A method and system for characterizing the geophysical response of the degree of enrichment of elements in coal rocks

Abstract

The application discloses a kind of geophysical response characterization methods and systems of element enrichment degree in coal rock, and characterization method includes the following steps: obtaining the original sample of the same research block coal rock, and dividing subdivision layer;The sample of the subdivision layer is processed to obtain a variety of sizes of cube samples and its remaining material to meet different test conditions;Collect remaining material and acidolysis into solution, select target element and determine the element enrichment degree of acidolysis solution;Determine the multiple geophysical responses of the cube sample;Calculate geophysical derived parameters;According to the test and calculation of the above steps, a plurality of geophysical responses corresponding to the element content are obtained for a certain number of subdivision layer samples, and a geophysical response characterization model of element enrichment degree is constructed using a multi-factor analysis method;Different characterization models constructed by different multi-factor analysis methods are optimized, and the model with high precision and easy operation is determined as the final characterization model.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of coal rock element enrichment capacity, and particularly relates to a method and system for quantitatively characterizing element enrichment degree based on multi-geophysical response analysis technology. BACKGROUND

[0002] Strategic metal element mineral resources are important raw materials for new energy, new materials, national defense and information technology in China. The distribution of these strategic metal elements in nature is scattered and the reserves are not high. It is of great significance to explore the mineral resources of these elements for the resource security of China. Due to its special geological and chemical conditions, coal measures have the ability to enrich strategic metal elements, and this enrichment capacity can be quantitatively studied by geochemical methods, including XRD-EDS technology for quantitative analysis of mineral composition of coal rock, ICP-MS technology for quantitative analysis of element content, and enrichment coefficient CC index for evaluating enrichment capacity. Through geochemical methods, element enrichment capacity analysis can be performed on discrete sampling points, but it is impossible to continuously measure whether the mining area is enriched in a certain strategic metal element, which may lead to incorrect judgment of the element enrichment degree of the unsampled block.

[0003] To solve this problem, geophysical methods such as seismic exploration technology and logging technology can be combined. This method can continuously detect various geophysical responses (including electromagnetic response, elastic wave velocity and modulus response, radioactivity response, anisotropy response, etc.) in the planar distribution and depth variation of the mining area. However, the element enrichment degree in geochemistry cannot be clearly related to various geophysical responses. Therefore, the continuous detection advantage of geophysical methods cannot effectively play a role in the element enrichment degree of coal rock.

[0004] On the other hand, although the types of geophysical responses are wide-ranging and the acquisition methods are relatively mature, there is still no obvious rule in quantitatively characterizing the element enrichment degree, and mathematical and statistical methods still need to be combined to build a characterization model. In this regard, the feature combination method can integrate geophysical responses with too high similarity; the stepwise regression method can gradually input geophysical responses into the characterization model and automatically fit them according to statistical rules; the regularization method can filter out geophysical responses irrelevant to the element enrichment degree with biased estimation and give the importance of the remaining responses; principal component analysis can reduce the dimension while retaining a large amount of geophysical response information, and use the reduced principal components as new responses to simplify the parameters. The above methods are combined to quantitatively characterize the geophysical responses of the element enrichment degree. After mastering the characterization model, the continuous detection of the content of strategic metal elements in coal rock in the mining area can be completed by using geophysical methods such as seismic exploration technology and logging technology, and the accuracy of the content distribution and the precision of the spatial distribution of the reserve prediction can be improved. SUMMARY

[0005] The present solution is directed to the problems and needs presented above, and proposes a geophysical response characterization method for the element enrichment degree in coal rock, which can achieve the above technical purposes and bring other technical effects due to the following technical features.

[0006] One object of the present application is to propose a geophysical response characterization method for the element enrichment degree in coal rock, comprising the following steps:

[0007] S10: Obtain original samples of coal rock in the same research block and divide the samples into sub-layers;

[0008] S20: Process the sub-layer samples into multiple sizes to obtain cubic samples and residual materials that meet different test conditions;

[0009] S30: Collect the residual materials and acid-dissolve them into a solution, select target elements, and determine the element enrichment degree of the acid-dissolved solution;

[0010] S40: Determine multiple geophysical responses of the cubic samples; wherein the multiple geophysical responses include density DEN, relative dielectric constant DC, magnetic susceptibility MS, natural gamma radiation GR, radial direct current resistivity p ∥ , vertical direct current resistivity p ⊥ , radial P-wave velocity Vp ∥ , radial S-wave velocity Vs ∥ , vertical P-wave velocity Vp ⊥ , and vertical S-wave velocity Vs ⊥ ;

[0011] S50: Calculate geophysical derived parameters; wherein the derived parameters include anisotropy parameters and elastic modulus parameters, the anisotropy parameters include resistivity anisotropy A ρ and wave velocity anisotropy A V , and the elastic modulus parameters include P-wave to S-wave velocity ratio η, bulk modulus K, shear modulus μ, Young's modulus E, and Poisson's ratio σ;

[0012] S60: Obtain multiple geophysical responses corresponding to element content by testing and calculating a certain number of sub-layer samples according to S30 to S50 above, and use a multi-factor analysis method to construct a geophysical response characterization model for the element enrichment degree;

[0013] S70: Optimize different characterization models constructed by different multi-factor analysis methods and determine a model with high precision and easy operation as the final characterization model.

[0014] In addition, the geophysical response characterization method for the element enrichment degree in the coal rock according to the present application can have the following technical features.

[0015] In one example of the present application, in the step S10, the same research block coal rock is the same coal seam of the same mine, different coal seams of the same mine, or the same coal seam of different mines; and the original sample is a complete coal system stratum profile including coal roof, coal, coal interburden, and coal floor from top to bottom.

[0016] In one example of the present application, in the step S10, the division of the sub-layers includes division according to lithology variation or certain thickness along the bedding direction of the rock stratum.

[0017] In one example of the present application, in the step S20, the test conditions include element enrichment degree test and geophysical response test; wherein the element enrichment degree test requires processing into powder and acidolysis into solution; and the geophysical response test requires processing into a cube with a side length of 4-6 cm, and one set of symmetry faces of the cube should be parallel to the bedding direction of the rock stratum.

[0018] In one example of the present application, in the step S30, after the target element is selected, the step further includes:

[0019] determining whether the element enrichment degree meets the condition;

[0020] if the target element enrichment ability of all the sub-layers is weak and the difference is small, the condition is not met, the research block needs to be replaced, and the sampling and processing are performed again according to the steps S10 to S20;

[0021] if the target element enrichment ability of all the sub-layers is strong and the difference is large, the condition is met, and the target element enrichment degree of each sub-layer is output.

[0022] In one example of the present application, in the step S40, the step of measuring the natural gamma radiation GR of the cube sample is as follows:

[0023] S441: providing a natural gamma detector and a radiation shielding cavity;

[0024] S442 (test noise): placing the natural gamma detector into the radiation shielding cavity, taking the average value GRb as the natural gamma background value after multiple measurements;

[0025] S443: placing the natural gamma detector into the radiation shielding cavity (31) containing the sample with the measured natural gamma radiation, taking the average value GRt as the natural gamma test value after multiple measurements;

[0026] S444 (de-noising): subtracting the natural gamma background value GRb from the natural gamma test value GRt to obtain a natural gamma value GRr from the sample itself;

[0027] S445 (volume correction): volume correcting GRr by using the calculated sample volume V, and the correction formula is GR=GRr / V, to obtain the natural gamma radiation GR of the sample.

[0028] In one example of the present application, in the step S60, the analysis method comprises a feature combination method, a stepwise regression method, a regularization method and a principal component analysis method.

[0029] In one example of the present application, the step S70 comprises the following steps:

[0030] obviously unreasonable models are screened out according to the geophysical knowledge background;

[0031] the remaining characterization models are sorted according to the accuracy;

[0032] combined with the difficulty of obtaining the geophysical response parameters used in the high-precision characterization model under the actual situation of the research block, a model with high precision and easy operation is optimized as the final characterization model.

[0033] Another object of the present application is to provide a geophysical response characterization system for the element enrichment degree in coal rock, comprising:

[0034] a sample division module configured to obtain original samples of coal rock in the same research block and divide the samples into sub-layers;

[0035] a sample processing module configured to process the sub-layer samples into cubic samples and residual materials in multiple sizes to meet different test conditions;

[0036] an element determination module configured to collect the residual materials and acid-dissolve them into a solution, select target elements and determine the element enrichment degree of the acid-dissolved solution;

[0037] a geophysical response calculation module configured to determine multiple geophysical responses of the cubic samples; wherein the multiple geophysical responses comprise density DEN, relative dielectric constant DC, magnetic susceptibility MS, natural gamma radiation GR, radial direct current resistivity ∥ , vertical direct current resistivity ⊥ , radial longitudinal wave velocity Vp ∥ , radial transverse wave velocity Vs ∥ , vertical longitudinal wave velocity Vp ⊥ , and vertical transverse wave velocity Vs ⊥ .

[0038] a derivative parameter calculation module configured to calculate geophysical derivative parameters; wherein the derivative parameters include anisotropy parameters and elastic modulus parameters, the anisotropy parameters include resistivity anisotropy A ρ and wave velocity anisotropy A V , and the elastic modulus parameters include the ratio of longitudinal wave velocity to transverse wave velocity η, bulk modulus K, shear modulus μ, Young's modulus E, and Poisson's ratio σ

[0039] a characterization model construction module configured to obtain a plurality of geophysical response characterization models of element enrichment degree by using a multi-factor analysis method according to the element determination module, the geophysical response calculation module, and the derivative parameter calculation module

[0040] a characterization model selection module configured to select and determine a model with high precision and easy operation as a final characterization model from different characterization models constructed by different multi-factor analysis methods.

[0041] In an example of the present application, the element determination module includes:

[0042] a judgment unit configured to judge whether the element enrichment degree meets a condition; wherein if the enrichment ability of the target element of all the sub-layers is weak and the difference is small, the condition is not met and the research block needs to be replaced, and sampling and processing are performed again according to the sample division module and the sample processing module; if the enrichment ability of the target element of all the sub-layers is strong and the difference is large, the condition is met and the target element enrichment degree of each sub-layer is output.

[0043] In an example of the present application, the characterization model selection module includes:

[0044] a screening unit configured to screen out obviously unreasonable models according to geophysical knowledge background;

[0045] a sorting unit configured to sort the remaining characterization models according to precision;

[0046] a selection unit configured to select a model with high precision and easy operation as a final characterization model by combining the difficulty of obtaining geophysical response parameters used by the high-precision characterization model under the actual situation of the research block.

[0047] The optimal embodiments of the present application will be described in more detail below with reference to the accompanying drawings, so that the features and advantages of the present application can be easily understood. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings of the embodiments of the present application will be briefly introduced below. The drawings are merely used to show some embodiments of the present application, and the present application is not limited to the drawings.

[0049] Figure 1 The flow chart of the method for characterizing the geophysical response of the element enrichment degree in coal rock according to the embodiment of the present application is shown in the figure.

[0050] Figure 2 The schematic diagram of the coal stratum profile subdivision layer division according to the embodiment of the present application is shown in the figure.

[0051] Figure 3 The schematic diagram of the cubic sample according to the embodiment of the present application is shown in the figure.

[0052] Figure 4 The structural schematic diagram of the natural gamma detector and the ray shielding cavity according to the embodiment of the present application is shown in the figure.

[0053] List of reference signs:

[0054] The roof 1r;

[0055] The first layer coal 1c;

[0056] The first layer parting 1p;

[0057] The second layer coal 2c;

[0058] The second layer parting 2p;

[0059] The third layer coal 3c;

[0060] The floor 1f;

[0061] The subdivision layer 101r of the roof;

[0062] The first subdivision layer 101c of the first layer coal;

[0063] The second subdivision layer 102c of the first layer coal;

[0064] The third subdivision layer 103c of the first layer coal;

[0065] The subdivision layer 101p of the first layer parting;

[0066] The subdivision layer 201c of the second layer coal;

[0067] The subdivision layer 201p of the second layer parting;

[0068] The first subdivision layer 301c of the third layer coal;

[0069] The second subdivision layer 302c of the third layer coal;

[0070] Subdivision layer 101f of the base plate;

[0071] Cube sample 100;

[0072] Top surface 10;

[0073] Bottom surface 20;

[0074] Side surface 30;

[0075] Natural gamma detector 11;

[0076] Ray shielding cavity cover 21;

[0077] Ray shielding cavity 31;

[0078] Sample 41 of the measured natural gamma radiation. DETAILED DESCRIPTION

[0079] In order to make the purpose, technical scheme and advantages of the technical scheme of the present application more clear, the technical scheme of the embodiments of the present application will be described clearly and completely below in combination with the drawings of the embodiments of the present application. The same reference signs in the drawings represent the same components. It should be noted that the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the described embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without any creative effort fall within the scope of protection of the present application.

[0080] Unless otherwise defined, technical terms or scientific terms used herein should be understood as having the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terms "first", "second", and similar terms used in the description and the claims of the present application do not necessarily mean any order, number, or importance, but are only used to distinguish different components. Similarly, the terms "one" or "a" or similar terms do not necessarily mean a quantity limitation. The terms "include" or "contain" or similar terms mean that the elements or objects before the terms cover the elements or objects listed after the terms and their equivalents, without excluding other elements or objects. The terms "connect" or "connected" or similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The terms "up", "down", "left", "right", and the like only represent relative positional relationships, which can change accordingly when the absolute position of the described object changes.

[0081] According to the method for characterizing the geophysical response of the enrichment degree of elements in coal rock according to the first aspect of the present application, as shown in Figure 1 the method comprises the following steps:

[0082] S10: Obtain the original sample of coal and rock in the same study block and divide the sublayer;

[0083] For example, the study block is the complete coal system profile of multiple coal seams in the same mine. When the sublayer is divided, as shown in FIG. 1, the top layer 1r is directly divided into the top layer sublayer 101r; the first coal seam 1c sandwiched by the top layer 1r and the first layer of gangue 1p has a uniform thickness and coal quality, and can be divided into three sublayers, i.e., the first sublayer of the first layer of coal 101c, the second sublayer of the first layer of coal 102c, and the third sublayer of the first layer of coal 103c; the first layer of gangue 1p is directly divided into the first layer of gangue sublayer 101p; the second coal seam 2c sandwiched by the first layer of gangue 1p and the second layer of gangue has a thin thickness, and is directly divided into the second layer of coal sublayer 201c; the second layer of gangue 2p is directly divided into the second layer of gangue sublayer 201p; the third coal seam 3c sandwiched by the second layer of gangue 2p and the floor 1f has a moderate thickness and uniform coal quality, and can be divided into two sublayers, i.e., the first sublayer of the third layer of coal 301c and the second sublayer of the third layer of coal 302c; and the lowermost floor 1f is directly divided into the floor sublayer 101f. Figure 2 S20: Process the sublayer sample to obtain a cubic sample and residual material that meet different test conditions; for example, as shown in FIG. 2, the cubic sample 100 is made after the irregular sublayer sample is processed, the dashed line represents the bedding of the rock layer, the top surface 10 and the bottom surface 20 of the cubic sample are parallel to the bedding direction, and the side surface 30 of the cubic sample is perpendicular to the bedding direction; the residual material generated during the processing of the cubic sample 100 is reserved, and a part of the residual material is processed into a powder and acid-dissolved into a solution.

[0084] Figure 3 S30: Collect the residual material and acid-dissolve it into a solution, select a target element, and measure the element enrichment degree of the acid-dissolved solution; the measurement method is inductively coupled plasma mass spectrometry (ICP-MS), and the national standard GB / T 30903-2014 is referred to.

[0085] S40: Measure the multiple geophysical responses of the cubic sample; the multiple geophysical responses include density DEN, relative dielectric constant DC, magnetic susceptibility MS, natural gamma radiation GR, radial direct current resistivity p ∥ , vertical direct current resistivity p ⊥ , radial longitudinal wave velocity Vp ∥ , radial transverse wave velocity Vs ∥ , vertical longitudinal wave velocity Vp ⊥ , and vertical transverse wave velocity Vs ⊥ .

[0086] S40: Measure the multiple geophysical responses of the cubic sample; the multiple geophysical responses include density DEN, relative dielectric constant DC, magnetic susceptibility MS, natural gamma radiation GR, radial direct current resistivity p ∥ , vertical direct current resistivity p ⊥ , radial longitudinal wave velocity Vp ∥ , radial transverse wave velocity Vs ∥ , vertical longitudinal wave velocity Vp ⊥ , and vertical transverse wave velocity Vs ⊥ .

[0087] S50: calculating geophysical derived parameters; wherein, the derived parameters include anisotropy parameters and elastic modulus parameters, the anisotropy parameters include resistivity anisotropy A ρ and wave velocity anisotropy A V , and the elastic modulus parameters include P-S wave velocity ratio η, bulk modulus K, shear modulus μ, Young's modulus E and Poisson's ratio σ;

[0088] S60: obtaining a plurality of geophysical responses corresponding to the element content by testing and calculating according to S30 to S50 above for a certain number of sublayer samples, and using a multi-factor analysis method to construct a geophysical response characterization model of the element enrichment degree;

[0089] S70: optimizing different characterization models constructed by different multi-factor analysis methods and determining a model with high precision and easy operation as the final characterization model.

[0090] The geophysical responses involved in the method are very comprehensive, and are all accurate parameters that can be directly or indirectly measured by experiments, and the method has high feasibility and reliable results; the method highly combines mathematical methods with theoretical practice, and based on the mathematical method of multi-factor analysis, geophysical parameters related to the element enrichment degree can be screened from multiple aspects, and a plurality of feasible mathematical characterization models can be established, and then the models can be optimized in combination with the relevant expert knowledge background and the actual situation of the research area, so that the accuracy and usability of the models can be ensured; the method is used for the enrichment degree evaluation, enrichment area delineation and reserve calculation of the associated strategic metal element mineral in coal rock, and expands the application range of geophysical methods such as seismic exploration technology and logging technology, and ensures the persuasiveness and reliability of the detection results, and provides support services for national resource security.

[0091] In an example of the present application, in the step S10, the same research block coal rock is the same coal seam of the same mine, different coal seams of the same mine or the same coal seam of different mines; and the original sample is a complete coal system stratigraphic section including coal roof, coal, coal gangue and coal floor from top to bottom.

[0092] In an example of the present application, in the step S10, the division of the sublayer includes: dividing according to the change of lithology or a certain thickness along the bedding direction of the rock stratum.

[0093] In an example of the present application, in the step S20, the test conditions include the test of the element enrichment degree and the test of the geophysical response; wherein, the test of the element enrichment degree requires processing into powder and acidolysis into solution; and the test of the geophysical response requires processing into a cube with a side length of 4-6 cm, and one set of symmetric faces of the cube should be parallel to the bedding direction of the rock stratum.

[0094] In one example of the present invention, step S30, after selecting the target element, further includes:

[0095] To determine whether the enrichment level of the measured element meets the conditions;

[0096] If the target elements in all subdivided layers have weak enrichment ability and small differences, the conditions are not met and the research block needs to be changed, and sampling and processing should be carried out again according to steps S10 to S20.

[0097] If all sub-layers have strong target element enrichment capabilities and significant differences, then the condition is met and the target element enrichment degree of each sub-layer is output.

[0098] For example, if the strategic metallic element Li is selected as the target element for the study block, and the Li content of the subdivided layers is determined by inductively coupled plasma mass spectrometry (ICP-MS) and compared with the average Li content in coal and rock worldwide, it is found that the Li content of some subdivided layers is 5 to 10 times the average Li content in coal and rock worldwide, indicating that these subdivided layers have a strong Li enrichment capacity; the Li content of a few subdivided layers can reach more than 10 times, indicating that these subdivided layers have an extremely strong Li enrichment capacity; the Li content of the remaining subdivided layers is 0.5 to 5 times, which is close to the average Li content in coal and rock worldwide, indicating a moderate enrichment capacity. The target element Li enrichment capacity of this study block is good and has significant differences, so there is no need to change the study block.

[0099] In one example of the present invention, in step S40, various geophysical testing methods are as follows:

[0100] S41: Density (DEN) of the test sample;

[0101] S411: Provides electronic balances and vernier calipers;

[0102] S412: Use an electronic balance to measure the mass m of the cube sample, and use a vernier caliper to measure the side length of the cube sample multiple times.

[0103] S413: Calculate the volume V of the square sample using the average side length, and calculate the density DEN of the sample using the density formula DEN=m / V;

[0104] S42: Relative permittivity DC of the test sample;

[0105] S421: Provides a relative permittivity meter;

[0106] S422: Use the probe of the relative permittivity meter to make close contact with the six faces of the fully dried cube sample, and read and record the relative permittivity values ​​of the six faces;

[0107] S423: Average the above 6 relative permittivity measurements to obtain the relative permittivity DC of the sample;

[0108] S43: Magnetic susceptibility MS of the test sample;

[0109] S431: Provides a magnetic susceptibility meter;

[0110] S432: Use the probe of the magnetic susceptibility meter to make close contact with the six faces of the fully dried cube sample, and read and record the magnetic susceptibility values ​​of the six faces.

[0111] S433: The magnetic susceptibility MS of the sample is obtained by averaging the above 6 magnetic susceptibility measurements.

[0112] S44: Natural gamma radiation (GR) of the test sample;

[0113] Rocks in nature constantly emit detectable natural gamma rays. Because different rocks contain varying amounts of radioactive elements, the lithology or other properties of underground rock formations can be inferred from the natural gamma radiation levels of rocks. Natural gamma logging technology in geophysical well logging can detect the natural gamma radiation levels in different sections of a wellbore. Correspondingly, patterns derived from testing the natural gamma radiation of rocks of different properties in the laboratory can be used as a reference in natural gamma logging. Therefore, a rapid, accurate, and low-cost laboratory testing method is needed to obtain the natural gamma radiation intensity of rocks.

[0114] However, laboratory testing differs from logging in the wellbore—in the wellbore, the logging probe is completely embedded in the rock formation of the section being logged, making it difficult for other radiation sources to interfere, and the rock formations in the wellbore are huge, making it easier for the probe to capture the large amount of natural gamma rays produced. In contrast, in the laboratory, the volume of rock samples that a natural gamma testing probe can test is limited, and its weak natural gamma radiation is easily masked by environmental noise from other radiation sources in the environment.

[0115] Based on experimental requirements, this invention designs a cavity to shield against environmental gamma rays, requiring the cavity to have the following characteristics:

[0116] (1) Strong natural gamma shielding ability. Natural gamma rays are gamma rays and have extremely strong penetrating power. Some materials with high atomic number and high density have a good effect in blocking gamma rays, and the blocking effect increases with the increase of material thickness. Commonly used gamma ray shielding materials include lead plates and steel plates.

[0117] (2) It has convenient opening and closing functions. The sample and the probe of the testing instrument need to be placed in the cavity, and the sample should be kept as intact as possible when changing the sample, and it is easy to clean the sample residue in the cavity.

[0118] (3) It has a data monitoring function. The radioactivity data measured by the probe can be monitored when the shielded cavity is closed, which facilitates data recording.

[0119] In summary, the design and production of such... Figure 4 A stainless steel radiation shielding cavity 31 with a radiation shielding cavity cover 21 is provided for inserting a natural gamma detector 11. The sample 41 for measuring the natural gamma radiation is placed at the bottom of the radiation shielding cavity 31. The radiation shielding cavity cover 21 has a certain weight and is placed directly on the open end of the radiation shielding cavity 31. The radiation shielding cavity cover 21 has a through hole that is connected to the radiation shielding cavity 31. The natural gamma detector 11 is placed into the radiation shielding cavity 31 through the through hole during placement.

[0120] The procedure for determining the natural gamma radiation (GR) of the test sample is as follows:

[0121] S441: Provides a natural gamma detector 11 and a radiation shielding cavity 31; for example, such as Figure 4 The image shows a cross-section of the radiation shielding cavity in operation. 11 is the natural gamma meter, and 21 is the cover of the radiation shielding cavity.

[0122] S442 (Test Noise): Place the natural gamma detector 11 into the radiation shielding cavity 31, and take the average value GRb as the natural gamma background value after multiple measurements.

[0123] S443: Place the natural gamma detector 11 into the radiation shielding cavity 31 containing the sample 41 containing the natural gamma radiation to be measured, and take the average value GRt as the natural gamma test value after multiple measurements.

[0124] S444 (Denoising): Subtract the natural gamma background value GRb from the natural gamma test value GRt to obtain the natural gamma value GRr from the sample itself;

[0125] S445 (Volume Correction): The sample volume V calculated in S413 is used to correct GRr for volume. The correction formula is GR=GRr / V, which gives the natural gamma radiation GR of the sample.

[0126] Before using the shielded cavity, the environmental noise is usually about 5 times the measured value of the rock sample being tested, and the measured value fluctuates greatly after being affected by the noise. The noise reduction process and the actual testing process are very difficult, and there may even be cases where the measured value is negative after noise reduction.

[0127] After using the shielded cavity, the environmental noise inside the cavity was suppressed to less than 10% of its original level, and the noise measurement was very stable, making it easy to remove the environmental noise inside the cavity. During the actual testing process after the sample was placed inside, the readings of the tester were also very stable, and the measured values ​​were all positive after noise removal, which is consistent with the actual situation.

[0128] The above comparisons demonstrate that using this shielded cavity effectively improves the accuracy of natural gamma radioactivity measurements in rock samples, and the stable readings reduce the number of repeated tests, significantly improving testing efficiency. The laboratory natural gamma testing method using this shielded cavity meets the expectations of being fast, accurate, and cost-effective.

[0129] S45: Resistivity ρ of the test sample;

[0130] S451: Provides a DC resistance tester;

[0131] S452: After applying conductive adhesive to a set of planes perpendicular to the stratification of a cube sample, use two copper sheets to make full contact with the two adhesive-coated planes. Connect a resistance meter with wires to measure the DC resistance R of the sample along the stratification. ∥ ;

[0132] S453: Using the side length of the cube sample from step S412 as the propagation distance L of the direct current, and the square of the side length as the cross-sectional area S of the sample, calculate the radial DC resistivity ρ of the sample using the resistivity formula ρ=RS / L. ∥ ;

[0133] S454: Apply conductive adhesive to a plane parallel to the strata of the cube sample and repeat steps S452 and S453 to obtain the vertical DC resistivity ρ of the sample. ⊥ ;

[0134] S46: Wave velocity V of the test sample;

[0135] S461: Provides an intelligent ultrasonic longitudinal and transverse wave automatic tester;

[0136] S462: Input the side length of the cube sample in step S412 as the propagation distance L of the ultrasonic wave in the sample into the software of the automatic testing instrument.

[0137] S463: Select a set of planes perpendicular to the strata of the cube sample, apply ultrasonic coupling agent, and then use a pair of ultrasonic probes to make full contact with the two planes coated with coupling agent to obtain the radial longitudinal wave waveform and ultrasonic transverse wave waveform.

[0138] S464: Remove the ultrasonic probe from the sample and wipe off the coupling agent. Then, select a set of planes parallel to the strata of the cubic sample and apply ultrasonic coupling agent. Obtain the vertical longitudinal wave waveform and ultrasonic transverse wave waveform according to the method in S463.

[0139] S465: Manually pick up the first arrival time tp of the above radial ultrasonic longitudinal wave waveform. ∥ Calculate the radial longitudinal wave velocity Vp ∥ ;

[0140] S466: Manually pick up the first arrival time ts of the above radial ultrasonic shear wave waveform. ∥ Calculate the radial shear wave velocity Vs ∥ ;

[0141] S467: Manually pick up the first arrival time tp of the above vertical ultrasonic longitudinal wave waveform. ⊥ Calculate the vertical P-wave velocity Vp ⊥ ;

[0142] S468: Manually pick up the first arrival time ts of the above vertical ultrasonic shear wave waveform. ⊥ Calculate the vertical shear wave velocity Vs ⊥ .

[0143] In one example of the present invention, the calculation of geophysical derived parameters in step S50 includes the following steps:

[0144] S51: Calculate two anisotropic parameters, resistivity anisotropy A. ρ With wave velocity anisotropy A V ;

[0145] S511: Provides the radial DC resistivity ρ of the sample. ∥ With vertical DC resistivity ρ ⊥ ;

[0146] S512: The ratio ρ of radial DC resistivity to vertical DC resistivity ∥ :ρ ⊥ As resistivity anisotropy A ρ ;

[0147] S513: Provides the radial longitudinal wave velocity Vp of the sample. ∥ With vertical longitudinal wave velocity Vp ⊥ ;

[0148] S514: The ratio of radial P-wave velocity to vertical P-wave velocity, Vp ∥ :Vp ⊥ As wave velocity anisotropy A V ;

[0149] S52: Calculate five elastic modulus parameters: longitudinal and transverse wave velocity ratio η, volumetric modulus K, shear modulus μ, Young's modulus E, and Poisson's ratio σ;

[0150] S521: Provides the sample density DEN, P-wave velocity Vp, and S-wave velocity Vs;

[0151] S522: The ratio of P-wave velocity Vp to S-wave velocity Vs can be used to obtain the P-wave / S-wave velocity ratio η;

[0152] S523: Calculate the bulk modulus K, shear modulus μ, Young's modulus E, and Poisson's ratio σ using density, P-wave velocity, S-wave velocity, and the P-wave / S-wave velocity ratio;

[0153] It should be noted that, firstly, two anisotropic parameters are calculated: resistivity anisotropy A. ρ With wave velocity anisotropy A V ;

[0154] The specific calculation formula is as follows:

[0155] A ρ =ρ ∥ / ρ ⊥

[0156] A V = V ∥ / V ⊥

[0157] Among them, A ρ It is resistivity anisotropy, ρ ∥ It is the radial DC resistivity, ρ ⊥ It is the vertical DC resistivity, V ρ It is wave speed anisotropy, V ∥ It is the radial longitudinal wave velocity, V ⊥ It is the vertical longitudinal wave velocity;

[0158] Then, calculate five elastic modulus parameters: the ratio of longitudinal to transverse wave velocity η, the volumetric modulus K, the shear modulus μ, Young's modulus E, and Poisson's ratio σ.

[0159] The specific calculation formula is as follows:

[0160]

[0161]

[0162]

[0163]

[0164]

[0165] Where η is the ratio of P-wave velocity to S-wave velocity, Vp is the P-wave velocity, Vs is the S-wave velocity, K is the bulk modulus, DEN is the density, μ is the shear modulus, E is Young's modulus, and σ is Poisson's ratio.

[0166] In one example of the present invention, in step S60, the analysis method includes feature combination method, stepwise regression method, regularization and principal component analysis method;

[0167] S61: Multifactor analysis using the feature combination method;

[0168] S611: Prepare all geophysical response test results for all samples; including 10 basic geophysical responses (density DEN, relative permittivity DC, magnetic susceptibility MS, natural gamma radiation GR, radial DC resistivity ρ). ∥ Vertical DC resistivity ρ ⊥ Radial longitudinal wave velocity Vp ∥ Radial transverse wave velocity Vs ∥ Vertical longitudinal wave velocity Vp ⊥ Vertical transverse wave velocity Vs ⊥ ), two anisotropy parameters (resistivity anisotropy A) ρ Wave velocity anisotropy A V ), 10 elastic moduli (radial and vertical longitudinal and transverse wave velocity ratio η, volumetric modulus K, shear modulus μ, Young's modulus E and Poisson's ratio σ), a total of 22 geophysical responses;

[0169] S612: Calculate the Pearson correlation coefficient between pairs of different geophysical responses;

[0170] S613: Combine geophysical responses with Pearson coefficients higher than 0.9 in pairs to obtain the combined geophysical response; for example, the P-wave velocity ratio η is combined into the Poisson ratio σ, and the radial wave velocity and modulus are combined into the radial volume modulus K. ∥ In the middle, the vertical wave velocity and modulus are combined into the vertical Young's modulus E. ⊥ Ultimately, 9 geophysical responses remained, and after merging, 12 geophysical responses were remaining (density DEN, relative permittivity DC, magnetic susceptibility MS, natural gamma radiation GR, radial DC resistivity ρ). ∥ Vertical DC resistivity ρ ⊥ Resistivity anisotropy A ρ Wave velocity anisotropy A V Radial Poisson's ratio σ ∥ Vertical Poisson's ratio σ ⊥ Radial volume modulus K ∥ Vertical Young's modulus E ⊥ );

[0171] S62: Multivariate analysis using stepwise regression;

[0172] S621: Prepare the combined geophysical response from step S613;

[0173] S622: Randomly introduce a geophysical response as a variable and perform linear regression with the elemental content;

[0174] S623: Calculate the correlation coefficients, T-test parameters, and p-test parameters of all remaining geophysical responses with elemental enrichment, and introduce the next geophysical response with the highest correlation coefficient that passes both the T-test and p-test as a new variable.

[0175] S624: If the introduced variable causes other variables already in the linear regression to no longer satisfy the T-test or p-test, then the variable that does not satisfy the test will be removed.

[0176] S625: Repeat steps S623 to S624 until all geophysical responses that meet the linear regression conditions have been input into the regression model, which can then be used as an alternative representation method (Type 1); for example, four different representation models (Type 1.1, Type 1.2, Type 1.3, Type 1.4) were extracted using the stepwise regression method.

[0177] S63: Multifactor analysis using regularization;

[0178] S631: Prepare the combined geophysical response from step S613;

[0179] S632: Use all geophysical responses to construct a regression model with elemental enrichment, and add the L1 norm to the objective function of the regression model to establish an L1-regularized Lasso regression model;

[0180] S633: Select a series of ordered λ as coefficients of the L1 norm and add them to the Lasso regression model. As λ gradually increases, geophysical responses are continuously removed from the Lasso regression model. At the same time, the mean square error of the model is also constantly changing and gradually increases as the remaining geophysical responses decrease.

[0181] S634: Select the model with the smallest mean square error as an alternative representation method (Type 2), and then select the model with the smallest mean square error but the simplest as another alternative representation method (Type 3).

[0182] S64: Multifactor analysis using principal component analysis;

[0183] S641: Prepare the 12 geophysical responses merged in step S613;

[0184] S642: Extract the principal components of all geophysical responses and calculate the component matrix of the contribution of the original geophysical responses in the principal components.

[0185] S643: Using element enrichment as the fitting object, principal components are added one by one to the linear fitting model, and the fitting R is calculated. 2 R 2 It will increase with the addition of new principal components;

[0186] S644: For improving R 2 The top few principal components (up to 3) are extracted, and the linear fitting model of these principal components and the enrichment of elements is used as an alternative characterization method (Type 4); for example, the 1st, 6th and 7th principal components are extracted, and the linear fitting model of these principal components and the enrichment of elements is used as an alternative characterization method (Type 4).

[0187] S645: Observe the component matrix in step S642 and find the main geophysical responses corresponding to the principal components extracted in step S644. Use the linear fitting model of these geophysical responses and element enrichment as an alternative characterization method (Type 5); for example, the main geophysical responses corresponding to the first principal component are density DEN and vertical Young's modulus E. ⊥ The principal geophysical response corresponding to the 6th principal component is the natural gamma radiation GR, and the principal geophysical response corresponding to the 7th principal component is the vertical Poisson's ratio σ. ⊥ Linear fitting models of these geophysical responses and elemental enrichment levels are used as an alternative characterization method (Type 5).

[0188] In one example of the present invention, step S70 includes the following steps:

[0189] Based on geophysical knowledge, obviously unreasonable models were filtered out.

[0190] The remaining representation models are sorted by accuracy;

[0191] Considering the ease of obtaining geophysical response parameters used in the high-precision characterization model under the actual conditions of the research block, a model with high accuracy and easy operation is selected as the final characterization model.

[0192] For example, a comparison of the eight models (Type 1.1~Type 1.4, Type 2~Type 5) extracted from the multifactor analysis showed that none of the models had any obviously unreasonable features; in terms of the accuracy of the model representation, Type 2 and Type 3, based on regularization, had the highest accuracy (R²). 2 ≥0.97), but they all used 9 geophysical responses, some of which were difficult to obtain in the study area, resulting in high model complexity and potential overfitting, and were therefore not included in the preferred selection; the next best in terms of model accuracy were Type 1.2 and Type 1.3 (R) based on stepwise regression.2 ≥0.95), all of them used 5 geophysical responses, among which the geophysical responses used by Type 1.3 were mainly obtained from seismic exploration, and the feasibility was higher than that of Type 1.2; the Type 4 and Type 5 (R) based on principal component analysis had moderate accuracy. 2 ≥0.90), among which Type 4 requires all 12 merged geophysical responses to extract principal components, which is difficult to implement in practice; Type 5 uses 4 relatively easy-to-obtain geophysical responses, which is relatively easy to implement in practice; finally, the remaining Type 1.1 and Type 1.4 each use only 2 geophysical responses, which are the simplest representation models, but also have the lowest accuracy (R 2 <0.90); After comprehensively considering the differences between the above representation models, Type 1.3 and Type 5 were selected as the final preferred representation models.

[0193] A geophysical response characterization system for the enrichment degree of elements in coal and rock according to a second aspect of the present invention includes:

[0194] The sample segmentation module is configured to acquire raw coal and rock samples from the same study block and segment them into sub-layers.

[0195] The sample processing module is configured to process the subdivided layer sample into multiple sizes to meet different testing conditions to obtain a cube sample and its remaining material.

[0196] The element determination module is configured to collect the remaining material and acid-dissolve it into a solution, select the target element, and determine the element enrichment degree of the acid-dissolved solution.

[0197] The geophysical response calculation module is configured to determine various geophysical responses of the cube sample; these responses include density (DEN), relative permittivity (DC), magnetic susceptibility (MS), natural gamma radiation (GR), and radial DC resistivity (ρ). ∥ Vertical DC resistivity ρ ⊥ Radial longitudinal wave velocity Vp ∥ Radial transverse wave velocity Vs ∥ Vertical longitudinal wave velocity Vp ⊥ Vertical transverse wave velocity Vs ⊥ ;

[0198] The derived parameter calculation module is configured to calculate geophysical derived parameters; these parameters include anisotropy parameters and elastic modulus parameters, with the anisotropy parameters including resistivity anisotropy A. ρ and wave velocity anisotropy A V The elastic modulus parameters include the ratio of longitudinal to transverse wave velocity η, volumetric modulus K, shear modulus μ, Young's modulus E, and Poisson's ratio σ;

[0199] The characterization model construction module is configured to test and calculate a certain number of subdivided layer samples according to the above-mentioned element determination module, geophysical response calculation module and derived parameter calculation module, to obtain a variety of geophysical responses that correspond one-to-one with the element content, and to construct a geophysical response characterization model of the element enrichment degree using a multi-factor analysis method.

[0200] The characterization model selection module is configured to optimize different characterization models constructed by different multifactor analysis methods and determine the model with high accuracy and ease of operation as the final characterization model.

[0201] This characterization system encompasses a comprehensive range of geophysical responses, all of which are accurate parameters that can be directly or indirectly measured experimentally, ensuring high feasibility and reliable results. It highly integrates mathematical methods with theoretical practice, utilizing multi-factor analysis to screen geophysical parameters related to element enrichment levels from multiple perspectives and establish several feasible mathematical characterization models. These models are then optimized based on relevant expert knowledge and the actual conditions of the study area, ensuring both accuracy and usability. This system is used for evaluating the enrichment levels, delineating enrichment zones, and calculating reserves of associated strategic metallic minerals in coal and rock. It expands the applicability of geophysical methods such as seismic exploration and well logging techniques, ensuring the persuasiveness and reliability of the detection results and providing support for national resource security.

[0202] In one example of the present invention, the element determination module includes:

[0203] The judgment unit is configured to determine whether the enrichment level of the measured element meets the conditions. If the enrichment ability of the target element in all sub-layers is very weak and the difference is small, the conditions are not met and the study block needs to be changed. Sampling and processing should be carried out again according to the sample division module and sample processing module. If the enrichment ability of the target element in all sub-layers is strong and the difference is large, the conditions are met and the enrichment level of the target element in each sub-layer is output.

[0204] For example, if the strategic metallic element Li is selected as the target element for the study block, and the Li content of the subdivided layers is determined by inductively coupled plasma mass spectrometry (ICP-MS) and compared with the average Li content in coal and rock worldwide, it is found that the Li content of some subdivided layers is 5 to 10 times the average Li content in coal and rock worldwide, indicating that these subdivided layers have a strong Li enrichment capacity; the Li content of a few subdivided layers can reach more than 10 times, indicating that these subdivided layers have an extremely strong Li enrichment capacity; the Li content of the remaining subdivided layers is 0.5 to 5 times, which is close to the average Li content in coal and rock worldwide, indicating a moderate enrichment capacity. The target element Li enrichment capacity of this study block is good and has significant differences, so there is no need to change the study block.

[0205] In one example of the present invention, the characterization model selection module includes:

[0206] The filtering unit is configured to filter out obviously unreasonable models based on geophysical knowledge background.

[0207] A sorting unit, configured to sort the remaining representation models by precision;

[0208] The selection unit is configured to select a high-precision and easy-to-operate model as the final characterization model by considering the ease of obtaining geophysical response parameters used in the high-precision characterization model under the actual conditions of the research block.

[0209] For example, a comparison of the eight models (Type 1.1~Type 1.4, Type 2~Type 5) extracted from the multifactor analysis showed that none of the models had any obviously unreasonable features; in terms of the accuracy of the model representation, Type 2 and Type 3, based on regularization, had the highest accuracy (R²). 2 ≥0.97), but they all used 9 geophysical responses, some of which were difficult to obtain in the study area, resulting in high model complexity and potential overfitting, and were therefore not included in the preferred selection; the next best in terms of model accuracy were Type 1.2 and Type 1.3 (R) based on stepwise regression. 2 ≥0.95), all of them used 5 geophysical responses, among which the geophysical responses used by Type 1.3 were mainly obtained from seismic exploration, and the feasibility was higher than that of Type 1.2; the Type 4 and Type 5 (R) based on principal component analysis had moderate accuracy. 2≥0.90), among which Type 4 requires all 12 merged geophysical responses to extract principal components, which is difficult to implement in practice; Type 5 uses 4 relatively easy-to-obtain geophysical responses, which is relatively easy to implement in practice; finally, the remaining Type 1.1 and Type 1.4 each use only 2 geophysical responses, which are the simplest representation models, but also have the lowest accuracy (R 2 <0.90); After comprehensively considering the differences between the above representation models, Type 1.3 and Type 5 were selected as the final preferred representation models.

[0210] The foregoing description of the exemplary implementation of the geophysical response characterization method for element enrichment in coal and rock proposed in this invention has been described in detail with reference to preferred embodiments. However, those skilled in the art will understand that various modifications and alterations can be made to the above specific embodiments without departing from the concept of this invention, and various combinations can be made to the various technical features and structures proposed in this invention without exceeding the protection scope of this invention, which is determined by the appended claims.

Claims

1. A geophysical response characterization method for the enrichment degree of elements in coal and rock, characterized in that, Includes the following steps: S10: Obtain original coal and rock samples from the same study block and divide them into sub-layers; S20: Process the subdivided layer sample into multiple sizes to meet different test conditions to obtain a cube sample and its remaining material; S30: Collect the remaining material and acid-dissolve it into a solution, select the target element and determine the element enrichment degree of the acid-dissolved solution; S40: Measure various geophysical responses of the cube sample; wherein, the various geophysical responses include density DEN, relative permittivity DC, magnetic susceptibility MS, natural gamma radiation GR, and radial DC resistivity ρ. ∥ Vertical DC resistivity ρ ⊥ Radial longitudinal wave velocity Vp ∥ Radial transverse wave velocity Vs ∥ Vertical longitudinal wave velocity Vp ⊥ Vertical transverse wave velocity Vs ⊥ ; The steps for determining the natural gamma radiation (GR) of a cube sample are as follows: S441: Provides a natural gamma detector (11) and a radiation shielding cavity (31); S442: Test noise: Place the natural gamma detector (11) into the radiation shielding cavity (31), and take the average value GRb as the natural gamma background value after multiple measurements; S443: Place the natural gamma detector (11) into the radiation shielding cavity (31) containing the sample (41) containing the natural gamma radiation to be measured, and take the average value GRt as the natural gamma test value after multiple measurements. S444: Denoising: Subtract the natural gamma background value GRb from the natural gamma test value GRt to obtain the natural gamma value GRr from the sample itself; S445: Volume Correction: The calculated sample volume V is used to correct GRr. The correction formula is GR=GRr / V, which gives the natural gamma radiation GR of the sample. S50: Calculate geophysical derived parameters; among which, derived parameters include anisotropy parameters and elastic modulus parameters, and anisotropy parameters include resistivity anisotropy A. ρ and wave velocity anisotropy A V The elastic modulus parameters include the ratio of longitudinal to transverse wave velocity η, volumetric modulus K, shear modulus μ, Young's modulus E, and Poisson's ratio σ; S60: For a certain number of sub-layer samples, according to the tests and calculations in S30 to S50 above, obtain a variety of geophysical responses that correspond one-to-one with the element content, and use multi-factor analysis methods to construct a geophysical response characterization model of element enrichment degree; wherein, the analysis methods include characteristic combination method, stepwise regression method, regularization and principal component analysis method. S70: Optimize the different characterization models constructed using various multifactor analysis methods and determine the model with high accuracy and ease of operation as the final characterization model; specifically including the following steps: Based on geophysical knowledge, obviously unreasonable models were filtered out. The remaining representation models are sorted by accuracy; Considering the ease of obtaining geophysical response parameters used in the high-precision characterization model under the actual conditions of the research block, a model with high accuracy and ease of operation is selected as the final characterization model.

2. The geophysical response characterization method for element enrichment in coal and rock according to claim 1, characterized in that, In step S10, the coal and rock in the same study block refers to the same coal seam in the same mine, different coal seams in the same mine, or the same coal seam in different mines; the original sample is a complete coal-bearing stratigraphic profile from top to bottom, including the coal roof, coal, coal interbedded with gangue, and coal floor.

3. The geophysical response characterization method for element enrichment in coal and rock according to claim 1, characterized in that, In step S10, the subdivision of the strata includes: dividing the strata along the bedding direction according to changes in lithology or a certain thickness.

4. The geophysical response characterization method for element enrichment in coal and rock according to claim 1, characterized in that, In step S20, the test conditions include testing the degree of element enrichment and testing the geophysical response; wherein, the test of the degree of element enrichment requires processing into powder and acid-dissolving into a solution; the test of the geophysical response requires processing into a cube with a side length of 4-6 cm, and one set of symmetry planes of the cube should be parallel to the bedding direction of the rock layer.

5. The geophysical response characterization method for element enrichment in coal and rock according to claim 1, characterized in that, In step S30, after selecting the target element, the following is also included: To determine whether the enrichment level of the measured element meets the conditions; If the target elements in all subdivided layers have weak enrichment ability and small differences, the conditions are not met and the research block needs to be changed, and sampling and processing should be carried out again according to steps S10 to S20. If all sub-layers have strong target element enrichment capabilities and significant differences, then the condition is met and the target element enrichment degree of each sub-layer is output.

6. A geophysical response characterization system for the enrichment degree of elements in coal and rock, characterized in that, The geophysical response characterization method for implementing the elemental enrichment degree in coal and rock as described in any one of claims 1 to 5 includes: The sample segmentation module is configured to acquire raw coal and rock samples from the same study block and segment them into sub-layers. The sample processing module is configured to process the subdivided layer sample into multiple sizes to meet different testing conditions to obtain a cube sample and its remaining material. The element determination module is configured to collect the remaining material and acid-dissolve it into a solution, select the target element, and determine the element enrichment degree of the acid-dissolved solution. The geophysical response calculation module is configured to determine various geophysical responses of the cube sample; these responses include density (DEN), relative permittivity (DC), magnetic susceptibility (MS), natural gamma radiation (GR), and radial DC resistivity (ρ). ∥ Vertical DC resistivity ρ ⊥ Radial longitudinal wave velocity Vp ∥ Radial transverse wave velocity Vs ∥ Vertical longitudinal wave velocity Vp ⊥ Vertical transverse wave velocity Vs ⊥ ; The derived parameter calculation module is configured to calculate geophysical derived parameters; these parameters include anisotropy parameters and elastic modulus parameters, with the anisotropy parameters including resistivity anisotropy A. ρ and wave velocity anisotropy A V The elastic modulus parameters include the ratio of longitudinal to transverse wave velocity η, volumetric modulus K, shear modulus μ, Young's modulus E, and Poisson's ratio σ; The characterization model construction module is configured to test and calculate a certain number of subdivided layer samples according to the above-mentioned element determination module, geophysical response calculation module and derived parameter calculation module, to obtain a variety of geophysical responses that correspond one-to-one with the element content, and to construct a geophysical response characterization model of the element enrichment degree using a multi-factor analysis method. The characterization model selection module is configured to optimize different characterization models constructed by different multifactor analysis methods and determine the model with high accuracy and ease of operation as the final characterization model.

7. The geophysical response characterization system for element enrichment in coal and rock according to claim 6, characterized in that, The elemental determination module includes: The judgment unit is configured to determine whether the enrichment level of the measured element meets the conditions. If the enrichment ability of the target element in all sub-layers is very weak and the difference is small, the conditions are not met and the study block needs to be changed. Sampling and processing should be carried out again according to the sample division module and sample processing module. If the enrichment ability of the target element in all sub-layers is strong and the difference is large, the conditions are met and the enrichment level of the target element in each sub-layer is output.

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