CN116104486A - Acoustic wave signal measuring and processing method based on drill rod propagation - Google Patents
Acoustic wave signal measuring and processing method based on drill rod propagation Download PDFInfo
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Abstract
The invention provides a sound wave signal measuring and processing method based on drill rod propagation, which comprises the following steps: step one, layout sensors; step two, collecting background noise signals; step three, calculating a transfer function; step four, subtracting and filtering EMD domain spectrums; and step five, deconvolution filtering. Compared with the traditional drill rod acoustic wave communication and identification method, the acoustic wave signal processing method based on drill rod propagation provided by the invention can bring two parts of improvement and promotion, firstly, the problem of background noise in acoustic wave communication while drilling is solved by adopting an EMD domain subtraction filtering method, and the signal to noise ratio of a received signal is improved. And secondly, deconvolution is adopted to eliminate the waveform distortion effect caused by uneven transfer function, so that the recognition rate of the waveform is obviously improved. The vibration sensors 1 and 3 are used for detecting background noise and identifying a system response function, the vibration sensors 2 and 4 are used for receiving digital electric signals of the sound wave transmitting device in real time, interference of the background noise is filtered through subtraction of an EMD domain, and signal to noise ratio is improved.
Description
Technical Field
The invention belongs to the technical field of coal mine tunnel drilling, relates to measurement while drilling, and in particular relates to a method for measuring and processing acoustic signals based on drill rod propagation.
Background
The measurement while drilling directional drilling technology is an important technological method for underground coal mine drilling construction, and has the advantages of controllable drilling track, long extension distance along a target stratum, large advanced coverage area, remarkably improved construction quantity and the like. In order to transmit measurement parameters while drilling in underground coal mines to a ground receiving unit in real time, a reliable transmission medium and a communication method are needed to be used, and an acoustic wave transmission technology is a reliable wireless communication mode of a mining measurement while drilling system.
The working principle of the acoustic transmission of measurement while drilling data is as follows: firstly, a near-bit attitude parameter is acquired at a transmitting end by using an inclinometry sensor. And then, the bit attitude parameter information is converted into a digital electric signal through an encoder, the acoustic wave transmitting transducer is excited to transmit acoustic waves, and the digital electric signal to be transmitted is converted into an acoustic signal to be transmitted in a drill rod channel. And finally, installing a vibration sensor at the receiving end to acquire sound waves transmitted by the drill rod, converting the sound signals into electric signals, decoding and identifying the electric signals, and recovering the original information.
The quality of the sound wave signal of the receiving end becomes a key factor influencing the measurement while drilling sound wave communication, and the sound wave can generate a large amount of low-frequency noise signals due to the complex working condition under the mine in the transmission process of the drill rod, so that the signal-to-noise ratio of the receiving end is reduced.
On the other hand, because the multi-section drill rod adopts a threaded connection mode and the sections are changed, acoustic impedance at the drill rod joint is not matched, and sound waves can be attenuated and scattered through the propagation of the drill rod, the integral transfer function has the characteristics of attenuation and non-flatness.
The received sound wave signal becomes 'blurred' due to the influence of background noise and uneven transfer function, the signal to noise ratio is further reduced, the recognition and demodulation of the sound wave signal are influenced, and the error rate of sound wave communication is increased.
In order to solve the above problems, it is necessary to effectively filter the received acoustic wave signal and recover the original transmission signal from the received mixed signal with noise.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention aims to provide a sound wave signal measuring and processing method based on drill rod propagation, which solves the technical problem of low signal-to-noise ratio of received sound waves caused by uneven background noise and transfer function in measurement-while-drilling sound wave communication in the prior art.
In order to solve the technical problems, the invention adopts the following technical scheme:
a sound wave signal measuring and processing method based on drill rod propagation comprises the following steps:
step one, layout sensor:
arranging a double-channel acoustic wave receiving sensor at a receiving end; the dual-channel acoustic wave receiving sensor comprises a vibration sensor 1, a vibration sensor 2, a vibration sensor 3 and a vibration sensor 4.
The vibration sensor 1 and the vibration sensor 3 are used for collecting background noise and for responding to measurements.
The vibration sensor 2 and the vibration sensor 4 are used for collecting coded vibration signals in the drilling holes in real time.
The sensitivity of the vibration sensor 1 and the sensitivity of the vibration sensor 3 are both larger than those of the vibration sensor 2 and the vibration sensor 4.
Step two, collecting background noise signals:
the drill rod machine is kept in a normal working state, the sound wave transmitting transducer of the sound wave transmitting device stops working, the vibration sensor 1 or the vibration sensor 3 senses background noise of the surrounding environment and converts the background noise into an electric signal, and the vibration sensor 1 or the vibration sensor 3 collects underground background noise with the sampling frequency as fs and stores the background noise.
Step three, calculating a transfer function:
the sound wave transmitting device generates a unit impact signal, the unit impact signal is transmitted to the vibration sensor 1 or the vibration sensor 3 through the drill rod, the vibration sensor 1 or the vibration sensor 3 collects and stores the signal, and a transfer function in a frequency domain is obtained through a Fourier transform method.
Step four, EMD domain spectrum subtraction filtering:
and respectively carrying out EMD decomposition on a background noise signal acquired by the vibration sensor 1 or the vibration sensor 3 and a digital electric signal containing noise acquired by the vibration sensor 2 or the vibration sensor 4, and then carrying out EMD domain spectrum subtraction on the obtained IMF to obtain a digital electric signal without noise.
Step five, deconvolution filtering:
according to the transfer function H (omega) obtained in the third step, decomposing the transfer function H (omega) into a minimum phase part and an all-pass part, designing a wiener deconvolution filter for the minimum phase part, and deconvoluting and filtering the digital electric signal filtered by the vibration sensor 2 or the vibration sensor 4 in the fourth step by adopting the wiener deconvolution filter.
And step six, adopting a machine learning recognition algorithm to recognize and decode the received digital electric signals to obtain original signals excited by the acoustic wave transmitting transducer.
Compared with the prior art, the invention has the following technical effects:
compared with the traditional drill rod acoustic communication and identification method, the acoustic signal processing method based on drill rod propagation provided by the invention can bring two parts of improvement and promotion, firstly, the problem of background noise in acoustic communication during measurement while drilling is solved by adopting an EMD domain subtraction filtering method, and the signal to noise ratio of a received signal is improved. And secondly, deconvolution is adopted to eliminate the waveform distortion effect caused by uneven transfer function, so that the recognition rate of the waveform is obviously improved.
In the aspect of sound wave receiving, the method of the invention comprises the steps of designing a double-channel sound wave receiving sensor, using the vibration sensor 1 and the vibration sensor 3 to detect background noise and identify a system response function, using the vibration sensor 2 and the vibration sensor 4 to receive digital electric signals of a sound wave transmitting device in real time, and filtering interference of the background noise through subtraction of an EMD domain, thereby improving signal to noise ratio.
And (III) due to the uneven and attenuation characteristics of the transfer function, the digital electric signal emitted by the acoustic wave under the mine has obvious attenuation phenomenon in the transmission process, so that the amplitude attenuation and waveform characteristics of the two-channel acoustic wave receiving sensor are not obvious, and the identification accuracy is further affected. The invention eliminates the influence of background noise and system transmission, improves the signal-to-noise ratio of signals, reduces the distortion degree of the signals, enhances the recognition precision and reduces the error rate of sound wave communication by designing a detection method for measuring a multichannel sensor and a signal processing method for subtracting filtering and deconvolution filtering of an EMD domain.
Drawings
FIG. 1 is a flow chart of the acoustic signal measurement and processing based on drill pipe propagation of the present invention.
Fig. 2 is a schematic diagram of a sensor layout of the present invention.
Fig. 3 is a flow chart of the EMD domain spectral subtraction filtering of the present invention.
Fig. 4 is a wiener deconvolution filter flow chart of the present invention.
Fig. 5 is a time domain diagram of a received sound wave of an application example.
Fig. 6 is an explanatory diagram showing the effect of the EMD domain subtraction filter of the application example.
FIG. 7 is a schematic diagram illustrating the effect of deconvolution filtering in response to the system of the application example.
The following examples illustrate the invention in further detail.
Detailed Description
All the devices according to the present invention, unless otherwise specified, are known in the art.
EMD, empirical Mode Decomposition, refers to empirical mode decomposition.
MLS, maximum Length Sequence, refers to the maximum length sequence.
IMF, intrinsic Mode Function, refers to the eigenmode function.
In the present invention, the system refers to the whole transmission system from the sound wave transmitting transducer to the drill rod channel to the vibration sensor.
In the present invention, the transfer function refers to a transfer function of a transmission system.
The following specific embodiments of the present invention are provided, and it should be noted that the present invention is not limited to the following specific embodiments, and all equivalent changes made on the basis of the technical solutions of the present application fall within the protection scope of the present invention.
Examples:
the embodiment provides a method for measuring and processing acoustic signals based on drill rod propagation, as shown in fig. 1, the method comprises the following steps:
step one, layout sensor:
as shown in fig. 2, a dual-channel acoustic wave receiving sensor is arranged at a receiving end; the dual-channel acoustic wave receiving sensor comprises a vibration sensor 1, a vibration sensor 2, a vibration sensor 3 and a vibration sensor 4.
The vibration sensor 1 and the vibration sensor 3 are used for collecting background noise and for transmitting system response measurement.
The vibration sensor 2 and the vibration sensor 4 are used for collecting coded vibration signals in the drilling holes in real time.
The sensitivity of the vibration sensor 1 and the sensitivity of the vibration sensor 3 are both larger than those of the vibration sensor 2 and the vibration sensor 4. In this embodiment, since the background noise signal in the field is weak and the unit impact signal duration of the response of the measurement transmission system is short, the sensitivity of the vibration sensor 1 and the vibration sensor 3 is required to be larger than that of the vibration sensor 2 and the vibration sensor 4.
In the first step, preferably, the sensitivity of the vibration sensor 1 is lower than the sensitivity of the vibration sensor 3; the sensitivity of the vibration sensor 2 is lower than the sensitivity of the vibration sensor 4. In this embodiment, the working principles of different sensitivities are: when the drilling depth is deeper and the signal transmitted to the receiving end is weak, selecting to receive the high-sensitivity vibration sensor signal, and avoiding that the weak signal cannot be identified; when the drilling depth is shallow, and the amplitude of the signal transmitted to the receiving end is strong, the signal of the vibration sensor with low sensitivity is selected to be received, so that the saturation of the signal of the sensor is avoided.
In the first step, preferably, the two-channel acoustic wave receiving sensors are piezoelectric vibration sensors.
Step two, collecting background noise signals:
the drill rod machine is kept in a normal working state, the sound wave transmitting transducer of the sound wave transmitting device stops working, the vibration sensor 1 or the vibration sensor 3 senses background noise of the surrounding environment and converts the background noise into an electric signal, and the vibration sensor 1 or the vibration sensor 3 collects underground background noise with the sampling frequency as fs and stores the background noise.
In the second step, preferably, the sampling frequency of the vibration sensor 1 or the vibration sensor 3 is fs which is greater than 2 times of the highest operating frequency of the transmitted acoustic wave signal, and the sampling time of the vibration sensor 1 is not less than 20s.
In this embodiment, the acoustic wave emitting device is a conventional acoustic wave emitting device known in the art; the acoustic wave transmitting transducer adopts an acoustic wave transmitting transducer commonly used as known in the art.
Step three, calculating a transfer function:
the sound wave transmitting device generates a unit impact signal, the unit impact signal is transmitted to the vibration sensor 1 or the vibration sensor 3 through the drill rod, the vibration sensor 1 or the vibration sensor 3 collects and stores the signal, and a transfer function in a frequency domain is obtained through a Fourier transform method.
In the third step, preferably, the expression of the unit impact signal is:
wherein:
delta represents a unit impact signal;
n represents a discrete form of time t.
In the third step, preferably, the unit impact signal collected by the vibration sensor 1 or the vibration sensor 3 is denoted as h (n), and the transfer function in the frequency domain can be obtained by performing fast fourier transform on h (n):
H(ω)＝FFT(h(n))；
wherein:
ω represents a frequency variable;
h (ω) represents a transfer function in the frequency domain;
the FFT represents the fast fourier transform.
In the third step, preferably, the unit impulse signal may be a gaussian white noise signal or an MLS sequence.
Step four, EMD domain spectrum subtraction filtering:
as shown in fig. 3, the background noise signal collected by the vibration sensor 1 or the vibration sensor 3 and the digital electric signal containing noise collected by the vibration sensor 2 or the vibration sensor 4 are respectively subjected to EMD decomposition, and then the obtained IMF is subjected to EMD domain spectrum subtraction to obtain a digital electric signal without noise.
In the fourth step, the specific process of the step is preferably as follows:
in this embodiment, the original signal is processed first, each local maximum point is obtained first, then the points are connected by interpolation to form an upper envelope, the minimum point is found to be connected to form a lower envelope, the average value of the envelope is subtracted from the original signal to obtain the first residual error below, but the first residual error does not satisfy the IMF definition, so the above operation is continuously repeated with the residual error as the original signal until the residual error satisfies the IMF definition condition.
Let x (t) be the signal and u (t) and v (t) be the upper and lower envelopes of the signal, respectively, the average curve of the upper and lower envelopes is m (t), m (t) = [ u (t) +v (t) ]/2.
The residual part after subtracting m (t) from x (t) is h 1 (t)，h 1 (t)＝x(t)-m(t)。
Wherein:
t represents a time variable.
Replacing x (t) with residual error, then obtaining the sum of the upper envelope curve and the lower envelope curve, and repeating the previous process until the obtained IMF condition is met; finally, all IMFs and residual amounts are obtained, the original x (t) is the sum of all IMFs and residual amounts, and signals obtained after EMD decomposition of background noise and digital electric signals are respectively:
wherein:
x 1 (t) represents a background noise signal received by the sensor;
x 2 (t) represents the digital electrical signal received by the sensor;
n represents the number of IMF components;
c 1i representing IMF components obtained after EMD decomposition of the background noise signal;
c 2i representing IMF components obtained after EMD decomposition of the digital electrical signals;
r 1n representing residual signals obtained by decomposing background noise signals IMF;
r 2n representing the residual signal obtained by IMF decomposition of the digital electrical signal.
For x 1 (t) and x 2 (t) subtracting to obtain denoised IMF and residue, and summing the denoised IMF and residue to recover denoisedThe digital electrical signal after that.
Step five, deconvolution filtering:
the transmission system is defined as a transmission system, the transfer function of the transmission system is defined as H (t) or H (omega), and then the electric signal received by the vibration sensor is convolved in the time domain of the digital electric signal and the transfer function, so that the whole process can be expressed by the following mathematical model:
the time domain of the signal received by the double-channel acoustic wave receiving sensor is expressed as follows: x (t) =h (t) ×s (t).
The frequency domain of the signal received by the double-channel sound wave receiving sensor is expressed as follows: x (ω) =h (ω) ×s (ω).
Wherein:
x (t) represents the time domain form of the signal received by the sensor;
h (t) represents the time domain form of the transfer function;
s (t) represents a digital electrical signal time domain form;
x (ω) represents the frequency domain of the signal received by the sensor;
h (ω) represents a frequency domain representation of the transfer function;
s (ω) represents a frequency domain form of the digital electrical signal.
The acoustic wave signal transmitted by the acoustic wave transmitting transducer is equivalent to the multiplication operation of the acoustic wave signal X (omega) and the transfer function H (omega) in the frequency domain through the transmission system, so that the amplitude and the phase characteristics of the transmitted acoustic wave signal are changed, the change can cause the time domain distortion of the waveform, and the deconvolution operation is required to be carried out on the electric signal received by the vibration sensor in order to eliminate the signal quality degradation caused by the transmission system.
As shown in fig. 4, the transfer function H (ω) is decomposed into a minimum phase portion and an all-pass portion according to the transfer function H (ω) obtained in the step three, H (ω) =h min (ω)H all (ω)。
Wherein:
H min (ω) represents the minimum phase portion;
H all (ω) represents the all-pass portion.
And (3) designing a wiener deconvolution filter for the minimum phase part, deconvolution filtering is carried out on the digital electric signal filtered by the vibration sensor 2 or the vibration sensor 4 in the fourth step by adopting the wiener deconvolution filter, so that waveform distortion caused by transmission response of a transmission system is further suppressed, and the digital electric signal s (t) generated by the sound wave transmitting device is recovered.
In this embodiment, the transfer function H (ω) has a multi-zero pole condition due to the periodic variable cross-section connection, and exhibits a non-minimum phase transfer system characteristic, which belongs to a class of unstable transfer systems, so that in order to improve the deconvolution accuracy, the transfer function H (ω) needs to be subjected to a minimum phase processing.
In the fifth step, specifically, the minimum phase portion may be obtained by:
C p (ω)＝log(H(ω))
c p (n)＝IFFT(C p (ω))
wherein:
C p (ω) represents a frequency domain representation of the complex cepstrum of H (ω);
c p (n) represents a time domain representation of the complex cepstrum of H (ω);
H min (ω) represents the minimum phase of H (ω);
n represents a discrete form of time t;
n represents the number of data points;
the IFFT represents an inverse fourier transform;
log represents a log operation;
exp represents an exponential operation.
In the fifth step, specifically, the wiener deconvolution filter is expressed in the frequency domain as:
wherein:
w (ω) represents the frequency domain representation of the designed wiener deconvolution filter;
H min * (ω) represents the transfer function minimum phase portion |H min Conjugated forms of (ω) |;
epsilon is a regularization constant; in general, in order to prevent "pathological solution" caused by the presence of a value of 0 for H (ω).
And step six, adopting a machine learning recognition algorithm to recognize and decode the received digital electric signals to obtain original signals excited by the acoustic wave transmitting transducer.
In this embodiment, the machine learning recognition algorithm is a machine learning recognition algorithm commonly used in the art.
Application example:
the application example provides a sound wave signal measuring and processing method based on drill rod propagation, and the method is based on the sound wave signal measuring and processing method based on drill rod propagation.
The application example builds a test platform which consists of three parts: an acoustic signal transmitting device, a threaded drill string and an acoustic signal receiving device, refer specifically to fig. 2.
In the test platform of the application example, the number of drill rods is 200, the transmission distance is 120m, the type of the sound wave transmitting transducer is a rare earth giant magnetostrictive transducer, the driving voltage is about 400V, the output current peak value is about 40A, the instantaneous maximum output power can reach 16KW, the vibration impact force is 31N, and the transmitting frequency of sound waves is 5Hz. The double-channel sound wave receiving sensor is a vibration piezoelectric acceleration sensor. The sensitivity is 500mV/g and 250mV/g respectively, and the sampling frequency of the data of the acoustic wave receiving sensor is 62500 points/s.
For the hardware experiment system constructed in fig. 2, 1000 sound wave communication experiments are performed according to the specific operation cases implemented by the sound wave signal measurement and processing method based on drill rod propagation provided in the above embodiment, experimental data are shown in fig. 5, 6 and 7, when data "1" is transmitted at the transmitting end, the sound wave transmitting transducer is driven to transmit the sound wave signal, the original signal received by the receiving sensor is shown in fig. 5, the signal-to-noise ratio is 20.2dB, the signal-to-noise ratio after subtraction filtering by adopting the EMD domain is shown in fig. 6, and the signal-to-noise ratio is 28.5dB. The filtered signal is subjected to wiener deconvolution again, and the obtained acoustic signal time domain diagram is shown in fig. 7, and it can be seen that the waveform of fig. 7 has significant unit digital electrical signal characteristics.
Comparative example 1:
this comparative example shows a drill pipe acoustic wave communication detection method which differs from the embodiment in that only a pair of receiving sensors of the sensor 1 and the sensor 3 is used in step one, and other steps are the same as the embodiment.
Comparative example 2:
this comparative example shows a drill pipe acoustic wave communication detection method which differs from the embodiment in that only a pair of receiving sensors of the sensor 2 and the sensor 4 is used in the first step, and other steps are the same as the embodiment.
Comparative example 3:
the comparative example shows a drill pipe acoustic communication detection method, which is different from the embodiment in that the third and fourth steps are omitted, and other steps are the same as the embodiment.
Comparative example 4:
the comparative example shows a drill pipe acoustic wave communication detection method, and the method is different from the embodiment in that the fifth step is omitted, and other steps are the same as the embodiment.
Comparative tests were carried out on the above comparative examples 1 to 4 and examples of the present invention in the test platforms as given in the above application examples, and specific results are shown in table 1.
Table 1 test results of comparative examples 1 to 4 and examples of the present invention
| Method | Signal to noise ratio of received digital electrical signal | Digital electric signal identification accuracy |
| Comparative example 1 | 26.5dB | 88.1％ |
| Comparative example 2 | 23.7dB | 80.3％ |
| Comparative example 3 | 20.2dB | 65.3％ |
| Comparative example 4 | 28.5dB | 91.6％ |
| Embodiments of the invention | 28.5dB | 98.5％ |
Analysis of results: as shown in table 1, in comparative example 1, only the high sensitivity sensor 1 and the sensor 3 are used to receive the background noise and the digital electric signal, respectively, the high sensitivity characteristic makes the signal-to-noise ratio of the received signal high, and the recognition accuracy of the digital signal reaches 88.1%, but when the amplitude of the transmitted signal is large, the signal waveform distortion caused by overload of the receiving circuit occurs, and a recognition error with a certain probability occurs. In comparative example 2, only the low-sensitivity sensor 2 and the sensor 4 are used for respectively receiving the background noise and the digital electric signal, the sensitivity limit makes the signal-to-noise ratio of the sensor to the received signal low, only 23.7dB, and the recognition accuracy of the digital signal is only 80.3%. In comparative example 3, the digital electric signal received by the vibration sensor is not filtered and noise reduced, so that the quality of the received signal is seriously affected by the existence of surrounding background noise, which is directly expressed by that the signal-to-noise ratio is not high, only 20.2dB, and the time domain signal waveform is shown in fig. 5. In comparative example 4, the signal-to-noise ratio of the received electrical signal is significantly improved and the final digital electrical signal recognition rate is also significantly improved due to the filtering noise reduction, but due to the transfer function of the transmission system, the phenomenon of time domain waveform distortion may occasionally occur, and the signal recognition unit may misjudge the received "1" as "0", or misjudge the received "0" as "1", and although the probability of occurrence of this situation is not large, the method implemented in comparative example 4 cannot be completely eliminated. In the case of implementing the method according to the invention, noise reduction is carried out aiming at background noise, the signal-to-noise ratio of a received signal is improved, deconvolution compensation is carried out aiming at the transfer function influence of a transmission system, and meanwhile, two vibration sensors with different sensitivities are arranged according to different amplitude ranges of the received signal, so that distortion caused by overload of the amplitude of the signal is avoided, the final recognition rate reaches 98.5%, and the signal measurement and recognition requirements of acoustic wave communication under the mine are met.
Claims (9)
1. The method for measuring and processing the acoustic signal based on the drill rod propagation is characterized by comprising the following steps:
step one, layout sensor:
arranging a double-channel acoustic wave receiving sensor at a receiving end; the double-channel acoustic wave receiving sensor comprises a vibration sensor 1, a vibration sensor 2, a vibration sensor 3 and a vibration sensor 4;
the vibration sensor 1 and the vibration sensor 3 are used for collecting background noise and for responding to measurement;
the vibration sensor 2 and the vibration sensor 4 are used for collecting coded vibration signals in the drilling in real time;
the sensitivity of the vibration sensor 1 and the sensitivity of the vibration sensor 3 are larger than those of the vibration sensor 2 and the vibration sensor 4;
step two, collecting background noise signals:
the drill rod machine is kept in a normal working state, an acoustic wave transmitting transducer of an acoustic wave transmitting device stops working, a vibration sensor 1 or a vibration sensor 3 senses background noise of surrounding environment and converts the background noise into an electric signal, and the vibration sensor 1 or the vibration sensor 3 collects underground background noise with sampling frequency as fs and stores the background noise;
step three, calculating a transfer function:
the sound wave transmitting device generates a unit impact signal, the unit impact signal is transmitted to the vibration sensor 1 or the vibration sensor 3 through the drill rod, the vibration sensor 1 or the vibration sensor 3 acquires and stores the signal, and a transfer function in a frequency domain is obtained through a Fourier transform method;
step four, EMD domain spectrum subtraction filtering:
respectively carrying out EMD (empirical mode decomposition) on a background noise signal acquired by the vibration sensor 1 or the vibration sensor 3 and a digital electric signal containing noise acquired by the vibration sensor 2 or the vibration sensor 4, and then carrying out EMD domain spectrum subtraction on the obtained IMF to obtain a digital electric signal without noise;
in the fourth step, the specific process of the step is as follows:
let x (t) be the signal, u (t) and v (t) be the upper and lower envelopes of the signal, respectively, and then the average curve of the upper and lower envelopes is m (t), m (t) = [ u (t) +v (t) ]/2;
the residual part after subtracting m (t) from x (t) is h 1 (t)，h 1 (t)＝x(t)-m(t)；
Wherein:
t represents a time variable;
replacing x (t) with residual error, then obtaining the sum of the upper envelope curve and the lower envelope curve, and repeating the previous process until the obtained IMF condition is met; finally, all IMFs and residual amounts are obtained, the original x (t) is the sum of all IMFs and residual amounts, and signals obtained after EMD decomposition of background noise and digital electric signals are respectively:
wherein:
x 1 (t) represents a background noise signal received by the sensor;
x 2 (t) represents the digital electrical signal received by the sensor;
n represents the number of IMF components;
c 1i representing IMF components obtained after EMD decomposition of the background noise signal;
c 2i representing IMF components obtained after EMD decomposition of the digital electrical signals;
r 1n representing residual signals obtained by decomposing background noise signals IMF;
r 2n representing residual signals obtained by IMF decomposition of the digital electric signals;
for x 1 (t) and x 2 (t) subtracting to obtain denoised IMF and residual, and summing the denoised IMF and residual to recover denoised digital electrical signals;
step five, deconvolution filtering:
from the transfer function H (ω) obtained in step three, the transfer function H (ω) is decomposed into a minimum phase portion and an all-pass portion, H (ω) =h min (ω)H all (ω)；
Wherein:
H min (ω) represents the minimum phase portion;
H all (ω) represents an all-pass portion;
and (3) designing a wiener deconvolution filter for the minimum phase part, and deconvoluting and filtering the digital electric signal filtered by the vibration sensor 2 or the vibration sensor 4 in the fourth step by adopting the wiener deconvolution filter.
2. The method of measuring and processing acoustic signals based on drill pipe propagation according to claim 1, wherein in step one, the sensitivity of the vibration sensor 1 is lower than the sensitivity of the vibration sensor 3; the sensitivity of the vibration sensor 2 is lower than the sensitivity of the vibration sensor 4.
3. The method for measuring and processing acoustic signals based on drill rod propagation according to claim 1, wherein in the first step, the two-channel acoustic receiving sensors are piezoelectric vibration sensors.
4. The method for measuring and processing acoustic signals based on drill rod propagation according to claim 1, wherein in the second step, the sampling frequency of the vibration sensor 1 or the vibration sensor 3 is fs which is 2 times greater than the highest working frequency of the transmitted acoustic signals, and the sampling time of the vibration sensor 1 is not less than 20s.
5. The method for measuring and processing acoustic signals based on drill pipe propagation according to claim 1, wherein in the third step, the expression of the unit impact signal is:
wherein:
delta represents a unit impact signal;
n represents a discrete form of time t.
6. The method for measuring and processing acoustic signals based on drill rod propagation according to claim 1, wherein in the third step, the unit impact signal collected by the vibration sensor 1 or the vibration sensor 3 is denoted as h (n), and the transfer function in the frequency domain can be obtained by performing fast fourier transform on h (n):
H(ω)＝FFT(h(n))；
wherein:
ω represents a frequency variable;
h (ω) represents a frequency domain representation of the transfer function;
the FFT represents the fast fourier transform.
7. The method of claim 1, wherein in step three, the unit impulse signal is a gaussian white noise signal or an MLS sequence.
8. The method for measuring and processing acoustic signals based on drill pipe propagation according to claim 1, wherein in the fifth step, the minimum phase portion can be obtained by:
C p (ω)＝log(H(ω))
c p (n)＝IFFT(C p (ω))
wherein:
C p (ω) represents a frequency domain representation of the complex cepstrum of H (ω);
c p (n) represents a time domain representation of the complex cepstrum of H (ω);
H min (ω) represents the minimum phase of H (ω);
n represents a discrete form of time t;
n represents the number of data points;
the IFFT represents an inverse fourier transform;
log represents a log operation;
exp represents an exponential operation.
9. The method for measuring and processing acoustic signals based on drill pipe propagation according to claim 1, wherein in the fifth step, the wiener deconvolution filter is expressed in a frequency domain as:
wherein:
w (ω) represents the frequency domain representation of the designed wiener deconvolution filter;
H min * (ω) represents the transfer function minimum phase portion |H min Conjugated forms of (ω) |;
epsilon is the regularization constant.
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