Detection device for detecting earthquake motion and prediction device for predicting the intensity of earthquake motion based on the detection results

Abstract

To provide a seismic motion detection / prediction system capable of further quickly and highly precisely detecting P-waves and estimating intensity of S-waves based on the detected P-waves.SOLUTION: A P-wave detector in a seismic motion detection / prediction system includes a plurality of seismometers, a calculation part for calculating a determination value for determining detection of P-waves, a determination part for determining whether or not to detect the P-waves based on the determination value, and a communication part for transmitting P-wave data for use in prediction of S-waves to the outside when the determination part determines that the P-waves are detected. An S-wave prediction device in the seismic motion detection / prediction system includes a communication part for receiving the P-wave data transmitted from the P-wave detector, and an S-wave prediction part for predicting intensity of incoming S-waves based on the P-wave data.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to an earthquake motion detection and prediction technology. More specifically, the present invention relates to an earthquake motion detection and prediction system that detects P waves that generate initial tremors and predicts S waves that generate major tremors based on the detected P waves, the system including a P wave detection device that detects P waves using multiple seismometers, and an S wave prediction device that predicts the intensity of S waves based on the P waves detected by the P wave detection device. [Background technology]

[0002] It is extremely difficult to predict the occurrence of an earthquake. For this reason, earthquake early warning systems have been proposed that calculate the time of occurrence, epicenter location, and magnitude of an earthquake based on earthquake observation information observed at many seismological observation points immediately after the occurrence of an earthquake, and then use this information to predict the arrival time and intensity of the main shock to areas that have not yet reached the earthquake.

[0003] For example, Patent Document 1 (JP 2015-25714 A) discloses a disaster warning linkage system that issues a warning based on information from a seismometer installed in a building in an area near the epicenter and links to an on-site warning for earthquake response according to the seismic intensity and damage situation. This system is configured to include various home appliances installed in the building and communicably connected to the seismometer, and a central server communicably connected to seismometers throughout the country and aggregating earthquake information from the seismometers throughout the country.

[0004] The applicant of the present application has also proposed an earthquake warning system disclosed in Patent Document 2. This earthquake warning system is configured to install three or more seismometers within the premises of a building or business, to determine whether P waves have been detected based on the correlation coefficient between data signals measured by these seismometers, and to predict the strength of S waves based on the P wave data received from the P wave detection points and to issue a warning based on this. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2015-25714 [Patent Document 2] Patent No. 6887310 Summary of the Invention [Problem to be solved by the invention]

[0006] However, in conventional systems, including the system disclosed in Patent Document 1, it is assumed that each seismometer is installed at a considerable distance from each other, and even if the information from these seismometers is collected in a central server, it is difficult to quickly and accurately detect the P waves that generate the initial tremors.

[0007] Furthermore, conventional systems aggregate information from multiple seismometers into a central server, and even if the central server predicts the arrival of S waves that generate major earthquakes based on the information from the multiple seismometers, it takes time to process the vast amount of data from the seismometers, and is not capable of issuing an emergency alert.

[0008] The technology proposed in Patent Document 2 is a useful technology that can detect P waves quickly and with high accuracy compared to conventional systems including the system disclosed in Patent Document 1. However, in recent years, in consideration of responding to epicentral earthquakes, which are feared to be occurring at an increasing rate, particularly in the Tokyo metropolitan area, there is a demand for faster and more accurate seismic motion prediction technology.

[0009] The present invention aims to provide an earthquake motion detection and prediction system that can detect P waves more quickly and with higher accuracy, and can predict the intensity of S waves based on the detected P waves. [Means for solving the problem]

[0010] The present invention provides a P-wave detection device that is used in a seismic motion detection and prediction system that detects P-waves that generate initial tremors and predicts S-waves that generate main tremors based on the detected P-waves, and is placed at a P-wave observation point to detect P-waves. The P-wave detection device includes multiple seismometers, a calculation unit that calculates a judgment value for determining whether P-waves have been detected, a judgment unit that determines whether P-waves have been detected based on the judgment value, and a communication unit that transmits P-wave data used to predict S-waves to the outside when the judgment unit determines that P-waves have been detected. The judgment value for determining whether P-waves have been detected is calculated using multiple real-time measurement data measured by each of the multiple seismometers at predetermined time intervals (referred to herein as "element intervals").

[0011] In one embodiment, the judgment value is calculated using the standard deviation of multiple pieces of real-time measurement data. In another embodiment, the judgment value is calculated using the average deviation of multiple pieces of real-time measurement data. In yet another embodiment, the judgment value is calculated using a correlation function of multiple pieces of real-time measurement data between multiple seismometers. The judgment may also be calculated using a combination of these.

[0012] The P-wave detection device preferably further includes a preceding rupture filter that detects preceding rupture seismic motion associated with a P-wave that occurs immediately before the P-wave and distinguishes the preceding rupture seismic motion from the P-wave. The P-wave detection device preferably further includes a minute event filter that detects minute events unrelated to the P-wave that occur immediately before the P-wave and distinguishes the minute events from the P-wave.

[0013] The present invention also provides an S-wave prediction device for predicting S-waves based on detected P-waves, which is used in a seismic motion detection and prediction system that detects P-waves that generate initial tremors and predicts S-waves that generate primary tremors based on the detected P-waves. The S-wave prediction device includes a communication unit that receives P-wave data transmitted from the P-wave detection device, and an S-wave prediction unit that predicts the intensity of arriving S-waves based on the P-wave data.

[0014] The S-wave prediction device may be a mobile body, in which case it is preferable to further include a position measurement unit that measures the position of the S-wave prediction device, and a grace period calculation unit that calculates the grace period until the S-wave arrives based on the measured position and the position of the P-wave detection device that detected the P-wave. [Effects of the Invention]

[0015] The earthquake motion detection and prediction system according to the present invention is configured to detect and determine P-waves based on multiple seismometers installed within a building or on the premises of a business, and to predict the intensity of S-waves based on P-wave data received from P-wave detection points. Therefore, the earthquake motion detection and prediction system according to the present invention is capable of quickly and accurately detecting P-waves that generate initial tremors, and is also capable of quickly issuing emergency alerts regarding major tremors. Furthermore, the earthquake motion detection and prediction system according to the present invention is equipped with a filter for distinguishing between P-waves and micro-events unrelated to the earthquake that occur before an earthquake and precursor ruptures related to the earthquake, thereby preventing P-waves from being overlooked or underestimated, and enabling more accurate P-wave detection and prediction. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a schematic diagram showing an example of the installation of a main system in an earthquake motion detection and prediction system according to one embodiment of the present invention. [Figure 2] FIG. 1 is a diagram showing an image of the installation of the main system in the earthquake motion detection and prediction system. [Figure 3A] This is a block diagram showing an overview of the seismic motion detection and prediction system, in which each of the main systems functions as a P-wave detection device and an S-wave prediction device. [Figure 3B] FIG. 2 is a block diagram showing the functions of a data processing unit of the main system. [Figure 4] This is a flowchart of the basic processing in the earthquake motion detection and prediction system, where (a) shows the flowchart when the main system performs P-wave detection processing, and (b) shows the flowchart when the main system performs S-wave prediction processing. [Figure 5] An example of measurement data from a seismometer is shown below. [Figure 6] 10 is a flowchart of a process for determining whether a P-wave has been detected using standard deviation as an index when the main system of the earthquake motion detection and prediction system functions as a P-wave detection device. [Figure 7] 10 is a flowchart of a process for determining whether a P wave has been detected using the mean deviation as an index when the main system of the earthquake motion detection and prediction system functions as a P wave detection device. [Figure 8] 10 is a flowchart of a process for determining whether a P wave has been detected using a correlation coefficient as an index when the main system of the earthquake motion detection and prediction system functions as a P wave detection device. [Figure 9] 10 is a flowchart showing the processing of a preceding destruction filter in the P-wave detection device. [Figure 10] 10 is a flowchart showing the processing of a minute event filter in the P wave detection device. [Figure 11] 4 is a flowchart showing a specific example of a P-wave detection process in the P-wave detection device. [Figure 12] 1 is a flowchart showing a process for predicting the intensity of an S wave in an S wave prediction device. [Figure 13] FIG. 10 is a block diagram showing an outline of a mobile S wave prediction device according to another embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0017] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. The present invention provides an earthquake motion detection and prediction system that quickly and accurately detects P waves that generate initial tremors and predicts the strength of S waves that generate primary motion based on the detected P waves. The earthquake motion detection and prediction system quickly and accurately detects P waves (primary waves), which are earthquake motions that generate initial tremors, using multiple seismometers, and can predict S waves (secondary waves), which are earthquake motions that generate primary motion, based on the detected P waves.

[0018] The earthquake motion detection and prediction system can further detect earthquake motion due to preceding ruptures related to the earthquake, which may occur immediately before the rupture that causes the earthquake, and distinguish this earthquake motion from P waves. The earthquake motion detection and prediction system can also detect earthquake motion due to micro-events unrelated to the earthquake, which may occur independently immediately before the earthquake, and distinguish this earthquake motion from P waves. In this way, by detecting earthquake motion due to preceding ruptures and micro-events and distinguishing these earthquake motions from P waves, P wave detection can be more reliably performed.

[0019] [Outline of the earthquake motion detection and prediction system] FIG. 1 is a schematic diagram showing an example of the installation of a main system in an earthquake motion detection and prediction system according to one embodiment of the present invention. In the earthquake motion detection and prediction system 1 according to the embodiment of the present invention, three seismometers are installed within a building or on the premises of a business, and P-wave detection and determination are performed based on measurement data from these three seismometers. In this embodiment, as shown in FIG. 1, an example is shown in which three seismometers, namely, first seismometer 101, second seismometer 102, and third seismometer 103, are installed. However, this is not limited to this, and any number of seismometers may be installed, as long as it is plural, and may be two, four, or more. However, if P-wave detection is determined by majority vote using multiple seismometers, it is preferable that the number of seismometers is odd.

[0020] Furthermore, in this embodiment, an example is shown in which the first seismometer 101 and the second seismometer 102 are installed inside a building, and the third seismometer 103 is installed on the site of the building, but the installation method is not limited to this. However, it is preferable that the three seismometers are installed at a certain distance apart on the site of the building (including inside the building). If the seismometers are placed close to each other, for example, multiple seismometers may measure vibrations from a truck traveling on a nearby road, which may interfere with P-wave detection. The appropriate distance between the seismometers is set appropriately depending on the building and site where the seismometers are installed, but is expected to be, for example, approximately 30 m to 100 m.

[0021] In the earthquake motion detection and prediction system 1, the first seismometer 101, the second seismometer 102, the third seismometer 103, and the system that processes the data measured by these seismometers are referred to as the main system 100. Fig. 2 is a diagram showing an image of the installation of the main system 100 of the earthquake motion detection and prediction system 1 according to an embodiment of the present invention, and shows a state in which the main system 100 is installed at points A, B, C, ...

[0022] In the earthquake motion detection and prediction system 1 of this embodiment, the main systems 100 are arranged in various locations as described above, and are configured so that the main systems 100 can communicate with each other via a network. Therefore, a main system 100 that detects a P-wave can transmit the measured P-wave data to another main system 100. At this time, each main system 100 performs P-wave detection determination using measurement data measured by the three seismometers 101, 102, and 103, enabling rapid and highly accurate P-wave detection.

[0023] Fig. 3A is a block diagram showing an overview of the earthquake motion detection and prediction system 1 according to the present invention, and shows a state in which, for example, multiple main systems 100A, 100B, 100C, ..., 100X are connected to one another via a network N. In Fig. 3A, main systems installed at points A, B, C, ..., and X are designated as 100A, 100B, 100C, ..., 100X, respectively. These systems have similar configurations, so only one main system, 100A, will be described here.

[0024] The main system 100A used in the earthquake motion detection and prediction system 1 according to the present invention has a first seismometer 101, a second seismometer 102, and a third seismometer 103 installed at an appropriate distance apart at one location. Data measured by the first seismometer 101, the second seismometer 102, and the third seismometer 103 (the measurement data signals of the respective seismometers are designated S1, S2, and S3) are transmitted to a data processing unit 110. The data processing unit 110 in the main system 100A is a general-purpose information processing device comprising a CPU, a ROM for storing programs running on the CPU, and a RAM as a work area for the CPU. The data measured by the first seismometer 101, the second seismometer 102, and the third seismometer 103 is processed by the data processing unit 110.

[0025] 3B is a block diagram showing the functions of the data processing unit 110. The data processing unit 110 has a calculation unit, a determination unit, and a prediction unit, as well as a minor event filter and a leading destruction filter. The detailed functions of each will be described later.

[0026] The data processing unit 110 operates in cooperation with each of the components shown connected to the data processing unit 110. Various control processes in the earthquake motion detection and prediction system 1 according to the present invention are realized by the CPU executing programs stored in storage means such as ROM and RAM in the data processing unit 110.

[0027] The data processing unit 110 is connected to a storage unit 120, such as a hard disk or solid-state drive. The storage unit 120 can store an S-wave prediction function F used to predict S-wave intensity. Furthermore, the storage unit 120 can store programs, initial data, intermediate processing data, and the like required for the operation of the seismic motion detection and prediction system 1, facility information (facility name, location information (latitude, longitude), etc.), and site information (ground amplification, average shallow earthquake depth, etc.), allowing the data processing unit 110 to reference various data. The S-wave prediction function F used to derive the S-wave waveform intensity based on the P-wave measurement data depends on each location. Therefore, the storage unit 120 can store the respective S-wave prediction functions F for each location. For example, the S-wave prediction function F for location A is the S-wave prediction function FA for location A, the S-wave prediction function FB for location B is the S-wave prediction function FB for location B, and so on. Thus, the S-wave prediction functions F for each location can be prepared in advance and stored in the storage unit 120.

[0028] In addition, a communication unit 150 that enables wireless or wired communication with the outside is connected to the data processing unit 110. The communication unit 150 is configured to transmit data transferred from the data processing unit 110 to main systems 100B, 100C, ..., 100X other than the data processing unit 110 via an external network N. In addition, the communication unit 150 is configured to receive data transmitted from main systems 100B, 100C, ..., 100X other than the data processing unit 110 via the external network N, and transmit the received data to the data processing unit 110.

[0029] [Processing in earthquake motion detection and prediction systems] Next, the processing in the earthquake motion detection and prediction system 1 according to the present invention will be described. The earthquake motion detection and prediction system 1 can measure earthquake motion, determine whether P waves have been detected, and predict the intensity of S waves based on the P wave data. When determining whether P waves have been detected, it can also detect earthquake motion due to micro-events and / or preceding ruptures and perform processing to distinguish these earthquake motions from P waves.

[0030] FIG. 4 is a diagram showing a flowchart of the processing of the earthquake motion detection and prediction system 1 according to the embodiment of the present invention. FIG. 4(a) shows the processing of one of the main systems 100 (e.g., main system 100A) that detects P waves, and FIG. 4(b) shows the processing of one of the main systems 100 (main systems 100B, 100C, . . . , 100X) that predicts S-wave intensity based on P-wave data from the main system 100 that detected P waves. All of the main systems 100A, 100B, 100C, . . . , 100X that constitute the earthquake motion detection and prediction system 1 according to the present invention can execute the processing of FIG. 4. When executing the processing of FIG. 4(a), the main system 100 functions as a P-wave detection device that detects P waves, and when executing the processing of FIG. 4(b), the main system 100 functions as an S-wave prediction device that predicts S waves.

[0031] (P wave detection judgment) In FIG. 4(a), for example, the main system 100A, which is closest to the epicenter, starts the P-wave detection process (S4a-1). Next, in S4a-2, seismic motion is measured by each of the three seismometers 101, 102, and 103. As shown in FIG. 5, the seismic motion is detected as follows: the waveform of the first seismometer 101 is S1, the waveform of the second seismometer 102 is S2, and the waveform of the third seismometer 103 is S3. Generally, each seismometer detects a random noise waveform, as shown in the waveform of S1, for example. Then, a P-wave waveform is detected as the earthquake occurs, and then an S-wave begins. In the present invention, the occurrence of P-waves can be detected earlier with high accuracy, and the intensity of S-waves can be estimated based on the P-wave data. The sampling frequency of the data S1, S2, and S3 from each seismometer 101, 102, and 103 can generally be 100 Hz or 200 Hz, but is not limited to this and can be any frequency that can be used to detect and determine P waves in the present invention. In this specification, the measurement data S1, S2, and S3 sampled at any frequency are referred to as "real-time measurement data."

[0032] Each seismometer 101, 102, and 103 normally measures random noise, but begins measuring P waves when an earthquake occurs. In the main system 100A, in s4a-3, P-wave detection is determined based on the measurement values ​​of each seismometer 101, 102, and 103. P-wave detection can be determined using a determination value calculated using a predetermined index, which can be a standard deviation, a mean deviation, a correlation coefficient, or a combination of these. The index is calculated using multiple real-time measurement data sets at predetermined time intervals. In this specification, this predetermined time interval is referred to as a "primary interval." From the perspective of measurement reliability and P-wave determination reliability, the size of one primary interval is preferably 0.05 to 0.2 seconds, but is not limited thereto. If the size of the primary interval is small, the number of real-time measurement data sets to be averaged is reduced, making it easier for fluctuations in individual real-time measurement data sets to affect P-wave detection accuracy. On the other hand, if the size of the elementary section is large, the number of real-time measurement data included in the elementary section increases, which increases the processing time, and it may take time to quickly detect and confirm the P wave.

[0033] For example, if the size of the elementary interval is 0.1 seconds and the sampling frequency of the seismometer is 100 Hz, the number of real-time measurement data in one elementary interval is 10, and if the size of the elementary interval is 0.05 seconds and the sampling frequency of the seismometer is 100 Hz, the number of real-time measurement data in one elementary interval is 5. The method of determining the detection of P waves using each index will be described later.

[0034] In this way, in the present invention, instead of using the real-time measurement data from the seismometer itself, multiple real-time measurement data acquired over one elementary interval are averaged and treated, and the data from multiple elementary intervals are further averaged and treated. This reduces the variation in predicted seismic intensity that may arise due to data fluctuations when real-time measurement data is processed as is, thereby enabling faster and more accurate predictions to be achieved.

[0035] If it is determined that a P wave has been detected, the main system 100A transmits the measured P wave data to the main systems 100B, 100C, ..., 100X via the network N (s4a-6). The P wave data includes at least raw data of the measured P waves. The P wave data to be transmitted may be, but is not limited to, data measured by one of the three seismometers 101, 102, and 103, whose noise level during calm conditions is intermediate. The data transmitted from the main system 100A is not limited to P wave data and may include, for example, S wave intensity predicted by the main system 100A. The S wave intensity included in the transmitted data is preferably predicted using a method similar to that described later in this specification, but is not limited thereto and may be predicted using a known method.

[0036] If the earthquake detection and prediction system 1 includes a preceding rupture filter and a small event filter, it is preferable that the P-wave detection determination (s4a-3) be followed by a provisional detection determination (s4a-4) of earthquake motion and a confirmation determination (s4a-5). These will be described in detail later.

[0037] (Determination using standard deviation) In one embodiment for determining the detection of a P wave, the standard deviation of real-time measurement data is used as an index for the determination. FIG. 6 is a flowchart of a process for determining the detection of a P wave using the standard deviation as an index. In this embodiment, the detection of a P wave can be determined based on a determination value calculated as the difference between the standard deviation σ [elementary interval] of multiple real-time measurement data in an elementary interval and a first threshold value LT1. The first threshold value LT1 is calculated by multiplying a constant K by an average value σ01 obtained by averaging the standard deviations of multiple real-time measurement data calculated for each elementary interval under normal conditions over multiple elementary intervals.

[0038] Each of the three seismometers 101, 102, and 103 measures random noise under normal circumstances, and the data of the measured random noise is sent to a data processing unit 110. A calculation unit provided in the data processing unit 110 uses the random noise data from each seismometer to calculate an average value σ01 by averaging the standard deviations of multiple random noise data calculated for each elementary interval over multiple elementary intervals. σ01 is calculated, for example, by averaging the standard deviations of multiple random noise data calculated for each elementary interval over, but not limited to, a period of 100 seconds (equivalent to 1000 elementary intervals when the elementary interval size is 0.1 seconds). It is preferable that σ01 be constantly updated.

[0039] The threshold value LT1 is obtained by multiplying the average value σ01 by a constant K, that is, LT1=K×σ01 The constant K is an empirical value determined from past earthquake data, and can be, for example, a value between 1.5 and 2.0, but is not limited to this. The value of K can be determined arbitrarily by the system administrator.

[0040] When an earthquake occurs, the P-wave detection determination process is started (s6-1). The calculation unit uses the multiple real-time measurement data sent from each of the seismometers 101, 102, 103 to calculate the standard deviation σ [elementary interval] of the multiple real-time measurement data in one elemental interval for each of the seismometers 101, 102, 103. Before calculating σ [elementary interval], it is preferable to perform drift correction on the data of each seismometer using a well-known method. The calculation unit calculates the difference D1 between this σ [elementary interval] and the normal threshold LT1, D1 = σ [prime interval] - LT1 The difference data D1 is sent to a determination unit provided in the data processing unit 110 (s6-2).

[0041] The determination unit generates a determination value of 1 if the difference data D1 is a positive value, and a determination value of 0 if the difference data D1 is a negative value, based on whether the difference data D1 is a positive value or a negative value. The determination unit performs a majority decision using the respective determination values ​​of the three seismometers 101, 102, and 103 (s6-3). That is, if one of the three seismometers 101, 102, and 103 has a determination value of 1, it is determined that no P waves have been detected in that elementary interval, and a similar determination is made in the next elementary interval (NO in s6-3). On the other hand, if two or more of the three seismometers 101, 102, and 103 have a determination value of 1, it is determined that P waves have been detected in that elementary interval (s6-4). If it is determined that P waves have been detected (after provisional detection and confirmation of earthquake, if necessary, as shown in Figure 4(a)), the measured P wave data can be transmitted to the main system 100B, 100C, ..., 100X via the network N, as described above (s4a-7 in Figure 4).

[0042] (Determination using mean deviation) Next, in another embodiment for determining P-wave detection, the mean deviation of real-time measurement data is used as a determination index. FIG. 7 is a flowchart of a process for determining P-wave detection using the mean deviation as an index. In this embodiment, P-wave detection can be determined based on a determination value calculated as the difference between the average value Z [elementary interval] of multiple real-time measurement data in an elementary interval and a second threshold value LT2. The second threshold value LT2 is calculated using an average value Z0 obtained by averaging the average values ​​of multiple real-time measurement data calculated for each elementary interval during normal conditions over multiple elementary intervals, and a value obtained by multiplying the standard deviation σ02 of multiple real-time measurement data calculated for each elementary interval during normal conditions by a constant K.

[0043] The calculation unit of the data processing unit 110 calculates an average value Z0 by averaging the average value of multiple real-time measurement data (random noise data) obtained for each elementary interval for each seismometer under normal conditions over multiple elementary intervals. Z0 is calculated, for example, by averaging the average value of multiple random noise data obtained for each elementary interval over 100 seconds (for example, if the elementary interval size is 0.1 seconds, this corresponds to 1000 elementary intervals). The calculation unit also calculates σ02 using the random noise data from each seismometer. σ02 is preferably constantly updated.

[0044] The threshold value LT2 is calculated by using the above Z0, σ02, and constant K as follows: LT2=Z0+K×σ02 Here, the constant K is as described above.

[0045] When an earthquake occurs, the P-wave detection determination process is started (s7-1). The calculation unit uses the multiple real-time measurement data sent from each of the seismometers 101, 102, 103 to calculate the average value Z [elementary interval] of the multiple real-time measurement data in one elementary interval for each of the seismometers 101, 102, 103. Before calculating Z [elementary interval], it is preferable to perform drift correction on the data of each seismometer using a well-known method. The calculation unit calculates the difference D2 between this Z [elementary interval] and the normal threshold value LT2, D2=Z[prime interval]-LT2 The difference data D2 is sent to a determination unit provided in the data processing unit 110 (s7-2).

[0046] The determination unit generates a determination value of 1 if the difference data D2 is positive, and a determination value of 0 if the difference data D2 is negative, based on whether the difference data D2 is positive or negative. The determination unit performs a majority decision using the respective determination values ​​of the three seismometers 101, 102, and 103 (s7-3). The majority decision is as described above with reference to FIG. 6. If it is determined that a P-wave has been detected (s7-4) (after provisional detection and earthquake confirmation, as necessary, as shown in FIG. 4(a)), the measured P-wave data can be transmitted to the main systems 100B, 100C, ..., 100X via the network N (s4a-7 in FIG. 4), as described above.

[0047] (Determination using correlation coefficient) In yet another embodiment for determining P-wave detection, a correlation function of multiple real-time measurement data obtained at a predetermined time interval between multiple seismometers is used as an index. In this embodiment, P-wave detection is determined based on a determination value calculated as the difference between the correlation coefficients r12, r23, and r31 of multiple real-time measurement data obtained between seismometers 101, 102, and 103 in a single interval and a third threshold LT3. The third threshold LT3 is determined based on the correlation coefficients C012, C023, and C031 of multiple real-time measurement data obtained between seismometers 101, 102, and 103 under normal conditions. The threshold LT3 can be, for example, a value between 0.5 and 1.0, but is not limited thereto and can be arbitrarily determined by a system administrator based on past earthquake data, empirical values, etc. Figure 8 is a flowchart of a process for determining P-wave detection using a correlation coefficient as an index.

[0048] The calculation unit of the data processing unit 110 uses multiple random noise data from the three seismometers 101, 102, and 103 to determine the correlation coefficients C012, C023, and C031 between each of the two seismometers. That is, it calculates the correlation coefficient C012 between seismometer 101 and seismometer 102 under normal conditions, the correlation coefficient C023 between seismometer 102 and seismometer 103 under normal conditions, and the correlation coefficient C031 between seismometer 103 and seismometer 101 under normal conditions. The correlation coefficients under normal conditions are calculated using, for example, 100 seconds of random noise data.

[0049] When an earthquake occurs, the P-wave detection and determination process is started (s8-1). The calculation unit uses the multiple real-time measurement data sent from each of the seismometers 101, 102, and 103 to calculate correlation coefficients r12, r23, and r31 of the multiple real-time measurement data in one elementary interval between each two of the seismometers 101, 102, and 103. Before calculating the correlation coefficients, it is preferable to perform drift correction on the data from each seismometer. The calculation unit calculates the difference between these correlation coefficients r12, r23, and r31 and the normal threshold value LT3, D12=r12-LT3 D23=r23-LT3 D31=r31-LT3 The difference data D12, D23, and D31 are sent to a determination unit provided in the data processing unit 110.

[0050] The determination unit generates a determination value of 1 if the difference data D12, D23, and D31 are positive, and a determination value of 0 if the difference data D12, D23, and D31 are negative, based on whether the difference data D12, D23, and D31 are positive or negative. The determination unit performs a majority decision using the three determination values ​​(s8-3). If only one of the three determination values ​​is 1, it is determined that no P waves have been detected in that elementary interval, and a similar determination is made in the next elementary interval ("NO" in s8-3). On the other hand, if two of the three determination values ​​are 1 ("YES" in s8-3), it is determined that P waves have been detected in that elementary interval (S8-4). If it is determined that P waves have been detected (after provisional detection and earthquake confirmation determinations, as necessary, as shown in FIG. 4(a)), the measured P-wave data can be transmitted to the main systems 100B, 100C, ..., 100X via the network N, as described above (s4a-7 in FIG. 4).

[0051] (Predestruction filter) It is known that when an earthquake occurs, a small rupture (preceding rupture) occurs in the Earth's crust immediately before the earthquake. The seismic motion caused by this preceding rupture is related to the main shock, but it can be an obstacle from the perspective of detecting and determining P waves. Specifically, when such a preceding rupture occurs, the seismic motion caused by the preceding rupture may be mistaken for a true P wave, or the magnitude of the P wave may be underestimated, resulting in an underestimation of the S wave. Therefore, it is preferable that the earthquake motion detection and prediction system 1 includes a preceding rupture filter for distinguishing and removing the preceding rupture seismic motion, which is the seismic motion caused by the preceding rupture, from the P wave.

[0052] The preceding fracture filter uses the preceding fracture index to determine whether the measured seismic motion is a preceding fracture seismic motion. The preceding fracture index can be calculated by averaging multiple real-time measurement data in each elementary section, calculating the difference deviation between the average values ​​of two elementary sections, and calculating the moving average of the two difference deviations. The predetermined number of elementary sections for determining preceding fracture (first number) is not limited, and can be set appropriately taking into account the speed and reliability of the preceding fracture determination.

[0053] Figure 9 is a flowchart of the process of determining whether a preceding rupture has occurred using the preceding rupture filter. When seismic motion is detected by each of the seismometers 101, 102, and 103, the preceding rupture filter first detects seismic motion using a method similar to the method of detecting and determining P waves using any of the methods described above (the methods explained using Figures 4(a) to 8). If, in an elementary section, two or more of the three seismometers 101, 102, and 103 have a determination value of 1 (majority decision), it is determined that seismic motion has been "tentatively detected" in that elementary section. This process is performed over a predetermined number (first number) of elementary sections.

[0054] When it is determined that seismic motion has been tentatively detected consecutively in a predetermined number (first number) of elementary sections, the seismic motion is designated as a "candidate earthquake" (s9-2), and the following processing is performed. While processing the predetermined number of elementary sections, the leading rupture filter calculates a leading rupture index using real-time measurement data for each elementary section (s9-3), and determines whether negative leading rupture indexes appear in a predetermined number (second number) or more elementary sections in the latter half of the predetermined number of elementary sections (referred to as "determination sections") (s9-4). If negative values ​​appear consecutively, the leading rupture filter determines that the seismic motion is due to a leading rupture (s9-5). The number of determination sections and the number of elementary sections for determining that a seismic motion is a leading rupture seismic motion are not limited. The number of elementary sections can be set appropriately, taking into account the speed and reliability of the determination of leading rupture.

[0055] (minor event filter) Large earthquakes are often preceded by independent microearthquakes. In particular, in the case of so-called epicentral earthquakes, where the epicenter is close to the point where P-wave detection and determination is performed, small earthquakes with a magnitude of a few gals and a duration of a few seconds may occur before the earthquake, and these seismic motions may interfere with the detection and determination of P-waves. Specifically, when such microearthquakes occur, the seismic motions caused by the microearthquakes may be mistaken for true P-waves, or the magnitude of the P-waves may be underestimated, resulting in an underestimation of S-waves. Therefore, it is preferable that the seismic motion detection and prediction system 1 include a micro-event filter for distinguishing and removing micro-event seismic motions caused by these microearthquakes from P-waves.

[0056] FIG. 10 is a flowchart of a process for determining a minute event using a minute event filter. The minute event filter uses data in a predetermined number of elementary sections to determine a minute event. The predetermined number can be the same as the number of elementary sections (first number) for determining the preceding destruction described above. The predetermined number of elementary sections for determining a minute event is not limited, and can be set appropriately taking into consideration the speed and reliability of the minute event determination.

[0057] First, as in the case of the preceding rupture described above, when seismic motion is detected by each of the seismometers 101, 102, 103, the seismic motion is detected using a method similar to that for determining P-wave detection using any of the methods described above (methods explained using Figures 4(a) to 8). If two or more of the three seismometers in a given section have a determination value of 1 (majority decision), it is determined that seismic motion has been "tentatively detected" in that section. This process is carried out over a predetermined number (first number) of section.

[0058] When seismic motion is tentatively detected consecutively in a predetermined number (first number) of elementary intervals, the seismic motion is designated as a "candidate earthquake" (s10-2), and the following process, i.e., "earthquake confirmation determination," is performed. First, a micro-event index value Z10 used to determine a micro-event is calculated (s10-3) using one or all three seismometers arbitrarily selected from the three seismometers. The micro-event index value Z10 is a value obtained by averaging the average values ​​of multiple real-time measurement data in each of the predetermined number (first number) of elementary intervals, shifting the average value for each elementary interval (i.e., the moving average value of the elementary intervals). The seismometer to be selected can be, for example, the one with the median random noise level under normal conditions among the three seismometers, but is not limited to this.

[0059] The difference D4 between this minute event index value Z10 and the minute event threshold ST, D4=Z10-ST is calculated (s10-4). The small event threshold ST may be a predetermined value that is given to the earthquake motion detection and prediction system 1, or may be configured so that the administrator can change it as needed.

[0060] The micro-event filter generates a judgment value of 1 if the difference data D4 is positive, and a judgment value of 0 if the difference data D4 is 0 or negative (s10-5). When a judgment value of 1 is generated, it is determined that the seismic motion is a true P-wave, i.e., a P-wave has been detected (a confirmed earthquake) (s10-7). When it is determined that a P-wave has been detected, the measured P-wave data can be transmitted to main systems 100B, 100C, ..., 100X via network N (s4a-6 in Figure 4). On the other hand, when a judgment value of 0 is generated, i.e., when the difference data D4 is 0 or a negative value, it is determined that the seismic motion is a seismic motion due to a micro-event and not a P-wave (s10-6).

[0061] As described above, the seismic motion detection and prediction system 1 of the present invention uses indices calculated from data measured by multiple seismometers and distinguishes micro-vibrations from true P waves, thereby enabling faster and more accurate detection, and transmits the measurement data of the detected P waves to other main systems 100 to help predict S-wave intensity in the other main systems 100.

[0062] (Example of a typical earthquake detection process) Figure 11 shows a flowchart of a typical P-wave detection and determination process that combines the above methods. In this example, real-time measurement data measured in 0.01-second intervals is used. After checking whether or not there is real-time measurement data in a certain elementary space, the elementary section average of the real-time measurement data is calculated. In this example, the size of the elementary space is 0.1 seconds.

[0063] Next, the elementary section average is used to determine the elementary section. As described above, elementary section determination can be performed using any one of the following methods: comparing the standard deviation of the real-time measurement data with a threshold; comparing the average deviation of the real-time measurement data with a threshold; or comparing the correlation coefficient of the real-time measurement data between the three seismometers with a threshold; or a combination of these methods. It is determined whether the index of each of the three seismometers 101, 102, and 103 exceeds the threshold, and if two or more of the three seismometers 101, 102, and 103 exceed the threshold, it is determined that a P wave has been tentatively detected in the elementary section.

[0064] The elementary section determination is then repeated, and when a tentative detection is determined in 10 consecutive elementary sections, the seismic motion becomes an earthquake candidate. At this time, at the point of the 10th elementary section, it is determined whether the earthquake candidate is a preceding rupture. The determination of a preceding rupture is performed as described above using the preceding rupture index (moving average of differential deviation). Since a preceding rupture is performed only once between tentative detection and the preceding rupture determination, if a preceding rupture has already been performed, proceed to the next step. If the current earthquake candidate is determined to be a preceding rupture, start from the first tentative detection.

[0065] If the current earthquake candidate is determined not to be a preceding rupture, or if a preceding rupture determination has already been made, it is determined whether the earthquake candidate is a micro-event. The determination of micro-events is performed as described above using the micro-event index (moving average of 10 elementary intervals). If the current earthquake candidate is determined not to be a micro-event, the current earthquake candidate is determined to be a true earthquake and is confirmed as an earthquake. When a confirmed earthquake is determined, the measurement result of the seismometer with the median value among the three seismometers 101, 102, and 103 is used as the P-wave intensity. Note that the number of seismometers is not limited to three. For example, if two seismometers are used, the measurement result of the seismometer with the larger value can be used as the P-wave intensity, and if only one seismometer is used, the value of the seismometer with the larger value can be used as the P-wave intensity. Even if four or more seismometers are used, the P-wave intensity can be determined using an appropriate method. The P-wave intensity is the average value of the real-time measurement data in the 10th elementary interval.

[0066] (S-wave intensity prediction) Next, we will explain the process for predicting S-wave intensity performed by main systems 100 other than the main system 100 that detected P-waves. Figure 4(b) is a flowchart of the process executed by main systems 100 (main systems 100B, 100C, ..., 100X) that predict S-wave intensity based on P-wave data from main system 100A that detected P-waves (main system 100A here functions as a P-wave detection device), as described above. These 100B, 100C, ..., 100X function as S-wave prediction devices that predict S-waves. In the following, we will explain the process assuming that main system 100B is the S-wave prediction device that predicts S-waves.

[0067] In FIG. 4(b), the S-wave detection process begins in s4b-1. Next, in s4b-2, the main system 100B receives P-wave data transmitted from the main system 100A that detected the P-wave. When the main system 100B receives the P-wave data, in s4b-3, the prediction unit of the data processing unit 110 of the main system 100B predicts the S-wave intensity using the received P-wave data and a distance attenuation equation. If the predicted S-wave intensity is equal to or greater than a threshold, in s4b-4, the main system 100B issues an alarm according to the intensity level. If the predicted S-wave intensity is less than the threshold, the process ends without issuing an alarm (s4b-5).

[0068] FIG. 12 is a flowchart of a process for predicting S-wave intensity and determining whether to issue an alarm. In FIG. 12, the process starts (s12-1). When P-wave data is received at s12-2, the main system 100B acquires a predetermined S-wave prediction function F for the point A where the P-wave was detected at s12-3. This S-wave prediction function F is used to predict S-wave intensity from P-wave measurement data. It can be calculated, for example, as the ratio of P-wave intensity to S-wave intensity for each point of the main system 100, based on an analysis of past earthquake data, without being limited thereto. The S-wave prediction function F may be transmitted from the main system 100A together with the P-wave, or it may be read from data previously stored in the memory unit 120 of the main system 100B. For example, when the main system 100A predicts S-waves from P-waves detected by its own seismometer, it can predict S-waves using an S-wave prediction function stored in its own system. Alternatively, when the main system 100B predicts S waves based on P waves received from the main system 100A via the network, the S waves can be predicted using an S wave prediction function transmitted together with the P waves detected by the main system 100A. In s12-4, the main system 100B multiplies the acquired S wave prediction function F by the P wave measurement data to calculate the S wave intensity S at the P wave detection point A. A Predict.

[0069] The main system 100B further generates a signal with intensity S at point A. A The main system 100B then determines how the predicted S-waves will affect point B from the distance attenuation of the ground vibrations traveling from point A to point B. In s12-5, the main system 100B calculates the distance attenuation between point A and point B using a distance attenuation formula that is predetermined, preferably incorporated into the program, and well known to those skilled in the art. In s12-6, the main system 100B calculates the distance attenuation between point A and point B using a distance attenuation formula that is predetermined, preferably incorporated into the program, and well known to those skilled in the art. A Based on this, the S-wave intensity S at point B is B Calculate.

[0070] Strength S B is predicted, the main system 100B calculates the obtained intensity S B The intensity S is compared with a predetermined threshold value THS. B If the intensity S is smaller than the threshold value THS, the process ends without issuing an alarm (s12-9). B If the threshold value THS is equal to or greater than the threshold value THS, the main system 100B applies the intensity S B The alarm unit 130 issues an alarm according to the level (step s12-8).

[0071] [An embodiment in which an S-wave prediction device is mounted on a moving object] In the seismic motion detection and prediction system 1 according to the present invention, the S-wave prediction device can be mounted on a mobile object to create a mobile S-wave prediction device, thereby more effectively reducing damage in the event of an earthquake. For example, if a large tremor (S-wave) suddenly strikes during rush hour, severe damage is predicted, especially in large cities. If an earthquake with a seismic intensity of 6 or 7 occurs on a train overpass or expressway, damage such as train derailment and overturning, trains and vehicles falling from the overpass, and multiple vehicle collisions is expected. By mounting the S-wave prediction device on a mobile object and providing early earthquake information, damage can be mitigated. Here, mobile objects include not only trains and automobiles, but also portable electronic devices such as smartphones, tablets, and smartwatches.

[0072] 13 is a block diagram showing an outline of a mobile S-wave prediction device 200 according to another embodiment of the present invention. The mobile S-wave prediction device 200, mounted on a mobile vehicle, includes a communication unit 151 for receiving various data via the Internet, a location measurement unit 161 for identifying its own location using a GPS system or the like, a storage unit 121 for storing various programs, initial data, prediction results, and other data, as well as an S-wave prediction function F as necessary, and a notification unit 131 for issuing an alarm based on the prediction results. When the main system 100A described above detects a P-wave, the mobile S-wave prediction device 200 receives the P-wave data from the main system 100A via the communication unit 151. The mobile S-wave prediction device 200 can predict the intensity of S-waves at its own position (the point where the P-wave data was received) in the prediction section of the data processing unit 111 based on the received P-wave data, an S-wave prediction function F at the point in the main system 100A where the P-wave was detected, and the distance attenuation from point A to the mobile unit calculated using a predetermined distance attenuation formula, preferably incorporated into the program and well known to those skilled in the art. The S-wave prediction function F and distance attenuation formula are as explained above in the section on S-wave intensity prediction in the main system.

[0073] Mobile S-wave prediction device 200 further includes a grace time calculation unit in data processing unit 111 that calculates the grace time until the arrival of an S-wave. The grace time calculation unit uses its own position measured by position measurement unit 161 and the position of the P-wave detection device (e.g., main system 100A) that detected the P-wave to determine the predicted grace time until the S-wave arrives at the location of mobile S-wave prediction device 200. The grace time is expressed by the following equation using the distance L between its own position and the position of the P-wave detection device that detected the P-wave. T=L / 4-L / 7-a Here, the propagation speed of P waves is 7 km / s, the propagation speed of S waves is 4 km / s, and a is the time required for the P wave detection device to detect a P wave. The mobile S wave prediction device 200 issues an alarm including the predicted S wave intensity and grace period from the alarm unit 131.

[0074] In yet another embodiment, mobile S-wave prediction device 200 can also be configured to receive Earthquake Early Warnings from the Japan Meteorological Agency. When the Earthquake Early Warning is received, notification unit 131 issues an alert based on the earlier of the time when the S-wave intensity prediction is completed by the S-wave prediction unit or the time when the Earthquake Early Warning is received. Furthermore, when the Earthquake Early Warning is received, the grace period calculation unit uses its own position measured by position measurement unit 161 and the epicenter position included in the Earthquake Early Warning to calculate the predicted grace period T' until the S-wave reaches the location of mobile S-wave prediction device 200. Grace period T' can be calculated as described above using the distance L' between its own position and the epicenter position included in the Earthquake Early Warning. The mobile S-wave prediction device 200 compares the grace period T calculated based on the position of the P-wave detection device that detected the P-wave with the grace period T' calculated based on the position of the epicenter included in the emergency earthquake warning, and issues an alert including the shorter grace period from the alarm unit 131.

Claims

1. In a seismic motion detection and prediction system that detects P-waves that generate initial tremors and predicts S-waves that generate main tremors based on the detected P-waves, a P-wave detection device is provided at a P-wave observation point to detect P-waves, a plurality of seismometers installed within a predetermined range at an observation point; a calculation unit that calculates a determination value for determining whether a P wave has been detected, using a plurality of real-time measurement data for each elementary section, which is a predetermined time interval, measured by each of the plurality of seismometers; and a determination unit that determines whether a P wave is detected based on the determination value; a communication unit that transmits P-wave data used to predict S-waves to an external device when the determination unit determines that a P-wave has been detected; Equipped with When a seismic motion is detected, the system includes a preceding rupture filter that calculates an average value of multiple real-time measurement data in each elementary section, calculates the difference deviation between the average values ​​of two elementary sections, and determines whether the seismic motion is a preceding rupture seismic motion caused by a preceding rupture using a preceding rupture index, which is a value calculated as a moving average of the two difference deviations, and distinguishes the preceding rupture seismic motion from a P wave and removes it. P wave detector.

2. When it is determined that an earthquake motion has been detected by the determination unit, the determination of the detection of the earthquake motion is repeated for a first number of elementary sections, and when the earthquake motion is detected (provisionally detected) continuously in the first number of elementary sections, The preceding destruction filter is Calculating the preceding fracture indicator over the first number of prime intervals; When the preceding fracture index having a negative value appears consecutively in a second number or more of elementary sections in any judgment section within the first number of elementary sections, the detected earthquake motion is judged to be a preceding fracture earthquake motion. The P wave detection device according to claim 1 .

3. The apparatus further includes a small event filter that detects a small event that occurs immediately before a P wave and is unrelated to the P wave, and distinguishes the small event from the P wave.

3. The P wave detection device according to claim 1 or 2.

4. When it is determined that an earthquake motion has been detected by the determination unit, the determination of the detection of the earthquake motion is repeated for a first number of elementary sections, and when the earthquake motion is detected (provisionally detected) continuously in the first number of elementary sections, The small event filter calculating a moving average value of average values ​​of a plurality of real-time measurement data in each of the first number of prime intervals; Calculating the difference between the moving average value and a predetermined micro-event threshold value; When the difference is positive, it is determined that a P wave has been detected. The P wave detection device according to claim 3 .

5. The determination value is calculated using one of a standard deviation of a plurality of real-time measurement data, an average deviation of a plurality of real-time measurement data, and a correlation function of a plurality of real-time measurement data between the plurality of seismometers, or a combination thereof. The P wave detection device according to any one of claims 1 to 4.

6. the determination value is calculated based on a difference between a standard deviation of a plurality of real-time measurement data in the elementary section and a first threshold value; the first threshold value is calculated using an average value obtained by averaging standard deviations of a plurality of real-time measurement data calculated for each of the elementary intervals under normal conditions over the plurality of elementary intervals; The P wave detection device according to claim 5 .

7. the determination value is calculated based on a difference between an average value of a plurality of real-time measurement data in the elementary section and a second threshold value; the second threshold value is calculated using an average value obtained by averaging the average values ​​of the plurality of real-time measurement data obtained for each elementary interval under normal conditions over the plurality of elementary intervals, and a value obtained by multiplying the standard deviation of the plurality of real-time measurement data obtained for each elementary interval under normal conditions by a constant. The P wave detection device according to claim 5 .

8. the determination value is calculated based on a difference between a correlation coefficient of a plurality of real-time measurement data of the elementary section calculated between the plurality of seismometers and a third threshold value; the third threshold is determined using a correlation coefficient of a plurality of real-time measurement data under normal conditions obtained among the plurality of seismometers; The P wave detection device according to claim 5 .

9. In a seismic motion detection and prediction system that detects P-waves that generate initial tremors and predicts S-waves that generate major tremors based on the detected P-waves, an S-wave prediction device for predicting S-waves based on the detected P-waves comprises: a communication unit that receives P-wave data transmitted from the P-wave detection device according to any one of claims 1 to 8; an S-wave prediction unit that predicts the intensity of arriving S-waves based on the P-wave data; An S-wave prediction device comprising:

10. The S wave prediction unit receiving a predetermined S-wave prediction function for predicting an S-wave intensity at a point where the P-wave data is transmitted; predicting the intensity of S waves at a point where the P wave data is received based on the P wave data, the S wave prediction function, and distance attenuation between a point where the P wave data is transmitted and a point where the S wave prediction device is located; The S wave prediction device according to claim 9.

11. a position measurement unit that measures the position of the S-wave prediction device; a grace time calculation unit that calculates a grace time until the arrival of the S wave based on the measured position and the position of a P wave detection device that detected the P wave; The S wave prediction device according to claim 9 or 10, further comprising:

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