Method and system for simulating seismic source during excavation based on pseudo-random signal
By using pseudo-random signal processing methods, a simulated waveform signal of the seismic source during excavation is constructed, which solves the problem of insufficient accuracy and flexibility of simulation data in existing technologies and realizes higher accuracy and flexibility in the simulation of seismic sources during excavation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-05
- Publication Date
- 2026-04-10
AI Technical Summary
Existing technologies struggle to accurately describe and load the complex characteristics of earthquake sources during excavation, resulting in significant discrepancies between simulated and real data, and a lack of flexibility and universality.
A pseudo-random signal-based method is adopted to construct the simulated waveform signal of the excavated seismic source by acquiring white noise sequence, bandpass filtering, linear superposition, envelope processing, wavelet convolution and normalization.
It improves the accuracy and flexibility of simulated seismic source waveform signals during excavation, adapting to the technical research needs of different scenarios.
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Figure CN121831870A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of seismic exploration technology, and in particular to a method and system for simulating seismic sources during excavation based on pseudo-random signals. Background Technology
[0002] Seismic exploration during tunneling, as an emerging method for mine geophysical exploration, enables the acquisition of seismic wave data simultaneously with tunnel boring machine (TBM) construction, allowing for advanced detection of geological structures ahead and in the surrounding area. This is of great significance for ensuring safe and efficient coal mine production. However, the seismic source of seismic exploration during tunneling is the mechanical vibration generated by the TBM cutting coal and rock mass. Its excitation method, wavelet morphology, and signal characteristics differ significantly from traditional controlled seismic sources and explosive sources. Seismic sources during tunneling exhibit complex characteristics such as multiple sources (multiple points including cutting teeth and mechanical vibrations), randomness (uncertain excitation time and intensity), continuity (continuous excitation), frequency variation (frequency components change with the cutting state), and non-stationarity (energy is not constant over time). These characteristics make it difficult to accurately describe and effectively load the seismic source signal during tunneling in numerical simulations.
[0003] Currently, in numerical simulation research of seismic signals during tunneling, source loading mainly relies on two methods: using simplified theoretical source functions, such as a single Ricker wavelet or sine wave. While this method is computationally simple, it completely fails to reflect the rich frequency components and random, non-stationary characteristics of the tunneling signal, leading to significant differences between simulated and real data and reducing the guiding value of numerical simulation. Directly loading measured source signals: This method, to some extent, restores the randomness and continuity of the signal. However, it heavily relies on measured data under specific working conditions, lacking universality and flexibility. When tunneling conditions, machinery models, or geological environments change, the original measured data becomes inapplicable, failing to provide a universal source model for technical research in different scenarios. Therefore, there is an urgent need for a source simulation scheme that can fully simulate the complex characteristics of tunneling sources while possessing flexibility, repeatability, and universality to fill the gaps in existing technology and promote the further development of tunneling seismic technology. Summary of the Invention
[0004] This application provides a method and system for simulating seismic sources during excavation based on pseudo-random signals, in order to at least solve the technical problem that existing simulation data has low accuracy and flexibility.
[0005] The first aspect of this application proposes a method for simulating seismic sources during excavation based on pseudo-random signals, the method comprising: The sampling duration and sampling frequency of the seismic source during excavation are obtained, and multiple white noise sequences are generated based on the sampling duration and sampling frequency. Based on the preset frequency band distribution and probability density of the seismic signal during excavation, the multiple white noise sequences are bandpass filtered to obtain signal sequences for each frequency band; The signal sequences of each frequency band are linearly superimposed to obtain a pseudo-random mixed signal, and the pseudo-random mixed signal is enveloped to obtain a signal with non-stationary characteristics in the time domain. The signal, which exhibits non-stationary characteristics in the time domain, is subjected to wavelet convolution processing to obtain a smooth wavelet signal; The smoothed wavelet signal is normalized to obtain the simulated waveform signal of the seismic source during excavation.
[0006] Preferably, the white noise sequence is a zero-mean Gaussian white noise sequence; The length of the white noise sequence is N = T × fs, where N is the length, T is the sampling duration, and fs is the sampling frequency. The frequency band distribution includes: 1-50Hz, 50-200Hz, and full-band noise.
[0007] Furthermore, the envelope processing of the pseudo-random mixed signal to obtain a signal exhibiting non-stationary characteristics in the time domain includes: The pseudo-random mixed signal is modulated using a Gaussian window function to obtain a signal that exhibits non-stationary characteristics in the time domain.
[0008] Furthermore, the formula for calculating the Gaussian window function is as follows:
[0009] In the formula, It is a Gaussian window function. , To control window width, .
[0010] Furthermore, the step of performing wavelet convolution processing on the signal that exhibits non-stationary characteristics in the time domain to obtain a smoothed wavelet signal includes: The signal, which exhibits non-stationary characteristics in the time domain, is convolved with the Ricker wavelet to obtain a smooth wavelet signal.
[0011] Furthermore, the normalization process of the smoothed wavelet signal to obtain the simulated waveform signal of the excavated seismic source includes: Using formula The smoothed wavelet signal is normalized to obtain the simulated waveform signal of the seismic source during excavation. in, This is the simulated waveform signal of the earthquake source at time t. Let be the smoothed wavelet signal at time t.
[0012] A second aspect of this application proposes a tunnel-based seismic source simulation system based on pseudo-random signals, comprising: The acquisition module is used to acquire the sampling duration and sampling frequency of the seismic source during excavation, and to generate multiple white noise sequences based on the sampling duration and sampling frequency; The filtering module is used to perform bandpass filtering on the multiple white noise sequences based on the preset frequency band distribution and probability density of the seismic signal during excavation to obtain signal sequences for each frequency band. The envelope processing module is used to linearly superimpose signal sequences from each frequency band to obtain a pseudo-random mixed signal, and to perform envelope processing on the pseudo-random mixed signal to obtain a signal with non-stationary characteristics in the time domain. The convolution processing module is used to perform wavelet convolution processing on the signal that exhibits non-stationary characteristics in the time domain to obtain a smooth wavelet signal. The normalization module is used to normalize the smoothed wavelet signal to obtain the simulated waveform signal of the seismic source during excavation.
[0013] Preferably, the white noise sequence is a zero-mean Gaussian white noise sequence; The length of the white noise sequence is N = T × fs, where N is the length, T is the sampling duration, and fs is the sampling frequency. The frequency band distribution includes: 1-50Hz, 50-200Hz, and full-band noise.
[0014] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the method described in the first aspect embodiment.
[0015] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method described in the first aspect.
[0016] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects: This application proposes a method and system for simulating seismic sources during excavation based on pseudo-random signals. The method includes: acquiring the sampling duration and sampling frequency of the seismic source during excavation, and generating multiple white noise sequences based on the sampling duration and sampling frequency; performing bandpass filtering on the multiple white noise sequences based on a preset frequency band distribution and probability density of the seismic signal during excavation to obtain signal sequences for each frequency band; linearly superimposing the signal sequences of each frequency band to obtain a pseudo-random mixed signal, and performing envelope processing on the pseudo-random mixed signal to obtain a signal with non-stationary characteristics in the time domain; performing wavelet convolution processing on the signal with non-stationary characteristics in the time domain to obtain a smoothed wavelet signal; and normalizing the smoothed wavelet signal to obtain the simulated waveform signal of the seismic source during excavation. The technical solution proposed in this application improves the determination accuracy of the simulated waveform signal of the seismic source during excavation.
[0017] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description
[0018] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a method for simulating seismic sources in tunneling based on pseudo-random signals, according to an embodiment of this application. Figure 2 This is a structural diagram of a tunnel-based seismic source simulation system based on pseudo-random signals, according to an embodiment of this application. Detailed Implementation
[0019] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.
[0020] This application proposes a method and system for simulating seismic sources during excavation based on pseudo-random signals. The method includes: acquiring the sampling duration and sampling frequency of the seismic source during excavation, and generating multiple white noise sequences based on the sampling duration and sampling frequency; performing bandpass filtering on the multiple white noise sequences based on a preset frequency band distribution and probability density of the seismic signal during excavation to obtain signal sequences for each frequency band; linearly superimposing the signal sequences of each frequency band to obtain a pseudo-random mixed signal, and performing envelope processing on the pseudo-random mixed signal to obtain a signal with non-stationary characteristics in the time domain; performing wavelet convolution processing on the signal with non-stationary characteristics in the time domain to obtain a smoothed wavelet signal; and normalizing the smoothed wavelet signal to obtain the simulated waveform signal of the seismic source during excavation. The technical solution proposed in this application improves the determination accuracy of the simulated waveform signal of the seismic source during excavation.
[0021] The following description, with reference to the accompanying drawings, illustrates an embodiment of this application of a method and system for simulating seismic sources based on pseudo-random signals during excavation.
[0022] Example 1 Figure 1 This is a flowchart of a method for simulating seismic sources during excavation based on pseudo-random signals, according to an embodiment of this application. Figure 1 As shown, the method includes: Step 1: Obtain the sampling duration, sampling frequency, and number of seismic sources during excavation, and generate multiple white noise sequences based on the sampling duration and sampling frequency; It should be noted that the white noise sequence is a zero-mean Gaussian white noise sequence; The length of the white noise sequence is N = T × fs, where N is the length, T is the sampling duration, and fs is the sampling frequency.
[0023] Specifically, determine the sampling duration T and the sampling frequency fs. The sampling frequency is related to the maximum effective frequency; to satisfy the sampling theorem, the sampling frequency should be at least twice the maximum effective frequency. Generate n zero-mean Gaussian white noise sequences with a sequence length N = T × fs as the initial excitation.
[0024] Step 2: Based on the preset frequency band distribution and probability density of the seismic signal during excavation, bandpass filtering is performed on the multiple white noise sequences to obtain signal sequences for each frequency band; It should be noted that the frequency band distribution includes: 1-50Hz, 50-200Hz, and full-band noise.
[0025] Based on the frequency band distribution characteristics of the tunneling signal, bandpass filters of different frequency bands were designed. White noise sequences were filtered according to their frequency probability densities to obtain sequences with different frequency components. Through literature review and actual data analysis, the frequency probability density can be given empirically, as shown in Table 1, which illustrates the frequency band distribution range and proportion of the tunneling signal.
[0026] Table 1
[0027] Step 3: Linearly superimpose the signal sequences of each frequency band to obtain a pseudo-random mixed signal, and perform envelope processing on the pseudo-random mixed signal to obtain a signal with non-stationary characteristics in the time domain; In this embodiment of the disclosure, step 3 specifically includes: The pseudo-random mixed signal is modulated using a Gaussian window function to obtain a signal that exhibits non-stationary characteristics in the time domain.
[0028] Furthermore, the formula for calculating the Gaussian window function is as follows:
[0029] In the formula, It is a Gaussian window function. , To control window width, .
[0030] It should be noted that, in order to simulate the non-stationarity of the tunneling signal, a Gaussian window function is used to modulate the signal, making it exhibit gradually changing characteristics. The modulation formula is as follows:
[0031] In the formula, The signal at time t exhibits non-stationary characteristics in the time domain, i.e., the signal after envelope processing. Let be the pseudo-random mixed signal at time t.
[0032] G(t) represents the Gaussian window function, and for discrete sequences: , α is the control window width, which is usually taken as 0.3-0.5.
[0033] Step 4: Perform wavelet convolution processing on the signal that exhibits non-stationary characteristics in the time domain to obtain a smoothed wavelet signal; In this embodiment of the disclosure, step 4 specifically includes: The signal, which exhibits non-stationary characteristics in the time domain, is convolved with the Ricker wavelet to obtain a smooth wavelet signal.
[0034] It should be noted that convolving with the Lake wavelet gives the signal a smoother wavelet waveform. In the formula, w(t) is the signal with a smooth wavelet waveform after convolution, i.e., the smooth wavelet signal, s(t) is the signal after envelope processing, and R(t) is the Ricker wavelet.
[0035] Step 5: Normalize the smoothed wavelet signal to obtain the simulated waveform signal of the seismic source during excavation.
[0036] In this embodiment of the disclosure, step 5 specifically includes: Using formula The smoothed wavelet signal is normalized to obtain the simulated waveform signal of the seismic source during excavation. in, This is the simulated waveform signal of the earthquake source at time t. Let be the smoothed wavelet signal at time t.
[0037] This embodiment proposes a method for simulating seismic sources during tunneling based on pseudo-random signals. It utilizes the superposition of multi-frequency band pseudo-random sequences to simulate the multi-source, random, continuous, and frequency-variable characteristics of the signals during tunneling, constructing the source time function of the seismic source for numerical simulation of earthquakes during tunneling. This provides a foundation for the design of methods for acquiring, processing, and interpreting earthquake data during tunneling.
[0038] In summary, the proposed method for simulating seismic sources during excavation based on pseudo-random signals in this embodiment improves the accuracy and flexibility of determining the simulated waveform signals of seismic sources during excavation.
[0039] Example 2 Figure 2 This is a structural diagram of a tunnel-based seismic source simulation system based on pseudo-random signals, according to an embodiment of this application. Figure 2 As shown, the system includes: The acquisition module 100 is used to acquire the sampling duration and sampling frequency of the seismic source during excavation, and to generate multiple white noise sequences based on the sampling duration and sampling frequency. It should be noted that the white noise sequence is a zero-mean Gaussian white noise sequence; The length of the white noise sequence is N = T × fs, where N is the length, T is the sampling duration, and fs is the sampling frequency.
[0040] The filtering module 200 is used to perform bandpass filtering on the multiple white noise sequences based on the preset frequency band distribution and probability density of the seismic signal during excavation to obtain signal sequences for each frequency band. It should be noted that the frequency band distribution includes: 1-50Hz, 50-200Hz, and full-band noise.
[0041] Envelope processing module 300 is used to linearly superimpose signal sequences from each frequency band to obtain a pseudo-random mixed signal, and to perform envelope processing on the pseudo-random mixed signal to obtain a signal with non-stationary characteristics in the time domain. The convolution processing module 400 is used to perform wavelet convolution processing on the signal that exhibits non-stationary characteristics in the time domain to obtain a smooth wavelet signal. The normalization module 500 is used to normalize the smoothed wavelet signal to obtain the simulated waveform signal of the seismic source during excavation.
[0042] In this embodiment of the disclosure, the envelope processing module 300 is further configured to: The pseudo-random mixed signal is modulated using a Gaussian window function to obtain a signal that exhibits non-stationary characteristics in the time domain.
[0043] It should be noted that the formula for calculating the Gaussian window function is as follows:
[0044] In the formula, It is a Gaussian window function. , To control window width, .
[0045] In this embodiment of the disclosure, the convolution processing module 400 is further configured to: The signal, which exhibits non-stationary characteristics in the time domain, is convolved with the Ricker wavelet to obtain a smooth wavelet signal.
[0046] In this embodiment of the disclosure, the normalization module 500 is further configured to: Using formula The smoothed wavelet signal is normalized to obtain the simulated waveform signal of the seismic source during excavation. in, This is the simulated waveform signal of the earthquake source at time t. Let be the smoothed wavelet signal at time t.
[0047] In summary, the pseudo-random signal-based seismic source simulation system proposed in this embodiment improves the accuracy and flexibility of determining the simulated waveform signal of the seismic source during excavation.
[0048] Example 3 To implement the above embodiments, this disclosure also proposes an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the method described in Embodiment 1.
[0049] Example 4 To implement the above embodiments, this disclosure also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in Embodiment 1.
[0050] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.
[0051] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.
[0052] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.
Claims
1. A method of seismic source simulation based on a pseudo-random signal, characterized by, The method comprises: acquiring a sampling time length and a sampling frequency of a while-drilling seismic source, and generating a plurality of white noise sequences based on the sampling time length and the sampling frequency; performing band-pass filtering on the plurality of white noise sequences based on a preset frequency band distribution and probability density of a while-drilling seismic signal to obtain signal sequences of each frequency band; linearly superimposing the signal sequences of each frequency band to obtain a pseudo-random mixed signal, and performing envelope processing on the pseudo-random mixed signal to obtain a signal with non-stationary characteristics in the time domain; performing wavelet convolution processing on the signal with non-stationary characteristics in the time domain to obtain a smooth wavelet signal; performing normalization processing on the smooth wavelet signal to obtain an analog waveform signal of the while-drilling seismic source.
2. The method of claim 1, wherein, The white noise sequence is a zero-mean Gaussian white noise sequence. The length of the white noise sequence is N=T*fs, N is the length, T is the sampling time length, and fs is the sampling frequency. The frequency band distribution comprises 1-50Hz, 50-200Hz, and full-band noise.
3. The method of claim 2, wherein, The envelope processing on the pseudo-random mixed signal to obtain a signal with non-stationary characteristics in the time domain comprises: modulating the pseudo-random mixed signal using a Gaussian window function to obtain a signal with non-stationary characteristics in the time domain.
4. The method of claim 3, wherein, The calculation formula of the Gaussian window function is as follows: wherein is a Gaussian window function, , is a control window width, .
5. The method of claim 4, wherein, The wavelet convolution processing on the signal with non-stationary characteristics in the time domain to obtain a smooth wavelet signal comprises: convolving the signal with non-stationary characteristics in the time domain with a Ricker wavelet to obtain a smooth wavelet signal.
6. The method of claim 5, wherein, The normalization processing on the smooth wavelet signal to obtain an analog waveform signal of the while-drilling seismic source comprises: Using the formula The smooth wavelet signal is normalized to obtain a simulated waveform signal of the excavation seismic source. wherein, is the simulated wave signal of the seismic source at time t as the excavation progresses, is the smoothed wavelet signal at time t.
7. A seismic source simulation system based on a pseudo-random signal, characterized by, The system comprises: an acquisition module configured to acquire a sampling time length and a sampling frequency of a while-drilling seismic source, and generate a plurality of white noise sequences based on the sampling time length and the sampling frequency; a filtering processing module configured to perform band-pass filtering on the plurality of white noise sequences based on a preset frequency band distribution and probability density of a while-drilling seismic signal to obtain signal sequences of each frequency band; an envelope processing module configured to linearly superimpose the signal sequences of each frequency band to obtain a pseudo-random mixed signal, and perform envelope processing on the pseudo-random mixed signal to obtain a signal with non-stationary characteristics in the time domain; a convolution processing module configured to perform wavelet convolution processing on the signal with non-stationary characteristics in the time domain to obtain a smooth wavelet signal; a normalization module configured to perform normalization processing on the smooth wavelet signal to obtain an analog waveform signal of the while-drilling seismic source.
8. The system of claim 7, wherein, The white noise sequence is a zero-mean Gaussian white noise sequence. The length of the white noise sequence is N=T*fs, N is the length, T is the sampling time length, and fs is the sampling frequency. The frequency band distribution comprises 1-50Hz, 50-200Hz, and full-band noise.
9. An electronic device, comprising: The computer program is stored in the memory and executable on the processor, and when the processor executes the program, the method of any one of claims 1-6 is implemented. The program is executed by the processor to implement the method of any one of claims 1-6.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that,