JP6033563B2 - Crustal failure prediction method - Google Patents

Description

  The present invention relates to a crustal destruction prediction method based on an electromagnetic field intensity signal obtained from a measuring instrument such as an underground antenna from an electrode pair, and a crustal destruction prediction system using the same.

  In order to put into practical use the prediction of rapid crustal destruction, such as earthquakes, volcanic eruptions, and landslides, it is essential to find the phenomena that occur immediately before crustal destruction, so-called precursors.

Precursor phenomena of crustal destruction are roughly classified into earthquake phenomena, crustal movements, groundwater abnormalities, electromagnetic field fluctuations, and other (so-called magnificent phenomena such as animal abnormalities). Below this major classification, there are second, third, etc. levels. In the second level, about 20 types of precursor phenomena are found.

As a method of using electromagnetic field fluctuation as a precursor phenomenon of crustal destruction, for example, in Patent Document 1 (Japanese Patent Application Laid-Open No. 2002-357666), the prediction target regions where the ground is expected to be unstable have different lengths. A ground characterized in that a plurality of survey lines are set and electrodes are installed at both ends thereof, and a ground collapse or a preclusion phenomenon of the ground in the prediction target region is evaluated from change data of a ground potential difference measured between the electrodes. It is disclosed as an example of the collapse / destruction prediction method.
JP 2002-357666 A

  The fact that a precursory phenomenon is closely related to the occurrence of crustal destruction means that it occurs with a high probability before the crustal destruction and does not occur with a high probability when there is no crustal destruction. The greater the two probabilities, the higher the benefit, and it is necessary to exceed several tens of percent for each of these two probabilities for practical use.

  However, many observational researches have been conducted since 1965 when full-fledged predictive research began, but no phenomenon or detection method that satisfies this condition has been found.

In order to solve the above-described problem, the invention according to claim 1 includes an electric field strength signal acquisition step of acquiring an electric field strength signal by a counter electrode embedded in the ground, and the electric field strength signal acquired by the electric field strength signal acquisition step. A waveform determination step for determining whether or not the waveform pattern is similar to the predetermined waveform pattern, and a frequency counting step for counting the number of times determined to be similar to the predetermined waveform in the waveform determination step within a predetermined time, depending on the counting result of the frequency counting step, possess a prediction step of predicting the event of occurrence of the crust breaking, and the predetermined waveform pattern, the SLF / ELF band, again sharply immediately after the steep rise A first waveform pattern that is a falling waveform pattern and a wave that gradually decreases over a predetermined time after a steep rise in the DC band and then falls sharply A second waveform pattern that is a pattern, and the prediction step is performed according to a counting result of the frequency counting step related to the first waveform pattern and a counting result of the frequency counting step related to the second waveform pattern, It is a crustal destruction prediction method characterized by making predictions related to crustal destruction.

The invention according to claim 2 is the crustal fracture prediction method according to claim 1, wherein the prediction step includes a counting result of the frequency counting step relating to the first waveform pattern and the frequency relating to the second waveform pattern. It is characterized by predicting the occurrence of crustal destruction according to the counting result of the counting step.

The invention according to claim 3 is the crustal fracture prediction method according to claim 1, wherein the prediction step includes a counting result of the frequency counting step related to the first waveform pattern and the frequency related to the second waveform pattern. The time of occurrence of crustal destruction is predicted according to the counting result of the counting process.

The invention according to claim 4 is the crustal fracture prediction method according to claim 1, wherein the prediction step includes a counting result of the frequency counting step related to the first waveform pattern and the frequency related to the second waveform pattern. According to the counting result of the counting step, whether or not the generated earthquake is a main shock is predicted.

  The crustal fracture prediction method of the present invention and the crustal fracture prediction system using the crustal fracture prediction system according to the present invention have found that the waveform pattern of the electromagnetic field intensity signal acquired by the electromagnetic field intensity signal acquisition step has been found by the inventor through intensive research. A waveform determination step for determining whether or not the waveform pattern is similar, a frequency counting step for counting the number of times determined to be similar to the predetermined waveform in the waveform determination step within a predetermined time, and a determination in the frequency determination step A prediction step of predicting an event related to the occurrence of crustal destruction according to the result.

  That is, according to the crustal destruction prediction method and the crustal destruction prediction system of the present invention, since prediction is performed based on a characteristic waveform pattern observed with high probability as a precursor phenomenon, events related to occurrence of crustal destruction Can be predicted more accurately.

It is a figure which shows the outline | summary of the underground antenna which consists of the electrode pair 10 as an example of the electric field measurement used with the crust destruction prediction system which concerns on embodiment of this invention. It is a figure which shows the block configuration of the crust destruction prediction system which concerns on embodiment of this invention. It is a figure which shows the waveform pattern memorize | stored in each waveform memory | storage part of the crustal destruction prediction system which concerns on embodiment of this invention. The first pattern may be another waveform. It is a figure which shows the time change of the frequency of each waveform pattern observed when crustal destruction, such as an earthquake, generate | occur | produces. It is a figure which shows typically a mode that several underground antennas were embed | buried in several observation points. It is a figure which shows the block configuration of the crustal destruction prediction system which concerns on other embodiment of this invention. It is a figure which shows the map of several observation points.

  Embodiments of the present invention will be described below with reference to the drawings. In the following examples, the crustal destruction prediction method according to the embodiment of the present invention will be described by taking an earthquake as an example of the predicted crustal destruction, but the method of the present invention is also applicable to prediction of volcanic eruptions and landslides. Is possible.

  FIG. 1 is a diagram showing an outline of an underground antenna composed of electrode pairs 10 used in a crustal destruction prediction system according to an embodiment of the present invention. The underground antenna is constituted by an electrode pair 10 including a cylindrical electrode 5 made of a conductive casing pipe and embedded in an observation point, and an annular electrode 6 buried in the ground around the electrode. is there. In addition, the system can be configured by adopting an advanced waveform recognition process also by measurement of a normal horizontal component of an electric field or a magnetic field sensor.

From the electrode pair 10 constituting such an underground antenna, it is possible to acquire an electric field strength signal based on a minute crack generated in a breakdown region in the ground. In addition, regarding the technology related to crustal destruction prediction using the underground antenna as described above, the inventors have issued a patent No. 3341040,
-JP 9-105781 A,
・ National Institute for Science and Technology for Disaster Prevention, Research Institute for Electronic Technology, Communications Research Laboratory (1996): Evaluation of electromagnetic phenomena as precursors of earthquakes, Report on the Earthquake Prediction Research Committee,
・ National Institute of Science and Technology for Disaster Prevention ・ Communication Research Laboratory (2001): Relationship between underground electromagnetic field fluctuations and earthquake and volcanic activity (2) Information disclosed in the Report on Earthquake Prediction Research Liaison Committee can be used as appropriate As such, the disclosure content here is incorporated herein by reference.

  Next, a system for performing prediction based on the electric field strength signal obtained from the electrode pair 10 constituting the underground antenna as described above will be specifically described. FIG. 2 is a diagram showing a block configuration of the crustal fracture prediction system according to the embodiment of the present invention.

  In FIG. 2, the electric field strength signal acquired from the electrode pair 10 of the underground antenna is converted from an analog value into digital data by the A / D conversion unit 50, sampled by the data acquisition unit 70, and taken into the data processing unit 60. . Sampling is performed at 5-18 kHz.

The data captured by the data processing unit 60 passes through both the SLF / ELF band transmission filter 100 and the DC band transmission filter 200, thereby including the data included in the SLF / ELF band and the DC band. To separate data. Here, the SLF / ELF band is 1.5 Hz-9 kHz, and the DC band is 0-1.5 Hz. The inventors have obtained as knowledge that there is a possibility that the waveform pattern that can be regarded as the first waveform pattern is not limited to that shown in FIG.

  From the data included in the SLF / ELF band that has passed through the SLF / ELF band transmission filter 100, the first waveform extraction unit 110 then extracts abnormal waveform data sufficiently larger than the noise level.

  The abnormal waveform data extracted by the first waveform extraction unit 110 is determined by the first waveform determination unit 120 whether or not it is similar to the first waveform pattern stored in advance in the first waveform storage unit 130. The

  Here, the first waveform pattern stored in the first waveform storage unit 130 is as shown in FIG. As a feature of this waveform pattern, in the SLF / ELF band, the waveform pattern falls sharply again immediately after the sharp rise.

  In the first storage unit 140, the first waveform determination unit 120 determines the generation timing and waveform of abnormal waveform data determined to be similar to the first waveform pattern stored in the first waveform storage unit 130 in advance. Remember the log.

  The first frequency counting unit 150 refers to the log stored in the first storage unit 140 and counts the number of occurrences of abnormal waveform data similar to the first waveform pattern within a predetermined time. .

  The counting result in the first frequency counting unit 150 is input to the prediction unit 300.

  On the other hand, the data included in the DC band that has passed through the DC band transmission filter 20 is extracted by the second waveform extraction unit 210 for abnormal waveform data that greatly deviates from the noise level.

  The abnormal waveform data extracted by the second waveform extraction unit 210 is determined by the second waveform determination unit 220 whether or not it is similar to the second waveform pattern stored in advance in the second waveform storage unit 230. The

  Here, the second waveform pattern stored in the second waveform storage unit 230 is as shown in FIG. As a feature of this waveform pattern, in the DC band, after a steep rise, the waveform pattern gradually decreases over a predetermined time and then falls steeply. The inventors have obtained as knowledge that there is a possibility that the waveform pattern that can be regarded as the second waveform pattern is not limited to that shown in FIG.

  In the second storage unit 240, the second waveform determination unit 220 logs a generation timing log of abnormal waveform data determined to be similar to the second waveform pattern stored in advance in the second waveform storage unit 230. Remember.

  The second frequency counting unit 250 refers to the log stored in the second storage unit 240 and counts the number of occurrences of abnormal waveform data similar to the second waveform pattern within a predetermined time. .

  The counting result in the second frequency counting unit 250 is input to the prediction unit 300.

FIG. 4 is a diagram showing the frequency of occurrence of each waveform pattern observed when a crustal failure such as an earthquake occurs. FIG. 4A shows the frequency of occurrence of abnormal waveform data similar to the first waveform pattern. FIG. 4B shows the frequency of occurrence of abnormal waveform data similar to the second waveform pattern. That is, the histogram in FIG. 4A corresponds to that calculated by the first frequency counting unit 150, and the histogram in FIG. 4B corresponds to that calculated by the second frequency counting unit 250.

  As shown in FIG. 4, the frequency of occurrence of abnormal waveform data similar to the first waveform pattern increases at the timing before the main shock and decreases after the main shock. On the other hand, the frequency of occurrence of abnormal waveform data similar to the second waveform pattern is low before the mainshock and increases after the mainshock. This is an actual fact found by the inventor through intensive observation and research.

  The cause of the phenomenon shown in FIG. 4 must be further studied academically. The above-mentioned causes considered at this stage are as follows. That is, the occurrence of the first waveform pattern based on minute cracks increases before the occurrence of the mainshock, and the occurrence of the second waveform pattern based on large cracks in the ground becomes significant after the occurrence of the mainshock. Therefore, it is considered that the phenomenon shown in FIG. 4 occurs.

Now, the count data from the first frequency counting unit 150 and the second frequency counting unit 250 is input to the prediction unit 300. Based on the knowledge shown in FIG. It can be seen that Hereinafter, the prediction method in the prediction unit 300 to be described is merely an example.
(A) Prediction related to whether crustal destruction such as an earthquake will occur in the future In this case, in the prediction unit 300, the result of the first frequency counting unit 150 and a threshold value stored in the reference value storage unit 310 (for example, In comparison with N ₁ ) shown in FIG. 4, if the result of the first frequency counting unit 150 exceeds the threshold, it can be predicted that crustal destruction such as an earthquake will occur in the future. . Such prediction by the prediction unit 300 is notified by the notification unit 330 serving as an output unit.
(B) Predicting the time when crustal destruction such as an earthquake will occur in this case In this case, in the prediction unit 300, the result of the first frequency counting unit 150 and the threshold value stored in the reference value storage unit 310 (for example, in FIG. 4) N ₂ ), and if the result of the first frequency counting unit 150 exceeds the threshold value, it can be predicted that crustal destruction such as an earthquake will occur within 24 hours, for example. it can. Such prediction by the prediction unit 300 is notified by the notification unit 330 serving as an output unit.

Further, for example, the prediction unit 300 refers to the result of the first frequency counting unit 150 over time T ₁ (see FIG. 4), and for example, as the output value of the first frequency counting unit 150 within this time T ₁ . When the threshold value (for example, N ₂ shown in FIG. 4) stored in the reference value storage unit 310 exceeds a predetermined number of times, the occurrence of crustal destruction such as an earthquake occurs within 12 hours, for example. Can be predicted. Such prediction by the prediction unit 300 is notified by the notification unit 330 serving as an output unit.

Quantitatively, an empirical rule according to Saito (1969) and Varnes (1989), where N (t) is the number of occurrences per unit time (4 hours) at an arbitrary time t and T _j is the predicted occurrence time,
dN (t) / dt = k / (T _j −t) ⁿ (1)
To predict the predicted occurrence time. Here, n and k are obtained from time series N (1), N (2), N (3),... Obtained after the abnormal waveform starts to be detected.
(C) Predicting whether crustal destruction such as an earthquake occurred is a main shock The prediction unit 300 refers to the result of the second frequency counting unit 250 over time T ₂ (see FIG. 4). If the output value of the second frequency counting unit 250 exceeds the threshold value stored in the reference value storage unit 310 within T ₂ and exceeds a predetermined number of times, the occurrence of crustal destruction such as an earthquake that has occurred first will occur. It can be predicted that it was a mainshock. Such prediction by the prediction unit 300 is notified by the notification unit 330 serving as an output unit.

  As described above, in the crustal fracture prediction method and the crustal fracture prediction system of the present invention, the inventor of the present application has found the waveform pattern of the electric field strength signal acquired by the electric field strength signal acquisition step through intensive research. A waveform determining step for determining whether or not the waveform is similar to a predetermined waveform pattern, a frequency counting step for counting the number of times determined to be similar to the predetermined waveform in the waveform determining step within a predetermined time, and the frequency A prediction step of predicting an event related to the occurrence of crustal destruction according to the determination result in the determination step.

  That is, according to the crustal destruction prediction method and the crustal destruction prediction system of the present invention, since prediction is performed based on a characteristic waveform pattern observed with high probability as a precursor phenomenon, events related to occurrence of crustal destruction Can be predicted more accurately.

  Next, another embodiment of the present invention will be described. In the previous embodiment, there was one observation point where the underground antenna composed of the electrode pair 10 was embedded, whereas in this embodiment, a plurality of observation points were embedded where the underground antenna composed of the electrode pair 10 was embedded. I am trying to provide it.

  FIG. 5 is a diagram schematically showing a state in which a plurality of underground antennas are embedded at a plurality of observation points. The density of such observation points is preferably about 9 points (distance D = 25 km), 16 (distance D = 17 km), and 25 points (distance D = 13 km) in one prefecture in consideration of the detection range. . Of course, the more you have, the more accurately you can know the state of the basement.

  FIG. 6 is a diagram showing a block configuration of a crustal fracture prediction system according to another embodiment of the present invention. The data processing unit 60 acquires electric field strength signals related to a plurality of observation points, and performs data processing related to the electric field strength signals at the respective observation points.

  Here, the data processing in the data processing unit 60 is performed in the same manner as in the previous embodiment. The difference between this embodiment and the previous embodiment is that in this embodiment, it is possible to acquire data related to a plurality of observation points, so it is possible to predict the scale of crustal destruction such as earthquakes. That is.

  Hereinafter, in another embodiment, processing of the prediction unit 300 when performing crustal destruction scale prediction will be described with reference to FIG. 7. FIG. 7 is a diagram showing a map of a plurality of observation points. In this map, black circles indicate points where the counting result of the first frequency counting unit 150 exceeds a predetermined value and occurrence of crustal destruction such as an earthquake is predicted, and white circles indicate counting results of the first frequency counting unit 150. Indicates a point where crustal destruction such as an earthquake is not predicted. S indicates the area calculated based on the observation points with black circles.

As a method for obtaining such an area S, for example, there is a method in which a unit area is determined in advance for one observation point, and the number of observation points indicated by a black circle is multiplied by this unit area. Note that the calculation method is not limited to this as long as the area S is increased so as to correspond to the actual topography according to the number of observation points indicated by black circles.

  Based on the area S obtained as described above, the magnitude of the magnitude of crustal destruction such as an earthquake is calculated by the following equation (1) showing the relationship between the well-known failure area and the magnitude (moment) Mw. Can be calculated.

Mw = a (logS−2.0) (2)
Here, a is a coefficient, which is initially 1.0, but is adjusted by accumulating observation data.

  According to the other embodiments as described above, it is possible to enjoy the same effect as the previous embodiment, and furthermore, it is possible to accurately predict the scale of crustal destruction such as an earthquake. Become.

5 ... cylindrical electrode 6 ... annular electrode 10 ... electrode pair 50 ... A / D conversion unit 60 ... data processing unit 70 ... data acquisition unit 100 ... SLF / ELF band Transmission filter 110 ... first waveform extraction unit 120 ... first waveform determination unit 130 ... first waveform storage unit 140 ... first storage unit 150 ... first frequency counting unit 200 ... DC band transmission filter 210 ... second waveform extraction unit 220 ... second waveform determination unit 230 ... second waveform storage unit 240 ... second storage unit 250 ... second frequency counting unit 300 ..Prediction unit 310 ... reference value storage unit 330 ... notification unit

Claims (4)

An electric field strength signal acquisition step of acquiring an electric field strength signal by a counter electrode embedded in the ground;
A waveform determination step of determining whether or not the waveform pattern of the electric field strength signal acquired by the electric field strength signal acquisition step is similar to a predetermined waveform pattern;
A frequency counting step of counting the number of times determined to be similar to the predetermined waveform in the waveform determination step within a predetermined time; and
Depending on the counting result of the frequency counting step, possess a prediction step of predicting the event of occurrence of the crust breaking, the,
In the SLF / ELF band, the predetermined waveform pattern includes a first waveform pattern that is a waveform pattern that sharply falls again immediately after a steep rise, and a gradual decrease over a predetermined time after a steep rise in the DC band, Then there is a second waveform pattern that is a waveform pattern that falls sharply,
The prediction step includes
A counting result of the frequency counting step with respect to the first waveform pattern;
A crustal destruction prediction method, wherein prediction related to crustal destruction is performed according to a counting result of the frequency counting step regarding the second waveform pattern . The prediction step includes
A counting result of the frequency counting step with respect to the first waveform pattern;
2. The crustal destruction prediction method according to claim 1, wherein the presence or absence of crustal destruction is predicted according to a counting result of the frequency counting step with respect to the second waveform pattern. The prediction step includes
A counting result of the frequency counting step with respect to the first waveform pattern;
The crustal destruction prediction method according to claim 1, wherein the time of occurrence of crustal destruction is predicted according to the counting result of the frequency counting step with respect to the second waveform pattern. The prediction step includes
A counting result of the frequency counting step with respect to the first waveform pattern;
2. The crustal fracture prediction method according to claim 1, wherein whether or not the generated earthquake is a main shock is predicted according to a counting result of the frequency counting step regarding the second waveform pattern.

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