A gradient detection method and system based on geochemical exploration technology

Abstract

The application relates to the technical field of chemical exploration, in particular to a gradient detection method and system based on geochemical exploration technology. The method comprises the following steps: sampling a to-be-explored area to obtain sampling point information and a number; setting an abnormal flag bit through element content after screening the abnormal sampling points, and obtaining abnormal sampling points; presetting local sampling points for the abnormal sampling points, determining local correlation based on the similarity of element content and the stability of local abnormal flag bits; normalizing associated elements, forming a gradient of the abnormal sampling points and the local sampling points, and determining gradient correlation in combination with the spatial distance of the two; calculating a target area probability value through the local correlation and the gradient correlation, and obtaining a gold mine target area thermal map to provide data support for subsequent detection. The application improves the positioning accuracy of the gold mine target area.

Description

Technical Field

[0001] This application relates to the field of chemical exploration technology, specifically to a gradient detection method and system based on geochemical exploration technology. Background Technology

[0002] In the field of gold exploration, the content of gold in geology is usually very low, which increases the difficulty of gold exploration. However, during the formation of gold, associated elements are often produced. Therefore, based on the anomalies of gold and associated elements, gold exploration can be effectively achieved.

[0003] Traditional gold exploration methods rely on statistical analysis of background element content within a region. Based on these statistical results, fixed thresholds are set for elemental anomalies to identify them. However, the actual gold mineralization process involves complex elemental evolution mechanisms. Setting fixed thresholds only reflects elemental content characteristics and fails to capture crucial information such as spatial relationships between sampling points and the symbiotic combination patterns of elemental anomalies. Therefore, traditional statistical methods for identifying elemental anomalies and determining target areas are prone to significant bias, making it difficult to identify gold target areas and often leading to misjudgments such as "anomalies present but no mineral deposits." Summary of the Invention

[0004] To address the technical challenge of identifying gold deposit target areas, this application provides a gradient detection method and system based on geochemical exploration technology. The specific technical solution adopted is as follows: Firstly, this application proposes a gradient detection method based on geochemical exploration technology, which includes the following steps: Sampling is carried out at pre-set sampling points in the exploration area to obtain the element content, three-dimensional coordinates and rock type of the sampling points, and the sampling points are numbered. Each element is sorted based on the sampling point number, and extreme values ​​are removed to obtain a subset. The background value and fluctuation value of each element are determined based on the content of elements in the subset, thereby obtaining the abnormal lower limit. An abnormal flag is set for each sampling point by comparing each element with the corresponding abnormal lower limit. Sampling points with an abnormal flag greater than 1 are recorded as abnormal sampling points. For anomaly sampling points, a predetermined number of sampling points with the same local rock properties are selected as local sampling points; element similarity is calculated based on the similarity of element content between the anomaly sampling points and their local sampling points; the reciprocal of the coefficient of variation of the anomaly markers of the anomaly sampling points and their local sampling points is calculated as the anomaly stability; local correlation is calculated based on element similarity and anomaly stability. The content of associated elements is standardized by the gold content; the gradient is constructed by the difference in the standardized associated element content between anomalous sampling points and local sampling points; the gradient correlation of anomalous sampling points is determined by the uniformity of the spatial distance and gradient amplitude ratio between anomalous sampling points and their local sampling points. The probability value of the target area is calculated based on the local correlation and gradient correlation of abnormal sampling points, and the resulting heat map of the gold mine target area provides data support for subsequent exploration.

[0005] In the aforementioned scheme, this application, based on the preliminary screening of anomalous sampling points using traditional methods, further obtains local correlations by analyzing the correlation between changes in gold and associated element content within a local area, thereby improving the assessment capability of spatial distribution. Furthermore, it combines gradient correlation analysis with the gradient changes of associated elements within a local area to avoid the isolated analysis of single elements, and further processes the data to obtain a heat map of the gold ore target area within the exploration region. This scheme effectively addresses the shortcomings of traditional methods in isolated anomaly analysis and lack of spatial correlation, improving the positioning accuracy of gold ore target areas and significantly resolving the dilemma of "anomalies without ore deposits."

[0006] In one embodiment, the background value is the mean of the element content in the corresponding subset for each element, and the fluctuation value is the standard deviation of the element content in the corresponding subset for each element.

[0007] In one embodiment, the lower limit of anomalies is the sum of the background value and 1.5 times the fluctuation value.

[0008] In one embodiment, the method of setting an anomaly flag for a sampling point by comparing each element of the sampling point with the corresponding anomaly lower limit is as follows: If the content of each element at the sampling point is less than or equal to the lower limit of abnormality, the abnormality flag is set to 0; if the content of each element at the sampling point is greater than the lower limit of abnormality but less than or equal to twice the lower limit of abnormality, the abnormality flag is set to 1; if the content of each element at the sampling point is greater than twice the lower limit of abnormality but less than or equal to four times the lower limit of abnormality, the abnormality flag is set to 2; if the content of each element at the sampling point is greater than four times the lower limit of abnormality, the abnormality flag is set to 4.

[0009] In one embodiment, the method for calculating element similarity based on the similarity of element content between abnormal sampling points and their local sampling points is as follows: For each sampling point, the content of all its elements is used to form an element sequence. The mean absolute value of the similarity coefficient between the element sequences of the outlier sampling point and all local sampling points is calculated and used as the element similarity.

[0010] In one embodiment, the local correlation is positively correlated with element similarity and anomaly stability, respectively.

[0011] In one embodiment, the method for constructing a gradient based on the standardized differences in associated element content between outlier sampling points and local sampling points is as follows: The three standard accompanying elements of the abnormal sampling point and the local sampling point are respectively used to form an accompanying vector. The difference between the accompanying vectors of the abnormal sampling point and the local sampling point is calculated as the difference vector, and the gradient magnitude is the magnitude of the difference vector.

[0012] In one embodiment, the method for determining the gradient correlation of anomaly sampling points by the uniformity of the spatial distance and gradient magnitude ratio between anomaly sampling points and their local sampling points is as follows: , This represents the gradient magnitude between the k-th local sampling point and the outlier sampling point. This represents the distance between the k-th local sampling point and the outlier sampling point. This represents the mean of the gradient magnitudes of the outlier sampling point and all local sampling points. This represents the average distance between the outlier sampling point and all local sampling points. This indicates the number of local sampling points corresponding to the abnormal sampling points. This indicates gradient correlation.

[0013] In one embodiment, the target region probability value is positively correlated with local correlation and negatively correlated with gradient correlation.

[0014] On the other hand, this application also provides a gradient detection system based on geochemical exploration technology, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the above-described gradient detection method based on geochemical exploration technology.

[0015] The beneficial effects of this application are as follows: This application, based on the traditional method of initially screening anomalous sampling points, further obtains local correlations by analyzing the correlation between changes in gold and associated element content within a local area, thus improving the ability to assess spatial distribution. Furthermore, it combines gradient correlation analysis with the analysis of gradient changes in associated elements within a local area, avoiding the analysis of single elements in isolation, and further processes the data to obtain a heat map of the gold target area within the exploration region. This approach effectively solves the shortcomings of traditional methods in isolated anomaly analysis and lack of spatial correlation, improving the accuracy of gold target area location and significantly resolving the dilemma of "anomalies without mineral deposits." Attached Figure Description

[0016] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of a gradient detection method based on geochemical exploration technology, provided as an embodiment of this application. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a gradient detection method and system based on geochemical exploration technology proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] 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 pertains.

[0020] An embodiment of a gradient detection method and system based on geochemical exploration technology: The following description, in conjunction with the accompanying drawings, details a specific scheme for a gradient detection method and system based on geochemical exploration technology provided in this application.

[0021] Please see Figure 1 The diagram illustrates a gradient detection method based on geochemical exploration technology according to an embodiment of this application. The method includes the following steps: Step S001: Sample the preset sampling points in the area to be explored, obtain the element content, three-dimensional coordinates and rock type of the sampling points, and number the sampling points.

[0022] For the exploration area, an exploration ratio is set. The larger the ratio, the more sampling points are collected per unit area. Exploration is carried out based on this ratio. For each sampling point, the altitude of the corresponding sampling point is recorded using GPS or RTK (real-time dynamic differential positioning) equipment to determine the three-dimensional spatial coordinates of the individual sampling point.

[0023] Different sampling methods were used based on different geological types, collecting rock samples, soil samples, and stream sediment samples respectively. After preservation, the sample information was labeled. For rock samples, fresh, unweathered ore bodies were selected, avoiding strongly altered fracture zones and oxide layers; for soil samples, soil samples from precipitate layers were selected, avoiding surface humus and parent rock; for stream sediment samples, fine sand or silt was collected from slow-flowing areas such as river confluences, river bends, and shallows.

[0024] Elemental analysis was performed on the collected samples. The content of gold (Au) was determined using inductively coupled plasma mass spectrometry (ICP-MS), and the contents of associated elements were determined using the same method. The selected associated elements included Ag, As, Bi, Cd, Co, Cr, Cu, Hg, Mo, Ni, Pb, Sb, Sn, W, and Zn. The unit for each element's content was ppb (dimensionless concentration unit). Each sampling point was numbered Au-i to indicate its information. Furthermore, based on the mineralogical chemical composition and structural characteristics of each sample, the corresponding rock type was determined according to the Chinese national standard GB / T17412.1-1998. The elemental content, three-dimensional coordinates, and rock type of each sampling point were obtained.

[0025] In this embodiment, an exploration scale of 1:10,000 is selected to obtain samples from each sampling point in the area to be explored, and the element content of each sample is determined through the above method.

[0026] This completes the measurement of elements in the area to be explored.

[0027] Step S002: After screening the sampling points for anomalies, set an anomaly flag bit based on their element content to obtain the abnormal sampling points.

[0028] Traditional statistical anomaly detection methods analyze global data, enabling preliminary analysis of background values ​​and "outliers" (ore-forming anomalies, human-caused anomalies, misjudged anomalies, etc.). Therefore, this application utilizes a global approach to achieve preliminary judgment of anomalies at sampling points.

[0029] Let's take a single element from all sampling points in the area to be explored as an example for analysis. First, outliers in the single element are removed. In an optional implementation, the elements are sorted based on the sampling point numbers, and the maximum or minimum values ​​are obtained through a peak detection algorithm. The subset of the element's content after removing the extreme values ​​is statistically analyzed, and the mean of the subset is calculated as the background value, and the standard deviation of the subset is obtained as the fluctuation value.

[0030] In order to achieve the initial judgment of element anomalies in this application, it is necessary to filter out as many abnormal cases as possible. Therefore, a lower anomaly limit is obtained in this application. The specific method is: anomaly limit = background value + 1.5 times the fluctuation value, where the background value and the fluctuation value refer to the background value and standard deviation of the remaining subset after removing extreme values.

[0031] This yields the lower limit of anomalies for each element. Different anomaly levels exist based on the degree to which the element's content deviates from the outlier. Based on this, the anomaly flag for a single sampling point for that element is obtained.

[0032] Specifically, if the content of each element at the sampling point is less than or equal to the lower limit of abnormality, it indicates that there is no abnormality for that element at that sampling point, and the abnormality flag is set to 0; if the content of each element at the sampling point is greater than the lower limit of abnormality but less than or equal to twice the lower limit of abnormality, it indicates that the sampling point has a weak abnormality for that element, and the abnormality flag is set to 1; if the content of each element at the sampling point is greater than twice the lower limit of abnormality but less than or equal to four times the lower limit of abnormality, it indicates that the sampling point has a moderate abnormality for that element, and the abnormality flag is set to 2; if the content of each element at the sampling point is greater than four times the lower limit of abnormality, it indicates that the sampling point has a strong abnormality for that element, and the abnormality flag is set to 4.

[0033] This process iterates through all elements corresponding to all sampling points to identify anomalies in individual elements. Further analysis is then performed on each individual sampling point based on this initial global anomaly assessment, enabling precise exploration of the mineral target area. It should be noted that for a single sampling point, if the anomaly flag of any single element is greater than or equal to 1, that sampling point is marked as an anomalous sampling point, and subsequent analysis will focus on these anomalous sampling points.

[0034] At this point, each sampling point is marked to identify abnormal sampling points.

[0035] Step S003: Preset local sampling points for abnormal sampling points, and determine the local correlation based on the similarity of element content between the two and the stability of local abnormality markers.

[0036] Within the distribution range of adjacent sampling points, similar geological types are often found, providing a basis for analyzing the similarity of elemental content variations. In this embodiment, a 1:10,000 soil geochemical survey was used for the area to be explored, indicating the existence of 5 sampling points per unit square kilometer. Furthermore, in the mechanism of ore deposit formation, the variations in the content of gold and associated elements are essentially the result of the combined effects of elemental geochemical properties and ore-forming physical conditions.

[0037] In terms of elemental geochemistry, gold exhibits strong chalcophilic and cubinophilic properties, making it readily precipitated in reducing environments and migrating as complexes in oxidizing environments. This leads to the presence of elements with similar chalcophilic or cubinophilic properties alongside gold. Chalcophiles (such as As, Sb, Hg, Bi, and Te) often precipitate in association with gold, while cubinophiles (Cu, Pb, Zn, and Ag) frequently occur together with gold.

[0038] Under the physical conditions of mineralization, the analysis is mainly based on the physical properties of gold. For example, gold tends to precipitate easily in low-temperature environments (150~400℃), often migrates in the form of complexes in high-pressure environments, and easily precipitates in low-pressure environments. Therefore, elements that exhibit similar or close physical properties to gold will undergo similar changes. Thus, gold exploration can be achieved based on the analysis of gold and associated elements.

[0039] The above analysis, focusing on the ore-forming mechanism, indicates that the contents of gold and associated elements often exhibit similar variation characteristics due to the influence of the ore-forming mechanism, resulting in significant differences in the contents of gold and associated elements in the gold deposit target area compared to the background elements. However, in actual processes, human production activities or non-geological natural factors, such as geological structures and differences in climate and hydrology, can lead to anomalies in the elements at individual sampling points.

[0040] Compared to elemental anomalies caused by non-ore-forming factors, which are mainly manifested as obvious anomalies at a single point or in some elements, elemental anomalies caused by ore-forming mechanisms are often accompanied by a long gold deposit deposition process, resulting in a stronger correlation between elements.

[0041] For genuine anomaly sampling points formed by mineralization mechanisms, the geological changes among sampling points with the same rock properties, at least locally, exhibit a certain degree of homogeneity. The elemental contents are essentially the same, leading to a high correlation between elemental contents. Furthermore, due to the homogeneity of geological changes, the degree of anomaly among elements is relatively similar, further resulting in consistent values ​​for anomaly markers and a high degree of anomaly stability, ultimately leading to a strong local correlation. However, for anomalies not caused by mineralization mechanisms, they often exhibit heterogeneity. The anomaly may be in the content of one or more elements, with significant differences between sampling points within the local area, ultimately resulting in a lower value for local correlation.

[0042] Therefore, for a single anomalous sampling point, based on the rock properties of that anomalous point, M anomalous sampling points with the same rock properties as the nearest anomalous point are obtained as its local sampling points; in this embodiment, M is 10. If the anomalous sampling point is formed by a gold mineralization mechanism, then sampling points that are close to the anomalous sampling point and have the same lithology should also be anomalous sampling points formed by a mineralization mechanism, and the overall elemental contents should have a certain correlation.

[0043] For each sampling point, the content of all elements within it is treated as an element sequence. The mean of the absolute values ​​of the similarity coefficients between the abnormal sampling point and the element sequences of all its local sampling points is calculated as the element similarity. This method assesses the similarity of element content between the abnormal sampling point and other sampling points within the local area, and is a common approach to data evaluation. In this embodiment, the similarity coefficient is calculated using the Pearson correlation coefficient, which ranges from -1 to 1.

[0044] Furthermore, for all local sampling points of the anomalous sampling point, the anomalous stability is analyzed based on its anomalous flag bit. Specifically, for the anomalous sampling point and all its local sampling points, the reciprocal of the coefficient of variation of the screening anomalous flag bit corresponding to the sampling point is calculated as the anomalous stability. To prevent the denominator from being zero and thus unable to be calculated, a very small positive number is added to the denominator; in this embodiment, it is 0.01. It is worth noting that the screening anomalous flag bit is the largest anomalous flag bit corresponding to all elements in the sampling point.

[0045] Calculate local correlation based on element similarity and anomaly stability; The local correlation is positively correlated with element similarity and anomaly stability, respectively.

[0046] It should be noted that positive correlation means that when one variable increases, the other variable also increases, and the two variables change in the same direction. When one variable changes from large to small or from small to large, the other variable also changes from large to small or from small to large. The specific relationship is determined by the actual application, and this application does not impose any special restrictions.

[0047] Preferably, in this embodiment, the expression for local correlation is: , This represents the element similarity of outlier sampling points. This indicates the abnormal stability of the abnormal sampling points. This indicates the local correlation of abnormal sampling points.

[0048] At this point, the local correlation of the abnormal sampling points has been obtained.

[0049] Step S004: Standardize the associated elements to form the gradients of the abnormal sampling points and local sampling points, and determine the gradient correlation by combining the spatial distance between the two.

[0050] The above analysis often relies on the spatial distribution and content relationships between anomalous sampling points to determine the anomalies in the elements forming the mineralization mechanism. Essentially, this relies on the similarity of elemental content among anomalous sampling points. In practice, gold deposition and enrichment often occur within a specific range, i.e., within the gold ore target area. The closer to the center of the target area, the higher the enrichment of gold elements; conversely, the closer to the outer layer, the lower the enrichment of gold and associated elements. This results in a gradient relationship between elemental contents among sampling points as the target area progresses.

[0051] However, in practice, due to the relatively small content of various elements, and especially the smaller content of gold compared to other associated elements, directly obtaining the gradient relationship from the changes in element content will not be obvious. Therefore, further processing of the content data is required. In this embodiment, gold is used for standardization. Specifically, for a single abnormal sampling point, the ratio of the content of each associated element to the content of gold is calculated to standardize the associated elements. It should be noted that if the Au content corresponding to the current abnormal sampling point is zero, to avoid the denominator being zero and unable to be calculated during standardization, the Au content of the current abnormal sampling point is set as the background value.

[0052] For a single anomalous sampling point, the three elements with the highest standardized content among the associated elements at that point are obtained. This is to screen associated elements, as not all associated elements are present in a single gold ore target area. The gradient magnitude of the local sampling point relative to the anomalous sampling point in terms of the three standard associated elements is calculated. Specifically, an associated vector is constructed from the three standard associated elements of both the anomalous and local sampling points. The difference between the associated vectors of the anomalous and local sampling points is calculated as the difference vector, and the gradient magnitude is the magnitude of the difference vector.

[0053] Although the enrichment of gold within the gold ore target area is not uniform, the distance between sampling points is relatively large over the entire area. This dilutes the non-uniformity of the enrichment, allowing it to be approximated as a uniform change. Consequently, the ratio of sampling point distance to gradient amplitude is approximately uniform, and the overall gradient direction approximately points towards the center of gold content within the gold ore target area.

[0054] The gradient correlation of outlier sampling points is determined by the uniformity of the distance between outlier sampling points and their local sampling points, the ratio of gradient magnitude, and the fitting radius.

[0055] Preferably, in this embodiment, the expression for gradient correlation is: , This represents the gradient magnitude between the k-th local sampling point and the outlier sampling point. This represents the distance between the k-th local sampling point and the outlier sampling point. This represents the mean of the gradient magnitudes of the outlier sampling point and all local sampling points. This represents the average distance between the outlier sampling point and all local sampling points. This indicates the number of local sampling points corresponding to the abnormal sampling points. This indicates gradient correlation.

[0056] In gradient correlation, the uniformity of gold enrichment within a local area is measured. Overall, the enrichment state is correlated with distance. The greater the distance, the greater the difference in enrichment state. For anomalies formed by the real mineralization mechanism, the smaller the difference in the second term, the smaller the final value of D.

[0057] At this point, the gradient correlation of the abnormal sampling points has been obtained.

[0058] Step S005: Calculate the target area probability value through local correlation and gradient correlation, and obtain the gold mine target area heat map to provide data support for subsequent exploration.

[0059] Through the above steps, the correlation between anomalies in sampling points within the exploration area and the mineralization mechanism is obtained, and the distribution probability of a single anomaly sampling point is derived based on this. The target area probability value is then calculated based on the local correlation and gradient correlation of the anomaly sampling points.

[0060] The target region probability value is positively correlated with local correlation and negatively correlated with gradient correlation.

[0061] Negative correlation means that when one variable increases, the other variable decreases accordingly. The two variables change in opposite directions. When one variable changes from large to small or from small to large, the other variable also changes from small to large or from large to small. The specific relationship is determined by the actual application, and this application does not impose any special restrictions.

[0062] Preferably, the expression for the target area probability value is: , Indicates the local correlation of abnormal sampling points. Indicates the gradient correlation of outlier sampling points. Represents the normalization function. This represents the target region probability value of the abnormal sampling point. This represents a very small positive number, which is 0.01 in this embodiment. The normalization function in this embodiment is a maximum-minimum normalization function, with a value range of [0,1]. Its maximum value is the maximum value of the target area probability value corresponding to all abnormal sampling points, and its minimum value is the minimum value of the target area probability value corresponding to all abnormal sampling points.

[0063] Furthermore, the probability value of the target area corresponding to the sampling point with an anomaly flag of zero is set to zero, thereby obtaining a heat map of the gold target area in the current exploration area. The higher the probability value, the higher the probability of the existence of a gold target area, thus providing data support for further gold mine location.

[0064] Thus, a thermal map of the gold mine target area was obtained, providing data support for exploration.

[0065] Based on the same inventive concept as the above method, this embodiment of the invention also provides a gradient detection system based on geochemical exploration technology, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described gradient detection methods based on geochemical exploration technology.

[0066] It should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

[0067] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A gradient detection method based on geochemical exploration technology, characterized in that, The method includes the following steps: Sampling is carried out at pre-set sampling points in the exploration area to obtain the element content, three-dimensional coordinates and rock type of the sampling points, and the sampling points are numbered. Each element is sorted based on the sampling point number, and extreme values ​​are removed to obtain a subset. The background value and fluctuation value of each element are determined based on the content of elements in the subset, thereby obtaining the lower limit of abnormality. An abnormality flag is set for each sampling point by comparing each element with the corresponding lower limit of abnormality. Sampling points with an abnormality flag greater than or equal to 1 are recorded as abnormal sampling points. For anomaly sampling points, a predetermined number of sampling points with the same local rock properties are selected as local sampling points; element similarity is calculated based on the similarity of element content between the anomaly sampling points and their local sampling points; the reciprocal of the coefficient of variation of the anomaly markers of the anomaly sampling points and their local sampling points is calculated as the anomaly stability; local correlation is calculated based on element similarity and anomaly stability. The content of associated elements is standardized by the gold content; the gradient is constructed by the difference in the standardized associated element content between anomalous sampling points and local sampling points; the gradient correlation of anomalous sampling points is determined by the uniformity of the spatial distance and gradient amplitude ratio between anomalous sampling points and their local sampling points. The probability value of the target area is calculated based on the local correlation and gradient correlation of abnormal sampling points, and the resulting heat map of the gold mine target area provides data support for subsequent exploration.

2. The gradient detection method based on geochemical exploration technology as described in claim 1, characterized in that, The background value is the mean of the element content in the corresponding subset for each element, and the fluctuation value is the standard deviation of the element content in the corresponding subset for each element.

3. The gradient detection method based on geochemical exploration technology as described in claim 1, characterized in that, The lower limit of the anomaly is the sum of the background value and 1.5 times the fluctuation value.

4. The gradient detection method based on geochemical exploration technology as described in claim 1, characterized in that, The method for setting an anomaly flag for a sampling point by comparing each element of the sampling point with the corresponding anomaly lower limit is as follows: If the content of each element at the sampling point is less than or equal to the lower limit of abnormality, the abnormal flag bit is set to 0; if the content of each element at the sampling point is greater than the lower limit of abnormality but less than or equal to twice the lower limit of abnormality, the abnormal flag bit is set to 1. If the content of each element at the sampling point is greater than twice the lower limit of abnormality but less than or equal to four times the lower limit of abnormality, the abnormality flag is set to 2; if the content of each element at the sampling point is greater than four times the lower limit of abnormality, the abnormality flag is set to 4.

5. The gradient detection method based on geochemical exploration technology as described in claim 1, characterized in that, The method for calculating element similarity based on the similarity of element content between abnormal sampling points and their local sampling points is as follows: For each sampling point, the content of all its elements is used to form an element sequence. The mean absolute value of the similarity coefficient between the element sequences of the outlier sampling point and all local sampling points is calculated and used as the element similarity.

6. The gradient detection method based on geochemical exploration technology as described in claim 1, characterized in that, The local correlation is positively correlated with element similarity and anomaly stability, respectively.

7. The gradient detection method based on geochemical exploration technology as described in claim 1, characterized in that, The method for constructing a gradient based on the standardized differences in the content of associated elements between outlier and local sampling points is as follows: The three standard accompanying elements of the abnormal sampling point and the local sampling point are respectively used to form an accompanying vector. The difference between the accompanying vectors of the abnormal sampling point and the local sampling point is calculated as the difference vector, and the gradient magnitude is the magnitude of the difference vector.

8. The gradient detection method based on geochemical exploration technology as described in claim 1, characterized in that, The method for determining the gradient correlation of outlier sampling points by considering the uniformity of the spatial distance and gradient magnitude ratio between outlier sampling points and their local sampling points is as follows: , This represents the gradient magnitude between the k-th local sampling point and the outlier sampling point. This represents the distance between the k-th local sampling point and the outlier sampling point. This represents the mean of the gradient magnitudes of the outlier sampling point and all local sampling points. This represents the average distance between the outlier sampling point and all local sampling points. This indicates the number of local sampling points corresponding to the abnormal sampling points. This indicates gradient correlation.

9. The gradient detection method based on geochemical exploration technology as described in claim 1, characterized in that, The target region probability value is positively correlated with local correlation and negatively correlated with gradient correlation.

10. A gradient detection system based on geochemical exploration technology, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the gradient detection method based on geochemical exploration technology as described in any one of claims 1-9.

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