CN112633225B - Mining microseism signal filtering method - Google Patents
Mining microseism signal filtering method Download PDFInfo
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Abstract
The disclosure relates to the technical field of microseismic monitoring, in particular to a mining microseismic signal filtering method. 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 through a signal variance ratio in a time period before and after a sliding window; performing synchronous extrusion wavelet transformation on the signal to be analyzed to remove noise, and performing inverse transformation on 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, and the wavelet coefficient threshold value is determined based on a probability distribution function of the wavelet coefficient of the first signal. The method can improve the signal-to-noise ratio, greatly reserve the characteristics of the original vibration signal monitored by the microseism and reduce the influence on the subsequent microseism signal processing and analysis.
Description
Technical Field
The application belongs to the technical field of data processing, and particularly relates to a mining microseismic signal filtering method, equipment and a computer readable storage medium.
Background
Along with the gradual decrease of shallow resources in recent years, deep mining gradually becomes a mining resource mining normal state in China, and the 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 the mine is restricted. With the rapid development of green, intelligent and deep well exploitation technologies, the microseismic monitoring technology is widely applied to safety monitoring of mine rock mass stability with the advantages of large scale, three-dimensional, foreseeable and the like. The microseismic monitoring technology can obtain stress, displacement and energy distribution conditions of mines by detecting and analyzing the microseisms under the wells, and provides scientific basis for mine production and safety evaluation of surrounding environments. However, in the face of mass data of a plurality of mines, microseismic signals monitored by the current microseismic monitoring system are extremely easy to be influenced by noise, the signal-to-noise ratio is low, the microseismic waveform P waves are difficult to extract, and the urgent requirements of ground pressure safety early warning and disaster prevention and control under deep complex geological conditions of the mines are difficult to meet.
In the existing noise reduction filtering treatment of the rock breaking microseism signals, the traditional low-pass, high-pass and band-pass filtering methods are often adopted, but under the condition that the microseism signals are not known enough, the filtering effect of the noise reduction filtering treatment has a large influence on the original signals, so that the time, the phase, the waveform amplitude and other distortions of the microseism P wave first arrival are caused, and the accuracy of the seismic source information such as microseism event positioning is further influenced. The digital filtering method, such as an Empirical Mode Decomposition (EMD) method, is studied by a learner, signals to be analyzed are decomposed into a series of eigenmode function (IMF) signals with different scales according to the time scale characteristics of data, so that each IMF component signal is a stable narrow-band signal, hilbert spectrums are obtained after hilbert transformation is performed on the IMF component signals, and the hilbert spectrums of all the IMF component signals are superimposed, so that the hilbert spectrums of the whole signals 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, has low operability and is complicated in calculation process. The method for resolving and reconstructing the filter is utilized to obtain the signals with high misjudgment rate and easy signal distortion, and has great influence on the subsequent analysis of the microseismic signals.
Disclosure of Invention
First, the technical problem to be solved
In view of the foregoing drawbacks and deficiencies 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 above purpose, the present application adopts the following technical scheme:
in a first aspect, the present application provides a mining microseismic signal filtering method, the method comprising:
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 a time period before and after a sliding window in a time domain;
s30, performing synchronous extrusion wavelet transformation on the signal to be analyzed to remove noise, and performing inverse transformation on 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 performs synchronous extrusion wavelet transformation, 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 adopted by the weighting process is:
wherein DF is the accumulated value of the third wavelet coefficient along the time axis, t represents time, DF m 、t m Respectively, the maximum value of DF and the corresponding moment, and alpha and beta are respectively two constants for adjusting the weight, if alpha>Beta, indicating that the magnitude of the weighting function value is primarily dependent on t-t m The value of the I, whereas the magnitude of the weighted function value is mainly determined by DF/DF m The value determines that L is the length of the signal to be analyzed.
Optionally, the signal to be analyzed includes a microseismic signal and a pure background noise signal, the background noise signal is a stationary signal, and the microseismic signal is a non-stationary signal, and 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 in a time period before the demarcation point and a signal in a time period after the demarcation point through a sliding window;
s22, calculating the variance ratio of the signals of the time period before the demarcation point and the signals of the time period after the demarcation point through a formula:
wherein ROV (i) is the ratio of variances at the ith moment, var(s) 0,i ) Variance of signal for time period before demarcation point, var (s i,L ) The variance of the signal in the time period after the demarcation point is given, and L is the length of the signal to be analyzed.
S23, taking the moment corresponding to the minimum value of the variance ratio as a final determined demarcation point, taking the signal before the demarcation point as a first signal, and taking the signal after the demarcation point as a 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 coefficients based on the probability distribution function;
based on the cumulative distribution function, a cumulative distribution value of the first wavelet coefficient is obtained;
the cumulative distribution value is used as a wavelet coefficient threshold value.
Optionally, filtering the second wavelet coefficient based on a wavelet coefficient threshold may be expressed by the formula:
wherein lambda is the confidence interval, lambda > 0, T s2 Is the second wavelet coefficient, F is the accumulation of the first wavelet coefficientDistribution function:
wherein T is s1 Is 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 distribution, and the probability distribution function is:
wherein μ is a first wavelet coefficient T s1 Sigma is the first wavelet coefficient T s1 Standard 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, which when executed by the processor, performs the steps of the mining microseismic signal filtering method according to any one 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 one of the first aspects above.
(III) beneficial effects
The beneficial effects of this application are: the application provides a mining microseismic signal filtering method, 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 microseism signal and a background noise signal as a second signal through a signal variance ratio in a time period before and after a sliding window; performing synchronous extrusion wavelet transformation on the signal to be analyzed to remove noise, and performing inverse transformation on the transformed signal to obtain a 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. The method can improve the signal-to-noise ratio, greatly reserve the characteristics of the original vibration signal monitored by the microseism and reduce the influence on the subsequent microseism signal processing and analysis.
Drawings
The application is described with the aid of the following figures:
FIG. 1 is a schematic flow chart of a mining microseismic signal filtering method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of extracting background noise signals using variance ratios in one embodiment of the present application;
FIG. 3 is a graph illustrating extraction of background noise signals using variance ratios in one example of the present application;
fig. 4 is a schematic architecture diagram of an evaluation apparatus according to another embodiment of the present application.
Detailed Description
The invention will be better explained by the following detailed description of the embodiments with reference to the drawings. It is to be understood that the specific embodiments described below are merely illustrative of the related invention, and not restrictive of the invention. In addition, it should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other; for convenience of description, only parts 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 method is suitable for filtering the mine microseismic signals aiming at signal denoising, so that the signal to noise ratio of microseismic recording processing is improved, and the method has great significance for the mine microseismic monitoring system.
Fig. 1 is a schematic flow chart of a mining microseismic signal filtering method according to an embodiment of the present application, 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 a signal to be analyzed as a first signal through a signal variance ratio in a time period before and after a sliding window in a time domain;
s30, performing synchronous extrusion wavelet transformation on the signal to be analyzed to remove noise, and performing inverse transformation on 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.
According to the technical scheme provided by the embodiment of the invention, the variance is adopted to carry out stability estimation on the pure background noise of the signal, the range of the pure background noise is determined, and the probability density distribution function is utilized to form a microseismic signal threshold filtering method based on the pure background noise, so that the self-adaptive threshold filtering method for mine microseismic monitoring signals under different background noise levels is realized, and the arrival, the phase and the shape of the waveform of the microseismic signals before and after denoising are not influenced. The method can improve the signal-to-noise ratio of the microseismic signals, greatly maintain the characteristics of the original vibration signals monitored by the microseismic signals, and reduce the influence on the subsequent microseismic signal analysis.
The following describes each step related to the embodiment of the present invention in detail.
In the step S10, the microseismic monitoring signal of the preset duration needs to include a pure background noise signal and a superimposed signal including the background noise signal and the microseismic signal.
In step S20 described above, the range of the background noise is estimated using the variance ratio. The pure background noise in this embodiment is a stationary signal, while the vibration signal is a non-stationary signal. The variance is a parameter that describes whether the current signal is stationary, so the range of the background noise can be estimated by using the variance ratio of the pure background noise signal to 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 ith moment, var(s) 0,i ) And var(s) i,L ) The variance of the signals in the time periods before and after the ith moment is respectively, 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 monitoring signal, and (c) in fig. 2 is a variance ratio diagram. As shown in fig. 2, the pure background noise is a waveform in a steady state, and the arrival of the vibration signal breaks the original steady state and can be regarded as a non-steady state, and when the microseismic signal arrives, the variance starts to increase gradually. The time corresponding to the ROV minimum is thus 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 step S30, the microseismic monitoring signal is decomposed by using synchronous extrusion wavelet transformation to form an energy spectrum in a two-dimensional space of a time-frequency domain, and a wavelet coefficient in the two-dimensional space is obtained.
And filtering and thresholding the wavelet coefficient of the signal to be analyzed by adopting a cumulative distribution function. In this embodiment, the wavelet coefficient of the pure background noise accords with the gaussian distribution, and the wavelet coefficient with the vibration signal does not accord with the gaussian distribution, so that the wavelet coefficient of the vibration signal more easily falls outside the confidence interval of the preset gaussian distribution and belongs to the low probability distribution.
By estimating the level of Gaussian pure background noise for the segment of pure background noise signal obtained in step S20, i.e., the first signal, the segment is obtainedWavelet coefficient T of noise signal s1 The cumulative distribution function of the wavelet coefficients resulting in pure background noise is shown in equation (1) as the mean μ and variance δ.
Where f (x) is a probability distribution function.
The probability distribution function of the pure background noise is shown in formula (2).
Wherein μ is a first wavelet coefficient T s1 Sigma is the first wavelet coefficient T s1 Standard deviation of (2).
Thus, the threshold filtering based on the cumulative distribution function can be expressed as shown in formula (3).
Wherein lambda is the confidence interval, lambda > 0, T s2 Is the second wavelet coefficient.
Since 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 be the vibration signal with a high probability and is reserved; if the wavelet coefficient is less than lambda, the wavelet coefficient is considered to be noise-dependent and is rejected.
And performing synchronous extrusion wavelet inverse transformation on the wavelet coefficient 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 adopts Gaussian distribution to search parameters of the pure background noise to obtain a probability distribution density function of the pure noise; threshold filtering is carried out on wavelet coefficients obtained based on synchronous extrusion wavelet transformation on a time-frequency domain by utilizing a probability density distribution function; the method is more suitable for processing microseismic monitoring signals under the condition of unknown mine noise level, and has stronger self-adaptive capacity and universality. The method can retain the characteristics of the original vibration signal monitored by the microseism to a great extent, ensure the consistency of the initial arrival time, the phase and the shape of vibration and reduce the influence on the analysis of the subsequent microseism signals.
The filtering method provided by the embodiment can analyze the signals with high precision, and decompose the two-dimensional signals into three-dimensional time domain-frequency domain-energy spectrum for analysis, so that the problem that effective vibration signals and noise signals cannot be distinguished only from time domain angles or frequency domain angles can be solved.
Further, in one embodiment, the weight suppression function is also used in the step S30 to reprocess the few noise wavelet coefficients still existing after the threshold filtering. In the denoised time-frequency domain, the magnitude of the wavelet coefficients of the shock signal is typically higher than the wavelet coefficients of the noise, and the wavelet coefficients of the denoised shock signal are continuous, while the non-completely removed noise wavelet coefficient distribution is typically discontinuous. This feature may be utilized to suppress or eliminate these wavelet coefficients in the residual noise.
And (3) superposing wavelet coefficients of all scales along a time axis to obtain a time marginal amplitude function DF, wherein the expression of the time marginal amplitude function DF is shown in a formula (4).
Wherein n is a The number of scales, N is the signal length, and t is the time.
The DF function gives the wavelet coefficients on each scale and the distribution curve on the time axis. In order to suppress the wavelet coefficient magnitudes of the noise, a weighting function is defined as shown in formula (5).
Wherein DF is m ，t m The maximum value of DF and the corresponding time are respectively, and alpha and beta are two parameters for adjusting the time distance and DF value.
If alpha > beta represents the magnitude of the weighting function value is dependent primarily on t-t m The magnitude of the weighting function value is mainly formed by DF/DF when the I value and alpha < beta m And (5) value determination.
The influence of the residual noise wavelet coefficient is eliminated by adopting the weight function, so that noise interference can be further eliminated, unknown noise interference such as discontinuous noise, sporadic 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 synthetic microseismic signal contaminated with known noise. Fig. 3 is an exemplary diagram of extracting background noise signals by using variance ratio in an embodiment of the present application, please refer to fig. 3, in which (a 1) and (a 2) in fig. 3 are a time domain waveform diagram and a time frequency diagram of an original microseismic signal respectively, energy in the diagram is changed from black to white from small to large, the lighter the color is, the greater the energy collected in the image is, the microseismic signal is from actual data with high signal-to-noise ratio, and the energy is mainly concentrated between 200 Hz and 400Hz at about 0.2 seconds. Adding complex noise with random frequency and fixed frequency to the original signal to obtain a noise-containing microseismic signal with a signal-to-noise ratio of 2.5367; in fig. 3, (b 1) and (b 2) are respectively a time domain waveform diagram and a time frequency diagram of the noise-containing microseismic signal, and in fig. 3, (b 1) and (b 2) show that the original microseismic signal is submerged by noise, so that the original microseismic signal is difficult to pick up in the first arrival.
The noise-containing microseismic signal is filtered through the following steps.
Step one: and drawing a variance ratio ROV curve for the synthesized signal to obtain a pure background noise range.
Step two: the composite signal is subjected to synchronous extrusion wavelet transformation:
in general, conventional time-varying signals 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 is k (t) is the instantaneous amplitude of the kth harmonic component, θ k (t) is the instantaneous phase of the kth harmonic component, e (t) is noise or error, and K is the number of resolvable components.
Continuous Wavelet Transform (CWT) is carried out on the mining microseismic signal f (t), and the obtained wavelet coefficient W f (a, b) as shown in formula (7).
Wherein a is a scale factor, b is a translation factor, and ψ * Is a conjugate wavelet function.
Which can be equivalently transformed in the frequency domain into:
wherein xi represents the frequency,the fourier transforms denoted f (t), ψ (t), respectively.
The energy can be concentrated by adding a main frequency band energy compression algorithm on the basis of traditional wavelet transformation. Let n be given a length of the original signal f (t) of n=2l+1 and a sampling time interval Δt v =64, take n a ＝Ln v ，
Let omega l ＝2 lΔω ω 0 ,l＝0,1,…,n a 1 dividing the range in which the original signal lies into different frequency bins,transforming coefficients of a wavelet transform, wherein a threshold is defined as the formula(9) As shown.
Wherein media is the median function.
Then at the center frequency omega l The synchronous extrusion transformation is carried out through the formula (10) to realize the frequency band extrusion, and a transformation value T is obtained f (ω l B) is shown in formula (10).
Wherein (Δa) i ＝a i -a i-1 。
Step three: with continued reference to fig. 3, in fig. 3, (c 1) and (c 2) are respectively a time domain waveform diagram and a time-frequency diagram of a microseismic signal obtained by performing filtering threshold processing on the wavelet coefficients of the signal by using a cumulative distribution function, where the time domain waveform diagram and the time-frequency diagram are shown in the figure.
Step four: and (3) adopting a weight pressing function to reprocess few noise wavelet coefficients still existing after the step three filtering. The result is shown in fig. 3 (d 2). In fig. 3, (d 2) is a time-frequency diagram of the microseismic signal obtained by processing the wavelet coefficient of the signal by using a weight suppression function, and it can be seen from the diagram that noise can be further removed by using the weight suppression function, so as to improve the signal-to-noise ratio of the signal.
Step five: and (3) reconstructing the wavelet coefficients subjected to time-frequency domain filtering into a time domain signal through synchronous extrusion wavelet inverse transformation, and finally obtaining a time domain waveform diagram shown in (d 1) in fig. 3. Fig. 3 (d 1) is a time domain waveform diagram of the microseismic signal obtained by processing the wavelet coefficients of the signal by using a weight suppression function.
According to the method, the two-dimensional signals can be decomposed into three-dimensional space for display, and 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.
A second aspect of the present application provides, by another embodiment, a mining microseismic signal analysis apparatus, including: 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 according to any of the above embodiments.
The mining microseismic signal analysis equipment can acquire abundant information such as the position, the magnitude and the seismic source mechanism of mine rock fracture by detecting and analyzing the microseismic in the pit, can improve the reliability of microseismic data and the accuracy of parameter extraction by denoising the microseismic signal, and provides necessary data support for security risk assessment.
Fig. 4 is a schematic architecture diagram of a mining microseismic signal analysis device according to another embodiment of the present application. The method described in the above figure 1 can 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 individual 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 connected communications between these 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 fig. 4.
The user interface 43 may include, among other things, a display, a keyboard, or a pointing device (e.g., a mouse, a trackball (trackball), or a touch pad, etc.).
It will be appreciated that the memory 42 in this embodiment may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile memory may be a Read-only memory (ROM), a programmable Read-only memory (ProgrammableROM, PROM), an erasable programmable Read-only memory (ErasablePROM, EPROM), an electrically erasable programmable Read-only memory (ElectricallyEPROM, EEPROM), or a flash memory, among others. The volatile memory may be a random access memory (RandomAccessMemory, RAM) that acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), dynamic random access memory (DynamicRAM, DRAM), synchronous dynamic random access memory (SynchronousDRAM, SDRAM), double data rate synchronous dynamic random access memory (ddr SDRAM), enhanced Synchronous Dynamic Random Access Memory (ESDRAM), synchronous link dynamic random access memory (SynchlinkDRAM, SLDRAM), and direct memory bus random access memory (DirectRambusRAM, 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 the following elements, executable units or data structures, or a subset thereof, or an extended set thereof: an operating system 421 and applications 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 application programs, such as an industrial control device operation management system, for implementing various application services. A program implementing the method of the embodiment of the present invention may be included in the application 422.
In an 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, where the method steps include 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 the background noise signal in the signal to be analyzed as a second signal through 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;
and when the second signal is subjected to synchronous extrusion wavelet transformation, taking the wavelet coefficient of the second signal as a second wavelet coefficient, filtering the second wavelet coefficient based on a wavelet coefficient threshold, wherein the wavelet coefficient threshold 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.
The method disclosed in the above embodiment 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 with signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in the processor 41 or by instructions in the form of software. The processor 41 may be a general purpose processor, a digital signal processor (DigitalSignalProcessor, DSP), an application specific integrated circuit (application specific IntegratedCircuit, ASIC), an off-the-shelf programmable gate array (FieldProgrammableGateArray, FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component. The disclosed methods, steps, and logic blocks 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 embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software elements in a decoding processor. The software elements may be located in a random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory 42 and the processor 41 reads information in the memory 42 and in combination with its hardware performs the steps of the method described above.
In addition, in combination with the mining microseismic signal filtering method in the above embodiment, the embodiment of the present invention may provide a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements any one of the mining microseismic signal filtering methods in the above method embodiments.
It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or a 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 (dsppdevices), 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 that perform 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 of the present disclosure, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus and method embodiments are merely illustrative, for example, flow diagrams and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods, apparatuses 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 terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to a specific feature, structure, material, or characteristic described in connection with the embodiment or example being included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed 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 upon learning the basic inventive concepts. Therefore, the appended claims should be construed to include preferred embodiments and all such variations and modifications as 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 to the present invention without departing from the spirit or scope of the invention. Thus, the present invention should also include such modifications and variations provided that they come within the scope of the following claims and their equivalents.
Claims (7)
1. The mining microseismic signal filtering method is characterized by comprising the following steps of:
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 a time period before and after a sliding window in a time domain;
s30, performing synchronous extrusion wavelet transformation on the signal to be analyzed to remove noise, and performing inverse transformation on 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, taking a wavelet coefficient of the signal to be analyzed as a second wavelet coefficient, and filtering the second wavelet coefficient based on a wavelet coefficient threshold, wherein the wavelet coefficient threshold 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; when the signal to be analyzed is subjected to synchronous extrusion wavelet transformation, the method further comprises the following steps:
taking a wavelet coefficient obtained after filtering based on a wavelet coefficient threshold as a third wavelet coefficient, and carrying out weighting treatment on the third wavelet coefficient; the weighting function adopted by the weighting process is as follows:
wherein DF is the accumulated value of the third wavelet coefficient along the time axis, t represents time, DF m 、t m Respectively, the maximum value of DF and the corresponding moment, and alpha and beta are respectively two constants for adjusting the weight, if alpha>Beta, indicating |t-t m The influence of the I value on the size of the weighting function value is larger than DF/DF m The effect of the value on the magnitude of the weighting function value, and vice versa, indicates DF/DF m The influence of the value on the magnitude of the weighting function value is greater than |t-t m Influence of the value on the size of the weighting function value;
l is the length of the signal to be analyzed.
2. The mining microseismic signal filtering method according to claim 1, wherein the signal to be analyzed includes a microseismic signal and a pure 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 in a time period before the demarcation point and a signal in a time period after the demarcation point through a sliding window;
s22, calculating the variance ratio of the signals of the time period before the demarcation point and the signals of the time period after the demarcation point through a formula:
wherein ROV (i) is the ratio of variances at the ith moment, var(s) 0,i ) Variance of signal for time period before demarcation point, var (s i,L ) The variance of the signal in the time period after the demarcation point is given, and L is the length of the signal to be analyzed;
s23, taking the moment corresponding to the minimum value of the variance ratio as a final determined demarcation point, taking the signal before the demarcation point as a first signal, and taking the signal after the demarcation point as a second signal.
3. The mining microseismic signal filtering method according to claim 1, wherein the determining method of the wavelet coefficient threshold comprises:
determining a probability distribution function of the first wavelet coefficients;
obtaining a cumulative distribution function of the first wavelet coefficients based on the probability distribution function;
based on the cumulative distribution function, a cumulative distribution value of the first wavelet coefficient is obtained;
the cumulative distribution value is used as a wavelet coefficient threshold value.
4. A mining microseismic signal filtering method according to claim 3, characterized in that the filtering of the second wavelet coefficients based on wavelet coefficient thresholds is formulated as:
wherein lambda is the confidence interval, lambda > 0, T s2 Is a second wavelet coefficient, F is a cumulative distribution function of said first wavelet coefficient:
wherein T is s1 Is a first wavelet coefficient and f (x) is a probability distribution function of the first wavelet coefficient.
5. A mining microseismic signal filtering method according to claim 3, characterized in that the probability distribution of the first wavelet coefficients is gaussian, and the probability distribution function is:
wherein μ is a first wavelet coefficient T s1 Sigma is the first wavelet coefficient T s1 Standard deviation of (2).
6. A mining microseismic signal analysis device, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the mining microseismic signal filtering method according to any one of claims 1 to 5 above.
7. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the mining microseismic signal filtering method according to any one of the preceding claims 1 to 5.
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