JP7849671B2 - Disaster information acquisition device and disaster information acquisition system - Google Patents

Description

This disclosure relates to a disaster information acquisition device and a disaster information acquisition system.

Disaster (hazard) information, particularly information on sediment-related disasters such as landslides, mudslides, and debris flows, is measured primarily by wire sensors installed and managed by the Ministry of Land, Infrastructure, Transport and Tourism and local governments in Japan. This information is then notified to local governments and used to prevent secondary disasters and for evacuation during disasters.

The wire sensors mentioned above cannot detect landslides, such as debris flows, unless they are installed in the path of such flows. Therefore, while they are prioritized for installation in areas with a high risk of debris flows, they may not be installed in areas with a relatively low risk due to cost and other factors. However, even in areas with a relatively low risk, landslides are a major concern for nearby residents, and there are many locations where sensor installation is desired.

Furthermore, as mentioned above, wire sensors directly detect debris flows by pre-installing wires in the path of the flow. Therefore, their detection accuracy heavily depends on the knowledge and experience of the installer. Consequently, there is a high demand for devices that are easy to install and capable of detecting widespread landslides.

In view of the above, this disclosure aims to provide a disaster information acquisition device and a disaster information acquisition system that have a relatively simple structure and are capable of detecting the occurrence of widespread landslides.

To achieve the above objective, the disaster information acquisition device according to the first aspect of this disclosure includes a vibration meter capable of detecting vibrations at the installation location, and a data processing unit for acquiring disaster information at the installation location, which includes a sediment disaster detection unit that detects the occurrence of sediment disasters at and around the installation location from the output results of the vibration meter.

In disaster information acquisition devices like the one described above, it is possible to detect not only the location of the vibration meter but also landslides occurring in the surrounding area. Furthermore, since vibration meters can be installed simply by placing them at any desired location, their installation is easier than that of wire sensors, and their detection accuracy is less prone to variation.

The disaster information acquisition device according to the second aspect of this disclosure is the disaster information acquisition device according to the first aspect of this disclosure, wherein the data processing unit further includes an earthquake detection unit that detects an earthquake at the installation location from the output result of the vibration meter.

In the disaster information acquisition device described above, it will be possible to acquire not only information on landslides but also information on earthquakes using a single device.

The disaster information acquisition device according to the third aspect of this disclosure, in the disaster information acquisition device according to the first or second aspect of this disclosure, detects the occurrence of a landslide at the installation location and its surroundings based on the number of times the output result detected by the vibration meter exceeds a third threshold within a unit time.

In the disaster information acquisition device described above, it becomes possible to accurately detect landslides that occurred particularly close to the vibration meter's installation location, based on the vibration meter's output.

A disaster information acquisition device according to a fourth aspect of this disclosure is a disaster information acquisition device according to any of the first to third aspects of this disclosure, wherein the landslide detection unit includes a high-pass filter that extracts vibration components with frequencies above a predetermined frequency from the output results detected by the vibration meter, and detects the occurrence of landslides at the installation location and in the vicinity of the installation location from the vibration components extracted by the high-pass filter.

In the disaster information acquisition device described above, the output results from the vibration meter allow for the detection of landslides occurring particularly close to the vibration meter's installation location, distinguishing them from vibrations caused by other disasters.

A disaster information acquisition device according to a fifth aspect of this disclosure is a disaster information acquisition device according to any of the first to fourth aspects of this disclosure, wherein the sediment disaster detection unit includes a fitting unit that fits a curve represented by a Gaussian function to the Fourier spectrum obtained by Fourier transforming the output result of the vibration meter, and detects the difference, thereby detecting the occurrence of sediment disasters at the installation location and in the vicinity of the installation location based on the difference.

In the disaster information acquisition device described above, it becomes possible to accurately detect landslides that occur relatively far from the vibration meter's installation location, based on the vibration meter's output.

The disaster information acquisition system according to the sixth aspect of this disclosure includes a vibration meter connected to a communication network and capable of detecting vibrations at the installation location, and a data processing unit for acquiring disaster information at the installation location, which is capable of acquiring the output results of the vibration meter via the communication network and includes a rainfall intensity calculation unit that calculates an estimated value of the rainfall intensity at the installation location from the output results of the vibration meter.

In the disaster information acquisition system described above, it is possible to detect not only the location of the vibration meter but also landslides occurring in the surrounding area. Furthermore, since vibration meters can be installed simply by placing them at any desired location, their installation is easier than that of wire sensors, and their detection accuracy is less prone to variation.

The disaster information acquisition device and disaster information acquisition system described herein enable the detection of widespread landslides with a relatively simple structure.

This is a functional block diagram showing an example of a disaster information acquisition device according to one embodiment of the present disclosure. Figure 1 shows a graph illustrating the relationship between the maximum acceleration detected by the first vibration meter and the wind speed. Figure 1 shows a graph illustrating the relationship between RMS acceleration detected by the first vibration meter and wind speed. Figure 1 shows a graph of the Fourier spectra obtained by Fourier transforming the output results detected by the first and second vibration meters, separated into three directional components. This graph shows the results of measuring the duration of vibrations caused by earthquakes and wind using Trifunac's cumulative power method. This graph shows the results of measuring the duration of vibrations caused by earthquakes and wind using Jennings' envelope method. Figure 1 shows a graph illustrating the relationship between the acceleration detected by the second vibration meter and the rainfall intensity. This graph shows the Fourier spectra obtained by Fourier transforming the output results of the second vibration meter shown in Figure 1, which were observed at varying rainfall intensities. Figure 1 shows a graph illustrating the relationship between the amplitude value of the Fourier spectrum obtained by Fourier transforming the output results detected by the second vibration meter and the rainfall intensity. Figure 1 shows a graph illustrating an example of an acceleration waveform detected by the vibration meter. Figure 1 shows another example of an acceleration waveform detected by a vibration meter, and a graph illustrating its relationship to pulse density. Figure 11(C) is a graph that shows the time series changes of pulse density and the time series changes of pulse density of acceleration waveforms caused by earthquakes side by side. This graph, similar to Figure 12, was generated after applying a high-pass filter to the acceleration waveform detected by the vibration meter and the acceleration waveform caused by the earthquake. This graph shows the Fourier spectra obtained by two different vibration meters that detected vibrations caused by landslides.

The following describes various embodiments for implementing this disclosure with reference to the drawings. The following descriptions will schematically show only the extent necessary to achieve the objectives of this disclosure, and will primarily focus on explaining the relevant parts of this disclosure. Any parts omitted from the explanation will be based on prior art.

<Overall configuration of the device>
Figure 1 is a functional block diagram showing an example of a disaster information acquisition device according to one embodiment of the present disclosure. As shown in Figure 1, the disaster information acquisition device 1 according to this embodiment includes at least a vibration meter 10 capable of detecting vibration at a specific installation location, and a data processing unit 20 for acquiring disaster information for that installation location from the output result of the vibration meter 10.

The vibration meter 10 can be installed at any location where disaster information is to be acquired, and can be configured to measure vibrations in three dimensions (for example, the X, Y, and Z directions in Figure 1) occurring at that location. As the vibration meter 10, for example, piezoelectric, servo, electromagnetic, or semiconductor type vibration sensors (accelerometers, velocity sensors) or MEMS (Micro Electro Mechanical Systems) vibration sensors (accelerometers, velocity sensors) can be employed. An example of the vibration meter 10 in this embodiment includes two vibration meters: a first vibration meter 11 installed on a structure 2 immersed in an air-fluid system so as to be exposed to surrounding wind, and a second vibration meter 12 installed on the ground (surface) 3. The number and arrangement of the vibration meters 10 are not limited to this and can be appropriately changed considering the disaster information to be acquired.

The structure 2 on which the first vibration meter 11 is installed can be composed of a columnar member erected above the ground 3, for example, in a location exposed to wind. The shape and size of structure 2 are not particularly limited, as long as it can vibrate when exposed to wind. Furthermore, existing structures such as utility poles or streetlights can be reused as structure 2. On the other hand, the "ground" on which the second vibration meter 12 is installed should be understood to include not only the ground 3 but also structures installed on the ground 3, as long as they do not vibrate even when exposed to wind.

The data processing unit 20 may be a component for acquiring desired disaster information based on the output results of the vibration meter 10. This data processing unit 20 can be implemented by, for example, a Programmable Logic Controller (PLC) or a well-known computer connected to a power supply (not shown). Therefore, various calculations performed within the data processing unit 20 can be carried out by a processor or the like within the data processing unit 20 (not shown). The data processing unit 20 in this embodiment includes, for example, a data acquisition unit 21 that collects the output results of the vibration meter 10, a wind speed calculation unit 22 that calculates an estimated wind speed at the installation location from the output results of the vibration meter 10 collected by the data acquisition unit 21, a rainfall intensity calculation unit 23 that calculates an estimated rainfall intensity at the installation location from the output results of the vibration meter 10 collected by the data acquisition unit 21, a landslide detection unit 24 that detects the occurrence of landslides at and around the installation location from the output results of the vibration meter 10 collected by the data acquisition unit 21, an earthquake detection unit 25 that detects earthquakes at the installation location from the output results of the vibration meter 10 collected by the data acquisition unit 21, a memory 26 capable of storing various data, and a communication interface 27. Note that the data processing unit 20 does not need to have all of the above-mentioned components, and can be appropriately selected and adopted.

The data acquisition unit 21 may be electrically connected to the vibration meter 10 via wired or wireless communication and capable of collecting the output results detected by the vibration meter 10. The output results from the vibration meter 10 may be transmitted to the data acquisition unit 21 in the form of an electrical signal, indicating the time-dependent change in the detected acceleration (vibration) (specifically, acceleration waveform or vibration waveform), or corresponding information. The data acquisition unit 21 may then function to store the received data at least temporarily in the memory 26.

The wind speed calculation unit 22, rainfall intensity calculation unit 23, landslide detection unit 24, and earthquake detection unit 25 may be components for calculating or detecting desired disaster information, specifically information regarding the presence or absence of strong winds, heavy rain, landslides, and earthquakes, from the output results of the vibration meter 10 collected by the data collection unit 21. In this embodiment, the wind speed calculation unit 22, rainfall intensity calculation unit 23, landslide detection unit 24, and earthquake detection unit 25 are provided within the data processing unit 20. This allows disaster information data to be generated within the disaster information acquisition device 1, significantly reducing the amount of data transmitted via the communication interface 27 compared to transmitting the output results of the vibration meter 10, thereby suppressing an increase in communication traffic. Specific calculation or detection methods for each component will be described in detail later.

The memory 26 can be composed of a well-known volatile or non-volatile recording medium and can store data collected by the data acquisition unit 21 and information used when calculating or detecting various types of disaster information. The communication interface 27 may be for sending and receiving various types of disaster information calculated or detected within the data processing unit 20. This communication interface 27 allows for the transmission of predetermined disaster information to the management server 4 and client terminal 5 via a communication network NW connected via wired or wireless communication, for example, and the reception of control signals transmitted from the management server 4 or client terminal 5 to perform disaster information calculation or detection. While this communication interface 27 is exemplified as being connected to the communication network NW via wired or wireless communication, it may also be for local connection to the management server 4 or client terminal 5.

The disaster information acquisition device 1 according to this embodiment, having the configuration described above, is capable of identifying multiple types of disaster information from the output results of the vibration meter 10, which serves as the measurement means. Therefore, the methods for acquiring disaster information using the wind speed calculation unit 22, rainfall intensity calculation unit 23, landslide detection unit 24, and earthquake detection unit 25 will be described in order below.

<Method for estimating wind speed>
When measuring wind speed, it is common to use a dedicated measuring device for wind speed, such as a wind turbine-type anemometer. In contrast, the wind speed calculation unit 22 of the disaster information acquisition device 1 according to this embodiment measures wind speed, or more precisely, calculates an estimated value of wind speed, using the output result of the vibration meter 10.

The wind speed calculation unit 22 in this embodiment can calculate an estimated wind speed based on the output results detected by the vibration meter 10, particularly the first vibration meter 11, which are collected by the data acquisition unit 21. That is, the wind speed calculation unit 22 may calculate an estimated wind speed using the vibration waveform detected when the first vibration meter 11 and the structure 2 on which the first vibration meter 11 is installed vibrate due to wind.

To determine the correlation between the output of the first vibration meter 11 and the wind speed generated around the first vibration meter 11, the output results when wind of an arbitrary wind speed was applied to the first vibration meter 11 are shown in Figures 2 and 3. Here, Figure 2 is a graph showing the relationship between the maximum acceleration detected by the first vibration meter of the disaster information acquisition device shown in Figure 1 and the wind speed. Figure 2(A) plots the maximum acceleration actually observed by the first vibration meter 11 as observation points in the graph, and Figure 2(B) shows a function that models the observation points shown in Figure 2(A). Furthermore, Figure 3 is a graph showing the relationship between RMS (root mean square, also called "effective value") acceleration detected by the first vibration meter of the disaster information acquisition device shown in Figure 1, and wind speed. Figure 3(A) plots the RMS accelerations actually observed multiple times by the first vibration meter 11 as observation points, while Figure 3(B) shows a function modeling the observation points shown in Figure 3(A). Incidentally, as can be seen by comparing Figure 2 and Figure 3, it can be confirmed that the variation in observation points tends to be smaller for RMS acceleration than for maximum acceleration.

The points shown as dots in Figures 2 and 3 represent the observation points. From these observation points, it can be seen that the first vibration meter 11 and the structure 2, exposed to wind, are undergoing forced vibration due to a so-called gust response (or buffeting). Here, the forced vibration due to the gust response can be modeled using an exponential function, as shown in sections A and C enclosed by dotted lines in Figures 2(A) and 3(A).

On the other hand, the first vibration meter 11 and structure 2 may experience vortex excitation (or galloping) due to the emission of Karman vortices downstream when exposed to wind. The increase in acceleration at specific wind speeds caused by this vortex excitation (indicated by arrows B and D in Figures 2(A) and 3(A)) can be modeled using a Gaussian function.

Based on the above, the wind speed calculation unit 22 can use, for example, the regression model shown in equation (1) below to calculate an estimated wind speed at the installation location where the first vibration meter 11 is installed, from the output result detected by the first vibration meter 11, for example, the RMS acceleration.

Here, x is the wind speed, y is the output result of the first vibration meter, and a, b, c, α, and β are arbitrary constants.

The wind speed calculation method used by the wind speed calculation unit 22 is not limited to the method using equation (1) described above. Specifically, the estimated wind speed at the installation location can also be calculated from the Fourier spectrum obtained by Fourier transforming the output results detected by the first vibration meter 11. A positive correlation is observed between the spectral intensity (average value of Fourier amplitude in a predetermined frequency range) of the Fourier spectrum obtained by Fourier transforming the output results detected by the first vibration meter 11 and the wind speed. Therefore, the spectral intensity in each direction can be modeled using regression analysis in the regression model shown in equation (1) above.

Therefore, by using the modeled regression equation (1) described above, it is possible to calculate the estimated wind speed x at the installation location where the first vibration meter 11 is installed, from the output result of the first vibration meter 11.

Incidentally, the vibrations detected by the first vibration meter 11 are not limited to those caused by wind. For example, if an earthquake occurs at the installation location of the first vibration meter 11, vibrations caused by the earthquake (earthquake motion) may also be detected. Therefore, in order to accurately estimate the wind speed, it is necessary to identify the cause of the vibrations detected by the first vibration meter 11. Accordingly, the following describes a method for distinguishing between vibrations caused by wind and vibrations caused by other factors, as one method for calculating an estimated wind speed from the output results detected by the first vibration meter 11.

To distinguish whether the vibration detected by the first vibration meter 11 is caused by wind or by other factors, particularly earthquakes, the disaster information acquisition device 1 according to this embodiment includes, as an example, a vibration meter 10 that includes the second vibration meter 12 described above in addition to the first vibration meter 11. The second vibration meter 12 should be installed in the ground 3, particularly in the ground 3 relatively adjacent to the structure 2. Here, "adjacent" refers to a positional relationship such that, if an earthquake occurs, the seismic motion detected by the first vibration meter 11 and the seismic motion detected by the second vibration meter 12 are approximately the same. Therefore, the positions of the first vibration meter 11 and the second vibration meter 12 do not need to be strictly adjacent as shown in Figure 1; they may be separated by a certain distance.

In addition, the wind speed calculation unit 22 may include a first vibration determination unit 31, as shown in Figure 1, which determines whether the output of the first vibration meter 11 includes vibration components caused by wind, based on the difference between the output of the first vibration meter 11 and the output of the second vibration meter 12. Specifically, this first vibration determination unit 31 may determine whether the output of the first vibration meter 11 includes components caused by wind by comparing the amplitude value of the Fourier spectrum obtained by Fourier transforming the output of the first vibration meter 11 with the amplitude value of the Fourier spectrum obtained by Fourier transforming the output of the second vibration meter 12, and detecting whether the difference is greater than or equal to a first threshold.

Figure 4 is a graph showing the Fourier spectra obtained by Fourier transforming the output results detected by the first and second vibration meters, for each of the three directional components: X, Y, and Z. Figures 4(A) to 4(C) show the Fourier spectra of the X, Y, and Z directional components of the output results detected by the first vibration meter, respectively. Figures 4(D) to 4(F) show the Fourier spectra of the X, Y, and Z directional components of the output results detected by the second vibration meter, respectively. Figures 4(A) to 4(F) also include the Fourier spectra of the output results when different wind speeds (0.4 m/s, 3.1 m/s, and 6.2 m/s) were applied to each vibration meter.

As can be seen from Figure 4, the amplitude of the Fourier spectrum corresponding to the output result of the second vibration meter 12 installed on the ground 3 (Fourier amplitude) hardly changes even when the wind speed changes. On the other hand, the amplitude of the Fourier spectrum corresponding to the output result of the first vibration meter 11 installed on the structure 2 changes significantly in proportion to the wind speed. From this, the first vibration determination unit 31 can determine that vibration components that cannot be detected by the second vibration meter 12 among the output results of the first vibration meter 11 are vibration components caused by wind, thereby preventing the wind speed calculation unit 22 from mistakenly identifying vibration components other than those caused by wind as being caused by wind.

The determination method by the first vibration determination unit 31 can, for example, compare the amplitude values of the Fourier spectra of the output results of the first and second vibration meters 11 and 12, and make a determination based on whether the difference is greater than or equal to a first threshold. The first threshold can be set in advance based on experiments or other factors. Then, the wind speed calculation unit 22 calculates an estimated wind speed from the output result of the first vibration meter 11 when a difference greater than or equal to the first threshold is detected, thereby enabling the calculation of an estimated wind speed based on the vibration component caused by wind.

In addition, along with the calculation of the wind speed estimate by the wind speed calculation unit 22 described above, earthquake detection may be performed using the earthquake detection unit 25 (described later) based on the output result of the second vibration meter 11 when a difference exceeding the first threshold is detected. By performing the calculation of the wind speed estimate by the wind speed calculation unit 22 and earthquake detection in parallel, it is possible to obtain a calculation result that takes into account the vibration component caused by the earthquake when calculating the wind speed estimate by the wind speed calculation unit 22. Therefore, even in the event of a combined disaster involving earthquakes and strong winds, both types of disaster information can be provided to the user with high accuracy.

Another method for determining whether the output of the first vibration meter 11 includes a vibration component due to wind is to utilize the duration of the vibration. In this regard, the wind speed calculation unit 22 according to this embodiment may include a second vibration determination unit 32 in place of, or in addition to, the first vibration determination unit 31 described above.

The second vibration determination unit 32 may detect whether the duration of vibration detected by the first vibration meter 11 is equal to or greater than a second threshold. The method for measuring the duration of vibration by the second vibration determination unit 32 is not particularly limited, but for example, at least one of Trifunac's cumulative power method and Jennings' envelope method can be used. Here, Trifunac's cumulative power method defines the duration as a certain interval on the time axis of this cumulative power, obtained by integraling the squared amplitude of the time history from the beginning to the end of the vibration measurement record over time. Generally, the interval is often set to 5% to 95% of the cumulative power. Jennings' envelope method is a method devised to simulate the time-series characteristics of the time history waveform of an earthquake wave, and the envelope function consists of an initial part, a main part, and a coda wave. The vibration measurement record is applied to this envelope function, and the duration is defined as the time from the start of the initial phase to the end of the coda phase.

Figure 5 is a graph showing the duration of vibrations detected by a vibration meter, measured using Trifunac's cumulative power method. Figure 5(A) shows the duration of vibrations caused by several past earthquakes, and Figure 5(B) shows the duration of vibrations caused by wind at three different points in time. Figure 6 is a graph showing the duration of vibrations detected by a vibration meter, measured using Jennings' envelope method. Figure 6(A) shows the duration of vibrations caused by several past earthquakes, and Figure 6(B) shows the duration of vibrations caused by wind at three different points in time. As can be seen from Figures 5 and 6, in both measurement methods, vibrations caused by earthquakes had a relatively short duration of less than 100 seconds, while vibrations caused by wind had a relatively long duration of more than 1500 seconds. Therefore, the second vibration determination unit 32 can accurately determine whether the vibration detected by the vibration meter includes a wind-induced vibration component by setting the second threshold to an arbitrary value within the range of 100 seconds to less than 1500 seconds (for example, 200 seconds).

As explained above, according to the wind speed estimation method of the disaster information acquisition device 1 of this embodiment, the wind speed calculation unit 22 can calculate an estimated wind speed from the output result detected by the vibration meter 10. Therefore, disaster information related to strong winds can be acquired without using dedicated measurement means such as a wind turbine-type wind direction and speed meter. It should be noted that the above-described calculation method tends to have relatively lower accuracy compared to methods using dedicated measurement means. However, in the disaster information acquisition device 1 of this embodiment, if it is possible to determine, for example, whether or not strong winds exceeding 10 m/s are occurring, the information can be sufficiently used as disaster information. Therefore, it can be said that even with the above-described calculation method, disaster information with usable accuracy can be acquired.

Furthermore, since the method described above uses a vibration meter 10 as a means of measuring wind speed, the output results of the vibration meter 10 can be used to acquire disaster information other than wind speed, such as earthquakes. Therefore, it becomes possible to acquire multiple disaster information items simultaneously, thereby enabling comprehensive disaster countermeasures.

<Method for estimating rainfall intensity>
When measuring rainfall intensity, it is common to use specialized measurement methods such as tipping bucket rain gauges that collect and measure the actual rainfall, or distrometers that measure using laser light. On the other hand, in the rainfall intensity calculation unit 23 of the disaster information acquisition device 1 according to this embodiment, similar to the case of wind speed, the output results of the vibration meter 10 collected by the data acquisition unit 21 are used to measure the rainfall intensity, or more precisely, to calculate an estimated value of the rainfall intensity.

The rainfall intensity calculation unit 23 of the disaster information acquisition device 1 according to this embodiment can calculate an estimated rainfall intensity based on the output results detected by the vibration meter 10, for example, the second vibration meter 12. In other words, the rainfall intensity calculation unit 23 may calculate the estimated rainfall intensity using vibration waveforms caused by raindrops that have fallen on or near the second vibration meter 12. It should be noted that the output results detected by the first vibration meter 11 can also be used when calculating the estimated rainfall intensity in the rainfall intensity calculation unit 23. However, as mentioned above, the first vibration meter 11 may include vibrations caused by wind, so it is expected that using the output results of the second vibration meter 12, which substantially does not include vibrations caused by wind, will easily yield calculation results with higher accuracy.

To identify the correlation between the output of the second vibration meter 12 and rainfall intensity, Figure 7 shows the output when artificial rainfall was induced on the second vibration meter 12. Here, Figure 7 is a graph showing the relationship between the acceleration detected by the second vibration meter shown in Figure 1 and the rainfall intensity. Figures 7(A) to 7(C) plot the acceleration components in the X, Y, and Z directions of the output detected by the second vibration meter 12, particularly the RMS acceleration, at the observation points.

As can be seen from Figure 7, the observed RMS acceleration results for each rainfall intensity in each direction can be modeled for each of the three directional components using regression analysis, for example, by the dotted line in Figure 7 and the linear regression model shown in equation (2) below.

Here, x ₁ is the RMS acceleration, y ₁ is the rainfall intensity, and a and b are arbitrary constants.

Therefore, by using the modeled regression equation (2) described above, it is possible to calculate an estimated value _y1 of the rainfall intensity at the installation location where the second vibration meter 12 is installed from the time history amplitude (specifically, acceleration or velocity) of the output result of the second vibration meter 12.

Figure 8 is a graph showing the Fourier spectra obtained by Fourier transforming the output results of the second vibration meter shown in Figure 1, observed at varying rainfall intensities. Figures 8(A) to 8(C) show the Fourier spectra of the X, Y, and Z components of the output results detected by the second vibration meter, respectively. Furthermore, the R0 in Figures 8(A) to 8(C) represents the Fourier spectrum when the rainfall intensity is zero, while the others represent the Fourier spectra when rainfall is present (specifically, rainfall intensities set to 15 mm/h, 75 mm/h, 135 mm/h, and 300 mm/h, respectively). From Figure 8, it can be observed that the amplitude values of the Fourier spectra of the second vibration meter 12 tend to increase proportionally to the rainfall intensity in the relatively high frequency range. Based on this, Figure 9 shows the relationship between the spectral intensity of the above Fourier spectrum (the average value of the Fourier amplitude in a predetermined high-frequency range) and the rainfall intensity.

Figure 9 is a graph showing the relationship between the spectral intensity values in a specific frequency range of the Fourier spectrum obtained by Fourier transforming the output results detected by the second vibration meter shown in Figure 1, and the rainfall intensity. Figures 9(A) to 9(C) plot the spectral intensity values of the Fourier spectra of the X, Y, and Z components of the output results detected by the second vibration meter 12 as observation points. Here, the specific frequency range mentioned above can be appropriately set within a range of relatively high frequencies. As can be seen from Figure 9, the observation results of the spectral intensity in a specific frequency range for each rainfall intensity in each direction can be modeled using regression analysis for each of the three directional components, for example, as shown by the dotted line in Figure 9 and the linear regression model shown in equation (3) below.

Here, _x² is spectral intensity, _y² is rainfall intensity, and a and b are arbitrary constants.

Therefore, by using the modeled regression equation (3) described above, it is possible to calculate the estimated rainfall intensity _y2 at the installation location where the second vibration meter 12 is installed from the output result of the second vibration meter 12.

As explained above, according to the rainfall intensity estimation method of the disaster information acquisition device 1 of this embodiment, the rainfall intensity calculation unit 23 can calculate an estimated rainfall intensity from the output result detected by the vibration meter 10. Therefore, disaster information related to heavy rain can be acquired without using dedicated measurement means such as a tipping bucket rain gauge or a distrometer that measures rainfall using laser light. However, since the above calculation method calculates an estimated value, its accuracy tends to be relatively lower compared to methods that actually measure rainfall, such as the dedicated measurement means described above. Nevertheless, in the disaster information acquisition device 1 of this embodiment, if an approximate rainfall intensity (for example, rainfall intensity with an accuracy of two orders of magnitude) can be determined, it is sufficient for use as disaster information. Therefore, even with the above calculation method, it can be said that disaster information with usable accuracy can be acquired.

Furthermore, since the method described above uses a vibration meter 10 as a means of measuring rainfall intensity, the output results of the vibration meter 10 can be used to acquire disaster information other than rainfall intensity, such as earthquakes. Therefore, it becomes possible to acquire multiple disaster information items simultaneously, thereby enabling comprehensive disaster countermeasures.

<Method for detecting landslides>
Landslides, mudslides, and debris flows are generally detected using specialized measurement methods, such as wire sensors, that are pre-installed in areas with a high probability of occurring. On the other hand, the landslide detection unit 24 of the disaster information acquisition device 1 according to this embodiment uses the output results of the vibration meter 10 collected by the data collection unit 21 to detect the occurrence of a landslide.

The landslide detection unit 24 in this embodiment can detect the occurrence of a landslide based on the output results detected by the vibration meter 10 collected by the data acquisition unit 21. In other words, this landslide detection unit 24 may be capable of detecting whether the vibration detected by at least one of the first and second vibration meters 11 and 12 is due to a landslide. The detection of a landslide by the landslide detection unit 24 may utilize the output results of either the first or second vibration meters 11 or 12.

As a specific method for detecting the occurrence of a landslide, the landslide detection unit 24 can employ a method that detects the occurrence of a landslide based on the number of pulse peaks (i.e., the number of times the vibration waveform exceeds the third threshold) among the vibration waveforms detected by the vibration meter 10. In order to detect the occurrence of a landslide with high accuracy using this method, it is advisable to install the vibration meter 10 in a location or near a location where a landslide is likely to occur.

Figure 10 is a graph showing an example of an acceleration waveform detected by the vibration meter shown in Figure 1. The acceleration waveform shown in Figure 10 is an illustrative example of the acceleration waveform of the Z-direction component from the output result detected by the vibration meter 10. In the sediment disaster detection unit 24 according to this embodiment, as shown in Figure 10, the third threshold is set to ±100 cm/ ^s² . The sediment disaster detection unit 24 detects the peak of pulses that exceed this third threshold (the portion indicated by arrow P in Figure 10), and can detect the occurrence of a sediment disaster based on the number of detected pulses per unit time (e.g., 1 second) (i.e., pulse density). The specific value of the third threshold is not limited to the above and can be changed as appropriate considering the detection accuracy.

Figure 11 is a graph showing the relationship between another example of an acceleration waveform caused by sediment movement detected by the vibration meter shown in Figure 1 and its pulse density. Figure 11(A) shows the acceleration waveform, Figure 11(B) shows the time-series results of detecting pulse peaks that exceeded the third threshold in the acceleration waveform of Figure 11(A), and Figure 11(C) shows the time-series change in the number of pulse peaks (pulse density) detected in Figure 11(B). Among the acceleration waveforms shown in Figure 11(A), pulse peaks that exceeded the third threshold set to ±100 cm/ ^s² appeared at the timings shown in Figure 11(B), and their frequency of appearance is shown in Figure 11(C). Here, if the pulse density for determining that vibration is due to a sediment disaster is set to, for example, 40 times/second, the sediment disaster detection unit 24 can determine from Figure 11(C) that a sediment disaster occurred 12 seconds after the vibration meter 10 detected the acceleration waveform. Furthermore, the pulse density at which vibrations are judged to be caused by landslides can be adjusted as appropriate.

Incidentally, the vibrations detected by the vibration meter 10 are not limited to those caused by landslides, but can also detect vibrations caused by other disasters, such as earthquakes. Figure 12 is a graph showing the time series changes of the pulse density shown in Figure 11(C) and the time series changes of the pulse density of the acceleration waveform caused by an earthquake side by side. Figure 12(A) corresponds to Figure 11(C), and Figures 12(B) and 12(C) show the pulse density obtained by processing the results of detecting seismic motion from two past earthquakes using the same method as in Figure 11(C) in a time series. As can be seen from Figure 12, the pulse density derived from the acceleration waveform that the vibration meter 10 can detect when an earthquake occurs is, at first glance, similar to the pulse density derived from the acceleration waveform that the vibration meter 10 can detect when a landslide occurs. For this reason, in particular, when an earthquake occurs at the installation location of the vibration meter 10, there is a possibility of misidentifying vibrations caused by the earthquake (seismic motion) as vibrations caused by a landslide. Therefore, the following describes a method for distinguishing between vibrations caused by landslides and vibrations caused by earthquakes, as one way for the landslide detection unit 24 to reliably detect vibrations caused by landslides from the output results of the vibration meter 10.

Analysis of vibrations caused by earthquakes and vibrations caused by landslides revealed that earthquake vibrations consisted almost entirely of vibrations in a lower frequency range compared to those caused by landslides. Therefore, the landslide detection unit 24 in this embodiment utilizes a high-pass filter 41 that extracts vibration components with frequencies above a predetermined frequency from the output results detected by the vibration meter 10, thereby identifying whether the vibrations detected by the vibration meter 10 are caused by a landslide or by earthquake motion. An example of this is illustrated below.

The high-pass filter 41 may block vibration components in the relatively low frequency range from the output results detected by the vibration meter 10, while allowing vibration components in other frequency ranges to pass through. The threshold of this high-pass filter 41 should be set to a frequency that can block vibration components caused by earthquakes. Figure 13 shows the results of applying the above-described high-pass filter 41 to the graphs showing vibrations caused by landslides and vibrations caused by earthquakes, as shown in Figure 12. As can be seen from Figure 13, when the high-pass filter 41 is applied, there is almost no change in the pulse density of vibrations caused by landslides (see Figure 13(A)), whereas virtually all components of vibrations caused by earthquakes cannot pass through the high-pass filter 41, so pulse peaks exceeding the third threshold are no longer detected (see Figures 13(B) and 13(C)).

Therefore, by analyzing the vibration components after passing through the high-pass filter 41, the landslide detection unit 24 can avoid misidentifying vibrations caused by earthquakes as vibrations caused by landslides, thereby enabling accurate detection of landslide occurrences.

The detection of landslides occurring near the vibration meter 10 can be achieved with high accuracy using the method described above. However, if a landslide occurs at a location far from the vibration meter 10, the relatively high-frequency components (pulses with large amplitude) of the vibration components generated by that landslide will attenuate during the process of reaching the vibration meter 10. Therefore, the landslide detection method using pulse density described above may not be able to accurately detect landslides occurring at a location far from the vibration meter 10. Accordingly, the landslide detection unit 24 according to this embodiment may further include a fitting unit 42 for detecting landslides occurring at a location far from the vibration meter 10, in addition to the high-pass filter 41 described above. The fitting unit 42 according to this embodiment may fit an arbitrary curve to the Fourier spectrum obtained by Fourier transforming the output result detected by the vibration meter 10.

Figure 14 is a graph showing the Fourier spectra obtained by two different vibration meters detecting vibrations caused by landslides. Figures 14(A) to 14(C) show the Fourier spectra of the X, Y, and Z components of the output detected by one vibration meter, respectively, while Figures 14(D) to 14(F) show the Fourier spectra of the X, Y, and Z components of the output detected by the other vibration meter, respectively. Figure 14 also shows approximate curves obtained by fitting arbitrary curves to each Fourier spectrum. Furthermore, both the first vibration meter and the other vibration meter can be composed of vibration meters installed in the ground, similar to the second vibration meter 12, but their installation locations may be different. From the observation records shown in Figure 14, it can be said that the Fourier spectrum of vibrations generated by landslides is a stable, unimodal convex shape. Therefore, it can be said that the Fourier spectrum of vibrations generated by landslides can be modeled as a Gaussian function, for example, shown in equation (4) below.

Here, c is the amplitude of the spectral peak (cm/ ^s² ·s), α is the frequency of the spectral peak (Hz), and β is the width of the spectral peak (Hz). In this embodiment, the example shows that the output result of the vibration meter includes acceleration recording, so the unit of the spectral peak amplitude corresponding to c is (cm/ ^s² ·s). However, if the output result of the vibration meter includes velocity recording, the unit of the spectral peak amplitude corresponding to c will be (cm/s·s).

Considering the above, the sediment disaster detection unit 24, in the fitting unit 42, fits the curve represented by the Gaussian function shown in equation (4) to the Fourier spectrum of the output result of the vibration meter 10. Based on the difference between the two, i.e., the Fourier spectrum and the Gaussian function, it can be determined that the vibration detected by the vibration meter 10 is caused by a sediment disaster. For this determination, for example, the difference described above can be compared with a preset fourth threshold. Furthermore, indicators such as the sum of squared residuals or the normalized RMSE (root mean square error) can be used for the difference.

Therefore, the landslide detection unit 24, in the fitting unit 42, continuously creates an approximate curve represented by a Gaussian function from the detection results of the vibration meter 10, and by comparing it with the Fourier spectrum of the measured vibration, it becomes possible to detect landslides that occur at a distance from the vibration meter 10.

As described above, according to the landslide occurrence detection method of the disaster information acquisition device 1 of this embodiment, the landslide occurrence detection unit 24 can detect not only landslides occurring near the installation location of the vibration meter 10, but also landslides occurring at locations far from the installation location of the vibration meter 10, based on the output results detected by the vibration meter 10. Therefore, disaster information related to landslides can be acquired without using dedicated measurement means such as wire sensors. Furthermore, because the landslide occurrence detection method described above can detect landslides occurring at locations far from the installation location of the vibration meter 10, it is possible to detect landslides over a wider area more easily than by installing wire sensors. To further improve the accuracy of landslide occurrence detection, a geophone (vibration receiver) or the like may be used as a complement.

Furthermore, since the method described above uses a vibration meter 10 as a means of measuring landslides, the output results of the vibration meter 10 can be used to acquire information on disasters other than landslides, such as earthquakes. Therefore, it becomes possible to acquire multiple types of disaster information simultaneously, thereby enabling comprehensive disaster countermeasures.

<Earthquake detection method>
Finally, a brief explanation will be given regarding the detection of earthquakes using the disaster information acquisition device 1 according to this embodiment. The disaster information acquisition device 1 according to this embodiment may include an earthquake detection unit 25 that detects earthquakes at the installation location of the vibration meter 10 from the output results of the vibration meter 10. When the earthquake detection unit 25 detects the seismic intensity of an earthquake from the acceleration waveform detected by the vibration meter 10, a conventionally known conversion method may be used. Specifically, the acceleration components in the X, Y, and Z directions detected by the vibration meter are processed in the order of Fourier transform, filtering, and inverse Fourier transform, and a vector waveform is synthesized from the obtained values. Then, the seismic intensity of the earthquake can be detected by calculating I = 2logA + 0.94 using the synthesized value A of the obtained vector waveform to obtain the measured seismic intensity I. Furthermore, in order to further improve the accuracy of earthquake detection, a well-known magnetic sensor or the like may be separately adopted.

The disaster information acquired by the earthquake detection method described above does not interfere with the acquisition of other disaster information calculated or detected by the disaster information acquisition device 1 according to this embodiment. Specifically, when the wind speed calculation unit 22 calculates an estimated wind speed and the earthquake detection unit 25 detects an earthquake simultaneously, for example, the output result detected by the first vibration meter 11 can be used for calculating the estimated wind speed, and the output result detected by the second vibration meter 12 can be used for detecting the earthquake. Alternatively, as described above, considering that wind-induced vibrations have a longer duration than earthquake vibrations, the wind speed calculation unit 22 can calculate the estimated wind speed based on data from the first vibration meter 11 for times other than when the earthquake was detected, thereby enabling accurate calculation and detection of both earthquakes and wind speed.

Furthermore, when the calculation of estimated rainfall intensity by the rainfall intensity calculation unit 23 and the detection of earthquakes by the earthquake detection unit 25 are performed simultaneously, it is sufficient to determine whether the vibration is caused by rainfall or an earthquake based on the frequency characteristics of the vibration detected by the vibration meter. Specifically, if it is desired to detect earthquakes during a period of rainfall, a low-pass filter that allows only relatively low frequencies to pass through can be used to extract the desired frequency characteristic components from the output results detected by the vibration meter, and then the earthquake detection unit 25 can perform the earthquake detection. Moreover, when the detection of landslides by the sediment disaster detection unit 24 and the detection of earthquakes by the earthquake detection unit 25 are performed simultaneously, the two can be distinguished and detected by, for example, considering the frequency range of the vibration detected by the vibration meter 10.

As described above, according to the earthquake detection method of the disaster information acquisition device 1 in this embodiment, the earthquake detection unit 25 can detect an earthquake occurring at the installation location of the vibration meter 10 from the output result detected by the vibration meter 10, without interfering with the detection of other disaster information. Therefore, the disaster information acquisition device 1 can acquire multiple pieces of disaster information.

In addition, the disaster information acquisition device 1 according to this embodiment can calculate or detect disaster information other than earthquakes in parallel without interfering with each other's acquisition. Specifically, for example, the calculation of wind speed estimates and rainfall intensity estimates can be performed separately by using the output results of different vibration meters. Furthermore, since the magnitudes of the vibration components of rainfall intensity estimates and landslide occurrence detection are completely different, they can be distinguished and calculated and detected based on the magnitude of the vibration components. Moreover, similar to the calculation of wind speed estimates and rainfall intensity estimates, the calculation and detection of landslide occurrence can be performed separately by using the output results of different vibration meters.

Furthermore, while the disaster information acquisition device 1 according to the above-described embodiment was explained as a single device in which the vibration meter 10 and the data processing unit 20 are locally connected, this disaster information acquisition device 1 can also be modified into a system in which the vibration meter 10 and the data processing unit 20 exist as separate components. Specifically, it is also possible to create a disaster information acquisition system in which an information terminal (for example, the server 4 shown in Figure 1) installed at a location separate from the vibration meter 10 functions as the data processing unit 20. In this case, by connecting the information terminal functioning as the data processing unit 20 and the vibration meter 10 via a communication network, and enabling the transmission of the output results of the vibration meter 10 to the information terminal, the information terminal can acquire disaster information for the installation location where the vibration meter 10 is installed.

Furthermore, the regression equations used in the calculation or detection methods exemplified in the above-described embodiment can be replaced with those obtained from other regression analyses. That is, parametric regression equations other than those described above can be used as substitutes, or similar results can be obtained using non-parametric regression.

This disclosure is not limited to the embodiments described above, and can be implemented with various modifications without departing from the spirit of this disclosure. All such modifications are included within the technical concept of this disclosure.

1 Disaster information acquisition device 2 Structure 3 Ground 10 Vibration meter 11 First vibration meter 12 Second vibration meter 20 Data processing unit 21 Data acquisition unit 22 Wind speed calculation unit 23 Rainfall intensity calculation unit 24 Sediment disaster detection unit 25 Earthquake detection unit 26 Memory 27 Communication interface 31 First vibration determination unit 32 Second vibration determination unit 41 High-pass filter 42 Fitting unit

Claims (6)

A vibration meter capable of detecting vibrations at the installation location,
The system includes a sediment disaster detection unit that detects the occurrence of sediment disasters at and around the installation location from the output results of the vibration meter, and a data processing unit for acquiring disaster information for the installation location .
The sediment disaster detection unit detects the occurrence of a sediment disaster at the installation location and in the vicinity of the installation location based on the number of times the output result detected by the vibration meter exceeds a third threshold within a unit time.
Disaster information acquisition device. The data processing unit further includes an earthquake detection unit that detects an earthquake at the installation location from the output result of the vibration meter.
The disaster information acquisition device according to claim 1. The landslide detection unit includes a high-pass filter that extracts vibration components with frequencies above a predetermined frequency from the output results detected by the vibration meter, and detects the occurrence of landslides at the installation location and in the vicinity of the installation location from the vibration components extracted by the high-pass filter.
A disaster information acquisition device according to claim 1 or claim 2 . A vibration meter capable of detecting vibrations at the installation location,
The system includes a sediment disaster detection unit that detects the occurrence of sediment disasters at and around the installation location from the output results of the vibration meter, and a data processing unit for acquiring disaster information for the installation location.
The sediment disaster detection unit includes a fitting unit that fits a curve represented by a Gaussian function to the Fourier spectrum obtained by Fourier transforming the output result of the vibration meter, and detects the difference, and detects the occurrence of sediment disasters at the installation location and in the vicinity of the installation location based on the difference.
Disaster information acquisition device. A vibration meter connected to a communication network, capable of detecting vibrations at the installation location,
A data processing unit for acquiring disaster information for the installation location is provided, which is capable of acquiring the output results of the vibration meter via the communication network and includes a sediment disaster detection unit that detects the occurrence of sediment disasters at and around the installation location from the output results of the vibration meter ,
The sediment disaster detection unit detects the occurrence of a sediment disaster at the installation location and in the vicinity of the installation location based on the number of times the output result detected by the vibration meter exceeds a third threshold within a unit time.
Disaster information acquisition system. A vibration meter connected to a communication network, capable of detecting vibrations at the installation location,
A data processing unit for acquiring disaster information for the installation location is provided, which is capable of acquiring the output results of the vibration meter via the communication network and includes a sediment disaster detection unit that detects the occurrence of sediment disasters at and around the installation location from the output results of the vibration meter,
The sediment disaster detection unit includes a fitting unit that fits a curve represented by a Gaussian function to the Fourier spectrum obtained by Fourier transforming the output result of the vibration meter, and detects the difference, and detects the occurrence of sediment disasters at the installation location and in the vicinity of the installation location based on the difference.
Disaster information acquisition system.