Buried object estimation system, buried object estimation method, and storage medium for storing buried object estimation program

Abstract

Provided is a buried object estimation system comprising: a reflection point estimation unit that uses, as input, radar data obtained by scanning the ground with electromagnetic waves from the ground surface to estimate the position of a reflection point of an electromagnetic wave in a medium in the radar data and outputs the estimated position as reflection point data coordinates; and a buried object shape selection unit that uses, as input, the radar data and the reflection point data coordinates to execute function fitting on an object reflection model held in a buried object shape information unit, calculates a fitting score, a physical property parameter, and a shape parameter, selects a buried object shape corresponding to a buried object shape model having a high calculated fitting score, and outputs the selected object shape.

Description

Storage medium for storing a buried object estimation system, a buried object estimation method, and a buried object estimation program. Import by reference

[0001] This application claims priority to Japanese Patent Application No. 2025-40221, filed on March 13, 2025, and incorporates its contents by reference.

[0002] The present invention relates to a buried object estimation system, a buried object estimation method, and a storage medium for storing a buried object estimation program, which estimate the shape of a buried object based on radar data obtained by exploring for the buried object.

[0003] Traditionally, when planning or constructing utility poles or undergrounding projects, design drawings and construction drawings that consolidate information on buried objects and surrounding structures at the relevant location are used. These drawings are managed, edited, and viewed using computer-aided design (CAD) technology. However, the managed data may deviate from the actual situation at the relevant location because the timing of data preparation may differ from the timing of the planning or construction.

[0004] Therefore, buried objects in the area in question are generally investigated. In investigating buried objects, information including the location of buried objects measured by various methods is obtained. Since investigations by trial excavation are costly, non-destructive exploration using ground-penetrating radar devices, such as those disclosed in Patent Document 1, is often performed.

[0005] Radar data acquired by ground-penetrating radar devices is directly converted into images, and a person analyzes the characteristic patterns representing signals originating from underground structures to investigate these structures.

[0006] Patent Document 2 discloses a buried object discrimination system that generates information on buried objects using underground exploration images, comprising: an arithmetic unit that performs predetermined arithmetic processing; a storage unit that stores data referenced in the arithmetic processing; a buried object identification unit that uses a buried object learning model to derive buried object information including the location and size of buried objects from underground exploration images; a buried object information unit that stores the derived buried object information; and an output unit that outputs the derived buried object information.

[0007] The system disclosed in Patent Document 2 generates an image from underground exploration data and aperture-composite exploration data subjected to aperture-composite processing as disclosed in Non-Patent Document 1, and identifies buried objects from the generated image. The buried object learning model used for buried object identification is an artificial intelligence (AI) model composed of a neural network that has learned from underground exploration data of the object to be detected.

[0008] Non-patent document 2 discloses a technique for determining the depth of identified buried objects from ground exploration data. This technique assumes the shape of an unknown reflector is a point, and if a clear reflection image is present in the exploration data, it estimates a value that fits the theoretical arrival time to the reflection image in the exploration data while varying the electromagnetic wave velocity in the medium and the depth of the buried object.

[0009] Patent Document 3 discloses a buried pipe location estimation system comprising: a radar exploration device that moves on the ground, irradiates radio waves into the ground and receives radio waves reflected from underground buried objects; an exploration position measuring device that measures the position of the radar exploration device; and a buried pipe location estimation device that estimates the buried location of buried pipes. The buried pipe location estimation device includes a buried location estimation unit that estimates the position and depth of the underground buried object based on the position measured by the exploration position measuring device and the intensity pattern of the radio waves received by the radar exploration device; and a buried pipe determination unit that determines whether the underground buried object is linearly continuous based on the position and depth of the underground buried object calculated by the buried location estimation unit, and determines that the underground buried object determined to be linearly continuous is a buried pipe.

[0010] Patent Document 4 discloses a method for measuring relative permittivity, which involves moving along the surface of a concealed location where an object is buried, radiating electromagnetic waves to the concealed location, receiving reflected waves from the object, creating a raw image of the cross-section of the concealed location based on the time difference between the radiated electromagnetic waves and the reflected waves, displaying the raw image on a screen, specifying a portion of the screen that includes the image of the object, sequentially changing and setting the relative permittivity from the concealed location to the object, performing a migration calculation using each set relative permittivity and the image of the region to return the object from its apparent position to its true position, evaluating the degree of convergence of the image of the object obtained by the calculation, and determining that the relative permittivity with the highest degree of convergence is the relative permittivity from the concealed location to the object.

[0011] Patent Document 5 discloses a ground-penetrating radar device that uses radio waves to explore objects underground, characterized in that it calculates the speed and attenuation of radio waves underground from the degree of agreement between reference data of the same depth, which is created in advance by varying the speed and attenuation of radio waves underground, and the reflection data actually obtained.

[0012] JP 2006-47132 JP 2022-120327 JP 2023-128876 JP 7-270528 JP 5-142344 WWL Lai, JFC Sham and F. Xie, "Correction of GPR wave velocity with distorted hyperbolic reflection in underground utility's GPR survey," 2016 16th International Conference on Ground Penetrating Radar (GPR), Hong Kong, China, 2016, pp. 1-4.Q. Dou, L. Wei, DR Magee and AG Cohn, "Real-Time Hyperbola Recognition and Fitting in GPR Data," in IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 1, pp. 51-62, Jan. 2017.

[0013] The radar data disclosed in Patent Document 1 is acquired by a ground-penetrating radar system comprising a transmitter that transmits a predetermined electromagnetic wave, which is a transmission signal, toward the ground, and a receiver that receives the electromagnetic wave, which is a transmission signal, returning from the ground.

[0014] Electromagnetic waves transmitted into the ground generally spread with a constant radiation angle and beam width, so the receiver can also receive reflected waves from buried objects that are not directly beneath it. In radar systems, the beam width is determined by the antenna aperture length and the transmission frequency.

[0015] However, the frequency band of the transmitted electromagnetic waves used in radar equipment, particularly for ground-penetrating exploration, ranges from a few MHz to a few GHz. Furthermore, due to the physical size constraints based on ground-based scanning measurement methods, the beam width is wider compared to other applications such as air traffic control. Consequently, images originating from buried objects recorded in radar data are usually distorted.

[0016] This distortion makes it difficult to visualize buried objects using radar data.

[0017] Patent Document 2 discloses a radar data discrimination technique using a learning model. However, for learning, it is necessary to prepare radar data (ground truth data) in which the shape and location of buried objects are known, and to create learning data to which ground truth labels are attached. However, in order to prepare ground truth data, it is necessary to measure the shape and location of buried objects by excavation and direct observation of the exploration site, or to acquire radar data in an area in which buried objects with known shapes at known locations have been prepared, which is a problem because it is very expensive. In particular, in order to construct a system that can discriminate the shapes of buried pipes and various other buried objects without omission, it is necessary to prepare various types of ground truth data, which further increases the man-hours and costs.

[0018] Patent Document 3 discloses a technique for estimating reflection points from radar data using a predetermined method and reconstructing the shape of buried objects mainly based on their contours from the shape of the reflection images formed by these points. However, as mentioned above, the reflection images captured in radar data acquired in ground-penetrating surveys are usually distorted, so there is a problem that the reflection images and the shape of buried objects do not correspond, and the shape of buried objects may be misidentified.

[0019] The present invention aims to select and output the shape of buried objects from radar data.

[0020] A representative example of the invention disclosed in this application is as follows: A buried object estimation system that observes reflected waves from underground buried objects of electromagnetic waves transmitted from an antenna to estimate the location and shape of the buried object, comprising a computer having a computing device that performs predetermined processing and a storage device connected to the computing device, a reflection point estimation unit that takes radar data acquired by scanning the electromagnetic waves from the ground as input, estimates the position of the reflection point in the medium of the electromagnetic wave within the radar data, and outputs the estimated position as reflection point data coordinates, a plurality of buried object shape models that describe the shapes of a plurality of buried objects with shape parameters, and physical property parameters and shape parameters that represent the physical properties of the medium corresponding to the buried object shape models The system is characterized by comprising: an object shape information unit that holds an object reflection model describing the shape of the reflected image obtained by the shape of the buried object, using as a function of the delay time required to receive the reflected signal based on the electromagnetic wave propagation path from the transmitting and receiving point position of the radar exploration device to the reflection point; and an object shape selection unit that takes the radar data and the reflection point data coordinates as input, performs function fitting on the object reflection model held in the object shape information unit, calculates a fitting score, the physical property parameters and the shape parameters, selects a buried object shape corresponding to the buried object shape model with a high calculated fitting score, and outputs the selected object shape.

[0021] According to one aspect of the present invention, the shape of an underground structure can be selected from radar data. Problems, configurations, and effects other than those described above will be clarified by the following description of embodiments.

[0022] This is a diagram showing the overall configuration of the buried object estimation system of Embodiment 1. This is a diagram showing the hardware configuration of the buried object estimation system of Embodiment 1. This is a diagram showing an overview of the buried object exploration of Embodiment 1. This is a diagram showing the relationship between the exploration device and the buried object of Embodiment 1. This is a diagram showing an example of radar data of Embodiment 1. This is a diagram illustrating an object shape with a circular cross-section of Embodiment 1. This is a diagram illustrating an object shape with a rectangular cross-section of Embodiment 1. This is a diagram showing an example of the configuration of the buried object shape table of Embodiment 1. This is a diagram showing an example of the configuration of the buried object shape selection unit of Embodiment 1. This is a flowchart of the buried object estimation process of Embodiment 1. This is a diagram showing an example of a screen output by the buried object estimation system of Embodiment 1. This is a diagram showing the overall configuration of the buried object estimation system of Embodiment 2. This is a diagram showing an example of a screen output by the buried object estimation system of Embodiment 2. This is a diagram showing radar data of Embodiment 3. This is a diagram showing an example of a screen output by the buried object estimation system of Embodiment 3. This is a diagram showing the overall configuration of the buried object estimation system of Embodiment 4. This is a diagram showing an example of a screen output by the buried object estimation system of Embodiment 4. This is a diagram showing the overall configuration of the buried object estimation system of Embodiment 5.

[0023] <Embodiment 1> Figure 1 is a diagram showing the overall configuration of the buried object estimation system S01 of Embodiment 1, and Figure 2 is a diagram showing the hardware configuration of the buried object estimation system S01 of this embodiment.

[0024] The buried object estimation system S01 of this embodiment includes a reflection point estimation unit P01, a buried object shape selection unit P02, and a buried object shape table T01.

[0025] The buried object estimation system S01 may be executed on a server H1 with the hardware configuration illustrated in Figure 2. Server H1 can be composed of a general-purpose computer device having information processing resources such as a processor A01 such as a CPU (Central Processing Unit), memory A02, storage A03, and a communication interface A04. Storage A03 stores a buried object estimation program M1 and a buried object shape table T01 for server H1 to execute the buried object estimation method, and the buried object estimation program M1 is executed by these information processing resources. The execution of the buried object estimation program M1 realizes the reflection point estimation unit P01, the buried object shape selection unit P02, and other functional blocks.

[0026] The reflection point estimation unit P01 of the buried object estimation system S01 estimates the position of the buried object based on the reflection points of electromagnetic waves appearing in the input radar data D01, and outputs the estimated position of the buried object as reflection point data coordinates D02 to the buried object shape selection unit P02.

[0027] The buried object shape selection unit P02 refers to a plurality of object reflection models D05 stored in the buried object shape table T01, fits a function based on the input radar data D01 and reflection point data coordinates D02, and calculates a fitting score D08, estimated physical property parameters D06, and estimated shape parameters D07. The estimated physical property parameters D06 are physical quantities that characterize the propagation of electromagnetic waves, such as relative permittivity, permeability, complex permittivity, and conductivity. The fitting score D08 is a value that indicates the degree of agreement between the radar data D01 and the object reflection model D05. Then, the buried object shape selection unit P02 selects a buried object shape model D04 (for example, one with a high fitting score D08) based on the fitting score D08 for the plurality of object reflection models D05, and outputs the selected buried object shape D03.

[0028] Figure 3 shows an overview of buried object detection, Figure 4 shows the relationship between the detection device and the buried object, and Figure 5 shows an example of radar data D01.

[0029] Radar data D01 is acquired by scanning the ground using a radar exploration device that transmits and receives predetermined electromagnetic waves. As shown in Figure 4, the radar exploration device transmits and receives electromagnetic waves while moving to a predetermined position. The position for transmitting and receiving electromagnetic waves is generally determined based on the position of the integrated transmit / receive antenna or the positional relationship between the transmitting and receiving antennas. The electromagnetic waves transmitted from the transmitting antenna propagate through a medium and are reflected at the interface of different media. The intensity of the reflected electromagnetic waves changes mainly due to the difference in physical properties between the media, and the receiving antenna of the radar exploration device receives the reflected electromagnetic waves. This propagation path of electromagnetic waves is called the electromagnetic wave propagation path. As mentioned above, since electromagnetic waves are radiated in a beam of a certain width, it is difficult to describe the electromagnetic wave propagation path linearly, but in this embodiment, for the sake of simplicity of explanation, a typical linear path is treated as the electromagnetic wave propagation path. Figure 3 illustrates the case where the scanning direction is along the X axis, but the scanning direction does not have to be in this direction.

[0030] As shown in Figure 4, when an object is buried in a medium, electromagnetic waves are reflected at the interface between the medium and the object. The location where the electromagnetic waves are reflected is called the reflection point.

[0031] Figure 3 illustrates how a radar detection device is repeatedly measured while moving in the scanning direction to search for buried objects. For the sake of explanation, the X, Y, and Z axes are shown in three-dimensional space, and the scanning direction is assumed to be along the X-axis; however, this is not necessarily the case in actual exploration. The radar detection device records and stores electromagnetic waves received at discrete locations. In this way, the position of the reflection point originating from the buried object changes along with the electromagnetic wave propagation path, and the received waveform at each reception point can be recorded as radar data.

[0032] Figure 5 schematically shows an example of radar data D01 acquired by the survey. Radar data D01 is the received waveform at each transmit / receive point position and has at least one of the signal strength and phase corresponding to the delay time from the electromagnetic wave transmission time. For example, if the magnitude of the signal strength is converted to the magnitude of brightness and drawn with a constant width in the scanning direction to display the received waveform at the transmit / receive point position, the image shown in Figure 5 is obtained. The pattern schematically shown as a reflected image in Figure 5 is an image that arises when the electromagnetic wave propagation path changes due to changes in the transmit / receive point position during the survey process.

[0033] Next, a specific example of the processing of the reflection point estimation unit P01 of the buried object estimation system S01 in Embodiment 1 will be described. The reflection point estimation unit P01, for example, determines the received intensity of each coordinate (for example, the pixel coordinate of the image shown in Figure 5) of the input radar data D01 using a predetermined threshold, estimates coordinates with a received intensity above or below the threshold as reflection points, and outputs them as positions within the radar data D01. Even if there are multiple positions estimated as reflection points, the position with the highest signal intensity may be output. Furthermore, if multiple reflection points are estimated in the vicinity, they may be grouped using a clustering method, and the centroid point may be output as a representative point within the group. The threshold may be a value that has been stored in the system's memory in advance, or the value may be changed by user input. Furthermore, the threshold may be defined as an absolute value of signal intensity, such as electric field strength, or as a statistical indicator such as a deviation value that assumes a predetermined statistical distribution for the signal intensity distribution within the radar data D01.

[0034] Furthermore, electromagnetic waves generally attenuate as they travel along their propagation path. For this reason, the receiving circuit of the radar detection device or the digital signal processing after sampling may use radar data D01 with attenuation corrected, or the reflection point estimation unit P01 may correct the signal strength of the radar data D01 with respect to the delay time. For example, each row of the radar data D01 shown in Figure 5, i.e., data with the same delay time, may be standardized (processed to set the mean to zero and the variance to 1). In addition, the radar data D01 may be preprocessed to reduce noise, such as by frequency filtering or horizontal line removal, in order to improve the accuracy of reflection point estimation.

[0035] Furthermore, the reflection point estimation unit P01 may simultaneously estimate the reflection point data coordinates D02 and the estimated physical property parameter D06 based on the radar data D01. The object shape calculation unit P04 uses the estimated physical property parameter D06 as an initial value or a fixed value. For the method of simultaneously estimating the reflection point data coordinates D02 and the estimated physical property parameter D06 based on the radar data D01, for example, methods disclosed in Non-Patent Document 1 ("Correction of GPR wave velocity with distorted hyperbolic reflection in underground utility's GPR survey,") and Non-Patent Document 2 ("Real-Time Hyperbola Recognition and Fitting in GPR Data,") may be used.

[0036] The reflection point estimation unit P01 may estimate the reflection point data coordinates D02 from the radar data D01 using a pre-trained learning model. The learning model is an AI (Artificial Intelligence) model or a machine learning model configured by a neural network (CNN) trained on radar data D01 labeled with reflection point data coordinates of buried objects to be detected (for example, a radar image obtained by imaging the signal intensity recorded as the radar data D01). The learning model may be trained with radar data D01 obtained by surveying an underground region where no buried object exists, which is set as a non-detection target.

[0037] The neural network model can include a convolutional layer and a pooling layer. The convolutional layer generates feature map data by substituting each value obtained by convolving input data with a prescribed filter into an activation function. For example, a sigmoid function, a softmax function, or ReLU (Rectified Linear Unit) may be used as the activation function. The pooling layer is provided downstream of the convolutional layer, reduces the data amount of input feature map data using a prescribed filter, and outputs the reduced data. For example, a maximum value or an average value is calculated as the reduction method. The fully connected layer is used as an output layer in a CNN model, combines feature map data, and outputs the combined data. For example, input can be combined using a Flatten function. For example, the learning model may be trained using teacher data in which radar images are associated with positions of reflection points from buried objects. During learning, parameters of the learning model may be updated based on a difference between an estimated position of a reflection point from a buried object output by the learning model and a position of a reflection point from a buried object included in the teacher data. The difference between the estimated position of a reflection point from a buried object output by the learning model and the position of a reflection point from a buried object included in the teacher data may be quantified by a loss function such as mean squared error or cross-entropy loss. By including positions of reflection points from buried objects in the teacher data, the learning model estimates and outputs the position of a reflection point from a buried object in response to input of a radar image.

[0038] A reflection image of highly directional electromagnetic waves converges in a point shape, but as shown in Fig. 5, the above-described radar device for underground exploration obtains a distorted reflection image that spreads in the scanning direction.

[0039] Fig. 8 is a diagram showing a configuration example of a buried object shape table T01.

[0040] The buried object shape table T01 has multiple buried object shape models D04 and corresponding object reflection models D05. The buried object estimation system S01 may calculate a fitting score D08, estimated physical property parameters D06, and estimated shape parameters D07 during processing and store the calculated values ​​in the buried object shape table T01. In the buried object shape table T01, various data corresponding to the buried object shape model D04 can be obtained by referencing data in the row direction. The buried object shape table T01 stores a circular cross-sectional shape as shown in Figure 6 and a rectangular cross-sectional shape as shown in Figure 7 (Figure 8).

[0041] For example, reinforcing bars inside concrete and pipes underground often have a circular cross-section, while beams inside concrete and utility tunnels underground often have a rectangular cross-section. Therefore, quantitatively distinguishing these based on the object reflection model D05 is important for management purposes.

[0042] Figures 6 and 7 illustrate the shape of an object. The horizontal axis shows a survey line (the trajectory of the transmitting and receiving points during the survey), and the vertical axis shows two axes, delay time and depth, respectively. In a homogeneous medium, delay time and depth are proportional, and for simplicity, this explanation is limited to the extent that this proportional relationship is not lost. In practice, in the case of a survey along the Earth's surface, the position of the transmitting and receiving points is displaced in the depth axis direction in Figures 6 and 7 due to the vertical movement of the survey device, but for simplicity, this explanation uses the transmitting and receiving points along the horizontal axis.

[0043] Figure 6 illustrates an object shape with a circular cross-section, and Figure 7 illustrates an object shape with a rectangular cross-section. These circles and rectangles are referred to as the buried object shapes, the radius r and width w are called shape parameters, and typical positions such as the center are called the buried object positions. Here, circles and rectangles are used as examples, but the shapes may also be ellipses, arcs, polygons, combinations thereof, or columnar shapes formed by a solid of revolution around an arbitrary axis or an extrusion along an arbitrary trajectory. Furthermore, diameter, minor radius, major radius, vertical width, side length, diagonal length, and rotation angle around an arbitrary position may also be used as shape parameters.

[0044] In FIGS. 6 and 7, the shape of the buried object, the transmission / reception point position xi, and the representative electromagnetic wave propagation path li, which is the path from the buried object to the medium interface, are indicated by double-headed arrows. In addition, xm indicates the transmission / reception point position at which the electromagnetic wave propagation path is the shortest among the survey lines crossing the buried object, and the corresponding delay time tm to the reflection point and depth lm are also illustrated respectively. The depth can be obtained from the electromagnetic wave propagation velocity v in the medium ｓ multiplied by one half of the delay time. The reason why the delay time is halved here is that the electromagnetic wave propagation path travels back and forth between the transmission / reception point and the reflection point. However, since the radar exploration device may record the delay time as a one-way path in the radar data D01, this will not be explicitly distinguished hereinafter. The electromagnetic wave propagation velocity v s varies depending on the physical property parameters of the medium, and is particularly affected by the dielectric constant. The relative permittivity ε r the electromagnetic wave propagation velocity v traveling in a medium having the physical property parameter ｓ can be calculated by formula (1) using the speed of light c in vacuum.

[0045]

[0046] Using the physical property parameters representing the physical property values of the medium described above and the shape parameters described above, the delay time ti until reception of a reflected signal based on the transmission / reception point position of the radar exploration device and the electromagnetic wave propagation path to the reflection point is defined as a function, and stored as the object reflection model D05 in the buried object shape table T01. Like the buried object shape model D04 with ID=M002 and the object reflection model D05 shown in FIG. 8, description may be made with a plurality of functions for each predetermined range.

[0047] For the buried object shape model D04 of an object shape with a circular cross section, the depth lm and x diff (x i -x m ) and the radius r of the object the relationship between the electromagnetic wave propagation path l i represented by formula (2) is converted to the dimension of time using the electromagnetic wave propagation velocity v s to obtain formula (3). Similarly, for the buried object shape model D04 of an object shape with a rectangular cross section, within the range of the transmission / reception point position xi where the rectangular cross section exists, the position directly below the transmission / reception point is the shortest path for the electromagnetic wave propagation, so t i =t mOutside the range, depth lm and x diff (x i -x m ) and electromagnetic wave propagation path l i Equation (4) expressing the relationship with the electromagnetic wave propagation speed v s Using this, we can convert to the dimension of time and obtain equation (5).

[0048]

[0049] Next, the specific processing of the buried object shape selection unit P02 will be explained. Figure 9 is a diagram showing an example configuration of the buried object shape selection unit P02 in Embodiment 1.

[0050] The buried object shape selection unit P02 consists of a fitting calculation unit P03 and an object shape calculation unit P04. The fitting calculation unit P03 refers to a plurality of object reflection models D05 stored in the buried object shape table T01, performs function fitting based on the input radar data D01 and reflection point data coordinates D02, and stores the fitting score D08, estimated physical property parameters D06, and estimated shape parameters D07 in the buried object shape table T01 (see Figure 8). The object shape calculation unit P04 sorts the buried object shape table T01 in descending order, for example, based on the fitting score D08, selects the buried object shape model D04 in the first row, i.e., the row with the highest fitting score D08, substitutes the estimated shape parameters D07 and estimated physical property parameters D06 of the selected buried object shape model D04 into the buried object shape model D04, calculates the selected buried object shape D03, and outputs it.

[0051] Furthermore, in Figure 9, the reflection point data coordinates D02 may be input to the object shape calculation unit P04 by a connection not shown, and the object shape calculation unit P04 may output the selected buried object shape D03 with its position.

[0052] One example of a method for performing function fitting is to substitute the reflection point data coordinates D02 into the object reflection model D05, tentatively determine the remaining unknown parameters within a predetermined range, and calculate the average of the absolute values ​​of the electromagnetic wave intensity at the coordinate positions of the radar data D01 corresponding to the shape of the reflected image defined by substituting the tentatively determined parameters into the object reflection model D05, as the evaluation value. This evaluation value is calculated for multiple tentatively determined unknown parameters, and the parameter that was substituted when the largest evaluation value was obtained is adopted as the best (F104 to F0107 in Figure 10). This is a method known as the iterative method. Here, the initial values ​​of the unknown parameters can be, for example, user input of parameters suitable for the medium being explored, such as the inside of concrete, if the medium is known in advance, or the system can maintain predetermined values ​​for each medium and use physical property parameters such as relative permittivity that can be assumed from the composition of the medium. For example, the initial value of the relative permittivity of concrete can be set to 10, or the initial value of the relative permittivity of wet soil can be set to 20. If there are multiple unknown parameters, they may be estimated simultaneously, or they may be decomposed into several sets of parameters, and the estimation results may be calculated sequentially to estimate the parameters.

[0053] Alternatively, data within a predetermined range can be extracted from the reflection point data coordinates D02 of the radar data D01, evaluated using a predetermined threshold, and coordinates with a signal intensity equal to or greater than the threshold can be obtained. Unknown parameters of the object reflection model D05 can then be determined for the acquired set of coordinates using the least squares method.

[0054] These methods may be combined, or the method used may be changed for each object reflection model D05. However, in order to select the buried object shape model D04 based on the fitting score D08, the dimensions and numerical range of the fitting score D08 must be kept the same. Regardless of which method is used to estimate the parameters, it is advisable to calculate the fitting score D08 using, for example, the coefficient of determination or the aforementioned evaluation values.

[0055] Figure 10 is a flowchart of the buried object estimation process performed by the buried object shape selection unit P02, as described above.

[0056] After the buried object estimation system S01 receives radar data D01 as input (F0101), the reflection point estimation unit P01 estimates the position of the reflection point based on the radar data D01 and outputs it as reflection point data coordinates D02 to the buried object shape selection unit P02 (F102). The buried object shape selection unit P02 reads one object reflection model D05 stored in the buried object shape table T01 (F103), performs function fitting based on the radar data D01 and the reflection point data coordinates D02, and calculates the fitting score D08, estimated physical property parameters D06, and estimated shape parameters D07 (F0110). In the loop from F0103 to F0108, if it is the first time, the buried object shape selection unit P02 determines that the termination condition is not met (No in F0108), reads another object reflection model D05 stored in the buried object shape table T01 (F0103), and again performs function fitting based on the radar data D01 and reflection point data coordinates D02, and calculates the fitting score D08, estimated physical property parameters D06 and estimated shape parameters D07 (F0110). For the second time and beyond, in F0108, if the buried object shape selection unit P02 determines that the termination condition is not met, for example based on user input or whether all of the multiple object reflection models D05 held in the buried object shape table T01 have been calculated, it returns to F0103 again. If it determines that the termination condition is met, it selects the buried object shape model D04 based on the fitting score D08, outputs the selection result (F0109), and terminates the process.

[0057] The details of the processing in F0110 are as follows: The buried object shape selection unit P02 uses the object reflection model D05 read from the buried object shape table T01 to assume a reflected image shape based on the reflection point data coordinates, physical property parameters and shape parameters (F0104). Then, the buried object shape selection unit P02 calculates the residual of the assumed reflected image shape from the radar data D01 as the fitting score D08 (F0105) and determines whether the termination condition is met (F0106). If the termination condition is not met, at least one of the physical property parameters and shape parameters is changed (F0107), and the process returns to step F0104 to assume a reflected image shape based on the changed physical property parameter and shape parameter.

[0058] The buried object shape model D04 output by the buried object estimation system S01 can be confirmed by the user on a screen displayed on a display device (not shown). For example, in the screen example shown in Figure 11, the estimated shape parameter D07 indicates the possibility that a buried object with a circular cross-section of r = 100 mm exists at a position xm = 1500 mm from the starting point of the exploration line and at a depth of lm = 1000 mm, allowing the user to confirm the buried object at a glance on the screen. Alternatively, the image of the buried object and the radar data D01 may be displayed superimposed.

[0059] Furthermore, the buried object estimation system S01 may, for example, store a fitting score D08 for each object reflection model D05 in the buried object shape table T01 and display the fitting score D08 on the screen.

[0060] The user can easily determine a suitable buried object shape model D04 from the perspective of the buried object's shape based on the selected buried object shape D03 and fitting score D08 displayed on the screen. Therefore, from the shape of the buried object, it is possible to estimate the type of buried object, for example, if it has a circular cross-section it is a pipeline, and if it has a rectangular cross-section it is a utility tunnel, and the buried object can be managed appropriately. If the fitting score D08 of multiple object reflection models D05 and buried object shape models D04 is high, the user can select and manage, for example, a design-appropriate buried object shape model D04, or select the object shape that is least suitable for management, and manage the buried object while considering the risks.

[0061] As described above, according to Embodiment 1 of the present invention, the shape of the buried object can be selected from radar data by an object reflection model corresponding to the shape of the buried object, and the shape of the buried object can be estimated with high accuracy.

[0062] <Embodiment 2> Next, Embodiment 2 of the present invention will be described. In Embodiment 2, the same reference numerals are used for components that are the same as those in the buried object estimation system S01 of Embodiment 1, and their descriptions are omitted.

[0063] Figure 12 shows the overall configuration of the buried object estimation system S02 of Embodiment 2.

[0064] The buried object estimation system S02 of this embodiment includes a reflection point estimation unit P01, a buried object shape selection unit P02, a buried object integration unit P05, a buried object shape table T01, and a buried object information unit T02. The radar data D01 of Embodiment 2 includes positioning data, which is the position coordinates of the transmission and reception points when electromagnetic waves are transmitted and received, within the trajectory (survey line) of the radar exploration device during exploration.

[0065] The buried object shape selection unit P02 calculates estimated buried object information D09 by attaching the reflection point position coordinates to the selected buried object shape D03 based on positioning data that records the position coordinates of the transmitting and receiving points included in the radar data D01, and outputs the calculated estimated buried object information D09 to the buried object information unit T02. The buried object information unit T02 stores the estimated buried object information D09 output from the buried object shape selection unit P02. The buried object integration unit P05 outputs integrated buried object information D12, which integrates the estimated buried object information D09 stored in the buried object information unit T02. The operation and functions of the reflection point estimation unit P01 and the buried object shape selection unit P02 are the same as those of the buried object estimation system S01 of Embodiment 1.

[0066] The position coordinates may be absolute positions expressed in projected coordinate systems such as a plane rectangular coordinate system or a UTM coordinate system. Alternatively, a predetermined reference point and reference direction may be established, and the position may be expressed as a relative position using the distance from the reference point and the angle from the reference direction. The radar detection device may record the coordinate system of the position coordinates. If the radar detection device has recorded the coordinate system, the buried object estimation system S02 may integrate the position coordinates into the predetermined coordinate system using a coordinate system transformation formula.

[0067] In Embodiment 2, the buried object shape selection unit P02 uses the input positioning data to convert the reflection point data coordinates D02 into reflection point position coordinates. For example, if the positioning data is an array-based data structure and the index of the transmission / reception point position coordinates of the exploration starting point is 0, the reflection point position coordinates are obtained from the positioning data based on the horizontal index of the reflection point data coordinates D02. The obtained reflection point position coordinates are either substituted into the buried object shape model D04, or associated with the buried object shape model D04 and stored together with the estimated buried object information D09 in the same row of the buried object shape table T01, for example, and sent to the buried object information unit T02.

[0068] The buried object information unit T02 stores multiple estimated buried object information D09 for multiple radar data D01, associating them with location information. The buried object integration unit P05 creates integrated buried object information D12 in a predetermined way based on the location information associated with each of the multiple estimated buried object information D09 for a predetermined space, and outputs it to the outside of the buried object estimation system S02 in a predetermined way. Here, integration refers to the operation of mapping the location information of each estimated buried object information D09 to a location in a predetermined space in a predetermined way. For example, estimated buried object information A, whose location information is associated in the plane rectangular coordinate system I(1), and estimated buried object information B, whose location information is associated in the UTM (Universal Transverse Mercator) coordinate system 51, are both integrated into the latitude and longitude space by converting (mapping) the location information to latitude and longitude. The predetermined space may be defined as, for example, a latitude and longitude space, and all the location information of the source estimated buried object information D09 may be converted to latitude and longitude, or other estimated buried object information D09 may be integrated with any other estimated buried object information D09. The coordinate system of the integration destination space may be a coordinate system predetermined by the system, or it may be set based on user input (not shown). Information for conversion between coordinate systems may be held in advance by the buried object integration unit P05. In addition, the buried object integration unit P05 may output the estimated buried object information D09 filtered within a predetermined location range as integrated buried object information D12. Based on selection information of estimated buried object information D09 input from an input unit (not shown), the estimated buried object information D09 may be selected and the selected estimated buried object information D09 may be output. Multiple data may be integrated and output as integrated buried object information D12. The selected information may include, for example, the date, location, or the values ​​or ranges of the buried object shape model D04 or fitting score D08.

[0069] Figure 13 shows an example screen displaying integrated buried object information D12 output by the buried object estimation system S02 of Embodiment 2. Based on the integrated buried object information D12, multiple selected buried object shapes D03 are arranged on the screen using position coordinates as keys and displayed on a display device (not shown). The user can see at a glance the distribution of selected buried object shapes D03 in space. In the example screen in Figure 13, the possibility of a cylindrical object with a circular cross-section being buried is suggested in the upper right of the screen, i.e., in the y-axis direction, and the user can easily and reliably design pipeline laying in that area based on the estimated buried object information D09.

[0070] <Embodiment 3> Next, Embodiment 3 of the present invention will be described. In Embodiment 3, the same reference numerals are used for components that are the same as those in the buried object estimation systems S01 and S02 of Embodiments 1 and 2, and their descriptions are omitted.

[0071] As shown in Figure 12, the buried object estimation system S02 of this embodiment includes a reflection point estimation unit P01, a buried object shape selection unit P02, a buried object integration unit P05, a buried object shape table T01, and a buried object information unit T02.

[0072] In the buried object estimation system S02 of this embodiment, the buried object integration unit P05 estimates the possible three-dimensional shape of a buried object in a predetermined area based on a plurality of selected buried object shapes D03 for a plurality of radar data D01 and the reflection point position coordinates described in Embodiment 2, and outputs the three-dimensional shape as integrated buried object information D12 to the outside of the buried object estimation system S02. Other aspects are the same as the buried object estimation system S02 of Embodiment 2.

[0073] Referring to Figure 14, the general outline of the estimation of the three-dimensional shape in the buried object integration unit P05 will be explained. In the three radar data D01 shown in Figure 14, the selected buried object shape D03 when a circular selected buried object shape D03 is obtained in the center is shown by a solid line, and the estimated three-dimensional cylinder is shown by a dotted line. As one example of three-dimensional shape estimation, the least squares method or a method called RANZAC (Random Sample Consensus), which is robust to outliers, may be used. A cylinder containing at least one selected buried object shape D03 is assumed, and it is determined whether other selected buried object shapes D03 are contained within the assumed cylinder. As a result of the determination, the number of selected buried object shapes D03 contained within the assumed cylinder is used as the evaluation value, and the process is repeated until the evaluation value exceeds a predetermined threshold. Then, after the iteration process for all patterns is completed, or the evaluation value exceeds the predetermined threshold, or after the iteration process has been performed for a predetermined upper limit number of times, the cylinder with the highest evaluation value is determined to be the three-dimensional shape and output as integrated buried object information D12.

[0074] The initial assumed three-dimensional shape can be determined according to the selected buried object shape D03; if it has a circular cross-section, it can be a cylinder or a cylindrical shape, and if it has a rectangular cross-section, it can be a cube or a plane.

[0075] Figure 15 shows an example screen displaying a three-dimensional shape output by the buried object estimation system S02 of Embodiment 3. As shown in Figure 15, the integrated buried object information D12 in a predetermined area may be displayed as a plan view on the screen of a display device (not shown). The three-dimensional shape is output as a GUI by a calculation device (not shown) and a user operation input unit, and the size and angle of the three-dimensional shape may be changed, for example, by the user's mouse or keyboard operation. Alternatively, the integrated buried object information D12 placed in the virtual space may be rendered as an image viewed from a virtual viewpoint in the virtual space, and an image that changes the position and angle of the virtual viewpoint according to the user's operation may be output. Furthermore, even without directly rendering the three-dimensional shape, as shown in Figure 15, shape parameters such as pipe diameter and depth attached as metadata may be displayed as a panel or table.

[0076] Furthermore, the integrated buried object information D12 may be output as data converted into CAD data or three-dimensional object data according to a predetermined data format in the buried object integration unit P05. The user can then verify the outputted CAD data on a computer running predetermined CAD software.

[0077] <Embodiment 4> Next, Embodiment 4 of the present invention will be described. In Embodiment 4, the same reference numerals are used for components that are the same as those in the buried object estimation systems S01 and S02 of Embodiments 1, 2, and 3, and their descriptions are omitted.

[0078] Figure 16 shows the overall configuration of the buried object estimation system S03 of Embodiment 4.

[0079] The buried object estimation system S03 of this embodiment includes a reflection point estimation unit P01, a buried object shape selection unit P02, a buried object integration unit P05, a managed buried object extraction unit P06, a buried object shape table T01, and a buried object information unit T02.

[0080] The managed buried object extraction unit P06 extracts the shape and location of managed buried objects in a predetermined area from the input management data D10 as managed buried object information D11 and outputs it to the buried object information unit T02. The buried object information unit T02 stores the managed buried object information D11 and estimated buried object information D09, attaching data (e.g., a flag) that can distinguish between the two. The buried object integration unit P05 outputs integrated buried object information D12, which integrates the managed buried object information D11 and the estimated buried object information D09. The configuration other than that described above is the same as the buried object estimation system S02 of Embodiment 2 or Embodiment 3.

[0081] The management data D10 is data recorded to manage the location and shape of buried objects in a predetermined area, and may be CAD data or, for example, table-format data including the location and shape of buried objects. It may also receive input from the user via an input interface (not shown). The predetermined area that defines the range of the management data D10 is preferably the area in which the system estimates buried objects. The managed buried object extraction unit P06 receives the input of the management data D10, converts the location and shape of the buried objects recorded in the management data D10 into a data format that the buried object information unit T02 can hold, and outputs it to the buried object information unit T02. At this time, information on all buried objects recorded in the management data D10 may be extracted, or data may be selected based on a narrower area, for example, the location and surrounding location area of ​​a selected buried object shape D03 already stored in the buried object information unit T02, and the selected data may be output to the buried object information unit T02.

[0082] Furthermore, the location information held by the managed buried object information D11 and the estimated buried object information D09 may be converted to the same location coordinate system. This conversion process may be performed by the managed buried object extraction unit P06 or by the buried object integration unit P05.

[0083] Figure 17 shows an example screen displaying integrated buried object information D12 output by the buried object estimation system S03 of Embodiment 4. In the example screen shown in Figure 17, the managed buried object information D11 and the estimated buried object information D09 are integrated at the same coordinates and displayed on a display device (not shown) on the same screen. The managed buried object information D11 and the estimated buried object information D09 may be distinguished and displayed using different display modes (e.g., color, pattern). For example, the managed buried object information D11 and the estimated buried object information D09 held in the buried object information unit T02 may be displayed with predetermined colors or patterns based on distinguishable data.

[0084] Users can distinguish between managed buried object information D11 and estimated buried object information D09, and can, for example, immediately grasp the inaccuracies of managed buried object information D11.

[0085] Furthermore, the buried object shape selection unit P02 may refer to the management data D10 held in the buried object information unit T02 and extract shape parameters representing the shape of the buried object recorded in the management data D10 that are near the reflection point data coordinates D02 in the fitting calculation described in Embodiment 1. The extracted parameters may then be used as initial values ​​or fixed values ​​when fitting using the object reflection model D05. In particular, the location of buried pipelines may have errors because the information is not updated during construction work such as road widening, but shape parameters such as diameter can be considered to have high accuracy when it is determined that the buried members have not deformed much due to aging deterioration, etc. Therefore, by performing a fitting calculation using the shape parameters recorded in the management data D10 as initial values ​​or fixed values, the number of parameters that need to be estimated in the fitting calculation is reduced, the stability of the solution is increased, and as a result, highly accurate estimation results can be obtained.

[0086] Furthermore, if the buried object is a perfect reflector (for example, a metal object), electromagnetic waves may be reflected from the top of the buried object and not reach the bottom. In this case, while the horizontal shape parameters of a rectangular shape can be estimated from the reflected image from a rectangular object alone, the vertical shape parameters may not be estimated. By supplementing the information of the shape parameters to be estimated with the shape parameters recorded in management data D10, the aforementioned three-dimensional modeling becomes possible, or the accuracy of the estimation results can be improved.

[0087] <Embodiment 5> Next, Embodiment 5 of the present invention will be described. In Embodiment 5, the same reference numerals are used for components that are the same as those in the buried object estimation systems S01, S02, and S03 of Embodiments 1, 2, 3, and 4, and their descriptions will be omitted.

[0088] Figure 18 shows the overall configuration of the buried object estimation system S04 of Embodiment 5.

[0089] The buried object estimation system S04 of this embodiment includes a reflection point estimation unit P01, a buried object shape selection unit P02, a reflection image generation unit P07, a radar data comparison unit P08, a determination unit P09, and a buried object shape table T01.

[0090] The reflection image generation unit P07 receives the selected buried object shape D03 and estimated physical property parameters D06 as input, creates a predetermined physical simulation model, simulates the received electromagnetic wave signals for multiple pseudo transmission and reception point positions using a numerical electromagnetic field solver to generate pseudo radar data D13, and outputs the generated radar data D13. The radar data comparison unit P08 receives the pseudo radar data D13 and radar data D01 as input, compares the input radar data D01 and the pseudo radar data D13, calculates the similarity D14 using a predetermined calculation, and outputs the similarity D14. The determination unit P09 determines the similarity D14 using a predetermined threshold, outputs the selected buried object shape D03, estimated physical property parameters D06, and similarity D14 to the system, and outputs parameters to the reflection image generation unit P07 for modifying the physical simulation model according to the object shape and physical property values. Other than those mentioned above, the configuration is the same as that of the buried object estimation systems S02 and S03 of Embodiments 2, 3, and 4.

[0091] The comparison between the simulated radar data D13 and radar data D01 can be performed, for example, by imaging both data and calculating the MSE calculated by equation (6) and the PSNR (Peak Signal to Noise Ratio) calculated by equation (7) as the similarity score D14, and outputting the calculated similarity score D14. Other algorithms such as SSIM (Structural Similarity) may also be used. In equation (6), I is the image and N is the number of pixels in the image. Also, in equation (7), MAX I This is the maximum pixel value of the image.

[0092]

[0093] The determination unit P09 may determine whether the similarity D14 is similar to a predetermined threshold. If a negative determination result is obtained, the object shape and at least one of the physical properties of the physical simulation model may be changed, and the reflection image generation unit P07 may output pseudo-radar data D13 again. If the similarity D14 is greater than a predetermined threshold or exceeds a predetermined number of calculations, the determination unit P09 outputs a positive determination result and outputs the selected buried object shape D03, estimated physical property parameters D06, and similarity D14 to the system. The determination unit P09 may also output a set of multiple values ​​calculated during the processing for the selected buried object shape D03, estimated physical property parameters D06, and similarity D14, or it may determine the value to output based on the similarity D14. When changing the object shape and at least one of the physical properties of the physical simulation model, the updated value may be determined using previously calculated values.

[0094] The buried object estimation system S04 of Embodiment 5 uses a buried object shape model D04 from a pre-formulated buried object shape table T01, and repeatedly adjusts parameters through physical simulation to obtain shape parameters and physical properties that are closer to the true values. In addition, the physical simulation model may represent the material of the buried object and the heterogeneous medium in the electromagnetic wave propagation path, which are difficult to define in the buried object shape model D04, and as a result, the reliability of the obtained selected buried object shape D03 is increased.

[0095] It should be noted that the present invention is not limited to the embodiments described above, but includes various modifications and equivalent configurations within the spirit of the attached claims. For example, the embodiments described above are described in detail to make the present invention easier to understand, and the present invention is not necessarily limited to having all the configurations described above. Furthermore, some of the configurations of one embodiment may be replaced with those of another embodiment. Furthermore, some of the configurations of one embodiment may be added to those of another embodiment. Furthermore, some of the configurations of each embodiment may be added, deleted, or replaced with those of other embodiments.

[0096] Furthermore, each of the aforementioned configurations, functions, processing units, and processing means may be implemented in hardware, for example, by designing them as integrated circuits, or they may be implemented in software by having a processor interpret and execute programs that realize each function.

[0097] Information such as programs, tables, and files that implement each function can be stored in memory, hard disks, SSDs (Solid State Drives), or other storage media such as IC cards, SD cards, and DVDs.

[0098] Furthermore, the drawings show control lines and information lines that are considered necessary to explain the embodiments, and do not necessarily show all control lines and information lines included in actual products to which the present invention is applied. In practice, it can be assumed that almost all components are interconnected.

Claims

1. A buried object estimation system for estimating the location and shape of buried objects by observing reflected waves from underground buried objects of electromagnetic waves transmitted from a radar exploration device, comprising a computer having a processing unit that performs predetermined processing and a storage device accessible by the processing unit, a reflection point estimation unit that takes radar data acquired by scanning the electromagnetic waves from the ground as input, estimates the position of the reflection point of the electromagnetic waves in the medium within the radar data, and outputs the estimated position as reflection point data coordinates, a buried object shape information unit that holds an object reflection model that describes the reflected image shape obtained from the buried object shape as a function of the delay time required to receive the reflected signal that has passed through the electromagnetic wave propagation path from the transmitting / receiving point position of the radar exploration device to the reflection point, using a plurality of buried object shape models that describe the shapes of a plurality of buried objects using shape parameters, physical property parameters that represent the physical property values ​​of the medium, and the shape parameters, A buried object estimation system comprising: a buried object shape selection unit that takes the radar data and the reflection point data coordinates as inputs, performs function fitting with the input radar data and the object reflection model held in the buried object shape information unit, calculates a fitting score, physical property parameters and shape parameters by the function fitting, selects a buried object shape model with a high calculated fitting score, and outputs a buried object shape corresponding to the selected buried object shape model.

2. A buried object estimation system according to claim 1, wherein the buried object shape selection unit outputs the selected buried object shape in association with the fitting score.

3. A buried object estimation system according to claim 1, wherein the reflection point estimation unit estimates the reflection point data coordinates and the physical property parameters, and the buried object shape selection unit performs function fitting using the estimated physical property parameters as initial values ​​or fixed values, and calculates the fitting score, the physical property parameters and the shape parameters.

4. A buried object estimation system according to claim 1, wherein the buried object shape information unit holds at least a first buried object shape model that describes a circular cross-sectional shape with radius or diameter as the shape parameter, and a second buried object shape model that describes a rectangular cross-sectional shape with width as the shape parameter.

5. A buried object estimation system according to claim 1, wherein the reflection point estimation unit estimates the reflection point data coordinates using a machine learning model that has been trained on radar data to which the positions of reflection points are associated.

6. A buried object estimation system according to claim 1, wherein the radar data includes positioning data recording the position coordinates of measurement points when the radar exploration device scans the ground, the buried object shape selection unit calculates the position of the reflection point based on the reflection point data coordinates and the positioning data, the calculated position of the reflection point and the selected buried object shape are stored in the buried object shape information unit as estimated buried object information, and the buried object estimation system further comprises a buried object integration unit that integrates the estimated buried object information stored in the buried object shape information unit and outputs it as integrated buried object information.

7. A buried object estimation system according to claim 6, wherein the buried object integration unit further estimates the three-dimensional shape that the buried object can take in a predetermined area based on the shapes of a plurality of buried objects and the positions of the reflection points, and outputs the estimated three-dimensional shape as estimated buried object information.

8. A buried object estimation system according to claim 7, comprising: receiving input of management data including the location and shape of buried objects in a predetermined area; further comprising a managed buried object extraction unit that extracts the shape and location of a plurality of managed buried objects in a predetermined area from the management data as managed buried object information; the buried object shape information unit holds the estimated buried object information and the managed buried object information; and the buried object integration unit outputs integrated buried object information which integrates the estimated buried object information and the extracted managed buried object information.

9. A buried object estimation system according to claim 8, wherein the reflection point estimation unit calculates the position of the reflection point based on positioning data recording the position coordinates of measurement points when the radar exploration device scans the ground, which are included in the radar data, and the reflection point data coordinates, and refers to the shape of a managed buried object located near the position of the reflection point among the managed buried object information held in the buried object shape information unit, and performs the function fitting using a buried object shape model similar to the shape and the corresponding object reflection model.

10. A buried object estimation system according to claim 1, comprising: a reflection image generation unit that creates a predetermined physical simulation model based on the selected buried object shape and physical properties, simulates the reception signals of electromagnetic waves for a plurality of pseudo transmission and reception point positions using a numerical electromagnetic field solver with respect to the created physical simulation model, and creates pseudo radar data based on the results of the simulation calculation; a radar data comparison unit that calculates the similarity between the created pseudo radar data and the input radar data; a determination unit that maintains the correspondence between at least one of the buried object shape and physical properties and the calculated similarity, and compares the calculated similarity with a predetermined threshold, wherein the determination unit, if it is determined by the determination that the similarity is small, displaces at least one of the buried object shape and physical properties by a predetermined amount of displacement, and outputs at least one of the displaced buried object shape and physical properties to the reflection image generation unit; and if it is determined by the determination that the similarity is large, outputs at least one of the buried object shape and physical properties together with the similarity.

11. A buried object estimation method for estimating the location and shape of a buried object using reflected waves from underground buried objects of electromagnetic waves transmitted from a radar exploration device, observed by a buried object estimation system, wherein the buried object estimation system is composed of a computer having a processing unit that performs predetermined processing and a storage device accessible by the processing unit, the storage device holds an object reflection model that describes the shape of a reflected image obtained from the buried object shape as a function of the delay time required to receive the reflected signal that has passed through the electromagnetic wave propagation path from the transmitting / receiving point position of the radar exploration device to the reflection point, using a plurality of buried object shape models that describe the shapes of a plurality of buried objects using shape parameters, physical property parameters that represent the physical property values ​​of the medium, and the shape parameters, and the buried object estimation method is a data input step in which radar data acquired by scanning the electromagnetic waves from the ground is input, and a reflection point estimation step that estimates the position of the reflection point of the electromagnetic wave in the medium within the radar data and outputs the estimated position as reflection point data coordinates, A method for estimating a buried object, comprising: taking the radar data and the reflection point data coordinates as input, performing a function fitting on the object reflection model held in the storage device, calculating a fitting score, the physical property parameters and the shape parameters, selecting a buried object shape model with a high calculated fitting score, and outputting a buried object shape corresponding to the selected buried object shape model.

12. A storage medium for storing a buried object estimation program that causes the buried object estimation system to estimate the location and shape of a buried object using reflected waves from underground buried objects of electromagnetic waves transmitted from a radar exploration device, the buried object estimation system is composed of a computer having a processing unit that performs predetermined processing and a storage device accessible by the processing unit, the storage device holds an object reflection model that describes the shape of a reflected image obtained from the shape of a buried object as a function of the delay time required to receive the reflected signal that has passed through the electromagnetic wave propagation path from the transmitting / receiving point position of the radar exploration device to the reflection point, using a plurality of buried object shape models that describe the shapes of a plurality of buried objects using shape parameters, physical property parameters that represent the physical property values ​​of the medium, and the shape parameters, the buried object estimation program includes a data input procedure in which radar data acquired by scanning the electromagnetic waves from the ground is input, and a reflection point estimation procedure that estimates the position of the reflection point of the electromagnetic wave in the medium within the radar data and outputs the estimated position as reflection point data coordinates, A storage medium for storing a buried object estimation program that causes the computing device to perform a data output procedure that takes the radar data and the reflection point data coordinates as input, performs function fitting on the object reflection model held in the storage device, calculates a fitting score, the physical property parameters and the shape parameters, selects a buried object shape model with a high calculated fitting score, and outputs a buried object shape corresponding to the selected buried object shape model.

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