WO2023106148A1 - Detection device for detecting seismic motion and prediction device for predicting seismic motion strength on basis of detection result from same - Google Patents

Abstract

Provided is a seismic motion detection/prediction system that is capable of more quickly and highly accurately detecting P-waves and is capable of predicting the strength of S-waves on the basis of the detected P-waves. A P-wave detection device in a seismic motion detection/prediction system according to the present invention includes a plurality of seismographs, a calculation unit which calculates a determination value for determining the detection of P-waves, a determination unit which determines, on the basis of the determination value, whether or not P-waves have been detected, and a communication unit which, when the determination unit has determined that the P-waves have been detected, externally transmits P-wave data to be used for predicting S-waves. An S-wave prediction device in the seismic motion detection/prediction system includes a communication unit which receives the P-wave data transmitted from the P-wave detection device and an S-wave prediction unit which predictions the strength of the coming S-waves on the basis of the P-wave data.

Description

A detection device for detecting seismic motion and a prediction device for predicting the intensity of seismic motion based on the detection result

The present invention relates to seismic motion detection and prediction technology. More specifically, the present invention provides a seismic motion detection and prediction system that detects P waves that generate initial microtremors and predicts S waves that generate primary tremors based on the detected P waves. and an S-wave prediction device for predicting the intensity of an S-wave based on the P-wave detected by the P-wave detection device.

"It is extremely difficult to predict the occurrence of an earthquake." Therefore, immediately after an earthquake occurs, based on the observation information of the earthquake observed at many seismic stations, the time of occurrence, the location of the epicenter, and the scale of the earthquake are calculated, and the arrival time of the principal motion predicted from this information. An earthquake early warning system has been proposed to notify unreachable areas of the intensity of earthquakes and earthquakes.

For example, in Patent Document 1 (Japanese Patent Application Laid-Open No. 2015-25714), an alarm is issued based on information from a seismometer installed in a building in an area near the epicenter, and an earthquake response is performed according to the seismic intensity and the damage situation. Disclosed is a disaster warning interlocking system that interlocks with onsite warnings for. This system consists of various home appliances installed in buildings and communicatively connected to seismometers, and a center server that is communicatively connected to seismometers all over the country and collects seismic information from the seismographs all over the country. is configured to include

In addition, the applicant of the present application has proposed an earthquake warning system disclosed in Patent Document 2. In this earthquake warning system, three or more seismometers are installed in the premises of a building or office, and P-wave detection is determined based on the correlation coefficient between data signals measured by these seismometers. Based on the P-wave data received from the P-wave detection point, the intensity of the S-wave is predicted, and based on this, notification is made.

JP 2015-25714 A Japanese Patent No. 6887310

However, in conventional systems, including the system disclosed in Patent Document 1, it is assumed that each seismometer is dispersedly installed at a considerable distance, so information from these seismometers Even if these are aggregated in a center server, it has been difficult to quickly and accurately detect the P waves that generate the initial tremors.

In addition, the conventional system aggregates information from multiple seismometers in the center server, and based on the information from the multiple seismometers, even if the center server predicts the arrival of the S wave that causes the main motion. , it takes a long time to process a huge amount of seismometer data, and it is not something that can issue an emergency alert.

The technology proposed in Patent Document 2 is a useful technology that can detect P-waves quickly and with high precision compared to conventional systems including the system disclosed in Patent Document 1. In recent years, however, there is a need for faster and more accurate seismic motion prediction technology, especially in the metropolitan area, in view of the increasing likelihood of earthquakes occurring directly beneath the surface.

An object of the present invention is to provide a seismic motion detection and prediction system that can detect P-waves more quickly and accurately, and predict the intensity of S-waves based on the detected P-waves.

The present invention is used in a seismic motion detection and prediction system that detects P-waves that generate primary tremors and predicts S-waves that generate primary tremors based on the detected P-waves. provides a P-wave detector located at an observation point of . The P-wave detection device includes a plurality of seismographs, a calculation unit that calculates a judgment value for judging detection of P-waves, a judgment unit that judges whether P-waves have been detected based on the judgment value, and a judging unit. a communication unit configured to externally transmit P-wave data used for predicting S-waves when the unit determines that P-waves have been detected. A judgment value for judging the detection of a P-wave is calculated using a plurality of real-time measurement data for each predetermined time interval (herein referred to as an “elementary interval”) measured by each of a plurality of seismometers. be done.

In one embodiment, the judgment value is obtained using standard deviations of a plurality of real-time measurement data. In another embodiment, the determination value is obtained using the average deviation of multiple pieces of real-time measurement data. In still another embodiment, the determination value is determined using a correlation function of multiple real-time measurement data between multiple seismographs. A determination may be obtained using a combination of these.

It is preferable that the P-wave detector further includes a preceding rupture filter that detects a preceding destructive seismic motion associated with the P-wave that occurs immediately before the P-wave and distinguishes the preceding destructive seismic motion from the P-wave. Preferably, the P-wave detector further includes a minute event filter for detecting a minute event unrelated to the P-wave occurring immediately before the P-wave and distinguishing the minute event from the P-wave.

The present invention detects P-waves that generate primary tremors, and based on the detected P-waves, is used in a seismic motion detection and prediction system that predicts S-waves that generate primary tremors. An S-wave predictor for predicting S-waves is also provided. The S-wave prediction device includes a communication section for receiving P-wave data transmitted from the above-described P-wave detection device, and an S-wave prediction section for predicting the intensity of arriving S-waves based on the P-wave data.

The S-wave prediction device can also be a mobile object. It is preferable to further include a grace time calculation unit that calculates a grace time until the S wave arrives based on the above.

The seismic motion detection and prediction system according to the present invention performs P-wave detection and determination based on a plurality of seismometers installed in buildings and on the premises of business establishments, and based on P-wave data received from P-wave detection points. is configured to predict the intensity of the S-wave. Therefore, according to the seismic motion detection and prediction system according to the present invention, it is possible to quickly and highly accurately detect the P wave that causes the initial microtremor, and to quickly issue an emergency warning regarding the major motion. In addition, since the seismic motion detection and prediction system according to the present invention includes a filter for distinguishing micro-events unrelated to earthquakes occurring before earthquakes and preceding ruptures related to earthquakes from P-waves, Oversight and underestimation can be prevented, and P-wave detection and determination can be performed more accurately.

1 is a schematic diagram showing an installation example of a main system in a seismic motion detection and prediction system according to an embodiment of the present invention; FIG. It is a figure which shows the installation image of the main system in an earthquake motion detection prediction system. It is a block diagram which shows the outline of a seismic motion detection prediction system, and each of a main system functions as a P-wave detection apparatus and an S-wave prediction apparatus. 4 is a block diagram showing functions of a data processing unit of the main system; FIG. 1 is a flow chart of basic processing in a seismic motion detection and prediction system, (a) showing a flow chart when the main system performs P-wave detection processing, and (b) showing a flow chart when the main system performs S-wave prediction processing. be. An example of measurement data from a seismometer is shown. FIG. 10 is a flowchart of processing for detecting and determining P-waves using standard deviation as an index when the main system of the seismic motion detection and prediction system functions as a P-wave detector; FIG. FIG. 10 is a flowchart of processing for detecting and determining P-waves using average deviation as an index when the main system of the seismic motion detection and prediction system functions as a P-wave detector; FIG. FIG. 10 is a flowchart of processing for detecting and determining P-waves using a correlation coefficient as an index when the main system of the seismic motion detection and prediction system functions as a P-wave detector; FIG. 4 is a flow chart showing processing of a pre-destructive filter in the P-wave detector; 4 is a flow chart showing processing of a minute event filter in the P-wave detection device; 4 is a flowchart showing a specific example of P-wave detection processing in the P-wave detection device; 4 is a flow chart showing a process of predicting the intensity of an S wave in an S wave prediction device. FIG. 4 is a block diagram showing an overview of a mobile S-wave prediction device according to another embodiment of the present invention;

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.
The present invention provides a seismic motion detection and prediction system that rapidly and highly accurately detects P-waves that generate initial microtremors and predicts the intensity of S-waves that generate primary tremors based on the detected P-waves. The seismic motion detection and prediction system uses multiple seismographs to quickly and accurately detect P waves (Primary waves), which are seismic motions that generate initial microtremors, and generate major motions based on the detected P waves. S waves (secondary waves), which are seismic motions, can be predicted.

The seismic motion detection and prediction system can also detect seismic motions due to preceding ruptures associated with the earthquake, which may occur just before the rupture causing the earthquake occurs, and distinguish these seismic motions from P-waves. The seismic motion detection and prediction system can also detect seismic motions due to micro-events unrelated to the earthquake that may occur independently just prior to the earthquake and distinguish them from P-waves. In this way, by detecting seismic motions due to preceding ruptures and micro-events and distinguishing these seismic motions from P-waves, the detection of P-waves can be made more reliable.

[Outline of seismic motion detection and prediction system]
FIG. 1 is a schematic diagram showing an installation example of a main system in a seismic motion detection and prediction system according to one embodiment of the present invention. The seismic motion detection and prediction system 1 according to the embodiment of the present invention has three seismographs installed in a building or on the premises of a business office, and based on the measurement data from these three seismographs, detects and determines P waves. etc. In this embodiment, as shown in FIG. 1, an example is shown in which three seismometers consisting of a first seismometer 101, a second seismometer 102, and a third seismometer 103 are installed, but the present invention is not limited to this. The number of installed seismometers may be plural, and may be two or four or more. However, when a plurality of seismometers are used to determine the P-wave detection by majority decision, it is preferable that the number of seismometers is an odd number.

Further, in this embodiment, the first seismometer 101 and the second seismometer 102 are installed in a building, and the third seismometer 103 is installed on the site to which the building belongs. It is not limited to this. However, it is preferable that the three seismometers are installed with a certain distance in the site to which the building belongs (including the inside of the building). If the seismometers are arranged close to each other, for example, the vibrations of trucks traveling on nearby roads may be measured by a plurality of seismometers together, which may interfere with P-wave detection. An appropriate distance between the seismometers is appropriately set according to the building or site where the seismometers are installed, and is assumed to be, for example, about 30m to 100m.

In the seismic 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 called the main system 100. FIG. 2 is a diagram showing an installation image of the main system 100 of the earthquake motion detection and prediction system 1 according to the embodiment of the present invention, where the main system 100 is installed at points A, B, C, . represents the state.

The seismic motion detection and prediction system 1 in this embodiment is configured such that the main systems 100 are arranged in various locations as described above, and the respective main systems 100 can communicate with each other via a network. Therefore, the main system 100 that detects the P-wave can transmit the measured P-wave data to other main systems 100 . At this time, each main system 100 uses measurement data measured by the three seismographs 101, 102, and 103 to determine P-wave detection, so it is possible to detect P-waves quickly and accurately. .

FIG. 3A is a block diagram showing an overview of the seismic motion detection and prediction system 1 according to the present invention. For example, a plurality of main systems 100A, 100B, 100C, . show. 3A, the main systems installed at points A, B, C, . . . , and X are denoted by 100A, 100B, 100C, . Since these configurations are similar, only one main system 100A will be described here.

In the main system 100A used in the seismic motion detection and prediction system 1 according to the present invention, a first seismometer 101, a second seismometer 102, and a third seismometer are installed at an appropriate distance at one point. 103. Data measured by the first seismometer 101, the second seismometer 102, and the third seismometer 103 (measurement data signals of the seismometers are S1, S2, and S3) are transmitted to the data processing unit 110. . The data processing unit 110 in the main system 100A is a general-purpose information processing device including a CPU, a ROM that holds programs operating on the CPU, and a RAM that is a work area for the CPU. Data measured by the first seismometer 101 , the second seismometer 102 , and the third seismometer 103 are processed by the data processing unit 110 .

FIG. 3B is a block diagram showing the functions of the data processing unit 110. As shown in FIG. The data processing unit 110 has a calculation unit, a determination unit and a prediction unit, and further has a minute event filter and a preceding destruction filter. Detailed functions of each will be described later.

The data processing unit 110 operates in cooperation with each component connected to the illustrated data processing unit 110 . Further, various control processes in the seismic 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. .

A storage unit 120 such as a hard disk or solid state drive is connected to the data processing unit 110 . The storage unit 120 can store an S-wave prediction function F used to predict the intensity of the S-wave. Further, the storage unit 120 stores programs necessary for the operation of the seismic motion detection and prediction system 1, initial data, intermediate processing data, etc., facility information (facility name, position information (latitude, longitude), etc.), site information ( ground amplification, average depth of an epicenter earthquake, etc.) can be stored, and the data processing unit 110 can refer to various data. Since the S-wave prediction function F used for deriving the intensity of the S-wave waveform based on the P-wave measurement data depends on each point, the storage unit 120 stores each S-wave at each point. A wave prediction function F can be stored. For example, at point A, the S-wave prediction function FA at point A, at point B, the S-wave prediction function FB at point B, and so on, so that the S-wave prediction function F at each point is It can be prepared in advance and stored in the storage unit 120 .

In addition, the data processing unit 110 is connected to a communication unit 150 that enables communication with the outside by radio or wire. The communication unit 150 can transmit data transferred from the data processing unit 110 via an external network N to the main systems 100B, 100C, . Also, the communication unit 150 receives data transmitted from the main systems 100B, 100C, . It is possible to do so.

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

FIG. 4 is a diagram showing a flowchart of processing of the seismic motion detection and prediction system 1 according to the embodiment of the present invention. FIG. 4A shows the processing of the main system 100 (for example, the main system 100A) that detects P waves among the plurality of main systems 100, and FIG. 4B shows the processing of the main system that detects the P waves. 100B, 100C, . All the main systems 100A, 100B, 100C, . , the main system 100 functions as a P-wave detection device that detects P-waves, and when executing the process of FIG. It works as a device.

(P wave detection judgment)
In FIG. 4(a), for example, the main system 100A closest to the epicenter starts detection processing of P waves (S4a-1). Next, in S4a-2, each of the three seismographs 101, 102, and 103 measures seismic motion. As shown in FIG. 5, the seismic motion is detected as the waveform S1 of the first seismometer 101, the waveform S2 of the second seismometer 102, and the waveform S3 of the third seismometer 103. FIG. In general, each seismometer normally detects a random noise waveform, for example, as shown in the waveform of S1, then detects the waveform of the P wave with the occurrence of an earthquake, and then the S wave starts. be done. In the present invention, the occurrence of P-waves can be detected earlier and with higher accuracy, and the intensity of S-waves can be estimated based on the P-wave data. The sampling frequency of the data S1, S2, S3 in each of the seismometers 101, 102, 103 can generally be 100 Hz or 200 Hz, but is not limited to this, and is used for detection and determination of P waves in the present invention. can be any frequency possible. In this specification, each measurement data S1, S2, S3 sampled at an arbitrary frequency is referred to as "real-time measurement data".

Each of the seismometers 101, 102, and 103 normally measures random noise, and when an earthquake occurs, P waves begin to be measured. In the main system 100A, P wave detection determination is performed based on the measured values of the seismometers 101, 102, and 103 in s4a-3. P-wave detection determination can be performed using a determination value calculated using a predetermined index, and the predetermined index is standard deviation, average deviation, or correlation coefficient, or a combination thereof. be able to. The index is calculated using a plurality of pieces of real-time measurement data for each predetermined time interval. In this specification, this predetermined time interval is referred to as "elementary interval". The size of one elementary interval is preferably 0.05 seconds to 0.2 seconds from the viewpoint of measurement reliability and P-wave determination reliability, but is not limited to this. If the size of the elementary interval is small, the number of pieces of real-time measurement data to be averaged is small, and variations in individual pieces of real-time measurement data tend to affect the detection accuracy of P-waves. On the other hand, when the size of the elementary interval is large, the number of pieces of real-time measurement data included in the elementary interval increases, resulting in a long processing time, and it may take time to confirm the detection of the P wave quickly.

For example, when the size of an elementary interval is 0.1 second and the sampling frequency of a seismometer is 100 Hz, the number of pieces of real-time measurement data in one elementary interval is 10, and the size of an elementary interval is 0.05. If the sampling frequency of the seismometer is 100 Hz per second, the number of pieces of real-time measurement data in one unit interval is five. A method for determining P-wave detection using each index will be described later.

Thus, in the present invention, not the real-time seismic data itself, but a plurality of real-time measurement data acquired over one elementary interval are averaged, and the data of the plurality of elementary intervals are averaged. By handling the real-time measurement data as they are, it is possible to reduce variations in the predicted seismic intensity that may occur due to data fluctuations when the real-time measurement data is processed as it is, and to realize high-speed and high-precision prediction.

When it is determined that the P wave is 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 measured P-wave data. The P-wave data to be transmitted is not limited, but for example, data measured by a seismometer whose noise level changes at an intermediate value during quiet times among the three seismometers 101, 102, and 103 is selected. be able to. The data transmitted from the main system 100A is not limited to P-wave data, and may include, for example, the S-wave intensity predicted by the main system 100A. The intensity of the S-waves included in the transmitted data is preferably an intensity predicted by a method similar to the method described later herein, but is not limited thereto, and may be predicted by a known method. strength.

In addition, when the seismic motion detection and prediction system 1 includes the preceding destruction filter and the minute event filter, after the P wave detection determination (s4a-3), the seismic motion provisional detection (s4a-4) determination and earthquake confirmation (s4a-5 ) determination is preferably made. Details of these will be described later.

(Determination using standard deviation)
In one embodiment for determining P-wave detection, the standard deviation of the real-time measurement data is used as an indicator of determination. FIG. 6 is a flow chart of processing for detecting and judging P waves using the standard deviation as an index. In this embodiment, the P-wave detection determination is based on the determination value obtained as the difference between the standard deviation σ [elementary interval] of the plurality of real-time measurement data in the elementary interval and the first threshold value LT1. It can be carried out. The first threshold LT1 is calculated by multiplying a constant K by an average value σ01 obtained by averaging standard deviations of a plurality of real-time measurement data obtained for each elementary interval over a plurality of elementary intervals.

In each of the three seismographs 101 , 102 , and 103 , random noise is measured during normal times, and the measured random noise data is sent to the data processing unit 110 . The calculation unit provided in the data processing unit 110 uses the random noise data of each seismometer to calculate the average value σ01 by averaging the standard deviations of a plurality of random noise data obtained for each elementary interval over a plurality of elementary intervals. demand. σ01 is, for example, the standard deviation of a plurality of random noise data obtained for each elementary interval, which is not limited to 100 seconds (if the size of the elementary interval is 0.1 second, 1000 elementary (corresponding to the interval). σ01 is preferably updated all the time.

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

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

Based on whether the difference data D1 is a positive value or a negative value, the determination unit generates a determination value of 1 if the difference data D1 is a positive value, and generates a determination value of 0 if the difference data D1 is a negative value. to generate The judging unit uses the respective judgment values of the three seismographs 101, 102, and 103 to make majority judgment (s6-3). That is, if only one of the three seismometers 101, 102, and 103 has a judgment value of 1, it is determined that the P wave is not detected in the corresponding elementary interval, and in the next elementary interval A similar determination is made (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 a P wave has been detected in the segment (s6-4). When it is determined that P-waves have been detected (as shown in FIG. 4(a), after provisional detection and determination of earthquake confirmation as necessary), as described above, the measured P-wave data , network N to the main systems 100B, 100C, . . . , 100X (s4a-7 in FIG. 4).

(Determination using average deviation)
Next, in another embodiment for determining detection of P-waves, the average deviation of real-time measurement data is used as an index for determination. FIG. 7 is a flow chart of processing for detecting and judging P waves using the average deviation as an index. In this embodiment, the P-wave detection determination is based on the determination value obtained as the difference between the average value Z [elementary interval] of a plurality of real-time measurement data in the elementary interval and the second threshold value LT2. It can be carried out. The second threshold LT2 is the average value Z0 obtained by averaging the average values of a plurality of real-time measurement data obtained for each elementary interval in normal times over a plurality of elementary intervals, and the average value Z0 obtained for each elementary interval in normal times. and a value obtained by multiplying the standard deviation σ02 of the real-time measurement data by a constant K.

The calculation unit of the data processing unit 110 calculates an average value Z0 obtained by averaging the average values of a plurality of real-time measurement data (random noise data) obtained for each elementary interval in each seismometer during normal operation over a plurality of elementary intervals. Ask for Z0 is, for example, the average of a plurality of random noise data obtained for each elementary interval over 100 seconds (for example, if the elementary interval size is 0.1 seconds, it corresponds to 1000 elementary intervals). It is calculated by Also, the calculation unit obtains σ02 using the random noise data of each seismometer. σ02 is preferably constantly updated

Using the above Z0, σ02, and constant K, the threshold LT2 is
LT2=Z0+K×σ02
can be Note that the constant K is as described above.

When an earthquake occurs, P-wave detection determination processing is started (s7-1). The calculator calculates a plurality of real-time measurement data in one unit interval for each of the seismometers 101, 102, and 103 using a plurality of real-time measurement data sent from each of the seismometers 101, 102, and 103. Find the average value Z [elementary interval] of . It should be noted that it is preferable to perform drift correction on the data of each seismometer by a well-known method before obtaining Z [elementary interval]. The calculation unit calculates the difference D2 between this Z [elementary interval] and the normal threshold LT2,
D2 = Z [elementary interval] - LT2
is calculated (s7-2). The difference data D2 is sent to the determination section provided in the data processing section 110 .

Based on whether the difference data D2 is a positive value or a negative value, the determination unit generates a determination value of 1 if the difference data D2 is a positive value, and generates a determination value of 0 if the difference data D2 is a negative value. to generate The judging unit uses the respective judgment values of the three seismographs 101, 102, and 103 to make majority judgment (s7-3). The majority decision is as described above with reference to FIG. When it is determined that a P-wave has been detected (s7-4) (as shown in FIG. 4(a), after provisional detection and determination of earthquake confirmation as necessary), measurement is performed as described above. The P-wave data obtained can be transmitted to the main systems 100B, 100C, . . . , 100X via the network N (s4a-7 in FIG. 4).

(Determination using correlation coefficient)
In yet another embodiment for determining P-wave detection, a correlation function for a plurality of real-time measurement data of elementary intervals between a plurality of seismometers is used as an index. In this embodiment, the P-wave detection determination is based on the correlation coefficients r12, r23, and r31 of the plurality of real-time measurement data in the elementary intervals obtained between the seismometers 101, 102, and 103, and the third threshold This can be done based on the determination value obtained as the difference from the value LT3. The third threshold LT3 is determined based on correlation coefficients C012, C023 and C031 of a plurality of real-time measurement data obtained between the seismometers 101, 102 and 103 during normal times. The threshold value LT3 can adopt, for example, a numerical value of 0.5 to 1.0, but is not limited to this. can decide. FIG. 8 is a flow chart of processing for detecting and determining a P wave using a correlation coefficient as an index.

The calculation unit of the data processing unit 110 uses a plurality of random noise data from the three seismometers 101, 102, and 103 to obtain correlation coefficients C012, C023, and C031 between the two seismometers, respectively. That is, the normal correlation coefficient C012 between the seismographs 101 and 102, the normal correlation coefficient C023 between the seismographs 102 and 103, and the correlation coefficient C023 between the seismographs 103 and 101. A normal correlation coefficient C031 between is calculated. The normal correlation coefficient is calculated using random noise data for 100 seconds, for example.

When an earthquake occurs, P-wave detection determination processing is started (s8-1). The calculation unit uses a plurality of real-time measurement data sent from each of the seismometers 101, 102, and 103 to calculate one Correlation coefficients r12, r23, and r31 of a plurality of pieces of real-time measurement data in an elementary interval are obtained. It should be noted that it is preferable to perform drift correction on the data of each seismometer before obtaining the correlation coefficient. The calculator 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
is calculated (s8-2). The difference data D12, D23, D31 are sent to the determination section provided in the data processing section 110. FIG.

Based on whether the difference data D12, D23, and D31 are positive values or negative values, the determination unit generates a determination value 1 if the difference data D12, D23, and D31 are positive values, If the value is , a judgment value of 0 is generated. The decision section uses the three decision values to make a majority decision (s8-3). If only one of the three determination values is 1, it is determined that the P wave is not detected in the elementary interval, and the same determination is made in the next elementary interval (" NO"). On the other hand, if two of the three determination values are 1 ("YES" in s8-3), it is determined that a P wave has been detected in the elementary interval (S8-4). When it is determined that P-waves have been detected (as shown in FIG. 4(a), after provisional detection and determination of earthquake confirmation as necessary), as described above, the measured P-wave data , network N to the main systems 100B, 100C, . . . , 100X (s4a-7 in FIG. 4).

(previous destruction filter)
It is known that when an earthquake occurs, a small rupture (preceding rupture) occurs in the crust immediately before the earthquake occurs. The ground motion caused by this pre-rupture is related to the mainshock but can be a hindrance in terms of P-wave detection and interpretation. Specifically, when such a preceding rupture occurs, the seismic motion caused by the preceding rupture may be mistaken for the true P-wave, or the magnitude of the P-wave may be underestimated, resulting in an underestimation of the S-wave. It is possible that they are connected. Therefore, the seismic motion detection and prediction system 1 preferably includes a preceding rupture filter for distinguishing and removing the preceding destructive seismic motion, which is the seismic motion that accompanies the preceding rupture, from the P wave.

The preceding rupture filter uses the preceding rupture index to determine whether the measured seismic motion is the preceding rupture seismic motion. As the preceding destruction index, it is possible to obtain the average value of a plurality of real-time measurement data in each elementary interval, calculate the difference deviation between the average values of the two elementary intervals, and use the value obtained as the moving average of the two differential deviations. can. The predetermined number (first number) of elementary intervals for judging preceding destruction is not limited, and can be appropriately set in consideration of promptness and reliability of judging preceding destruction.

FIG. 9 is a flowchart of the process of determining the preceding destruction by the preceding destruction filter. First, when seismic motion is detected by each seismometer 101, 102, 103, the preceding destruction filter detects P waves by any of the methods described above (methods described with reference to FIGS. 4(a) to 8). Detection of seismic motion is performed in a similar manner to determination. If two or more of the three seismometers 101, 102, and 103 have a judgment value of 1 in an elementary interval (majority decision), it is judged that the seismic motion has been "tentatively detected" in that elementary interval. . This processing is performed over a predetermined number (first number) of elementary intervals.

If it is determined that seismic motion has been tentatively detected continuously in a predetermined number (first number) of elementary intervals, that seismic motion becomes an "earthquake candidate" (s9-2), and the following processing is performed. While processing is performed on a predetermined number of elementary intervals, the preceding breaking filter calculates a preceding breaking index using the real-time measurement data of each elementary interval (s9-3), In an interval (referred to as a “judgment interval”), it is determined whether or not a negative precedent destruction index has appeared in a predetermined number (second number) or more of elementary intervals (s9-4), and if negative values are continuous , the preceding rupture filter determines that the seismic motion is due to the preceding rupture (s9-5). Note that the number of sections to be judged and the number of elementary sections for judging as preceding destructive seismic ground motion are not limited. The numbers of these elementary intervals can be appropriately set in consideration of promptness and reliability of preceding destruction determination. For example, the number of sections to be judged or the number of elementary sections for judging as preceding destructive earthquake ground motion or both of them can be set to 1, and the number of elementary sections for judging as preceding rupturing earthquake ground motion is set to the same number as the number of judgment sections. can also

Here, sampling of data, data processing thereof, and determination of provisional detection of P waves are normally performed sequentially for each elementary interval. That is, a plurality of real-time measurement data are sampled and data processed in one elementary interval, and after the sampling and data processing in that elementary interval are completed, provisional detection is determined and sampling is performed in the next elementary interval. and set to start data processing. However, sampling of data, data processing thereof, and determination of tentative detection of P waves are not limited to such settings. In another embodiment, sampling and data processing are performed on a plurality of pieces of real-time measurement data in one elementary interval, and after the sampling and data processing in that elementary interval are completed, the determination of provisional detection in that elementary interval is performed. However, in the middle of sampling and data processing in that unitary interval, it is also possible to set to start sampling and data processing in the next unitary interval. For example, if the size of an elementary interval is 0.1 second and the sampling frequency of the seismometer is 100 Hz, the number of pieces of real-time measurement data in one elementary interval is ten. In this case, when, for example, five pieces of data have been sampled and processed in one elementary interval, if sampling and data processing in the next elementary interval are started, it is possible to determine a certain seismic motion as an earthquake candidate. The time is halved, resulting in shorter P-wave detection times. The same applies to processing by a minute event filter, which will be described later.

(minor event filter)
Large earthquakes are often accompanied by independent microearthquakes before the occurrence of the earthquake. In particular, in the case of so-called epicenter earthquakes, where the epicenter is close to the point where P waves are detected and determined, a small earthquake with a magnitude of several gals and a duration of several seconds occurs before the earthquake, and this seismic motion becomes a P wave. may interfere with the detection and determination of Specifically, when such a microearthquake occurs, the seismic motion caused by the microearthquake may be mistaken for the true P wave, or the magnitude of the P wave may be underestimated, resulting in an underestimation of the S wave. It is possible that they are connected. Therefore, the seismic motion detection and prediction system 1 preferably includes a micro event filter for distinguishing and removing the micro event seismic motion, which is the seismic motion caused by this micro earthquake, from the P wave.

FIG. 10 is a flowchart of processing for determining minute events by the minute event filter. The minute event filter uses data in a predetermined number of elementary intervals to determine minute events. The predetermined number can be the same number as the number of elementary intervals (first number) for determining the above-described preceding destruction. The predetermined number of elementary intervals for determining a minute event is not limited, and can be appropriately set in consideration of the quickness and reliability of minute event determination.

First, in the same manner as in the case of preceding rupture described above, when seismic motion is detected by each of the seismometers 101, 102, and 103, any of the above methods (the method described using FIGS. 4(a) to 8) The detection of seismic motion is performed in the same manner as the P-wave detection determination by . If two or more of the three seismographs have a judgment value of 1 in an elementary interval (majority decision), it is judged that seismic motion has been "provisionally detected" in that elementary interval. This processing is performed over a predetermined number (first number) of elementary intervals.

If seismic motion is tentatively detected continuously in a predetermined number (first number) of elementary intervals, the seismic motion becomes an "earthquake candidate" (s10-2), and the following processing, that is, "earthquake confirmation determination" is performed. will be First, one seismometer arbitrarily selected from three seismometers or three seismometers are used to calculate a micro event index value Z10 used for micro event determination (s10-3). The minute event index value Z10 is a value obtained by shifting and averaging the average values of a plurality of real-time measurement data in each of a predetermined number (first number) of elementary intervals (that is, the moving average value of the elementary interval). ). The seismometer to be selected can be, for example, one with the median random noise level in normal times among the three seismometers, but is not limited to this.

The difference D4 between this minute event index value Z10 and the minute event threshold value ST,
D4=Z10-ST
is calculated (s10-4). As the minute event threshold value ST, a predetermined numerical value may be given to the seismic motion detection/prediction system 1, or may be arbitrarily changed by an administrator as necessary.

Based on whether the difference data D4 is a positive value or a negative value, the minute event filter generates a judgment value of 1 if the difference data D4 is a positive value, and generates a judgment value of 0 if it is a negative value. A judgment value of 0 is generated (s10-5). When the determination value 1 is generated, it is determined that this seismic motion is a true P wave, that is, that a P wave has been detected (earthquake confirmed) (s10-7). When it is determined that a P-wave has been detected, the measured P-wave data can be transmitted to the main systems 100B, 100C, . 6). On the other hand, when a determination value of 0 is generated, that is, when the difference data D4 is 0 or a negative value, this seismic motion is determined to be a seismic motion due to a minute event and not a P wave (s10-6).

As described above, the seismic motion detection and prediction system 1 according to the present invention uses indices calculated from data measured by a plurality of seismometers, and distinguishes minute vibrations from true P-waves. High-precision detection is performed, and the detected P-wave measurement data is transmitted to another main system 100 so as to contribute to prediction of S-wave intensity in the other main system 100 .

(Example of typical earthquake judgment processing)
FIG. 11 shows a flowchart of a typical P-wave detection determination process in which the above methods are combined. In this example, real-time measurement data measured in units of 0.01 second is used. After confirming the presence or absence of real-time measurement data in a certain elementary space, the average of elementary intervals of the real-time measurement data is calculated. In this example, the elementary space size is 0.1 second.

Next, the unitary interval is determined using the unitary interval average. As described above, the seismic interval determination is performed by comparing the standard deviation of real-time measurement data with a threshold value, the method of comparing the average deviation of real-time measurement data with a threshold value, or the method of comparing real-time measurement data between three seismographs. Any or a combination of methods of comparing the correlation coefficient of the measurement data and the threshold value can be used. It is determined whether the index exceeds the threshold value in each of the three seismometers 101, 102, 103, and if the number of the seismometers exceeding the threshold is two or more among the three seismometers 101, 102, 103, It is determined that the P wave has been tentatively detected in the elementary interval.

After that, the elementary interval judgment is repeated, and when it is judged to be provisionally detected in 10 consecutive elementary intervals, the seismic motion becomes an earthquake candidate. At this time, it is determined whether or not the earthquake candidate is a preceding rupture at the time of the tenth elementary interval. Determination of preceding destruction is performed as described above using the preceding destruction index (moving average of differential deviation). Since the preceding destruction is performed only once between the provisional detection and the preceding destruction determination, if the preceding destruction has already been implemented, the process proceeds to the next step. If the current earthquake candidate is determined to be a preceding rupture, the first provisional detection is started.

If it is determined that the current earthquake candidate is not a preceding rupture, or if a preceding rupture judgment has already been made, it is determined whether the earthquake candidate is a micro event. Minor event determination is performed as described above using a minor event index (moving average of 10 elementary intervals). If the current earthquake candidate is determined not to be a minor event, the current earthquake candidate is determined to be a true earthquake, and the earthquake is confirmed. When it is determined that an earthquake has been confirmed, the measurement result of the middle value of the three seismographs 101, 102, and 103 is taken as the intensity of the P wave. 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 is taken as the P-wave intensity, and the intensity of the one seismometer is In some cases, the value of the seismometer can be used as the intensity of the P wave. If more than four seismographs are used, the intensity of the P-wave can be determined by any suitable method. The intensity of the P wave is the average value of the real-time measurement data in the 10th elementary interval.

(S-wave intensity prediction)
Next, processing related to prediction of S-wave intensity performed by the main system 100 other than the main system 100 that detected the P-wave will be described. FIG. 4B, as described above, shows the S-wave based on the P-wave data from the main system 100A that detected the P-wave (here, the main system 100A functions as a P-wave detector). 2 is a flowchart of processing executed by a main system 100 (main systems 100B, 100C, . . . , 100X) that performs intensity prediction. These 100B, 100C, . In the following description, it is assumed that the main system 100B is an S-wave prediction device that predicts S-waves.

In FIG. 4(b), S-wave detection processing is started at s4b-1. Next, in s4b-2, the main system 100B receives the 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 strength of the S-wave using the received P-wave data and the distance attenuation formula. . If the predicted S-wave intensity is greater than or equal to the threshold, the main system 100B issues an alert according to the level of intensity at s4b-4. If the predicted S-wave intensity is smaller than the threshold value, the process ends without issuing an alarm (s4b-5).

FIG. 12 is a flow chart of the process of predicting the intensity of the S wave and determining whether to issue an alarm. In FIG. 12, the process starts (s12-1), and upon receiving P-wave data at s12-2, main system 100B, at s12-3, detects a predetermined S-wave at point A where the P-wave was detected. Get the prediction function F. This S-wave prediction function F is used to predict the intensity of the S-wave from the measurement data of the P-wave, and is not limited, but for example, the intensity of the P-wave at each point of the main system 100 The ratio to the intensity of the S wave can be obtained from the analysis of past earthquake data. The S-wave prediction function F may be transmitted together with the P-waves from the main system 100A, or may read data stored in advance in the storage 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 main system 100B predicts an S wave based on a P wave received from main system 100A via a network, the S wave prediction function transmitted together with the P wave detected by main system 100A is can be used to predict S-waves. In s12-4, the main system 100B multiplies the P-wave measurement data by the obtained S-wave prediction function F to predict the S-wave intensity S _A at the P-wave detection point A. FIG.

The main system 100B further grasps how the S wave predicted at the point A with the strength _SA affects the point B from the distance attenuation of the ground vibration from the point A to the point B. Therefore, at s12-5, the main system 100B calculates the distance attenuation between the point A and the point B using a predetermined, preferably programmed distance attenuation formula known to those skilled in the art, At s12-6, the strength S _B of the S wave at point B is calculated based on the calculated distance attenuation and strength S _A.

Once the strength S _B is predicted, the main system 100B compares the obtained strength S _B with a predetermined threshold THS at s12-7. If the intensity _SB is smaller than the threshold value THS, the process ends without issuing an alarm (s12-9). When the strength S _B is equal to or greater than the threshold THS, the main system 100B issues an alarm through the notification unit 130 according to the level of the strength S _B (step s12-8).

[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, furthermore, by mounting the S-wave prediction device on a mobile body to form a mobile S-wave prediction device, it is possible to more effectively reduce damage when an earthquake occurs. become able to. For example, if a sudden big tremor (S-wave) hits during rush hour, serious damage is expected, especially in large cities. Damage such as derailment and overturning, trains and vehicles falling from elevated structures, and multiple vehicle collisions are assumed. By installing an S-wave prediction device on a moving body and promptly reporting earthquake information, it is possible to mitigate damage. Here, moving objects include not only trains and automobiles, but also portable electronic devices such as smart phones, tablets, and smart watches.

FIG. 13 is a block diagram showing an overview of a mobile S-wave prediction device 200 according to another embodiment of the present invention. The mobile-type S-wave prediction device 200 mounted on a mobile object includes, for example, a communication unit 151 for receiving various data via the Internet, and a position measurement unit for specifying its own position using a GPS system or the like. 161, a storage unit 121 for storing various programs, data such as initial data and prediction results, and an S-wave prediction function F as necessary, and a notification unit 131 for issuing an alarm based on the prediction results. . For example, when main system 100A detects a P-wave, mobile-type S-wave prediction apparatus 200 receives P-wave data from main system 100A at communication unit 151 . In the prediction unit of the data processing unit 111, the mobile S-wave prediction device 200 uses the received P-wave data, the S-wave prediction function F at the point of the main system 100A where the P-wave was detected, and a predetermined, preferably program The intensity of the S-wave at your location (the point where the P-wave data was received) based on the distance attenuation from point A to the mobile calculated using the distance attenuation formula known to those skilled in the art incorporated in can be predicted. The S-wave prediction function F and the distance attenuation formula are as described in the S-wave intensity prediction section of the main system above.

Mobile-type S-wave prediction device 200 further includes, in data processing unit 111, a delay time calculation unit that calculates a delay time until the S-wave arrives. The grace time calculation unit uses the position of itself measured by the position measurement unit 161 and the position of the P wave detection device (for example, the main system 100A) that detected the P wave, and predicts that the S wave is a mobile type S wave. Determine the expected grace period to reach the point of the device 200 . The grace time is expressed by the following formula using the distance L between the self position and the position of the P-wave detection device that detected the P-wave.
T = L/4-L/7-a
Here, the transmission speed of the P wave is 7 km/s, the transmission speed of the S wave is 4 km/s, and a is the time required for the P wave detector to detect the P wave. The mobile S-wave prediction device 200 issues an alarm including the predicted intensity of the S-wave and the grace period through the notification unit 131 .

In yet another embodiment, the mobile S-wave prediction device 200 can also be configured to receive earthquake early warnings from the Japan Meteorological Agency. When an earthquake early warning is received, the notification unit 131 issues an alarm based on the earliest of the time when the S-wave prediction is completed in the S-wave prediction unit and the time when the earthquake early warning is received. Further, when receiving an earthquake early warning, the grace time calculation unit uses the position of itself measured by the position measurement unit 161 and the position of the epicenter included in the earthquake early warning to determine whether the S wave is a mobile S A predicted grace time T′ until reaching the point of the wave prediction device 200 is obtained. The grace period T' can be obtained as described above using the distance L' between the self position and the epicenter position included in the earthquake early warning. The mobile S-wave prediction device 200 calculates the grace period T calculated based on the position of the P-wave detection device that detected the P-wave, and the grace period T′ calculated based on the position of the hypocenter included in the earthquake early warning. , and the notification unit 131 issues an alarm including a shorter grace period.

Claims (12)

1. In a seismic motion detection and prediction system that detects P-waves that generate primary tremors and predicts S-waves that generate primary tremors based on the detected P-waves. A P-wave detection device comprising:
   A plurality of seismometers provided within a predetermined range at an observation point;
   a calculation unit that calculates a judgment value for judging detection of a P-wave using a plurality of real-time measurement data for each elementary interval that is a predetermined time interval measured by each of the plurality of seismometers;
   a determination unit that determines whether a P wave is detected based on the determination value;
   A P-wave detection device comprising: a communication section that transmits P-wave data used for predicting an S-wave to the outside when the determination section determines that a P-wave has been detected.
2. The judgment value is any of the standard deviation of a plurality of real-time measurement data, the average deviation of a plurality of real-time measurement data, or the correlation function of a plurality of real-time measurement data between the plurality of seismometers, or any of these determined using a combination of
   2. The P-wave detection device of claim 1.
3. The determination value is obtained based on a difference between a standard deviation of a plurality of real-time measurement data in the elementary interval and a first threshold,
   The first threshold is obtained using an average value obtained by averaging standard deviations of a plurality of real-time measurement data obtained for each elementary interval in normal times over a plurality of elementary intervals,
   3. The P-wave detection device of claim 2.
4. the determination value is obtained based on a difference between an average value of a plurality of real-time measurement data in the elementary interval and a second threshold;
   The second threshold value is the average value obtained by averaging the average values of the plurality of real-time measurement data obtained for each elementary interval during normal operation over the plurality of elementary intervals, and the average value calculated for each elementary interval during normal operation. obtained using the standard deviation of multiple real-time measurement data,
   3. The P-wave detection device of claim 2.
5. The determination value is obtained based on a difference between a correlation coefficient of a plurality of real-time measurement data of the elementary interval obtained between the plurality of seismometers and a third threshold,
   The third threshold is determined using a correlation coefficient of a plurality of normal real-time measurement data obtained between the plurality of seismographs,
   3. The P-wave detection device of claim 2.
6. a pre-rupture filter for detecting a pre-rupture seismic motion associated with the P-wave occurring immediately prior to the P-wave and distinguishing the pre-rupture seismic motion from the P-wave;
   A P-wave detection device according to any one of claims 1 to 7.
7. When it is determined that the seismic motion is detected by the determination unit, the determination of the detection of the seismic motion is repeated for the first number of elementary intervals, and the seismic motion is continuously detected in the first number of elementary intervals (provisional detection). If
   The preceding destruction filter is
   Over the first number of elementary intervals, the average value of a plurality of real-time measurement data in each elementary interval is obtained, the differential deviation between the average values of the two elementary intervals is calculated, and the moving average of the two differential deviations is obtained. Calculate the leading fracture index, which is the value of
   In an arbitrary judgment interval within the first number of elementary intervals, when the negative preceding rupture index continuously appears in the second number or more of elementary intervals, the detected seismic motion is the preceding rupture seismic motion. determine that there is
   7. The P-wave detector of claim 6.
8. further comprising a minor event filter for detecting minor events unrelated to the P-wave that occur immediately prior to the P-wave and distinguishing the minor events from the P-wave;
   A P-wave detection device according to any one of claims 1 to 7.
9. When it is determined that the seismic motion is detected by the determination unit, the determination of the detection of the seismic motion is repeated for the first number of elementary intervals, and the seismic motion is continuously detected in the first number of elementary intervals (provisional detection). If
   The minute event filter is
   calculating a moving average value of average values of a plurality of real-time measurement data in each of the first number of elementary intervals;
   calculating a difference between the moving average value and a predetermined minute event threshold;
   determining that a P-wave is detected when the difference is positive;
   9. The P-wave detection device of claim 8.
10. To predict S-waves based on the detected P-waves in a seismic motion detection and prediction system that detects P-waves that generate primary tremors and predicts S-waves that generate primary tremors based on the detected P-waves An S-wave predictor of
    a communication unit that receives P-wave data transmitted from the P-wave detection device according to any one of claims 1 to 9;
    an S-wave prediction unit that predicts the intensity of an arriving S-wave based on the P-wave data;
    An S-wave predictor comprising:
11. The S-wave prediction unit
    configured to receive a predetermined S-wave prediction function for predicting an S-wave intensity at the point from which the P-wave data was transmitted;
    Based on the P-wave data, the S-wave prediction function, and the distance attenuation between the point of transmission of the P-wave data and the point of the S-wave predictor, S predict wave strength,
    The S-wave prediction device according to claim 10.
12. a position measuring unit that measures the position of the S-wave prediction device;
    Claim 10 or claim 10, further comprising a delay time calculation unit that calculates a delay time until the S wave arrives based on the measured position and the position of the P wave detection device that detected the P wave. 12. The S-wave prediction device according to 11.
    
    

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