Method for predicting coal quality of magma contact metamorphic zone based on organic geochemical data
By using sparse sampling and geochemical data analysis, transition points were identified and matched with a template library, enabling efficient and accurate prediction of coal quality in magmatic contact metamorphic zones. This solved the problems of high cost, long cycle, and poor interpretability in existing technologies and provided a clear reliability assessment.
Patent Information
- Application Number
- CN202610180449.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-02-09
- Publication Date
- 2026-05-19
AI Technical Summary
Existing technologies are costly, time-consuming, and inaccurate in predicting coal quality in magmatic contact metamorphic zones, with poor interpretability and difficulty in effectively characterizing coal rank abrupt changes.
Organic geochemical data were obtained through sparse sampling, transition points of geochemical indicators were identified, a geochemical gradient field was constructed and matched with a pre-established response template library, and spatial prediction of coal quality parameters was performed using a transition point-distance relationship model. Uncertainty assessment was conducted by combining multi-profile consistency verification and supplementary validation.
It reduces sampling and analysis costs, shortens the prediction cycle, improves the accuracy and interpretability of prediction results, provides clear reliability classifications, and enhances the practicality and universality of predictions.
Smart Images
Figure CN122067642A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of coal quality prediction technology, specifically to a method for predicting coal quality in magmatic contact metamorphic zones based on organic geochemical data. Background Technology
[0002] Magmatic contact metamorphic zone coal refers to a special type of coal whose physical structure and chemical composition have undergone significant changes due to the high-temperature thermal baking of intermediate-acidic magmatic intrusions during burial. Within the contact metamorphic zone, natural coke, high-rank anthracite, and bituminous coal of varying metamorphic degrees can be formed sequentially from the intrusion outwards, exhibiting a distinct zonal distribution. Its coal quality is characterized by high fixed carbon, low volatile matter, low hydrogen content, and a sharp increase in vitrinite reflectance at the microscopic level, with some associated graphitization. Predicting the coal quality of magmatic contact metamorphic zones has multiple important implications. First, in coal resource exploration and development, prediction can accurately delineate coal quality anomaly zones, avoiding development deployment errors or resource evaluation deviations caused by sudden changes in coal quality, thus ensuring mine production efficiency and safety. Second, contact metamorphic coal may possess high added value utilization potential due to its special physicochemical properties (such as high conductivity and high reactivity), and prediction helps to comprehensively evaluate its potential as a new material or key metal carrier resource. Furthermore, metamorphism strongly influences the generation and storage characteristics of coalbed methane, and its prediction can provide crucial geological evidence for the prevention and control of gas outburst disasters and the exploration of coalbed methane resources. In addition, contact metamorphic zones are often responses to tectonic-thermal events, and their spatial distribution studies are of significant scientific value in revealing the history of regional tectonic-magmatic activity.
[0003] Traditional methods typically rely on dense sampling and complex numerical simulations, requiring hundreds of sample points to build reliable heat conduction models or train machine learning algorithms. This results in a large workload in the field, high laboratory analysis costs, and prediction cycles lasting several months, leading to poor economic efficiency and timeliness. These methods are often based on the ideal assumption of "continuous and gradual changes in geochemical indicators," making it difficult to effectively characterize the abrupt changes in coal rank commonly seen in actual metamorphic processes. The prediction results have significant errors near transition boundaries. Thermodynamic simulations require precise magma intrusion parameters (such as initial temperature and intrusion duration), which are difficult to obtain accurately, introducing significant uncertainties. Data-driven machine learning models, on the other hand, are black boxes; their prediction logic lacks geological process interpretation, and the results are difficult for geologists to directly understand and verify. Summary of the Invention
[0004] To address the shortcomings of existing technologies, this invention provides a method for predicting coal quality in magmatic contact metamorphic zones based on organic geochemical data, which solves the problems of high cost, long cycle, inaccurate prediction, and poor interpretability of existing technologies.
[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for predicting coal quality in magmatic contact metamorphic zones based on organic geochemical data, comprising the following steps:
[0006] S1. At least two sampling profiles are arranged in the direction perpendicular to the contact zone between the magmatic rock mass and the coal seam, one perpendicular to the coal seam sedimentary layer and the other parallel to the coal seam sedimentary layer. Coal samples are collected at equal intervals on each sampling profile to obtain organic geochemical data of key control points.
[0007] S2. Based on the geochemical data of each profile obtained in step S1, identify and locate the transition points of geochemical indicators on each profile, and construct a geochemical gradient field.
[0008] S3. Match the transition patterns identified in step S2 with the pre-established regional geochemical transition-coal quality response template library to determine the geochemical response pattern category of the study area;
[0009] S4. Based on the geochemical response model obtained by matching, the transition point-distance relationship model is used to make spatial predictions of coal quality parameters at unsampled locations, and generate coal quality prediction profiles or planar maps.
[0010] S5. Through multi-profile consistency testing and / or supplementary key point verification sampling, perform uncertainty assessment and reliability classification on the prediction results of step S4.
[0011] Preferably, in step S1, the sparse sampling follows the principle of minimum sampling, with 7-11 sampling points for each sampling profile, and the sampling points are mainly arranged in the inferred coal quality change gradient zone and the stable zone.
[0012] Preferably, in step S2, identifying and locating the transition points of geochemical indicators specifically includes:
[0013] S21. Select at least one first-order response index that is sensitive to thermal degradation as the core index for transition analysis.
[0014] S22. For the selected core indicator data sequence, calculate the local gradient rate of change at each internal data point i along the profile direction. ;
[0015] S23. Identify the transition point location by determining whether the local gradient change rate exceeds the adaptive threshold.
[0016] Preferably, in step S22, the local gradient change rate The calculation formula is: ;in, , , These represent the core geochemical index values of three consecutive sampling points along the profile direction.
[0017] Preferably, in step S23, the method for determining the adaptive threshold is as follows: ;in, For adaptive threshold, This represents the maximum local gradient rate of change at all calculated points on the cross section. This is an empirical coefficient, ranging from 0.15 to 0.35; when at a certain point... When this point is reached, it is determined to be a potential transition point.
[0018] Preferably, in step S3, the geochemical transition-coal quality response template library pre-stores various typical response patterns established based on known study areas, including at least:
[0019] a) Threshold transition mode: The characteristic of coal rank undergoing abrupt changes within a short distance;
[0020] b) Linear gradual variation mode: This corresponds to the characteristic that coal quality parameters change approximately linearly with distance;
[0021] c) Segmented stabilization mode: characterized by the presence of a distinct coal quality stabilization plateau zone;
[0022] The matching process is accomplished by calculating the morphological similarity between the profile data of the area under study and each template pattern.
[0023] Preferably, in step S4, the method for spatial prediction using the transition point-distance relationship model is as follows:
[0024] For each interval divided by the jump point, the corresponding interpolation function is selected to predict coal quality parameters based on the matched template pattern.
[0025] When the matching is a threshold transition mode, step function interpolation is used; when the matching is a linear gradient mode, linear function interpolation is used; when the matching is a piecewise stable mode, interval mean assignment is used.
[0026] Preferably, the step function interpolation specifically involves: for transition points... Two adjacent intervals separated and Predicted coal quality values within the range Take the measured coal quality values at both ends of the interval respectively , or , The average value.
[0027] Preferably, in step S5, the uncertainty assessment and reliability grading include:
[0028] The consistency of major transition locations identified by different profiles and geochemical indicators was statistically analyzed.
[0029] If at least two profiles and at least two independent indicators indicate that the transition points coincide within the spatial tolerance range, then the transition point is considered reliable, and the reliability of the coal quality prediction results for the area is classified as Level 1.
[0030] If only one profile or index indicates a certain transition point, then the transition point is considered unreliable, and the coal quality prediction results of the area where it is located need to be verified by supplementary sampling. The reliability is level two or three.
[0031] Preferably, the organic geochemical data includes at least vitrinite reflectance, and the coal quality parameters include at least volatile matter yield and vitrinite content.
[0032] This invention provides a method for predicting coal quality in magmatic contact metamorphic zones based on organic geochemical data. It has the following beneficial effects:
[0033] This invention provides a method for predicting coal quality in magmatic contact metamorphic zones based on organic geochemical data. This technology, through a designed transition point identification algorithm and a regional response template library, requires only sparse key point samples to accurately capture coal quality abrupt change boundaries, reducing sampling and analysis costs and shortening the cycle to several weeks. The method has a clear physical concept, directly establishing the correlation between geochemical signal abrupt change characteristics and coal quality changes. The prediction results have strong geological interpretability and are easy to verify. The developed automatic transition point detection algorithm and intelligent interpolation model do not require difficult-to-obtain thermodynamic parameters, avoiding complex simulations and improving the method's practicality and universality. The entire process emphasizes multi-index, multi-profile cross-validation and provides clear reliability grading, making the uncertainty of the prediction results transparent and greatly improving the effectiveness of decision support. Attached Figure Description
[0034] Figure 1 This is a schematic diagram of the method flow of the present invention;
[0035] Figure 2 This is a schematic diagram of the system framework of the present invention;
[0036] Figure 3 This is a schematic diagram of the prediction-verification iterative optimization of the present invention. Detailed Implementation
[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0038] like Figure 1-3 As shown, this embodiment of the invention provides a method for predicting coal quality in magmatic contact metamorphic zones based on organic geochemical data, including the following steps:
[0039] S1. At least two sampling profiles are arranged in the direction perpendicular to the contact zone between the magmatic rock mass and the coal seam, one perpendicular to the coal seam sedimentary layer and the other parallel to the coal seam sedimentary layer. Coal samples are collected at equal intervals on each sampling profile to obtain organic geochemical data of key control points.
[0040] S2. Based on the geochemical data of each profile obtained in step S1, identify and locate the transition points of geochemical indicators on each profile, and construct a geochemical gradient field.
[0041] S3. Match the transition patterns identified in step S2 with the pre-established regional geochemical transition-coal quality response template library to determine the geochemical response pattern category of the study area;
[0042] S4. Based on the geochemical response model obtained by matching, the transition point-distance relationship model is used to make spatial predictions of coal quality parameters at unsampled locations, and generate coal quality prediction profiles or planar maps.
[0043] S5. Through multi-profile consistency testing and / or supplementary key point verification sampling, perform uncertainty assessment and reliability classification on the prediction results of step S4.
[0044] In step S1, sparse sampling follows the principle of minimum sampling, with 7-11 sampling points per sampling profile, and these points are primarily located in the inferred coal quality gradient zone and stable zone. In step S2, the transition points of geochemical indicators are identified and located, specifically including:
[0045] S21. Select at least one first-order response index that is sensitive to thermal degradation as the core index for transition analysis.
[0046] S22. For the selected core indicator data sequence, calculate the local gradient rate of change at each internal data point i along the profile direction. ;
[0047] S23. Identify the transition point location by determining whether the local gradient change rate exceeds the adaptive threshold.
[0048] In step S22, the local gradient change rate The calculation formula is: ;in, , , These represent the core geochemical index values of three consecutive sampling points along the profile direction.
[0049] In step S23, the method for determining the adaptive threshold is as follows: ;in, For adaptive threshold, This represents the maximum local gradient rate of change at all calculated points on the cross section. This is an empirical coefficient, ranging from 0.15 to 0.35; when at a certain point... When this point is reached, it is determined to be a potential transition point.
[0050] In step S3, the geochemical transition-coal quality response template library pre-stores various typical response models established based on known study areas, including at least:
[0051] a) Threshold transition mode: The characteristic of coal rank undergoing abrupt changes within a short distance;
[0052] b) Linear gradual variation mode: This corresponds to the characteristic that coal quality parameters change approximately linearly with distance;
[0053] c) Segmented stabilization mode: characterized by the presence of a distinct coal quality stabilization plateau zone;
[0054] The matching process is accomplished by calculating the morphological similarity between the profile data of the area under study and each template pattern.
[0055] In step S4, the method for spatial prediction using the jump point-distance relationship model is as follows:
[0056] For each interval divided by the jump point, the corresponding interpolation function is selected to predict coal quality parameters based on the matched template pattern.
[0057] When the matching is a threshold transition mode, step function interpolation is used; when the matching is a linear gradient mode, linear function interpolation is used; when the matching is a piecewise stable mode, interval mean assignment is used.
[0058] Step function interpolation specifically refers to: for transition points... Two adjacent intervals separated and Predicted coal quality values within the range Take the measured coal quality values at both ends of the interval respectively , or , The average value.
[0059] In step S5, uncertainty assessment and reliability grading include:
[0060] The consistency of major transition locations identified by different profiles and geochemical indicators was statistically analyzed.
[0061] If at least two profiles and at least two independent indicators indicate that the transition points coincide within the spatial tolerance range, then the transition point is considered reliable, and the reliability of the coal quality prediction results for the area is classified as Level 1.
[0062] If only one profile or index indicates a certain transition point, then the transition point is considered unreliable, and the coal quality prediction results of the area where it is located need to be verified by supplementary sampling. The reliability is level two or three.
[0063] Organic geochemical data should include at least vitrinite reflectance, and coal quality parameters should include at least volatile matter yield and vitrinite content. Typically, vitrinite reflectance is included to reflect the degree of thermal evolution, organic element content - carbon, hydrogen, oxygen, nitrogen, and sulfur content, and industrial analysis data: ash, moisture, volatile matter, coal petrographic composition: vitrinite, inertinite, and scabra content, and biomarker compound composition (optional).
[0064] This technique first employs a sparse data acquisition strategy and rapidly constructs a geochemical gradient field. Specifically, several sampling profiles are arranged perpendicular to the contact zone between the magmatic intrusion and the coal seam. The number of sampling points on each profile is optimized and controlled within a limited range; these points are deliberately placed near key locations of anticipated coal quality changes, such as potential abrupt change boundaries. The collected coal samples are then analyzed for key organic geochemical indicators, focusing on acquiring core data most sensitive to thermal responses. Based on these sparse but highly representative point data, a unique automatic transition point identification algorithm is used. This algorithm can keenly capture the local abrupt changes in geochemical indicators along the profile direction, thereby accurately identifying the boundary locations where coal quality may undergo abrupt changes. By spatially connecting and integrating these discrete transition points, the core framework of the geochemical gradient field of the study area can be rapidly delineated at extremely low cost. The key advantage of this stage is that it completely changes the high-cost model of traditional methods that rely on large-scale intensive sampling. It extracts the most critical geological information with the fewest number of samples, which can save time and economic costs in the early stage of the project and lay an efficient and reliable data foundation for subsequent analysis.
[0065] After successfully identifying key transition points, the process enters the pattern matching and geological interpretation stage. At this point, the system compares and matches the spatial variation patterns of geochemical indicators discovered in the area with a pre-established regional "geochemical transition-coal quality response" template library. This template library compiles classic response patterns of magmatic thermal metamorphism under different geological backgrounds, such as the "step-like" pattern indicating abrupt changes in coal rank and the "slope-like" pattern reflecting gradual changes in coal quality. By performing morphological similarity analysis between the measured indicator variation curves and these templates, the dominant response pattern category of the study area can be quickly determined, and the possible spatial variation behavior of coal quality can be qualitatively inferred. The innovative advantage of this step lies in transforming complex geological processes into identifiable and comparable patterns, greatly reducing the reliance on complex numerical simulations. This allows geologists to make rapid and reasonable judgments about metamorphic zone characteristics based on experience and limited data, even without precise thermodynamic parameters, significantly improving the practicality of the method and the intuitiveness of the interpretation.
[0066] The next stage involves the construction of the quantitative prediction model and spatial extrapolation. Based on the response mode category determined in the previous stage, the system automatically calls the appropriate prediction interpolation algorithm. For example, for intervals matching the "step-like" transition mode, an interpolation prediction based on the step function controlled by the transition point will be used, and the coal quality parameters in the regions on both sides of the transition point will be considered relatively stable; while for the "linear gradual" mode, a linear interpolation algorithm will be used. These algorithms use the coal quality data of the actual sampling points as constraints and the identified transition points as key control nodes to automatically complete the calculation of coal quality parameters for unsampled points in the entire profile and even in a planar region composed of multiple profiles. Finally, the system can generate intuitive coal quality prediction profile maps, contour maps, or three-dimensional trend models. The advantage of this stage is that the prediction model closely matches the geological reality, and the algorithm is directly derived from the mathematical description of geochemical transition phenomena, rather than a general black-box model. Therefore, the prediction results have clear geological significance and higher reliability. At the same time, the entire calculation process is lightweight, requiring no high-performance computing resources, and is easy to apply quickly in the field.
[0067] The final process loop concludes with the system verification, optimization, and reliability delivery phase. Preliminary prediction results must undergo rigorous multi-source consistency verification. This includes comparing whether the transition locations revealed by different sampling profiles spatially coincide, and whether the abrupt change boundaries indicated by different geochemical indicators corroborate each other. Based on the results of this cross-validation, the system will classify the reliability of the predicted region, clearly marking high-confidence areas and questionable areas requiring further verification. For questionable areas, a very small number of supplementary verification samples can be deployed selectively to calibrate and correct the prediction model with minimal additional cost. This dynamic, iterative verification mechanism constitutes another core advantage of the method, making prediction work no longer a one-way, one-time estimation, but a process that can continuously improve and approximate reality. The final delivery is not only a set of prediction maps, but also includes a clear reliability assessment, allowing users to accurately understand the uncertainties in the prediction conclusions, thereby making more scientific and risk-controlled decisions.
[0068] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A method for predicting coal quality in magmatic contact metamorphic zones based on organic geochemical data, characterized in that, Includes the following steps: S1. At least two sampling profiles are arranged in the direction perpendicular to the contact zone between the magmatic rock mass and the coal seam, one perpendicular to the coal seam sedimentary layer and the other parallel to the coal seam sedimentary layer. Coal samples are collected at equal intervals on each sampling profile to obtain organic geochemical data of key control points. S2. Based on the geochemical data of each profile obtained in step S1, identify and locate the transition points of geochemical indicators on each profile, and construct a geochemical gradient field. S3. Match the transition patterns identified in step S2 with the pre-established regional geochemical transition-coal quality response template library to determine the geochemical response pattern category of the study area; S4. Based on the geochemical response model obtained by matching, the transition point-distance relationship model is used to make spatial predictions of coal quality parameters at unsampled locations, and generate coal quality prediction profiles or planar maps. S5. Through multi-profile consistency testing and / or supplementary key point verification sampling, perform uncertainty assessment and reliability classification on the prediction results of step S4.
2. The method for predicting coal quality in magmatic contact metamorphic zones based on organic geochemical data according to claim 1, characterized in that: In step S1, the sparse sampling follows the principle of minimum sampling, with 7-11 sampling points for each sampling profile, and the sampling points are mainly arranged in the inferred coal quality change gradient zone and stable zone.
3. The method for predicting coal quality in magmatic contact metamorphic zones based on organic geochemical data according to claim 1, characterized in that: In step S2, identifying and locating the transition points of geochemical indicators specifically includes: S21. Select at least one first-order response index that is sensitive to thermal degradation as the core index for transition analysis. S22. For the selected core indicator data sequence, calculate the local gradient rate of change at each internal data point i along the profile direction. ; S23. Identify the transition point location by determining whether the local gradient change rate exceeds the adaptive threshold.
4. The method for predicting coal quality in magmatic contact metamorphic zones based on organic geochemical data according to claim 3, characterized in that: In step S22, the local gradient change rate The calculation formula is: ;in, , , These represent the core geochemical index values of three consecutive sampling points along the profile direction.
5. The method for predicting coal quality in magmatic contact metamorphic zones based on organic geochemical data according to claim 3, characterized in that: In step S23, the method for determining the adaptive threshold is as follows: ;in, For adaptive threshold, This represents the maximum local gradient rate of change at all calculated points on the cross section. This is an empirical coefficient, ranging from 0.15 to 0.35; when at a certain point... When this point is reached, it is determined to be a potential transition point.
6. The method for predicting coal quality in magmatic contact metamorphic zones based on organic geochemical data according to claim 1, characterized in that: In step S3, the geochemical transition-coal quality response template library pre-stores various typical response modes established based on known study areas, including at least: a) Threshold transition mode: The characteristic of coal rank undergoing abrupt changes within a short distance; b) Linear gradual variation mode: This corresponds to the characteristic that coal quality parameters change approximately linearly with distance; c) Segmented stabilization mode: characterized by the presence of a distinct coal quality stabilization plateau zone; The matching process is accomplished by calculating the morphological similarity between the profile data of the area under study and each template pattern.
7. The method for predicting coal quality in magmatic contact metamorphic zones based on organic geochemical data according to claim 1, characterized in that: In step S4, the method for spatial prediction using the transition point-distance relationship model is as follows: For each interval divided by the jump point, the corresponding interpolation function is selected to predict coal quality parameters based on the matched template pattern. When the matching is a threshold transition mode, step function interpolation is used; when the matching is a linear gradient mode, linear function interpolation is used; when the matching is a piecewise stable mode, interval mean assignment is used.
8. The method for predicting coal quality in magmatic contact metamorphic zones based on organic geochemical data according to claim 7, characterized in that: The step function interpolation specifically refers to: for the transition point... Two adjacent intervals separated and Predicted coal quality values within the range Take the measured coal quality values at both ends of the interval respectively , or , The average value.
9. The method for predicting coal quality in magmatic contact metamorphic zones based on organic geochemical data according to claim 1, characterized in that: In step S5, the uncertainty assessment and reliability grading include: The consistency of major transition locations identified by different profiles and geochemical indicators was statistically analyzed. If at least two profiles and at least two independent indicators indicate that the transition points coincide within the spatial tolerance range, then the transition point is considered reliable, and the reliability of the coal quality prediction results for the area is classified as Level 1. If only one profile or index indicates a certain transition point, then the transition point is considered unreliable, and the coal quality prediction results of the area where it is located need to be verified by supplementary sampling. The reliability is level two or three.
10. The method for predicting coal quality in magmatic contact metamorphic zones based on organic geochemical data according to claim 1, characterized in that: The organic geochemical data include at least vitrinite reflectance, and the coal quality parameters include at least volatile matter yield and vitrinite content.