A tailings phreatic line analysis method and system

Abstract

The application discloses a tailing dam seepage line analysis method and system, and belongs to the technical field of geotechnical engineering monitoring and geophysical exploration. The method first acquires three-component continuous waveform data of a tailing dam observation point, uses a sliding time window to segment and calculate the cross-spectral density between each component, and constructs a horizontal-vertical spectral ratio (HVSR) curve after removing the autocorrelation term; then, using a historical average HVSR curve as a reference template, the single-window HVSR curve is filtered and superimposed and averaged through a global, local or combined amplitude threshold cross-correlation coefficient judgment strategy, and a single-day HVSR curve with high stability is obtained. The application effectively eliminates the redundant amplitude information introduced by environmental noise interference and autocorrelation terms, significantly improves the stability of the HVSR curve and the accuracy of the peak frequency identification, and thus more accurately inverts the position of the wave impedance interface inside the tailing dam, and realizes dynamic monitoring and safety evaluation of the seepage line.

Description

Technical Field

[0001] This invention relates to the fields of geotechnical engineering monitoring and geophysical exploration, and in particular to a tailings wetting line analysis method and system based on template matching and improved CSD-HVSR. Background Technology

[0002] Tailings dams, as man-made mining waste storage facilities, have a significant impact on downstream ecological and public safety due to their structural stability. Within a tailings dam, the phreatic line, the boundary between saturated and unsaturated tailings, directly determines the distribution of seepage and stress fields within the dam body, making it a core engineering parameter for evaluating dam stability. An abnormally high phreatic line will lead to increased pore water pressure and decreased effective stress within the dam body, significantly increasing the risk of seepage deformation, local instability, and even overall dam failure. Therefore, accurate and efficient monitoring and analysis of the phreatic line is a key technological requirement for current tailings dam safety early warning and risk control.

[0003] In the field of tailings dam seepage line monitoring, in addition to traditional manual observation, automated monitoring and physical exploration techniques such as resistivity methods have also been applied. Considering that the transition of the medium above and below the seepage line from an unsaturated state to a saturated state will form a significant wave impedance interface and cause a sudden change in shear wave velocity, the HVSR (horizontal-vertical spectral ratio) method based on seismic motion signal analysis has been proposed in recent years to identify such interfaces. This method provides an innovative technical approach for dynamic monitoring of the seepage line and safety assessment of the dam body.

[0004] In traditional HVSR calculations, the Fourier spectral ratio (FAS) is the ratio of the horizontal to the vertical components of the time-domain signal after conversion to the frequency domain; the power spectral ratio (PSD) is the ratio of their energy distributions in the frequency domain; and the autocorrelation method calculates HVSR based on the correlation of the signal's Fourier spectrum. These methods are susceptible to environmental interference, leading to large fluctuations and high uncertainties in the HVSR curve during actual monitoring, affecting the stability of the results and increasing the difficulty of interpretation and application.

[0005] To overcome the shortcomings of traditional HVSR calculations, such as significant environmental noise interference and poor result stability, an improved method (a tailings wetting line identification method and system based on cross-spectral density, patent number 202410699711.6) has been proposed in the prior art. This method enhances the stability of the calculation results by calculating the cross-spectral ratio (HVSR) between each component. However, this method still has its drawbacks: in the calculation of the window curve, there is a lack of effective methods for removing interference information, which introduces a certain degree of uncertainty into the results; the autocorrelation term introduced during the calculation process will superimpose redundant amplitude information, causing the calculated HVSR curve to exhibit abnormally high amplitude characteristics, affecting the convergence and accuracy of subsequent parameter inversion.

[0006] To address the above issues, this invention proposes a novel strategy: applying template matching to the window filtering of HVSR to effectively eliminate interference information, remove autocorrelation terms, and retain other cross-spectral components to obtain a more reasonable and accurate spectral ratio curve, thereby obtaining more precise information about the internal structure of the dam. Summary of the Invention

[0007] The purpose of this invention is to provide an improved method for analyzing the seepage line of tailings dams. By using template matching for window screening and calculating the horizontal-vertical spectral ratio (HVSR) based solely on the cross-spectral density between multi-component signals, the interference caused by autocorrelation components is avoided. This results in obtaining a more accurate spectral ratio characteristic that reflects the wave impedance interface of the dam body. This method can then be applied to tailings monitoring to improve the accuracy and reliability of tailings dam seepage line monitoring.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] In a first aspect, the present invention provides a method for analyzing tailings wetting lines, the method comprising the following steps: S1: Obtain three-component continuous waveform data from the tailings dam observation point, wherein the three components include east-west, north-south, and vertical components; perform sliding time window segmentation on the three-component continuous waveform data, calculate the cross-spectral density between any two different components within each time window; calculate the horizontal-to-vertical spectral ratio (HVSR) in a single direction based on the cross-spectral density, and average and smooth the HVSR in the two directions to obtain the HVSR curve for a single time window; S2: Using the average HVSR curve calculated from historical observation data as a reference curve template, calculate the cross-correlation coefficient between the reference curve and the HVSR curve of each time window; sort and filter all time window HVSR curves according to the cross-correlation coefficient, and select a preset number of HVSR curves at the front of the sorting for superposition and averaging to obtain a stable HVSR curve within the time period; analyze the position and stability of the tailings dam phreatic line based on the peak frequency change of the stable HVSR curve.

[0010] Furthermore, in S1, the formula for calculating the cross-spectral density is:

[0011] In the formula, Indicates the first With the The cross-spectral density between components, i.e. Component signal spectrum and The product of the spectral conjugates of the component signals; and i ≠ jThis represents the three orthogonal components: east-west, north-south, and vertical. , and These represent the north-south, east-west, and vertical directions, respectively. Indicates spatial location First The observed vibration signal components at angular frequency The frequency domain representation below is obtained by Fourier transform of the corresponding time domain vibration record; Indicates the first j The complex conjugate of the spectral signal of each component, sign This indicates the complex conjugate operation.

[0012] Furthermore, in S1, based on the six cross-spectral density results formed by pairwise combinations of the three components, the single-direction [spectral density] is calculated. The curve, its calculation formula is:

[0013]

[0014] In the formula, and These represent the horizontal vertical spectral ratios (HVSRs) in the east-west and north-south directions, respectively, calculated based on the cross-power spectral density. , , , , and These represent the cross-power spectral density between different components; The HVSR in two directions is calculated, then averaged and smoothed using a Konno-Ohmachi logarithmic window with a b value of 40 to obtain a single time window HVSR curve, which is then used for subsequent cross-correlation coefficient calculation and screening.

[0015] Furthermore, in S2, the cross-correlation coefficient calculation strategy includes global cross-correlation coefficient determination: by comparing the overall similarity of HVSR curves across the entire effective frequency band, the consistency between different curves is quantitatively evaluated; the formula for calculating the global cross-correlation coefficient is:

[0016] In the formula, Represents the global cross-correlation coefficient; It is a normalized reference curve. It is a normalized single-window HVSR curve; Indicates the frequency sampling point number. The number of effective frequency points participating in the global cross-correlation calculation; and The reference HVSR curve and the HVSR curve to be determined, respectively, represent the HVSR curve after amplitude normalization on the [missing information] th [missing information]. Spectral values ​​at each frequency point.

[0017] Furthermore, in S2, the calculation strategy for the cross-correlation coefficient also includes a combination of global and local cross-correlation coefficients: simultaneously calculating the global cross-correlation coefficient and the local cross-correlation coefficient in the local frequency band near the main peak frequency; the formula for calculating the local cross-correlation coefficient is:

[0018] In the formula, This represents the local cross-correlation coefficient near the peak value. The reference is the frequency index position corresponding to the main peak of the HVSR curve; The half-window width selected on both sides of the main peak is used to limit the frequency range participating in the local cross-correlation calculation; This represents the total number of frequency points involved in the calculation within the local frequency band; Then, assign weights to the global cross-correlation coefficients and the local cross-correlation coefficients, and perform a weighted summation:

[0019] In the formula, This represents the weighted sum of the global and local correlation coefficients. and These are the weight coefficients corresponding to the global cross-correlation coefficient and the local cross-correlation coefficient, respectively, and satisfying the following conditions: .

[0020] Furthermore, in S2, the cross-correlation coefficient calculation strategy also includes the introduction of a global and local cross-correlation coefficient determination with an amplitude threshold: based on the combined determination of global and local cross-correlation coefficients, an amplitude threshold constraint of the HVSR curve is introduced to pre-screen and quality control the curves participating in the determination, specifically including: ① Set the amplitude threshold; ② Determine whether the maximum value of the HVSR curve of a single window is within the threshold range; ③ If the value is within the threshold, the cross-correlation coefficient is calculated and filtered based on a combination of global and local cross-correlation coefficients; if the value exceeds the threshold, the curve is removed and does not participate in the cross-correlation coefficient calculation and filtering process.

[0021] Secondly, the present invention also provides a tailings wetting line analysis system, which includes: an autocorrelation-free HVSR calculation module and a template matching-based screening module, and performs tailings wetting line analysis using the above-mentioned method.

[0022] Thirdly, the present invention also provides an electronic device, including a processor and a memory, the memory storing machine-executable instructions executable by the processor, the processor executing the machine-executable instructions to perform the above-described method.

[0023] As can be seen from the above technical solution, compared with the prior art, the beneficial effects of the present invention include: 1. This invention, while ensuring peak frequency consistency, can effectively suppress amplitude anomalies and multi-peak phenomena, improve the stability and reliability of HVSR curve results, and provide more robust input results for subsequent underground structure parameter inversion.

[0024] 2. The CSD-HVSR with autocorrelation terms removed in this invention can obtain more accurate impedance surface spectrum ratio characteristics, which is beneficial for subsequent velocity interface inversion and obtaining more accurate internal structure information.

[0025] 3. In this invention, template matching is applied to window filtering, which can effectively eliminate interference signals and improve the accuracy of the results.

[0026] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0027] 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

[0028] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0030] Figure 1 This is a schematic diagram of the tailings wetting line analysis method provided in an embodiment of the present invention.

[0031] Figure 2 A schematic diagram of a 1-hour three-component waveform provided for an embodiment of the present invention.

[0032] Figure 3This is a schematic diagram of HVSR results provided in an embodiment of the present invention; the gray curve in the figure is the HVSR calculated for each window, the light red curve is the HVSR selected based on template matching, and the dark red curve is the HVSR obtained by superimposing the light red curves.

[0033] Figure 4 This diagram illustrates a comparison of the results of two methods provided in this embodiment of the invention; the blue curve represents the result of the prior method, and the red curve represents the result of the method of this invention. The peak frequencies of the two results are consistent. The left side shows the amplitude scale corresponding to the red curve, and the right side shows the amplitude scale corresponding to the blue curve.

[0034] Figure 5 This is a schematic diagram of the electronic device structure provided in an embodiment of the present invention. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 some embodiments of the present invention, but not all embodiments.

[0036] In the description of this invention, it should be noted that some processes described in this application specification and drawings include multiple operations that appear in a specific order. However, it should be clearly understood that these operations may be performed in any order or in parallel. Furthermore, various numbers are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0037] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0038] See Figure 1 As shown, this invention proposes an improved method for analyzing the seepage line of tailings dams. By employing template matching for window selection and calculating the horizontal-to-vertical spectral ratio (HVSR) based solely on the cross-spectral density between multi-component signals, interference from autocorrelation components is avoided. This results in a more accurate spectral ratio characteristic reflecting the wave impedance interface of the dam body, which is then applied to tailings monitoring. The main contents of this method include: (1) When calculating the HVSR curve, remove the autocorrelation term to obtain a more reasonable HVSR curve.

[0039] (2) Historical data is used as a template to monitor the HVSR curve of the tailings area on a daily basis, thereby selecting a more accurate curve and reducing the influence of interference signals.

[0040] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings: Step 1: Calculate the 6-component HVSR without autocorrelation: like Figure 2 As shown, three-component continuous waveform data from the tailings dam observation point were obtained. Each data segment was 1 hour long, corresponding to the E–W (east-west), N–S (north-south), and Z (vertical) components, respectively. The original waveform was segmented using a sliding time window of 30 seconds with 50% overlap, and the cross-spectral density (CSD) between the three components was calculated within each time window. The calculation formula is as follows:

[0041] In the formula, and i ≠ j This represents the three orthogonal components: east-west, north-south, and vertical. Indicates the first With the The cross-spectral density between components, i.e. i Component signal spectrum and j The cross-spectral density is calculated based on frequency domain correlation analysis: First, a Fourier transform is performed on the two time-series signals, and then the spectrum of one signal is multiplied by the conjugate of the spectrum of the other signal, where the sign... This represents the complex conjugation operation, thereby obtaining the energy distribution characteristics of the two components in the frequency domain. This process can characterize the frequency correlation between different components and identify their potential resonant frequencies. Based on the six cross-spectral density results formed by pairwise combinations of the three components, the energy distribution in a single direction is further calculated. The curve is calculated using the following formula:

[0042]

[0043] In the formula, and These represent the horizontal vertical spectral ratios (HVSRs) in the east-west and north-south directions, respectively, calculated based on the cross-power spectral density. , , , , and Let represent the cross-power spectral density between different components, where , and These represent north-south, east-west, and vertical directions, respectively. For example, This represents the cross-power spectral density between the north-south and east-west components. This represents the cross-power spectral density between the north-south component and the vertical component; the other symbols have similar meanings.

[0044] The HVSR in both directions is calculated, then averaged and smoothed using a Konno-Ohmachi logarithmic window with a b-value of 40 to obtain a single-time-window HVSR curve, which is then used for subsequent cross-correlation coefficient calculation and screening. The HVSR calculation formula is as follows:

[0045] Step 2: Window filtering based on template matching: Using the average HVSR curve calculated from historical observation data as a reference template, the similarity between the two curves is quantitatively evaluated by calculating the cross-correlation coefficient between the reference curve and the HVSR curve of a single time window (the result of step 1). A higher cross-correlation coefficient indicates greater consistency between the corresponding curves. All time window curves are sorted from largest to smallest cross-correlation coefficient, and the HVSR curves corresponding to the highest-ranking coefficients (e.g., the total number of windows obtained from a single hour's data segmentation × 10%) are selected and averaged to obtain the stable HVSR curve for that hour. Different cross-correlation calculation strategies can be adopted depending on different application scenarios and data quality conditions. Specific methods include the following: Method 1: Global cross-correlation coefficient determination: The global cross-correlation coefficient (CCR) method quantitatively evaluates the consistency between different HVSR curves by comparing their overall similarity across the entire effective frequency band. This method uses the complete spectral shape as the criterion, reflecting the overall matching degree of the curves over a wide frequency range. Using the global CCR helps avoid the instability caused by relying solely on local features, constraining the variation characteristics of the HVSR curves at the overall level, and improving the stability and reliability of the screening results. The calculation formula is as follows:

[0046] In the formula, It is a normalized reference curve. It is a normalized single-window HVSR curve. Indicates the frequency sampling point number. This refers to the number of effective frequency points participating in the global cross-correlation calculation. The magnitude of the global cross-correlation coefficient reflects the degree of consistency between the single-window HVSR curve and the reference HVSR curve across the entire effective frequency band. A larger value indicates that the two curves have a high degree of consistency over a wide frequency range; conversely, a smaller value indicates that the overall spectral shapes of the two curves are quite different.

[0047] Method 2: Determine based on a combination of global and local cross-correlation coefficients: The method combining global and local cross-correlation coefficients comprehensively evaluates the matching relationship between HVSR curves from two levels: overall spectral consistency and key frequency band feature similarity. The global cross-correlation coefficient constrains the overall consistency of the curves across the entire effective frequency band, while the local cross-correlation coefficient characterizes the local spectral features near the dominant peak frequency. By combining global and local assessments, the sensitivity to key frequency features is enhanced while ensuring overall stability, effectively reducing the impact of local anomalies or overall deviations on the assessment results, thereby improving the accuracy and reliability of HVSR curve screening and consistency determination. The formula for calculating the local cross-correlation coefficient is as follows:

[0048] In the formula, and The reference HVSR curve and the HVSR curve to be determined, respectively, represent the HVSR curve after amplitude normalization on the [missing information] th [missing information]. Spectral values ​​at each frequency point; The reference is the frequency index position corresponding to the main peak of the HVSR curve; The half-window width selected on both sides of the main peak is used to limit the frequency range participating in the local cross-correlation calculation; This represents the total number of frequency points involved in the calculation within the local frequency band. By calculating the cross-correlation coefficient within the local frequency band near the main peak, the degree of matching between the two HVSR curves in key feature regions can be effectively characterized, highlighting the consistency of the peak frequency and its neighborhood spectral shape. Then, the global correlation coefficient and the local correlation coefficient are assigned weights and summed using a weighted average:

[0049] In the formula, This represents the global cross-relationship number. This represents the local cross-correlation coefficient near the peak value. and These are the weight coefficients corresponding to the global cross-correlation coefficient and the local cross-correlation coefficient, respectively, and satisfying the following conditions: .

[0050] Method 3: Determining global and local cross-correlation coefficients by introducing amplitude thresholds: Based on the joint determination of global and local cross-correlation coefficients, an amplitude threshold constraint for the HVSR curve is further introduced to pre-screen and control the quality of the curves involved in the determination. The specific method is as follows: ① Set the amplitude threshold; ② Determine whether the maximum value of the HVSR curve of a single window is within the threshold range; ③ If the value is within the threshold, the cross-correlation coefficient can be calculated and filtered according to method 2; if the value exceeds the threshold, the curve is removed and will not participate in the subsequent cross-correlation coefficient calculation and filtering process.

[0051] This method, while evaluating overall spectral consistency and key frequency band similarity, introduces amplitude level as an auxiliary criterion to avoid interference from curves with low signal-to-noise ratios or unclear physical meanings. By applying an amplitude threshold constraint before correlation determination, abnormal curves that are strongly affected by noise or have insignificant peak characteristics can be effectively eliminated, enhancing the stability and reliability of global and local cross-correlation coefficient determination results. Specific implementation examples: The following provides comparative examples of the method of the present invention and a prior method (the tailings wetting line identification method based on cross-spectral density in the background section): The 24 hourly stable HVSR curves obtained from the screening process were superimposed to form the corresponding daily HVSR curves, and a long-term continuous monitoring curve sequence was constructed based on this. The peak frequency in the monitoring curves is used to indicate the location of the high impedance difference interface in the underground medium of the tailings area, which corresponds to the phreatic line in the tailings dam. By analyzing the changes of the peak frequency over a long time scale, the stability of the phreatic line can be determined, thereby realizing the monitoring and assessment of the safety status of the tailings dam. To further verify the effectiveness of the method of the present invention, the threshold judgment criteria proposed by SESAME (2004) were used to compare and analyze the stability of the peak frequency before and after the screening process. The judgment criteria are shown in Table 1. Table 1 Judgment Criteria

[0053] In Table 1, f0 is the average peak frequency, and ε f0 It is a threshold used to assess its stability over time; SESAME criterion: if the standard deviation of the peak frequency (σ) f0 ) less than ε f0 If the peak frequency is stable, then it is considered to be in a stable state.

[0054] One hour of observation data acquired under strong interference conditions was selected as the data for an embodiment of the method of this invention. Figure 2 ). Figure 3This displays the HVSR results calculated from one hour of observation data acquired under strong interference conditions. The gray curve represents the original HVSR curve for each time window, the light red curve represents the set of HVSR curves retained after filtering, and the dark red line is the average HVSR curve of the filtered results. The blue dots indicate the location of the maximum peak of the average HVSR curve, corresponding to a peak frequency of 1.42 Hz. Figure 3 As can be seen, the dispersion of the HVSR curves near the main peak frequency is significantly reduced after screening, and the spectral shape tends to be concentrated. The average HVSR curve can better characterize the main spectral features within this time period.

[0055] For the same observation data, a comparison of the results of prior methods and the method of this invention is shown below. Figure 4 As shown, the peak frequencies of the HVSR curves obtained by the two methods are consistent, both being 1.42 Hz. However, the amplitude of the result obtained by the prior method is too high and exhibits a bimodal characteristic; in contrast, the amplitude distribution of the HVSR curve obtained by the method of this invention is more reasonable, with a single and clear main peak, which is more conducive to the subsequent inversion of underground structure parameters. Furthermore, the reliability of the results from the two methods is evaluated according to the SESAME criterion. The standard deviation (σ) of the peak frequency obtained by the prior method... f0 The value is 0.265. Based on this criterion, the corresponding threshold ε is... f0 =0.1 1.42 = 0.142, since σ f0 >ε f0 The results are not stable enough; the standard deviation (σ) of the peak frequency obtained by the method of this invention is insufficient. f0 The value is 0.046, satisfying σ. f0 <ε f0 This indicates that the results obtained by this method have good stability and reliability.

[0056] Therefore, under the same data conditions, the method of the present invention effectively suppresses amplitude anomalies and multi-peak phenomena while ensuring the consistency of peak frequency, improves the stability and reliability of HVSR curve results, and provides more robust input results for subsequent underground structure parameter inversion.

[0057] As described in the above embodiments, those skilled in the art will understand that the present invention provides a tailings phreatic line analysis method. This method employs a novel strategy: removing the autocorrelation term and retaining the other six cross-spectral components, while applying template matching to the window filtering of the HVSR, effectively eliminating interference information and obtaining a more reasonable and accurate spectral ratio curve. This, in turn, helps to obtain more accurate information on the internal structure of the dam body. Applying this method to tailings monitoring is beneficial to improving the accuracy and reliability of tailings dam phreatic line monitoring.

[0058] Furthermore, the present invention also provides a tailings wetting line analysis system, which includes: an autocorrelation-free HVSR calculation module (including sub-modules such as data acquisition, preprocessing, cross-spectral density calculation and single-window HVSR calculation), a template matching-based screening module, and an analysis and evaluation module, etc., and performs tailings wetting line analysis using the above-mentioned methods.

[0059] The system provided in this embodiment of the invention has the same implementation principle and technical effects as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the system embodiment can be referred to the corresponding content in the aforementioned method embodiment, and will not be repeated here.

[0060] Additionally, refer to Figure 5 As shown, this embodiment of the invention also provides an electronic device, which may include a processor, a memory, a communication bus and a communication interface, and may also include a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the above-described method.

[0061] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, software systems, electronic devices, or computer program products, etc. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0062] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0063] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for analyzing tailings wetting lines, characterized in that, The method includes the following steps: S1: Obtain three-component continuous waveform data from the tailings dam observation point, wherein the three components include east-west, north-south, and vertical components; perform sliding time window segmentation on the three-component continuous waveform data, calculate the cross-spectral density between any two different components within each time window; calculate the horizontal-to-vertical spectral ratio (HVSR) in a single direction based on the cross-spectral density, and average and smooth the HVSR in the two directions to obtain the HVSR curve for a single time window; S2: Using the average HVSR curve calculated from historical observation data as a reference curve template, calculate the cross-correlation coefficient between the reference curve and the HVSR curve of each time window; sort and filter all time window HVSR curves according to the cross-correlation coefficient, and select a preset number of HVSR curves at the front of the sorting for superposition and averaging to obtain a stable HVSR curve within the time period; analyze the position and stability of the tailings dam phreatic line based on the peak frequency change of the stable HVSR curve.

2. The method according to claim 1, characterized in that, In S1, the formula for calculating the cross-spectral density is: In the formula, Indicates the first With the Cross-spectral density between components and i ≠ j This represents the three orthogonal components: east-west, north-south, and vertical. , and These represent the north-south, east-west, and vertical directions, respectively. Indicates spatial location First The observed vibration signal components at angular frequencies The frequency domain representation below is obtained by Fourier transform of the corresponding time domain vibration record; Indicates the first j The complex conjugate of the spectral signal of each component, sign This indicates the complex conjugate operation.

3. The method according to claim 2, characterized in that, In S1, based on the six cross-spectral density results formed by pairwise combinations of the three components, the calculation is performed in a single direction. The curve, its calculation formula is: In the formula, and These represent the horizontal vertical spectral ratios (HVSRs) in the east-west and north-south directions, respectively, calculated based on the cross-power spectral density. , , , , and These represent the cross-power spectral density between different components; The HVSR in two directions is calculated, then averaged and smoothed using a Konno-Ohmachi logarithmic window with a b value of 40 to obtain a single time window HVSR curve, which is then used for subsequent cross-correlation coefficient calculation and screening.

4. The method according to claim 1, characterized in that, In S2, the cross-correlation coefficient calculation strategy includes global cross-correlation coefficient determination: by comparing the overall similarity of HVSR curves across the entire effective frequency band, the consistency between different curves is quantitatively evaluated; the formula for calculating the global cross-correlation coefficient is: In the formula, Represents the global cross-correlation coefficient; It is a normalized reference curve. It is a normalized single-window HVSR curve; Indicates the frequency sampling point number. The number of effective frequency points participating in the global cross-correlation calculation; and The reference HVSR curve and the HVSR curve to be determined, respectively, represent the HVSR curve after amplitude normalization on the [missing information] th [missing information]. Spectral values ​​at each frequency point.

5. The method according to claim 4, characterized in that, In step S2, the calculation strategy for the cross-correlation coefficient also includes a combination of global and local cross-correlation coefficients: simultaneously calculating the global cross-correlation coefficient and the local cross-correlation coefficient in the local frequency band near the main peak frequency; the formula for calculating the local cross-correlation coefficient is as follows: In the formula, This represents the local cross-correlation coefficient near the peak value. The reference is the frequency index position corresponding to the main peak of the HVSR curve; The half-window width selected on both sides of the main peak is used to limit the frequency range participating in the local cross-correlation calculation; This represents the total number of frequency points involved in the calculation within the local frequency band; Then, assign weights to the global cross-correlation coefficients and the local cross-correlation coefficients, and perform a weighted summation: In the formula, This represents the weighted sum of the global and local correlation coefficients. and These are the weight coefficients corresponding to the global cross-correlation coefficient and the local cross-correlation coefficient, respectively.

6. The method according to claim 5, characterized in that, In S2, the cross-correlation coefficient calculation strategy also includes the introduction of a global and local cross-correlation coefficient determination with an amplitude threshold: based on the combined determination of global and local cross-correlation coefficients, an amplitude threshold constraint of the HVSR curve is introduced to pre-screen and quality control the curves participating in the determination, specifically including: ① Set the amplitude threshold; ② Determine whether the maximum value of the HVSR curve of a single window is within the threshold range; ③ If the value is within the threshold, the cross-correlation coefficient is calculated and filtered based on a combination of global and local cross-correlation coefficients; if the value exceeds the threshold, the curve is removed and does not participate in the cross-correlation coefficient calculation and filtering process.

7. A tailings wetting line analysis system, characterized in that, The system includes: an autocorrelation-free HVSR calculation module, a template matching-based screening module, and performs tailings wetting line analysis using the method described in any one of claims 1–6.

8. An electronic device, characterized in that, It includes a processor and a memory, the memory storing machine-executable instructions that can be executed by the processor, the processor executing the machine-executable instructions to perform the method as described in any one of claims 1-6.

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