A gamma spectrum generation method based on Monte Carlo simulation, a storage medium and a system

Abstract

The present application relates to a kind of gamma spectrum generation methods based on Monte Carlo simulation, comprising the following steps: gamma ray transport space is defined as cube, and is cut into multiple equal small blocks, and in multiple equal small blocks, according to relative probability value, fill predetermined material;Detector geometry setting is carried out, and cylindrical body is used to represent detector geometry;Gamma spectrum broadening setting is carried out;Determine the kind of gamma ray, and define source particle quantity and position.The present application also provides a kind of storage medium and gamma spectrum generation system based on Monte Carlo simulation, using a kind of gamma spectrum generation method based on Monte Carlo simulation, storage medium and system can generate a large number of high simulation general gamma spectrum sample, so that the sample generated can cover the real measurement in the diversified measurement scene under the spectrum real form feature, to break through the limitation of existing sample generation technique only for single measurement scene, improve the versatility, universality of gamma spectrum sample and the analysis algorithm related therewith.

Description

Technical Field

[0001] This invention belongs to the field of nuclear radiation detection, specifically relating to a method, storage medium, and system for generating gamma spectra based on Monte Carlo simulation. Background Technology

[0002] A gamma-ray spectrum is a statistical distribution map of the signal count (arranged in ascending order of signal amplitude) formed by the energy deposited in a detector by gamma rays emitted by radioactive nuclides. Since the signal amplitude is directly proportional to the energy deposited by the gamma rays in the detector, the gamma-ray spectrum is essentially a statistical distribution map of the number of gamma rays by energy. Because the gamma-ray spectrum contains characteristic information about radioactive nuclides, gamma-ray spectrum measurement is an important means of quantitative radioactivity analysis in laboratories or on-site. Accurate interpretation of the gamma-ray spectrum is a key step in ensuring the accuracy of quantitative radioactivity analysis. The morphology of the gamma-ray spectrum is extremely complex; therefore, to achieve accurate spectral interpretation, it is necessary to understand the changing patterns of the spectral morphology, which requires obtaining a large number of spectral samples. Currently, machine learning technology is increasingly used in the field of gamma-ray spectrum analysis. This method uses a large number of gamma-ray spectrum samples to train deep neural networks to achieve the interpretation of unknown spectral types. Its performance far exceeds that of traditional filtering analysis methods, and therefore it is gradually becoming the mainstream method. The construction of gamma-ray spectrum samples is becoming an increasingly core technical aspect.

[0003] Due to limitations in practical measurement conditions, obtaining a large number of gamma-ray spectrum samples through direct measurement is difficult. Unlike actual measurements, generating simulated gamma-ray spectrum samples using computer simulation is not limited by practical measurement conditions, offering greater flexibility and operability. Therefore, it has become an important method for constructing gamma-ray spectrum samples.

[0004] Currently, there are generally two computer simulation methods for generating simulated gamma-ray spectrum samples: the Monte Carlo simulation method and the empirical formula method. The Monte Carlo simulation method, based on the Monte Carlo algorithm, simulates all processes of gamma rays from emission to interaction with matter and finally energy deposition in the detector through random sampling, and then statistically analyzes the final calculation results to generate simulated gamma-ray spectrum samples. The empirical formula method generates simulated gamma-ray spectrum samples by fitting and interpolating piecewise numerical values ​​to the measured gamma-ray spectrum.

[0005] However, regardless of the method used, existing simulated gamma spectrum sample generation techniques are all based on a single measurement scenario. That is, the generated simulated gamma spectrum corresponds only to a specific scenario set during simulation (for example, a scenario where a φ3×3-inch NaI(Tl) detector measures 1460keV gamma rays emitted by a radionuclide from a Ф25×50cm cylindrical water body in unshielded air). Since the morphology of the gamma spectrum is closely related to the interaction process between the rays and matter, the morphology of the gamma spectrum varies greatly under different measurement scenarios (including different types of radionuclides, different geometric and physical states of the radioactive source, different detector types, and different geometric and physical conditions of the surrounding matter). Therefore, the gamma spectrum sample generated under a specific single scenario can only reflect the gamma spectrum characteristics of that scenario, and the spectrum analysis algorithm learned from it can only be used for that scenario, and is ineffective for other scenarios. This greatly reduces the practical value of these gamma spectrum samples and limits the applicability of analysis algorithms trained based on these samples. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the purpose of this invention is to provide a method, storage medium, and system for generating gamma spectra based on Monte Carlo simulations. This enables the generated samples to cover the true morphological characteristics of gamma spectra under diverse measurement scenarios in actual measurements, thereby overcoming the limitation of existing sample generation technologies that are only applicable to a single measurement scenario, expanding the applicability of samples, and ultimately enhancing the universality of gamma spectrum analysis technology.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is: a method for generating a gamma spectrum based on Monte Carlo simulation, comprising the following steps: defining the gamma-ray transport space as a cube and cutting it into multiple equal parts, and filling the multiple equal parts with a predetermined material according to the relative probability value; setting the detector geometry, using a cylinder to represent the detector geometry; setting the gamma spectrum broadening; determining the gamma-ray type and defining the number and position of source particles.

[0008] Furthermore, the γ-ray transport space is defined as a 3m×3m×3m cube, and the equally divided small blocks are 1000 small grid cells of 0.3m×0.3m×0.3m.

[0009] Furthermore, the preset materials include air, concrete, water, stainless steel, and lead.

[0010] Furthermore, when setting the detector geometry, the cylinder diameter and axial length are both taken as random floating-point numbers, ranging from 2.5 to 8.0 cm.

[0011] Furthermore, the broadening formula for setting the γ-ray spectrum broadening is as follows:

[0012]

[0013] Where G(E) represents the broadening at energy E, in MeV; E is the energy, in MeV; and a is a floating-point number, ranging from 0.02439 to 0.0813.

[0014] Furthermore, random integers are used to determine the total number of gamma ray types, ranging from 1 to 10; for each gamma ray, random floating-point numbers are used to determine its specific energy, ranging from 0.01 to 3.00 MeV, and random floating-point numbers are used to determine its relative sampling probability, ranging from 1.00 to 10.00.

[0015] Furthermore, the number of source particles is defined using random integers, ranging from 10. 6 -10 10 .

[0016] Furthermore, when setting the source particle position, the number of positions is determined by random integers, ranging from 1 to 5. The x, y, and z coordinate values ​​of each position are taken as random floating-point numbers, ranging from 0 to 3m, and it is ensured that the source particle is not inside the detector. The relative sampling probability of the source particle at each position is taken as a random floating-point number, ranging from 1.0 to 10.0.

[0017] The present invention also provides a storage medium storing a computer program thereon, which, when executed by a processor, implements a method for generating gamma spectra based on Monte Carlo simulation.

[0018] The present invention also provides a gamma-ray spectrum generation system based on Monte Carlo simulation, comprising: a cutting and filling unit for defining the gamma-ray transport space as a cube and cutting it into multiple equal parts, and filling the multiple equal parts with a predetermined material according to a relative probability value; a detector definition unit for setting the detector geometry, using a cylinder to represent the detector geometry; a broadening definition unit for setting the gamma-ray spectrum broadening; and a ray and source particle definition unit for determining the type of gamma ray and defining the number and position of source particles.

[0019] The advantages of this invention are: by using a special Monte Carlo technique, a large number of highly realistic general gamma spectrum samples can be generated, so that the generated samples can cover the real morphological characteristics of the energy spectrum under diverse measurement scenarios in actual measurements. This breaks through the limitation of existing sample generation technologies that are only applicable to a single measurement scenario, and improves the versatility and universality of gamma spectrum samples and related analysis algorithms. Attached Figure Description

[0020] Figure 1 This is a flowchart of the steps of a method for generating a gamma spectrum based on Monte Carlo simulation in this invention. Detailed Implementation

[0021] The present invention will now be further described with reference to the accompanying drawings and specific embodiments.

[0022] The simulated gamma spectrum is generated based on the Monte Carlo simulation method. By using special Monte Carlo simulation techniques, such as special transport space settings, special source term generation methods, and special counter configurations, the diversity of measurement scenarios that may occur in actual measurements (including the types of radionuclides, detector energy resolution, surrounding medium geometry, etc.) can be reproduced. This allows the generated gamma spectrum samples to cover the true morphological characteristics of the spectrum under various conditions.

[0023] like Figure 1 As shown, this invention proposes a method for generating γ-ray spectra based on Monte Carlo simulations, comprising the following steps:

[0024] S1, define the γ-ray transport space as a cube, cut it into multiple equal parts, and fill the multiple equal parts with a predetermined material according to the relative probability value;

[0025] Specifically, considering the diversity of space size and the composition and structure of surrounding materials (excluding radiation sources and detectors) in the measurement scenario, the gamma-ray transport space is defined as a 3m×3m×3m cube, which is then divided into 1000 small cells of 0.3m×0.3m×0.3m. The preset materials include air, concrete, water, stainless steel, and lead; that is, each cell is randomly filled with air, concrete, water, stainless steel, or lead. The specific relative probability values ​​for each material are shown in Table 1. These relative probabilities are estimated based on the volumetric composition of surrounding materials in typical radiation detection scenarios.

[0026]

[0027] Table 1

[0028] Since the maximum transport distance of gamma rays in conventional actual measurements in the field of radiation detection is generally several meters, with a typical value of 3 meters, and the typical surrounding materials are air, building walls, water, steel structural materials, and shielding lead blocks, with typical volumes as shown in Table 1, filling 1000 small grid cells of the above five materials in a 3m×3m×3m transport space in a random manner can better cover the common space size in actual measurements and can better reproduce the diversity of the surrounding materials in terms of geometric distribution.

[0029] S2, configure the detector geometry, using a cylinder to represent the detector geometry;

[0030] Specifically, considering the diversity of gamma-ray detector sizes, a cylinder is used to represent the detector geometry when setting up the detector geometry. The diameter of the cylinder is a random floating-point number (range 2.5-8.0, unit is cm), and the length of the cylinder axis is a random floating-point number (range 2.5-8.0, unit is cm).

[0031] Since scintillator detectors, such as NaI(Tl) and LaBr3(Ce), are commonly used in conventional measurements in the field of radiation detection, their typical shape is a standard cylinder, and their typical dimensions are generally φ1×1 (unit: inch) or φ3×3 (unit: inch). Therefore, setting the detector geometry to a cylinder with a diameter and axial length randomly selected between 2.5-8.0 cm can better reproduce the diversity of detector dimensions in actual measurements.

[0032] S3, configure γ-ray spectrum broadening;

[0033] Specifically, considering the diversity of energy resolution in gamma-ray detectors, the following broadening formula is used when setting the gamma-ray spectrum broadening:

[0034]

[0035] Where G(E) represents the broadening at energy E, in MeV; E is the energy, in MeV; and a is a floating-point number, ranging from 0.02439 to 0.0813.

[0036] Since the formula for calculating energy resolution is:

[0037] FWHM = G(E0) / E0 × 100%

[0038] Where FWHM is the energy resolution of the gamma-ray detector, which is dimensionless; E0 = 0.661 MeV.

[0039] Therefore, calculations show that the energy resolution of the simulated gamma spectrum sample is between 3% and 7%. Since scintillator detectors, such as NaI(Tl) and LaBr3(Ce), are commonly used in actual measurements in the field of radiation detection, their typical energy resolution is between 3% and 7%. Thus, the gamma spectrum sample generated in this way can better cover the diverse factors of detector energy resolution in actual measurements.

[0040] S4, determine the type of gamma ray and define the number and location of source particles;

[0041] Specifically, considering the diversity of radionuclides, when setting the source particles, a random integer (range 1-10) is used to determine the total number of gamma-ray types. For each gamma-ray, a random floating-point number (range 0.01-3.00, unit MeV) is used to determine its specific energy, and a random floating-point number (range 1.00-10.00) is used to determine its relative sampling probability. This results in simulated gamma-ray energy spectrum samples containing 1-10 types of gamma rays, with ray energies covering 0.01-3.00 MeV, and a relative ratio of 1 / 10 to 10 between the number of rays.

[0042] In the field of radiation detection, the types of radioactive nuclides used in routine measurements are generally 1-5, and the gamma rays emitted are generally no more than 10; while common nuclides such as 137 Cs、 60 Co, etc., have energies ranging from 0.01 to 3.00 MeV; furthermore, among different gamma rays, if the quantity of one type is less than 1 / 10 of the others, that ray can generally be ignored. Therefore, in summary, the gamma spectrum samples generated in this way can better cover the diversity of radionuclide species in actual measurements.

[0043] To account for the diversity of radionuclide activity and measurement time, a random integer (range 10) is used when setting the number of source particles. 6 -10 10 Define the number of source particles.

[0044] Since the typical activity of radionuclides in conventional practical measurements in the field of radiation detection ranges from several microcitrants (μCi) to several millicitrants (mCi), and the typical measurement time is several minutes, based on a 100% gamma-ray yield, a 1 μCi source emits approximately 10 gamma rays per minute. 6 A 1 mCi radiation source emits approximately 10 gamma rays in 10 minutes. 10 Therefore, this method can better cover the comprehensive diversity of radionuclide activity and measurement time in actual measurements.

[0045] To address the spatial diversity of radionuclides, the number of locations (range 1-5) is determined by random integers when setting the source particle positions. The x, y, and z coordinates of each location are taken as random floating-point numbers (range 0-3, unit m). However, it must be ensured that the source particles are not inside the detector. The relative sampling probability of the source particles at each location is taken as a random floating-point number (range 1.0-10.0).

[0046] Since the number of source particle locations in conventional actual measurements in the field of radiation detection is generally no more than 5, this can better cover the diversity of the spatial distribution of radionuclides in actual measurements.

[0047] Taking the simulated gamma spectrum generated by the Monte Carlo simulation program MCNP as an example, this paper illustrates how to implement the method:

[0048] MCNP uses input cards to define all simulation parameters (including transport space size, number of source particles, energy, etc.). A single input card can only generate a gamma spectrum with fixed parameters. To generate diverse gamma spectra with varying parameters, external programs (such as C++ or Matlab programs) should be used to automatically generate input cards in batches to change the parameters in the input cards for each simulation.

[0049] In practice, an infinite sequence of grid cells in space is first generated using an infinite grid cell generation card (lat). Filler cells (u = 1 / 2 / 3 / 4 / 5) represent air, concrete, water, stainless steel, and lead grid cells, respectively. Then, 1000 small grid cells (0.3m × 0.3m × 0.3m) are generated using a grid cell filling card (fill) and randomly filled with air, concrete, water, stainless steel, and lead grid cells. The relative filling probability of each material is controlled by an external program.

[0050] Then, the detector cylinder is defined using geometric surfaces such as CZ and PZ, and the diameter and axial length of the cylinder in each input card are randomly selected within the range of 2.5-8.0 cm by an external program.

[0051] Next, the γ-ray spectrum broadening function is defined using the FT8 card. The specific format of the FT8 card is as follows:

[0052] GEB 0a 0

[0053] The value of 'a' is controlled by an external program and is randomly selected between 0.02439 and 0.0813 each time.

[0054] Finally, the types and energies of gamma rays are defined using the SDEF card and the distribution function D1, as follows:

[0055] SDEF erg=D1

[0056] SI1…

[0057] SP1 d…

[0058] The specific contents of SI1 and SP1 in each input card are controlled by an external program, so that the total number of gamma ray types used is between 1 and 10, the energy of each gamma ray is between 0.01 and 3.00 MeV, and the relative sampling probability of each gamma ray is randomly selected between 1.00 and 10.00.

[0059] The number of source particles is defined using an NPS card, and its value is randomly selected within the range of 10⁶ to 10¹⁰ by an external program.

[0060] The source particle position is defined using a POS card and the distribution function D2, as follows:

[0061] POS = D2

[0062] SI2…

[0063] SP2 d…

[0064] The specific contents of SI2 and SP2 in each input card are controlled by an external program, so that the number of source particle positions is between 1 and 5, the x, y, and z coordinates of each source particle position are between 0 and 3m, and the relative sampling probability of the source particle at each position is randomly selected between 1.0 and 10.0.

[0065] The present invention also provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of a method for generating a gamma spectrum based on Monte Carlo simulation.

[0066] It should be noted that the storage medium shown in this application can be a computer-readable signal medium or a storage medium, or any combination of the two. The storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or any combination thereof. More specific examples of the storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, the storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. In this application, the storage medium can include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The storage medium may also be any computer-readable medium other than a storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, system, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0067] The present invention also provides a γ-ray spectrum generation system based on Monte Carlo simulation, comprising:

[0068] A cutting and filling unit is used to define the gamma-ray transport space as a cube, cut it into multiple equal parts, and fill the multiple equal parts with a predetermined material according to a relative probability value.

[0069] The detector definition unit is used to set the detector geometry, and a cylinder is used to represent the detector geometry.

[0070] The broadening definition unit is used to set the γ-ray spectrum broadening.

[0071] The ray and source particle definition unit is used to determine the type of gamma ray and define the number and location of source particles.

[0072] As can be seen from the above embodiments, the beneficial effects of the present invention are as follows: by using a special Monte Carlo technique, a large number of highly realistic general gamma spectrum samples can be generated, so that the generated samples can cover the real morphological characteristics of the energy spectrum under diverse measurement scenarios in actual measurements, thereby breaking through the limitation of existing sample generation technologies that are only for a single measurement scenario, and improving the versatility and universality of gamma spectrum samples and related analysis algorithms.

[0073] The present invention is not limited to the embodiments described in the specific implementation. Other implementation methods derived by those skilled in the art based on the technical solution of the present invention also fall within the scope of the technical innovation of the present invention.

Claims

1. A method for generating gamma-ray spectra based on Monte Carlo simulations, characterized in that, include: The gamma-ray transport space is defined as a cube, and cut into multiple equal parts. A predetermined material is filled into the multiple equal parts according to the relative probability value. The detector geometry is set up using a cylinder to represent it. Configure γ-ray spectrum broadening settings; Determine the type of gamma ray and define the number and location of source particles; When setting the detector geometry, the cylinder diameter and axial length are both taken as random floating-point numbers, ranging from 2.5 to 8.0 cm. The broadening formula for setting the γ-ray spectrum broadening is as follows: Where G(E) represents the broadening at energy E, in MeV; E is the energy, in MeV; and a is a floating-point number, ranging from 0.02439 to 0.0813. The total number of gamma ray species was determined using random integers, ranging from 1 to 10. The number of source particles is defined using random integers, ranging from 10. 6 -10 10 ; When setting the source particle position, a random integer is used to determine the number of positions, ranging from 1 to 5.

2. The method for generating a γ-ray spectrum based on Monte Carlo simulation as described in claim 1, characterized in that: The gamma-ray transport space is defined as a 3m×3m×3m cube, and the equally divided small blocks are 1000 small grid cells of 0.3m×0.3m×0.3m.

3. The method for generating a γ-ray spectrum based on Monte Carlo simulation as described in claim 1, characterized in that: The predetermined materials include air, concrete, water, stainless steel, and lead.

4. The method for generating a γ-ray spectrum based on Monte Carlo simulation as described in claim 1, characterized in that: For each type of gamma ray, a specific energy was determined using random floating-point numbers, ranging from 0.01 to 3.00 MeV, and a relative sampling probability was determined using random floating-point numbers, ranging from 1.00 to 10.

00.

5. The method for generating a γ-ray spectrum based on Monte Carlo simulation as described in claim 1, characterized in that: When setting the source particle position, the x, y, and z coordinate values ​​of each position are taken as random floating-point numbers, ranging from 0 to 3m, and it is ensured that the source particle is not inside the detector. The relative sampling probability of the source particle at each position is taken as a random floating-point number, ranging from 1.0 to 10.

0.

6. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the method for generating a gamma spectrum based on Monte Carlo simulation as described in any one of claims 1 to 5.

7. A γ-ray spectrum generation system based on Monte Carlo simulation, characterized in that, include: A cutting and filling unit is used to define the gamma-ray transport space as a cube, cut it into multiple equal parts, and fill the multiple equal parts with a predetermined material according to a relative probability value. The detector definition unit is used to set the detector geometry, and a cylinder is used to represent the detector geometry. The broadening definition unit is used to set the γ-ray spectrum broadening. The ray and source particle definition unit is used to determine the type of gamma ray and define the number and location of source particles; When setting the detector geometry, the cylinder diameter and axial length are both taken as random floating-point numbers, ranging from 2.5 to 8.0 cm. The broadening formula for setting the γ-ray spectrum broadening is as follows: Where G(E) represents the broadening at energy E, in MeV; E is the energy, in MeV; and a is a floating-point number, ranging from 0.02439 to 0.0813. The total number of gamma ray species was determined using random integers, ranging from 1 to 10. The number of source particles is defined using random integers, ranging from 10. 6 -10 10 ; When setting the source particle position, a random integer is used to determine the number of positions, ranging from 1 to 5.

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