Geological anomaly body identification method and system based on c-s fractal joint analysis

Abstract

The application belongs to the technical field of geological anomaly body identification, and particularly discloses a geological anomaly body identification method and system based on C-S fractal joint analysis, which comprises the following steps: obtaining spectral information of a region to be measured; extracting characteristic spectral line information corresponding to target elements and performing quantitative inversion to obtain content values of each target element in surrounding rock of the region to be measured; dividing a three-dimensional space of the region to be measured into regular grids; for each indicating element, calculating a first fractal dimension of each grid unit by using an improved C-A fractal model; calculating a second fractal dimension of each grid unit by using an S-A fractal model; starting from a predetermined starting point, calculating joint fractal dimensions of adjacent grids in each direction; and identifying a geological anomaly body boundary corresponding to the indicating element based on changes in the joint fractal dimensions. The application reduces the interference of directional differences caused by structural control or uneven spatial sampling.

Description

Technical Field

[0001] This invention relates to the field of geological anomaly identification technology, and in particular to a geological anomaly identification method and system based on C–S fractal joint analysis. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] In tunnel engineering, identifying and delineating geological anomalies can promptly reveal potential geological hazards that may affect construction safety and stability, such as fault zones, alteration zones, or mineralized areas. Accurate identification and spatial delineation of anomalies provide a scientific basis for the design phase, allowing for the optimization of excavation methods and support parameters, and the development of targeted disaster prevention and mitigation measures.

[0004] Traditional methods for identifying and delineating geological anomalies mainly rely on geophysical exploration or manual experience, which suffers from drawbacks such as high cost and poor timeliness. UAVs can serve as platforms for various sensors to quickly acquire large amounts of data from the tunnel face, but the data density collected in the tunnel excavation direction is sparse due to the constraints of project progress and the time constraints of data collection personnel.

[0005] Fractal methods are widely used for delineating anomalies. However, the fractal dimension of a single method has different shortcomings in delineating geological anomalies. For example, the traditional C-A fractal model usually assumes that the study area is spatially isotropic. Its fractal dimension mainly reflects the statistical characteristics of the anomaly intensity and is difficult to effectively characterize the directional differences caused by tectonic control or spatial sampling heterogeneity. When the anomaly exhibits significant heterogeneity in different directions, the model's identification accuracy is significantly limited.

[0006] Furthermore, due to the inconsistencies in physical meaning and scale response among different fractal methods, existing technologies cannot directly combine them, thus failing to achieve joint and accurate delineation of anomalous bodies using different fractal dimensions. Summary of the Invention

[0007] To address the aforementioned issues, this invention proposes a geological anomaly identification method and system based on C–S fractal joint analysis. Spatial directionality and relevant scale are introduced into the threshold-area statistics process of the C–A fractal model, ensuring it shares the same directional structural orientation as the SA fractal model. Then, the fractal dimension of each grid is calculated using both the improved CA and SA fractal models, and C–S fractal joint analysis is employed to achieve accurate joint delineation of geological anomalies.

[0008] In some implementations, the following technical solutions are adopted: A method for identifying geological anomalies based on C–S fractal joint analysis includes: Acquire the spectral information of the region to be measured and perform preprocessing; The characteristic spectral information corresponding to the target element is extracted and quantitatively inverted to obtain the content value of each target element in the surrounding rock of the area to be tested; the target element is the indicator element of different geological anomalies. The three-dimensional space of the region to be tested is divided into a regular grid, and a semi-variogram function is introduced to improve the CA fractal model; For each indicator element, the first fractal dimension of each grid cell is calculated using the improved CA fractal model; the second fractal dimension of each grid cell is calculated using the SA fractal model. Starting from a predetermined starting point, the joint fractal dimension of adjacent grids is calculated in each direction. The joint fractal dimension is obtained by weighted calculation based on the first fractal dimension and the second fractal dimension. The boundary of the geological anomaly corresponding to the indicator element is identified based on the change in the joint fractal dimension.

[0009] As a further approach, a semi-variogram function is introduced to improve the CA fractal model, specifically: ; in, ; As the baseline fractal dimension, It is the value of the anisotropic semivariogram.

[0010] As a further approach, the semi-variogram function in the three-dimensional spatial direction is specifically as follows: ; In the formula, It is a semi-variogram function in three-dimensional space, used to characterize the spatial variation of element concentration at the corresponding spatial lag distance. P i =( x i , y i , z i () represents the spatial coordinates of the sampling point. h =( h x , h y , h z ) is a three-dimensional spatial lag vector. h x , h y , h zThese represent the spatial lag distances in the x, y, and z directions, respectively. N ( h To satisfy the lag distance Data pairs of quantities , These are the element concentration values ​​at the corresponding positions.

[0011] As a further approach, the joint fractal dimension is calculated based on a weighted average of the first fractal dimension and the second fractal dimension, specifically as follows: ; in, The anisotropy index of the grid. for The local gradient magnitude, This represents the average directional anisotropy index for the entire region. This represents the 75th percentile. The first fractal dimension, The second fractal dimension, , These are the weights for the first and second fractal dimensions, respectively.

[0012] As a further solution, the weights of the first fractal dimension and the second fractal dimension are determined as follows: ; ; ; in, For grid G i Anisotropy index, The SA fractal dimension is a specific direction that measures the fractal dimension within a grid. G i In the middle, along a certain specific angle Frequency-energy fractal characteristics in the direction. The second fractal dimension is the dimension of the grid. G i All directions inside The result of the arithmetic mean; This represents the k-th angle interval. cover to ; Represents all grids of the median.

[0013] As a further approach, the boundary of the geological anomaly corresponding to the indicator element is identified based on the change in the joint fractal dimension, specifically as follows: During the delineation of anomalies, the joint fractal dimension of adjacent grid cells is calculated in each direction starting from a predetermined starting point. When the t-th grid is added, the calculation is performed before... The average value of the joint fractal parameters of the meshes: ; in, This indicates the number of grids that have been included. Represents a grid G i The joint fractal parameters; If it exists: (previous) The average value of the joint fractal parameters of the grids and the previous -1 The difference between the average values ​​of the joint fractal parameters of the meshes is greater than a set threshold. Then determine the first Each grid represents the boundary of the new anomaly.

[0014] As a further solution, the joint fractal dimension change threshold The method for determining it is as follows: ; in, , This is the sensitivity coefficient; for The average value, for Standard deviation; , The fractal dimensions are calculated for the improved CA fractal and SA fractal, respectively.

[0015] In other embodiments, the following technical solutions are adopted: A geological anomaly identification system based on C–S fractal joint analysis includes: The data acquisition module is used to acquire the spectral information of the area to be measured and perform preprocessing. The element inversion module is used to extract the characteristic spectral information corresponding to the target element and perform quantitative inversion to obtain the content value of each target element in the surrounding rock of the area to be tested; the target element is the indicator element of different geological anomalies. The fractal dimension calculation module is used to divide the three-dimensional space of the area to be measured into a regular grid, and to improve the CA fractal model by introducing a semi-variogram function. For each indicator element, the first fractal dimension of each grid cell is calculated using the improved CA fractal model; the second fractal dimension of each grid cell is calculated using the SA fractal model. The joint fractal dimension determination module is used to calculate the joint fractal dimension of adjacent grids in each direction starting from a predetermined starting point. The joint fractal dimension is obtained by weighted calculation based on the first fractal dimension and the second fractal dimension. The geological anomaly identification module is used to identify the boundary of the geological anomaly body corresponding to the indicator element based on the change of the joint fractal dimension.

[0016] In other embodiments, the following technical solutions are adopted: A terminal device includes a processor and a memory, wherein the processor is used to implement instructions; and the memory is used to store multiple instructions adapted to be loaded and executed by the processor for the above-described method for identifying geological anomalies based on C–S fractal joint analysis.

[0017] In other embodiments, the following technical solutions are adopted: A computer-readable storage medium storing a plurality of instructions adapted for loading and execution by a processor of a terminal device of the above-described method for identifying geological anomalies based on C–S fractal joint analysis.

[0018] Compared with the prior art, the beneficial effects of the present invention are: (1) By introducing a semi-variogram into the C-A fractal model, the present invention explicitly considers spatial correlation and directional characteristics in the process of anomaly intensity threshold-area statistics. This makes the improved C-A fractal dimension not only reflect the intensity distribution characteristics of the anomaly, but also have sensitivity to changes in spatial structure. This overcomes the shortcomings of the traditional C-A model, which can only characterize the anomaly intensity and is difficult to reflect the spatial structure characteristics, and reduces the interference of directional differences caused by structural control or uneven spatial sampling.

[0019] (2) This invention introduces anisotropic constraints into the C-A model, ensuring that its fractal characteristics have the same directional structural orientation as the S-A model in terms of physical meaning and scale response. This fundamentally solves the problems of inconsistent physical meaning and unstable fusion results in the joint application of the two types of fractal models. Based on this, the geological anomaly delineation in the CS fractal joint analysis enables more accurate identification of anomaly boundaries, geometric shapes, and weak anomaly areas, effectively avoiding the problems of boundary ambiguity or anomaly omission in traditional methods.

[0020] Other features and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0021] Figure 1 This is a flowchart of the geological anomaly identification method based on C–S fractal joint analysis in an embodiment of the present invention. Detailed Implementation

[0022] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0023] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0024] Example 1 In one or more embodiments, a method for identifying geological anomalies based on C–S fractal joint analysis is disclosed, combining... Figure 1 Specifically, it includes the following processes: S101: Obtain the spectral information of the area to be measured and perform preprocessing.

[0025] In this embodiment, taking drill-and-blast tunnel construction as an example, after each blasting excavation of the tunnel, a drone equipped with laser-induced breakdown spectroscopy (LIBS) is deployed. When the robot reaches a pre-marked test point, it excites the rock surface at the test point on the tunnel face with one or more laser-induced breakdown spectral pulses, collecting the corresponding spectral information. Each flight lasts approximately 5 minutes and can cover the entire tunnel face area.

[0026] After acquisition, the raw spectral data undergoes preprocessing operations such as background removal, noise filtering, and normalization to correct for the effects of instrument drift and baseline drift. Peak position correction is then performed on the spectrum. Specifically, based on the NIST atomic spectral database, reference materials with known peak positions are introduced. By analyzing the deviation between the actual and theoretical positions of the reference peaks, linear or nonlinear calibration is performed on the target peaks. Characteristic spectral line information corresponding to the target elements is extracted to reduce errors caused by self-absorption and rectangular effects.

[0027] S102: Extract the characteristic spectral information corresponding to the target element and perform quantitative inversion to obtain the content value of each target element in the surrounding rock of the area to be tested; the target element is the indicator element of different geological anomalies.

[0028] In this embodiment, after completing the preprocessing of spectral data, the elemental content of known standard samples is combined with the establishment of a calibration model between spectral intensity and target element content. Multiple linear regression, partial least squares regression (PLSR), or machine learning algorithms are used to quantitatively invert the characteristic spectral lines to obtain the content values ​​of each target element in the surrounding rock to be tested.

[0029] In this embodiment, the target element refers to the element that is closely related to the target anomaly (fault zone, alteration zone, or mineralization) obtained through screening, i.e., the indicator element of the target anomaly. As a specific implementation method, elements with significant response relationships to the target anomaly (fault zone, alteration zone, or mineralization) are screened through principal component analysis (PCA), cluster analysis, or correlation analysis. Combining geological background, mineral assemblage, and material migration mechanism, it is determined whether the geochemical behavior of these elements during the anomaly formation process is stable and representative. Finally, elements with both statistical significance and geological indicativeness are selected as indicator elements for subsequent anomaly delineation.

[0030] Taking principal component analysis as an example, fault zones are often accompanied by rock fracturing and fluid activity. If the absolute values ​​of Si, Fe, and Ca loads in a certain principal component are large and their high values ​​correspond to the fault location, it indicates that these elements are closely related to the mineral transformation in the fault fracture zone. Fault zone indicator elements: Si, Fe, Ca, Na, K; Alteration zone indicator elements: K, Ca, Fe, Mg, Na, Al.

[0031] S103: Divide the three-dimensional space of the region to be measured into a regular grid and introduce anisotropic semi-variograms to improve the CA fractal model.

[0032] A semivariogram is a function that describes the degree to which the value of a variable (such as element content) changes with distance. When using a semivariogram, the spatial variability of data in different directions is calculated, and spatial correlation and directionality can be incorporated into fractal methods, making it suitable for quantifying the anisotropy of data. This embodiment introduces anisotropic semivariograms to describe the degree to which element content changes with distance in the horizontal and vertical directions, and embeds them into the CA fractal model.

[0033] Specifically, considering the characteristics of data collected by UAVs in tunnel excavation, the data density collected during the drill-and-blast tunnel excavation process is sparse, often spaced several meters to tens of meters apart, due to the influence of project progress and the time constraints of data collection personnel. UAV-based data acquisition methods obtain a higher data density along the tunnel excavation cross-section (face), but the spatial distribution, density, and representativeness of the data are non-uniform due to differences in spatial resolution and directionality. This inconsistency directly affects the reliability of traditional CA fractal models; therefore, this embodiment introduces anisotropic semi-variograms to correct for the influence of spatial structure.

[0034] This embodiment divides the three-dimensional space of the tunnel into a regular grid, and for each target grid whose fractal dimension needs to be calculated... G i ,coordinate( x i , y i , z i The local neighborhood is defined by the cube centered on it, and the cube radius covers 20-50 neighboring grids. The vertical neighborhood can be slightly larger than the horizontal neighborhood to accommodate data anisotropy.

[0035] The basic formula for the CA fractal model is: ; In the formula, N The element concentration value is greater than the threshold. 0 grid cells It is the fractal dimension. Here, the element concentration value refers to the concentration value of any of the previously selected indicator elements; when constructing the CA fractal model, different concentration thresholds are traversed. The number of corresponding grid cells N is counted, and a double logarithmic relationship curve is established; the geochemical anomaly classification threshold is determined based on the slope change characteristics or inflection point position of the double logarithmic curve.

[0036] However, in three-dimensional data, lateral sparsity can lead to inaccurate statistical cell counts when directly applying this formula. Since the anisotropic semivariogram quantifies the anisotropy of spatial data, it is introduced into the CA model, and the formula is improved as follows: ; in, ; As the baseline fractal dimension, It is the anisotropic semivariogram value, which depends on the spatial lag distance. ; Calculations are performed separately in the horizontal and vertical directions to reflect anisotropy.

[0037] ; In the formula, It is a semi-variogram function in three-dimensional space, used to characterize the spatial variation of element concentration at the corresponding spatial lag distance. P i =( x i , y i , z i () represents the spatial coordinates of the sampling point. h =( h x , h y , h z ) is a three-dimensional spatial lag vector. h x , h y , h z These are the spatial lag distance components in the x, y, and z directions, respectively. N ( h To satisfy the lag distance Data pairs of quantities , These are the element concentration values ​​at the corresponding positions.

[0038] To reflect the anisotropy of the data, the semi-variogram in three-dimensional space is decomposed into three directional components: , , , representing the spatial variation characteristics in the transverse direction (face dimension), the lateral direction (face dimension), and the longitudinal direction (tunnel excavation direction), respectively. By comparing the semi-variogram values ​​in each direction, a spatial anisotropy index is constructed and used to correct the fractal dimension in the CA fractal model, thereby enabling the model to simultaneously reflect the element concentration distribution characteristics and spatial structural anisotropy characteristics.

[0039] Since the semivariogram is a function of the lag distance, it is difficult to directly participate in fractal calculations. Therefore, a characteristic lag distance is selected. Extract the variogram values ​​in each direction: ; ; ; in, It can be taken as a representative distance of the local neighborhood scale (such as a certain proportion of the neighborhood radius).

[0040] By comparing the semivariogram values ​​in each direction, a spatial anisotropy index is constructed and used to correct the fractal dimension in the CA fractal model, so that the model can simultaneously reflect the element concentration distribution characteristics and the spatial structure anisotropy characteristics.

[0041] Looking at the improved CA model formula, when A larger value indicates that the elements are discontinuous vertically. An increase in size reflects a more complex fractal; conversely, when... and When the levels are relatively low, the elemental content is horizontally continuous. Increase. When the longitudinal spatial variation increases, the fractal dimension increases accordingly, thus reflecting the complexity and anisotropy of the anomalous structure.

[0042] This embodiment introduces the directional semivariogram as a dynamic correction factor for fractal dimension into the three-dimensional CA fractal model, and establishes a directional adaptive statistical compensation mechanism for borehole data structures that are vertically dense and horizontally sparse. This can reduce the scale bias of the traditional CA model in three-dimensional sparse data, improve the stability and continuity of deep geochemical anomaly boundary identification, and reduce anomaly fragmentation caused by uneven sampling direction. At the same time, it enhances the ability to identify weak anomaly information of concealed ore bodies under complex geological conditions and improves the consistency between anomaly extraction results and actual mineralization spatial distribution.

[0043] S104: For each indicator element, the first fractal dimension of each grid cell is calculated using the improved CA fractal model; the second fractal dimension of each grid cell is calculated using the SA fractal model.

[0044] For each target grid G i Select a series of thresholds Count the number of unweighted units: ; Among them, unweighted units refer to units with an element content greater than 100%. The number of grid cells; This is an indicator function.

[0045] Further analysis of different thresholds The statistical results below were subjected to double logarithmic fitting: ; in, The intercept of the fitted line, As the baseline fractal dimension, and All results were obtained through double logarithmic fitting.

[0046] Therefore, based on the results It can be finally calculated .

[0047] Compared to CA fractals, SA fractals are more suitable for datasets with varying observation data densities in different directions. Therefore, the original formula for SA fractals can be used directly to calculate the fractal dimension: ; In the formula: A(S≥s) is the area of ​​the region where the energy spectral density S is greater than a given threshold s; β s Let be the fractal dimension of SA. As the value of s increases, A(S≥s) decreases, and the relationship between A(S≥s) and s depends on... β s Size.

[0048] Target mesh calculated based on SA fractal principle G i SA fractal dimension (i.e., the second fractal dimension).

[0049] S105: Starting from a predetermined starting point, calculate the joint fractal dimension of adjacent grids in each direction. The joint fractal dimension is obtained by weighted summation of the first fractal dimension and the second fractal dimension.

[0050] The CA fractal model studies the variation of the area exceeding a certain concentration threshold with the threshold, and is therefore better at characterizing the intensity distribution of anomalies. The SA fractal model, on the other hand, measures the spatial structural complexity and directionality through the power-law characteristics of frequency energy distribution, and is better at characterizing the geometric features and boundary structure of anomalies. Therefore, based on the complementary properties of the two, the joint and accurate delineation of geological anomalies can be achieved.

[0051] For each target grid G i In the frequency domain, according to angular intervals ( ,cover to Extract the radial energy spectrum and calculate the SA fractal dimension in each direction. .

[0052] Define the anisotropy index of the mesh. for: ; in, The SA fractal dimension is a specific direction that measures the fractal dimension within a grid. G i In the middle, along a certain specific angle Frequency-energy fractal characteristics in the direction. The average SA fractal dimension (i.e., the second fractal dimension) is the fractal dimension of the grid.G i All directions inside The result of the arithmetic mean. The superscript (i) indicates the attribute of a specific spatial location on the i-th grid.

[0053] Anisotropy Index Used to capture the fractal directional differences of SA, this index increases significantly at geometric abrupt changes such as lithological boundaries and fractures.

[0054] Based on this, the weights of the first fractal dimension and the second fractal dimension can be calculated: ; ; in, , These are the weights for the first and second fractal dimensions, respectively. This is the median of the anisotropy index across all grid directions.

[0055] Finally, to avoid boundary ambiguity that may be caused by linear mixing, a fusion form with geometric mean properties is adopted, and neighborhood constraints are introduced to suppress noise. Joint fractal parameters are defined. D CS The calculation formula is: ; in, for The local gradient magnitude, This represents the mean of the anisotropy index of the grid across the entire region. for The 75th percentile.

[0056] The exp exponential term plays a role in preserving structure fidelity. Within the anomalous body and at regular boundaries, the anisotropic exponential gradient is small, and this term approaches 1. In irregular noise regions, the anisotropic exponential gradient oscillates abnormally, and the joint parameters are suppressed, thereby effectively suppressing false anomalies.

[0057] This scheme transforms the directional discrimination capability unique to SA fractals into a weighted control quantity through the anisotropy index, achieving an adaptive dynamic balance between CA intensity characterization and SA boundary characterization: for blocky anomalies, the CA contribution is automatically enhanced internally, highlighting the concentration center; for strip-shaped and fractured anomalies, the SA contribution is automatically enhanced at the edges, accurately locking the boundary. Compared with the original fixed variance weighting, the boundary fit and spatial continuity of the delineation results are significantly improved, and it is insensitive to local noise, exhibiting stronger robustness in geological interpretation.

[0058] S106: Identify the boundary of the geological anomaly corresponding to the indicator element based on the change in the joint fractal dimension.

[0059] During the delineation of anomalies, starting from the initial point (usually the grid at the center of the first face cross-section), adjacent grids are sequentially incorporated in each direction: ; in, This indicates the number of grids that have been included. It was before The average value of the joint fractal parameters of the grid; Represents a grid G i The joint fractal parameters; When the t-th grid is added, if: ; Then the t-th grid position is determined to be the new anomaly boundary. This represents the three-dimensional coordinates of the location.

[0060] This embodiment determines the boundary of anomalies by calculating the average value of joint fractal parameters. This can effectively suppress random fluctuations or local noise that may exist in a single grid, making the judgment more focused on significant changes in the overall trend. At the same time, the cumulative average change can capture the abrupt change point of this cumulative effect, reducing misjudgments.

[0061] Parameters in this embodiment This is the threshold for changes in joint fractal characteristics, used to determine whether changes in fractal characteristics are significant. When adding a new mesh causes a change in the joint fractal parameters exceeding [a certain threshold], [the threshold is set]. At that time, the system automatically identifies the boundary of a geological anomaly. Parameters The calculation method is as follows: ; in, , Select 1.5–2.5, and adjust according to abnormal sensitivity; for The average value, for Standard deviation; , The fractal dimensions are calculated for the improved CA fractal and SA fractal, respectively.

[0062] This embodiment introduces... and , making the threshold Instead of a fixed empirical value, it is derived statistically based on the actual data distribution in the study area. This allows the method to adapt to regions with different geological backgrounds and data characteristics, avoiding the subjectivity and uncertainty caused by artificially setting thresholds.

[0063] Ultimately, an anomaly region based on this indicator element is obtained. This anomaly region is a key target area for finding the target geological body, which can be a geological anomaly such as a fault or alteration zone. If a more specific and accurate anomaly region and type are desired, the analysis results of multiple elements can be combined to make a comprehensive determination.

[0064] Example 2 In one or more embodiments, a geological anomaly identification system based on C–S fractal joint analysis is disclosed, specifically including: The data acquisition module is used to acquire the spectral information of the area to be measured and perform preprocessing. The element inversion module is used to extract the characteristic spectral information corresponding to the target element and perform quantitative inversion to obtain the content value of each target element in the surrounding rock of the area to be tested; the target element is the indicator element of different geological anomalies. The fractal dimension calculation module is used to divide the three-dimensional space of the area to be measured into a regular grid, and to improve the CA fractal model by introducing a semi-variogram function. For each indicator element, the first fractal dimension of each grid cell is calculated using the improved CA fractal model; the second fractal dimension of each grid cell is calculated using the SA fractal model. The joint fractal dimension determination module is used to calculate the joint fractal dimension of adjacent grids in each direction starting from a predetermined starting point. The joint fractal dimension is obtained by weighted calculation based on the first fractal dimension and the second fractal dimension. The geological anomaly identification module is used to identify the boundary of the geological anomaly body corresponding to the indicator element based on the change of the joint fractal dimension.

[0065] It should be noted that the specific implementation methods of the above modules are exactly the same as those in Example 1, and will not be described in detail again.

[0066] Example 3 In one or more embodiments, a terminal device is disclosed, comprising a processor and a memory, wherein the processor is used to implement instructions; and the memory is used to store multiple instructions adapted to be loaded by the processor and executed by the processor to identify geological anomalies based on C–S fractal joint analysis as described in Embodiment 1.

[0067] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can 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 other conventional processor.

[0068] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of the memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0069] In the implementation process, each step of the above method can be completed by the integrated logic circuits in the processor hardware or by software instructions.

[0070] Example 4 In one or more embodiments, a computer-readable storage medium is disclosed, wherein a plurality of instructions are stored, the instructions being adapted to be loaded by a processor of a terminal device and executed by the geological anomaly identification method based on C–S fractal joint analysis as described in Embodiment 1.

[0071] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for identifying geological anomalies based on C–S fractal joint analysis, characterized in that, include: Acquire the spectral information of the region to be measured and perform preprocessing; The characteristic spectral information corresponding to the target element is extracted and quantitatively inverted to obtain the content value of each target element in the surrounding rock of the area to be tested; the target element is the indicator element of different geological anomalies. The three-dimensional space of the region to be tested is divided into a regular grid, and a semi-variogram function is introduced to improve the CA fractal model; For each indicator element, the first fractal dimension of each grid cell is calculated using the improved CA fractal model; The second fractal dimension of each grid cell is calculated using the SA fractal model; Starting from a predetermined starting point, the joint fractal dimension of adjacent grids is calculated in each direction. The joint fractal dimension is obtained by weighted calculation based on the first fractal dimension and the second fractal dimension. Identify the boundary of the geological anomaly corresponding to the indicator element based on the change in the joint fractal dimension; The joint fractal dimension is calculated based on a weighted average of the first fractal dimension and the second fractal dimension, specifically as follows: ； in, The anisotropy index of the grid. for The local gradient magnitude, This represents the average directional anisotropy index for the entire region. This represents the 75th percentile. The first fractal dimension, The second fractal dimension, , These are the weights for the first fractal dimension and the second fractal dimension, respectively. The weights of the first fractal dimension and the second fractal dimension are determined as follows: ； ； ； in, For grid G i Anisotropy index, The SA fractal dimension is a specific direction that measures the fractal dimension within a grid. G i In the middle, along a certain specific angle Frequency-energy fractal characteristics in the direction; The second fractal dimension is the dimension of the grid. G i All directions inside The result of the arithmetic mean; This represents the k-th angle interval. cover to ; Represents all grids of the median.

2. The geological anomaly identification method based on C-S fractal joint analysis as described in claim 1, characterized in that, The CA fractal model is improved by introducing a semi-variogram function, specifically: ； in, ; As the baseline fractal dimension, It is the value of the anisotropic semivariogram.

3. The geological anomaly identification method based on C-S fractal joint analysis as described in claim 2, characterized in that, The semi-variogram in three-dimensional space is specifically as follows: ； In the formula, It is a semi-variogram function in three-dimensional space, used to characterize the spatial variation of element concentration at the corresponding spatial lag distance. P i =( x i , y i , z i () represents the spatial coordinates of the sampling point. h =( h x , h y , h z ) is a three-dimensional spatial lag vector. h x , h y , h z These represent the spatial lag distances in the x, y, and z directions, respectively. N ( h To satisfy the lag distance Data pairs of quantities , These are the element concentration values ​​at the corresponding positions.

4. The geological anomaly identification method based on C-S fractal joint analysis as described in claim 1, characterized in that, Identifying the boundaries of geological anomalies corresponding to indicator elements based on changes in joint fractal dimension, specifically: During the delineation of anomalies, the joint fractal dimension of adjacent grid cells is calculated in each direction starting from a predetermined starting point. When the t-th grid is added, the calculation is performed before... The average value of the joint fractal parameters of the meshes: ； in, This indicates the number of grids that have been included. Represents a grid G i The joint fractal parameters; If it exists: (previous) The average value of the joint fractal parameters of the grids and the previous -1 The difference between the average values ​​of the joint fractal parameters of the meshes is greater than a set threshold. Then determine the first Each grid represents the boundary of the new anomaly.

5. The geological anomaly identification method based on C-S fractal joint analysis as described in claim 4, characterized in that, The joint fractal dimension change threshold The method for determining it is as follows: ； in, , This is the sensitivity coefficient; for The average value, for Standard deviation; , The fractal dimensions are calculated for the improved CA fractal and SA fractal, respectively.

6. A system for implementing the geological anomaly identification method based on C-S fractal joint analysis as described in claim 1, characterized in that, include: The data acquisition module is used to acquire the spectral information of the area to be measured and perform preprocessing. The element inversion module is used to extract the characteristic spectral information corresponding to the target element and perform quantitative inversion to obtain the content value of each target element in the surrounding rock of the area to be tested; the target element is the indicator element of different geological anomalies. The fractal dimension calculation module is used to divide the three-dimensional space of the area to be measured into a regular grid and introduce a semi-variogram function to improve the CA fractal model. For each indicator element, the first fractal dimension of each grid cell is calculated using the improved CA fractal model. The second fractal dimension of each grid cell is calculated using the SA fractal model; The joint fractal dimension determination module is used to calculate the joint fractal dimension of adjacent grids in each direction starting from a predetermined starting point. The joint fractal dimension is obtained by weighted calculation based on the first fractal dimension and the second fractal dimension. The geological anomaly identification module is used to identify the boundary of the geological anomaly body corresponding to the indicator element based on the change of the joint fractal dimension.

7. A terminal device comprising a processor and a memory, the processor for implementing instructions; the memory for storing multiple instructions, characterized in that, The instructions are adapted to be loaded by a processor and executed as described in any one of claims 1-5, the geological anomaly identification method based on C–S fractal joint analysis.

8. A computer-readable storage medium storing a plurality of instructions, characterized in that, The instructions are adapted to be loaded and executed by the processor of the terminal device, and are based on the C–S fractal joint analysis method for identifying geological anomalies as described in any one of claims 1-5.

Images