Information processing device, information processing program, and information processing method

Abstract

To provide an information processing device capable of highly accurately estimating the magnitude and the distance from a ground motion observation location to the earthquake center, even when noise is superimposed on the earthquake motion.SOLUTION: An amplitude calculation unit 21 acquires observed digital data Dg(l*) from a ground motion sensor 10 and calculates an amplitude x(l*) caused by the ground motion. A frequency analysis unit 22 calculates frequency characteristics such as a running spectrum PS(l*,f) from the amplitude x(l*). A magnitude estimation unit 24 estimates the magnitude of an earthquake using the frequency characteristics such as the running spectrum PS(l*,f). A distance estimation unit 25 estimates the distance from a ground motion observation location to the earthquake center using the amplitude x(l*) and the magnitude. By means of the frequency analysis unit 22, the magnitude estimation unit 24, and the distance estimation unit 25, the magnitude and the distance to the earthquake center can be highly accurately estimated even when noise is superimposed on the earthquake motion.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present disclosure relates to a seismograph that estimates the magnitude and the distance to the earthquake source from the time series of observation data observed by a ground motion sensor.

Background Art

[0002] A seismograph is a device that immediately estimates damage immediately after an earthquake occurs and prevents damage. In order to estimate damage, the seismograph needs to accurately estimate the magnitude and the distance to the earthquake source.

[0003] As a method for estimating the magnitude and the distance to the earthquake source, Patent Document 1 discloses a method for estimating the magnitude and the distance to the earthquake source from the growth rate of the P-wave onset in the observation data.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, in the technique disclosed in Patent Document 1, when noise such as vibration by a vehicle overlaps with the P-wave onset, there is a problem that the growth rate of the P-wave onset cannot be accurately calculated, and the distance to the earthquake source and the magnitude may not be accurately estimated.

[0006] An object of the present disclosure is to provide a seismograph that can accurately estimate the distance to the earthquake source and the magnitude even when noise such as vibration by a vehicle overlaps with the P-wave onset.

Means for Solving the Problems

[0007] The information processing apparatus according to the present disclosure is An amplitude calculation unit that calculates the amplitude caused by ground motion from the observed data observed by a ground motion sensor, A frequency analysis unit that calculates frequency characteristics in real time from the amplitude using an autoregressive model, A magnitude estimation unit that estimates the magnitude of an earthquake that causes the ground motion by using the frequency characteristics, and is provided with.

Effect of the Invention

[0008] Since the information processing apparatus according to the present disclosure includes a frequency analysis unit, a magnitude estimation unit, and a distance estimation unit to the earthquake source, even if noise such as vibration by a vehicle overlaps with the P-wave first motion, it is possible to provide an apparatus that correctly estimates the magnitude of an earthquake and the distance to the earthquake source.

Brief Description of the Drawings

[0009]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Modes for Carrying Out the Invention

[0010] In the description of the embodiments and the drawings, the same elements and corresponding elements are denoted by the same reference numerals. The description of the elements denoted by the same reference numerals may be omitted or simplified as appropriate. In the following embodiments, "unit" may be appropriately read as "circuit", "process", "step", "processing", or "circuitry".

[0011] The symbols used in the first embodiment are summarized below.

[0012] In the descriptions other than Formula 1 to Formula 14, the lowercase letter l of L will be written as l to distinguish it from the number 1. * It is written as follows.

[0013] <Index and data sent and received> (1)l * :l * is a counter that counts up with each sampling. (2) m:m is the index of the AR model. (3) f: f is frequency (Hz). (4)Dg(l * ):Dg(l * ) is the observation digital data. (5)x(l * ):x(l * ) is the amplitude data in physical units. (6)φS m (l * ):φS m (l * ) are the AR coefficients of the AR model (m = 1, , M). (7)σS 2 (l * ):σS j 2 (l * ) is the variance of the estimation error of the amplitude data by the AR model. (8)PS(l * ,f):PS(l * ,f) is the running spectrum by the AR model. (9)fSd(l * ):fSd(l * ) is the dominant frequency. (10) Mag(l * ):Mag(l * ) is an estimate of the magnitude. (11) Dist(l * ):Dist(l *) is the estimated distance to the earthquake source.

[0014] <Internal variable> (1) μS(l * ): μS(l * ) is the mean by the AR model. (2) CS m (l * ): CSm(l * ) is the covariance function by the AR model (m = 0, ···, M). (3) xS(l * ): xS(l * ) is the estimated value of the amplitude data by the AR model. (4) Q: Q is the number of characteristic roots of the AR model. (5) zS q (l * ): zS q (l * ) is the characteristic root of the AR model (q = 1, ···, Q). (6) fS q (l * ): fS q (l * ) is the peak frequency of the running spectrum by the AR model (q = 1, ···, Q). (7) PS q (l * ): PS q (l * ) is the peak value of the running spectrum by the AR model (q = 1, ···, Q).

[0015] <Parameter> (1) fs: fs is the sampling frequency (Hz) of the observed digital data. (2) Cf: Cf is the physical value conversion coefficient. (3) M: M is the order of the AR model. (4) rS: rS is the forgetting coefficient of the AR model. It is a coefficient that determines the window size for smoothing the running spectrum. The larger rS is, the smaller the window size and the shorter the time average. (5) CMa: CMa is the coefficient 1 for calculating the estimated value of the magnitude. (6) CMb: CMb is the coefficient 2 for calculating the estimated magnitude. (7) CDa: CDa is the coefficient 1 for calculating the estimated distance to the earthquake source. (8) CDb: CDb is the coefficient 2 for calculating the estimated distance to the earthquake source. (9) CDc: CDc is the coefficient 3 for calculating the estimated distance to the earthquake source. (10) CDd: CDd is the coefficient 4 for calculating the estimated distance to the earthquake source. (11) CDe: CDe is the coefficient 5 for calculating the estimated distance to the earthquake source. (12) CDf: CDf is the coefficient 6 for calculating the estimated distance to the earthquake source. (13) CDg: CDg is the coefficient 7 for calculating the estimated distance to the earthquake source.

[0016] Embodiment 1. Referring to FIGS. 1 to 4, the seismometer 30 of Embodiment 1 will be described.

[0017] ***Description of the Configuration*** FIG. 1 is a system configuration diagram of the seismometer 30. The seismometer 30 includes a ground motion sensor 10 and an information processing device 20. The seismometer 30 can communicate with an external device 40 via a network 50.

[0018] FIG. 2 is a block configuration of the information processing device 20. The information processing device 20 includes, as functional elements, an amplitude calculation unit 21, a frequency analysis unit 22, a dominant frequency calculation unit 23, a magnitude estimation unit 24, and a distance estimation unit 25.

[0019] ***Description of the Operation*** FIG. 3 is a flowchart showing the operation of the information processing apparatus 20 to estimate the magnitude and the distance to the earthquake source from the time series of the observation data observed by the seismic sensor 10. The operation of the information processing apparatus 20 will be described with reference to FIG. 3. The operation of the information processing apparatus 20 corresponds to an information processing method. Further, the operation of the information processing method corresponds to the processing by the information processing program 231 described later. In FIG. 3, the operation of the information processing apparatus 20 is shown as processes P201 to P205. The information processing apparatus 20 obtains ground motion as observed digital data from the seismic sensor 10 at intervals of the reciprocal (1 / fs) of the sampling frequency fs -1 Ground motion is the vibration of the earth, and the vibration of the earth caused by an earthquake among ground motions is called "seismic motion". Hereinafter, the reciprocal (1 / fs) -1 is referred to as the sampling interval. For each sampling, the information processing apparatus 20 performs estimation processing of the magnitude of the earthquake causing the ground motion and the distance from the installation position of the seismic sensor 10 to the earthquake source.

[0020] <Process P201: Calculate the amplitude of the observation data> (1) The amplitude calculation unit 21 acquires the observed digital data Dg(l * ) of the ground motion observed by the seismic sensor 10 from the seismic sensor 10. The observed digital data Dg(l * ) is ground motion. (2) The amplitude calculation unit 21 calculates the amplitude data x(l * ) of the ground motion based on the acquired observed digital data Dg(l * ) by Equation 1. The amplitude calculation unit 21 calculates the amplitude data x(l * ), which is the amplitude, from the observed digital data Dg(l * ).

Equation

[0021] <Process P202: Calculate the frequency characteristics> The frequency analysis unit 22 calculates the frequency characteristics in real time from the amplitude data x(l * ) using an autoregressive model. Specifically, it is as follows. The frequency analysis unit 22 first performs smoothing by time averaging on the time series of the amplitude data x(l * ). Next, by expressing it with an autoregressive model, the time-averaged running spectrum of the amplitude data is calculated. Hereinafter, the autoregressive model is referred to as an AR model. The frequency characteristics refer to the combination of the AR coefficient φS m (l * ) of the AR model, the variance σS 2 (l * ) of the estimation error of the amplitude data by the AR model, and the running spectrum PS(l * , f) by the AR model. That is, the frequency characteristics include the AR coefficient φS m (l * ) of the AR model, the variance σS 2 (l * ) of the estimation error of the amplitude data by the AR model, and the running spectrum PS(l * , f) by the AR model. (1) The frequency analysis unit 22 calculates the average μS(l * ) by the AR model using Equation 2.

Equation

Equation

number

number

number

number

[0022] <Process P203: Calculate dominant frequency> (1) The dominant frequency calculation unit 23 calculates the dominant frequency from the frequency characteristics calculated by the frequency analysis unit 22 for the amplitude data x(l * ). Specifically, it is as follows. The dominant frequency calculation unit 23 solves the M-th order polynomial (characteristic equation) for z in Equation 8, and among the solutions, those with an imaginary part of 0 or positive are the characteristic roots zS of the AR model q (l * )(q is a serial number, q = 1, ···, Q).

Equation

Equation

Number

[0023] <Process P204: Calculate the estimated value of magnitude> The magnitude estimation unit 24 estimates the magnitude of the earthquake causing the ground motion by using the frequency characteristics. The magnitude estimation unit 24 estimates the magnitude by using the dominant frequency calculated by the dominant frequency calculation unit 23. Specifically, it is as follows The magnitude estimation unit 24 calculates the estimated value Mag of the magnitude in Equation 11 * ()

Number

[0024] <Process P205: Calculate the estimated value of the distance to the earthquake source> The distance estimation unit 25 estimates the distance Dist from the installation position of the ground motion sensor 10 to the earthquake source based on the amplitude data x(l * ) and the magnitude Mag(l * ). That is, the installation position of the ground motion sensor 10 is the position where the ground motion is observed. Specifically, it is as follows. The distance estimation unit 25 uses the Mag(l * ) estimated by Equation 11 to estimate the distance Dist(l * ) from the position where the ground motion is observed to the earthquake source. The position where the ground motion is observed is the installation position of the ground motion sensor 10. Hereinafter, "the distance to the earthquake source" means "the distance from the position where the ground motion is observed to the earthquake source". The distance estimation unit 25 calculates the estimated value Dist(l * ) of "the distance to the earthquake source" by Equation 12.

[0025]

Number

[0026] By the procedure of the above information processing apparatus 20, it is possible to estimate the magnitude and the distance to the seismic source with high accuracy.

[0027] Also, at a certain timing (counter) "l" * ", Mag(l * ) is calculated from Equation 11 and Dist(l * ) is calculated from Equation 12. The magnitude estimation unit 24 sets the calculated Dist(l * ) to DistInit. At a certain timing "l" * " after the timing "l" ** ", the amplitude data x(l ** ) becomes large. The magnitude estimation unit 24 may calculate the estimated value Mag(l * ) at the timing "l" ** " using DistInit at the timing "l" ** " and the amplitude data x(l ** ) at the timing "l" ** ) from Equation 13. The left side of Equation 13 is Mag(l ** ). The x(l) on the right side of Equation 13 is x(l ** ).

[0028]

Equation

[0029] By using Equation 13, Mag(l ** ) at timing "l ** " can be estimated more accurately.

[0030] <Modification Example> Figure 4 is a flowchart showing the operation of a modification example of Embodiment 1. The modification example will be described with reference to Figure 4.

[0031] <Process P205a> In Figure 3, Mag(l * ) was calculated using Equation 11, and then Dist(l * ) was calculated using Mag(l * ) in Equation 12. In the modification example described in Figure 4, in Process P205a, the distance estimation unit 25 calculates Dist(l * ) according to the following Equation 14. Equation 14 is a form in which Mag(l * ) in Equation 11 is replaced with Dist(l * ), and the coefficients CMa and CMb are replaced with CDf and CDg. That is, in Process P205a, the distance estimation unit 25 estimates the distance from the installation position of the ground motion sensor 10 to the seismic source that causes the ground motion by using the frequency characteristics. The distance estimation unit 25 estimates the distance from the installation position of the ground motion sensor 10 to the seismic source by utilizing the dominant frequency. Specifically, it is as follows.

[0032]

Equation

[0033] <Process P204a> Next, in process P204a, the magnitude estimation unit 24 estimates the magnitude of the earthquake based on the amplitude and the distance from the installation position of the ground motion sensor 10 to the earthquake source. Specifically, it is as follows. That is, the magnitude estimation unit 24 uses Dist(l * ) calculated by Equation 14 of Process P205a to calculate Mag(l * ) by Equation 12. After calculating Dist(l * ) as described above, Mag(l * ) may be calculated using that Dist(l * ).

[0034] Note that, as described in Equation 13, in the case of Figure 4 as well, as described in Equation 13, Dist(l * ) calculated in Process P205a is set to DistInit, and Mag(l ** ) may be calculated from Equation 13 using DistInit and x(l ** ).

[0035] ***Effects of Embodiment 1*** (1) In Embodiment 1, the dominant frequency fSd(l * ) of the ground motion obtained from Equation 10 is utilized. Therefore, even if noise such as vibration caused by a vehicle having a dominant frequency different from the general dominant frequency of the ground motion overlaps the ground motion, it is possible to accurately estimate the magnitude and the distance to the earthquake source. (2) In Embodiment 1, since Mag(l ** ) is calculated from DistInit and x(l ** ) in Equation 13, a highly accurate magnitude estimation value can be obtained.

[0036] In Embodiment 1, the epicentral distance was assumed as the distance from the installation position of the ground motion sensor 10 to the earthquake source, but embodiments using the hypocentral distance or the shortest fault distance are possible.

[0037] Also, in Figure 3, Mag(l* ) was calculated, and Dist(l * ) was calculated in Equation 12 using this Mag(l * ). Also, in the modification example described in FIG. 4, Dist(l * ) was calculated according to Equation 14, and Mag(l * ) was calculated in Equation 12 using this Dist(l * ). However, not limited to these, it is also possible to configure to simultaneously calculate Mag(l * ) and Dist(l * ) according to Equation 11 and Equation 14.

[0038] Further, the magnitude estimation unit 24 may learn the characteristics of the magnitude value Mag(l * , f) of the running spectrum PS(l * ) calculated by Equation 7 using machine learning such as deep learning. That is, the magnitude estimation unit 24 learns the correspondence between PS(l * , f) and Mag(l * ) using a plurality of sets of the running spectrum PS(l * , f) and the magnitude value Mag(l * ) as teacher data. Thereby, the magnitude estimation unit 24 can determine Mag(l * , f) for a new PS(l * ). The distance estimation unit 25 can calculate the distance Dist(l * ) to the earthquake source from the determined Mag(l * ) using Equation 12. Thereby, an embodiment capable of estimating the magnitude and the distance to the earthquake source with high accuracy is possible. Alternatively, the distance estimation unit 25 learns the correspondence between PS(l * , f) and Dist(l * ) using a plurality of sets of the running spectrum PS(l * , f) and the estimated distance Dist(l * ) as teacher data. Thereby, the distance estimation unit 25 can determine Dist(l * , f) for a new PS(l *) can be calculated. The magnitude estimation unit 24 calculates Mag(l * ) from the determined Dist(l * ) using Equation 12. Alternatively, in the case of the "configuration for simultaneously obtaining Mag(l * ) and Dist(l * ) by Equations 11 and 14 described above", it is as follows. That is, the magnitude estimation unit 24 uses a plurality of sets of the running spectrum PS(l * , f), the magnitude value Mag(l * ), and the estimated distance Dist(l * ) as teacher data to learn the correspondence between PS(l * , f), Mag(l * ), and Dist(l * ). Thereby, the magnitude estimation unit 24 can simultaneously calculate Mag(l * ) and Dist(l * ) for a new PS(l * ). This machine learning may be performed by the distance estimation unit 25 instead of the magnitude estimation unit 24. Alternatively, a learning unit including the magnitude estimation unit 24 and the distance estimation unit 25 may perform this machine learning.

[0039] Also, since zS q (l * ), fS q (l * ), and PS q (l * ) all correspond to reducing the dimension of the running spectrum over all frequencies f, by using all or part of these for effective learning of the characteristics of the magnitude value using machine learning such as deep learning, an embodiment capable of estimating the magnitude with high accuracy and the distance to the earthquake source is possible.

[0040] Also, in Embodiment 1, the amplitude data x(l *) was assumed to be one of the three spatial components, but embodiments are also possible when using multiple spatial components simultaneously or when using function values calculated from multiple spatial components.

[0041] Also, in Embodiment 1, an autoregressive model was used in the frequency analysis unit 22 to calculate the running spectrum, but embodiments are also possible in which the running spectrum is calculated by other methods such as FFT (Fast Fourier Transform). For example, the frequency analysis unit 22 calculates the frequency characteristics from the amplitude in real time by other methods such as FFT. Then, the distance estimation unit 25 may estimate the distance from the installation position of the ground motion sensor 10 to the earthquake source that causes the ground motion by using the calculated frequency characteristics.

[0042] Also, the functional forms of Equation 11 and Equation 13 for calculating the estimated value of magnitude Mag(l * ) may be other than these, and the functional form of Equation 12 for calculating the estimated value of the distance to the earthquake source Dist(l * ) may be other than these.

[0043] (Supplement on Hardware) FIG. 5 shows the hardware configuration of the information processing apparatus 20. The hardware configuration of the information processing apparatus 20 is shown. The information processing apparatus 20 is a computer. The information processing apparatus 20 includes a processor 210. As hardware, the information processing apparatus 20 includes a processor 210, a main storage device 220, an auxiliary storage device 230, and a communication interface 240. The processor 210 is connected to other hardware via a signal line 250 and controls other hardware.

[0044] The information processing apparatus 20 includes an amplitude calculation unit 21, a frequency analysis unit 22, a dominant frequency calculation unit 23, a magnitude estimation unit 24, and a distance-to-source estimation unit 25. These functions are realized by an information processing program 231.

[0045] The processor 210 is a device that executes an information processing program 231. When the processor 210 executes the information processing program 231, the functions of an amplitude calculation unit 21, a frequency analysis unit 22, a dominant frequency calculation unit 23, a magnitude estimation unit 24, and a distance-to-epicenter estimation unit 25 are realized. The processor 210 is an integrated circuit (IC) that performs arithmetic processing.

[0046] A specific example of the main memory device 220 is an SRAM (Static Random Access Memory). The main memory 220 stores the results of calculations performed by the processor 210.

[0047] The auxiliary storage device 230 is a storage device that stores data in a non-volatile manner. A specific example of the auxiliary storage device 230 is a hard disk drive (HDD). The auxiliary storage device 230 may also be a portable recording medium. The auxiliary storage device 230 stores an information processing program 231.

[0048] The communication interface 240 is a communication port that allows the processor 210 to communicate with other devices. The communication interface 240 is connected to a network 50.

[0049] The processor 210 loads the information processing program 231 from the auxiliary storage device 230 into the main storage device 220. The processor 210 reads the loaded information processing program 231 from the main storage device 220 and executes it.

[0050] The information processing program 231 is a program that causes a computer to execute each process, each procedure, or each step, where each "unit" of the information processing device 20 is replaced with a "process," a "procedure," or a "step."

[0051] In addition, the method performed by the information processing apparatus 20, which is a computer, by executing the information processing program 231 is an information processing method. The information processing program 231 may be stored and provided in a computer-readable recording medium, or may be provided as a program product.

Explanation of Signs

[0052] 10 Seismic sensor, 20 Information processing apparatus, 21 Amplitude calculation unit, 22 Frequency analysis unit, 23 Dominant frequency calculation unit, 24 Magnitude estimation unit, 25 Distance estimation unit, 30 Seismograph, 40 External device, 50 Network, 210 Processor, 220 Main memory device, 230 Auxiliary storage device, 231 Information processing program, 240 Communication interface, 250 Signal line.

Claims

1. An amplitude calculation unit that calculates the amplitude caused by ground motion from the observation data observed by a ground motion sensor, a frequency analysis unit that calculates frequency characteristics in real time from the amplitude using an autoregressive model, a magnitude estimation unit that estimates the magnitude of an earthquake that causes the ground motion by using the frequency characteristics, and an information processing apparatus comprising the same.

2. The information processing apparatus further comprises a dominant frequency calculation unit that calculates the dominant frequency of the amplitude from the frequency characteristics, wherein the magnitude estimation unit estimates the magnitude by using the dominant frequency, according to the information processing apparatus of claim 1.

3. The information processing apparatus further comprises a distance estimation unit that estimates the distance from the installation position of the ground motion sensor to the earthquake epicenter based on the amplitude and the magnitude, according to the information processing apparatus of claim 1 or claim 2.

4. An information processing program for causing a computer to execute an amplitude calculation process for calculating the amplitude caused by ground motion from the observation data observed by a ground motion sensor, a frequency analysis process for calculating frequency characteristics in real time from the amplitude using an autoregressive model, and a magnitude estimation process for estimating the magnitude of an earthquake that causes the ground motion by using the frequency characteristics.

5. The information processing program causes the computer to further execute a dominant frequency calculation process for calculating the dominant frequency of the amplitude from the frequency characteristics, wherein the magnitude estimation process estimates the magnitude by using the dominant frequency, according to the information processing program of claim 4.

6. The information processing program causes the computer to further execute a distance estimation process for estimating the distance from the installation position of the ground motion sensor to the earthquake epicenter based on the amplitude and the magnitude, according to the information processing program of claim 4 or claim 5.

7. An information processing method, wherein a computer calculates the amplitude caused by ground motion from the observation data observed by a ground motion sensor, calculates frequency characteristics in real time from the amplitude using an autoregressive model, and estimates the magnitude of an earthquake that causes the ground motion by using the frequency characteristics.

8. An amplitude calculation unit that calculates the amplitude caused by ground motion from the observation data observed by a ground motion sensor, and a frequency analysis unit that calculates frequency characteristics in real time from the amplitude. A distance estimation unit that estimates the distance from the installation position of the ground motion sensor to the earthquake source that causes the ground motion by using the frequency characteristics. An information processing apparatus comprising the above.

9. The frequency analysis unit The information processing apparatus according to claim 8, wherein the frequency characteristics are calculated in real time from the amplitude using an autoregressive model.

10. The information processing apparatus further comprises A dominant frequency calculation unit that calculates the dominant frequency of the amplitude from the frequency characteristics, and The distance estimation unit The information processing apparatus according to claim 8 or claim 9, wherein the distance from the installation position of the ground motion sensor to the earthquake source is estimated by using the dominant frequency.

11. The information processing apparatus further comprises A magnitude estimation unit that estimates the magnitude of the earthquake based on the amplitude and the distance from the installation position of the ground motion sensor to the earthquake source. The information processing apparatus according to claim 8 or claim 9, comprising the above.

12. The information processing apparatus further comprises A magnitude estimation unit that estimates the magnitude of the earthquake based on the amplitude and the distance from the installation position of the ground motion sensor to the earthquake source. The information processing apparatus according to claim 10, comprising the above.

13. Causing a computer to Perform an amplitude calculation process for calculating the amplitude caused by ground motion from the observation data observed by a ground motion sensor, A frequency analysis process for calculating frequency characteristics in real time from the amplitude, and A distance estimation process for estimating the distance from the installation position of the ground motion sensor to the earthquake source that causes the ground motion by using the frequency characteristics. An information processing program.

14. The frequency analysis process The information processing program according to claim 13, wherein the frequency characteristics are calculated in real time from the amplitude using an autoregressive model.

15. The information processing program Further causing the computer to Perform a dominant frequency calculation process for calculating the dominant frequency of the amplitude from the frequency characteristics, and The distance estimation process The information processing program according to claim 13 or claim 14, wherein the distance from the installation position of the ground motion sensor to the earthquake source is estimated by using the dominant frequency.

16. The information processing program Further causing the computer to a magnitude estimation process for estimating the magnitude of the earthquake based on the amplitude and the distance from the installation position of the ground motion sensor to the epicenter; 15. The information processing program according to claim 13, which causes the computer to execute the above steps.

17. The information processing program The computer further comprises: a magnitude estimation process for estimating the magnitude of the earthquake based on the amplitude and the distance from the installation position of the ground motion sensor to the epicenter; 16. The information processing program according to claim 15, which causes the program to execute the following:

18. The computer The amplitude caused by ground motion is calculated from the observation data obtained by the ground motion sensor. Calculating frequency characteristics from the amplitude in real time; Using the frequency characteristics, a distance from the installation position of the ground motion sensor to the epicenter of the earthquake that caused the ground motion is estimated. Information processing methods.

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