere, includes data receiving, transmission, and analysis stations on the earth'"'"'s surface; data from satellite transmitters; and mechanisms to extract earthquake-related signatures that are correlated, processed, interpreted, and disseminated as warnings from central analysis centers. Additionally, data from ground-based radars that display unique clutter maps during earthquake pre-cursor activity and data from cellular telephone networks that display signal fading may also be used.

Claims

1. A method of processing geospheric data to predict a large entity event, said method comprising the steps of:

acquiring data at receiving stations located near the earth'"'"'s surface, wherein the acquired data comprises one or more of high orbiting radio-tomography (HORT), low orbiting radio-tomography (LORT), radar clutter, and cellular telephone signal fading and noise data;

correlating said acquired data with secondary data, wherein the secondary data comprises at least one of archival data, magnetospheric data, seismic data from a region of interest, solar data, data relating to man-made activities that may influence the prediction, and other cosmic, solar, geophysical, or terrestrial data which may have an impact on the prediction;

comparing said correlated data with archived data sets, in an iterative process, revisiting the correlated data to remove false signals or irrelevant noise and to extract meaningful trends; and

evaluating said data for presence of large entity event precursors.

2. The method of claim 1, further comprising the step of disseminating an alert if it is determined from said evaluated data that a large entity event is imminent.

3. The method of claim 1, wherein said large entity event is an earthquake.

4. The method of claim 1, wherein said large entity event is a seaquake.

5. The method of claim 1, wherein said large entity event is a tsunami.

6. The method of claim 3, wherein said acquired data comprises HORT data and said act of acquiring said HORT data comprises the steps of:

acquiring and combining the HORT data at a plurality of receiving sites in the region of interest, wherein the HORT data comprises the characteristics of wave fields, phases, group delays, amplitude of navigational signals at two coherent frequencies f₁ and f₂, as transmitted from high orbiting navigational satellites;

transferring the acquired and combined data to at least one data processing and analysis center; and

pre-processing said transferred acquired data by averaging, filtering, removal of phase ambiguities and related preparatory procedures.

7. The method of claim 6, wherein said correlating step further comprises the steps of:

measuring combined signals in said combined data;

measuring, at numerous receiving sites in the given region of the wave field, characteristics (phases, phase delays, group delays, phase and amplitude variances, correlation functions, coherence functions, moments and distribution functions, and amplitudes of navigational satellites at two or three coherent frequencies f₁, f₂ and f₃) transmitted from specialized satellite systems;

measuring, at numerous receiving sites in a given region of fluctuation, characteristics of the wave fields (variances, correlation functions, coherence functions, corresponding moments and distribution functions of phases and amplitudes of signals at two coherent frequencies f₁ and f₂);

space-time imaging of ionospheric electron density and refraction index distribution in the neutral atmosphere by tomographic methods employing phase-difference radio tomography employing phase difference data in time or location of the satellite-borne transmitter, reducing the reconstruction problem to a function of differences of linear integrals, avoiding ambiguity of phase measurements and increasing reconstruction accuracy;

space-time imaging of statistic characteristics of ionospheric electron concentration and density of the neutral atmosphere by tomographic methods using the data on statistical characteristics of the wave fields;

determining structural parameters of regular and quasi-regular ionosphere;

determining structure and parameters of the disturbed ionosphere, including determination of the intensity and sizes of perturbations;

patches, blobs, bubbles, and other irregularities;

calculating characteristics and parameters of ionospheric and atmospheric wave perturbations in the ionosphere and atmosphere including amplitude, frequency, wavelength, and velocity of acoustic, internal gravity waves, acoustic-gravity waves in the ionosphere and atmosphere, and estimation of the location of the wave sources; and

calculating characteristics and parameters of solitary waves, soliton-like waves, isolated perturbations in the ionosphere and atmosphere, including determination of amplitudes and velocities of the perturbations, to aid in location of the wave sources.

8. The method of claim 7, wherein said comparing step further comprises the steps of:

calculating and estimating an earthquake occurrence probability in a given seismic region from the characteristics and parameters of wavelike disturbances in the ionosphere and atmosphere, using amplitude, frequency, wavelength, velocity of acoustic, internal gravity wave (IGW), and acoustic gravity wave (AGW) in the atmosphere and ionosphere, by constructing a functional of the earthquake containing information about wave activity in said seismic region, determining spectral parameters of wave disturbances and the characteristics of perturbations using wavelet analysis, and separating acoustic, internal gravity wave, and acoustic gravity wave wavelike disturbances by analysis of the images, frequencies, wavelengths, and velocities of the perturbations;

calculating and estimating the earthquake occurrence probability in a given seismic region from the characteristics and parameters of solitary waves, soliton-like isolated perturbations in the ionosphere and atmosphere, by constructing the functional of the earthquake containing information about wave activity in said seismic region, where the wave activity is determined by energy parameters in solitary waves, and in soliton-like isolated perturbations in the ionosphere and atmosphere;

calculating and estimating the earthquake occurrence probability in a given seismic region from the characteristics and parameters of the structure of the ambient and quasi-ambient ionosphere (height and thickness of layers, concentration levels, parameters of ionization troughs in northern and southern latitudes, and parameters of the equatorial anomaly in equatorial latitudes), wherein the parameters of the structure of the ambient and quasi-ambient ionosphere which define the disturbance level of quasi-stationary electromagnetic fields in said seismic region are related to perturbations in geophysical fields within the region of earthquake preparation, and calculation of the earthquake occurrence probability is performed by constructing the functional of the earthquake, containing information about the parameters of the structure of the ambient and quasi-ambient ionosphere, the level of disturbance in the parameters of the structure of ambient and quasi-ambient ionosphere defining the magnitude of the oncoming earthquake;

calculating and estimating the earthquake occurrence probability in a given seismic region from the characteristics and parameters of the spatial-temporal structure of the statistic characteristics of the electron concentration in the ionosphere and the neutral density in the atmosphere, and from the data about the level of turbulence in the medium, wherein estimation of the earthquake occurrence probability is performed by constructing the functional of an earthquake containing information about the statistical characteristics of the electron concentration in the ionosphere and neutral density in the atmosphere, and the perturbation level of the statistical characteristics of electron concentration in the ionosphere and neutral density in the atmosphere defines the level of turbulence in the medium and the magnitude of the oncoming earthquake;

determining a probability of epicenter coordinate distribution from the probability distribution of the coordinates of the sources of the waves, solitary waves, and soliton-like isolated perturbations in the ionosphere and atmosphere, wherein the probability distribution of the coordinates of all wave and wavelike disturbances during periods of high probability of an earthquake define the probability of distribution of the coordinates of the oncoming earthquake;

determining a probability of the epicenter coordinates distribution from the distribution of perturbation amplitudes in the characteristics and parameters of the structure of the ambient and quasi-ambient ionosphere, wherein the distribution of the perturbation amplitudes in the characteristics and parameters of the structure of the ambient and quasi-ambient ionosphere during periods of high probability of an earthquake defines the probability of distribution of the coordinates of the oncoming earthquake; and

determining a probability of the epicenter coordinates distribution from the distribution of the level of perturbations in the characteristics and parameters of the spatial-temporal structure of statistical characteristics of electron concentrations in the ionosphere and the neutral density in the atmosphere, and from the level of turbulence in the medium, wherein the distribution of amplitudes of the statistical characteristics of electron density in the ionosphere, the neutral density in the atmosphere, and the distribution of the level of turbulence in the medium during periods of high probability of an earthquake defines the probability of distribution of the coordinates of the oncoming earthquake.

9. The method of claim 8, wherein said step of evaluating said data for presence of large entity event precursors further comprises the steps of:

discriminating between earthquake precursors based on RTI effects caused by sources of other origin;

estimating the earthquake occurrence probability by constructing the functional of an earthquake containing information about the characteristics and parameters of the ionosphere and atmosphere within a given seismic region by separating the ionospheric effects caused by the earthquake preparation processes and influences of other factors, accounting for the level of solar-geophysical disturbance, by acquisition of relevant geophysical data and introducing those data into the process of constructing the earthquake predictor; and

estimating the earthquake occurrence probability by constructing the functional of an earthquake containing information about the characteristics and parameters of the ionosphere and atmosphere within a given seismic region by construction of the predictor with an iterative, self-learning procedure, using all available information about all previous strong earthquakes in a given region, archival data, lessons learned in the course of successful and failed predictions, and drawing on information about subsequent earthquakes.

10. The method of claim 3, wherein said acquired data comprises LORT data and said act of acquiring said LORT data comprises the steps of:

acquiring and combining the LORT data at a plurality of receiving sites in a region of interest, wherein the LORT data comprises the characteristics of wave fields, further comprising phases, group delays, amplitude of navigational signals at two coherent frequencies f₁ and f₂, as transmitted from orbiting navigational satellites,

transferring the acquired and combined data to at least one data processing and analysis center;

pre-processing said transferred acquired data by averaging, filtering, removal of phase ambiguities, and related preparatory procedures, and recording for permanent archival access.

11. The method of claim 10, wherein said correlating step further comprises the steps of:

measuring combined signals in said combined data;

measuring, at numerous receiving sites in a given region of the wave field, characteristics (phases, phase delays, group delays, phase and amplitude variances, correlation functions, coherence functions, moments and distribution functions, and amplitudes of navigational satellites at two or three coherent frequencies f₁, f₂ and f₃) transmitted from specialized satellite systems;

wherein the measured RTI quantities are all related to a distribution of electron density;

measuring, at numerous receiving sites in a given region of fluctuation, characteristics of the wave fields (variances, correlation functions, coherence functions, their moments and distribution functions of phases and amplitudes of signals at two coherent frequencies f₁ and f₂);

space-time imaging of ionospheric electron density and refraction index distribution in the neutral atmosphere by tomographic methods, using phase-difference radio tomography employing phase difference data in time or location of the satellite-borne transmitter, reducing the reconstruction problem to a function of differences of linear integrals, avoiding ambiguity of phase measurements and increasing reconstruction accuracy;

space-time imaging of statistic characteristics of ionospheric electron concentration and density of the neutral atmosphere by tomographic methods using the data on statistical characteristics of the wave fields;

determining structural parameters of regular and quasi-regular ionosphere;

determining structure and parameters of the disturbed ionosphere, including determination of the intensity and sizes of perturbations;

patches, blobs, bubbles, and other irregularities;

calculating the characteristics and parameters of ionospheric and atmospheric wave perturbations in the ionosphere and atmosphere;

amplitude, frequency, wavelength, and velocity of acoustic, internal gravity waves, acoustic-gravity waves in the ionosphere and atmosphere, and estimation of the location of the wave sources; and

calculating the characteristics and parameters of solitary waves, soliton-like waves, isolated perturbations in the ionosphere and atmosphere, including determination of amplitudes and velocities of the perturbations, to aid in location of the wave sources.

12. The method of claim 11, wherein said comparing step further comprises the steps of:

calculating and estimating an earthquake occurrence probability in a given seismic region from the characteristics and parameters of wavelike disturbances in the ionosphere and atmosphere, using amplitude, frequency, wavelength, velocity of acoustic, internal gravity wave (IGW), and acoustic gravity wave (AGW) in the atmosphere and ionosphere, by constructing the functional of the earthquake containing information about wave activity in said seismic region, determining spectral parameters of wave disturbances and the characteristics of perturbations using wavelet analysis, and separating acoustic, IGW, and AGW wavelike disturbances by analysis of the RTI images, frequencies, wavelengths, and velocities of the perturbations;

calculating and estimating the earthquake occurrence probability in a given seismic region from the characteristics and parameters of solitary waves, soliton-like isolated perturbations in the ionosphere and atmosphere, by constructing the functional (predictor) of the earthquake containing information about wave activity in said seismic region, where the wave activity is determined by energy parameters in solitary waves, and in soliton-like isolated perturbations in the ionosphere and atmosphere;

calculating and estimating the earthquake occurrence probability in a given seismic region from the characteristics and parameters of the structure of the ambient and quasi-ambient ionosphere (height and thickness of layers, concentration levels, parameters of ionization troughs in northern and southern latitudes, and parameters of the equatorial anomaly in equatorial latitudes), wherein the parameters of the structure of the ambient and quasi-ambient ionosphere which define the disturbance level of quasi-stationary electromagnetic fields in said seismic region are related to perturbations in geophysical fields within the region of earthquake preparation, and calculation of the earthquake occurrence probability is performed by constructing the functional (predictor) of the earthquake, containing information about the parameters of the structure of the ambient and quasi-ambient ionosphere with the level of disturbance in the parameters of the structure of ambient and quasi-ambient ionosphere defining the magnitude of the oncoming earthquake;

calculating and estimating the earthquake occurrence probability in a given seismic region from the characteristics and parameters of the spatial-temporal structure of the statistic characteristics of the electron concentration in the ionosphere and the neutral density in the atmosphere, and from the data about the level of turbulence in the medium, wherein estimation of the earthquake occurrence probability is performed by constructing the functional of an earthquake containing information about the statistical characteristics of the electron concentration in the ionosphere and neutral density in the atmosphere, and the perturbation level of the statistical characteristics of electron concentration in the ionosphere and neutral density in the atmosphere defines the level of turbulence in the medium and the magnitude of the oncoming earthquake;

determining a probability of epicenter coordinate distribution from the probability distribution of the coordinates of the sources of the waves, solitary waves, and soliton-like isolated perturbations in the ionosphere and atmosphere, wherein the probability distribution of the coordinates of all wave and wavelike disturbances during periods of high probability of an earthquake define the probability of distribution of the coordinates of the oncoming earthquake;

determining a probability of the epicenter coordinates distribution from the distribution of perturbation amplitudes in the characteristics and parameters of the structure of the ambient and quasi-ambient ionosphere, wherein the distribution of the perturbation amplitudes in the characteristics and parameters of the structure of the ambient and quasi-ambient ionosphere during periods of high probability of an earthquake defines the probability of distribution of the coordinates of the oncoming earthquake; and

determining a probability of the epicenter coordinates distribution from the distribution of the level of perturbations in the characteristics and parameters of the spatial-temporal structure of statistical characteristics of electron concentrations in the ionosphere and the neutral density in the atmosphere, and from the level of turbulence in the medium, wherein the distribution of amplitudes of the statistical characteristics of electron density in the ionosphere, the neutral density in the atmosphere, and the distribution of the level of turbulence in the medium during periods of high probability of an earthquake defines the probability of distribution of the coordinates of the oncoming earthquake.

13. The method of claim 12, wherein said step of evaluating said data for presence of large entity event precursors further comprises the steps of:

discriminating between earthquake precursors based on RTI effects caused by sources of other origin;

estimating the earthquake occurrence probability by constructing the functional of an earthquake containing information about the characteristics and parameters of the ionosphere and atmosphere within a given seismic region by separating the ionospheric effects caused by the earthquake preparation processes and influences of other factors, accounting for the level of solar-geophysical disturbance, by acquisition of relevant geophysical data and introducing those data into the process of constructing the earthquake predictor; and

estimating the earthquake occurrence probability by constructing the functional of an earthquake containing information about the characteristics and parameters of the ionosphere and atmosphere within a given seismic region by construction of the predictor with an iterative, self-learning procedure, using all available information about all previous strong earthquakes in a given region, archival data, lessons learned in the course of successful and failed predictions, and drawing on information about subsequent earthquakes.

14. The method of claim 6, wherein said acquired data further comprises cellular telephone signal fading (CTSF) data;

and wherein said method further comprises;

acquiring signal strength and noise data from a plurality of ground stations, mobile stations, and cellular telephones in the region of interest;

comparing and contrasting said acquired signal strength and noise data over an extended period of time, recording trends in said signal strength and noise data;

eliminating known diurnal, seasonal, and associated effects from said data;

correlating signal strength and noise trends against a predictive model; and

refining the geo-spatial and temporal predictions relating to the impending earthquake based on the knowledge that the earthquake will be focused in the region of maximum noise change.

15. The method of claim 14, wherein said model predicts a gradual rise in noise, and correlative decrease in signal strength, followed by a sharp decrease shortly before the earthquake, with a rapid rise to the previous maximum just minutes or seconds prior to the event.

16. The method of claim 10, wherein said acquired data further comprises cellular telephone signal fading (CTSF) data;

and wherein said method further comprises;

acquiring signal strength and noise data from a plurality of ground stations, mobile stations, and cellular telephones in the region of interest;

comparing and contrasting said acquired signal strength and noise data over an extended period of time, recording trends in said signal strength and noise data;

eliminating known diurnal, seasonal, and associated effects from said data;

correlating signal strength and noise trends against a predictive model; and

refining the geo-spatial and temporal predictions relating to the impending earthquake based on the knowledge that the earthquake will be focused in the region of maximum noise change.

17. The method of claim 6 or 10, wherein said acquired data further comprises radar clutter map data and said method further comprises the steps of:

acquiring the radar clutter map data from Nexrad and K-band radars over an extended period of time in areas identified as at risk for impending earthquakes;

performing frame-to-frame comparisons of the clutter map data over varying periods of time to determine the presence of persistent versus sporadic clutter;

eliminating from consideration clutter typically present due to seasonal, daily, or diurnal changes in the atmosphere;

eliminating from consideration clutter typically present due to man-made or natural anomalies other than earthquake preparation process;

eliminating from consideration clutter which is associated with changing weather patterns;

focusing the K-band radar on those sectors of the clutter map showing persistent clutter and in the general vicinity of the impending earthquake as previously predicted;

determining from K-band radar data the presence of active aerosols, which are indicative of precursory earthquake activity;

refining the geo-spatial and temporal predictions relating to the impending earthquake based on knowledge that the earthquake will be focused near the region of maximum Nexrad and K-band clutter.

18. A system for processing geospheric data to predict a large entity event, said system comprising:

a plurality of receiving stations for acquiring data, the acquired data comprising one or more of high orbiting radio-tomopraphy (HORT), low orbiting radio-tomopraphy (LORT), radar clutter, and cellular telephone signal fading and noise data;

means for correlating said acquired data with secondary data, wherein the secondary data comprises at least one of archival data, magnetospheric data, seismic data from a region of interest, solar data, data relating to man-made activities that may influence the prediction, and other cosmic, solar, geophysical, or terrestrial data which may have an impact on the prediction;

means for comparing said correlated data with archived data sets, in an iterative process, revisiting the correlated data to remove false signals or irrelevant noise and to extract meaningful trends; and

means for evaluating said data for presence of large entity event precursors.

19. The system of claim 18, further comprising means for disseminating an alert if it is determined from said evaluated data that a large entity event is imminent.

20. The system of claim 18, wherein said large entity event is an earthquake.

21. The system of claim 18, wherein said large entity event is a seaquake.

22. The system of claim 18, wherein said large entity event is a tsunami.