CN112633225A - Mining microseismic signal filtering method - Google Patents
Mining microseismic signal filtering method Download PDFInfo
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Abstract
The disclosure relates to the technical field of micro-seismic monitoring, in particular to a mining micro-seismic signal filtering method. The method comprises the following steps: acquiring a microseismic monitoring signal with preset duration as a signal to be analyzed; in a time domain, extracting a background noise signal as a first signal through a signal variance ratio in a time period before and after a sliding window; synchronously performing extrusion wavelet transformation on a signal to be analyzed to remove noise, and performing inverse transformation on a denoised signal wavelet coefficient to obtain a denoised time domain microseismic signal; when the synchronous extrusion wavelet transformation is carried out on the signals to be analyzed, the wavelet coefficients of the signals to be analyzed are used as second wavelet coefficients, the second wavelet coefficients are filtered based on wavelet coefficient threshold values, and the wavelet coefficient threshold values are determined based on the probability distribution function of the wavelet coefficients of the first signals. The method can improve the signal-to-noise ratio, greatly retain the characteristics of the original vibration signal monitored by the microseisms, and reduce the influence on the subsequent processing and analysis of the microseismic signals.
Description
Technical Field
The application belongs to the technical field of data processing, and particularly relates to a mining microseismic signal filtering method, mining microseismic signal filtering equipment and a computer readable storage medium.
Background
With the gradual reduction of shallow resources in recent years, deep well mining gradually becomes the normal state of mineral resource mining in China, and mine safety is seriously threatened by deep high stress and ground pressure disasters induced by mining disturbance, so that the efficient and sustainable development of mines is restricted. With the rapid development of green, intelligent and deep well mining technologies, the microseism monitoring technology is widely applied to safety monitoring of mine rock stability with the advantages of large scale, three-dimensional, predictability and the like. The microseismic monitoring technology can obtain the stress, displacement and energy distribution conditions of a mine by detecting and analyzing underground microseisms, and provides scientific basis for the safety evaluation of mine production and the surrounding environment. However, in the face of mass data of numerous mines, the microseismic signals monitored by the current microseismic monitoring system are very easily influenced by noise, the signal to noise ratio is low, the extraction of microseismic waveform P waves is difficult, and the urgent requirements of ground pressure safety early warning and disaster prevention and control under complex geological conditions at the deep part of the mine are difficult to meet.
In the existing noise reduction filtering processing of the rock fracture micro-seismic signal, the traditional low-pass, high-pass and band-pass filtering methods are usually adopted, but under the condition of insufficient understanding of the micro-seismic signal, the filtering effect has a large influence on the original signal, so that the first arrival time, the phase, the waveform amplitude and the like of the micro-seismic P wave are distorted, and the accuracy of seismic source information such as micro-seismic event positioning and the like is further influenced. Some researchers have also studied digital filtering methods, such as Empirical Mode Decomposition (EMD) methods, which decompose a signal to be analyzed into a series of eigen-mode function (IMF) signals of different scales according to the time scale characteristics of data itself, so that each IMF component signal is a stable narrow-band signal, and then hilbert spectrum is obtained after hilbert transform is performed on the IMF component signals, and hilbert spectra of all the IMF component signals are superimposed, so that the hilbert spectrum of the entire signal to be analyzed can be obtained. However, in practical engineering application, the EMD decomposition method is difficult to accurately distinguish and distinguish the noise signal and the effective signal in a plurality of IMFs, and has poor operability and a complicated calculation process. The decomposition reconstruction filtering method is used for obtaining the micro-seismic signal, the misjudgment rate of the signal is high, the signal is easy to distort, and the subsequent analysis of the micro-seismic signal is greatly influenced.
Disclosure of Invention
Technical problem to be solved
In view of the above disadvantages and shortcomings of the prior art, the present application provides a mining microseismic signal filtering method, apparatus and computer readable storage medium.
(II) technical scheme
In order to achieve the purpose, the technical scheme is as follows:
in a first aspect, the application provides a mining microseismic signal filtering method, which includes:
s10, acquiring a microseismic monitoring signal with preset duration as a signal to be analyzed;
s20, extracting a background noise signal in the signal to be analyzed as a first signal through a signal variance ratio in time periods before and after a sliding window in a time domain;
s30, synchronously extruding wavelet transformation on the signal to be analyzed to remove noise, and inversely transforming the denoised signal wavelet coefficient to obtain a denoised time domain microseismic signal;
when the signal to be analyzed is subjected to synchronous extrusion wavelet transformation, the wavelet coefficient of the signal to be analyzed is used as a second wavelet coefficient, the second wavelet coefficient is filtered based on a wavelet coefficient threshold value, the wavelet coefficient threshold value is determined based on a probability distribution function of a first wavelet coefficient, and the first wavelet coefficient is the wavelet coefficient of the first signal.
Optionally, in step S30, when the signal to be analyzed is subjected to synchronous wavelet transform, the method further includes:
and taking the wavelet coefficient obtained by filtering based on the wavelet coefficient threshold as a third wavelet coefficient, and carrying out weighting processing on the third wavelet coefficient.
Optionally, the weighting function used in the weighting process is:
wherein DF is the accumulated value of the third wavelet coefficient along the time axis, t represents time, DFm、tmRespectively the maximum value of DF and the corresponding time, alpha and beta are respectively two constants for adjusting the weight if alpha is>Beta, indicating that the magnitude of the weighted function value depends mainly on t-tmThe value of | is, otherwise, the value of the weighting function is mainly determined by DF/DFmThe value determines, L, the length of the signal to be analyzed.
Optionally, the signal to be analyzed includes a microseismic signal and a simple background noise signal, where the background noise signal is a stationary signal, and the microseismic signal is a non-stationary signal, then step S20 includes:
s21, respectively taking each sampling moment of the signal to be analyzed as a demarcation point, and dividing the signal to be analyzed into a signal of a time period before the demarcation point and a signal of a time period after the demarcation point through a sliding window;
s22, calculating the variance ratio of the signal of the time period before the demarcation point and the signal of the time period after the demarcation point by a formula:
where ROV (i) is the ratio of the variances at time i, var(s)0,i) Is the variance of the signal of the time period before the demarcation point, var(s)i,L) Is the variance of the signal for the time period after the cut-off point, and L is the length of the signal to be analyzed.
S23, the time corresponding to the minimum variance ratio is set as the finally determined demarcation point, the signal before the demarcation point is set as the first signal, and the signal after the demarcation point is set as the second signal.
Optionally, the method for determining the wavelet coefficient threshold includes:
determining a probability distribution function of the first wavelet coefficients;
obtaining a cumulative distribution function of the first wavelet coefficient based on the probability distribution function;
obtaining a cumulative distribution value of the first wavelet coefficient based on the cumulative distribution function;
and taking the cumulative distribution value as a wavelet coefficient threshold value.
Optionally, the second wavelet coefficient is filtered based on a wavelet coefficient threshold, which can be expressed by the formula:
wherein λ is the confidence interval and λ > 0, Ts2Is a second wavelet coefficient, F is the cumulative distribution function of the first wavelet coefficient:
wherein, Ts1Is a first wavelet coefficient, and f (x) is a probability distribution function of the first wavelet coefficient.
Optionally, the probability distribution of the first wavelet coefficient is gaussian, and its probability distribution function is:
where mu is the first wavelet coefficient Ts1σ is the first wavelet coefficient Ts1Standard deviation of (2).
In a second aspect, the present application provides a mining microseismic signal analysis device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program when executed by the processor implementing the steps of the mining microseismic signal filtering method of any of the first aspects above.
In a third aspect, the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the mining microseismic signal filtering method of any of the first aspects above.
(III) advantageous effects
The beneficial effect of this application is: the application provides a mining microseismic signal filtering method, mining microseismic signal filtering equipment and a computer readable storage medium. The method comprises the following steps: acquiring a microseismic monitoring signal with preset duration as a signal to be analyzed; in the time domain, extracting a background noise signal as a first signal and extracting a signal containing a microseismic signal and the background noise signal as a second signal according to a signal variance ratio in a time period before and after a sliding window; synchronously extruding wavelet transform to a signal to be analyzed to remove noise, and inversely transforming the transformed signal to obtain a time domain microseismic signal; when the synchronous extrusion wavelet transformation is carried out on the signals to be analyzed, the wavelet coefficients of the signals to be analyzed are used as second wavelet coefficients, the second wavelet coefficients are filtered based on wavelet coefficient threshold values, the wavelet coefficient threshold values are determined based on probability distribution functions of first wavelet coefficients, and the first wavelet coefficients are wavelet coefficients of the first signals. The method can improve the signal-to-noise ratio, greatly retain the characteristics of the original vibration signal monitored by the microseisms, and reduce the influence on the subsequent processing and analysis of the microseismic signals.
Drawings
The application is described with the aid of the following figures:
FIG. 1 is a schematic flow chart illustrating a mining microseismic signal filtering method according to an embodiment of the present application;
FIG. 2 is a diagram illustrating an embodiment of extracting a background noise signal by using a variance ratio;
FIG. 3 is an exemplary diagram of extracting a background noise signal using a variance ratio according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an evaluation device in another embodiment of the present application.
Detailed Description
For the purpose of better explaining the present invention and to facilitate understanding, the present invention will be described in detail by way of specific embodiments with reference to the accompanying drawings. It is to be understood that the following specific examples are illustrative of the invention only and are not to be construed as limiting the invention. In addition, it should be noted that, in the case of no conflict, the embodiments and features in the embodiments in the present application may be combined with each other; for convenience of description, only portions related to the invention are shown in the drawings.
The microseismic monitoring technology is an important technical means for judging whether mine ground pressure is safe or not, and the basic flow of the microseismic monitoring technology comprises signal denoising, microseismic event detection, P/S phase extraction and seismic source positioning. The application provides a filtering processing method suitable for mine micro-seismic signals aiming at signal denoising, so as to improve the signal-to-noise ratio of micro-seismic record processing, and has great significance for the mine micro-seismic monitoring system.
Fig. 1 is a schematic flow chart of a mining microseismic signal filtering method in an embodiment of the present application, and as shown in fig. 1, the method includes the following steps:
s10, acquiring a microseismic monitoring signal with preset duration as a signal to be analyzed;
s20, extracting a background noise signal in the signal to be analyzed as a first signal through a signal variance ratio in time periods before and after a sliding window in a time domain;
s30, synchronously extruding wavelet transformation to the signal to be analyzed to remove noise, and inversely transforming the denoised signal wavelet coefficient to obtain a denoised time domain microseismic signal;
when the synchronous extrusion wavelet transformation is carried out on the signals to be analyzed, the wavelet coefficients of the signals to be analyzed are used as second wavelet coefficients, the second wavelet coefficients are filtered based on wavelet coefficient threshold values, the wavelet coefficient threshold values are determined based on probability distribution functions of first wavelet coefficients, and the first wavelet coefficients are wavelet coefficients of the first signals.
The technical scheme provided by the embodiment of the invention adopts the variance to carry out stability estimation on the pure background noise of the signal, determines the range of the pure background noise, and forms a micro-seismic signal threshold filtering method based on the pure background noise by utilizing the probability density distribution function, thereby not only realizing the self-adaptive threshold filtering method for mine micro-seismic monitoring signals under different background noise levels, but also ensuring that the arrival, phase and shape of the micro-seismic signal waveform before and after denoising are not influenced. The method can improve the signal-to-noise ratio of the microseismic signal, greatly reserve the characteristics of the original microseismic signal monitored by the microseismic, and reduce the influence on the subsequent microseismic signal analysis.
The following describes each step of the embodiment of the present invention in detail.
In step S10, the microseismic monitor signal with the preset duration includes a pure background noise signal and a superimposed signal including a background noise signal and a microseismic signal.
In the above step S20, the range of the background noise is estimated using the variance ratio. In this embodiment, the pure background noise is a stationary signal, and the vibration signal is a non-stationary signal. The variance is a parameter that can describe whether the current signal is in a steady state, so the range of the background noise can be estimated by using the variance ratio of the pure background noise signal and the vibration-containing signal, where the variance ratio is defined as:
wherein: ROV (i) is the ratio of the variances of the signals in the time periods before and after the i-th time, var(s)0,i) And var(s)i,L) Respectively, the variance of the signal in the time period before and after the ith time, and L is the length of the signal to be analyzed.
Fig. 2 is a schematic diagram of extracting a background noise signal by using a variance ratio in an embodiment of the present application, where (a) in fig. 2 is a schematic diagram of a time domain waveform of a microseismic signal, (b) in fig. 2 is a schematic diagram of a time domain waveform of a microseismic monitor signal, and (c) in fig. 2 is a variance ratio diagram. As shown in fig. 2, the pure background noise is a steady waveform, and the arrival of the vibration signal breaks the original steady state, which can be regarded as a non-steady state, and when the microseismic signal arrives, the variance begins to gradually increase. The time corresponding to the ROV minimum is therefore the approximate time range that can be used to estimate the background noise level, as indicated by the dashed lines in fig. 2 (b) and (c).
In the above step S30, the microseismic monitor signal is decomposed by using the synchronous squeeze wavelet transform to form an energy spectrum in the two-dimensional space of the time-frequency domain, and a wavelet coefficient in the two-dimensional space is obtained.
And performing filtering threshold processing on the wavelet coefficient of the signal to be analyzed by adopting a cumulative distribution function. In the embodiment, the wavelet coefficient of the pure background noise conforms to gaussian distribution, and the wavelet coefficient with the vibration signal does not conform to gaussian distribution, so that the wavelet coefficient of the vibration signal is more easily outside a preset confidence interval of gaussian distribution and belongs to low probability distribution.
The wavelet coefficient T of the segment of the noise signal is obtained by estimating the level of the gaussian pure background noise for the segment of the pure background noise signal, i.e. the first signal, obtained in step S20s1The mean μ and the variance δ to obtain a cumulative distribution function of the wavelet coefficients of the pure background noise as shown in equation (1).
Where f (x) is a probability distribution function.
The probability distribution function of pure background noise is shown in equation (2).
Where mu is the first wavelet coefficient Ts1σ is the first wavelet coefficient Ts1Standard deviation of (2).
Therefore, the threshold filtering based on the cumulative distribution function can be expressed as shown in equation (3).
Wherein λ is the confidence interval and λ > 0, Ts2Is the second wavelet coefficient.
Because the vibration signal belongs to a low probability event, if the cumulative probability corresponding to the wavelet coefficient is greater than lambda, the wavelet coefficient is considered to belong to the vibration signal with a high probability and is reserved; if the wavelet coefficient is less than lambda, the wavelet coefficient is considered as noise and eliminated.
And performing synchronous extrusion wavelet inverse transformation on the wavelet coefficients on the processed time-frequency domain to obtain a filtered waveform based on the time domain.
The embodiment provides a self-adaptive threshold denoising method based on pure background noise, which is characterized in that parameters of the pure background noise are searched by adopting Gaussian distribution to obtain a probability distribution density function of the pure noise; performing threshold filtering on wavelet coefficients obtained based on synchronous extrusion wavelet transform on a time-frequency domain by using a probability density distribution function; the method is more suitable for processing the microseismic monitoring signal under the condition that the mine noise level is unknown, and has stronger self-adaptive capacity and universality. The method can greatly retain the characteristics of the original vibration signal monitored by the micro-vibration, ensure the consistency of the initial arrival time, the phase and the shape of the vibration and reduce the influence on the analysis of the subsequent micro-vibration signal.
The filtering method provided by the embodiment can analyze a signal with high precision, and decompose a two-dimensional signal into a three-dimensional time domain-frequency domain-energy spectrum for analysis, so that the problem that an effective vibration signal and a noise signal cannot be distinguished only from a time domain angle or a frequency domain angle can be solved.
Further, in one embodiment, the weighting function is further used to re-process the few noise wavelet coefficients still existing after the threshold filtering in step S30. In the denoised time-frequency domain, the wavelet coefficients of the seismic signal are generally higher in magnitude than those of the noise, and the wavelet coefficients of the denoised seismic signal are continuous, while the distribution of the incompletely rejected noise wavelet coefficients is generally discontinuous. This feature can be used to suppress or eliminate these wavelet coefficients in the residual noise.
And (3) superposing the wavelet coefficients of all scales along a time axis to obtain a time margin amplitude function DF, wherein the expression of the time margin amplitude function DF is shown in a formula (4).
Wherein n isaIs the scale degree, N is the signal length, and t represents the time.
The DF function gives the wavelet coefficients at each scale and the distribution curve on the time axis. In order to suppress the magnitude of the wavelet coefficients of the noise, a weighting function is defined as shown in equation (5).
Wherein, DFm，tmThe maximum value of DF and its corresponding time, α and β are two parameters for adjusting the time distance and DF value.
If alpha > beta represents that the magnitude of the weighting function value is mainly dependent on t-tmThe magnitude of the weighting function value is mainly determined by DF/DF if alpha is less than betamAnd (4) determining the value.
The influence of the wavelet coefficient of the residual noise is eliminated by adopting the weight function, and the noise interference can be further eliminated, so that the unknown noise interference such as discontinuous noise, accidental noise and the like can be processed, and the signal-to-noise ratio of the microseismic signal is improved.
To better illustrate the present invention, an example of microseismic signal filtering using the method of the present invention is provided below.
This example uses a composite microseismic signal contaminated with known noise. Fig. 3 is an exemplary diagram of extracting a background noise signal by using a variance ratio in an example of the present application, please refer to fig. 3, where (a1) and (a2) in fig. 3 are a time domain waveform diagram and a time-frequency diagram of an original microseismic signal, respectively, where energy is changed from black to white from small to large, and the lighter the color indicates that the more energy is gathered at the point, the microseismic signal is from actual data with a high signal-to-noise ratio, and the energy is mainly concentrated between 200 Hz and 400Hz at a time of about 0.2 seconds. Adding complex noise with random frequency and fixed frequency to an original signal to obtain a noise-containing microseismic signal with the signal-to-noise ratio of 2.5367; fig. 3 (b1) and (b2) are respectively a time-domain waveform diagram and a time-frequency diagram of a noise-containing microseismic signal, and fig. 3 (b1) and (b2) show that the original microseismic signal is submerged by noise and is difficult to pick up in the first arrival.
The noisy microseismic signal is filtered by the following steps.
The method comprises the following steps: and drawing a variance ratio ROV curve for the synthesized signal to obtain a pure background noise range.
Step two: performing synchronous squeeze wavelet transform on the composite signal:
in general, a conventional time-varying signal can be represented as a superposition of multiple harmonic signals, i.e., the signal f (t) can be represented as shown in equation (6).
Wherein A isk(t) is the instantaneous amplitude of the kth harmonic component, θk(t) is the instantaneous phase of the kth harmonic component, e (t) is the noise or error, and K is the number of resolvable components.
Carrying out Continuous Wavelet Transform (CWT) on the mining microseismic signal f (t) to obtain a wavelet coefficient Wf(a, b) as shown in formula (7).
Where a is the scale factor, b is the translation factor,. phi*Is a conjugate wavelet function.
Which is equivalently transformed in the frequency domain into:
where ξ denotes the frequency,
the fourier transforms denoted f (t), ψ (t), respectively.
The main frequency band energy compression algorithm can be added on the basis of the traditional wavelet transformationThe energy is more concentrated. Let n be 2L +1, the original signal f (t) length, and the sampling time interval Δ tvWhen the value is 64, take na＝Lnv，
Let omegal＝2lΔωω0,l＝0,1,…,na-1 dividing the range in which the original signal is located into different frequency intervals,
the coefficients of the wavelet transform are transformed, wherein the threshold is defined as shown in equation (9).
Wherein, the mean is a median function.
Then at the center frequency omegalThe synchronous extrusion transformation is carried out by the formula (10) to realize the frequency band extrusion, and a transformation value T is obtainedf(ωlAnd b) is shown in equation (10).
Wherein, (Delta a)i＝ai-ai-1。
Step three: with reference to fig. 3, fig. 3 (c1) and (c2) show a time domain waveform diagram and a time-frequency diagram of the microseismic signal, which are obtained by performing the filtering threshold thresholding on the wavelet coefficient of the signal by using the cumulative distribution function, respectively.
Step four: and (4) reprocessing the few noise wavelet coefficients still existing after the filtering in the third step by adopting a weight suppressing function. The result is shown in fig. 3 (d 2). In fig. 3, (d2) is a time-frequency diagram of the microseismic signal obtained by processing the wavelet coefficient of the signal with the weight suppression function, and it can be seen from the diagram that the noise can be further removed by the weight suppression function, and the signal-to-noise ratio of the signal is improved.
Step five: and (d1) reconstructing the wavelet coefficients after the time-frequency domain filtering into time-domain signals through synchronous extrusion wavelet inverse transformation, and finally obtaining a time-domain waveform diagram shown in figure 3. In fig. 3, (d1) is a time domain waveform diagram of the microseismic signal obtained by processing the wavelet coefficients of the signal by using the weight reduction function.
According to the method, the two-dimensional signals can be decomposed into the three-dimensional space to be displayed, the energy distribution of the signals in the time-frequency domain can be clearly distinguished, so that the difference between effective signals and noise is distinguished, the influence of frequency domain similar noise on the effective signals is further solved, and the signal-to-noise ratio is improved.
The second aspect of the present application provides a mining microseismic signal analysis device, including: the mining microseismic signal filtering method comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the computer program realizes the steps of the mining microseismic signal filtering method in any one of the above embodiments when the computer program is executed by the processor.
The mining micro-seismic signal analysis equipment can obtain rich information such as the position, the magnitude and the seismic source mechanism of the mine rock fracture by detecting and analyzing the underground micro-seismic, can improve the reliability of micro-seismic data and the accuracy of parameter extraction by denoising the micro-seismic signal, and provides necessary data support for safety risk assessment.
Fig. 4 is a schematic structural diagram of a mining microseismic signal analysis device in another embodiment of the present application. The method described above with reference to fig. 1 may be implemented by a mining microseismic signal analysis device.
The evaluation apparatus shown in fig. 4 may include: at least one processor 41, at least one memory 42, at least one network interface 44, and other user interfaces 43. The various components in the evaluation device are coupled together by a bus system 45. It will be appreciated that the bus system 45 is used to enable communications among the components. The bus system 45 includes a power bus, a control bus, and a status signal bus in addition to the data bus. But for clarity of illustration the various buses are labeled as bus system 45 in figure 4.
The user interface 43 may include, among other things, a display, a keyboard, or a pointing device (e.g., a mouse, trackball, or touch pad, among others.
It will be appreciated that the memory 42 in this embodiment can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. The non-volatile memory may be a Read-only memory (ROM), a programmable Read-only memory (PROM), an erasable programmable Read-only memory (erasabprom, EPROM), an electrically erasable programmable Read-only memory (EEPROM), or a flash memory. The volatile memory may be a Random Access Memory (RAM) which functions as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (staticiram, SRAM), dynamic random access memory (dynamic RAM, DRAM), synchronous dynamic random access memory (syncronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM ), Enhanced Synchronous DRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and direct memory bus RAM (DRRAM). The memory 42 described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
In some embodiments, memory 42 stores elements, executable units or data structures, or a subset thereof, or an expanded set thereof as follows: an operating system 421 and application programs 422.
The operating system 421 includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, for implementing various basic services and processing hardware-based tasks. The application 422 includes various applications, such as an industrial control device operation management system, for implementing various application services. A program implementing methods of embodiments of the present invention may be included in application 422.
In the embodiment of the present invention, the processor 41 is configured to execute the method steps provided in the first aspect by calling a program or an instruction stored in the memory 42, specifically, a program or an instruction stored in the application 422, for example, including the following steps:
acquiring a microseismic monitoring signal with preset duration as a signal to be analyzed;
in the time domain, extracting a background noise signal in a signal to be analyzed as a first signal and extracting a signal containing a microseismic signal and a background noise signal in the signal to be analyzed as a second signal according to the variance ratio of signals in adjacent time periods;
performing synchronous extrusion wavelet transformation and inverse transformation on the second signal to obtain a time domain microseismic signal;
when the second signal is subjected to synchronous extrusion wavelet transform, the wavelet coefficient of the second signal is used as the second wavelet coefficient, the second wavelet coefficient is filtered based on a wavelet coefficient threshold, the wavelet coefficient threshold is determined based on a probability distribution function of the first wavelet coefficient, and the first wavelet coefficient is the wavelet coefficient of the first signal.
The method disclosed in the above embodiments of the present invention may be applied to the processor 41, or implemented by the processor 41. The processor 41 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 41. The processor 41 may be a general-purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, or discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software elements in the decoding processor. The software elements may be located in ram, flash, rom, prom, or eprom, registers, among other storage media that are well known in the art. The storage medium is located in the memory 42, and the processor 41 reads the information in the memory 42 and performs the steps of the above method in combination with the hardware thereof.
In addition, with reference to the mining microseismic signal filtering method in the foregoing embodiment, an embodiment of the present invention may provide a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the mining microseismic signal filtering method in any one of the above method embodiments is implemented.
It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or any combination thereof. For a hardware implementation, the processing units may be implemented within one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described herein, or a combination thereof.
For a software implementation, the techniques described herein may be implemented by means of units performing the functions described herein. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.
In the above embodiments disclosed in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The apparatus and method embodiments described above are illustrative only, as the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods, apparatus, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. Furthermore, it should be noted that in the description of the present specification, the description of the term "one embodiment", "some embodiments", "examples", "specific examples" or "some examples", etc., means that a specific feature, structure, material or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, the claims should be construed to include preferred embodiments and all changes and modifications that fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention should also include such modifications and variations.
Claims (9)
1. A mining microseismic signal filtering method is characterized by comprising the following steps:
s10, acquiring a microseismic monitoring signal with preset duration as a signal to be analyzed;
s20, extracting a background noise signal in the signal to be analyzed as a first signal through a signal variance ratio in time periods before and after a sliding window in a time domain;
s30, synchronously extruding wavelet transformation on the signal to be analyzed to remove noise, and inversely transforming the denoised signal wavelet coefficient to obtain a denoised time domain microseismic signal;
when the signal to be analyzed is subjected to synchronous extrusion wavelet transformation, the wavelet coefficient of the signal to be analyzed is used as a second wavelet coefficient, the second wavelet coefficient is filtered based on a wavelet coefficient threshold value, the wavelet coefficient threshold value is determined based on a probability distribution function of a first wavelet coefficient, and the first wavelet coefficient is the wavelet coefficient of the first signal.
2. The mining microseismic signal filtering method of claim 1 wherein in step S30, when the signal to be analyzed is subjected to synchronous squeeze wavelet transform, the method further comprises:
and taking the wavelet coefficient obtained by filtering based on the wavelet coefficient threshold as a third wavelet coefficient, and carrying out weighting processing on the third wavelet coefficient.
3. The mining microseismic signal filtering method of claim 2 wherein the weighting function used for weighting is:
wherein DF is the accumulated value of the third wavelet coefficient along the time axis, t represents time, DFm、tmRespectively the maximum value of DF and the corresponding time, alpha and beta are respectively two constants for adjusting the weight if alpha is>Beta, indicating that the magnitude of the weighted function value depends mainly on t-tmThe value of | is, otherwise, the value of the weighting function is mainly determined by DF/DFmThe value determines, L, the length of the signal to be analyzed.
4. The mining microseismic signal filtering method of claim 3 wherein the signal to be analyzed includes a microseismic signal and a simple background noise signal, the background noise signal is a stationary signal, and the microseismic signal is a non-stationary signal, then step S20 includes:
s21, respectively taking each sampling moment of the signal to be analyzed as a demarcation point, and dividing the signal to be analyzed into a signal of a time period before the demarcation point and a signal of a time period after the demarcation point through a sliding window;
s22, calculating the variance ratio of the signal of the time period before the demarcation point and the signal of the time period after the demarcation point by a formula:
where ROV (i) is the ratio of the variances at time i, var(s)0,i) Is the variance of the signal of the time period before the demarcation point, var(s)i,L) Is the variance of the signal for the time period after the cut-off point, and L is the length of the signal to be analyzed.
S23, the time corresponding to the minimum variance ratio is set as the finally determined demarcation point, the signal before the demarcation point is set as the first signal, and the signal after the demarcation point is set as the second signal.
5. The mining microseismic signal filtering method of claim 3 wherein the wavelet coefficient threshold determination method comprises:
determining a probability distribution function of the first wavelet coefficients;
obtaining a cumulative distribution function of the first wavelet coefficient based on the probability distribution function;
obtaining a cumulative distribution value of the first wavelet coefficient based on the cumulative distribution function;
and taking the cumulative distribution value as a wavelet coefficient threshold value.
6. The mining microseismic signal filtering method of claim 5 wherein the second wavelet coefficient is filtered based on a wavelet coefficient threshold, which can be expressed by the formula:
wherein λ is the confidence interval and λ > 0, Ts2Is a second wavelet coefficient, F is the cumulative distribution function of the first wavelet coefficient:
wherein, Ts1Is a first wavelet coefficient, and f (x) is a probability distribution function of the first wavelet coefficient.
7. The mining microseismic signal filtering method of claim 5 wherein the probability distribution of the first wavelet coefficients is a Gaussian distribution with a probability distribution function of:
where mu is the first wavelet coefficient Ts1σ is the first wavelet coefficient Ts1Standard deviation of (2).
8. A mining microseismic signal analysis device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program when executed by the processor implementing the steps of the mining microseismic signal filtering method of any of the above claims 1-7.
9. A computer readable storage medium, having stored thereon a computer program which, when executed by a processor, carries out the steps of the mining microseismic signal filtering method of any one of the above claims 1 to 7.
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