WO2024004215A1 - Ground movement analysis device and ground movement analysis method - Google Patents

Abstract

This ground movement analysis device comprises an inclination projecting unit (12) which converts LoS movement data acquired for each of a plurality of predetermined unit areas included in a region of interest into inclination movement data in a direction along an inclination direction of the region of interest, an offset correcting unit (13) which performs offset correction of the inclination movement data, and a hotspot extracting unit (14) which uses the offset-corrected inclination movement data to extract, as a movement hotspot, a region in which movement of the ground surface is greater than in a surrounding region, wherein the LoS movement data observed in an area having an undulating topography are converted into inclination movement data in a direction along the inclination direction of the undulations, and noisy data components are removed by means of the offset correction, after which the region having a large ground surface movement is extracted as the movement hotspot, thereby enabling the state of ground movements to be accurately captured, even in areas having an undulating topography.

Description

Ground movement analysis device and ground movement analysis method

The present invention relates to a ground deformation analysis device and a ground deformation analysis method, and is particularly suitable for use in a device and method that analyze the state of ground deformation using observation data measured using a synthetic aperture radar.

Ground failures such as landslides on slopes are one of the major geological hazard phenomena that require monitoring. Landslides have become more frequent in recent years due to abnormal weather and unplanned human activities. Therefore, it is becoming important to identify and monitor slopes where landslides may occur in order to prevent or reduce the various losses associated with landslides.

Conventionally, methods for monitoring ground deformation on slopes have included frequently repeated field surveys or measurements using ground sensors of the Global Navigation Satellite System (GNSS). However, these methods have the problem that monitoring information can only be obtained in a limited area, such as the site where a field survey was conducted or the location where ground sensors were installed.

On the other hand, a monitoring method using synthetic aperture radar (SAR) as an earth observation technology has also been proposed (see, for example, Patent Documents 1 and 2). According to a monitoring method using synthetic aperture radar, it is possible to obtain data indicating changes in the ground surface in the direction of the line-of-sight (LoS) when looking at the ground surface from a satellite. By analyzing LoS fluctuation data, it is possible to monitor ground deformation in a relatively wide area.

Patent Document 1: Japanese Patent Application Publication No. 2021-56008 Patent Document 2: Patent No. 3987451

The landslide area detection device described in Patent Document 1 uses at least a combination of landslide characteristics in each of phase difference data and coherence data obtained by performing interference analysis on ground surface data obtained by radar measurements at different timings. Based on this information, landslide-prone ground movements are detected. Here, based on the topographical data corresponding to the range of ground surface data, the range where landslide-prone ground movements are not detected is identified, and this range is excluded from the ground movement area, thereby eliminating the need for detection processing. This reduces the amount of time and effort required and also suppresses the occurrence of misjudgments.

In the ground surface variation measurement method described in Patent Document 2, the ground surface is measured by performing synthetic aperture interference processing on the ground surface reflected waves of pulsed radio waves emitted from one synthetic aperture radar mounted on a satellite. Find the amount of variation. Specifically, by performing synthetic aperture interference processing on the reflected waves obtained twice in the ascending orbit and the descending orbit, the distances from the ascending orbit and the descending orbit to the measurement target point are determined, and then By adding the condition of the direction of ground surface movement specified by the ground structure (the condition that landslide movement occurs along the slope's maximum slope line), the actual amount of change from the past position of the measurement target point is determined.

When using synthetic aperture radar, in areas with flat topography without undulations, LoS variation data can be converted into vertical ground movement, which helps in meaningfully interpreting the ground movement situation. Furthermore, in the case of a slope whose aspect direction is parallel to the LoS direction, the LoS fluctuation data can be interpreted as movement along the slope.

However, in areas with undulating topography, it is difficult to interpret LOS fluctuation data or vertical fluctuation data obtained by converting the LOS fluctuation data in the vertical direction as the movement of the ground in that area. That is, for example, mountainous areas typically slope in all directions and have undulations within even a small area. Therefore, even if LoS fluctuation data and vertical fluctuation data were used, there was a problem in that the situation of ground deformation could not be accurately captured.

The present invention has been made to solve such problems, and it is possible to accurately capture the ground deformation situation in areas with undulating topography using observation data from synthetic aperture radar. The purpose is to

In order to solve the above problems, the present invention uses LoS fluctuation data acquired for each of a plurality of predetermined unit areas included in a region of interest based on information regarding the orientation of the satellite and information regarding the tilt characteristics of the region of interest. Convert the tilt variation data in the direction along the tilt direction of the region of interest, and convert each of the tilt variation data obtained for each of the plurality of predetermined unit areas to the average value of the tilt variation data obtained for each of the plurality of predetermined unit areas. Correct the offset using . Then, using the offset-corrected slope variation data, areas where the ground surface variation is larger than the surrounding area are extracted as variation hot spots.

According to the present invention configured as described above, LoS fluctuation data observed in an area with undulating topography is converted into slope fluctuation data in a direction along the slope direction of the undulations, and further noise reduction is performed by offset correction. After these data components are removed, areas with large ground surface fluctuations are extracted as fluctuation hotspots. As a result, even in areas with undulating topography, the state of ground deformation can be accurately captured using observation data from synthetic aperture radar.

FIG. 1 is a diagram showing a network configuration of an analysis system according to the present embodiment. FIG. 1 is a block diagram showing an example of the functional configuration of a ground deformation analysis device according to the present embodiment. FIG. 3 is a diagram for explaining the LoS direction. FIG. 2 is a block diagram showing a specific functional configuration example of a hot spot extraction unit according to the present embodiment. FIG. 2 is a diagram for explaining the processing content of the difference analysis unit according to the present embodiment, and is a diagram showing a state in which a certain area of the ground surface is viewed from above. It is a diagram for explaining the processing content of the difference analysis unit according to the present embodiment, and is a diagram showing the transition of slope fluctuation data (time-series slope) in one predetermined unit area. FIG. 6 is a diagram for explaining the processing content of the adjacent area extraction unit according to the present embodiment. It is a flow chart showing an example of the operation of the ground deformation analysis device according to the present embodiment.

Hereinafter, one embodiment of the present invention will be described based on the drawings. FIG. 1 is a diagram showing a network configuration of an analysis system 100 according to this embodiment. The analysis system 100 includes a satellite base station 50 that receives satellite data from a satellite S flying in a satellite orbit in space, a database DB that records satellite data, and a ground deformation analysis device 10 that analyzes the satellite data.

The satellite S is equipped with a satellite data acquisition unit (including, for example, an optical sensor, a synthetic aperture radar, etc.), and transmits satellite data including images of the ground taken by the satellite data acquisition unit to the satellite base station 50.

The satellite base station 50 receives satellite data from the satellite S and records it in the database DB. The database DB is connected to a communication network N such as the Internet. The ground deformation analysis device 10 acquires satellite data stored in the database DB via the communication network N.

FIG. 2 is a block diagram showing an example of the functional configuration of the ground deformation analysis device 10 according to this embodiment. As shown in FIG. 2, the ground deformation analysis device 10 of this embodiment includes a LoS fluctuation data acquisition section 11, a tilt projection section 12, an offset correction section 13, and a hot spot extraction section 14 as functional configurations.

The functional blocks 11 to 14 can be configured by hardware, DSP (Digital Signal Processor), or software. For example, when configured by software, the functional blocks 11 to 14 are configured with a computer's CPU, RAM, ROM, etc., and an analysis program stored in a storage medium such as the RAM, ROM, hard disk, or semiconductor memory runs. This is achieved by The analysis program may be provided stored in a storage medium or may be provided via a communication network.

Note that the physical configuration described here is an example and does not necessarily have to be an independent configuration. For example, the ground deformation analysis device 10 may include an LSI (Large-Scale Integration) in which a CPU, RAM, and ROM are integrated. Furthermore, in this embodiment, a case will be described in which the ground deformation analysis device 10 is composed of one computer, but the ground deformation analysis device 10 may be realized by combining a plurality of computers.

The LoS fluctuation data acquisition unit 11 analyzes time-series observation data measured using a synthetic aperture radar, and determines the line-of-sight direction when looking at the ground surface from the satellite for each of a plurality of predetermined unit areas included in the region of interest. Obtain LoS fluctuation data, which is data indicating fluctuations in . The region of interest refers to an area whose ground deformation is to be monitored. The observation data used in this embodiment is, for example, time-series SAR images stored in a storage medium (not shown) based on daily observations using synthetic aperture radar.

A satellite equipped with a synthetic aperture radar observes all points on the earth from two directions: a northward orbit (Ascending) and a southward orbit (Descending) from a pre-designated orbit of the satellite. As a result, a time-series SAR image observed by a satellite whose traveling direction is a northward orbit (hereinafter referred to as a "northern orbit SAR image") and a time-series SAR image observed by a satellite whose satellite traveling direction is a southward orbit. A SAR image (hereinafter referred to as a southbound orbit SAR image) is input to the LoS fluctuation data acquisition unit 11.

The LoS variation data acquisition unit 11 analyzes the northbound orbit SAR image and the southbound orbit SAR image, thereby obtaining data indicating variations in the ground surface in the direction of line of sight when looking at the ground surface from a satellite in the northbound orbit. (hereinafter referred to as northbound LoS variation data) and data indicating variations in the ground surface in the line-of-sight direction when looking at the ground surface from a satellite in a southbound orbit (hereinafter referred to as southbound LoS variation data). Here, the known InSAR analysis can be used as the analysis performed by the LoS fluctuation data acquisition unit 11.

FIG. 3 is a diagram for explaining the LoS direction, which is the line of sight direction when looking at the earth's surface from a satellite. Radar waves emitted from a satellite are not irradiated perpendicularly to the earth's surface, but in directions with incident angles λasc and λdesc deviated from the perpendicular direction. That is, radar waves are irradiated in directions with slightly different incident angles λasc and λdesc between the satellite in the northward orbit shown in FIG. 3(a) and the satellite in the southward orbit shown in FIG. 3(b). As a result, in the InSAR analysis, the amount of variation in the LoS direction of the satellite is determined instead of the amount of variation in the vertical direction of the ground surface.

The LoS fluctuation data acquisition unit 11 replaces the northbound LoS fluctuation data and southbound LoS fluctuation data acquired as described above with fluctuation data for each predetermined unit time and for each predetermined unit area. Here, the LoS fluctuation data acquisition unit 11 matches the time axis and the spatial axis between the northbound LoS fluctuation data and the southbound LoS fluctuation data, and the matched northbound LoS fluctuation data and southbound LoS fluctuation data and detect.

The northbound LoS fluctuation data and the southbound LoS fluctuation data may not match in the time axis direction and the spatial axis direction. Therefore, the LoS fluctuation data acquisition unit 11 sets a threshold value in the time axis direction, and if there is northbound LoS fluctuation data and southbound LoS fluctuation data that can be considered the same within the range of the threshold value, it matches them and detects them. do. Similarly, a threshold is set in the spatial axis direction, and if there is northbound LoS variation data and southbound LoS variation data that can be considered the same within the range of the threshold, they are matched and detected. Matching here refers to the process of determining whether there is a point within a threshold set in the time axis direction or the spatial axis direction where northbound LoS fluctuation data and southbound LoS fluctuation data can be considered the same. .

In this embodiment, as an example of the time-axis threshold and the spatial-axis threshold used when performing matching, we use thresholds commonly set on the Sentinel-1 satellite, which is used during measurements that require strictness and precision. Is possible. Alternatively, the matching conditions may be set loosely by making at least one of the time axis threshold and the spatial axis threshold larger than the threshold generally set for the Sentinel-1 satellite. For example, the threshold on the time axis can be set to 11 days, and the threshold on the spatial axis can be set to a rectangular area of 20 m square.

For example, the LoS fluctuation data acquisition unit 11 sets the northbound LoS fluctuation data as master data and the southbound LoS fluctuation data as slave data, and determines the same data as the northbound LoS fluctuation data within a time range of 11 days and within a distance difference of 20 m square. Detect southbound LoS fluctuation data that can be considered. Then, only when conditions that can be considered to be the same can be detected, the northbound LoS fluctuation data and the southbound This is adopted as row LoS fluctuation data. In this case, the predetermined unit time and predetermined unit area are specified based on the northbound LoS fluctuation data used as master data.

The oblique projection unit 12 is configured to obtain information about the orientation of the satellite (azimuth angle φ and incident angle λ) and information about the inclination characteristics of the region of interest (inclination angle S and aspect angle A) by the LoS fluctuation data acquisition unit 11. The LoS variation data is converted into tilt variation data in a direction along the tilt direction of the region of interest.

Here, the LoS fluctuation data to be converted is the northbound LoS fluctuation data used as master data. Further, as information regarding the orientation of the satellite (azimuth angle φ and incident angle λ), information regarding northbound LoS fluctuation data is used. Information regarding the slope characteristics (slope angle S and aspect angle A) of the region of interest can be obtained, for example, using topographic data published by public institutions such as the Geospatial Information Authority of Japan.

Specifically, the slope projection unit 12 converts the LoS fluctuation data V _LOS into slope fluctuation data V _slope based on the following (Equation 1).
V _slope = V _LOS /C [mm/predetermined unit time]... (Formula 1)
here,
C=η _LOS ×η _slope =N _LOS ×N _slope +E _LOS ×E _slope +Z _LOS ×Z _slope
(N: north direction, E: east direction, Z: vertical direction perpendicular to north and east)
η _LOS = [sinφ* sinλ, -cosφ*sinλ, cosλ]
η _slope = [-cosA*cosS, -sinA*cosS, sinS]
A: Aspect angle indicating the direction of the slope of the observation area as seen from the satellite S: Inclination angle of the slope of the observation area φ: Azimuth angle with respect to the North Pole (true north) in the orbit λ: Incident angle from the satellite to the ground surface

The offset correction unit 13 performs offset correction on each of the tilt variation data obtained for each of the plurality of predetermined unit areas by the tilt projection unit 12 using the average value of the tilt variation data obtained for each of the plurality of predetermined unit areas. do. Specifically, the offset correction unit 13 calculates the average value of time-series gradients representing the transition of slope fluctuation data regarding all the matched predetermined unit areas within the observation area, and calculates the average value of the time-series gradient from the time-series gradient of each predetermined unit area. Offset correction is performed by subtracting the respective average values.

The hot spot extraction unit 14 uses the time-series slope variation data offset-corrected by the offset correction unit 13 to extract an area where the ground surface variation is larger than the surrounding area as a variation hot spot. FIG. 4 is a block diagram showing a specific functional configuration example of the hot spot extracting section 14. As shown in FIG. As shown in FIG. 4, the hot spot extraction unit 14 of this embodiment includes a difference analysis unit 14a, a first filter unit 14b, a second filter unit 14c, and a nearby area extraction unit 14d as specific functional configurations. We are prepared.

The difference analysis unit 14a takes one of the predetermined unit areas as an area of interest, and analyzes the difference between slope fluctuation data of a local area that includes the area of interest and slope fluctuation data of a control area that includes the local area and is wider than the local area. Then, an area of interest that satisfies the first condition regarding the large difference is extracted as a candidate for a fluctuation hot spot.

5 and 6 are diagrams for explaining the processing contents of the difference analysis section 14a. FIG. 5 shows a bird's-eye view of a certain area of the ground surface from above, and each position indicated by a black mark indicates a plurality of matched predetermined unit areas. Although there are actually many more predetermined unit areas, they are shown in a simplified manner for convenience of explanation. FIG. 6 shows the time-series slope of the slope fluctuation data V _slope in one predetermined unit area, obtained by the processing of the tilt projection unit 12 and the offset correction unit 13, for each predetermined unit time (every 11 days). , and each dot indicates slope fluctuation data V _slope acquired every 11 days.

In FIG. 5, the predetermined unit area indicated by the symbol P is the area of interest. Centering on this area of interest P, a circular area with radius r1 (for example, 100 m) is the local area A _local , and a circular area with radius r2 (r1 < r2, for example, r2 = 500 m) is the control area A _region . be. In the example of FIG. 5, the attention area P and other predetermined unit areas Q _L1 , Q _L2 , ..., Q _Lm (m=3 in the example of FIG. 5) exist in the local area A _local , and the control area In _region A, there are further predetermined unit areas Q _R1 , Q _R2 , . . . , Q _Rn (n=9 in the example of FIG. 5).

Note that, although the shapes of the local area A _local and the control area A _region are both circular here, they are not limited to this. For example, the shape of at least one of the local area A _local and the control area A _region may be rectangular.

In this embodiment, the slope fluctuation data of the local area A _local is used as _the slope fluctuation data of the attention area P and other predetermined unit areas Q _L1 , Q _L2 , ..., Q _Lm existing in the local area A _local . The average value of the data V _slope is used. In addition, as slope variation data V _region of the control area A _region , the attention area P and other predetermined unit areas Q _L1 , Q _L2 , ..., Q _Lm , Q _R1 , Q _R2 existing in the control area A _region are added. ,..., Q The average value of the slope fluctuation data V _slope of _Rn is used.

Here, the slope fluctuation data V _slope whose average value is to be calculated is obtained by sampling a predetermined number of slope fluctuation data V _slope from among the time series slopes shown in FIG. 6 . For example, k pieces of slope fluctuation data V _slope from the latest final time point _T1 to time point _Tk are used as the average value calculation target. The value of the sampling number k may be a fixed value or a variable value that can be set arbitrarily by the user. Alternatively, if environmental conditions such as the season and the climate of the observation area are set, the value of the sampling number k may be automatically calculated according to the set environmental conditions.

For example, the average value of the slope fluctuation data V _slope of the predetermined unit areas P, Q _L1 , Q _L2 , ..., Q _Lm at the time _{T 1} is used as the slope fluctuation data of the local area A _local at the time T 1; is written as V _local-T1 . Similarly, the slope fluctuation data of the local area A _local at time T _k is the average value of slope fluctuation data V _slope of predetermined unit areas P, Q _L1 , Q _L2 , ..., Q _Lm at time T _k , This is written as V _local-Tk .

Further, as slope fluctuation data of the control area A _region at time T ₁ , predetermined unit areas P, Q _L1 , Q _L2 , ..., Q _Lm , Q _R1 , Q _R2 , ..., Q _Rn at time T ₁ Using the average value of the slope fluctuation data V _slope of , this is expressed as V _region-T1 . Similarly, the slope fluctuation data of the control area A _region at the time _Tk is the slope of the predetermined unit area P, Q _L1 , Q _L2 , ..., Q _Lm , Q _R2 , ..., Q _Rn at the time T _k This is the average value of the fluctuation data V _slope , and is expressed as V _region-Tk .

In this case, the difference ΔV between the slope fluctuation data V _local of the local area A _local and the slope fluctuation data V _region of the control area A _region is expressed as follows.
ΔV=V _local −V _region
= {V _local-T1 −V _region-T1 , V _local-T2 −V _region-T2 , ..., V _local-Tk −V _region-Tk }

The first condition regarding the magnitude of the difference ΔV is defined as follows using the time-series average ΔVavg of the difference ΔV and the time-series standard deviation σΔV of the difference ΔV.
First condition: ΔVavg>2σΔV
Here, the time series average ΔVavg is the average value of V _local-T1 −V _region-T1 , V _local-T2 −V _region-T2 , . . . , V _local-Tk −V _region-Tk . Further, the time series standard deviation σΔV is the standard deviation of V _local-T1 −V _region-T1 , V _local-T2 −V _region-T2 , . . . , V _local-Tk −V _region-Tk .

The time-series average ΔVavg calculated as described above indicates how much the ground deformation in the area of interest P differs from the ground deformation in the control area A _region around the area of interest P. In this embodiment, when the first condition that the time series average ΔVavg is larger than twice the time series standard deviation σΔV is satisfied, the area of interest P is set to be a hotspot with large ground surface fluctuations compared to the surrounding area. Extract as a candidate.

The first filter unit 14b extracts the slope fluctuation data of the area of interest P extracted by the difference analysis unit 14a as satisfying the first condition, and the slope fluctuation data of the area of interest P and the area of interest of a plurality of predetermined unit areas included in the region of interest. By extracting an area of interest P in which the magnitude of variation in the area of interest P satisfies the second condition based on the slope variation data, candidates for variation hot spots are narrowed down.

For example, the first filter unit 14b selects k slopes sampled from the time-series slope of the slope fluctuation data _Vslope as described above for the area of interest P extracted by the difference analysis unit 14a as satisfying the first condition. The average value of the fluctuation data V _slope-T1 to V _slope-Tk is calculated, and this is expressed as V _P avg. The first filter unit 14b also calculates the standard deviation of k pieces of slope fluctuation data V _slope sampled from the time series slope of slope fluctuation data V _slope for all predetermined unit areas included in the region of interest. , this is written as σV _slope .

In this embodiment, the second condition regarding the magnitude of variation in the area of interest P is defined as follows.
Second condition: V _P avg > σV _slope
The first filter section 14b further extracts an area of interest P that satisfies the second condition from among the areas of interest P that have been extracted by the difference analysis section 14a as satisfying the first condition. As a result, only the area of interest P in which unstable ground movement occurs within the region of interest is extracted as a candidate for a movement hot spot.

Here, if the ground is sinking in the area of interest P, the slope change data V _P avg of the area of interest P has a negative value. In this case, the second condition requires that the negative value of the slope variation data V _P avg of the area of interest P be larger in absolute value than the negative value of the standard deviation σV _slope . On the other hand, when the ground in the area of interest P is uplifted, the slope variation data V _P avg of the area of interest P has a positive value. In this case, the second condition requires that the positive value of the slope variation data V _P avg of the area of interest P be larger in absolute value than the positive value of the standard deviation σV _slope .

Here, k pieces of slope fluctuation data sampled from the time-series slope of the slope fluctuation data are used to calculate the slope fluctuation data V _P avg of the area of interest P and the slope fluctuation data of a plurality of predetermined unit areas included in the region of interest. Although an example of calculating the standard deviation σV _slope has been described, the present invention is not limited thereto. For example, V _P and σV _slope may be calculated using slope fluctuation data at the most recent final time point T ₁ . In this case, V _P =V _slope-T1 .

The second filter unit 14c extracts slope fluctuation data of the attention area P extracted by the first filter unit 14b as satisfying the second condition, and slope fluctuation data included in the control area A _region . The change hot spot candidates are further narrowed down by comparing the above and extracting an area of interest P that satisfies the third condition in which the magnitude of variation in the area of interest P is larger than the magnitude of variation in the control area A _region .

For example, the second filter unit 14c defines a third condition in which the magnitude of variation in the area of interest P is larger than the magnitude of variation in the control area A _region , and defines an area of interest that satisfies this third condition. P is extracted as a candidate for a fluctuation hotspot.
Third condition: sign of V _P avg ≠ sign of ΔVavg

For example, when the sign of V _P avg is positive, it means that the ground in the area of interest P is uplifted. At this time, if the sign of ΔVavg is positive, it means that the ground deformation in the local area A _local is larger than the ground deformation in the control area A _region (the area of interest P is rising at a faster rate than the surrounding area). If the sign of ΔVavg is negative, it means that the ground deformation in the local area A _local is smaller than the ground deformation in the control area A _region (the area of interest P is rising at a slower rate than the surrounding area).

Furthermore, when the sign of V _P avg is negative, it means that the ground in the area of interest P is sinking. At this time, if the sign of ΔVavg is negative, it means that the ground deformation in the local area A _local is larger than the ground deformation in the control area A _region (the area of interest P is sinking at a faster rate than the surrounding area). If the sign of ΔVavg is positive, it means that the ground deformation in the local area A _local is smaller than the ground deformation in the control area A _region (the area of interest P is sinking at a slower rate than the surrounding area).

Therefore, the sign of the slope fluctuation data V _P avg of the area of interest _P and the time series average ΔVavg which is the average value of the difference ΔV between the slope fluctuation data V _{local of the local area A local and} the slope fluctuation data V _region of the control area A region. By performing the processing in the second filter unit 14c with the third condition that the signs of P and P are different, only the area of interest P whose fluctuation speed is faster than the surrounding area is extracted as a candidate for a fluctuation hot spot.

Note that a third condition in which the magnitude of variation in the area of interest P is larger than the magnitude of variation in the control area A _region may be defined as follows.
Third condition: V _P avg > V _region avg
Here, V _region avg is the average value of the time-series slope of the slope fluctuation data V _region of the control area A _region , and is expressed as follows.
V _region avg=V _region-T1 +V _region-T2 +...+V _region-Tk /k

The adjacent area extraction unit 14d extracts a plurality of areas of interest P (extracted by the processing of the difference analysis unit 14a, the first filter unit 14b, and the second filter unit 14c), which are extracted as having large fluctuations in the ground surface compared to the surrounding areas. Among the plurality of attention areas P), attention areas P existing at positions within a predetermined distance from each other are extracted, and an area including a set of the extracted attention areas P is extracted as a fluctuation hot spot.

Here, the value of the predetermined distance can be set to a different value depending on the SAR image used as input for the InSAR analysis. For example, when performing Sentinel-1-based InSAR analysis that includes both single-look and multi-look processing using the IPTA (Interferometric Point Target) method, the predetermined distance is, for example, 44 m.

FIG. 7 is a diagram for explaining the processing content of the adjacent area extraction unit 14d. FIG. 7 shows a bird's-eye view of a certain area of the ground surface from above, and the individual positions marked with ■ are the positions of the difference analysis section 14a, the first filter section 14b, and the second filter section 14c. It shows a plurality of areas of interest P ₁ to P ₅ extracted as candidates for fluctuation hot spots through processing. In reality, there may be many more attention areas P that are candidates for fluctuation hot spots, but FIG. 7 shows them in a simplified manner for convenience of explanation.

Among the plurality of attention areas P ₁ to P ₅ shown in FIG. 7, three attention areas P ₁ to P ₃ are located close to each other at intervals within a predetermined distance. On the other hand, the remaining two areas of interest P ₄ and P ₅ are located within a predetermined distance from each other. In this case, the adjacent area extraction unit 14d extracts an area including a set of three areas of interest P ₁ to P ₃ as a fluctuation hot spot HS. The area including the set of the three attention areas P ₁ to P ₃ is a rectangular area that inscribed and surrounds the three attention areas P ₁ to P ₃ , as shown in FIG. 7, for example. The remaining two areas of interest P ₄ and P ₅ are not extracted as variable hot spots HS. That is, attention areas P ₄ and P ₅ that are highly likely to have no spatial relationship with other attention areas P are excluded from the variable hot spots HS.

Here, the adjacent area extraction unit 14d extracts a set of the plurality of attention areas P only when there are a predetermined number (for example, 5) or more of the attention areas P existing at close positions within a predetermined distance from each other. The area including the above may be extracted as a fluctuation hotspot HS.

Note that the shape of the area of the fluctuation hot spot HS shown in FIG. 7 is an example, and the shape is not limited thereto. For example, it may be a rectangle, a circle, an ellipse, or another polygon with the minimum area that includes the areas of interest P ₁ to P ₃ .

As described above, by performing the processing of the difference analysis section 14a, the first filter section 14b, the second filter section 14c, and the adjacent area extraction section 14d, the fluctuation hot spot HS with a higher level of reliability can be obtained. Detection becomes possible.

FIG. 8 is a flowchart showing an example of the operation of the ground movement analysis device 10 according to the present embodiment (an example of the processing procedure of the ground movement analysis method). First, the LoS fluctuation data acquisition unit 11 analyzes time-series observation data measured using a synthetic aperture radar, and analyzes the time-series observation data for each of a plurality of predetermined unit areas included in the region of interest and for each of a plurality of predetermined unit times. Obtain LoS fluctuation data (step S1).

Next, the tilt projection unit 12 uses the above-mentioned (Equation 1) based on the information regarding the orientation of the satellite (azimuth angle φ and incident angle λ) and the information regarding the tilt characteristics of the region of interest (tilt angle S and aspect angle A). Accordingly, the LoS fluctuation data acquired by the LoS fluctuation data acquisition unit 11 is converted into slope fluctuation data (step S2).

Next, the offset correction section 13 converts each of the tilt variation data obtained for each of the plurality of predetermined unit areas by the tilt projection section 12 using the average value of the tilt variation data obtained for each of the plurality of predetermined unit areas. Offset correction is performed (step S3).

Next, the difference analysis unit 14a analyzes the difference between the slope fluctuation data of the local area A _local including the area of interest P and the slope fluctuation data of the control area A _region , which is wider than the local area A _local , and determines the magnitude of the difference. An area of interest P that satisfies the first condition is extracted as a variable hot spot candidate (step S4).

Next, with respect to the area of interest P extracted by the difference analysis unit 14a as satisfying the first condition, the first filter unit 14b extracts the tilt variation data of the area of interest P and a plurality of predetermined units included in the area of interest. Based on the area slope variation data, an area of interest P in which the magnitude of variation in the area of interest P satisfies the second condition is extracted (step S5).

Furthermore, the second filter unit 14c extracts the slope fluctuation data of the area of interest P extracted by the first filter unit 14b as satisfying the second condition, and the slope included in the control area A _region . The area of interest P is compared with the variation data, and an area of interest P that satisfies the third condition in which the variation of the area of interest P is larger than the magnitude of variation of the control area A _region is extracted (step S6).

Finally, the adjacent area extraction unit 14d extracts attention areas P that are located at intervals within a predetermined distance from each other from among the plurality of attention areas P extracted by the second filter unit 14c, and The area including the set of areas P is extracted as a fluctuation hotspot (step S7). With the above steps, the processing of the flowchart shown in FIG. 8 is completed.

As described in detail above, in this embodiment, the LoS fluctuation data obtained for each of a plurality of predetermined unit areas included in the region of interest is Convert the slope fluctuation data in the direction along the slope direction of the area, and convert each of the slope fluctuation data obtained for each of the plurality of predetermined unit areas to the average value of the slope fluctuation data obtained for each of the plurality of predetermined unit areas. Use this to correct the offset. Then, using the offset-corrected slope variation data, areas where the ground surface variation is larger than the surrounding area are extracted as variation hot spots.

According to this embodiment configured in this way, LoS variation data observed in an area with undulating topography is converted to slope variation data in a direction along the slope direction of the undulations, and further noise is reduced by offset correction. After these data components are removed, fluctuation hotspots are extracted from areas with large ground surface fluctuations. As a result, even in areas with undulating topography, the state of ground deformation can be accurately captured using observation data from synthetic aperture radar.

Furthermore, in the above embodiment, as the processing of the hot spot extraction section 14, the processing of the difference analysis section 14a, the first filter section 14b, and the second filter section 14c is performed, so that the ground movement is simply less than that of the surrounding area. This makes it possible to extract not only large areas but also areas with unstable ground deformation, where the rate of deformation is faster than the surrounding areas, as candidates for deformation hotspots. Further, as the processing of the hot spot extracting section 14, the processing of the adjacent area extracting section 14d is also performed, so that areas that are likely to have no spatial relationship can be excluded from candidates for variable hot spots. This makes it possible to detect fluctuation hotspots with a higher level of reliability.

Note that the hot spot extraction section 14 does not necessarily need to perform all the processing of the difference analysis section 14a, first filter section 14b, second filter section 14c, and adjacent area extraction section 14d. For example, only the processing of the difference analysis section 14a may be performed. In this case, the hot spot extracting unit 14 extracts an area including a set of areas of interest P in which the magnitude of the difference satisfies the first condition as a variable hot spot HS. In this case, the area is not processed by the adjacent area extraction unit 14d, so there is a possibility that the area exists in multiple locations. The processing of the difference analysis section 14a and the processing of the adjacent area extraction section 14d may be performed in combination so that the extraction locations of the fluctuating hot spots HS are not dispersed.

Alternatively, the processing of the difference analysis section 14a and the processing of any one or two of the first filter section 14b, the second filter section 14c, and the adjacent area extraction section 14d may be performed in combination. For example, the processing by the difference analysis section 14a and the processing by the first filter section 14b may be performed. In this case, the hot spot extraction unit 14 extracts an area including the set of attention areas P extracted by the first filter unit 14b as a fluctuating hot spot HS. In this case, there is also a possibility that the area will exist in multiple locations. The processing of the difference analysis section 14a, the processing of the first filter section 14b, and the processing of the adjacent area extraction section 14d may be performed in combination so that the extraction locations of the fluctuating hot spots HS are not dispersed.

Further, the processing by the difference analysis section 14a and the processing by the second filter section 14c may be performed. In this case, with respect to the area of interest P extracted by the difference analysis unit 14a as satisfying the first condition, the second filter unit 14c compares the slope variation data of the area of interest P and the slope variation data of the control area A _region . and extracts an area of interest P that satisfies the third condition in which the variation in the area of interest P is greater than the magnitude of variation in the control area A _region . The hot spot extraction unit 14 extracts an area including the set of attention areas P extracted by the second filter unit 14c as a fluctuating hot spot HS. In this case, there is also a possibility that the area will exist in multiple locations. The processing of the difference analysis section 14a, the processing of the second filter section 14c, and the processing of the adjacent area extraction section 14d may be performed in combination so that the extraction locations of the fluctuating hot spots HS are not dispersed.

Alternatively, the processing of the adjacent area extraction section 14d may be omitted, and the processing of the difference analysis section 14a, the first filter section 14b, and the second filter section 14c may be performed. In this case, the hot spot extracting unit 14 extracts an area including the set of attention areas P extracted by the second filter unit 14c as a fluctuating hot spot HS.

In addition, the above-mentioned embodiments are merely examples of implementation of the present invention, and the technical scope of the present invention should not be construed to be limited thereby. That is, the present invention can be implemented in various forms without departing from its gist or main features.

10 Ground deformation analysis device 11 LoS fluctuation data acquisition unit 12 Incline projection unit 13 Offset correction unit 14 Hot spot extraction unit 14a Difference analysis unit 14b First filter unit 14c Second filter unit 14d Proximity area extraction unit

Claims (7)

LoS is data that analyzes time-series observation data measured using synthetic aperture radar and shows changes in the line-of-sight direction when looking at the ground surface from a satellite for each of multiple predetermined unit areas included in the region of interest. an LoS fluctuation data acquisition unit that acquires fluctuation data;
Based on the information regarding the orientation of the satellite and the information regarding the tilt characteristics of the region of interest, the LoS fluctuation data acquired by the LoS fluctuation data acquisition unit is converted into tilt fluctuation data in a direction along the tilt direction of the region of interest. a tilted projection section for converting;
an offset correction unit that offsets and corrects each of the slope fluctuation data obtained for each of the plurality of predetermined unit areas using an average value of the slope fluctuation data obtained for each of the plurality of predetermined unit areas;
a hotspot extracting unit that extracts an area where the ground surface has a large variation compared to the surrounding area as a variation hotspot using the slope variation data offset-corrected by the offset correction unit; Analysis device. The above hot spot extraction section is
One of the predetermined unit areas is set as an area of interest, and the difference between the slope variation data of a local area that includes the area of interest and the slope variation data of a control area that includes the local area and is wider than the local area is analyzed. Equipped with a differential analysis section,
2. The ground deformation analysis device according to claim 1, wherein an area including a set of said areas of interest where the magnitude of said difference satisfies a first condition is extracted as said deformation hot spot. The above hot spot extraction section is
Regarding the area of interest extracted by the difference analysis unit as satisfying the first condition, the slope variation data of the area of interest and the slope variation data of a plurality of predetermined unit areas included in the region of interest are further comprising a first filter unit that extracts the area of interest that satisfies a second condition in which the magnitude of variation in the area of interest is large based on the area;
3. The ground deformation analysis device according to claim 2, wherein an area including the set of the areas of interest extracted by the first filter section is extracted as the deformation hot spot. The above hot spot extraction section is
Regarding the area of interest extracted by the difference analysis unit as satisfying the first condition, the slope variation data of the area of interest is compared with the slope variation data included in the control area, and the area of interest is further comprising a second filter unit that extracts the area of interest that satisfies a third condition in which the variation is larger than the variation in the control area;
3. The ground deformation analysis device according to claim 2, wherein an area including the set of the areas of interest extracted by the second filter section is extracted as the deformation hot spot. The above hot spot extraction section is
Regarding the area of interest extracted by the first filter unit as satisfying the second condition, the slope variation data of the area of interest is compared with the slope variation data included in the control area, and the area of interest is further comprising a second filter unit that extracts the area of interest that satisfies a third condition in which the variation in the area is larger than the variation in the control area;
4. The ground deformation analysis device according to claim 3, wherein an area including the set of the areas of interest extracted by the second filter section is extracted as the deformation hot spot. The above hot spot extraction section is
further comprising a proximate area extracting unit that extracts the areas of interest that are located at intervals within a predetermined distance from each other among the plurality of areas of interest that are extracted as having large ground surface fluctuations compared to the surrounding area;
The ground deformation analysis device according to any one of claims 2 to 5, wherein an area including the set of the areas of interest extracted by the adjacent area extraction unit is extracted as the deformation hot spot. The LoS fluctuation data acquisition unit of the ground deformation analysis device analyzes time-series observation data measured using synthetic aperture radar, and views the ground surface from the satellite for each of multiple predetermined unit areas included in the region of interest. a first step of acquiring LoS fluctuation data, which is data indicating fluctuations in the line of sight direction;
The tilt projection unit of the ground deformation analysis device converts the LoS fluctuation data acquired by the LoS fluctuation data acquisition unit into the area of interest based on information regarding the orientation of the satellite and information regarding the slope characteristics of the region of interest. a second step of converting into slope variation data in a direction along the slope direction;
The offset correction unit of the ground deformation analysis device calculates each of the slope fluctuation data obtained for each of the plurality of predetermined unit areas using the average value of the slope fluctuation data obtained for each of the plurality of predetermined unit areas. a third step of correcting the offset;
A fourth method in which the hot spot extracting unit of the ground deformation analysis device extracts an area where the ground surface fluctuation is larger than the surrounding area as a fluctuation hot spot using the slope fluctuation data offset-corrected by the offset correction unit. A ground deformation analysis method characterized by comprising steps.