A coal water-structure state recognition method based on terahertz spectrum

Abstract

The application discloses a coal water-structure state recognition method based on terahertz spectroscopy, and relates to the technical field of material property characterization. The method prepares a characteristic state coal sample containing raw coal, collects a terahertz frequency band transmission spectrum and constructs a difference spectrum; three typical mechanism spectra of water-induced polarization absorption, structure scattering enhancement and cavity polarization nonlinearity are extracted through non-negative matrix decomposition to construct a frequency spectrum-mechanism-structure mapping model; the spectrum of a coal sample to be measured is collected and a difference spectrum is constructed, and after solving an activation weight vector, the difference spectrum is matched with characteristic samples to inverse the water-structure state thereof. The method can realize nondestructive and accurate recognition of the water-structure state of coal, adapt to disaster precursor warning such as water inrush in coal mines, and expand the engineering practical value of terahertz technology.

Description

Technical Field

[0001] This invention relates to the field of material property characterization technology, and in particular to a method for identifying the water-structure state of coal based on terahertz spectroscopy. Background Technology

[0002] The moisture state and microstructure of coal significantly influence its dielectric response behavior. During drying, immersion, and re-drying, the reorganization of pore structure and the evolution of water-skeleton coupling can trigger local dielectric disturbances. These changes often serve as precursors to disasters such as gas outbursts, rock bursts, and water inrushes, reflecting changes in coal permeability, stress response, and structural stability. Traditional detection methods, such as infrared spectroscopy, low-field nuclear magnetic resonance, and mass determination, can reflect moisture content, but they struggle to accurately identify the water state and its coupling relationship with the structure. Furthermore, they generally suffer from limitations such as high destructiveness, insufficient resolution, and difficulty in adapting to field applications. Terahertz time-domain spectroscopy, due to its sensitivity to polar molecules, strong penetrating power, and frequency coverage of water polarization and structural resonance regions, has become an important means of identifying microscopic dielectric disturbances and has shown potential in non-destructive coal detection.

[0003] Due to the complex structure and highly coupled response mechanisms of coal bodies, their terahertz spectra often exhibit characteristics such as multi-mechanism aliasing, spectral overlap, and weak signal differences. Traditional methods such as baseline correction and peak extraction struggle to accurately identify and separate physically significant typical perturbation mechanisms, limiting the ability to convert spectral data into structural state identification and accident early warning information. Therefore, there is an urgent need to develop a terahertz spectral data analysis method with high-resolution feature extraction and physical attribution capabilities. This method should be able to effectively separate various weak mechanism signals, such as water-induced polarization and structural reorganization, from coal body difference spectra and construct a mapping relationship between these signals and structural state evolution, providing technical support for the accurate identification of accident precursor features in complex coal bodies. Summary of the Invention

[0004] The purpose of this invention is to provide a method for identifying the water-structure state of coal based on terahertz spectroscopy, so as to achieve non-destructive and accurate identification of the water-structure state of coal, adapt to early warning of disasters such as coal mine water inrush, and expand the engineering application value of terahertz technology.

[0005] To achieve the above objectives, this invention provides a method for identifying the water-structure state of coal based on terahertz spectroscopy, comprising the following steps: S1. Sample preparation and terahertz spectral acquisition: Select coal samples and process them into raw coal samples of a predetermined thickness. Use the raw coal samples as raw coal and subject a portion of the raw coal to various water treatments and structural evolution-related treatments to obtain at least two types of characteristic state coal samples that are different from the raw coal. Use terahertz spectral detection equipment to measure the transmission spectra of each characteristic state coal sample in a predetermined terahertz frequency band. Based on the transmission spectra of the raw coal, construct the difference spectra of the remaining characteristic state coal samples. S2. Difference spectral mechanism decomposition modeling: Combine multiple difference spectra to form a spectral matrix, execute a mechanism decomposition algorithm on the spectral matrix, extract at least two types of typical mechanism spectra, establish the correspondence between each typical mechanism spectrum and the dielectric response mechanism of coal body, and construct a mapping model between the water-structure state of coal body and the characteristics of difference spectral decomposition. S3. State Inversion and Matching Identification of the Sample to be Tested: The transmission spectrum of the coal sample to be tested is collected, and the difference spectrum of the coal sample to be tested is constructed based on the transmission spectrum of the raw coal; the typical mechanism spectrum obtained in step S2 is fixed, and the activation weight vector of the coal sample to be tested is solved by non-negative least squares method; the activation weight vector of the coal sample to be tested is compared with the activation weight distribution of each characteristic state coal sample by using distance metric method or cluster analysis method, and the water-structure state of the coal sample to be tested is inferred by combining the mapping model. S4. State Space Projection and Result Characterization: The activation weight vector of the coal sample to be tested is projected onto the state space constructed based on the mechanism spectrum to realize the identification and quantitative characterization of the microstate of the coal sample to be tested.

[0006] Preferably, in step S1, the various water treatments and structural evolution-related treatments include high-pressure saturation, high-temperature drying, and saturation followed by drying. The corresponding coal samples with characteristic states different from raw coal include high-pressure saturated coal samples, high-temperature dried coal samples, and saturated coal samples followed by drying.

[0007] Preferably, in step S1, the preset thickness is 1-3 cm, the terahertz spectroscopy detection device is a transform infrared spectrometer, and the preset terahertz frequency band is 0.1 THz-1 THz.

[0008] Preferably, in S1, the formula for constructing the difference spectrum is:

[0009] in, This refers to the transmission spectra of coal samples or coal samples under test that exhibit other characteristic states. This is the transmission spectrum of raw coal.

[0010] Preferably, in S2, the spectral matrix is ,in The number of coal samples with characteristic states different from raw coal. fThe number of frequency sampling points; the mechanism decomposition algorithm is a non-negative matrix factorization algorithm, and the decomposition formula is:

[0011] in: For the mechanism spectrum matrix, each row A spectroscopic pattern representing a potential mechanism; For the sample activation weight matrix, For global offset items, This represents the typical mechanism spectrum number.

[0012] Preferably, the typical mechanism spectrum number k is determined by the Silhouette coefficient and the error inflection point.

[0013] Preferably, k=3 is selected, and the three typical mechanism spectra correspond to the water-induced polarization absorption mechanism, the structure scattering enhancement mechanism, and the cavity polarization nonlinear mechanism, respectively. The frequency band corresponding to the water-induced polarization absorption mechanism is 0.12–0.15THz, the frequency band corresponding to the structure scattering enhancement mechanism is 0.28–0.32THz, and the frequency band corresponding to the cavity polarization nonlinear mechanism is 0.42–0.48THz.

[0014] Preferably, in S3, the constraint condition satisfied by solving the activation weight vector is:

[0015] in, The difference spectrum of the coal sample to be tested. For single-sample offset terms, Let be the activation weight vector of the coal sample to be tested. This is a fixed mechanism spectrum matrix used in the modeling phase.

[0016] Preferably, in S3, the mapping model is a hierarchical mapping from spectral response characteristics to mechanism category to microstructure, specifically: When the typical mechanism spectrum corresponding to the water-induced polarization absorption mechanism is activated, there is free water, surface adsorbed water or weakly bound water inside the coal body; When the typical mechanism spectrum corresponding to the structure scattering enhancement mechanism is activated, the coal body undergoes skeleton compaction, fracture closure, or mineral grain rearrangement. When the typical mechanism spectrum corresponding to the cavity polarization nonlinear mechanism is activated, the coal body has a heterogeneous cavity structure, a multi-scale periodic dielectric interface, or metastable micro / nano structure units.

[0017] Preferably, in step S3, the distance measurement method includes Euclidean distance and cosine similarity.

[0018] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: (1) This method achieves a technological breakthrough in non-destructive detection of coal body water-structure state identification. Compared with traditional detection methods that rely on sampling and segmentation, it can directly monitor the in-situ state of coal body through non-contact acquisition of terahertz spectroscopy. This avoids secondary damage to the coal body structure during the detection process and can also track the water and structural evolution process of the same coal body for a long time, which is suitable for the actual needs of maintaining the original state of coal body in engineering scenarios such as coal mines.

[0019] (2) This method effectively solves the technical problem of aliasing of multiple response mechanisms in terahertz spectroscopy. Through differential spectral enhancement and mechanism decomposition algorithms, weak spectral signals from different sources such as water-induced polarization and structural scattering in coal can be accurately separated, breaking the limitation of "signal overlap cannot be distinguished" in traditional spectral analysis. It can clearly identify the types of moisture and microstructural change patterns in different occurrence states in coal, and achieve an upgrade from "general detection" to "precise attribution".

[0020] (3) This method has strong adaptability to field applications. Its operation process does not require complicated sample preprocessing, and the detection and analysis process is efficient and simple, which can quickly complete the identification and characterization of coal body status. Combined with the visualization of three-dimensional state space, it can transform abstract spectral data into intuitive state information, which makes it easy for field staff to quickly judge the coal body status. It provides practical technical support for early warning of disasters such as coal mine water inrush and rock bursts, and expands the practical value of terahertz technology in the field of coal body safety monitoring.

[0021] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This invention provides a method for analyzing coal water content based on terahertz spectroscopy. A flowchart illustrating an embodiment of the structural state recognition method. Detailed Implementation

[0024] 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0025] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0026] Example This embodiment details the method of the present invention in a laboratory environment, using methods such as... Figure 1 The example shown is a coal water analysis based on terahertz spectroscopy. The structural state identification method is the entire process of modeling a set of coal samples in known states and identifying the state of test coal samples in unknown states.

[0027] S1. Sample Preparation and Terahertz Spectroscopy Acquisition Lump coal was collected from the same coal seam at Shendong Coal Mine. Using cutting and grinding equipment, it was processed into coal flakes with a thickness of about 2 cm and a smooth surface, which were used as the benchmark raw coal sample and denoted as RC.

[0028] Three portions of raw coal from the same batch were separated and processed as follows to obtain representative characteristic states: High-pressure saturation treatment: A portion of the coal sample is placed in a high-pressure saturation device and saturated with water at a pressure of 15 MPa for 48 hours. After removal, the surface moisture is wiped off to obtain a high-pressure saturated coal sample, denoted as SC.

[0029] High-temperature drying treatment: A portion of the coal sample was placed in a 105℃ forced-air drying oven and dried continuously for 72 hours to completely remove moisture, resulting in a high-temperature dried coal sample, denoted as DC.

[0030] Water saturation followed by drying: A portion of the coal sample is first subjected to the above-mentioned high-pressure water saturation treatment, and then placed in a 60℃ constant temperature oven for slow drying to constant weight, simulating the natural drying process, to obtain a water saturated and then dried coal sample, denoted as DSC.

[0031] Using a Nicolet Is50 Fourier transform infrared spectrometer equipped with a terahertz time-domain spectroscopy module, the transmission spectra of four samples (RC, SC, DC, and DSC) in the frequency range of 0.1 THz to 1.0 THz were measured under a dry nitrogen atmosphere, and denoted as follows: , , , .

[0032] Using the spectrum of raw coal RC as a benchmark, the difference spectra of samples in other states are calculated, taking into account the weak spectral perturbations caused by changes in the outburst state. The calculation formula is as follows:

[0033] in, These can be SC, DC, and DSC, respectively. This yields a set of difference spectral data.

[0034] S2, Decomposition Modeling of Difference Spectral Mechanism The difference spectral data of the three samples (SC, DC, and DSC) are combined into a 3-row, 200-column spectral matrix. For the matrix Using the nonnegative matrix factorization algorithm, the model is:

[0035] Through multiple iterations and optimizations, the solution was obtained as follows: Mechanism Spectrum Matrix Each row represents a typical extracted spectral mechanism pattern.

[0036] Activation weight matrix Each row corresponds to a sample, and the element value represents the activation intensity of the sample on each mechanism.

[0037] Global offset It is used to compensate for baseline translation.

[0038] By calculating the silhouette coefficients of the decomposition results with different k values ​​and observing the inflection point of the reconstruction error, the optimal mechanism number k=3 was determined. This indicates that the state changes of the coal sample in the 0.1-1.0 THz frequency band can be mainly explained by three physical mechanisms.

[0039] The three mechanism spectra obtained from the analysis , , Spectral features and their activation patterns in known state samples: Mechanism spectrum A distinct absorption valley is observed around 0.12-0.15 THz. The activation weight is highest in the saturated sample SC and almost zero in the dry sample DC. Considering the strong polarity of water, this can be interpreted as a water-induced polarization absorption mechanism, mainly reflecting the dielectric loss between free and bound water in coal.

[0040] Mechanism spectrum It exhibits a broad response variation in the 0.28–0.32 THz frequency band. Activation is significant in both dry and re-dried samples (DC and DSC), but weaker in saturated samples. Considering the phenomena of coal dehydration shrinkage and pore closure, this can be interpreted as a structural scattering enhancement mechanism, reflecting the enhanced light scattering caused by coal skeleton compaction and increased interfaces.

[0041] Mechanism spectrum A sharp resonance peak appears in the 0.42–0.48 THz range. The activation is most prominent in the DSC of re-dried samples that have undergone wet-drying cycles. This is attributed to a cavity polarization nonlinear mechanism, presumably caused by localized polarization resonance resulting from micro / nano cavities or layered structures generated during the non-uniform wet-drying process.

[0042] Thus, the spectral characteristics were established. →Physical mechanism category→Hierarchical mapping model pointing to microstructure / water content state, and save the mechanism spectrum matrix H and the activation weight vector of each modeling sample as a benchmark database for subsequent identification.

[0043] S3. Sample State Inversion and Matching Recognition Take a coal sample from the same seam but with an unknown processing history, and denote it as... T A 2 cm thick slice was prepared using the same method as in step S1, and its transmission spectrum was measured using the same spectrometer. The difference spectrum relative to the RC of the raw coal was calculated.

[0044] With the mechanism spectrum matrix H determined in step S2 fixed, the activation weight vector of the test sample T is solved using the nonnegative least squares method. Solve the following optimization problem:

[0045] Solving for the results , respectively representing sample T in , , Activation intensity across the three mechanisms.

[0046] Calculate the activation weight vector of the test sample The cosine similarity between the modeling sample weight matrix W and each row (i.e., the weight vectors of SC, DC, and DSC).

[0047] Scenario Assumption 1: If The weight vector with the highest similarity to SC is, and The value is very large. If the value is small, it can be determined that sample T is in a high water content state and is rich in polarizable water.

[0048] Scenario 2: If The weight vector most similar to DC, and Significant, If the value is close to 0, it can be determined that the T sample is in a deeply dry state and the structure has undergone significant compaction.

[0049] Scenario Assumption 3: If The weight vector most similar to that of DSC, and It exhibits unique high activation, while and If the sample has a certain strength, it can be inferred that the T sample has undergone a wet-dry cycle and formed a complex cavity-interface composite structure inside.

[0050] S4. State-space projection and result representation The activation weight vector of the test sample TX As a three-dimensional coordinate point, it is projected onto a three-dimensional state space defined by the axes of water-induced polarization (axis 1), structural scattering (axis 2), and cavity resonance (axis 3). Simultaneously, the modeling samples RC (origin), SC, DC, and DSC are also projected into this space. The position of the TX point in this space is observed: Its location is close to point SC: visually, its moisture content is close to that of saturated coal.

[0051] Significant displacement along the structural scattering axis (axis 2): quantitative characterization of the degree of structural compaction.

[0052] The projection onto the cavity resonance axis (axis 3) indicates the presence of microstructural heterogeneity. This visualization projection enables a comprehensive and quantitative characterization of the water-structure state of the coal sample.

[0053] The remaining technical features in the above embodiments can be flexibly selected by those skilled in the art to meet different specific practical needs according to actual circumstances. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims. In the above description, numerous specific details have been set forth to provide a thorough understanding of the present invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to implement the present invention. In other instances, to avoid obscuring the present invention, well-known techniques, such as specific construction details, operating conditions, and other technical conditions, have not been specifically described.

[0054] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for identifying the water-structure state of coal based on terahertz spectroscopy, characterized in that, The steps are as follows: S1. Sample preparation and terahertz spectral acquisition: Select coal samples and process them into raw coal samples of a preset thickness. Use the raw coal samples as raw coal and perform various water treatments and structural evolution-related treatments on a portion of the raw coal to obtain at least two types of coal samples with characteristic states different from the raw coal. A terahertz spectroscopy detection device was used to measure the transmission spectrum of coal samples in each characteristic state in a preset terahertz frequency band. Based on the transmission spectrum of raw coal, the difference spectrum of coal samples in other characteristic states was constructed. S2. Difference spectral mechanism decomposition modeling: Combine multiple difference spectra to form a spectral matrix, execute a mechanism decomposition algorithm on the spectral matrix, extract at least two types of typical mechanism spectra, establish the correspondence between each typical mechanism spectrum and the dielectric response mechanism of coal body, and construct a mapping model between the water-structure state of coal body and the characteristics of difference spectral decomposition. S3. State Inversion and Matching Identification of the Sample to be Tested: The transmission spectrum of the coal sample to be tested is collected, and the difference spectrum of the coal sample to be tested is constructed based on the transmission spectrum of the raw coal; the typical mechanism spectrum obtained in step S2 is fixed, and the activation weight vector of the coal sample to be tested is solved by non-negative least squares method; the activation weight vector of the coal sample to be tested is compared with the activation weight distribution of each characteristic state coal sample by using distance metric method or cluster analysis method, and the water-structure state of the coal sample to be tested is inferred by combining the mapping model. S4. State Space Projection and Result Characterization: The activation weight vector of the coal sample to be tested is projected onto the state space constructed based on the mechanism spectrum to realize the identification and quantitative characterization of the microstate of the coal sample to be tested.

2. The method for identifying the water-structure state of coal based on terahertz spectroscopy according to claim 1, characterized in that: In step S1, the various water treatments and structural evolution-related treatments include high-pressure saturation, high-temperature drying, and saturation followed by drying. The corresponding coal samples with characteristic states different from raw coal include high-pressure saturated coal samples, high-temperature dried coal samples, and saturated coal samples followed by drying.

3. The method for identifying the water-structure state of coal based on terahertz spectroscopy according to claim 1, characterized in that: In step S1, the preset thickness is 1-3 cm, the terahertz spectroscopy detection device is a transform infrared spectrometer, and the preset terahertz frequency band is 0.1 THz-1 THz.

4. The method for identifying the water-structure state of coal based on terahertz spectroscopy according to claim 1, characterized in that: In S1, the formula for constructing the difference spectrum is: in, The transmission spectra of coal samples or coal samples to be tested in other characteristic states are shown. This is the transmission spectrum of raw coal.

5. The method for identifying the water-structure state of coal based on terahertz spectroscopy according to claim 1, characterized in that: In S2, the spectral matrix is ,in The number of coal samples with characteristic states different from raw coal. f The number of frequency sampling points; the mechanism decomposition algorithm is a non-negative matrix factorization algorithm, and the decomposition formula is: in: For the mechanism spectrum matrix, each row A spectroscopic pattern representing a potential mechanism; For the sample activation weight matrix, For global offset items, This represents the typical mechanism spectrum number.

6. The method for identifying the water-structure state of coal based on terahertz spectroscopy according to claim 1, characterized in that: The typical mechanism spectral number k is determined by the Silhouette coefficient and the error inflection point.

7. The method for identifying the water-structure state of coal based on terahertz spectroscopy according to claim 6, characterized in that: With k=3, the three typical mechanism spectra correspond to the water-induced polarization absorption mechanism, the structure-enhanced scattering mechanism, and the cavity-polarized nonlinear mechanism, respectively. The frequency band corresponding to the water-induced polarization absorption mechanism is 0.12–0.15 THz, the frequency band corresponding to the structure-enhanced scattering mechanism is 0.28–0.32 THz, and the frequency band corresponding to the cavity-polarized nonlinear mechanism is 0.42–0.48 THz.

8. The method for identifying the water-structure state of coal based on terahertz spectroscopy according to claim 5, characterized in that: In S3, the constraints satisfied by solving for the activation weight vector are: in, The difference spectrum of the coal sample to be tested. For single-sample offset terms, Let be the activation weight vector of the coal sample to be tested. This is a fixed mechanism spectrum matrix used in the modeling phase.

9. The method for identifying the water-structure state of coal based on terahertz spectroscopy according to claim 6, characterized in that: In S3, the mapping model is a hierarchical mapping from spectral response characteristics to mechanism category to microstructure, specifically: When the typical mechanism spectrum corresponding to the water-induced polarization absorption mechanism is activated, there is free water, surface adsorbed water or weakly bound water inside the coal body; When the typical mechanism spectrum corresponding to the structure scattering enhancement mechanism is activated, the coal body undergoes skeleton compaction, fracture closure, or mineral grain rearrangement. When the typical mechanism spectrum corresponding to the cavity polarization nonlinear mechanism is activated, the coal body has a heterogeneous cavity structure, a multi-scale periodic dielectric interface, or metastable micro / nano structure units.

10. The method for identifying the water-structure state of coal based on terahertz spectroscopy according to claim 1, characterized in that: In step S3, the distance measurement methods include Euclidean distance and cosine similarity.

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