A method and system for three-dimensional inversion of airborne radioactivity spectrum data

Abstract

This invention discloses a three-dimensional inversion method and system for airborne radiometric spectrum data, aiming to improve the accuracy of three-dimensional airborne gamma-ray spectrum data inversion methods under UAV platforms. The method first acquires airborne radiometric spectrum data from various measuring points within the inversion area using airborne detection equipment to construct an observation data vector. Then, an irregular triangular mesh is used to discretize the inversion area for three-dimensional terrain modeling. Next, a rock property model constraint function based on the principle of dissimilarity is introduced into the inversion objective function. Finally, the model parameter vector is output through iterative inversion calculations to obtain the distribution of radionuclides in the inversion area. This invention develops a three-dimensional inversion technology for airborne radiometric spectrum data based on rock property constraints of dissimilarity. By using a constraint function with multi-peak characteristics, a one-to-one correspondence between rock properties and extreme points is achieved, ensuring the principle of dissimilarity. Ultimately, this effectively reduces the problem of multiple solutions in the interpretation of airborne spectrum data inversion, improving the accuracy and effectiveness of interpretation.

Description

Technical Field

[0001] This invention relates to the field of three-dimensional inversion technology of airborne radiometric energy spectrum data under unmanned aerial vehicle (UAV) platforms in the field of radiometric geophysics, and particularly to a method and system for three-dimensional inversion of airborne radiometric energy spectrum data. Background Technology

[0002] In the field of airborne geophysical exploration, 3D airborne gamma-ray spectroscopy data inversion is an important radiophysical method widely used in mineral resource exploration. In the past, most traditional 3D airborne gamma-ray spectroscopy data were not inverted; only height correction was applied to represent the distribution of radionuclides in the subsurface medium. This method failed to accurately reflect the content of radionuclides. Only a small number of conventional optimized inversion studies were conducted. However, conventional optimized inversion does not consider prior information about the radionuclides in the subsurface medium, leading to significant discrepancies between the inversion results and the actual radionuclide content, resulting in poor inversion quality. Furthermore, traditional airborne radiometric inversion methods do not consider the impact of complex terrain, only enabling flat-surface modeling. Inversion of airborne radiometric data without considering terrain significantly affects the accuracy of data interpretation, easily masking effective signals and leading to distorted inversion results. Summary of the Invention

[0003] This invention aims to address the accuracy improvement problem of 3D airborne gamma-ray spectroscopy data inversion methods under UAV platforms and to solve the technical problem of inversion result distortion under complex and undulating terrain conditions. To this end, the invention proposes a 3D inversion method and system for airborne radiometric spectroscopy data. To adapt to complex and undulating geological environments, irregular triangular mesh discretization technology is directly used to discretize the exploration area, better fitting the complex and undulating terrain and reducing the impact of terrain. Based on this, an objective function for airborne radiometric spectroscopy data inversion is constructed. A distinct rock property function constraint technique is introduced into the objective function. This distinct rock property function constraint technique can effectively incorporate prior information. Combined with the Gauss-Newton inversion optimization algorithm, the accuracy of airborne radiometric spectroscopy data inversion results is significantly improved, providing reliable technical support for precise mineral resource exploration. This technology performs particularly well in complex geological environments.

[0004] Therefore, the present invention provides the following technical solution:

[0005] On the one hand, the present invention provides a three-dimensional inversion method for airborne radiometric energy spectrum data, comprising the following steps:

[0006] Step 1: Airborne detection. Airborne radiometric energy spectrum data of each measuring point in the inversion area are obtained through airborne detection equipment, and then the observation data vector is constructed.

[0007] Step 2: 3D terrain modeling. Based on mesh discretization, 3D terrain modeling is performed on the inversion area, and initial values ​​of the model parameter vector are set. Model parameter vector It consists of the rock properties of all underground physical property units, and the rock properties of underground physical property units are the values ​​or amounts of radionuclides;

[0008] Step 3: Constructing the inversion objective function. The inversion objective function is constructed by introducing the rock property model constraint function based on the principle of dissimilarity. The inversion objective function includes data fitting terms, model fitting terms, and rock property model constraint function terms.

[0009] Step 4: Inversion operation, based on the observation data vector from Step 1 and the initial values ​​of the model parameter vector from Step 2. The inversion objective function is optimized iteratively until the iteration termination condition is met, and the model parameter vector is output. The distribution of radionuclides in the inversion region was obtained.

[0010] Optionally, the constraint function term of the rock property model is expressed as:

[0011]

[0012] In the formula, the model parameter vector , Let J be the value of the radionuclide in the j-th subsurface physical property unit. Given the rock physical property vectors, As for rock types, For the first The values ​​of radioactive nuclides in known rocks, The number of underground physical property units. Step size, Step size, For the first The variance of the physical properties of a type of rock.

[0013] Optionally, the iterative calculation process in step 4 is as follows: first, the Gauss-Newton optimization algorithm is used to solve for the model change in the inversion objective function. Then update the model parameter vector. Then proceed to the next iteration.

[0014] Among them, when using the Gauss-Newton optimization algorithm, the model change in the k-th iteration is... The solution equation is:

[0015]

[0016] In the formula, For the sensitivity matrix, The variance matrix of the data. This represents the model weight function. For the first The corresponding model parameter vector. For the first The difference between the forward data vector calculated by the inversion model obtained in the next iteration and the observed data vector, where T is the matrix transpose. The weighting factors are the constraint functions of the rock property model. These are the weighting factors for the model fitting terms. The second derivative vector of the distinct rock property constraint function with respect to the model parameter vector , The first derivative vector of the rock property constraint function with respect to the model parameter vector is the distinct rock property constraint function. .

[0017] Optionally, the weighting factors of the model fit term It changes dynamically in different iteration rounds, satisfying: , For the first The weight factor for the (k-1)th inversion iteration.

[0018] Optionally, in step 2, an irregular triangular mesh is used to discretize the inversion region, wherein the inversion region is discretized into triangular units of variable size.

[0019] Secondly, the present invention provides a system based on the above-mentioned three-dimensional inversion method for airborne radiometric energy spectrum data, comprising:

[0020] The detection module is used to acquire airborne radiometric energy spectrum data of various measuring points in the inversion area through airborne detection equipment, and then construct the observation data vector.

[0021] The 3D terrain modeling module is used to perform 3D terrain modeling on the inverted area based on grid discretization and to set the initial values ​​of the model parameter vector. Model parameter vector It consists of the rock properties of all underground physical property units, and the rock properties of underground physical property units are the values ​​or amounts of radionuclides;

[0022] The inversion objective construction module is used to construct the inversion objective function by introducing the rock property model constraint function based on the principle of dissimilarity. The inversion objective function includes data fitting terms, model fitting terms, and rock property model constraint function terms.

[0023] The inversion operation module is used to perform operations based on the observation data vector and the initial values ​​of the model parameter vector. The inversion objective function is optimized iteratively until the iteration termination condition is met, and the model parameter vector is output. The distribution of radionuclides in the inversion region was obtained.

[0024] In three aspects, the present invention provides an inversion system based on the above-mentioned three-dimensional inversion method of airborne radiometric energy spectrum data, comprising: a UAV, an airborne gamma spectrometer mounted on the UAV, and a control terminal communicating with the airborne gamma spectrometer.

[0025] The control terminal acquires airborne radiometric energy spectrum data of the measuring point through an airborne gamma spectrometer, and uses the three-dimensional inversion method of the airborne radiometric energy spectrum data to obtain the distribution of radionuclides in the inversion region.

[0026] In four aspects, the present invention provides an electronic terminal, comprising: one or more processors and a memory storing one or more computer programs;

[0027] The processor invokes a computer program to achieve the following:

[0028] The steps of the above-mentioned three-dimensional inversion method for airborne radiometric energy spectrum data.

[0029] Fifthly, the present invention provides a computer-readable storage medium storing a computer program, which is invoked by a processor to implement the steps of the above-described three-dimensional inversion method for airborne radiometric energy spectrum data.

[0030] Compared with the prior art, the present invention achieves the following improvements:

[0031] 1. This invention effectively solves the problem of poor reliability of inversion results caused by insufficient introduction of rock properties during the inversion process by introducing a rock property constraint function based on the principle of dissimilarity into the inversion objective function. Simultaneously, this constraint function... This invention possesses multi-peak characteristics (multiple extreme points), enabling a one-to-one correspondence between rock properties and extreme points, thus achieving accurate property introduction. In contrast, traditional property functions only have a single extreme point, which can be assigned to different properties. Therefore, this invention solves the problem in traditional inversion where model units cannot belong to a single rock property, ensuring that each model unit belongs to only one rock property. Furthermore, it considers the variance characteristics of the properties during the inversion iteration process. This significantly improves the uniqueness and reliability of the inversion results.

[0032] 2. This invention utilizes unstructured triangular meshes to discretize geological models of complex undulating terrain, which can more accurately characterize the gradual boundaries of adjacent anomaly units, effectively solve the problem of false anomalies caused by data errors due to inaccurate terrain fitting, and improve the accuracy of data interpretation.

[0033] 3. This invention combines the Gauss-Newton optimization algorithm with parallel computing technology, which significantly improves the inversion efficiency of large-scale three-dimensional airborne gamma spectrum data. The calculation speed is 2-3 times faster than traditional methods, making it suitable for the rapid processing needs in actual exploration. Attached Figure Description

[0034] Figure 1 A flowchart of a three-dimensional inversion method for airborne radiometric energy spectrum data provided by the present invention;

[0035] Figure 2 It is a three-dimensional schematic diagram of the theoretical model;

[0036] Figure 3 This is a schematic diagram of the forward modeling results.

[0037] Figure 4 This is a schematic diagram of the theoretical model inversion results;

[0038] Figure 5 This is a schematic diagram of field data collection. In the diagram, o is the ground projection point of the UAV, p is a source point, R is the distance between o and p, r is the distance between the UAV and the source point, and h is the vertical altitude of the UAV. Let be the detection angle of a certain source point p. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. The technical features involved in the various embodiments of the invention described below can be combined with each other as long as they do not conflict with each other.

[0040] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0042] This invention provides a three-dimensional inversion method for airborne radiometric spectrum data. It incorporates rock property constraint functions based on the principle of dissimilarity into the radiometric inversion method. The inversion process involves rationally dividing the observation data into a grid to obtain an appropriate grid density, optimizing the objective function using the Gauss-Newton method, achieving soft partitioning of geological body boundaries and parameter gradient optimization. Furthermore, it can be combined with parallel computing technology, significantly improving the ability to characterize anomalies at adjacent boundaries and enhancing the accuracy and efficiency of the inversion. It should be understood that for the interpretation of UAV airborne radiometric spectrum exploration data in complex geological environments, utilizing a UAV platform equipped with a radiometric spectrum sensor to conduct radiometric spectrum data observation can solve the problem of detecting the total amount of geological radionuclides within the survey area. Therefore, this invention is highly suitable for the interpretation of airborne radiometric spectrum data in complex geological environments, greatly improving the interpretation level and accuracy of airborne radiometric spectrum data in exploration areas.

[0043] The invention will be further illustrated below with examples.

[0044] This invention provides a method for three-dimensional inversion of airborne radiometric energy spectrum data, comprising the following steps:

[0045] Step S1: Acquire airborne radiometric energy spectrum data at the measurement points and construct the observation data vector. The process involves determining the inversion region based on the measurement point location, then discretizing the inversion region using an irregular triangular mesh, and setting initial values ​​for the model parameter vectors corresponding to the initial mesh. .

[0046] Measurement point data: In this embodiment, measurement points are set up in the inversion area, and airborne radiometric spectrum data of the measurement points are acquired using an airborne gamma spectrometer mounted on an unmanned aerial vehicle. The airborne radiometric spectrum data of all measurement points are then used to construct an observation data vector. , , Number of measurement points; The nuclide count rate (gamma ray counts detected per unit time, commonly measured in s⁻¹) is the nuclide count rate measured at the i-th measuring point. The nuclide count rate at a measuring point is the number of rays detected by the flight platform and is related to factors such as the ground source term content, field of view, and flight altitude. Therefore, this data reflects the natural radionuclides (such as gamma rays detected per unit time) within a certain range on or near the Earth's surface. 40 K, 238 U、 232 The gamma radiation level released by the decay of Th series radioactive elements is the basis for calculating the content of radioactive elements, identifying nuclide types, and assessing radiation levels. This example uses the nuclide count rate as the acquired airborne radiometric energy spectrum data for illustration.

[0047] It should be understood that observation points exist within the inversion region. In some embodiments, the coordinates of the observation points are used... The length between the maximum and minimum values ​​in ( The inversion calculation region is determined by multiplying the region by 2-3 times.

[0048] Grid generation of the inversion area: To adapt to the complex undulating terrain and geological environment, it is preferable to directly use irregular triangular mesh discretization technology to discretize the exploration area, which better fits the complex undulating terrain and reduces the influence of terrain. Therefore, in this embodiment, a uniform mesh partitioning strategy is adopted to discretize the solution area. Each subsurface triangular unit after discretization is a physical property unit, and the model parameter vector... It consists of the rock properties of all physical property units, and the rock properties of the physical property units are the values ​​or amounts of radionuclides.

[0049] Step S2: Construct an objective function for airborne radiometric energy spectrum data based on rock property constraints according to the principle of distinctness, and use the Gauss-Newton optimization algorithm to optimize the objective function until the iteration termination condition is met, and obtain the inversion result.

[0050] First, the objective function for inverting airborne radiometric energy spectrum data is defined as:

[0051]

[0052] In the formula, It is the objective function. For model parameter vectors, It is an observation data vector composed of airborne radiometric energy spectrum data from measuring points. It is a data fitting term. This is a forward modeling operator, where the forward modeling is a linear problem, utilizing the model parameter vector. The measurement data obtained from the actual performance is achievable with existing technology; The variance matrix of the data; It is a model fitting term. This is the initial model (initial values ​​for the model parameter vector). The model space variation feature constraint matrix; For model fitting terms The weighting factor in different iteration rounds Dynamic changes, satisfying: , For the first The weight factor for the (k-1)th inversion iteration, based on the above formula, is... As the iteration process continues to decrease; The constraint functions for the rock property model based on the principle of distinctness are: Constraint functions for rock property model The weighting factors remain constant throughout the inversion process, and are used to balance the proportion of the rock property constraint functions that are dissimilar during the inversion process. for:

[0053]

[0054] In the formula, the model parameter vector , The value of the radionuclide (rock property) for the j-th subsurface property unit. This represents the known rock property vector, i.e., the value of the radionuclide in a known element; As for rock types, For the first The values ​​of radioactive nuclides in known rocks, The number of underground physical property units. Step size, Step size, For the first The variance of the physical properties of the rock It is a mathematical function of multiple products.

[0055] It should be understood that, based on the principle of distinctness, each subsurface triangular unit's physical property (content of reflective nuclides) can only correspond to one known rock physical property, and cannot correspond to multiple property values. Therefore, this invention proposes the aforementioned constraint function. The function has multiple peaks, each corresponding to a rock property value, ensuring a one-to-one correspondence between rock properties and extreme points.

[0056] The variance matrix of the data is expressed as follows:

[0057]

[0058] in, To ensure that the denominator is an arbitrary positive real number that is not zero, n is the number of observation data; Representing observation data The variance.

[0059] Based on the above formula, we can solve the problem of optimizing the inversion objective function, and obtain the Gauss-Newton iteration formula for the k-th inversion iteration:

[0060]

[0061] In the formula, For the sensitivity matrix, The variance matrix of the data. This represents the model weight function. The second derivative vector of the distinct rock property constraint function with respect to the model parameter vector , Model change;

[0062] For the first The model parameter vector of the corresponding inversion model. For the first The difference between the forward data calculated by the inversion model obtained in the next iteration and the observed data. This indicates the transpose of the sensitivity matrix. This represents the transpose of the model weight function. The first derivative vector of the rock property constraint function with respect to the model parameter vector is the distinct rock property constraint function. Specifically, and The expression is as follows:

[0063]

[0064]

[0065] Among them, a single rock property constraint function is defined. , , For model parameter vectors, This is the prior information for the model (known rock property vectors). This represents the variance of the model's physical properties.

[0066] Sensitivity matrix The calculation formula is:

[0067]

[0068] In the formula, the sensitivity matrix It is a partial derivative matrix, and it is a full matrix. It is calculated using the derivatives of the observed data with respect to the model parameters. Storing the partial derivative matrix requires a large amount of memory, and calculating the partial derivative matrix is ​​often the most time-consuming part of solving the inverse problem.

[0069] In addition, to ensure that the objective function decreases sufficiently in each iteration, a linear search is performed on the model update step size, expressed as:

[0070]

[0071] In the formula, for The parameters of the inversion model. for The parameters of the inversion model are as follows: To change the quantity for the model,

[0072] Based on the above theoretical statements, it can be concluded that, Figure 1As shown, the technical solution of this invention first determines the inversion region based on airborne radiometric energy spectrum data, and then discretizes the inversion region using an irregular triangular mesh to set an initial model. Next, an objective function for airborne radiometric energy dispersive spectroscopy (ERS) data constrained by rock physical properties based on the principle of distinctness is constructed. The Gauss-Newton optimization algorithm is then used to optimize and solve the objective function. In each iteration, the change in the model is calculated. And linear search step size, and use model to change the quantity and the initial model The next iteration model is calculated until the inversion termination condition is met, and the inversion ends.

[0073] It should also be understood that the above regularization factor This is a balancing parameter, its function is to balance the relationship between the data error function and the model error function. Typically, inversion involves first fitting the model error function; therefore, in this embodiment, the initial parameter is... Setting a relatively large value changes the relationship between data error and model error as the inversion process progresses, thereby reducing... This continues until the inversion is complete. In other feasible examples, a fixed value can also be selected; this invention does not impose specific limitations on this.

[0074] In this embodiment, the termination condition is set as: root mean square error of the data. The number of iterations is less than the preset threshold or the set number of iterations has been reached. The preset threshold is usually set to a small value. This represents the data from the i-th observation point. This represents the data at the observation point obtained by forward modeling after each inversion iteration. It should be understood that in this invention, inversion stops when either of the two termination conditions is met. Other feasible embodiments, adjusted according to actual application needs, also fall within the scope of this invention.

[0075] Design as Figure 2The typical three-dimensional radioactive element uranium distribution model shown is used to verify the proposed three-dimensional airborne gamma energy spectrum data inversion method with rock physical property constraints based on the principle of reciprocity. Considering the common regional scale in actual exploration work, the model space range is set to 600 meters in the x-axis direction and 600 meters in the y-axis direction. To simulate real geological conditions, digital terrain data is introduced. The terrain height is set between -20 and 20 meters. The background source activity of the model is 1 ppm. Abnormal bodies are set inside the model. The abnormal body is the Chinese character "王". The source content of the upper and lower horizontal lines is 20 ppm, and the source content of the middle plus sign is 10 ppm. The width of the letter is 40 meters, and the lengths in the x-axis and y-axis directions are both 320 meters. There are mainly two ways of unmanned aerial vehicle flight, namely fixed-height flight and parallel terrain flight. To show the influence of the terrain, in this example, fixed-height flight will be adopted, that is, the unmanned aerial vehicle maintains an absolute height and forms a relative height with the terrain. This method is more convenient in actual operation and can fully reflect the distortion effect of the terrain on the energy spectrum data. In this example, the flight height is set to 40 meters to ensure meeting the safe flight requirements.

[0076] Adopt the Gaussian integration calculation strategy to calculate the airborne radioactive gamma energy spectrum data at any point of view. Figure 3 The schematic diagram of the forward calculation result is shown. Among them, the x-axis direction represents the measuring point direction (400 meters), the measuring point spacing is set to 4 meters, the y-axis direction represents the survey line direction (400 meters), and the survey line spacing is set to 4 meters. Due to the influence of the terrain elevation, the original energy spectrum data shows obvious terrain undulation distortion characteristics, especially in the protruding areas.

[0077] Based on the model and airborne radioactive gamma energy spectrum data described above, implement the inversion method with rock physical property constraints based on the principle of reciprocity proposed by the present invention. The inversion result is as Figure 4 shown. By comparing Figure 2 and Figure 4 , it can be found that this method can accurately restore the position and size of the abnormal body, can accurately depict the adjacent abnormal boundaries. By comparing Figure 3 and Figure 4 , it is found that the method proposed by the present invention can effectively overcome the influence of terrain undulation. At the same time, compared with the traditional method, this method introduces the function into the inversion objective function, and at the same time introduces the OpenMP technology in the forward simulation process, which increases the inversion speed by 2 - 3 times and solves the requirements for the interpretation of airborne radioactive data under a large-scale unmanned aerial vehicle platform.

[0078] Example 2

[0079] The present invention embodiment also provides a three-dimensional inversion method and system based on the above airborne radioactive energy spectrum data, including: a detection module, a three-dimensional terrain modeling module, an inversion target construction module, and an inversion operation module connected in sequence.

[0080] Among them, the detection module is used to acquire airborne radiometric energy spectrum data of each measuring point in the inversion area through airborne detection equipment, and then construct the observation data vector;

[0081] The 3D terrain modeling module is used to perform 3D terrain modeling on the inverted area based on mesh discretization and to set the initial values ​​of the model parameter vector. ;

[0082] The inversion objective construction module is used to construct the inversion objective function by introducing the rock property model constraint function of the dissimilarity principle. The inversion objective function includes data fitting term, model fitting term, and rock property model constraint function term.

[0083] The inversion operation module is used to perform operations based on the observation data vector and the initial values ​​of the model parameter vector. The inversion objective function is optimized iteratively until the iteration termination condition is met, and the model parameter vector is output. .

[0084] It should also be understood that the specific implementation process of each module is described in the above method. This invention will not repeat it here. The division of the above functional modules is only for illustrative purposes. In some embodiments, some functional modules can be merged and some functional modules can be split. Each functional module can be implemented in software, hardware, or a combination of software and hardware. The software and hardware devices include, but are not limited to, general electronic terminals, programmable gate arrays, digital signal processors, microprocessors and their corresponding programming or burning software.

[0085] Example 3

[0086] This invention also provides an inversion system based on the aforementioned three-dimensional inversion method for airborne radiometric spectrum data, comprising: a UAV, an airborne gamma spectrometer mounted on the UAV, and a control terminal communicatively connected to the airborne gamma spectrometer. The control terminal acquires airborne radiometric spectrum data of measurement points through the airborne gamma spectrometer and uses the three-dimensional inversion method for airborne radiometric spectrum data to obtain the inversion result of the inversion region. Figure 5 The diagram shown illustrates field data collection, demonstrating how this invention utilizes drones for data acquisition.

[0087] Example 4

[0088] The present invention provides an electronic terminal comprising: one or more processors and a memory storing one or more computer programs; wherein the processor invokes the computer programs to implement the steps of the above-described three-dimensional inversion method for airborne radiometric energy spectrum data.

[0089] For example, the following steps are performed: Step S1: Acquire airborne radiometric spectrum data of the measurement points, determine the inversion region based on the measurement point data, then discretize the inversion region using an irregular grid and set initial values ​​for the inversion parameter vector corresponding to the initial grid. Step S2: Construct an objective function for the airborne radiometric spectrum data based on the rock property constraints of the principle of distinctness, and use the Gauss-Newton optimization algorithm to optimize the objective function until the iteration termination condition is met, thus obtaining the inversion result.

[0090] For details on the implementation of each step, please refer to the description in the foregoing inversion method embodiments. In some embodiments, the electronic terminal mentioned above is the control terminal in Embodiment 3.

[0091] In some embodiments, the electronic components of the electronic terminal include:

[0092] The processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention.

[0093] The memory can be implemented in the form of read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory and the processor calls and executes the algorithm program of the above-described inversion method.

[0094] Input / output interfaces are used to implement information input and output.

[0095] The communication interface is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0096] A bus is used to transfer information between various components of a device, such as processors, memory, input / output interfaces, and communication interfaces.

[0097] The processor, memory, input / output interfaces, and communication interfaces communicate with each other within the device via a bus.

[0098] Example 5

[0099] The present invention provides a computer-readable storage medium storing a computer program, which is called by a processor to implement the steps of the above-described three-dimensional inversion method for airborne radiometric energy spectrum data.

[0100] For example, the following steps are performed: Step S1: Acquire airborne radiometric spectrum data of the measurement points, determine the inversion region based on the measurement point data, then discretize the inversion region using an irregular grid and set initial values ​​for the inversion parameter vector corresponding to the initial grid. Step S2: Construct an objective function for the airborne radiometric spectrum data based on fuzzy C-means clustering constraints, and use the Gauss-Newton optimization algorithm to optimize the objective function until the iteration termination condition is met, thus obtaining the inversion result.

[0101] The readable storage medium is a computer-readable storage medium, which can be an internal storage unit of the hardware and software device described in any of the foregoing embodiments, such as the hard drive or memory of the controller. The readable storage medium can also be an external storage device of the controller, such as a plug-in hard drive, Smart MediaCard (SMC), Secure Digital (SD) card, or Flash Card equipped on the controller. Further, the readable storage medium can include both internal storage units and external storage devices of the controller. The readable storage medium is used to store the computer program and other programs and data required by the controller. The readable storage medium can also be used to temporarily store data that has been output or will be output.

[0102] It should be emphasized that the examples described in this invention are illustrative rather than limiting. Therefore, this invention is not limited to the examples described in the specific embodiments. Any other embodiments derived by those skilled in the art based on the technical solutions of this invention, without departing from the spirit and scope of this invention, whether modifications or substitutions, are also protected by this invention.

Claims

1. A method for three-dimensional inversion of airborne radiometric energy spectrum data, characterized in that, Includes the following steps: Step 1: Airborne detection. Airborne radiometric energy spectrum data of each measuring point in the inversion area are obtained through airborne detection equipment, and then the observation data vector is constructed. Step 2: 3D terrain modeling. Based on mesh discretization, 3D terrain modeling is performed on the inversion area, and initial values ​​of the model parameter vector are set. Model parameter vector It consists of the rock properties of all underground physical property units, and the rock properties of underground physical property units are the values ​​or amounts of radionuclides; Step 3: Constructing the inversion objective: Introduce the rock property model constraint function based on the principle of dissimilarity into the inversion objective function. The inversion objective function includes data fitting terms, model fitting terms, and rock property model constraint function terms. Step 4: Inversion operation, based on the observation data vector from Step 1 and the initial values ​​of the model parameter vector from Step 2. The inversion objective function is optimized iteratively until the iteration termination condition is met, and the model parameter vector is output. The distribution of radionuclides in the inversion region was obtained.

2. The method according to claim 1, characterized in that: The constraint function term of the rock property model is expressed as follows: ； In the formula, the model parameter vector , Let J be the value of the radionuclide in the j-th subsurface physical property unit. Given the rock physical property vectors, As for rock types, For the first The values ​​of radioactive nuclides in known rocks, The number of underground physical property units. Step size, Step size, For the first The variance of the physical properties of a type of rock.

3. The method according to claim 2, characterized in that: The iterative calculation process in step 4 is as follows: First, the Gauss-Newton optimization algorithm is used to solve the inversion objective function to obtain the model change. Then use the model to change the quantity Update the current model parameter vector Then proceed to the next iteration. Among them, when using the Gauss-Newton optimization algorithm to solve the inversion objective function, the model change in the k-th iteration is... The solution equation is: ； In the formula, This is the sensitivity matrix. The variance matrix of the data. This represents the model weight function. For the first The corresponding model parameter vector. For the first The difference between the forward data vector calculated by the inversion model obtained in the next iteration and the observed data vector, where T is the matrix transpose. The weighting factors are the constraint functions of the rock property model. These are the weighting factors for the model fitting terms. The second derivative vector of the distinct rock property constraint function with respect to the model parameter vector , The first derivative vector of the rock property constraint function with respect to the model parameter vector is the distinct rock property constraint function. .

4. The method according to claim 3, characterized in that: Weighting factors of model fit terms It changes dynamically in different iteration rounds, satisfying: , For the first The weight factor for the (k-1)th inversion iteration.

5. The method according to claim 1, characterized in that: In step 2, an irregular triangular mesh is used to discretize the inversion region, wherein the inversion region is discretized into triangular units of variable size.

6. A system based on the method of any one of claims 1-5, characterized in that: include: The detection module is used to acquire airborne radiometric energy spectrum data of various measuring points in the inversion area through airborne detection equipment, and then construct the observation data vector. The 3D terrain modeling module is used to perform 3D terrain modeling on the inverted area based on grid discretization and to set the initial values ​​of the model parameter vector. Model parameter vector It consists of the rock properties of all underground physical property units, and the rock properties of underground physical property units are the values ​​or amounts of radionuclides; The inversion objective construction module is used to construct the inversion objective function by introducing the rock property model constraint function based on the principle of dissimilarity. The inversion objective function includes data fitting terms, model fitting terms, and rock property model constraint function terms. The inversion operation module is used to perform operations based on the observation data vector and the initial values ​​of the model parameter vector. The inversion objective function is optimized iteratively until the iteration termination condition is met, and the model parameter vector is output. The distribution of radionuclides in the inversion region was obtained.

7. A system based on the method of any one of claims 1-5, characterized in that: include: The drone, the airborne gamma spectrometer mounted on the drone, and the control terminal that communicates with the airborne gamma spectrometer. The control terminal acquires airborne radiometric energy spectrum data of the measuring point through an airborne gamma spectrometer, and uses the three-dimensional inversion method of the airborne radiometric energy spectrum data to obtain the distribution of radionuclides in the inversion region.

8. An electronic terminal, characterized in that: include: One or more processors; A memory that stores one or more computer programs; The processor invokes a computer program to achieve the following: The steps of the three-dimensional inversion method for airborne radiometric energy spectrum data according to any one of claims 1-5.

9. A computer-readable storage medium, characterized in that: The computer program is stored and is invoked by the processor to implement: The steps of the three-dimensional inversion method for airborne radiometric energy spectrum data according to any one of claims 1-5.

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