Ground spring water evaluation method and ground spring water evaluation system

Abstract

To provide a ground spring water evaluation method and a ground spring water evaluation system which quantitatively evaluate the amount of spring water springing from the ground.SOLUTION: A ground spring water evaluation method includes steps of: acquiring an image of at least two or more different wavelengths of 650 nm or more and 1100 nm or less of reflection light reflected from a face 2 serving as an evaluation object, to which light in a predetermined wavelength band is applied; and calculating a spring water amount index value having correlation with the amount of spring water on the basis of a signal level in the image of at least the two or more different wavelengths.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a ground spring water evaluation method and a ground spring water evaluation system. [Background technology]

[0002] Patent Document 1 discloses an evaluation system that evaluates the state of water springing up at a tunnel face based on temperature distribution information and near-infrared intensity distribution information at the tunnel face. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2010-101799 A Summary of the Invention [Problem to be solved by the invention]

[0004] According to the evaluation system described in Patent Document 1, it is possible to identify the location and area of ​​the spring water generation area at the face. On the other hand, the evaluation system described in Patent Document 1 does not identify the amount of spring water at the face, so the amount of spring water is still estimated by workers visually. However, such subjective evaluation by workers is prone to individual differences, so it cannot be said to be suitable as an index for considering the safety of work at the face.

[0005] An object of the present invention is to provide a method and system capable of quantitatively evaluating the amount of spring water gushing out from the ground. [Means for solving the problem]

[0006] The present invention is a ground spring water evaluation method for evaluating the amount of spring water that springs out from the ground, and includes the steps of acquiring images of at least two or more different wavelengths of light between 650 nm and 1100 nm inclusive, which are reflected from the ground to be evaluated when light of a predetermined wavelength band is irradiated thereon, and calculating a spring water volume index value that is correlated with the amount of spring water based on the signal levels in the images of the at least two or more different wavelengths.

[0007] The present invention also relates to a ground spring water evaluation system for evaluating the amount of spring water that springs out from the ground, and includes a light source that irradiates light of a predetermined wavelength band onto the ground to be evaluated, an imaging device that can capture the light reflected by the ground, and a calculation device that determines the amount of spring water based on the signal level in an image of a wavelength of 650 nm or more and 1100 nm or less captured by the imaging device, and the calculation device calculates a spring water volume index value that is correlated with the amount of spring water based on the signal levels in the image of at least two or more different wavelengths. Effect of the Invention

[0008] According to the present invention, the amount of spring water welling up from the ground can be quantitatively evaluated. [Brief description of the drawings]

[0009] [Figure 1] 1 is a schematic diagram showing an example of ground to be evaluated by a ground spring water evaluation method according to an embodiment of the present invention. FIG. [Diagram 2] 1 is a block diagram of a ground spring water evaluation system according to an embodiment of the present invention. [Diagram 3] FIG. 1 is a diagram showing an example of spring water gushing out from the ground. [Figure 4] FIG. 4 is a cross-sectional view showing a cross section of the spring water taken along line AA in FIG. 3. [Diagram 5] FIG. 13 is a diagram for explaining a method for measuring light absorption characteristics. [Figure 6] 13 is a graph for explaining a difference in absorptivity. [Figure 7] 1 is a flowchart showing a processing procedure performed in a first embodiment of a subsurface seepage evaluation method according to an embodiment of the present invention. [Figure 8] 11 is a graph for explaining the calculation process of the spring water quantity index value. [Figure 9] FIG. 13 is a diagram showing an example of visualization of a spring water volume index value. [Figure 10] 13 is a graph for explaining the correlation between the spring water volume index value and the spring water thickness. [Figure 11] 10 is a flowchart showing a processing procedure performed in a second embodiment of a subsurface seepage evaluation method according to an embodiment of the present invention. [Figure 12] 11 is a diagram for explaining the calculation process of the spring water quantity index value. FIG. [Figure 13] FIG. 13 is a diagram showing an example of visualization of a spring water volume index value. [Figure 14] 13 is a graph for explaining the correlation between the spring water volume index value and the spring water thickness. [Figure 15] 13 is another graph for explaining the correlation between the spring water volume index value and the spring water thickness. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, a ground spring water evaluation system 100 and a ground spring water evaluation method according to an embodiment of the present invention will be described with reference to the drawings.

[0011] The ground spring water evaluation system 100 according to an embodiment of the present invention is a system for quantitatively evaluating the amount of spring water that springs from the ground. For example, as shown in FIG. 1, the system evaluates the amount of spring water that springs from a face 2 (ground), which is the excavation surface when constructing a tunnel 1 by the NATM method.

[0012] In the following, a case will be described in which the ground seepage evaluation system 100 is used to evaluate the amount of spring water that springs out at a face 2 (ground) as shown in Fig. 1. Note that the ground to be evaluated by the ground seepage evaluation system 100 is not limited to a bedrock such as the face 2, but may be any ground exposed by excavation, and may be, for example, an excavation surface other than the face 2 in the tunnel 1, or a cut slope.

[0013] In the NATM method, which is mainly used for constructing mountain tunnels and the like, a tunnel 1 as shown in Figure 1 is constructed by repeating the blasting process, the debris removal process, the primary concrete spraying process, the support construction process, and the secondary concrete spraying process every 1 to 3 meters.

[0014] In processes after the shoring construction process, workers may perform work near the face 2, so in order to increase the safety of the work, the face 2 is stabilized in advance by, for example, spraying concrete material or driving bolts into the face 2, which has a relatively large amount of welling water and is at risk of spalling.

[0015] In spraying concrete material against the face 2 or driving bolts into it, it is necessary to evaluate the amount of spring water gushing out from the face 2, and this evaluation is carried out, for example, by a worker estimating it visually or by measuring the flow rate of the spring water collected in a gutter. However, subjective evaluation by a worker is prone to individual differences even when the worker is highly skilled, so it cannot be said to be suitable as an index for considering measures to prevent spalling on the entire face 2, and furthermore, work near the face 2, such as actually measuring the flow rate of spring water, is dangerous and should be avoided.

[0016] In this embodiment, therefore, a ground spring water evaluation system 100 described below makes it possible to quantitatively evaluate the amount of spring water springing out at the tunnel face 2 (ground).

[0017] As shown in Figures 1 and 2, the ground seepage evaluation system 100 includes a light source 24 that irradiates light of a predetermined wavelength band onto the face 2 (ground) to be evaluated, an imaging device 20 that can capture the light reflected by the face 2, and a calculation device 10 that calculates the amount of ground seepage based on the signal level in the image captured by the imaging device 20.

[0018] The imaging device 20 is a general digital camera with a built-in image sensor such as a CCD (Charge Coupled Device) or a CMOS (Complementary Metal Oxide Semiconductor), and is a digital imaging device that can image and digitize the signal level, which is the output intensity of the image sensor that receives light of any wavelength in the near-infrared region and visible light region of 1100 nm or less, by switching optical filters. The imaging device 20 can image a predetermined wavelength even from a position 5 m or more away from the face 2 (ground) to be evaluated, but in order to improve the light receiving accuracy, it is better to install it as close to the face 2 as possible. On the other hand, from the viewpoint of safety, it is desirable to install it at a position away from the face 2, so the imaging device 20 is installed at a position, for example, 3 m or more away.

[0019] The imaging device 20 is connected to the calculation device 10 by wire or wirelessly, and an image of the face 2 captured by the imaging device 20 is sent to an image processing unit 11 (described later) of the calculation device 10. The imaging device 20 may be composed of a single camera capable of switching the wavelength to be captured, or may be composed of multiple single-wavelength cameras specialized in capturing images of specific wavelengths. The image sensor built into the imaging device 20 is not limited to CCD or CMOS.

[0020] The light source 24 is a halogen lamp capable of irradiating light including light in a wavelength band of 1100 nm or less toward the face 2 (ground) to be evaluated, and is installed together with the imaging device 20 at a position 3 to 5 m or more away from the face 2 (ground) to be evaluated. The light source 24 is not limited to a halogen lamp, and may be any type of lighting device capable of irradiating light including light in a wavelength band of 1100 nm or less. It is preferable that a plurality of light sources 24 are provided in order to irradiate the entire face 2 to be evaluated with light.

[0021] The calculation device 10 has an image processing unit 11 that performs processing such as normalization on the image captured by the imaging device 20, a calculation unit 12 that calculates a spring water volume index value, described below, based on the processed image, an evaluation unit 13 that evaluates the volume of spring water springing out at the face 2 (ground) based on the calculation results of the calculation unit 12, and a memory unit 14 that stores calculation formulas, judgment criteria, etc. used in the calculation unit 12 and the evaluation unit 13, as well as the calculation results and evaluation results.

[0022] Specifically, the arithmetic device 10 is composed of a microcomputer equipped with a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), and an I / O interface (Input / Output Interface). The RAM stores data for CPU processing, the ROM stores control programs and the like for the CPU in advance, and the I / O interface is used for inputting and outputting information to and from the imaging device 20 and the display unit 30 connected to the arithmetic device 10. The RAM and ROM correspond to a storage unit 14. Note that the image processing unit 11, the arithmetic unit 12, and the evaluation unit 13 are shown as virtual units representing the respective functions of the arithmetic device 10, and are not meant to exist physically.

[0023] The display unit 30 is a display device capable of displaying the calculation results calculated by the calculation unit 12 of the calculation device 10, the evaluation results evaluated by the evaluation unit 13, the images captured by the imaging device 20, etc. The display unit 30 may include an input unit capable of inputting various commands to the calculation device 10 and the imaging device 20.

[0024] Next, the spring water gushing out at the face 2 (ground) to be evaluated will be explained with reference to Figures 3 and 4. Figure 3 shows the state in which the spring water gushing out from one spring point S at the face 2 flows downward, and Figure 4 shows a cross section along line AA in Figure 3, i.e., a cross section of the flowing down spring water. Note that in reality, the surface of the face 2 (ground) to be evaluated has irregularities, and spring water tends to gush out from multiple points such as cracks, but for the sake of explanation, it is shown in a simplified manner.

[0025] As shown in Fig. 3, generally, spring water gushing out from a spring point S at the tunnel face 2 spreads horizontally and flows vertically downward along the tunnel face 2. As shown in Fig. 4, the thickness t of the flowing down spring water varies in the horizontal direction.

[0026] In addition, since the average flow velocity of a fluid along a wall generally changes depending on the thickness of the fluid, it is thought that the average flow velocity of the spring water flowing down also changes depending on the thickness t of the spring water.

[0027] Therefore, by experimentally determining the average flow velocity of the spring water according to the thickness t of the spring water in advance, and by determining the thickness t of the spring water flowing along the face 2 and the horizontal length of the spring water, it is possible to calculate the flow rate of the spring water flowing, for example, through a cross section along line AA in Figure 3, i.e., a cross section along the horizontal direction.

[0028] Here, as a method for detecting the presence or absence of moisture or the amount of moisture, a method utilizing the property of water absorbing light in the near infrared region is known, but the known method generally utilizes a wavelength of about 2000 nm where the light absorption property of water is prominent. However, sensors capable of detecting wavelengths of about 2000 nm, particularly sensors capable of imaging, are relatively expensive.

[0029] On the other hand, even for light with wavelengths of 1100 nm or less, which can be imaged using relatively inexpensive image sensors such as CCD and CMOS, the absorption characteristics of water vary depending on the wavelength.

[0030] For example, as shown in FIG. 5, when light is irradiated from the above-mentioned light source 24 to water in a container C and the reflected light is imaged by the above-mentioned imaging device 20, it can be seen that the signal level in the image of wavelengths of about 700 to 1000 nm changes for each wavelength according to the thickness t of the water in the container C, as shown in the graph of FIG. 6. In particular, it is clear that there is a difference in the degree of decrease in the signal level relative to the thickness t of the water, that is, the absorptivity absorbed by the water, between any two wavelengths with a difference of about 50 to 100 nm. Note that the horizontal axis of the graph shown in FIG. 6 is the wavelength imaged by the imaging device 20, and the vertical axis is the ratio of the signal level based on the case where there is no water in the container C.

[0031] In this embodiment, the difference in the light absorption characteristics of water is utilized to obtain the signal level obtained by imaging at least two or more different wavelengths between 650 nm and 1100 nm, and the thickness t of the spring water is calculated based on the signal level, and the amount of spring water is ultimately evaluated.

[0032] Next, a first embodiment of a ground spring water evaluation method performed using the above-mentioned ground spring water evaluation system 100 will be described with reference to Fig. 7 to Fig. 10. Fig. 7 is a flow chart showing the processing procedure performed in the first embodiment of the ground spring water evaluation method, Fig. 8 is a three-dimensional graph for explaining the calculation process of the spring water quantity index value, Fig. 9 is an example of an image of the spring water quantity index value, and Fig. 10 is a graph for explaining the correlation between the spring water quantity index value and the spring water thickness.

[0033] First, in step S10, the imaging device 20 captures an image of the tunnel face 2 (ground) to be evaluated.

[0034] The face 2 is irradiated with light including light in a wavelength band of 1100 nm or less from the light source 24, and the imaging device 20 acquires images of three different wavelengths of 650 nm or more and 1100 nm or less.

[0035] Specifically, in step S10, three image data are acquired: an image P1 with a first wavelength λ1 (e.g., 750 nm), an image P2 with a second wavelength λ2 (e.g., 850 nm) that is 100 nm longer than the first wavelength λ1, and an image P3 with a third wavelength λ3 (e.g., 950 nm) that is 100 nm longer than the second wavelength λ2. The difference between the wavelengths is not limited to 100 nm and may be about 50 nm, but it is preferable to provide a difference of about 100 nm in order to fully utilize the difference in the light absorption characteristics of water. In addition, it is preferable that the third wavelength λ3, which is the longest of the three different wavelengths, is 900 nm or more, at which the absorptivity of light absorbed by water (see FIG. 6) is relatively high.

[0036] Next, in step S11, the three images P1, P2, and P3 are normalized.

[0037] Each pixel of each image P1, P2, P3 has a pixel value that is a signal level, which is the output intensity of the image sensor receiving light, but since these are absolute values, it is difficult to simply compare the signal levels of each image P1, P2, P3. Therefore, in order to make the signal levels of each image P1, P2, P3 a relative value, in step S11, the signal levels of all pixels in the images P1, P2, P3 are converted to values ​​in the range from 0 to 1 so that the pixel value after normalization of the pixel with the highest signal level is 1 and the pixel value after normalization of the pixel with the lowest signal level is 0 for each image P1, P2, P3.

[0038] Once normalization is complete, the process proceeds to step S12, where the signal levels at each pixel of the three normalized images P1, P2, and P3 are treated as one vector to generate a composite vector.

[0039] Specifically, the signal levels of pixels at the same coordinate position in each of the images P1, P2, and P3 are extracted, and a composite vector is generated using a Spectral Angle Mapper (SAM) in which the X-axis value is the signal level of the first wavelength λ1, the Y-axis value is the signal level of the second wavelength λ2, and the Z-axis value is the signal level of the third wavelength λ3, as shown in Fig. 8. Note that the same number of composite vectors are generated as the number of pixels.

[0040] Then, in step S13, a reference vector to be compared with the composite vector is set.

[0041] The reference vector is a composite vector obtained by combining signal levels at points in the face 2 (ground) to be evaluated where it is estimated that there is no spring water, and is selected from the composite vectors generated in step S12.

[0042] Specifically, in areas where there is no spring water, none of the wavelengths are absorbed, and it is expected that the value obtained by normalization in step S11 will be 1 or a value closest to 1. Therefore, of the composite vectors generated in step S12, the one with the largest magnitude is selected as the reference vector.

[0043] The method of selecting the reference vector is not limited to this, and a pixel in an image captured by the imaging device 20 at a location where spring water is estimated not to exist may be extracted, and a composite vector obtained by combining the signal levels of the wavelengths λ1, λ2, and λ3 at the pixel may be set as the reference vector. Also, instead of selecting the reference vector from the composite vector, a vector in which the signal levels of the wavelengths λ1, λ2, and λ3 are 1 may be set as the reference vector.

[0044] Next, in step S14, a spring water quantity index value is calculated based on each resultant vector and the reference vector.

[0045] Specifically, the cosine value calculated from the angle θ between the resultant vector and the reference vector is calculated as the spring water amount index value. Note that, like the resultant vector, the spring water amount index value is calculated for each pixel.

[0046] Here, when there is almost no spring water and the thickness t of the spring water is thin, almost none of the wavelengths λ1, λ2, and λ3 are absorbed, so the angle θ between the composite vector and the reference vector becomes small, and the cosine value calculated from the angle θ, which is used as the spring water volume index value, becomes close to 1.

[0047] On the other hand, when the thickness t of the spring water is large, each of the wavelengths λ1, λ2, and λ3 is absorbed at a different rate, so that the angle θ between the resultant vector and the reference vector becomes relatively large, and the cosine value calculated from the angle θ, which is used as the spring water volume index value, approaches 0.

[0048] In this way, the cosine value calculated from the angle θ between the resultant vector and the reference vector is an index value that indicates the degree of correlation between the spring water thickness t and the spring water volume. Note that since the light absorption characteristics are different for the three wavelengths λ1, λ2, and λ3 (see Figure 6), when the spring water thickness t is relatively thick, the direction of the resultant vector does not coincide with the direction of the reference vector.

[0049] When the spring water volume index value (cosθ) calculated in this way is visualized for the area indicated by arrow B in Fig. 3, for example, the area where the spring water thickness t is relatively thick is shown as an area with a small spring water volume index value, and as the spring water thickness t becomes thinner, the spring water volume index value becomes gradually larger (closer to 1), as shown in Fig. 9. Therefore, by visualizing the spring water volume index value calculated in step S14, it is possible to grasp the position of the spring water point S from which the spring water gushes out and the extent to which the spring water spreads at the face 2.

[0050] In the next step S15, the thickness t of the spring water is calculated from the spring water volume index value.

[0051] In order to calculate the thickness t of the spring water, the correlation between the spring water volume index value and the thickness t of the spring water shown in FIG. 10 is obtained in advance.

[0052] The correlation between the spring water volume index value and the thickness t of the spring water is obtained in advance, for example, by using the test device shown in Fig. 5 to obtain the change in the signal level of each wavelength λ1, λ2, λ3 when the water thickness t is gradually increased from zero, and calculating a value equivalent to the spring water volume index value in the same manner as in step S14. Note that the wavelengths λ1, λ2, λ3 of the three images acquired in step S10 are the same as the wavelengths λ1, λ2, λ3 used in obtaining the correlation between the spring water volume index value and the thickness t of the spring water shown in Fig. 10.

[0053] Once the spring water thickness t is obtained from the correlation between the spring water volume index value and the spring water thickness t, the spring water volume is calculated in the next step S16.

[0054] The spring water volume, which is the flow rate per unit time, is calculated by multiplying the spring water thickness t for each pixel calculated in step S15 by the actual length of each pixel, calculated by converting the horizontal length of each pixel into the length at the face 2, and the average flow velocity of the spring water flowing along the face 2, and then integrating the calculated spring water volume in the horizontal direction to calculate the spring water volume flowing along the cross section along line AA in Fig. 3. Note that since the average flow velocity of the spring water flowing along the face 2 differs depending on the spring water thickness t, the average flow velocity of the spring water according to the spring water thickness t is experimentally calculated in advance and compiled into a map or table.

[0055] When the amount of spring water is calculated in this manner, in the next step S17, an evaluation value of the calculated amount of spring water relative to a preset reference amount of spring water is obtained.

[0056] The evaluation result of the amount of spring water obtained through the above-mentioned calculation process is displayed on the display unit 30 together with the image captured by the imaging device 20 and image data in the calculation process.

[0057] This completes the ground spring water evaluation method performed using the ground spring water evaluation system 100, and the worker can grasp the quantitatively evaluated state of spring water at the entire tunnel face 2 (ground) by looking at the content displayed on the display unit 30. The quantitatively determined evaluation value of the amount of spring water is used as an index for considering measures against spalling at the entire tunnel face 2. The content displayed on the display unit 30 may be displayed on a terminal carried by the worker.

[0058] As described above, according to the first embodiment of the ground spring water evaluation method, it is possible to obtain a spring water volume index value (cosine value) that is correlated with the volume of spring water flowing on the surface of the face 2 (ground) from the signal levels in images P1, P2, and P3 of three different wavelengths λ1, λ2, and λ3, and as a result, the volume of spring water springing out from the face 2 (ground) can be quantitatively evaluated.

[0059] In the first embodiment, the composite vector is three-dimensionally synthesized from the signal levels of three different wavelengths λ1, λ2, and λ3, but the composite vector may be two-dimensionally synthesized from the signal levels of two different wavelengths λ1 and λ2. In this case, the cosine value calculated from the angle θ between the composite vector and the reference vector is calculated as the spring water volume index value. In order to improve the estimation accuracy of the spring water volume, it is preferable to synthesize the composite vector from the signal levels of three different wavelengths λ1, λ2, and λ3.

[0060] In the first embodiment, the composite vector is generated for the number of pixels in each of the images P1, P2, and P3, but the composite vector may be generated only for pixels within a predetermined range in each of the images P1, P2, and P3. For example, if the face 2 (ground) to be evaluated has an area where the light from the light source 24 does not reach sufficiently, the area where the light from the light source 24 reaches sufficiently may be set as the evaluation object, and the composite vector may be generated only for pixels captured within this area.

[0061] Next, a second embodiment of the ground spring water evaluation method performed using the above-mentioned ground spring water evaluation system 100 will be described with reference to Fig. 11 to Fig. 14. Fig. 11 is a flow chart showing the processing procedure performed in the second embodiment of the ground spring water evaluation method, Fig. 12 is a diagram for explaining the calculation process of the spring water quantity index value, Fig. 13 is an example of an image of the spring water quantity index value, and Fig. 14 is a graph for explaining the correlation between the spring water quantity index value and the spring water thickness.

[0062] First, in step S20, the imaging device 20 captures an image of the tunnel face 2 (ground) to be evaluated.

[0063] The face 2 is irradiated with light including light in a wavelength band of 1100 nm or less from the light source 24, and the imaging device 20 acquires images of two different wavelengths, 650 nm or more and 1100 nm or less.

[0064] Specifically, in step S20, two pieces of image data are acquired: an image P1 with a first wavelength λ1 (e.g., 750 nm) and an image P2 with a second wavelength λ2 (e.g., 950 nm) that is 200 nm longer than the first wavelength λ1. The difference in wavelength is not limited to 200 nm and may be about 100 nm, but it is preferable to provide a difference of about 200 nm in order to fully utilize the difference in the light absorption characteristics of water. In addition, of the two different wavelengths, it is preferable that the second wavelength λ2, which has a longer wavelength, is 900 nm or more, which has a relatively high light absorption coefficient (see FIG. 6), while the first wavelength λ1, which has a shorter wavelength, is 800 nm or less, which has a relatively low light absorption coefficient (see FIG. 6).

[0065] Next, in step S21, the two images P1 and P2 are normalized.

[0066] Each pixel in the two images P1 and P2 has a signal level, which is the output intensity of the image sensor receiving light, as a pixel value, but since these are absolute values, it is difficult to simply compare the signal levels of each image P1 and P2. Therefore, in order to make the signal levels of each image P1 and P2 a relative value, in step S21, the signal levels of all pixels in the images P1 and P2 are converted to values ​​in the range from 0 to 1 so that the pixel value after normalization of the pixel with the highest signal level is 1 and the pixel value after normalization of the pixel with the lowest signal level is 0 for each image P1 and P2.

[0067] When normalization is completed, the process proceeds to step S22, where the difference in signal level at each pixel of the two normalized images P1, P2 is calculated.

[0068] Specifically, for example, as shown in Fig. 12, the difference in signal levels of pixels at the same coordinate positions in each image P1, P2 is calculated as the spring water amount index value by subtracting the normalized signal level value m of the image P2 of the second wavelength λ2 from the normalized signal level value n of the image P1 of the first wavelength λ1. Note that the signal level difference is calculated for the number of pixels.

[0069] Here, when there is almost no spring water and the thickness t of the spring water is thin, almost none of the wavelengths λ1 and λ2 are absorbed, so the difference in signal levels calculated as the spring water volume index value will be a value close to zero.

[0070] On the other hand, when the thickness t of the spring water is large, the wavelengths λ1 and λ2 are absorbed at different rates, and the difference in the absorption rates increases as the thickness t of the spring water increases (see Figure 6). Therefore, the difference in signal levels calculated as the spring water volume index value becomes greater than 0 depending on the thickness t of the spring water.

[0071] In this way, the difference in signal levels found from images P1 and P2 at two different wavelengths λ1 and λ2 is an index value that indicates the degree of spring water thickness t that is correlated with the spring water volume. Note that the two wavelengths λ1 and λ2 have different light absorption characteristics, and in particular, the first wavelength λ1 has a relatively low light absorption coefficient and the second wavelength λ2 has a relatively high light absorption coefficient, so that as the spring water thickness t increases, the difference does not approach 0 but gradually increases.

[0072] When the spring water volume index value (difference in signal levels) calculated in this way is visualized for the area indicated by arrow B in Fig. 3, for example, the area where the spring water thickness t is relatively thick is shown as an area where the spring water volume index value is large, and as the spring water thickness t becomes thinner, the spring water volume index value becomes gradually smaller (closer to 0), as shown in Fig. 13. Therefore, by visualizing the spring water volume index value calculated in step S22, it is possible to grasp the position of the spring water point S where the spring water gushes out and the extent to which the spring water spreads at the face 2.

[0073] In the next step S23, the thickness t of the spring water is calculated from the spring water volume index value.

[0074] In order to calculate the thickness t of the spring water, the correlation between the spring water volume index value and the thickness t of the spring water shown in FIG. 14 is obtained in advance.

[0075] The correlation between the spring water volume index value and the thickness t of the spring water is obtained in advance, for example, by using the test device shown in Fig. 5 to obtain the change in the signal level of each wavelength λ1, λ2 when the water thickness t is gradually increased from zero, and calculating a value equivalent to the spring water volume index value in the same manner as in step S22. Note that the wavelengths λ1, λ2 of the two images acquired in step S20 are the same as the wavelengths λ1, λ2 used in obtaining the correlation between the spring water volume index value and the thickness t of the spring water shown in Fig. 14.

[0076] Once the thickness t of the spring water is determined from the correlation between the spring water volume index value and the thickness t of the spring water, the volume of the spring water is calculated in the next step S24.

[0077] The spring water volume, which is the flow rate per unit time, is calculated by multiplying the spring water thickness t for each pixel calculated in step S23 by the actual length of each pixel, calculated by converting the horizontal length of each pixel into the length at the face 2, and the average flow velocity of the spring water flowing along the face 2, and then integrating the calculated spring water volume in the horizontal direction to calculate, for example, the spring water volume flowing through a cross section along line AA in Fig. 3. Note that since the average flow velocity of the spring water flowing along the face 2 differs depending on the spring water thickness t, the average flow velocity of the spring water according to the spring water thickness t is experimentally calculated in advance and compiled into a map or table.

[0078] When the spring water volume is calculated in this manner, in the next step S25, an evaluation value of the calculated spring water volume relative to a preset reference spring water volume is obtained.

[0079] The evaluation result of the amount of spring water obtained through the above-mentioned calculation process is displayed on the display unit 30 together with the image captured by the imaging device 20 and image data in the calculation process.

[0080] This completes the ground spring water evaluation method performed using the ground spring water evaluation system 100, and the worker can grasp the quantitatively evaluated state of spring water at the entire tunnel face 2 (ground) by looking at the content displayed on the display unit 30. The quantitatively determined evaluation value of the amount of spring water is used as an index for considering measures against spalling at the entire tunnel face 2. The content displayed on the display unit 30 may be displayed on a terminal carried by the worker.

[0081] As described above, according to the second embodiment of the ground spring water evaluation method, it is possible to obtain a spring water volume index value (difference in signal levels) that correlates with the volume of spring water flowing on the surface of the face 2 (ground) from the signal levels in images P1, P2 of two different wavelengths λ1, λ2, and as a result, the volume of spring water springing out from the face 2 (ground) can be quantitatively evaluated.

[0082] In the above second embodiment, the difference in signal levels is calculated as the spring water volume index value, but the value obtained by dividing the difference in signal levels by the wavelength difference (λ2-λ1) between two different wavelengths λ1 and λ2 may also be calculated as the spring water volume index value.

[0083] If the difference in signal levels is used as the spring water volume index value, the wavelengths λ1, λ2 of the two images P1, P2 must be the same size as the two wavelengths λ1, λ2 used when previously determining the correlation between the spring water volume index value and the spring water thickness t, and the imaging device 20 is limited to capturing images of the two specified wavelengths λ1, λ2.

[0084] On the other hand, when the spring water volume index value is the value obtained by dividing the difference in signal level by the wavelength difference (λ2-λ1), the correlation between the spring water volume index value and the thickness t of the spring water shown in Fig. 15 can be obtained, for example, by using the test device shown in Fig. 5 to determine the change in signal level of two arbitrary wavelengths λn and λm when the water thickness t is gradually increased from zero, and by dividing the difference in signal level obtained by the wavelength difference (λm-λn) between the two arbitrary wavelengths. Note that Fig. 15 is a graph showing the correlation between the spring water volume index value and the thickness of the spring water, similar to Fig. 14.

[0085] In other words, in this case, since there is no need to match the wavelengths λ1, λ2 of the two images P1, P2 with the two wavelengths λn, λm used when previously determining the correlation between the spring water volume index value and the spring water thickness t (see Figure 15), the wavelengths λ1, λ2 when capturing images P1, P2 can be set to wavelengths that can be captured by the imaging device 20.

[0086] In the second embodiment, the signal level difference is calculated for the number of pixels in each of the images P1 and P2, but the signal level difference may be calculated only for pixels within a predetermined range in each of the images P1 and P2. For example, if the face 2 (ground) to be evaluated has an area where the light from the light source 24 does not reach sufficiently, the area where the light from the light source 24 reaches sufficiently may be set as the evaluation object, and the signal level difference may be calculated only for pixels captured within this area.

[0087] According to the above embodiment, the following advantageous effects are achieved.

[0088] According to the above-mentioned ground spring water evaluation system 100 and ground spring water evaluation method, a spring water amount index value correlating with the amount of spring water flowing on the surface of the face 2 (ground) is calculated based on the signal levels in the images of at least two or more different wavelengths of 650 nm to 1100 nm. By calculating the spring water amount index value correlating with the amount of spring water in this way, the amount of spring water springing out from the face 2 (ground) can be quantitatively evaluated.

[0089] In addition, since the images used to calculate the spring water quantity index value are images with wavelengths of 1100 nm or less, it is possible to adopt a digital camera equipped with a relatively inexpensive image sensor as the imaging device 20. Therefore, the construction cost and operation cost of the ground spring water evaluation system 100 can be reduced.

[0090] In addition, the following modified examples are also within the scope of the present invention, and it is possible to combine the methods and configurations shown in the modified examples with the methods and configurations described in the above-mentioned embodiments, or to combine the methods and configurations described in the different modified examples below.

[0091] In the first and second examples of the above embodiment, the signal level of each pixel in the image is normalized before the spring water amount index value is calculated. Alternatively, if it is confirmed in advance that the signal levels of each image used in calculating the spring water amount index value are equivalent, for example, that the maximum value of each image is within a predetermined range and the minimum value of each image is within a predetermined range, the spring water amount index value may be calculated without normalization.

[0092] In the above embodiment, the amount of spring water is evaluated only at the time when the image is captured. Instead, the amount of spring water may be evaluated by capturing an image at a predetermined time interval. By evaluating the change in the amount of spring water over time in this way, it is possible to determine whether the spring water is caused by groundwater or temporary rainfall.

[0093] Although the embodiments of the present invention have been described above, the above-mentioned embodiments merely show some of the application examples of the present invention, and it is not intended that the technical scope of the present invention be limited to the specific configurations of the above-mentioned embodiments. [Explanation of symbols]

[0094] 100···Ground water spring evaluation system 2. Face (ground) 10...Arithmetic unit 20 Imaging device 24...Light source

Claims

1. A ground spring water evaluation method for evaluating the amount of spring water that springs out from the ground, comprising: acquiring images of at least two or more different wavelengths of light reflected from the ground to be evaluated, the light being irradiated with light of a predetermined wavelength band, the images having wavelengths of 650 nm to 1100 nm; A step of calculating a spring water volume index value having a correlation with the spring water volume based on signal levels in the image of at least two or more different wavelengths. Methods for assessing ground spring water.

2. A step of obtaining a resultant vector by combining signal levels of pixels at the same coordinate position in the image of at least two or more different wavelengths; establishing a reference vector to be compared with the resultant vector; A step of calculating a value according to an angle between the reference vector and each resultant vector as the spring water amount index value; The method further includes a step of calculating the amount of spring water from a correlation between the spring water amount index value and the amount of spring water that is calculated in advance. The method for evaluating ground seepage according to claim 1.

3. A step of calculating a difference in signal levels of pixels at the same coordinate position in the images of two different wavelengths as the spring water volume index value; The method further includes a step of calculating the amount of spring water from a correlation between the spring water amount index value and the amount of spring water that is calculated in advance. The method for evaluating ground seepage according to claim 1.

4. A step of calculating a spring water volume index value by dividing a difference in signal levels of pixels at the same coordinate position in the image of two different wavelengths by a wavelength difference between the two different wavelengths; The method further includes a step of calculating the amount of spring water from a correlation between the spring water amount index value and the amount of spring water that is calculated in advance. The method for evaluating ground seepage according to claim 1.

5. The method further includes a step of normalizing a signal level at each pixel in the image before calculating the spring water volume index value. A method for evaluating ground spring water according to any one of claims 1 to 4.

6. The wavelength difference between the different wavelengths is 100 nm or more; A method for evaluating ground spring water according to any one of claims 1 to 4.

7. The imaging device capable of capturing the image is installed at a position 3 m or more away from the ground. A method for evaluating ground spring water according to any one of claims 1 to 4.

8. A ground spring water evaluation system for evaluating the amount of spring water that springs out from the ground, A light source that irradiates the ground to be evaluated with light of a predetermined wavelength band; An imaging device capable of imaging light reflected by the ground; A calculation device that calculates the amount of spring water based on a signal level in an image of wavelengths of 650 nm or more and 1100 nm or less captured by the imaging device, The calculation device calculates a spring water volume index value having a correlation with the spring water volume based on signal levels in the image of at least two or more different wavelengths. Groundwater spring evaluation system.

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