Interlayer multiple extraction method for detecting coal thickness through borehole radar bedding hole
By employing Gaussian tuned wavelet and discrete wavelet transform techniques, the problem of the masking of interlayer multiple information during coal seam thickness detection by borehole radar was solved. This enabled accurate extraction of the time information of interlayer multiples even under instrument interference, thereby improving the accuracy of coal seam thickness detection.
Patent Information
- Application Number
- CN202510832731.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-20
- Publication Date
- 2025-10-21
AI Technical Summary
In existing technologies, when drilling radar detects the thickness of coal seams, the inter-layer multiple wave information is easily masked by horizontal interference between instrument channels, making extraction difficult.
Signal decomposition is performed using Gaussian tuned wavelets. By combining Hilbert transform and discrete wavelet transform, and constructing Gaussian tuned wavelet and discrete wavelet transform profiles, the target discrete wavelet transform profile that satisfies the characteristics of inter-layer multiples is extracted, and the time information corresponding to the inter-layer multiples is determined.
Despite horizontal interference between instrument channels, the time information of inter-layer multiple waves can be effectively extracted, improving the accuracy and precision of coal seam thickness detection.
Smart Images
Figure CN120820989A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of geophysical exploration, and in particular to a method for extracting interlayer multiple waves for detecting coal thickness along a layer hole using a borehole radar. Background Art
[0002] With the development of intelligent coal mining, current coal seam mining requires advanced exploration and prediction of coal seam thickness and its variations with high precision. Conventional geophysical methods for detecting coal seam thickness and its variations are performed at the surface or on the surface of the object being measured. However, due to the complex structure of the underground or the interior of the object being measured, it is difficult to accurately predict the subtle variations in coal seam thickness solely based on the data obtained from these methods. Borehole radar uses a high-frequency electromagnetic wave transceiver to penetrate directly into the stratum for detection. Its greatest advantages are high resolution and high precision. To ensure safe coal production, numerous boreholes are drilled along the coal seams in underground coal mines. Therefore, it is crucial to insert borehole radar into these boreholes to detect coal seam thickness and its variations. In actual detection, when coal thickness variations are not drastic, the inter-layer multiple events are approximately horizontal. Horizontal interference between traces caused by the borehole radar instrument itself can mask the inter-layer multiple information, making multiple extraction difficult. Summary of the Invention
[0003] In order to overcome at least one shortcoming in the prior art, the present application provides a method for extracting interlayer multiple waves for detecting coal thickness along the seam using a borehole radar.
[0004] In a first aspect, a method for extracting interlayer multiple waves for detecting coal thickness along a seam hole using a borehole radar is provided, comprising:
[0005] determining a plurality of signal decomposition frequencies, and constructing a Gaussian tuned wavelet at each signal decomposition frequency based on the plurality of signal decomposition frequencies;
[0006] Performing Hilbert transform on each signal in the borehole radar data to obtain each transformed signal;
[0007] Based on the Gaussian tuned wavelet at each signal decomposition frequency, the discrete wavelet transform is performed on each transformed signal to obtain the discrete wavelet transform results of each signal at each signal decomposition frequency;
[0008] For each signal decomposition frequency, a discrete wavelet transform profile is drawn based on the discrete wavelet transform results of each channel;
[0009] Determine a target discrete wavelet transform profile that satisfies the interlayer multiple wave characteristics among all discrete wavelet transform profiles;
[0010] The time information corresponding to the interlayer multiple waves is determined based on the target discrete wavelet transform profile.
[0011] In one embodiment, constructing a Gaussian tuned wavelet at each signal decomposition frequency based on multiple signal decomposition frequencies includes:
[0012] Construct the real part ψ of the Gaussian tuned wavelet r (τ):
[0013]
[0014] Among them, r0 is the envelope width of the Gaussian tuned wavelet, τ is the time of the Gaussian tuned wavelet, τ0 is the time center position of the Gaussian tuned wavelet, f c Decompose the frequency of the signal;
[0015] Construct the imaginary part ψ of the Gaussian tuned wavelet i (τ);
[0016]
[0017] Calculate the time center position τ0 of the Gaussian tuned wavelet;
[0018]
[0019] Where N is the number of wavelet sampling points, and Δt is the sampling interval;
[0020] Calculate the envelope width r0 of the Gaussian tuned wavelet;
[0021] r0=τ0×0.28289
[0022] Calculate the frequency domain length N(f c ):
[0023]
[0024] Among them, T * is the quality factor related parameter, Indicates rounding up. Indicates rounding down.
[0025] In one embodiment, the target discrete wavelet transform profile that satisfies the inter-layer multiple wave characteristics is:
[0026] The figure shows multiple interlayer multiple wave events that are equally spaced and parallel;
[0027] The time interval between the multiple wave phase axes between adjacent layers is twice the radar wave propagation time of the coal seam;
[0028] In the figure, the energy of the interlayer multiple waves gradually decreases as time increases.
[0029] In one embodiment, the method further comprises pre-processing the borehole radar data.
[0030] In a second aspect, a device for extracting interlayer multiple waves for detecting coal thickness along a seam hole using a borehole radar is provided, comprising:
[0031] A Gaussian tuned wavelet construction module is used to determine multiple signal decomposition frequencies and construct a Gaussian tuned wavelet at each signal decomposition frequency based on the multiple signal decomposition frequencies;
[0032] A Hilbert transform module is used to perform Hilbert transform on each signal in the borehole radar data to obtain a transformed signal of each channel;
[0033] A discrete wavelet transform module is used to perform discrete wavelet transform on each transformed signal based on the Gaussian tuned wavelet at each signal decomposition frequency, and obtain discrete wavelet transform results of each signal at each signal decomposition frequency;
[0034] A profile drawing module is used to draw a discrete wavelet transform profile based on the discrete wavelet transform results of each channel for each signal decomposition frequency;
[0035] A target discrete wavelet transform profile determination module is used to determine a target discrete wavelet transform profile that satisfies the interlayer multiple wave characteristics among all discrete wavelet transform profiles;
[0036] The interlayer multiple wave extraction module is used to determine the time information corresponding to the interlayer multiple waves according to the target discrete wavelet transform profile.
[0037] Compared to the existing technology, the present application has the following beneficial effects: Based on the Gaussian tuned wavelet at each signal decomposition frequency, the present application performs a discrete wavelet transform on each transformed signal, obtaining the discrete wavelet transform results for each channel at each signal decomposition frequency; for each signal decomposition frequency, a discrete wavelet transform profile is plotted based on the discrete wavelet transform results for each channel; a target discrete wavelet transform profile that satisfies the interlayer multiple wave characteristics is determined from all discrete wavelet transform profiles; and the time information corresponding to the interlayer multiple waves is determined based on the target discrete wavelet transform profile. The present application can effectively extract the time information corresponding to the interlayer multiple waves even when affected by horizontal interference between instrument channels. BRIEF DESCRIPTION OF THE DRAWINGS
[0038] The present application may be better understood by referring to the following description in conjunction with the accompanying drawings, which together with the following detailed description are incorporated into and form a part of this specification. In the drawings:
[0039] Figure 1 The flow chart of the method for extracting interlayer multiple waves for detecting coal thickness along the seam hole using borehole radar is shown;
[0040] Figure 2 shows a profile of the raw borehole radar data;
[0041] Figure 3 The target discrete wavelet transform profile after 710MHz single frequency decomposition is shown. DETAILED DESCRIPTION
[0042] Exemplary embodiments of the present application are described below with reference to the accompanying drawings. For the sake of clarity and conciseness, not all features of actual embodiments are described in this specification. However, it should be understood that in the process of developing any such actual embodiment, many implementation-specific decisions may be made to achieve the developer's specific goals, and these decisions may vary from one implementation to another.
[0043] It is also necessary to explain here that, in order to avoid obscuring the present application due to unnecessary details, the accompanying drawings only show the device structure closely related to the solution according to the present application, while other details that are not closely related to the present application are omitted.
[0044] It should be understood that the present application is not limited to the described embodiments due to the following description with reference to the accompanying drawings. In this document, where feasible, the embodiments may be combined with each other, features between different embodiments may be replaced or borrowed, and one or more features may be omitted in one embodiment.
[0045] The embodiment of the present application provides a method for extracting interlayer multiple waves by using borehole radar to detect coal thickness along the seam hole. Figure 1 The flow chart of the method for extracting interlayer multiple waves from the borehole radar to detect coal thickness along the seam hole is shown in FIG. Figure 1 , the method mainly includes the following steps:
[0046] Step S1: determining a plurality of signal decomposition frequencies, and constructing a Gaussian tuned wavelet at each signal decomposition frequency based on the plurality of signal decomposition frequencies.
[0047] Specifically, construct the real part ψ of the Gaussian tuned wavelet r (τ):
[0048]
[0049] Among them, r0 is the envelope width of the Gaussian tuned wavelet, τ is the time of the Gaussian tuned wavelet, τ0 is the time center position of the Gaussian tuned wavelet, f c Decompose the frequency of the signal;
[0050] Construct the imaginary part ψ of the Gaussian tuned wavelet i (τ);
[0051]
[0052] Calculate the time center position τ0 of the Gaussian tuned wavelet;
[0053]
[0054] Where N is the number of wavelet sampling points, and Δt is the sampling interval;
[0055] Calculate the envelope width r0 of the Gaussian tuned wavelet;
[0056] r0=τ0×0.28289
[0057] Calculate the frequency domain length N(f c ):
[0058]
[0059] Among them, T * is the quality factor related parameter, Indicates rounding up; Indicates rounding down.
[0060] In order to determine the effectiveness of the constructed Gaussian tuned wavelet, it can be verified from the following three aspects:
[0061] (1) Calculate the energy of the Gaussian tuned wavelet and check whether the accumulated energy is equal to 1. If it is equal to 1, the constructed tuned wavelet meets the requirements. If it is not equal to 1, the tuned wavelet does not meet the requirements.
[0062]
[0063] Where ψ(τ) is the Gaussian tuned wavelet.
[0064] (2) Calculate the zero phase of the Gaussian tuned wavelet and see if the zero phase is located at the central time position of the Gaussian tuned wavelet:
[0065]
[0066] (3) Calculate the bandwidth of the Gaussian tuned wavelet to see if the bandwidth meets the requirements of the following formula:
[0067]
[0068] Step S2: performing Hilbert transform on each signal in the borehole radar data to obtain each transformed signal.
[0069] Collect geological data of the detection area, including approximate coal thickness information, coal thickness change trend, lithology information of the coal seam roof and floor, and drilling slag return information, and collect borehole radar detection data. The borehole radar data includes various signals, that is, signals obtained at various depths, and each signal includes data at multiple times.
[0070] Furthermore, the borehole radar data is preprocessed, including DC elimination, depth correction, time correction, correction of uneven direct wave event axis, bad track removal, signal gain, etc.
[0071] Step S3, based on the Gaussian tuned wavelet at each signal decomposition frequency, the signal x(t) after each channel is transformed is subjected to discrete wavelet transform to obtain the discrete wavelet transform results of each channel at each signal decomposition frequency. The discrete wavelet transform results include the real part discrete wavelet transform results Re[X(f c ,t)] and the imaginary discrete wavelet transform result Im[X(f c ,t)], t represents time.
[0072]
[0073] Step S4: for each signal decomposition frequency, a discrete wavelet transform profile is drawn based on the discrete wavelet transform results of each channel.
[0074] Here, we can use the real part discrete wavelet transform result Re[X(f c ,t)] to draw the discrete wavelet transform profile.
[0075] Step S5: determining a target discrete wavelet transform profile that satisfies the interlayer multiple wave characteristics among all discrete wavelet transform profiles.
[0076] Specifically, the target discrete wavelet transform profile that meets the interlayer multiple wave characteristics is:
[0077] The figure shows multiple interlayer multiple wave events that are equally spaced and parallel;
[0078] The time interval between the multiple wave phase axes between adjacent layers is twice the radar wave propagation time of the coal seam;
[0079] In the figure, the energy of the interlayer multiple waves gradually decreases as time increases.
[0080] Step S6: determining the time information corresponding to the interlayer multiple waves according to the target discrete wavelet transform profile.
[0081] Here, the time corresponding to each multiple wave event in the target discrete wavelet transform profile is the time information corresponding to the determined interlayer multiple wave.
[0082] In order to further verify the effectiveness of the method of the present application, the following specific examples are given.
[0083] The original data of the borehole radar equipment with a center frequency of 200MHz in the coal seam hole test of a mine in Shanxi Province was decomposed into single frequency. Figure 2The raw data profile of the borehole radar is shown. The average thickness of the coal seam where the borehole is located is about 1.8m. The roof of the coal seam is mudstone and the floor is siltstone. The hole is opened 1.2m away from the roof. The borehole radar measures a depth of 80m. The borehole penetrates the coal seam at a depth of about 62m and there is no water in the hole.
[0084] The original data is decomposed into signals with a decomposition frequency of 710MHz. Figure 3 The target discrete wavelet transform profile after 710MHz single frequency decomposition is shown.
[0085] contrast Figure 2 and Figure 3 The original data has serious and uneven horizontal interference between channels, poor continuity of the horizontal event axis, and no multiple wave information can be seen. Figure 3 There is almost no interference information between the traces in the original data, and the time information of the multiple waves between layers is clearly shown (a total of 5 times on the profile: 60ns, 70ns, 80ns, 90ns, and 100ns). Figure 3 As time increases along the longitudinal axis, the energy of the interlayer multiple waves gradually decreases. The interlayer multiple waves disappear after the hole is drilled through at a depth of 61.5 m. The time difference between the multiple waves between each layer reflects the change in coal thickness.
[0086] Based on the same inventive concept as the method for extracting interlayer multiple waves for detecting coal thickness using borehole radar along the bed hole, this embodiment also provides a corresponding device for extracting interlayer multiple waves for detecting coal thickness using borehole radar along the bed hole, comprising:
[0087] A Gaussian tuned wavelet construction module is used to determine multiple signal decomposition frequencies and construct a Gaussian tuned wavelet at each signal decomposition frequency based on the multiple signal decomposition frequencies;
[0088] A Hilbert transform module is used to perform Hilbert transform on each signal in the borehole radar data to obtain a transformed signal of each channel;
[0089] A discrete wavelet transform module is used to perform discrete wavelet transform on each transformed signal based on the Gaussian tuned wavelet at each signal decomposition frequency, and obtain discrete wavelet transform results of each signal at each signal decomposition frequency;
[0090] A profile drawing module is used to draw a discrete wavelet transform profile based on the discrete wavelet transform results of each channel for each signal decomposition frequency;
[0091] A target discrete wavelet transform profile determination module is used to determine a target discrete wavelet transform profile that satisfies the interlayer multiple wave characteristics among all discrete wavelet transform profiles;
[0092] The interlayer multiple wave extraction module is used to determine the time information corresponding to the interlayer multiple waves according to the target discrete wavelet transform profile.
[0093] The device for extracting interlayer multiple waves by using a borehole radar to detect coal thickness along the layer hole in this embodiment has the same inventive concept as the method for extracting interlayer multiple waves by using a borehole radar to detect coal thickness along the layer hole in the above text. Therefore, the specific implementation method of the device can be seen in the embodiment part of the method for extracting interlayer multiple waves by using a borehole radar to detect coal thickness along the layer hole in the above text, and its technical effects correspond to the technical effects of the above method, which will not be repeated here.
[0094] In summary, this application has the following technical effects:
[0095] This application performs a discrete wavelet transform on each transformed signal channel based on a Gaussian tuned wavelet at each signal decomposition frequency, obtaining the discrete wavelet transform results for each channel at each signal decomposition frequency. For each signal decomposition frequency, a discrete wavelet transform profile is plotted based on the discrete wavelet transform results for each channel. A target discrete wavelet transform profile that satisfies the characteristics of interlayer multiples is determined from all discrete wavelet transform profiles. The time information corresponding to the interlayer multiples is determined based on the target discrete wavelet transform profile. This application can effectively extract the time information corresponding to the interlayer multiples, even when affected by horizontal interference between instrument channels.
[0096] The above descriptions are merely examples of various embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any modifications or substitutions that can be readily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included within the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.
Claims
1. A method for extracting interlayer multiple waves for detecting coal thickness using borehole radar along the seam, characterized in that: include: Determine a plurality of signal decomposition frequencies, and construct a Gaussian tuned wavelet at each signal decomposition frequency based on the plurality of signal decomposition frequencies; Performing Hilbert transform on each signal in the borehole radar data to obtain each transformed signal; Based on the Gaussian tuned wavelet at each signal decomposition frequency, performing discrete wavelet transform on each transformed signal to obtain a discrete wavelet transform result for each signal decomposition frequency; For each signal decomposition frequency, a discrete wavelet transform profile is drawn based on the discrete wavelet transform results of each channel; Determine a target discrete wavelet transform profile that satisfies the interlayer multiple wave characteristics among all discrete wavelet transform profiles; The time information corresponding to the interlayer multiple waves is determined according to the target discrete wavelet transform profile.
2. The method according to claim 1, wherein in, Constructing a Gaussian tuned wavelet at each signal decomposition frequency based on the multiple signal decomposition frequencies, comprising: Construct the real part ψ of the Gaussian tuned wavelet r (τ): Among them, r0 is the envelope width of the Gaussian tuned wavelet, τ is the time of the Gaussian tuned wavelet, τ0 is the time center position of the Gaussian tuned wavelet, f c Decompose the frequency of the signal; Construct the imaginary part ψ of the Gaussian tuned wavelet i (τ); Calculate the time center position τ0 of the Gaussian tuned wavelet; Where N is the number of wavelet sampling points, and Δt is the sampling interval; Calculate the envelope width r0 of the Gaussian tuned wavelet; r0=τ0×0.28289 Calculate the frequency domain length N(f c ): Among them, T * is the quality factor related parameter, Indicates rounding up. Indicates rounding down.
3. The method according to claim 1, wherein The target discrete wavelet transform profile that satisfies the interlayer multiple wave characteristics is: The figure shows multiple interlayer multiple wave events that are equally spaced and parallel; The time interval between the multiple wave phase axes between adjacent layers is twice the radar wave propagation time of the coal seam; In the figure, the energy of the interlayer multiple waves gradually decreases as time increases.
4. The method according to claim 1, wherein The method also includes pre-processing the borehole radar data.
5. An interlayer multiple wave extraction device for detecting coal thickness along the seam hole using a borehole radar, characterized in that: include: A Gaussian tuned wavelet construction module is used to determine a plurality of signal decomposition frequencies and construct a Gaussian tuned wavelet at each signal decomposition frequency based on the plurality of signal decomposition frequencies; A Hilbert transform module is used to perform Hilbert transform on each signal in the borehole radar data to obtain a transformed signal of each channel; A discrete wavelet transform module, configured to perform discrete wavelet transform on each of the transformed signals based on the Gaussian tuned wavelet at each signal decomposition frequency, to obtain discrete wavelet transform results for each signal at each signal decomposition frequency; A profile drawing module is used to draw a discrete wavelet transform profile based on the discrete wavelet transform results of each channel for each signal decomposition frequency; A target discrete wavelet transform profile determination module is used to determine a target discrete wavelet transform profile that satisfies the interlayer multiple wave characteristics among all discrete wavelet transform profiles; The interlayer multiple wave extraction module is used to determine the time information corresponding to the interlayer multiple waves according to the target discrete wavelet transform profile.