A high-resolution processing method for borehole radar data based on hybrid time-frequency analysis

Abstract

The application relates to a high-resolution processing method of borehole radar data based on mixed time-frequency analysis, which comprises the following steps: constructing discrete frequencies based on the scale resolution of borehole radar data; performing Hilbert transform on the borehole radar data to generate an analytical signal; filtering the signal, reconstructing the signal, and then obtaining weighted energy of different frequency bands, and finally outputting data processed by high-resolution processing. The application realizes dynamic weighting through low-frequency and high-frequency weighting factors, flexibly enhances or suppresses low-frequency / high-frequency components; through envelope normalization, pulse noise is suppressed, and the signal-to-noise ratio is improved; through conjugate symmetric padding, the original phase information is preserved when filtering in the frequency domain, signal distortion is avoided, and the time positioning accuracy can be maintained in time-frequency analysis; through different frequency band energy weighting processing, high and low frequency joint analysis is realized, the high and low frequencies are simultaneously widened, and high-resolution amplitude-preserving processing is realized.

Description

Technical Field

[0001] This application relates to the field of geophysical exploration, and more specifically, to a high-resolution processing method for borehole radar data using hybrid time-frequency analysis. Background Technology

[0002] Raw borehole radar data contains both useful information and various types of noise. When using borehole radar to detect coal-rock interfaces along coal seams, the useful information about the coal-rock interface can sometimes be masked by noise. The purpose of data processing is to suppress noise, enhance the useful signal, and improve the signal-to-noise ratio, allowing interpreters to better interpret the borehole radar data geologically. Furthermore, the superposition of reflected waves from various anomalies around the borehole makes it difficult to accurately extract the precise locations of these anomalies. Therefore, high-resolution processing of borehole radar data is necessary to suppress noise, improve data resolution, and accurately reflect the reflected information of various anomalies. Thus, high-resolution processing of borehole radar data is of great significance.

[0003] Existing high-resolution processing techniques generally involve widening the high frequency range, enhancing the high-frequency information in the frequency domain, and then inversely transforming it to the time domain to achieve high-resolution processing. However, they cannot simultaneously widen both high and low frequencies, resulting in the inability to preserve amplitude during high-resolution processing. Consequently, the amplitude of the processed data is significantly lower than that of the original data. Summary of the Invention

[0004] To overcome at least one deficiency in the prior art, this application provides a high-resolution processing method for borehole radar data using hybrid time-frequency analysis.

[0005] Firstly, a high-resolution processing method for borehole radar based on hybrid time-frequency analysis is provided, including:

[0006] Discrete frequencies are constructed based on the scale resolution of borehole radar data;

[0007] Perform Hilbert transform on the borehole radar data to generate an analytical signal;

[0008] A window function is constructed based on discrete frequencies; a Fourier transform is performed on the analytic signal to obtain the frequency domain signal; the window function and the frequency domain signal are convolved to obtain the convolved signal; the convolved signal is then filled with conjugate symmetry to obtain the filled frequency domain signal.

[0009] Perform an inverse Fourier transform on the filled frequency domain signal to obtain the inverse Fourier transform signal, extract the real part of the inverse Fourier transform signal as the real part signal, and perform a Hilbert transform on the real part signal to obtain the imaginary part signal.

[0010] The signal envelope is calculated based on the real and imaginary parts of the signal; the signal envelope is then smoothed to obtain a smoothed signal envelope; the real and imaginary parts of the signal are normalized based on the smoothed signal envelope to obtain normalized real and imaginary parts; the energy of different frequency bands is weighted based on the normalized real and imaginary parts to obtain weighted energy of different frequency bands.

[0011] The weighted energy of different frequency bands is normalized to obtain normalized energy; the normalized energy is then phase-cosine adjusted to obtain phase-cosine adjusted data; the unit value of the phase-cosine adjusted data is calculated and multiplied by the total energy of the borehole radar data to obtain the high-resolution output data.

[0012] In one embodiment, discrete frequencies are constructed based on the scale resolution of borehole radar data using the following formula:

[0013]

[0014] Where f(j) is the discrete frequency, j is the number of discrete frequency points, iv is the scale resolution, and f0 is the fundamental frequency.

[0015] In one embodiment, the window function is constructed based on discrete frequencies using the following formula:

[0016]

[0017] Where W(u,ff) is the window function, u is the logarithmic scaling parameter, ff is the current discrete frequency, and f0 is the fundamental frequency.

[0018] In one embodiment, the real and imaginary parts of the signal are normalized based on the smoothed signal envelope to obtain the normalized real and imaginary parts, using the following formula:

[0019]

[0020] Among them, rs norm (ii) is the normalized real part of the signal, ii is the sampling point, rs(ii) is the real part of the signal, a mdu (ii) is the smoothed signal envelope, δ is the small regularization term, and is(ii) is the imaginary part of the signal. norm (ii) is the normalized imaginary part of the signal.

[0021] In one embodiment, the energy of different frequency bands is weighted based on the normalized real part signal and the normalized imaginary part signal to obtain the weighted energy of different frequency bands, including:

[0022]

[0023] Where s2 is the real part of the low-frequency weighted energy, s3 is the real part of the high-frequency weighted energy, s7 is the imaginary part of the low-frequency weighted energy, s8 is the imaginary part of the high-frequency weighted energy, ii is the sampling point, and n sum rs represents the number of sampling points. norm (ii) is the normalized real part of the signal, Δu is the scale step size, Δu = 1 / iv, iv is the scale resolution, and a kg tq1 is the wavelet normalization constant, tq2 is the low-frequency weighting factor, and tq3 is the high-frequency weighting factor. norm (ii) is the normalized imaginary part of the signal.

[0024] In one embodiment, the weighted energy of different frequency bands is normalized to obtain the normalized energy, using the following formula:

[0025]

[0026] Where s4 is the normalized real part of energy, s5 is the normalized imaginary part of energy, s2 is the real part of low-frequency weighted energy, s3 is the real part of high-frequency weighted energy, s7 is the imaginary part of low-frequency weighted energy, s8 is the imaginary part of high-frequency weighted energy, and total_energy is the total energy of the borehole radar data.

[0027] In one embodiment, the normalized energy is phase-cosine adjusted to obtain the phase-cosine adjusted data, using the following formula:

[0028] s6=cos(∠(s4+is5))·s4

[0029] Where s6 is the data after phase cosine adjustment, s4 is the normalized real part of energy, s5 is the normalized imaginary part of energy, and ∠ represents the phase calculation.

[0030] Secondly, a high-resolution processing device for borehole radar data with hybrid time-frequency analysis is provided, comprising:

[0031] The discrete frequency construction module is used to construct discrete frequencies based on the scale resolution of borehole radar data.

[0032] The Hilbert transform module is used to perform Hilbert transform on borehole radar data to generate analytical signals;

[0033] The filtering module is used to construct a window function based on discrete frequencies; perform Fourier transform on the analytic signal to obtain the frequency domain signal; perform convolution calculation on the window function and the frequency domain signal to obtain the convolution signal; and perform conjugate symmetric filling on the convolution signal to obtain the filled frequency domain signal.

[0034] The signal reconstruction module is used to perform an inverse Fourier transform on the filled frequency domain signal to obtain the inverse Fourier transform signal, and extract the real part of the inverse Fourier transform signal as the real part signal. The real part signal is then subjected to a Hilbert transform to obtain the imaginary part signal.

[0035] The energy weighting module is used to calculate the signal envelope based on the real and imaginary parts of the signal, and to smooth the signal envelope to obtain a smoothed signal envelope. Based on the smoothed signal envelope, the real and imaginary parts of the signal are normalized to obtain normalized real and imaginary parts. Based on the normalized real and imaginary parts, the energy of different frequency bands is weighted to obtain the weighted energy of different frequency bands.

[0036] The calculation output module is used to normalize the weighted energy of different frequency bands to obtain the normalized energy; to perform phase cosine adjustment on the normalized energy to obtain the phase cosine adjusted data; to calculate the unit value of the phase cosine adjusted data and multiply it by the total energy of the borehole radar data as the high-resolution output data.

[0037] Thirdly, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the aforementioned high-resolution processing method for borehole radar data based on hybrid time-frequency analysis.

[0038] Fourthly, a computer program product is provided, including a computer program / instruction, which, when executed by a processor, implements the above-described high-resolution processing method for borehole radar data based on hybrid time-frequency analysis.

[0039] Compared with the prior art, this application has the following advantages: This application achieves dynamic weighting through low-frequency and high-frequency weighting factors, flexibly enhancing or suppressing low-frequency / high-frequency components; it suppresses impulse noise and improves the signal-to-noise ratio through envelope normalization; it preserves the original phase information during frequency domain filtering through conjugate symmetry filling, avoiding signal distortion and maintaining time positioning accuracy in time-frequency analysis; and it achieves simultaneous broadening of high and low frequencies and high-resolution amplitude preservation processing through energy weighting processing of different frequency bands and joint analysis of high and low frequencies. Attached Figure Description

[0040] This application can be better understood by referring to the description given below in conjunction with the accompanying drawings, which, together with the detailed description below, are incorporated in and form part of this specification. In the drawings:

[0041] Figure 1 A flowchart of a high-resolution processing method for borehole radar based on hybrid time-frequency analysis is shown.

[0042] Figure 2 This shows a profile of the raw data from the borehole radar detection.

[0043] Figure 3 This shows a data profile after processing by the method of this application;

[0044] Figure 4 The image shows a comparison of the spectrum before and after processing of the 32m deep data channel;

[0045] Figure 5 The image shows a comparison of the spectrum before and after processing of the 52m deep data channel. Detailed Implementation

[0046] Exemplary embodiments of the present application will be described below with reference to the accompanying drawings. For clarity and brevity, not all features of the actual embodiments are described in the specification. However, it should be understood that many embodiment-specific decisions can be made in the development of any such actual embodiment to achieve the developer’s specific objectives, and these decisions may vary as the embodiments differ.

[0047] It should also be noted that, in order to avoid obscuring this application with unnecessary details, only the device structure closely related to the solution according to this application is shown in the accompanying drawings, while other details that are not closely related to this application are omitted.

[0048] It should be understood that this application is not limited to the described embodiments by virtue of the following description with reference to the accompanying drawings. In this document, embodiments may be combined with each other, features may be substituted or borrowed between different embodiments, and one or more features may be omitted in one embodiment, where feasible.

[0049] This application provides a high-resolution processing method for borehole radar using hybrid time-frequency analysis. Figure 1 A flowchart of a high-resolution processing method for borehole radar using hybrid time-frequency analysis is shown. See [link / reference]. Figure 1 The method mainly includes the following steps:

[0050] Step S1: Based on the scale resolution of the borehole radar data, construct discrete frequencies.

[0051] Specifically, the following formula is used:

[0052]

[0053] Where f(j) is the discrete frequency, j is the number of discrete frequency points, iv is the scale resolution, and f0 is the fundamental frequency.

[0054] Step S2: Perform Hilbert transform on the borehole radar data to generate an analytical signal.

[0055] s analytic (t)=s1(t)+i·H(s1(t))

[0056] Among them, s analytic s1(t) is the analytic signal, s1(t) is the borehole radar data, H represents the Hilbert transform, and i is the imaginary unit.

[0057] Find the amplitude f3 and phase ph1 of the analytic signal:

[0058]

[0059] Step S3: Filtering.

[0060] (1) Construct a window function based on discrete frequencies, using the following formula:

[0061]

[0062] Where W(u,ff) is the window function, u is the logarithmic scaling parameter, u = j / iv, ff is the current discrete frequency, and f0 is the fundamental frequency.

[0063] (2) Perform Fourier transform on the analytic signal to obtain the frequency domain signal F3;

[0064] (3) Perform convolution calculation on the window function and the frequency domain signal to obtain the convolution signal F4;

[0065] (4) Perform conjugate symmetric filling on the convolutional signal F4 to obtain the filled frequency domain signal; using conjugate symmetric filling can maintain the frequency symmetry and ensure that the inverse transform is a real signal. The formula is as follows:

[0066] F4(n sum -ii+2)=F4(ii),ii=2,…,n sum / 2

[0067] Where ii is the sampling point, n sum This represents the number of sampling points.

[0068] Step S4: Reconstruct the signal.

[0069] Perform an inverse Fourier transform on the filled frequency domain signal to obtain the inverse Fourier transform signal, extract the real part of the inverse Fourier transform signal as the real part signal, and perform a Hilbert transform on the real part signal to obtain the imaginary part signal.

[0070] Step S5: Calculate the signal envelope based on the real and imaginary parts of the signal, and smooth the signal envelope to obtain a smoothed signal envelope; normalize the real and imaginary parts of the signal based on the smoothed signal envelope to obtain normalized real and imaginary parts of the signal; weight the energy of different frequency bands according to the normalized real and imaginary parts of the signal to obtain the weighted energy of different frequency bands.

[0071] Specifically, the signal envelope is calculated using the following formula:

[0072]

[0073] Where mdu(ii) is the signal envelope, rs(ii) is the real part of the signal, and is(ii) is the imaginary part of the signal.

[0074] The signal envelope mdu(ii) is smoothed using a five-point cubic smoothing method to obtain the smoothed signal envelope a. mdu (ii).

[0075] The normalized real part and the normalized imaginary part of the signal are obtained using the following formulas:

[0076]

[0077] Among them, rs norm (ii) is the normalized real part of the signal, ii is the sampling point, rs(ii) is the real part of the signal, a mdu (ii) is the smoothed signal envelope, δ is the small regularization term, and is(ii) is the imaginary part of the signal. norm (ii) is the normalized imaginary part of the signal.

[0078] Weighted energy across different frequency bands, including:

[0079]

[0080] Where s2 is the real part of the low-frequency weighted energy, s3 is the real part of the high-frequency weighted energy, s7 is the imaginary part of the low-frequency weighted energy, s8 is the imaginary part of the high-frequency weighted energy, ii is the sampling point, and n sum rs represents the number of sampling points. norm (ii) is the normalized real part of the signal, Δu is the scale step size, Δu = 1 / iv, a kg tq1 is the wavelet normalization constant, tq2 is the low-frequency weighting factor, and tq3 is the high-frequency weighting factor. The weighting factors are given numerical values ​​based on the actual signal characteristics. norm (ii) is the normalized imaginary part of the signal.

[0081] Step S6: Normalize the weighted energy of different frequency bands to obtain normalized energy; perform phase cosine adjustment on the normalized energy to obtain phase cosine adjusted data; calculate the unit value of the phase cosine adjusted data and multiply it by the total energy of the borehole radar data to obtain the high-resolution output data.

[0082] Specifically, the normalized energy is expressed using the following formula:

[0083]

[0084] Where s4 is the normalized real part of energy, s5 is the normalized imaginary part of energy, s2 is the real part of low-frequency weighted energy, s3 is the real part of high-frequency weighted energy, s7 is the imaginary part of low-frequency weighted energy, s8 is the imaginary part of high-frequency weighted energy, total_energy is the total energy of the borehole radar data, and ||| represents the norm.

[0085] The data after phase cosine adjustment is obtained using the following formula:

[0086] s6=cos(∠(s4+is5))·s4

[0087] Where s6 is the data after phase cosine adjustment, s4 is the normalized real part of energy, s5 is the normalized imaginary part of energy, and ∠ represents the phase calculation. ∠(s4+is5)=atan(s5,s4), where atan represents the arctangent function.

[0088] The high-resolution processed output data s_out is expressed using the following formula:

[0089]

[0090] Where total_energy represents the total energy of the borehole radar data, and ||| represents the norm.

[0091] In summary, this application achieves dynamic weighting through low-frequency and high-frequency weighting factors tq1 and tq2, flexibly enhancing or suppressing low-frequency / high-frequency components; it suppresses impulse noise and improves the signal-to-noise ratio through envelope normalization; it preserves the original phase information during frequency domain filtering through conjugate symmetry filling, avoiding signal distortion and maintaining time positioning accuracy in time-frequency analysis; and it achieves simultaneous broadening of high and low frequencies and high-resolution amplitude preservation processing through energy weighting processing of different frequency bands and joint analysis of high and low frequencies.

[0092] To further analyze the effectiveness of the method in this application, the following specific embodiments are provided.

[0093] The coal seam in a certain coal mine is approximately 3 meters thick, with mudstone on the roof and siltstone on the floor. Drilling was conducted along the coal seam to detect the coal-rock interface. Water drilling was used during the drilling process with a conventional drilling rig. One borehole reached a depth of 81 meters, with the coal seam at depths of 0-48 meters and mudstone at depths of 48-81 meters. The borehole trajectory was upward, and there was no water inside. A 100MHz borehole radar was used for detection. Figure 2 The diagram shows a profile of raw borehole radar detection data, using the method described in this application (MTF), with a window length of 50MHz-400MHz. Figure 3 The data profile after processing using the method described in this application is shown. The spectra at depths of 32m and 52m before and after processing, representing two locations with different lithological changes, are compared. Figure 4 The image shows a comparison of the spectrum before and after processing the 32m deep data channel. Figure 5 The image shows a comparison of the spectrum before and after processing of the 52m deep data channel.

[0094] according to Figure 2 and Figure 3 Clearly visible in-phase axis features penetrating the strata can be observed, combined with Figure 4 and Figure 5 It can be seen that the method of this application can broaden the effective frequency band, filter the low frequency to a certain extent, retain some low frequency information, and does not generate high frequency noise, thus maintaining the relative relationship of energy strength of the in-phase axis very well.

[0095] Based on the same inventive concept as the high-resolution processing method for borehole radar data using hybrid time-frequency analysis, this embodiment also provides a corresponding high-resolution processing apparatus for borehole radar data using hybrid time-frequency analysis, including:

[0096] The discrete frequency construction module is used to construct discrete frequencies based on the scale resolution of borehole radar data.

[0097] The Hilbert transform module is used to perform Hilbert transform on borehole radar data to generate analytical signals;

[0098] The filtering module is used to construct a window function based on discrete frequencies; perform Fourier transform on the analytic signal to obtain the frequency domain signal; perform convolution calculation on the window function and the frequency domain signal to obtain the convolution signal; and perform conjugate symmetric filling on the convolution signal to obtain the filled frequency domain signal.

[0099] The signal reconstruction module is used to perform an inverse Fourier transform on the filled frequency domain signal to obtain the inverse Fourier transform signal, and extract the real part of the inverse Fourier transform signal as the real part signal. The real part signal is then subjected to a Hilbert transform to obtain the imaginary part signal.

[0100] The energy weighting module is used to calculate the signal envelope based on the real and imaginary parts of the signal, and to smooth the signal envelope to obtain a smoothed signal envelope. Based on the smoothed signal envelope, the real and imaginary parts of the signal are normalized to obtain normalized real and imaginary parts. Based on the normalized real and imaginary parts, the energy of different frequency bands is weighted to obtain the weighted energy of different frequency bands.

[0101] The calculation output module is used to normalize the weighted energy of different frequency bands to obtain the normalized energy; to perform phase cosine adjustment on the normalized energy to obtain the phase cosine adjusted data; to calculate the unit value of the phase cosine adjusted data and multiply it by the total energy of the borehole radar data as the high-resolution output data.

[0102] The high-resolution processing device for borehole radar data with hybrid time-frequency analysis in this embodiment has the same inventive concept as the high-resolution processing method for borehole radar data with hybrid time-frequency analysis described above. Therefore, the specific implementation of this device can be found in the embodiment section of the high-resolution processing method for borehole radar data with hybrid time-frequency analysis described above, and its technical effects correspond to the technical effects of the above method, so it will not be repeated here.

[0103] This application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the above-described high-resolution processing method for borehole radar data using hybrid time-frequency analysis.

[0104] This application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the above-described high-resolution processing method for borehole radar data using hybrid time-frequency analysis.

[0105] The above descriptions are merely various embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A high-resolution processing method for borehole radar using hybrid time-frequency analysis, characterized in that, include: Discrete frequencies are constructed based on the scale resolution of borehole radar data; Perform Hilbert transform on the borehole radar data to generate an analytical signal; A window function is constructed based on the discrete frequencies; Perform a Fourier transform on the analytic signal to obtain a frequency domain signal; The convolution of the window function and the frequency domain signal is performed to obtain the convolution signal; The conjugate symmetric filler is applied to the conjugate signal to obtain the filled frequency domain signal. Perform an inverse Fourier transform on the filled frequency domain signal to obtain the inverse Fourier transform signal, extract the real part of the inverse Fourier transform signal as the real part signal, and perform a Hilbert transform on the real part signal to obtain the imaginary part signal. Based on the real part signal and the imaginary part signal, the signal envelope is calculated; and the signal envelope is smoothed to obtain a smoothed signal envelope. Based on the smoothed signal envelope, the real part signal and the imaginary part signal are normalized respectively to obtain the normalized real part signal and the normalized imaginary part signal; the energy of different frequency bands is weighted according to the normalized real part signal and the normalized imaginary part signal to obtain the weighted energy of different frequency bands. The weighted energy of the different frequency bands is normalized to obtain normalized energy; the normalized energy is then phase-cosine adjusted to obtain phase-cosine adjusted data. The unit value is obtained from the phase cosine adjusted data and multiplied by the total energy of the borehole radar data to obtain the high-resolution output data.

2. The method as described in claim 1, characterized in that, in, Based on the scale resolution of borehole radar data, discrete frequencies are constructed using the following formula: Where f(j) is the discrete frequency, j is the number of discrete frequency points, iv is the scale resolution, and f0 is the fundamental frequency.

3. The method as described in claim 1, characterized in that, in, The window function is constructed based on the discrete frequency using the following formula: Where W(u,ff) is the window function, u is the logarithmic scaling parameter, ff is the current discrete frequency, and f0 is the fundamental frequency.

4. The method as described in claim 1, characterized in that, in, Based on the smoothed signal envelope, the real part signal and the imaginary part signal are normalized respectively to obtain the normalized real part signal and the normalized imaginary part signal, using the following formula: Among them, rs norm (ii) is the normalized real part of the signal, ii is the sampling point, rs(ii) is the real part of the signal, a mdu (ii) is the smoothed signal envelope, δ is the small regularization term, and is(ii) is the imaginary part of the signal. norm (ii) is the normalized imaginary part of the signal.

5. The method as described in claim 1, characterized in that, in, The energy of different frequency bands is weighted based on the normalized real part signal and the normalized imaginary part signal to obtain the weighted energy of different frequency bands, including: Where s2 is the real part of the low-frequency weighted energy, s3 is the real part of the high-frequency weighted energy, s7 is the imaginary part of the low-frequency weighted energy, s8 is the imaginary part of the high-frequency weighted energy, ii is the sampling point, and n sum rs represents the number of sampling points. norm (ii) is the normalized real part of the signal, Δu is the scale step size, Δu = 1 / iv, iv is the scale resolution, and a kg tq1 is the wavelet normalization constant, tq2 is the low-frequency weighting factor, and tq3 is the high-frequency weighting factor. norm (ii) is the normalized imaginary part of the signal.

6. The method as described in claim 1, characterized in that, in, The weighted energies of the different frequency bands are normalized to obtain the normalized energy using the following formula: Where s4 is the normalized real part of energy, s5 is the normalized imaginary part of energy, s2 is the real part of low-frequency weighted energy, s3 is the real part of high-frequency weighted energy, s7 is the imaginary part of low-frequency weighted energy, s8 is the imaginary part of high-frequency weighted energy, and total_energy is the total energy of the borehole radar data.

7. The method as described in claim 1, characterized in that, in, The normalized energy is then subjected to phase cosine adjustment to obtain the phase cosine adjusted data, using the following formula: s6=cos(∠(s4+is5))·s4 Where s6 is the data after phase cosine adjustment, s4 is the normalized real part of energy, s5 is the normalized imaginary part of energy, and ∠ represents the phase calculation.

8. A high-resolution processing device for borehole radar data using hybrid time-frequency analysis, characterized in that, include: The discrete frequency construction module is used to construct discrete frequencies based on the scale resolution of borehole radar data. The Hilbert transform module is used to perform Hilbert transform on borehole radar data to generate analytical signals; The filtering module is used to construct a window function based on the discrete frequency; perform a Fourier transform on the analytic signal to obtain a frequency domain signal; and perform a convolution calculation on the window function and the frequency domain signal to obtain a convolution signal. The conjugate symmetric filler is applied to the conjugate signal to obtain the filled frequency domain signal. The signal reconstruction module is used to perform an inverse Fourier transform on the filled frequency domain signal to obtain the inverse Fourier transform signal, extract the real part of the inverse Fourier transform signal as the real part signal, and perform a Hilbert transform on the real part signal to obtain the imaginary part signal. An energy weighting module is used to calculate the signal envelope based on the real part signal and the imaginary part signal, and to smooth the signal envelope to obtain a smoothed signal envelope. Based on the smoothed signal envelope, the real part signal and the imaginary part signal are normalized respectively to obtain the normalized real part signal and the normalized imaginary part signal. The energy of different frequency bands is weighted based on the normalized real part signal and the normalized imaginary part signal to obtain the weighted energy of different frequency bands; The calculation output module is used to normalize the weighted energy of the different frequency bands to obtain the normalized energy; The normalized energy is then subjected to phase cosine adjustment to obtain the phase cosine adjusted data. The unit value is obtained from the phase cosine adjusted data and multiplied by the total energy of the borehole radar data to obtain the high-resolution output data.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the high-resolution processing method for borehole radar data with hybrid time-frequency analysis as described in any one of claims 1-7.

10. A computer program product, characterized in that, Includes a computer program / instruction, which, when executed by a processor, implements the high-resolution processing method for borehole radar data with hybrid time-frequency analysis as described in any one of claims 1-7.

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