Method and system for detecting glacier cracks by cosmic ray muons

Abstract

The invention discloses a method for detecting glacier cracks by using cosmic ray muons. The method comprises the following steps: establishing a system parameter model based on glacier appearance data; placing a muon detector at the bottom of the periphery of the glacier; and reconstructing a three-dimensional internal image of the glacier crack through transmission muon data acquired by the muon detector. The glacier detection system comprises a glacier appearance data measurement device, a muon detection device, a data processing device and an image reconstruction device. By utilizing the method and the system, the advantages of cosmic ray muons, such as strong penetrating power and strong anti-interference capability, can be fully played, the cosmic ray muons can be known without manually approaching glacier cracks, the safety of workers is ensured, and finally three-dimensional imaging of the glacier structure is realized.

Description

Technical Field

[0001] The present invention relates to the field of glacier crack detection, and in particular to a glacier crack detection method and system. Background Art

[0002] Glacier cracks refer to cracks or fissures formed on the surface or inside of a glacier. They are usually caused by factors such as glacier movement, pressure, temperature changes, or topography. Glacier cracks can provide scientists with important information about glacier movement, ice structure, climate change, etc. The formation and expansion of glacier cracks are closely related to the structure of the ice layer. Observing the morphology and distribution of cracks can reveal the structure, layers, and characteristics of the ice layer, such as the age, thickness, and density distribution of the glacier. By monitoring the morphological changes of cracks and the displacement of the surrounding ice body, scientists can infer the movement speed and direction of the glacier, thereby understanding the overall dynamics of the glacier. The formation of cracks may affect the stability of glaciers, especially in warming climate conditions, where cracks may lead to the rupture and intensified melting of glaciers. Therefore, studying cracks can help scientists assess the stability and future development trends of glaciers.

[0003] The main technical means currently used to detect glacier cracks include satellite remote sensing, aerial laser radar (LiDAR), ground measurement and monitoring, etc. Satellites can monitor cracks and changes on the glacier surface through high-resolution optical and radar remote sensing technology. This method can provide wide-area coverage and long-term monitoring, but the resolution may be insufficient for small cracks. LiDAR technology obtains precise topographic data of the surface and glacier surface through laser scanning, and can detect the location and morphology of cracks. However, its coverage is limited and is restricted by weather conditions and flight platforms. Ground measurement tools such as GPS, laser scanners and cameras can detect cracks directly on or near the glacier surface. This method provides high resolution and accuracy, but the coverage is limited and requires a lot of manpower and material resources.

[0004] Radiation imaging technology, as an advanced technology with multi-field applications today, has penetrated into various aspects such as medical care, national economy and scientific research. Its core advantage lies in its ability to accurately reveal key information such as the internal structure and density of an object in a non-destructive state. As an important branch of radiation imaging, muon imaging technology is mainly divided into two categories: scattering imaging and absorption imaging. Scattering imaging technology is based on the multiple Coulomb scattering phenomenon generated by cosmic ray muons when penetrating matter, and the size of its scattering angle is closely related to the atomic number of the material. This feature gives scattering imaging technology a unique advantage in the detection of small, high-density, and high-atomic-number objects. Muon transmission imaging technology, with its strong penetration ability and low sensitivity to environmental interference, shows excellent performance in observing large objects. For example, it plays an important role in detecting volcanic activity, glacial structure, and earthquake fault zones, providing strong technical support for geological research and disaster prevention. Summary of the invention

[0005] The main purpose of the present invention is to provide a method and system for detecting glacier cracks using muons, in order to make up for the deficiencies of existing glacier crack detection technology.

[0006] According to the first aspect of the present invention, a method and system for detecting glacier cracks using muons are provided, comprising the following steps: constructing a parameter model based on the glacier body shape data; generating an initial muon model; constructing a muon transport model based on the system parameter model and the initial muon model; setting a muon detector outside the glacier body; and reconstructing an internal three-dimensional image of cracks in the glacier body based on the muon data and muon transport model acquired by the muon detector. The details include:

[0007] 1. Glacier shape data is obtained through satellite remote sensing technology.

[0008] 2. Establish the initial muon model by calling Gaisser's empirical formula.

[0009] 3. Construct a muon transport model based on the energy loss of muons in glaciers.

[0010] 4. The steps of reconstructing the internal three-dimensional image of the cracks in the glacier mountain are to first reconstruct the single-view image, and then reconstruct the multi-view image based on the single-view image.

[0011] 5. The core theory of reconstructing multi-view images is the ASD-POCS algorithm, which uses the SART algorithm to reconstruct images while using the ideas of alternating direction projection (ASD) and projection consistency reconstruction algorithm (POCS) to gradually approximate the real distribution of the image through alternating iterations. The ASD algorithm handles data consistency and the POCS algorithm handles projection consistency, thereby improving the quality and accuracy of the reconstructed image.

[0012] According to the second aspect of the present invention, a system for detecting glacier cracks using muons is provided, characterized in that it is arranged outside the glacier mountain and includes: a mountain shape data measurement device for measuring mountain shape parameters; a muon detection device for collecting muon data; a data processing device for constructing a parameter model based on the glacier mountain shape data, generating an initial muon model, and constructing a muon transport model based on the system parameter model and the initial muon model; an image reconstruction device for reconstructing the internal structure image of the cave in the glacier crack mountain based on the muon data and the muon transport model. The detection system includes in detail:

[0013] 1. The muon detection device includes 6 sets of detectors placed outside the mountain.

[0014] 2. Each set of detectors is a cylindrical gas ionization chamber detector.

[0015] 3. The detector is placed underground through a pre-prepared drill hole, at an angle of 45° to the sea level.

[0016] Compared with the existing glacier crack monitoring methods, the technical solution of the present invention provides a safer and more effective means to meet the needs of monitoring glacier cracks. Due to its strong penetration and low environmental interference, muon transmission imaging technology is very suitable for observing large objects and can effectively solve the current difficulties encountered in monitoring glacier cracks. Compared with other non-destructive detection methods, muons come directly from the environment and pose no threat to human safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0018] Figure 1 shows a glacier detection map according to an embodiment of the present invention;

[0019] Figure 2 A flow chart of a glacier crack detection method according to an embodiment of the present invention is shown;

[0020] Figure 3 A schematic diagram of the design of a muon detector according to an embodiment of the present invention is shown;

[0021] Figure 4 The flowchart of the algorithm of the Simultaneous Algebraic Reconstruction Technique according to the embodiment of the present invention is shown;

[0022] Figure 5A flowchart of an adaptive gradient descent projection on convex sets (ASD-POCS) algorithm according to an embodiment of the present invention is shown;

[0023] Figure 6 The sub-pixel based ASDPOCS algorithm according to an embodiment of the present invention is shown; DETAILED DESCRIPTION

[0025] In order to better understand the technical solution of the present invention, the present invention is further explained below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention.

[0026] For the sake of simplicity, certain technical features well known to those skilled in the art are omitted in the following description.

[0027] The figure shows a glacier crack detection method according to the present invention. The method can be implemented near the glacier mountain, and can also be partially implemented in a remote location via the network.

[0028] Constructing the system model in step 1 refers to obtaining glacier shape data through remote sensing technology and then constructing model parameters based on the glacier shape data.

[0029] The muon model generated in step 2 is established by calling the Gaisser empirical formula to establish the initial muon model; based on the system model constructed in step 1 and the muon model in step 2, the muon transport model is established according to the energy loss of muons in the ice.

[0030] In steps 3 and 4, depending on the actual situation, the detectors are placed at appropriate locations outside the glacier, with a total of 6 locations placed, as shown in the figure. Those skilled in the art can clearly understand that the order of the above steps can be adjusted as needed.

[0031] The working principles of the cosmic ray muon imaging reconstruction method and detection device for cave detection according to the present invention are described in detail below.

[0032] In order to detect the flux changes after the absorption-only interaction between cosmic ray muons and carbonate mountain rocks, it is necessary to first determine the energy loss mechanism of muons.

[0033] To determine the energy loss mechanism of muons, the energy loss formula is:

[0034]

[0035] Where E represents the muon energy, X = ∫ρ(x)dx is the integral of the density of the muon passing through the material over the penetration length, a corresponds to the ionization energy loss rate, and bE is the energy loss rate caused by other effects.

[0036] Ionization loss refers to the energy lost when the incident charged particle transfers part of its energy to the extranuclear electrons of the medium during the interaction, causing the medium atoms to be ionized or excited. Muons have a much larger mass than electrons. When passing near the nucleus, they are less deflected and have a smaller acceleration, so there is only a small radiation loss. The main energy loss of muons interacting with matter is ionization loss. Based on Monte Carlo Geant4 simulation, the interaction mechanism between muons and glaciers is determined.

[0037] The first step is to construct a system parameter model of the glacier with cracks and the muon detector. The system parameter model is established based on the data obtained by satellite remote sensing technology and ground measurement as prior knowledge. Remote sensing technology emits a pulsed laser beam to the ground and measures the reflection time between the laser beam and the ground to obtain the elevation data of the ground object, which can accurately measure the height and shape of the glacier. The principle of this technology is to use the reflection time interval of the laser beam to calculate the elevation data, thereby generating a digital elevation model (DEM), and then analyzing the shape and terrain characteristics of the mountain. The latter method of measuring topographic maps is to use traditional topographic surveying instruments (such as total stations or theodolites) to establish a measurement network around the glacier, measure the level elevation and plane coordinates of the ground points, and then draw a topographic map to obtain the shape and terrain data of the glacier. The principle of this method is to deduce the shape and terrain characteristics of the glacier by measuring the coordinates and elevations of the ground points. In addition, the low-noise muon detector system is optimized and designed. The present invention will use a gas detector as a muon detector,

[0038] The second step is to generate initial muons. The initial muon model is generated by simulation using the empirical formula method, that is, the flux and angular distribution of cosmic ray muons reaching the sea level are obtained by sampling the integral of the formula, which is suitable for the field of glacier crack detection imaging. This example uses one of the classic models, the Gassier model, to simulate the muon source. The formula is as follows:

[0039]

[0040] Among them, I is the muon flux, E is the muon energy, and θ is the muon zenith angle.

[0041] The third step is to build a muon transport model. In the geant4 software, we will select and call the integrated physical library QGSP-BERT-HP, which can comprehensively describe the interaction between muons and matter.

[0042] The fourth step is to establish a detector measurement plan. Based on the models established in the previous three steps, optimize the design of parameters such as the number, position, and angle of detectors, and establish a measurement plan to guide on-site detection.

[0043] After simulation by geant4, the three-dimensional reconstruction of the crack structure in the glacier was performed based on the severely incomplete muon flux projection data. The first step is to reconstruct the image from a single perspective. The muon energy attenuation information received by the detector is closely related to the density of the object being measured. Through multiple scans, projection data at different angles are collected to accurately restore the density distribution of the object being measured. The restoration process is called image reconstruction. In view of the problem of incomplete projection data reconstruction, the iterative reconstruction algorithm can effectively reduce the reconstructed image artifacts. The iterative reconstruction algorithm uses the SART algorithm. Figure 3 This is the flow chart of the SART algorithm. The principle of the SART algorithm is:

[0044]

[0045] In the formula, k is the number of iterations, and are the pixel values ​​of the jth pixel block in the (k+1)th and kth iterations, respectively, 1≤i≤M, M is the total number of projection viewing angles, N is the total number of image pixels, λ(k) is the relaxation factor in the kth iteration, and a ij is the weighted value of the i-th ray to the j-th pixel block, I θ is the set of all rays under the projection angle θ. The specific implementation steps are:

[0046] 1) The image to be reconstructed x j Assign initial value

[0047] 2) Calculate the projection value of the i-th ray And the correction value

[0048] 3) Perform back projection and update the image:

[0049] 4) k = k + 1, repeat steps 2) - 3) until the calculations under all projection angles are completed, that is, one iteration is completed;

[0050] 5) Use the result of the previous iteration as the initial value and return to step 2) until the iterative convergence condition is met.

[0051] Through the above-mentioned SART algorithm, the projection data obtained by a single detector is reconstructed to obtain a single-view reconstructed image.

[0052] The second step is to reconstruct the 3D image under multiple perspectives. Based on the single-perspective reconstruction result of the first step, multi-perspective reconstruction is further performed. Considering the case of severely incomplete projection data in this example, the algorithm used in the present invention is the sub-pixel-based ASDPOCS algorithm, which is mainly aimed at the case of sparse projection data. Figure 5The flowchart of the ASD-POCS algorithm is shown in Figure 2. The present invention applies this algorithm to the field of muon transmission imaging for the first time. The algorithm steps specifically include:

[0053] 1) Initialize the reconstruction parameters, the initial reconstructed image is x=0;

[0054] 2) Obtain image x through SART algorithm;

[0055] 3) Enter the TV-POCS optimization algorithm;

[0056] The specific model of the TV algorithm is:

[0057]

[0058] where x i ≥0 represents the positive constraint of the image, It refers to the sparse transformation of image x, and the solution is:

[0059]

[0060] Gradient direction v of sparse image m，n for:

[0061]

[0062] In the above formula, σ represents the minimum constant, which is generally taken as 1*e -7 .

[0063] Specific steps of TV-POCS optimization algorithm:

[0064] a. Positive constraint, set all negative elements in image x to 0, and get the new image x pocs ;

[0065] b. Using formula (5), pocs Perform gradient descent iterations Where dA = α||x-x0||2;

[0066] c. When ||x is not satisfied pocs -x TV ||≤σ convergence condition, let x=x TV , return to the TV positive constraint step;

[0067] d. When the convergence condition ||x is met pocs -x TV When ||≤σ, exit the TV algorithm;

[0068] 4) Enter the ASD adaptive parameters to determine whether the convergence condition |x0-x is met tv-pocs |≤θ1 and |p0-p|≤θ2, where p is the projection data;

[0069] 5) When |x0-x is not satisfied tv-pocs |≤θ1 and |p0-p|≤θ2, return to step 3);

[0070] 6) When the condition |x0-X is met tv-pocs When |≤θ1 and |p0-p|≤θ2, TV optimization is changed, the number of iterations is reached, and the iterative reconstruction is terminated. Furthermore, the specific implementation steps of the sub-pixel-based ASDPOCS algorithm are as follows:

[0071] 1) Initialize the reconstruction parameters, the initial reconstructed image is x=0;

[0072] 2) Discretize the ray beam into equal angles and use the corresponding relationship between the ray and the detector to obtain the position information of each ray;

[0073] 3) Using the algebraic relationship between the ray and the image area, the starting point (x1, y1) and the end point (x2, y2) of the ray in the image area are obtained, and the coordinates of the starting point and the end point are doubled and recorded as (x3, y3) and (x4, y4), that is, (x3, y3) = (2x1, 2y1), (x4, y4) = (x2, y2);

[0074] 4) The enlarged image area is recorded as the new reconstruction area П, and the lower left corner coordinate set {(Indx1, Indy1)} of all pixel blocks that the ray passes through in the area ∏ is obtained;

[0075] 5) Calculate the intersection of the ray and each pixel block in the set {(Indx1, Indy1)}, and calculate the weight;

[0076] 6) Restore the sub-pixel image area to the unit pixel image area, and merge the sub-pixel values ​​to the unit pixel to obtain the weight coefficient matrix:

[0077] x(i,j)=x(i suub , j sub )+x(i sub +1,j sub )+x(i sub , j sub +1)+x(i sub +1,j sub +1) (7)

[0078] 7) Obtain the reconstructed image × through the SART reconstruction algorithm;

[0079] 8) Adaptive total variation minimization optimization is performed using the ASD-POCS algorithm to obtain x ASDPOCS ;

[0080] 9) When the convergence condition || xx = x is not met ASDPOCS When ||≤σ, let x=x ASDPOCS , return to step ② and iterate and rebuild again;

[0081] 10) When the convergence condition is met, exit the program.

[0082] After reconstruction using the above algorithm, a multi-view three-dimensional reconstructed image is obtained.

[0083] Finally, the muon absorption imaging detection device is composed of three parts: the detector system, the data acquisition and processing system, and the image reconstruction system. Among them, the detector system and the image reconstruction system will be based on the detector system optimization design scheme obtained by geant4 simulation and 3D reconstruction and the sub-pixel ASDPOCS 3D reconstruction algorithm. The design of the data acquisition and processing system is based on the geant4 simulation detector design and the actual measurement environment of the glacier site. Figure 6 A set of detector electronics construction diagrams are shown. The outer shell is made of plastic material, which is used to wrap the entire detector and protect its internal components. The electrode system consists of two parts: the central electrode and the ring electrode. The central electrode is located in the center of the detector and is a slender metal rod used to attract ionized particles and record their trajectories; the ring electrode is located around the central electrode to form a cylindrical structure used to provide an electric field to accelerate and focus ionized particles. The detector is filled with a gas, such as argon or helium, which is used to receive radiation from the radiation source and produce an ionization effect under the action of the electric field. This is followed by a signal acquisition system, which is used to receive the signal generated by the electrode system and convert it into a digital signal for processing and analysis.

[0084] The detection data is analyzed and processed, and compared with the simulation results in geant4 to verify, ultimately achieving three-dimensional imaging of the crack structure in the glacier and establishing corresponding glacier melting safety monitoring work.

[0085] The above descriptions are merely embodiments of the present invention and do not limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be within the scope of the claims of the present invention.

Claims

1. A method and system for detecting glacier cracks using cosmic ray muons, comprising: Construct a parametric model based on glacier mountain shape data; Generate an initial muon model; A muon transport model is constructed based on the system parameter model and the initial muon model; a muon detector is set up outside the glacier; and an internal three-dimensional image of the cracks in the glacier is reconstructed based on the muon data obtained by the muon detector and the muon transport model.

2. The detection system according to claim 1, characterized in that: It consists of three parts: detector system, data acquisition and processing system and image reconstruction system. The detector system and image reconstruction system will be implemented based on the detector system optimization design scheme obtained by geant4 simulation and 3D reconstruction and the 3D reconstruction algorithm based on sub-pixel adaptive gradient descent convex set projection (ASD-POCS). The design of the data acquisition and processing system is based on the geant4 simulation detector design and the actual measured environment of the cave. The detector is a cylindrical gas ionization chamber detector, which is placed underground through a pre-prepared borehole at a 45° angle to the sea level.

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