A tailings infiltration line identification method and system based on cross-spectral density

Abstract

The present invention discloses a tailings infiltration line identification method and system based on cross-spectral density, belonging to the technical field of infiltration line detection. The present invention first obtains three-component data of tailings observation points, then determines the cross-spectral density between any two component data in the three-component data, obtains the HVSR curve based on the cross-spectral density, and identifies the tailings infiltration line according to the HVSR curve. Compared with the prior art, the present invention can provide more accurate and stable monitoring results, and monitor the changes of the infiltration line in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of wetting line detection, and more particularly to a tailings wetting line identification method and system based on cross-spectral density. Background Art

[0002] In the field of mine safety management, the monitoring of tailings ponds is particularly important, especially the monitoring of the infiltration line. The infiltration line refers to the farthest limit of water penetration in the tailings pond dam body, and its position is directly related to the stability and safety of the dam body.

[0003] Traditional infiltration line monitoring methods mainly rely on manual observation, which has a long cycle and delayed information transmission, making it difficult to meet the timeliness requirements of monitoring work. With the development of technology, automated methods have been widely used, but the efficiency and accuracy of automated monitoring depend on the advantages and disadvantages of the instruments and equipment themselves. In addition, they also face the problems of high cost and post-maintenance. The resistivity method has poor anti-interference ability and is also limited by the target's burial depth and volume.

[0004] In the field of earthquake engineering, HVSR (Horizontal-to-Vertical Spectral Ratio), that is, the horizontal to vertical spectral ratio, is a new method proposed in recent years. This method can well identify strong impedance interfaces and can be used to monitor the changes in the infiltration line of tailings and evaluate the safety of tailings. HVSR is currently mainly used to evaluate the characteristics of seismic sites and obtain underground shear wave velocity structures. Existing HVSR calculation methods include Fourier spectral ratio (FAS), power spectral ratio (PSD) and autocorrelation method. However, since the results obtained by the above methods are easily affected by environmental changes, the HVSR curves obtained in actual monitoring are prone to volatility and uncertainty. This instability may have a negative impact on the results, thereby increasing the complexity of the interpretation and application of the results.

[0005] Therefore, the existing tailings infiltration line monitoring method has certain limitations, and a more accurate, stable and efficient tailings infiltration line identification method is needed. Summary of the invention

[0006] In view of this, the present invention provides a tailings infiltration line identification method and system based on cross-spectral density; it aims to analyze the correlation information between two signals through the cross-spectral density of seismic signals, so as to make the information extracted from the HVSR curve richer and more stable, thereby realizing accurate monitoring and evaluation of the stability of the tailings area.

[0007] Specifically, the present invention is implemented through the following scheme:

[0008] In a first aspect, the present application discloses a tailings infiltration line identification method based on cross-spectral density, comprising the following steps:

[0009] Obtain three-component data of tailings observation points;

[0010] Determine the cross-spectral density between any two component data in the three-component data, where the cross-spectral density is a product of the Fourier spectrum of one component data and the conjugate Fourier spectrum of another component data, and perform a discrete-time Fourier transform;

[0011] The HVSR curve is obtained based on the cross-spectral density, and the tailings infiltration line is identified according to the HVSR curve.

[0012] Furthermore, after obtaining the three-component data of the tailings observation point, the data is segmented according to a preset length to obtain the HVSR curve corresponding to each segment of data;

[0013] When the tailings infiltration line is identified according to the HVSR curve, the HVSR curve corresponding to each segment of data is smoothed to obtain a single-day HVSR curve, and the tailings infiltration line is identified according to the single-day HVSR curve.

[0014] Furthermore, the cross-spectral density between any two component data includes the cross-spectral density of each component data relative to itself and the cross-spectral density of any component data relative to the other two component data.

[0015] Furthermore, the process of obtaining the HVSR curve based on the cross-spectral density includes:

[0016] The HVSR curve in a single direction is obtained based on the cross-spectral density according to the following formula;

[0017]

[0018] Where HVSR ci represents the HVSR curve in direction i, i {E, N, Z}, E, N, Z represent the direction of component data, CSD iE , CSD iN and CSD iZ Represents the cross-spectral density between the component data in direction i and the component data in directions E, N and Z respectively;

[0019] The total HVSR curve is synthesized according to the HVSR curve in a single direction; the synthesis method is:

[0020] .

[0021] Furthermore, the tailings infiltration line is identified according to the HVSR curve, including: obtaining the peak frequency of the HVSR curve, and identifying the stable state of the tailings infiltration line according to the standard deviation of the peak frequency.

[0022] Furthermore, when the standard deviation of the peak frequency is less than a preset threshold, the tailings infiltration line is in a stable state; wherein the threshold is set as follows:

[0023] When the peak frequency changes <0.2Hz, the threshold is 0.25f0;

[0024] When 0.2Hz≤peak frequency change<0.5Hz, the threshold is 0.2f0;

[0025] When 0.5Hz≤peak frequency change<1.0Hz, the threshold is 0.15f0;

[0026] When 1.0Hz≤peak frequency change<2.0Hz, the threshold is 0.1f0;

[0027] When the peak frequency change was ≥2.0 Hz, the threshold was 0.05f0, where f0 was the average peak frequency.

[0028] In a second aspect, the present application discloses a tailings infiltration line identification system based on cross-spectral density, comprising:

[0029] A data acquisition module, used to obtain three-component data of tailings observation points;

[0030] A cross-spectral density calculation module is used to determine the cross-spectral density between any two component data in the three-component data, wherein the cross-spectral density is a discrete-time Fourier transform of the product of the Fourier spectrum of one component data and the conjugate Fourier spectrum of another component data;

[0031] The tailings infiltration line identification module is used to obtain the HVSR curve based on the cross-spectral density and identify the tailings infiltration line according to the HVSR curve.

[0032] Furthermore, the data acquisition module is also used to segment the acquired three-component data of the tailings observation point according to a preset length to obtain an HVSR curve corresponding to each segment of data.

[0033] Furthermore, the tailings infiltration line identification module includes an HVSR curve synthesis unit and a tailings infiltration line identification unit.

[0034] The HVSR curve synthesis unit is used to obtain the HVSR curve based on the cross-spectral density;

[0035] The tailings infiltration line identification unit is used to identify the tailings infiltration line according to the HVSR curve.

[0036] It can be seen from the above technical solutions that the present invention discloses a tailings infiltration line identification method and system based on cross-spectral density; compared with the prior art, the present invention can provide more accurate and stable monitoring results; it not only helps to better understand the micro-motion characteristics of the tailings area, but also can monitor the changes of the infiltration line in real time, providing important technical support for the safe operation of mines.

[0037] In addition, the application of this method and system can also be extended to the monitoring and early warning of other geological disasters, and has broad practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0039] Figure 1 The figure is a flow chart of the tailings infiltration line identification method based on cross-spectral density of the present invention;

[0040] Figure 2 A flow chart of a method for identifying tailings infiltration lines in an embodiment of the present invention;

[0041] Figure 3 This is a schematic diagram of the synthesis result of the HVSR curve of the present invention;

[0042] Figure 4 It is a flow chart of long-term tailings infiltration line monitoring of the present invention;

[0043] Figure 5 It is a schematic diagram of the infiltration line monitoring results of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] In view of the limitations of the prior art, the embodiments of the present invention analyze the relationship between two signals based on the cross-spectral density to extract more information, obtain detailed underground structure information, and increase the stability of the results.

[0046] The embodiment of the present invention discloses a tailings infiltration line identification method based on cross-spectral density, such as Figure 1 , including the following steps:

[0047] Obtain three-component data of tailings observation points;

[0048] Determine the cross-spectral density between any two component data in the three-component data, where the cross-spectral density is a product of the Fourier spectrum of one component data and the conjugate Fourier spectrum of another component data, and perform a discrete-time Fourier transform;

[0049] The HVSR curve is obtained based on the cross-spectral density, and the tailings infiltration line is identified according to the HVSR curve.

[0050] In an exemplary embodiment, Figure 2 As shown,

[0051] First, obtain the three-component data of the tailings observation point, which are EW (east-west), NS (north-south), and Z (vertical). In this embodiment, the sampling frequency is 100 Hz;

[0052] Secondly, the data is segmented according to a preset length. In this embodiment, the rotating window length is 20 minutes. The reason for window segmentation is that the microseismic records in different time windows may be affected by different noises and underground medium changes. Segmented averaging helps to reduce noise and improve the reliability of the curve.

[0053] Then, the corresponding HVSR curve is calculated according to each segment of data; the calculation process is: determining the cross-spectral density between any two component data in the three-component data, and obtaining the HVSR curve based on the cross-spectral density;

[0054] In this embodiment, the cross-spectral density between any two component data includes a 9-component cross-spectral density, that is, the cross-spectral density of each component data relative to itself, and the cross-spectral density of any component data relative to the other two component data;

[0055] The cross-spectral density is the discrete-time Fourier transform of the Fourier spectrum of one component data and the conjugate of the Fourier spectrum of another component data; the expression is as follows:

[0056]

[0057] where i {E ,N,Z}, corresponding to the time series data in the three directions of EW, NS, and Z, CSD ij (j {E, N, Z}) is the cross-spectral density of the time series data in the i direction and the j direction;

[0058] That is, firstly, the autocorrelation function of the two signals is calculated, which is the product of the Fourier spectrum of one signal and the conjugate of the Fourier spectrum of the other signal. The * in the formula represents the conjugate of the Fourier spectrum of the signal. Then, a discrete time Fourier transform (DTFT) is performed on the autocorrelation function to display the power distribution between the two signals on an arbitrary time spectrum, from which the mutual resonant frequency in the two signals is retrieved.

[0059] Furthermore, the process of obtaining the HVSR curve based on the cross-spectral density includes:

[0060] Based on the cross-spectral density, the HVSR curves in three directions are obtained according to the following formulas;

[0061]

[0062] Where HVSR ci represents the HVSR curve in direction i, i {E, N, Z}, E, N, Z represent the direction of component data, CSD iE , CSD iN and CSD iZ Represents the cross-spectral density between the component data in direction i and the component data in directions E, N and Z respectively;

[0063] The total HVSR curve is synthesized based on the HVSR curves in three single directions; the synthesis method is:

[0064] .

[0065] Finally, the HVSR curve corresponding to each segment of data is smoothed to reduce the mutation and oscillation of the curve, making it more continuous and easier to analyze. All data segments are then averaged to obtain a single-day HVSR curve, and the tailings infiltration line is identified based on the single-day HVSR curve.

[0066] Smoothing means sliding the curve over a specified length to obtain an average, which can remove interference. After smoothing the data of a single window, the data of all windows are averaged to obtain the curve for the whole day.

[0067] The HVSR curve represents the information in the frequency domain. The frequency domain information of the whole day is continuous in time, and the frequency domain information of different windows is superimposed in the frequency domain. In one embodiment, the single-day detection results of the tailings area are as follows: Figure 3 As shown, the grey curve is the HVSR calculated for each window, and the black curve is the single-day HVSR curve averaged over all windows.

[0068] Furthermore, when the present invention identifies the tailings infiltration line according to the HVSR curve, a long-term single-day HVSR curve can be obtained based on the daily monitoring results, and then the infiltration line state can be evaluated according to the long-term peak frequency change. The process is referred to Figure 4 The peak frequency of the HVSR curve represents the strong impedance interface of the underground layer in the tailings area, that is, the infiltration line. By analyzing the changes in peak frequency over a long period of time, it is possible to evaluate whether the infiltration line is stable and the safety of the tailings pond, which is of great reference value for ensuring the safe operation of the tailings pond.

[0069] In order to optimize the above technical solution, this embodiment identifies the stable state of the tailings infiltration line according to the standard deviation of the peak frequency change. Specifically:

[0070] When the standard deviation of the peak frequency is less than the preset threshold, the tailings infiltration line is in a stable state; the threshold is set as follows:

[0071] When the peak frequency changes <0.2Hz, the threshold is 0.25f0;

[0072] When 0.2Hz≤peak frequency change<0.5Hz, the threshold is 0.2f0;

[0073] When 0.5Hz≤peak frequency change<1.0Hz, the threshold is 0.15f0;

[0074] When 1.0Hz≤peak frequency change<2.0Hz, the threshold is 0.1f0;

[0075] When the peak frequency change was ≥2.0 Hz, the threshold was 0.05f0, where f0 was the average peak frequency.

[0076] In another embodiment, the present application discloses a tailings infiltration line identification system based on cross-spectral density, the system comprising:

[0077] A data acquisition module, used to acquire three-component data of tailings observation points; and to segment the acquired three-component data of tailings observation points according to preset lengths to obtain an HVSR curve corresponding to each segment of data;

[0078] A cross-spectral density calculation module is used to determine the cross-spectral density between any two component data in the three-component data, wherein the cross-spectral density is a discrete-time Fourier transform of the product of the Fourier spectrum of one component data and the conjugate Fourier spectrum of another component data;

[0079] The tailings infiltration line identification module is used to obtain the HVSR curve based on the cross-spectral density and identify the tailings infiltration line according to the HVSR curve.

[0080] Furthermore, the tailings infiltration line identification module includes an HVSR curve synthesis unit and a tailings infiltration line identification unit.

[0081] The HVSR curve synthesis unit is used to obtain the HVSR curve based on the cross-spectral density;

[0082] The tailings infiltration line identification unit is used to identify the tailings infiltration line according to the HVSR curve.

[0083] The calculation method of the cross-spectral density and the method of identifying the infiltration line in this embodiment are consistent with those in the above embodiment, and thus will not be repeated here.

[0084] The present invention determines the HVSR curve based on the cross-spectral density and is used to monitor the tailings pond, making it easier to obtain the underground structure and more accurately monitor the daily infiltration line changes, thereby significantly improving the stability of the monitoring results.

[0085] In order to verify the practicality and effectiveness of the proposed method, the data results for the whole month from July 1 to 31, 2023 are taken as an example to identify the infiltration line using the proposed method. The daily peak frequency is stable at around 2.17 Hz, so 0.05 times the average peak frequency is selected as the threshold. The statistical result σ f is 0.021, the threshold is 0.11, σ f is less than the threshold, the result is Figure 5 As shown, Figure 5 It shows that the underground infiltration line at the monitoring point remains in a stable state.

[0086] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0087] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one 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 present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A tailings infiltration line identification method based on cross-spectral density, characterized in that: include: Obtain three-component data of tailings observation points; Determine the cross-spectral density between any two component data in the three-component data, where the cross-spectral density is a product of the Fourier spectrum of one component data and the conjugate Fourier spectrum of another component data, and perform a discrete-time Fourier transform; The HVSR curve is obtained based on the cross-spectral density, and the tailings infiltration line is identified according to the HVSR curve; The process of obtaining the HVSR curve based on the cross-spectral density includes: The HVSR curve in a single direction is obtained based on the cross-spectral density according to the following formula; Where HVSR ci represents the HVSR curve in direction i, i∈{E,N,Z}, E, N, Z represent the directions of component data, CSD iE , CSD iN and CSD iZ Represents the cross-spectral density between the component data in direction i and the component data in directions E, N and Z respectively; The total HVSR curve is synthesized according to the HVSR curve in a single direction; the synthesis method is:

2. The tailings infiltration line identification method based on cross-spectral density according to claim 1 is characterized in that: After obtaining the three-component data of the tailings observation point, segment it according to the preset length to obtain the HVSR curve corresponding to each segment of data; When the tailings infiltration line is identified according to the HVSR curve, the HVSR curve corresponding to each segment of data is smoothed to obtain a single-day HVSR curve, and the tailings infiltration line is identified according to the single-day HVSR curve.

3. The tailings infiltration line identification method based on cross-spectral density according to claim 1 is characterized in that: The cross-spectral density between any two component data includes the cross-spectral density of each component data relative to itself, and the cross-spectral density of any component data relative to the other two component data.

4. The tailings infiltration line identification method based on cross-spectral density according to claim 1 is characterized in that: The tailings infiltration line is identified according to the HVSR curve, including: obtaining the peak frequency of the HVSR curve, and identifying the stable state of the tailings infiltration line according to the standard deviation of the peak frequency.

5. The tailings infiltration line identification method based on cross-spectral density according to claim 4 is characterized in that: When the standard deviation of the peak frequency is less than a preset threshold, the tailings infiltration line is in a stable state; wherein the threshold is set as follows: When the peak frequency changes <0.2Hz, the threshold is 0.25f0; When 0.2Hz≤peak frequency change<0.5Hz, the threshold is 0.2f0; When 0.5Hz≤peak frequency change<1.0Hz, the threshold is 0.15f0; When 1.0Hz≤peak frequency change<2.0Hz, the threshold is 0.1f0; When the peak frequency change was ≥2.0 Hz, the threshold was 0.05f0, where f0 was the average peak frequency.

6. A tailings infiltration line identification system based on cross-spectral density, characterized in that: include: A data acquisition module, used to obtain three-component data of tailings observation points; A cross-spectral density calculation module is used to determine the cross-spectral density between any two component data in the three-component data, wherein the cross-spectral density is a discrete-time Fourier transform of the product of the Fourier spectrum of one component data and the conjugate Fourier spectrum of another component data; A tailings infiltration line identification module is used to obtain the HVSR curve based on the cross-spectral density and identify the tailings infiltration line according to the HVSR curve; The process of obtaining the HVSR curve based on the cross-spectral density includes: The HVSR curve in a single direction is obtained based on the cross-spectral density according to the following formula; Where HVSR ci represents the HVSR curve in direction i, i∈{E,N,Z}, E, N, Z represent the directions of component data, CSD iE , CSD iN and CSD iZ Represents the cross-spectral density between the component data in direction i and the component data in directions E, N and Z respectively; The total HVSR curve is synthesized according to the HVSR curve in a single direction; the synthesis method is:

7. The tailings infiltration line identification system based on cross-spectral density according to claim 6 is characterized in that: The data acquisition module is also used to segment the acquired three-component data of the tailings observation point according to a preset length to obtain an HVSR curve corresponding to each segment of data.

8. The tailings infiltration line identification system based on cross-spectral density according to claim 6, characterized in that: The tailings infiltration line identification module includes the HVSR curve synthesis unit and the tailings infiltration line identification unit. The HVSR curve synthesis unit is used to obtain the HVSR curve based on the cross-spectral density; The tailings infiltration line identification unit is used to identify the tailings infiltration line according to the HVSR curve.

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