Earthquake observation equipment, earthquake observation method, and recording medium for recording earthquake observation program

Abstract

An earthquake observation device includes: a vibration detection status information acquisition device, which acquires vibration detection status information at each of a plurality of observation points; a multidimensionalization device, which generates two-dimensional or more dimensional vibration detection status information related to a geographical location and time based on the vibration detection status information at each of the plurality of observation points; and a group designation device, which classifies element information into groups for each vibration cause, the element information forming the two-dimensional or more dimensional vibration detection status information, and each piece of the element information indicates the vibration detection status at a specific geographical location and a specific time.

Description

Technical Field

[0001] The present invention relates to earthquake observation equipment, an earthquake observation method, and a recording medium for recording earthquake observation programs. Background Art

[0002] To perform earthquake observation, seismographs are installed at a plurality of locations, and earthquake intensity is measured at each location (for example, Patent Document 1).

[0003] [Prior art literature]

[0004] [Patent Document]

[0005] [Patent Document 1]

[0006] Japanese Unexamined Patent Application First Publication No. 2016-156712 Summary of the Invention

[0007] [Problems to be Solved by the Invention]

[0008] When multiple earthquakes occur very close in time and location, in order to infer the source of each earthquake, it is necessary to obtain a correspondence between the observed vibrations and the earthquake that caused them. To obtain this correspondence with high accuracy, it is preferable to use not only the positional relationship of the observation points but also a relatively large amount of information.

[0009] An object of the present invention is to provide an earthquake observation device, an earthquake observation method, and a recording medium for recording an earthquake observation program that can solve the above-mentioned problems.

[0010] [Methods of solving the problem]

[0011] According to a first aspect of the present invention, an earthquake observation device is provided, which includes: a vibration detection status information acquisition device for acquiring vibration detection status information at each of a plurality of observation points; a multidimensionalization device for generating two-dimensional or more dimensional vibration detection status information related to a geographical location and time based on the vibration detection status information at each of the plurality of observation points; and a group designation device for classifying element information into groups for each vibration cause, the element information forming the two-dimensional or more dimensional vibration detection status information, and each piece of the element information indicating the vibration detection status at a specific geographical location and a specific time.

[0012] According to a second aspect of the present invention, there is provided an earthquake observation method, comprising: acquiring vibration detection status information at each of a plurality of observation points; generating two-dimensional or more dimensional vibration detection status information related to a geographic location and a time based on the vibration detection status information at each of the plurality of observation points; and classifying element information into groups for each vibration cause, the element information forming the two-dimensional or more dimensional vibration detection status information, and each piece of the element information indicating the vibration detection status at a specific geographic location and a specific time.

[0013] According to a third aspect of the present invention, a recording medium for recording an earthquake observation program is provided, which enables a computer to: obtain vibration detection status information at each of a plurality of observation points; generate two-dimensional or more dimensional vibration detection status information related to a geographic location and time based on the vibration detection status information at each of the plurality of observation points; and classify element information into groups for each vibration cause, the element information forming the two-dimensional or more dimensional vibration detection status information, and each piece of the element information indicating the vibration detection status at a specific geographic location and a specific time.

[0014] [Beneficial effects of the present invention]

[0015] According to the present invention, a relatively large amount of information can be used to obtain a correspondence between observed vibrations and the earthquakes that caused the vibrations. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a block diagram showing an example of a functional configuration of an earthquake observation apparatus according to the embodiment.

[0017] Figure 2 is a block diagram showing an example of a functional configuration of a seismic processing system according to an embodiment.

[0018] Figure 3 is a graph showing an arrangement example of observation points arranged in a straight line according to the embodiment.

[0019] Figure 4 is a graph showing an example of two-dimensional trigger detection state information generated by using observation points arranged in a straight line according to the embodiment.

[0020] Figure 5 is a graph showing an example of two-dimensional or more-dimensional trigger detection state information in the encoding method according to the embodiment.

[0021] Figure 6 is a graph illustrating an example of a trigger group determined by a group specifying unit according to an embodiment.

[0022] Figure 7 is a block diagram showing an example of a functional configuration of a model generating apparatus according to an embodiment.

[0023] Figure 8 is a block diagram showing an earthquake observation apparatus according to an embodiment of a minimum configuration of the present invention.

[0024] Figure 9 : is a flowchart showing a processing procedure of an earthquake observation method according to an embodiment of a minimum configuration of the present invention.

[0025] Figure 10 is a block diagram showing a configuration of a computer according to at least one of the above-described embodiments. DETAILED DESCRIPTION

[0026] Hereinafter, embodiments of the present invention will be described, but the following embodiments do not limit the present invention according to the claims. Not all feature combinations described in the embodiments are essential to the solving means of the present invention.

[0027] Figure 1 : is a schematic block diagram showing an example of a functional configuration of an earthquake observation device according to an embodiment. Figure 1 As shown, the earthquake observation apparatus 100 includes a communication unit 110, a display unit 120, an operation input unit 130, a storage unit 180, and a control unit 190. The storage unit 180 includes a model storage unit 181. The control unit 190 includes a multidimensionalization unit 191 and a group specifying unit 192.

[0028] The earthquake observation device 100 groups the vibrations observed at a plurality of corresponding observation points according to each of the events causing the vibrations. Specifically, for each of the events causing the vibrations, the earthquake observation device 100 groups the vibrations shown as triggers on two-dimensional or more dimensional data having extensions in the time direction and the spatial direction.

[0029] The earthquake observation apparatus 100 is constructed by using a computer such as a workstation or a mainframe.

[0030] The above-mentioned observation point is a point where a sensor for observing vibrations such as earthquakes is installed. The observation point will also be called an earthquake observation point or an earthquake intensity observation point. The vibration measured by the sensor at the observation point will also be called the vibration at the observation point.

[0031] The above-mentioned event is a vibration cause (an event that causes vibration) such as an earthquake.

[0032] For example, if two earthquakes occur very close in time and location, in order to infer the source of each of the two earthquakes, it is necessary to specify which earthquake caused the vibration observed at each observation point. If the accuracy of this specification is low, the information used to infer the source will include information about other earthquakes. This information about other earthquakes acts as noise, thereby reducing the accuracy of the estimated source. Therefore, it is desirable for the earthquake observation device 100 to group each vibration event with high accuracy.

[0033] A trigger is a vibration that is different from ordinary vibrations generated in daily life (for example, vibrations at a level equal to or lower than the noise level). When the earthquake observation device 100 detects a vibration that is different from ordinary vibrations, the earthquake observation device 100 performs a process for earthquake observation on the vibration. In this regard, the vibration that is different from ordinary vibrations is a trigger for the earthquake observation device 100 to perform a process for earthquake observation.

[0034] Determining the presence or absence of vibrations different from ordinary vibrations will also be referred to as determining a trigger.

[0035] Information indicating a trigger detection state such as whether a trigger is detected will be referred to as trigger detection state information. The trigger detection state information corresponds to an example of vibration detection state information.

[0036] The earthquake observation device 100 can be used to infer the source of an earthquake.

[0037] Figure 2 : is a schematic block diagram showing an example of a functional configuration of a seismic processing system according to an embodiment. Figure 2 In the illustrated configuration, the earthquake processing system 1 includes a sensor 210, a sensor data collection device 220, an earthquake processing device 230, a data server device 240, a tsunami processing device 250, a real-time display terminal device 261, a maintenance terminal device 262, and an interactive processing terminal device 263. The earthquake processing device 230 includes a receiving unit 231, a single observation point trigger processing unit 232, an earthquake determination unit 233, a phase check unit 234, a source estimation unit 235, and a notification processing unit 236.

[0038] The earthquake processing device 230 , the tsunami processing device 250 , and the data server device 240 are configured by using computers such as workstations and mainframes.

[0039] The earthquake processing system 1 reports an earthquake warning when an earthquake occurs. In the earthquake processing system 1, the sensor data collection device 220 collects measurement data from the sensor 210 installed at the observation point, transmits the collected data to the earthquake processing device 230, and registers the collected data in the data server device 240.

[0040] Earthquake processing device 230 determines whether an earthquake has occurred based on the measurement data from sensor 210. If an earthquake has occurred, earthquake processing device 230 estimates the earthquake source (center of the earthquake) and notifies a notification destination of the estimated result along with the tsunami information from tsunami processing device 250. The notification destination may be, for example, a terminal device of a person in charge of earthquake analysis or an organization that reports earthquake information, such as a television station.

[0041] The receiving unit 231 receives the measurement data from the sensor 210 from the sensor data collecting device 220 and outputs the data to each unit of the subsequent stage.

[0042] The single observation point trigger processing unit 232 determines a trigger for each observation point based on the measurement data from the sensor 210. The single observation point trigger processing unit 232 outputs the determination result as trigger detection status information for each observation point. The trigger detection status information referred to here is information indicating whether a trigger has been detected at the observation point (i.e., whether a trigger has been detected).

[0043] The trigger detection state information corresponds to an example of the vibration detection state information. The vibration detection state information is information indicating the vibration detection state at the observation point. In the vibration detection state information, the trigger detection state information indicates the trigger detection state as the vibration detection state.

[0044] Each observation point will also be referred to as a single observation point.

[0045] The earthquake determination unit 233 determines whether an earthquake has occurred based on the results of trigger determination at multiple observation points. For example, if the proportion of observation points determined to be triggered by an earthquake among the observation points included in a predetermined range is equal to or greater than a predetermined proportion, the earthquake determination unit 233 determines that an earthquake has occurred.

[0046] In the case where the earthquake determination unit 233 determines that an earthquake has occurred, the phase check unit 234 checks seismic waves of respective phases (eg, P wave, S wave, and T wave). An existing method can be used as the checking method.

[0047] In the case where the earthquake determination unit 233 determines that an earthquake has occurred, the earthquake source estimation unit 235 estimates the earthquake source based on the check result of the phase check unit 234. An existing method can be used as a method of estimating the earthquake source.

[0048] In the case where the earthquake determination unit 233 determines that an earthquake has occurred, the notification processing unit 236 notifies a predefined notification destination of the earthquake source estimated by the earthquake source estimation unit 235 and tsunami information generated by the tsunami processing device 250 .

[0049] The data server device 240 stores various data related to seismic observations, such as measurement data from the sensor 210. The data server device 240 also stores parameter values used by the seismic processing device 230 to perform various processes. For example, if the seismic processing device 230 performs processing in each unit using a machine learning-based training model, the data server device 240 may store the model parameter values resulting from the machine learning.

[0050] If earthquake determination unit 233 determines that an earthquake has occurred, tsunami processing device 250 estimates whether a tsunami has occurred. Tsunami processing device 250 estimates the presence of a tsunami using a known method. Tsunami processing device 250 transmits tsunami information indicating the estimated presence of a tsunami to notification processing unit 236. As described above, notification processing unit 236 notifies the notification destination of both earthquake source information and tsunami information.

[0051] The real-time display terminal device 261 displays the measurement data from the sensor 210 in real time.

[0052] The maintenance terminal device 262 is a terminal device used to maintain the seismic processing system 1. For example, a maintenance operator of the seismic processing system 1 uses the maintenance terminal device 262 to update the model parameter values stored in the data server device 240. The maintenance operator of the seismic processing system 1 uses the maintenance terminal device 262 to check whether each of the sensors 210 is operating normally and perform maintenance on the sensors 210 as needed.

[0053] The interactive processing terminal device 263 interactively presents to the user portions of the seismic processing system 1 that require manual processing, and accepts the user's processing. For example, if the user processes the phase inspection unit 234, the interactive processing terminal device 263 may display information for the inspection, such as a seismic waveform, and accept the user's processing.

[0054] Among the components of the earthquake processing system 1, the earthquake determination unit 233 is associated with the earthquake observation apparatus 100. When multiple earthquakes occur very close in time and location, the earthquake observation apparatus 100 not only detects the earthquakes that have occurred but also determines the correspondence as to which earthquake caused the trigger.

[0055] The earthquake observation device 100 may perform the processing of the earthquake determination unit 233. In this case, the earthquake processing device 230 may be configured as one device, and the earthquake observation device 100 and a portion of the earthquake processing device 230 may be associated with each other. Alternatively, the earthquake processing device 230 may be configured as a plurality of devices including the earthquake observation device 100.

[0056] Earthquake observation equipment 100( Figure 1 ) communicates with other devices through the communication unit 110. For example, the communication unit 110 receives the trigger detection state information of each observation point from the single observation point trigger processing unit 232. The communication unit 110 corresponds to an example of a vibration detection state information acquisition unit.

[0057] The display unit 120 includes a display screen such as a liquid crystal panel or a light emitting diode (LED) panel and displays various images. For example, the display unit 120 displays the trigger detection state information generated by the multidimensionalization unit 191 and two-dimensionalized with a geographical location axis and a time axis.

[0058] The operation input unit 130 includes input devices such as a keyboard and a mouse, and accepts user operations.

[0059] The storage unit 180 stores various data. The storage unit 180 is configured by using a storage device included in the earthquake observation apparatus 100.

[0060] The model storage unit 181 stores a training model. The training model referred to here is a model obtained through machine learning. The model storage unit 181 can store a training model used for some or all of the processing performed by the multidimensionalization unit 191 and the group specifying unit 192. The model storage unit 181 stores a training model obtained as a result of machine learning.

[0061] For example, the model storage unit 181 may store a model including parameters and parameter values obtained through machine learning as a training model. The model including parameters may be a neural network such as a convolutional neural network (CNN), but is not limited thereto.

[0062] The control unit 190 controls each unit of the earthquake observation apparatus 100 to perform various processes. The functions of the control unit 190 are performed by a central processing unit (CPU) included in the earthquake observation apparatus 100 reading a program from the storage unit 180 and executing the program.

[0063] The multi-dimensionalization unit 191 generates two-dimensional or more-dimensional trigger detection status information associated with a geographical location and time from the trigger detection status information at each of a plurality of observation points.

[0064] Group specifying unit 192 classifies each piece of element information, which forms the two-dimensional or more dimensional trigger detection status information generated by multidimensionalization unit 191 and indicates the trigger detection status at a specific geographical location and a specific time, into a group for each vibration cause. That is, group specifying unit 192 uses the trigger detection status information multidimensionalized by multidimensionalization unit 191 to classify the trigger detection status information at each observation point and each time as its elements into a group for each event (e.g., for each earthquake).

[0065] <Visualization method>

[0066] One method by which the earthquake observation apparatus 100 groups triggers using two-dimensional or more-dimensional trigger detection state information related to geographical location and time will be referred to as a visualization method. In this visualization method, the earthquake observation apparatus 100 performs the following steps 11 to 15.

[0067] (Step 11)

[0068] The multidimensionalization unit 191 generates two-dimensional trigger detection state information associated with a geographical location axis to which a plurality of observation points arranged in a straight line are assigned (a coordinate axis indicating positions on a straight line connecting the plurality of observation points) and a time axis.

[0069] Figure 3 is a diagram showing an example of arrangement of observation points arranged in a straight line. Figure 3 In the example in , the observation points are indicated by circles (O). In the area A11 , the observation points are arranged substantially in a straight line.

[0070] Multidimensionalization unit 191 assigns the observation points arranged in a straight line as described above to coordinate axes of the geographic location in two dimensions of geographic location and time. Multidimensionalization unit 191 generates two-dimensional trigger detection status information by displaying the trigger detection status at each time at each assigned observation point in the two-dimensional coordinate system. The term "each time" here refers to, for example, a division of the time axis into predetermined unit time periods.

[0071] Figure 4 is a diagram showing an example of vibration measurement data at each of observation points arranged in a straight line. Figure 4 The horizontal axis of the graph in represents time. The vertical axis represents the position on the straight line where the observation point is set ( Figure 3 Each of the observation points arranged in a straight line is assigned to the vertical axis. Figure 4The graph of refers to 100 continuous waveform images disclosed on the website of the National Institute of Earth Science and Disaster Prevention (address: http: / / www.hinet.bosai.go.jp / mtrace / ?tm=&pv=&eq=&LANG=ja).

[0072] In the horizontal direction (direction parallel to the time axis) of the position to which the observation point is assigned, the amplitude at each time at the observation point is shown. As described above, the trigger is a vibration different from ordinary vibration (for example, vibration with a level equal to or lower than the noise level), and Figure 4 The portion with a larger amplitude (black portion) in the graph of can be regarded as the portion where the trigger is detected.

[0073] Alternatively, the multidimensionalization unit 191 may generate a graph that plots the trigger detection state for each observation point and each time, instead of generating a graph of the amplitude for each observation point and each time.

[0074] For example, a black plot point may indicate that a trigger has been detected, while a white plot point or no plotting may be used to indicate that no trigger has been detected. A plot point indicating that a trigger has been detected will also be referred to as a trigger plot point.

[0075] (Step 12)

[0076] The group specifying unit 192 determines a trigger group.

[0077] Here, the group specifying unit 192 classifies the triggers for each event (vibration cause such as a single earthquake). The group obtained by this classification is called a trigger group.

[0078] The group specifying unit 192 may classify triggers into groups based on geometric shapes in the two-dimensional trigger detection status information.

[0079] Since the observation points are arranged in a straight line, if the seismic waves propagate concentrically from the earthquake source, the trigger is described as concentric circles or arcs on the two-dimensional curve graph (on the two-dimensional trigger detection status information). Specifically, the start time of the trigger (i.e., the arrival time of the seismic wave at the observation point) is displayed as concentric circles or arcs. Figure 4 In the example of , the sequence of time points at which the amplitude increases corresponding to the start of the trigger is roughly arc-shaped.

[0080] When the distance between observation points arranged in a straight line varies, by allocating observation points at intervals proportional to the intervals between observation points also on the geographical location axis, the same shape as when equally spaced observation points are allocated to the vertical axis at equal intervals is displayed.

[0081] The group specifying unit 192 classifies triggers whose trigger start times are arranged in a roughly arc shape in the two-dimensional trigger detection state information into the same trigger group. The group specifying unit 192 may automatically perform this classification, or the group specifying unit 192 may manually associate the triggers with the trigger groups (classify the triggers into the trigger groups) based on user operations.

[0082] When group designation unit 192 automatically performs the above classification, for example, group designation unit 192 may select three or more trigger start times from the trigger start times indicated in the two-dimensional trigger detection status information, each of which has a predetermined range of time and distance between observation points. Group designation unit 192 may calculate an arc passing through all selected points or an arc approximating all selected points. Approximation in this case may be performed using the least squares method, but is not limited thereto.

[0083] The group specifying unit 192 may classify triggers whose start times are within a predetermined time from the calculated arc into the same trigger group.

[0084] When manually classifying a trigger into a trigger group, the group designation unit 192 calculates an arc of the start time of the trigger selected by the user, or an arc approximate to the start time of the trigger selected by the user, on the display screen of the two-dimensional trigger detection status information, and displays the arc on the screen. The user (the person performing the classification) can refer to the displayed arc to determine the start time of the trigger that is hidden by noise and difficult to see, and decide whether to include the trigger in the trigger group.

[0085] The group specifying unit 192 or a person may classify triggers into trigger groups based on other information such as a vibration waveform in addition to the shape indicated by the trigger start time in the two-dimensional trigger detection state information.

[0086] (Step 13)

[0087] The multidimensionalization unit 191 estimates the location of the temporary earthquake source. The temporary earthquake source referred to here is a temporary earthquake source. It is called a temporary earthquake source because the earthquake source is processed by the earthquake processing device 230 ( Figure 2 ) is re-estimated by the processing of the earthquake source estimation unit 235.

[0088] The multidimensionalization unit 191 checks the waveform of the vibration at each observation point based on the classification of the triggers into trigger groups, and estimates the position of the temporary earthquake source based on the inspection results. As a method for estimating the position of the temporary earthquake source, a publicly known temporary earthquake source calculation method such as a grid search method can be used.

[0089] The multidimensionalization unit 191 may automatically estimate the position of the temporary earthquake source. Alternatively, a person may estimate the position of the temporary earthquake source, and the multidimensionalization unit 191 may set the position of the temporary earthquake source on the two-dimensional trigger detection state information according to a user operation.

[0090] (Step 14)

[0091] The multidimensionalization unit 191 generates two-dimensional trigger detection status information associated with a geographic location axis and a time axis. The geographic location axis is an axis to which all observation points are assigned in order of distance from the temporary earthquake source. As the distance from the temporary earthquake source, the earthquake source distance or the epicenter distance can be used.

[0092] For example, if the time axis is set as the horizontal axis and the geographic location axis is set as the vertical axis, and the observation points are assigned to the geographic location axis in order from bottom to top starting from the observation point closest to the temporary earthquake source (in order of increasing distance from the epicenter), the triggering caused by the seismic wave from the temporary earthquake source is drawn as a line inclined to the upper right (or a strip shape inclined to the upper right).

[0093] In addition, for example, in the case where the distance from the temporary earthquake source to the observation point varies, the distance from the position corresponding to the temporary earthquake source to the position to which the observation point is assigned on the geographical location axis may be proportional to the distance from the temporary earthquake source to the observation point (epicenter distance). If the seismic wave from the temporary earthquake source propagates concentrically, the triggering due to the seismic wave from the temporary earthquake source is drawn in a straight line shape (or a rectangular shape).

[0094] In the case where the temporary earthquake source is at a shallow position, the same applies even if the distance from the epicenter to the observation point is used as the distance from the temporary earthquake source to the observation point.

[0095] (Step 15)

[0096] The group specifying unit 192 determines a trigger group.

[0097] As described above, the triggers due to the seismic waves from the temporary earthquake source are plotted as a line (or a stripe) in the two-dimensional graph generated by the multidimensionalization unit 191. Therefore, the group specifying unit 192 classifies the triggers plotted as the same line (or the same stripe) in the two-dimensional graph generated by the multidimensionalization unit 191 into the same group.

[0098] When multiple earthquakes close in position and time occur, the multidimensionalization unit 191 estimates the position of a temporary earthquake source for each earthquake. Next, the multidimensionalization unit 191 and the group specifying unit 192 group the triggers for each temporary earthquake source.

[0099] The earthquake observation apparatus 100 may repeatedly perform the processing from step 13 to step 15 .

[0100] For example, the earthquake observation apparatus 100 may repeatedly perform the processing from step 13 to step 15 until the position of the temporary earthquake source converges. For example, in the case where the multidimensionalization unit 191 performs the processing of estimating the temporary earthquake source position in step 13 two or more times, the distance between the temporary earthquake source position obtained in this processing and the temporary earthquake source position obtained in the previous processing may be calculated, and the processing from step 13 to step 15 may be repeatedly performed until the distance is equal to or less than a predetermined threshold.

[0101] If the accuracy of the trigger grouping in step 12 is determined to be sufficiently high, the seismic observation apparatus 100 may suppress (not execute) the processes from step 13 to step 15. For example, for each of the trigger groups obtained in step 12, the group designation unit 192 may evaluate the error between the arc approximating the trigger start position and the trigger start position in the two-dimensional trigger detection state information. If this error is equal to or less than a predetermined threshold, the group designation unit 192 may suppress the processes from step 13 to step 15.

[0102] <Encoding method>

[0103] Another method in which the earthquake observation apparatus 100 groups triggers by using two or more dimensions of trigger detection state information related to geographical location and time will be referred to as an encoding method. In the encoding method, the earthquake observation apparatus 100 performs the processing in the following steps 21 and 22.

[0104] (Step 21)

[0105] The multidimensionalization unit 191 generates three-dimensional or more-dimensional trigger detection state information based on one-dimensional or more-dimensional coordinate axes in which a plurality of observation points are arranged and a time axis.

[0106] The multidimensionalization unit 191 develops the trigger detection state information (drawing trigger) in a multidimensional coordinate space having a geographical location axis and a time axis in the same manner as in the case of the visualization method.

[0107] On the other hand, the encoding method differs from the visualization method in that the object observation points are not limited to observation points on a straight line. The encoding method differs from the visualization method in that any order of assigning observation points to the geographical location axis can be adopted.

[0108] However, the order in which observation points are assigned to the geographic location axis is fixed according to the encoding method. Specifically, when machine learning is performed on trigger grouping according to a trigger drawing mode according to the encoding method, the order in which observation points are assigned to the geographic location axis is the same both when learning and when applying.

[0109] The encoding method differs from the visualization method in that the multidimensionalization unit 191 can expand the trigger detection state information in a three-dimensional or higher-dimensional coordinate space. For example, the multidimensionalization unit 191 can set the latitude and longitude of the observation point as two axes and expand the trigger detection state information in a three-dimensional coordinate space including a time axis.

[0110] In the encoding method, the multi-dimensionalization unit 191 can add additional information to the element information of the two-dimensional or more dimensional trigger detection state information (the trigger drawn, each observation point and each time). For example, the single observation point trigger processing unit 232 ( Figure 2 ) The trigger is classified according to the event type (e.g., far-field earthquake, near-field earthquake, low-frequency earthquake, and artificial earthquake), and the multidimensionalization unit 191 can add information about the event type of the trigger to the element information.

[0111] By increasing the amount of information to be referred to, the group specifying unit 192 can be expected to perform grouping for each trigger event with higher accuracy.

[0112] Figure 5 is a diagram showing an example of two-dimensional or more-dimensional trigger detection state information in an encoding method. Figure 5 The horizontal axis of the graph represents time, and the observation points are assigned to the vertical axis.

[0113] Figure 5 The example in FIG is an example of a case where observation points arranged in a straight line are assigned to the vertical axis in the order of arrangement in order to make the drawings easier to see. However, in the encoding method, as described above, the observation points can be assigned to the vertical axis in the order of arrangement. Figure 5 any arrangement on the vertical axis in the example).

[0114] exist Figure 5 In the example of , the type of trigger event (cause of vibration) is shown for each piece of element information (for each observation point and for each time).

[0115] (Step 22)

[0116] The group specifying unit 192 determines a trigger group.

[0117] Figure 6 is a diagram showing an example of a trigger group determined by the group specifying unit 192 . Figure 6 Shows the Figure 5An example of grouping triggers as shown in .

[0118] exist Figure 6 In the example of FIG, triggers are generated by four earthquakes such as earthquake A, earthquake B, earthquake C, and earthquake D, and the group specifying unit 192 classifies the event into four groups such as "A," "B," "C," and "D" for each earthquake. "X" indicates that the event is not an earthquake and therefore does not belong to any of the four groups.

[0119] In the coding method, the order in which observation points are assigned to the geographic location axis is arbitrary, so observation points that are geographically adjacent to each other are not necessarily assigned to be adjacent to each other on the geographic location axis. Therefore, in the trigger generation status information in the coding method, triggers caused by the same earthquake are not always represented as a group. For example, Figure 6 As shown in Groups C and D in , triggering due to multiple earthquakes may be represented as a group.

[0120] On the other hand, since the time axis is set in the trigger generation state information in the encoding method, Figure 6 The arrival time differences of seismic waves between observation points are shown in . This arrival time difference indicates the positional relationship between the observation points and the positional relationship between the observation points and the earthquake source, and serves as a clue for grouping triggers for each earthquake.

[0121] Therefore, a model can be generated through machine learning. This model receives input from the trigger generation state information (two-dimensional or more-dimensional trigger generation state information) in the encoding method and outputs trigger groups. In this model, by reflecting the seismic wave arrival time differences between observation points in the grouping rules, it is expected that triggers can be grouped with high accuracy.

[0122] Specifically, when triggers at multiple observation points are caused by the same earthquake, statistical information showing that this positional relationship is often found in two-dimensional or higher-dimensional trigger detection status information can be reflected in the machine learning results (training model). Based on this reflection, if the two-dimensional or higher-dimensional trigger detection status information being processed has this positional relationship, it is determined that the triggers were caused by the same earthquake, or that the triggers were caused by the same earthquake as the trigger group, and thus it is expected that the triggers can be appropriately grouped.

[0123] In this case, the grouping process performed by the training model can be regarded as the following process: receiving two-dimensional or more-dimensional trigger generation state information as an input code and performing statistical analysis on the input code to determine the trigger group. Hence, the name of the encoding method is used.

[0124] <Combination of visualization and encoding methods>

[0125] The earthquake observation device 100 can use both the visualization method and the encoding method in combination and integrate the classification results (grouping results) of the two methods. For example, the group designation unit 192 can integrate the classification results according to the visualization method and the classification results according to the encoding method by performing the processing in the following steps 31 and 32.

[0126] (Step 31)

[0127] The group specifying unit 192 associates the trigger group in the visualization method with the trigger group in the encoding method.

[0128] The classification based on the visualization method and coding method before integration will be called provisional classification.

[0129] In the visualization method and the encoding method, a trigger group is required for each observation point and each time. Therefore, the group specifying unit 192 associates the trigger group in the visualization method with the trigger group in the encoding method for each observation point and each time.

[0130] (Step 32)

[0131] The group specifying unit 192 gives an evaluation to the classification result based on the degree of coincidence of the classification result (degree of coincidence of the trigger group).

[0132] In the case where the triggers are classified into the same trigger group in the visualization method and the encoding method (i.e., when the classification results are consistent with each other), the reliability of the classification is considered to be relatively high. Therefore, the group designation unit 192 gives a high evaluation to the classification result in this case. For example, the group designation unit 192 adds a relatively large weighting factor (e.g., "1") to the trigger group in this case (the trigger group for each observation point and each time).

[0133] On the other hand, in the case where triggers are classified into different trigger groups in the visualization method and the encoding method, the reliability of this classification is considered to be relatively low. Therefore, the group designation unit 192 gives a low evaluation to the classification result in this case. For example, the group designation unit 192 adds a relatively small weighting factor (e.g., "0.3") to the trigger group in this case (the trigger group for each observation point and each time).

[0134] For example, when the earthquake source estimation unit 235 ( Figure 2 ) When inferring the earthquake source, the weighting factor added to the trigger group by the group designation unit 192 can be used.

[0135] If the correlation between a trigger and the earthquake that caused it is accurate, using information related to the trigger (e.g., the arrival time of the seismic wave) to estimate the earthquake's hypocenter increases the amount of information used to estimate the hypocenter, thereby potentially improving estimation accuracy. On the other hand, if the correlation between a trigger and the earthquake that caused it is incorrect, using information related to the trigger to estimate the earthquake's hypocenter will act as noise in the hypocenter estimate, potentially reducing estimation accuracy.

[0136] Therefore, when estimating the earthquake source, the earthquake source estimation unit 235 uses the weighting factors described above to reduce the contribution of information associated with classification results with low reliability to the earthquake source estimation. As a result, even when the correlation between the trigger and the earthquake that caused the trigger is incorrect, the impact on the estimation accuracy of the earthquake source can be reduced.

[0137] If the trigger groups resulting from the classification of triggers according to the visualization method and the encoding method are different, the group specifying unit 192 may retain only one of the trigger groups. For example, the group specifying unit 192 may delete the trigger group obtained according to the encoding method. Alternatively, the group specifying unit 192 may retain both trigger groups. Alternatively, the group specifying unit 192 may delete both trigger groups to be excluded from use in subsequent processing steps.

[0138] When classifying triggers into groups (trigger groups) for each earthquake that caused the trigger, group designation unit 192 can refer to the waveform of the seismic wave in addition to the trigger detection status information. Specifically, in the case of a far-field earthquake, there is a high probability that similar waveforms will be observed at multiple observation points. By referring to the waveform of the seismic wave, group designation unit 192 classifies triggers with similar waveforms into the same trigger group, thereby enabling classification with relatively high accuracy.

[0139] The combination of methods used by the earthquake observation apparatus 100 is not limited to the visualization method and the encoding method. In addition to the visualization method and the encoding method, or instead of any of them, the earthquake observation apparatus 100 may also use other methods for classifying triggers into groups for each earthquake that caused the trigger.

[0140] In the case where the processing in each unit of the control unit 190 is generated by machine learning, the earthquake observation apparatus 100 may perform machine learning, or an apparatus other than the earthquake observation apparatus 100 may perform machine learning. Figure 7 A case where the model generation apparatus 300 , which is different from the earthquake observation apparatus 100 , performs machine learning is described.

[0141] Figure 7 is a schematic block diagram illustrating an example of a functional configuration of a model generating apparatus according to an embodiment.

[0142] exist Figure 7 In the configuration shown, the model generation device 300 includes a machine training data generation unit 310, a trigger information learning unit 321, a temporary earthquake source estimation processing learning unit 322 and a visualization method grouping learning unit 323.

[0143] The model generating device 300 performs machine learning to generate a model for processing in each unit in the control unit 190 .

[0144] The model generating apparatus 300 is constructed by using a computer such as a workstation or a mainframe.

[0145] The machine training data generation unit 310 generates supervised machine training data. Specifically, the machine training data generation unit 310 acquires trigger detection state information (encoding), trigger detection state information (temporary source estimation), and trigger detection state information (visualization) as input data of the model.

[0146] The trigger detection state information (code) is two-dimensional or more-dimensional trigger detection state information generated by the multi-dimensionalization unit 191 according to the encoding method.

[0147] The trigger detection status information (temporary earthquake source estimation) is data generated by the multidimensionalization unit 191 for inferring the temporary earthquake source based on the visualization method. In this data, the trigger detection status information at the observation point not located on the straight line, which is used to determine on which side of the straight line the temporary earthquake source is located, is added to the two-dimensional trigger detection status information at the observation point arranged in a straight line.

[0148] The trigger detection status information (visualization) is two-dimensional trigger detection status information generated by the multidimensionalization unit 191 for determining a trigger group according to a visualization method, in which observation points are assigned to geographic location axes according to distances from temporary earthquake sources.

[0149] The machine training data generating unit 310 acquires event determination data (encoding), temporary earthquake source estimated position information, and event determination data (visualization) as correct answer data.

[0150] The event determination data (code) is information indicating a trigger group corresponding to a correct answer to the trigger detection status information (code).

[0151] The temporary earthquake source estimated position information is information indicating the temporary earthquake source position corresponding to the correct answer of the trigger detection state information (temporary earthquake source estimation).

[0152] The event determination data (visualization) is information indicating a trigger group corresponding to a correct answer to the trigger detection status information (visualization).

[0153] The machine training data generating unit 310 generates training data as a combination of input data and correct answer data for each learning unit (i.e., the trigger information learning unit 321, the temporary source estimation processing learning unit 322, and the visualization method grouping learning unit 323), and provides the training data for machine learning.

[0154] The machine training data generating unit 310 generates a combination of trigger detection state information (code) and event determination data (code) corresponding thereto as training data for the trigger information learning unit 321 .

[0155] The machine training data generating unit 310 generates a combination of trigger detection state information (temporary earthquake source estimation) and the corresponding temporary earthquake source inferred position information as training data for the temporary earthquake source estimation processing learning unit 322.

[0156] The machine training data generating unit 310 generates a combination of trigger detection state information (visualization) and event determination data (visualization) corresponding thereto as training data for the visualization method grouping learning unit 323 .

[0157] The trigger information learning unit 321 adjusts the parameter values of the encoding model (learned parameter values) through machine learning. The encoding model is a model for performing processing according to the encoding method. The encoding model receives input of two-dimensional or more-dimensional trigger detection state information in the encoding method and outputs a classification result that classifies the triggers into groups (trigger groups) for each earthquake.

[0158] The temporary earthquake source estimation processing learning unit 322 adjusts the parameter values (learned parameter values) of the temporary earthquake source estimation model through machine learning. The temporary earthquake source estimation model is a model used to estimate the temporary earthquake source position. The temporary earthquake source estimation model receives input of two-dimensional trigger detection status information at observation points arranged in a straight line and trigger detection status information at observation points not located on the straight line, and outputs the estimated position of the temporary earthquake source.

[0159] However, as described above, a person can infer the position of a temporary earthquake source. In this case, the model generation device 300 does not need to include the temporary earthquake source estimation processing learning unit 322.

[0160] The visualization method group learning unit 323 adjusts the parameter values (learning parameter values) of the visualization model through machine learning. The visualization model is a model for classifying triggers into groups (trigger groups) for each earthquake based on the visualization method. The visualization model receives input of two-dimensional trigger detection state information in which observation points are assigned to geographical location axes based on their distance from the temporary earthquake source, and outputs a classification result that classifies the triggers into groups (trigger groups) for each earthquake.

[0161] As described above, the communication unit 110 acquires vibration detection status information at each of the multiple observation points. Based on the vibration detection status information at each of the multiple observation points, the multidimensionalization unit 191 generates two-dimensional or higher-dimensional vibration detection status information associated with a geographic location and time. The group designation unit 192 classifies each piece of element information into a group for each vibration cause. The element information referred to here is information that forms the two-dimensional or higher-dimensional vibration detection status information generated by the multidimensionalization unit 191 and indicates the vibration detection status at a specific geographic location and a specific time.

[0162] As a result, the earthquake observation apparatus 100 can use the trigger generation time information in order to obtain the correspondence between the trigger and the earthquake that caused the trigger, and thus can use a relatively large amount of information.

[0163] Specifically, the seismic wave arrival time difference between observation points can be calculated from the trigger generation time information, thereby showing the positional relationship between the observation points. By using this information, the earthquake observation apparatus 100 is expected to be able to group triggers with relatively high accuracy.

[0164] The multidimensionalization unit 191 generates two-dimensional vibration detection state information associated with a geographical location axis to which observation points are allocated in order of distance from a temporarily set earthquake source and a time axis.

[0165] In this two-dimensional vibration detection state information, triggers caused by the same earthquake are shown in a line or strip shape. The group specifying unit 192 is expected to be able to group the triggers with relatively high accuracy based on this information.

[0166] The multidimensionalization unit 191 generates two-dimensional vibration detection state information associated with a coordinate axis indicating a position on a straight line connecting a plurality of observation points arranged in a straight line and a time axis, and temporarily sets a seismic source by using the vibration detection state information.

[0167] The two-dimensional vibration detection state information geometrically shows the direction of the temporary earthquake source. By using this information, the multidimensionalization unit 191 can estimate the position of the temporary earthquake source relatively easily.

[0168] The multidimensionalization unit 191 generates two-dimensional or more-dimensional vibration detection state information based on one-dimensional or more-dimensional coordinate axes in which a plurality of observation points are arranged and a time axis.

[0169] In this two-dimensional or more-dimensional vibration detection state information, the coding pattern indicated by the trigger is correlated with the correspondence between the trigger and the earthquake that caused the trigger. Specifically, because a time axis is provided in the trigger generation state information in the coding method, the arrival time difference of the seismic wave between the observation points is shown in the graph. This arrival time difference indicates the positional relationship between the observation points and the positional relationship between the observation points and the earthquake source. Based on this information, the group designation unit 192 is expected to be able to group the triggers with relatively high accuracy.

[0170] The multi-dimensionalization unit 191 acquires type information indicating the type of the vibration cause of the vibration indicated by the element information, and generates two-dimensional or more dimensional vibration detection state information including the acquired type information.

[0171] The group specifying unit 192 can group triggers using this information, and thus can be expected to use a relatively large amount of information and thus can be expected to perform grouping with relatively high accuracy.

[0172] The group specifying unit 192 adds evaluation information for the classification result of classifying the element information into groups based on the consistent states of the plurality of temporary classifications that classify the element information into groups.

[0173] By reflecting this evaluation information in the earthquake source position estimation, it is expected that the earthquake source position can be estimated with relatively high accuracy.

[0174] According to the earthquake observation apparatus 100 , the temporal relationship and the distance relationship of the triggers can be evaluated, and thus the triggers caused by a plurality of close-in earthquakes can be separated for each close-in earthquake.

[0175] By performing statistical processing using machine learning in the earthquake observation apparatus 100 , it is possible to eliminate unlikely correlations between triggers and earthquakes that cause the triggers.

[0176] According to the earthquake observation device 100, even when different types of earthquakes are mixed, if the earthquakes can be separated by trigger level, the earthquake that caused the trigger can be separately determined. Therefore, the earthquake observation device 100 can avoid determining multiple earthquakes as a single earthquake, thereby preventing, for example, a decrease in the accuracy of earthquake source estimation.

[0177] By performing machine learning on the determination of the correspondence between the trigger and the earthquake that caused the trigger in the earthquake observation device 100, a determination close to that performed by a human can be performed. Therefore, according to the earthquake observation device 100, the possibility of the correspondence between the trigger and the earthquake that caused the trigger being incorrectly determined is low, and the location of the earthquake source can be estimated with relatively high accuracy.

[0178] Next, we will refer to Figure 8 and Figure 9The configuration of an embodiment of the minimum configuration of the present invention is described.

[0179] Figure 8 An earthquake observation apparatus according to an embodiment of a minimum configuration is shown. Figure 8 The earthquake observation apparatus 400 shown in FIG. 4 includes a vibration detection state information acquisition unit 401 , a multidimensionalization unit 402 , and a group specifying unit 403 .

[0180] In this configuration, the vibration detection state information acquisition unit 401 acquires vibration detection state information at each of a plurality of observation points. The multidimensionalization unit 402 generates two-dimensional or higher-dimensional vibration detection state information associated with a geographic location and time based on the vibration detection state information at each of the plurality of observation points. The group specification unit 403 categorizes element information into groups for each vibration cause, forming two-dimensional or higher-dimensional vibration detection state information, with each piece of element information indicating the vibration detection state at a specific geographic location and a specific time.

[0181] Therefore, the earthquake observation apparatus 400 can use the vibration generation time information in order to obtain the correspondence between the vibration and the earthquake that caused the vibration, and thus can use a relatively large amount of information.

[0182] Specifically, the arrival time difference of seismic waves between observation points can be calculated from the vibration generation time information, thereby showing the positional relationship between the observation points. The earthquake observation device 400 is expected to be able to obtain the correspondence between vibration and the earthquake that caused the vibration with higher accuracy by using this information.

[0183] Figure 9 The processing procedure in the earthquake observation method according to the embodiment of the minimum configuration is shown.

[0184] Figure 9 The processing shown in includes: a vibration detection status information acquisition step (step S211), acquiring the vibration detection status information at each of a plurality of observation points; a multidimensionalization step (step S212), generating two-dimensional or more dimensional vibration detection status information related to a geographic location and time based on the vibration detection status information at each of a plurality of observation points; and a group designation step (step S213), classifying element information into groups for each vibration cause, the element information forming two-dimensional or more dimensional vibration detection status information, and each piece of element information indicating the vibration detection status at a specific geographic location and a specific time.

[0185] according to Figure 9 In the processing, the vibration generation time information can be used to obtain the correspondence between the vibration and the earthquake that caused the vibration, so a relatively large amount of information can be used.

[0186] Specifically, the seismic wave arrival time difference between the observation points can be calculated from the vibration generation time information, and thus the positional relationship between the observation points can be shown. Figure 9 By using this information in the processing, it is expected that the correspondence between the vibration and the earthquake that caused the vibration can be obtained with higher accuracy.

[0187] Figure 10 is a schematic block diagram showing a configuration of a computer according to at least one of the above-described embodiments.

[0188] exist Figure 10 In the configuration shown, computer 700 includes a central processing unit (CPU) 710 , a primary storage device 720 , a secondary storage device 730 , and an interface 740 .

[0189] Any one or more of the earthquake observation device 100, earthquake processing device 230, model generation device 300, and earthquake observation device 400 may be installed on the computer 700. In this case, the operation of each of the above-mentioned processing units is stored in the form of a program in the auxiliary storage device 730. The CPU 710 reads the program from the auxiliary storage device 730, loads the program into the main storage device 720, and executes the above-mentioned processing according to the program. The CPU 710 ensures that the storage area corresponding to the above-mentioned storage unit is in the main storage device 720 according to the program.

[0190] When the earthquake observation device 100 is installed on the computer 700, the operation of the control unit 190 and each unit thereof is stored in the form of a program in the auxiliary storage device 730. The CPU 710 reads the program from the auxiliary storage device 730, loads the program to the main storage device 720, and executes the processing of the control unit 190 and each unit thereof according to the program.

[0191] The CPU 710 secures a storage area corresponding to the storage unit 180 and each unit thereof in the main storage device 720 according to a program. Communication performed by the communication unit 110 is performed by the interface 740, which has a communication function and performs communication under the control of the CPU 710. The function of the display unit 120 is performed by the interface 740, which has a display screen and performs display under the control of the CPU 710. The function of the operation input unit 130 is performed by the interface 740, which has an input device and accepts user operations under the control of the CPU 710.

[0192] When the earthquake processing device 230 is installed on the computer 700, the operation of each of the single observation point trigger processing unit 232, earthquake determination unit 233, phase check unit 234, earthquake source estimation unit 235, and notification processing unit 236 is stored in the form of a program in the auxiliary storage device 730. The CPU 710 reads the program from the auxiliary storage device 730, loads the program into the main storage device 720, and executes the processing in each of these units according to the program. The communication performed by the receiving unit 231 is performed by the interface 740 having a communication function and performing communication under the control of the CPU 710.

[0193] In the case where the model generation device 300 is installed on the computer 700, the operation of each of the machine training data generation unit 310, the trigger information learning unit 321, the temporary earthquake source estimation processing learning unit 322, and the visualization method grouping learning unit 323 is stored in the form of a program in the auxiliary storage device 730. The CPU 710 reads the program from the auxiliary storage device 730, loads the program into the main storage device 720, and executes the processing in each of these units according to the program.

[0194] In the case where the earthquake observation device 400 is installed on the computer 700, the operation of each of the multidimensionalization unit 402 and the group specifying unit 403 is stored in the form of a program in the auxiliary storage device 730. The CPU 710 reads the program from the auxiliary storage device 730, loads the program into the main storage device 720, and executes the processing in each of these units according to the program.

[0195] Data acquisition performed by the vibration detection state information acquisition unit 401 is performed by the interface 740 which has a communication function and performs communication under the control of the CPU 710 .

[0196] A program for executing all or some of the processes performed by the earthquake observation device 100, the earthquake processing device 230, the model generation device 300, and the earthquake observation device 400 may be recorded on a computer-readable recording medium, and the processes in each unit may be executed by reading the program recorded on the recording medium into a computer system and executing the program. The term "computer system" referred to herein includes an operating system or hardware such as peripheral devices.

[0197] "Computer-readable recording medium" refers to removable media such as floppy disks, magneto-optical disks, ROMs, and CD-ROMs, or storage devices such as hard disks built into a computer system. The aforementioned program may be a program for implementing some of the aforementioned functions, or may be a program that implements the aforementioned functions in combination with a program already stored in the computer system.

[0198] Although the embodiments of the present invention are described in detail with reference to the accompanying drawings, specific configurations are not limited to these embodiments and include designs and the like within the scope not departing from the concept of the present invention.

[0199] This application claims the benefit of Japanese Patent Application No. 2019-150631, filed on August 20, 2019, the contents of which are incorporated herein by reference.

[0200] Industrial Applicability

[0201] The present invention can be applied to earthquake observation equipment that measures the seismic intensity of an earthquake and infers the earthquake source, and can use a relatively large amount of information to obtain a correspondence between the observed vibration and the earthquake that caused the vibration.

[0202] [Explanation of Reference Numerals]

[0203] 1 Seismic processing system

[0204] 100, 400 earthquake observation equipment

[0205] 110 Communication Unit

[0206] 120 display units

[0207] 130 Operation input unit

[0208] 180 storage units

[0209] 181 Model Storage Unit

[0210] 190 control unit

[0211] 191, 402 Multidimensional Unit

[0212] 192, 403 groups of designated units

[0213] 210 Sensors

[0214] 220 sensor data collection equipment

[0215] 230 Seismic Processing Equipment

[0216] 231 Receiving Unit

[0217] 232 Single observation point trigger processing unit

[0218] 233 Earthquake Determination Unit

[0219] 234 Phase Check Unit

[0220] 235 Source Estimation Unit

[0221] 236 Notification Processing Unit

[0222] 240 Data Server Equipment

[0223] 250 Tsunami treatment equipment

[0224] 261 Real-time display terminal equipment

[0225] 262 Maintenance Terminal Equipment

[0226] 263 Interactive Processing Terminal Equipment

[0227] 300 Model generation equipment

[0228] 310 Machine training data generation unit

[0229] 321 Trigger Information Learning Unit

[0230] 322 Temporary Source Estimation Processing Learning Unit

[0231] 401 Vibration detection state information acquisition unit.

Claims

1. An earthquake observation device comprising: a vibration detection state information acquiring device, configured to acquire vibration detection state information at each of a plurality of observation points; a multidimensionalization device for generating two-dimensional vibration detection state information associated with a geographical location axis and a time axis, wherein the geographical location axis is the geographical location axis to which the observation points arranged in a straight line among the plurality of observation points are allocated in order of distance from the epicenter of the temporarily set earthquake source; as well as A group specifying device is used to classify the reason why the element information forming the two-dimensional vibration detection state information and indicating the vibration detection state at a specific geographical location and a specific time is drawn as a line or a strip into multiple groups for each earthquake.

2. The earthquake observation device according to claim 1, in, The multidimensionalization device generates two-dimensional vibration detection state information associated with a coordinate axis indicating a position on a straight line connecting a plurality of observation points arranged in a straight line and a time axis, and temporarily sets the earthquake source by using the vibration detection state information.

3. The earthquake observation device according to claim 1 or 2, in, The multidimensionalization means generates two-dimensional or more-dimensional vibration detection state information associated with one-dimensional or more-dimensional coordinate axes on which the plurality of observation points are arranged and a time axis.

4. The earthquake observation device according to claim 3, in, The multidimensional device: acquiring type information indicating a type of a vibration cause of the vibration indicated by the element information, and The two-dimensional or more dimensional vibration detection state information including the type information is generated.

5. The earthquake observation device according to claim 1 or 2, in, The group specifying means adds evaluation information for a classification result of classifying the element information into the groups based on a consistent state of a plurality of temporary classifications of classifying the element information into the groups.

6. A method for earthquake observation, comprising: Acquiring vibration detection state information at each of a plurality of observation points; generating two-dimensional vibration detection state information associated with a geographical location axis and a time axis, the geographical location axis being the geographical location axis to which the observation points arranged in a straight line among the plurality of observation points are assigned in order of distance from the epicenter of the temporarily set earthquake source; as well as The causes of element information forming the two-dimensional vibration detection state information and indicating the vibration detection state at a specific geographical location and a specific time as the element information drawn in a line or a band are classified into a plurality of groups for each earthquake.

7. A recording medium for recording an earthquake observation program, the earthquake observation program causing a computer to: Acquiring vibration detection state information at each of a plurality of observation points; generating two-dimensional vibration detection state information associated with a geographical location axis to which the observation points arranged in a straight line among the plurality of observation points are assigned in order of distance from the epicenter of the temporarily set earthquake source and a time axis; and The causes of element information forming the two-dimensional vibration detection state information and indicating the vibration detection state at a specific geographical location and a specific time as the element information drawn in a line or a band are classified into a plurality of groups for each earthquake.

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