Method and system for calculating interval transit time

Abstract

The invention relates to the technical field of oil exploration and development, in particular to a method and system for calculating interval transit time. The method comprises the following steps: S1, processing acoustic waveform data sampled by an acoustic logging instrument; s2, determining a positive peak threshold value, a negative peak threshold value and a cut-off search window according to front noise existing in the sound wave waveform data; s3, searching all maximum values and minimum values in the waveform sequence in the initial search window and the cut-off search window, dividing the maximum values and the minimum values into a plurality of maximum value-minimum value arrays, and calculating to obtain variances of the arrays; s4, determining the positions of a positive peak and a negative peak in the maximum-minimum array; s5, calculating the final interval transit time according to the interval transit time in the receiving mode and the emission mode of the sound wave receiver; s6, determining the interval transit time selection range; the accuracy of calculating the interval transit time in acoustic logging is improved, and a new verification method is provided for acoustic logging information quality control.

Description

Technical Field

[0001] The present invention relates to the technical field of oil exploration and development, and in particular to a method and system for calculating acoustic wave time difference. Background Art

[0002] Acoustic velocity logging is a logging method that measures the acoustic velocity of the rock formation downhole, and then determines the lithology of the rock formation outside the well, and estimates the reservoir porosity. This method places a transmitting probe and a receiving probe in the well, records the time difference of the sound wave from the transmitting probe through the formation to the receiving probe, and uses the time difference logging curve to calculate the reservoir porosity and identify the lithology accordingly, especially for identifying gas-bearing reservoirs. The algorithm for calculating the acoustic time difference in acoustic logging is the threshold method. This algorithm uses the fact that the longitudinal wave arrives first and that the longitudinal wave has a large waveform amplitude (strong energy). By setting a waveform amplitude threshold value, the noise before the longitudinal wave is separated from the longitudinal wave arrival, so as to obtain the arrival time of the first longitudinal wave arrival and then calculate the acoustic time difference. However, since the acoustic logging instrument is affected by factors such as wellbore expansion, mud invasion, formation thickness and the instability of the instrument itself, the waveform data is not consistent with the waveform obtained by theoretical calculation. The first arrival of the longitudinal wave may be delayed or the noise amplitude may be too large, resulting in the first arrival obtained by the threshold method being not the first arrival of the longitudinal wave.

[0003] The prior art CN115407405A discloses a method for calculating array acoustic wave slowness in a laboratory, and describes a method for measuring acoustic wave time difference in a laboratory. The method has less human interference, simple parameter setting, and strong generalization ability, and is suitable for longitudinal wave slowness extraction of conventional digital acoustic wave instruments and array acoustic wave instruments; the unsupervised machine learning method using conditional fuzzy clustering and BIC information criterion has high computational efficiency.

[0004] Prior art CN112558159A discloses a method for first arrival picking of acoustic logging, describes a method for improving the signal-to-noise ratio calculation method based on the conventional matrix singular value decomposition method by using subspace classes and weighting functions, and proposes a new method for full wave time difference extraction of array acoustic logging. Compared with the STC time difference extraction result, the improved singular value decomposition method can reliably extract the array acoustic wave time difference with high calculation efficiency.

[0005] However, the above two methods have a relatively fixed selection of threshold values, which may lead to inaccurate confirmation of the first arrival of the longitudinal wave, thereby leading to inaccurate calculation of the acoustic wave time difference.

[0006] Therefore, there is an urgent need to provide a method and system for calculating the acoustic wave time difference, which can improve the accuracy of the acoustic wave time difference calculation compared with the prior art. Summary of the invention

[0007] The present invention solves the technical problems existing in the prior art and provides a method and system for calculating the time difference of sound waves.

[0008] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0009] A method for calculating the time difference of sound waves comprises the following steps:

[0010] S1. Processing the acoustic waveform data sampled by the acoustic logging instrument to obtain waveform data with unified mean, standard deviation and sampling interval;

[0011] S2, determining a positive peak threshold value, a negative peak threshold value and a cutoff search window according to the front noise present in the sound wave waveform data;

[0012] S3, searching for all the maximum values ​​and minimum values ​​in the waveform sequence within the start search window and the end search window, dividing these maximum values ​​and minimum values ​​into a plurality of maximum value-minimum value arrays, and calculating the variances of these maximum value-minimum value arrays;

[0013] S4, comparing the maximum-minimum array obtained in S3 with the set constraints respectively, and confirming the positive peak and negative peak positions in the maximum-minimum array according to the position determination method corresponding to the constraints;

[0014] S5. Calculate the final sound wave time difference according to the sound wave time difference in the receiving mode and the transmitting mode of the sound wave receiver;

[0015] S6. Determine the range of the sound wave time difference.

[0016] Furthermore, S1 specifically includes the following steps:

[0017] S101, performing a preprocessing operation on the acoustic waveform data sampled by the acoustic logging instrument to remove outliers in the acoustic waveform data;

[0018] S102, segmenting the acoustic waveform data according to the number of receiving channels of the acoustic logging instrument;

[0019] S103, performing standardization processing on the acoustic waveform data to form acoustic waveform data with a unified mean and a unified standard deviation;

[0020] S104, interpolating the sound wave waveform data standardized in S103 to form sound wave waveform data with uniform intervals.

[0021] Furthermore, the specific method of step S101 is: replacing the outlier value in the sound wave waveform data sampled in step S1 with the average value near the value, wherein the outlier value is defined as a value exceeding the upper percentile threshold and the lower percentile threshold.

[0022] Furthermore, the upper percentile threshold is set to 97 and the lower percentile threshold is set to 3.

[0023] Furthermore, the specific method of step S103 is: using the normalize function to process the acoustic waveform data after waveform segmentation into acoustic waveform data with a mean of 0 and a standard deviation of 1.

[0024] Furthermore, the interval of the sound waveform data formed in step S104 is set to 1 us.

[0025] Furthermore, S2 specifically includes the following steps:

[0026] S201, calculating the mean and variance of the front noise present in the sound wave waveform data, setting a mean threshold, comparing the mean of the front noise with the set mean threshold, and obtaining corresponding positive peak threshold and negative peak threshold according to different comparison results;

[0027] S202, setting a waveform amplitude threshold, and setting the position of the cutoff search window to a time series position where the acoustic waveform amplitude is greater than or equal to the set amplitude threshold.

[0028] Furthermore, the specific comparison method of the pre-noise mean and the set mean threshold in step S201 is:

[0029] (1) When the mean value of the current noise is greater than or equal to the mean threshold, then:

[0030] Positive Thresholds=abs(mean+3×variance)

[0031] Negative Thresholds=-abs(3×variance)

[0032] (2) When the mean of the current noise is less than the mean threshold, then:

[0033] Positive Thresholds=abs((2×mean+3×variance)×2)

[0034] Negative Thresholds=-abs(3×variance)

[0035] In the above formula, Positive Thresholds represents the positive peak threshold, abs represents its absolute value, mean represents the mean, variance represents the variance, and Negative Thresholds represents the negative peak threshold.

[0036] Furthermore, the set mean threshold is 0.15.

[0037] Furthermore, the waveform amplitude threshold is set to 2.

[0038] Furthermore, there are three kinds of constraints in step S4, namely, the first constraint, the second constraint and the third constraint, and there are also three corresponding position determination methods. The first constraint corresponds to the first position determination method, the second constraint corresponds to the second position determination method, and the third constraint corresponds to the third position determination method.

[0039] Furthermore, the specific contents of the first constraint condition, the second constraint condition, and the third constraint condition are as follows:

[0040] (1) The first constraint:

[0041] 1) The minimum value is after the initial search window;

[0042] 2) The minimum value is less than the minimum value threshold;

[0043] 3) The minimum value is before the cutoff search window;

[0044] 4) The maximum value is after the initial search window;

[0045] 5) The maximum value is greater than the maximum value threshold;

[0046] 6) The difference between the two maximum values ​​in the maximum-minimum value is greater than the difference between the two minimum values;

[0047] 7) The variance value of the maximum-minimum array is greater than the set variance threshold;

[0048] (2) Second constraint:

[0049] 1) The maximum value is after the initial search window;

[0050] 2) The maximum value is greater than the maximum value threshold;

[0051] 3) The maximum value is before the cutoff search window;

[0052] 4) The variance of the maximum-minimum array is greater than the set variance threshold;

[0053] (3) The third constraint:

[0054] 1) The minimum value is after the initial search window;

[0055] 2) The minimum value is less than the minimum value threshold;

[0056] 3) The minimum value is before the cutoff search window;

[0057] 4) The variance of the maximum-minimum array is greater than the set variance threshold;

[0058] The specific contents of the first location determination method, the second location determination method, and the third location determination method are as follows:

[0059] (1) A first position determination method: setting the position of the maximum value in the maximum value-minimum value array to the positive peak position of the first longitudinal wave, and the position of the minimum value to the negative peak position of the first longitudinal wave;

[0060] (2) A second position determination method: setting the position of the maximum value in the maximum-minimum array as the positive peak position of the first longitudinal wave, and then confirming the negative peak position of the first longitudinal wave according to the positional relationship between the positive peak and the negative peak in the maximum-minimum array;

[0061] (3) The third position determination method: set the position of the minimum value in the maximum-minimum array to the negative peak position of the first wave of the longitudinal wave, and then confirm the positive peak position of the first wave of the longitudinal wave based on the positional relationship between the positive peak and the negative peak in the maximum-minimum array.

[0062] Furthermore, the final acoustic wave time difference in step S5 is calculated by the following formula:

[0063]

[0064]

[0065]

[0066] In the above formula, is the time difference of the ith sound wave in the receiving mode, Indicates the time position of the i+1th peak in the receiving mode, Indicates the time position of the i-th peak in the receiving mode, Indicates the time difference of the i-th sound wave in the transmission mode, Indicates the time position of the i+1th peak in the emission mode, represents the time position of the ith wave peak in the transmission mode, R represents the spacing of the sound wave receivers, Δt i Represents the i-th final sound wave time difference.

[0067] Furthermore, the maximum-minimum array described in step S3 includes two maximum values ​​and two minimum values.

[0068] A system for calculating sound wave time difference comprises a data processing module, a noise processing module, a peak processing module and a calculation module which are connected in sequence, wherein the data processing module is used to execute the operation content in step S1, the noise processing module is used to execute the operation content in step S2, the peak processing module is used to execute the operation content in step S3, and the calculation module is used to execute the operation content in steps S4-S6.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] (1) The present invention first performs unified processing on the acoustic waveform data, then determines the threshold value and the cutoff search window according to the front noise of the acoustic waveform data, further confirms the time position of the positive peak and the negative peak in the acoustic waveform, and finally takes the average value of the acoustic time difference under different modes as the final acoustic time difference, thereby improving the accuracy of acoustic logging in calculating the acoustic time difference and providing a new verification method for the quality control of acoustic logging data. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 is a flow chart of the method of the present invention.

[0072] Figure 2 It is a schematic diagram of the system of the present invention. DETAILED DESCRIPTION

[0073] The technical solution of the present invention will be clearly described below in conjunction with the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention, and all other embodiments obtained by ordinary technicians in the field without making creative work are within the protection scope of the present invention.

[0074] like Figure 1 As shown, the present invention provides a method for calculating the time difference of sound waves, comprising the following steps:

[0075] S1, processing the acoustic waveform data sampled by the acoustic logging instrument to obtain waveform data with a mean of 0, a standard deviation of 1, a sampling interval of 1us and no outliers, specifically comprising the following steps:

[0076] S101, preprocessing the acoustic waveform data sampled by the acoustic logging instrument, specifically: replacing the outlier value in the original sampled waveform data with the average value of the sum of the first 5 sampling points and the last 5 sampling points of the value, wherein the outlier value is defined as a value exceeding the upper percentile threshold and the lower percentile threshold, the upper percentile threshold number is set to 97, and the lower percentile threshold number is set to 3;

[0077] S102, segmenting the waveform data after waveform preprocessing according to the number of receiving channels of the acoustic logging instrument, specifically: since the original waveform data is to merge all the receiving channels into one data set, which is inconvenient for subsequent processing, in this step, the preprocessed waveform data is segmented according to the number of receiving channels of the acoustic logging instrument. The conventionally used acoustic logging instrument is generally a single transmitter and four receivers or a dual transmitter and four receivers, and the number of receiving channels is generally 4 or 8;

[0078] Furthermore, each specific waveform is divided in turn according to the number of sampling points in each channel, and the number of sampling points in each channel = the total number of sampling points / the number of receiving channels.

[0079] S103, standardizing the waveform data. Since there are many types of logging instruments in this work area, the logging instruments of different manufacturers have different settings in instrument parameters such as measurement method, acquisition method, waveform amplitude, etc. In order to make different types of logging instruments suitable for a sound wave time difference calculation method, it is necessary to standardize the sound wave waveform data collected by different types of logging instruments so that the sound wave time difference of different instruments remains consistent when reflecting the same formation. The specific method of standardization in this step is: use the normalize function to standardize the waveform data, and process the waveform data after waveform segmentation into waveform data with a mean of 0 and a standard deviation of 1;

[0080] S104, interpolate the waveform data after data standardization, and interpolate the waveform data of different sampling intervals into waveform data of sampling interval of 1us, so as to unify the different sampling intervals of different acoustic logging instruments, facilitate the subsequent accurate calculation of the first arrival of the longitudinal wave and other subsequent processing.

[0081] S2, according to the front noise existing in the sound wave waveform data, determine the positive and negative peak thresholds and the cutoff search window, specifically including the following steps:

[0082] S201. Aiming at the common pre-noise in the acoustic logging waveform data, a large amount of acoustic logging waveform data is analyzed to set appropriate pre-noise range parameters, which include pre-noise range, pre-noise mean threshold, and pre-noise variance, so as to calculate the statistical characteristics of the pre-noise, which are mean and variance. The mean threshold is used as another parameter for judging the noise distribution, and the mean threshold is set to 0.15. The corresponding relationship between the positive and negative peak thresholds and the noise statistical distribution is obtained by analyzing a large amount of waveform data. The positive and negative peak thresholds are specifically calculated by the following formula:

[0083] (1) When the mean value of the current noise is greater than or equal to the mean threshold, then:

[0084] Positive Thresholds=abs(mean+3×variance)

[0085] Negative Thresholds=-abs(3×variance)

[0086] (2) When the mean of the current noise is less than the mean threshold, then:

[0087] Positive Thresholds=abs((2×mean+3×variance)×2)

[0088] Negative Thresholds=-abs(3×variance)

[0089] In the above formula, Positive Thresholds represents the positive peak threshold, abs represents its absolute value, mean represents the mean, variance represents the variance, and Negative Thresholds represents the negative peak threshold.

[0090] S202. Determine the cutoff search window. The standardized sound wave waveform is a time series with a mean of 0 and a variance of 1, and the waveform amplitude of the longitudinal wave in the sound wave waveform sequence is much smaller than the waveform amplitude of the subsequent wave. Therefore, when the subsequent calculation of the first arrival of the longitudinal wave is performed, the cutoff search window should be truncated before the subsequent wave. By analyzing the difference between the longitudinal wave waveform amplitude and the subsequent wave waveform amplitude, the waveform amplitude threshold is set to 2. When the sound wave waveform amplitude is greater than or equal to 2, the position of the cutoff search window is set to the position of this time series.

[0091] S3. Search for all the maximum and minimum values ​​in the waveform sequence within the starting search window and the ending search window. Among the multiple maximum values ​​searched, there is the first positive peak of the longitudinal wave, and among the minimum values ​​searched, there is the first negative peak of the longitudinal wave. These maximum and minimum values ​​are divided into several combinations of four maximum values ​​and four minimum values. Each combination can be expressed as: maximum value-minimum value-maximum value-minimum value. Calculate the variance of these combinations to obtain an array of multiple combinations and the variance of each combination.

[0092] S4. Compare the multiple maximum-minimum arrays obtained in S3 with the set constraints respectively, and determine the positions of all positive peaks and negative peaks in the maximum-minimum arrays according to the position determination method corresponding to the constraints.

[0093] Furthermore, there are three types of constraints, which are expressed as the first constraint, the second constraint, and the third constraint. The specific contents are as follows:

[0094] (1) The first constraint:

[0095] 1) The minimum value is after the initial search window;

[0096] 2) The minimum value is less than the minimum value threshold;

[0097] 3) The minimum value is before the cutoff search window;

[0098] 4) The maximum value is after the initial search window;

[0099] 5) The maximum value is greater than the maximum value threshold;

[0100] 6) The difference between the two maximum values ​​in the maximum-minimum value is greater than the difference between the two minimum values;

[0101] 7) The variance value of the maximum-minimum array is greater than the set variance threshold.

[0102] (2) Second constraint:

[0103] 1) The maximum value is after the initial search window;

[0104] 2) The maximum value is greater than the maximum value threshold;

[0105] 3) The maximum value is before the cutoff search window;

[0106] 4) The variance of the maximum-minimum array is greater than the set variance threshold.

[0107] (3) The third constraint:

[0108] 1) The minimum value is after the initial search window;

[0109] 2) The minimum value is less than the minimum value threshold;

[0110] 3) The minimum value is before the cutoff search window;

[0111] 4) The variance of the maximum-minimum array is greater than the set variance threshold.

[0112] Furthermore, the variance threshold is set to 0.06.

[0113] Furthermore, there are also the position determination methods corresponding to the three constraints, namely the first position determination method, the second position determination method and the third position determination method, the specific contents are as follows:

[0114] (1) The first position determination method: setting the positions of the two maximum values ​​in the maximum value-minimum value array as the positive peak positions of the first wave of the longitudinal wave, and the positions of the two minimum values ​​as the negative peak positions of the first wave of the longitudinal wave;

[0115] (2) A second position determination method: setting the positions of the two maximum values ​​in the maximum-minimum array as the positive peak positions of the first wave of the longitudinal wave, and then confirming the negative peak position of the first wave of the longitudinal wave according to the positional relationship between the positive peak and the negative peak in the maximum-minimum array;

[0116] (3) The third position determination method: set the positions of the two minimum values ​​in the maximum-minimum array as the negative peak positions of the first wave of the longitudinal wave, and then confirm the positive peak position of the first wave of the longitudinal wave based on the positional relationship between the positive peak and the negative peak in the maximum-minimum array.

[0117] S5. Calculate the acoustic time difference in the receiving mode and the transmitting mode, and then calculate the average of the acoustic time difference obtained in the two modes as the final acoustic time difference; the acoustic receivers in the acoustic logging instrument can be arranged in two ways in the depth direction, the first is the common transmitter mode, and the second is the common receiver mode. The common transmitter mode is when the selected depth interval is within the range of 4 receivers, 5 waveform combinations can be formed by moving the wave train, and the common receiver mode is a waveform combination composed of different waveforms at the same depth point in the horizontal direction. When the wellbore becomes smaller, the acoustic time difference value of the data combination in the common receiver mode will become smaller, while the acoustic time difference value of the data combination in the common transmitter mode will become larger. After averaging the time differences calculated in the common receiver mode and the common transmitter mode, the influence of some wellbore changes can be eliminated. The specific calculation formula is as follows:

[0118]

[0119]

[0120]

[0121] In the above formula, is the time difference of the ith sound wave in the receiving mode, Indicates the time position of the i+1th peak in the receiving mode, Indicates the time position of the i-th peak in the receiving mode, Indicates the time difference of the i-th sound wave in the transmission mode, Indicates the time position of the i+1th peak in the emission mode, represents the time position of the ith wave peak in the transmission mode, R represents the spacing of the sound wave receivers, Δt i Represents the i-th final sound wave time difference.

[0122] Furthermore, the acoustic time difference of the receiving mode and the transmitting mode are both calculated by the waveform in S4, but the two modes select different waveform combinations. The transmitting mode selects the waveform combination that records the same depth segment to calculate the acoustic time difference, and the receiving mode selects the receiver waveform at the same distance from the acoustic transmitter to calculate the acoustic time difference. The acoustic time difference finally output is the average of the acoustic time differences calculated by these two modes. The waveforms finally determined by these two modes and S4 are exactly the same, and the difference is the waveform combination.

[0123] S6. Due to the instability of the instrument, abnormal acoustic time differences calculated from abnormal waveforms may occur. Therefore, based on the analysis of a large amount of data in the work area, the approximate acoustic time differences of each layer can be known, thereby determining the range of acoustic time differences.

[0124] The present invention improves the accuracy of acoustic logging in calculating acoustic time difference and provides a new verification method for acoustic logging data quality control.

[0125] like Figure 2 As shown, the present invention also provides a system for calculating the time difference of sound waves, including a data processing module, a noise processing module, a peak processing module and a calculation module, the output end of the data processing module is connected to the noise processing module, the output end of the noise processing module is connected to the peak processing module, the output end of the peak processing module is connected to the calculation module, the data processing module is used to execute the operation content involved in step S1, the noise processing module is used to execute the operation content involved in step S2, the peak processing module is used to execute the operation content involved in step S3, and the calculation module is used to execute the operation content involved in S4-S6.

[0126] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions of the technical solution of the present invention by ordinary technicians in this field do not deviate from the essence and scope of the technical solution of the present invention.

Claims

1. A method for calculating the time difference of sound waves, It is characterized in that The following steps are involved: S1. Processing the acoustic waveform data sampled by the acoustic logging instrument to obtain waveform data with unified mean, standard deviation and sampling interval; S2, determining a positive peak threshold value, a negative peak threshold value and a cutoff search window according to the front noise present in the sound wave waveform data; S3, searching for all the maximum values ​​and minimum values ​​in the waveform sequence within the start search window and the end search window, dividing these maximum values ​​and minimum values ​​into a plurality of maximum value-minimum value arrays, and calculating the variances of these maximum value-minimum value arrays; S4, comparing the maximum-minimum array obtained in S3 with the set constraints respectively, and confirming the positive peak and negative peak positions in the maximum-minimum array according to the position determination method corresponding to the constraints; S5. Calculate the final sound wave time difference according to the sound wave time difference in the receiving mode and the transmitting mode of the sound wave receiver; S6. Determine the range of the sound wave time difference.

2. A method for calculating the time difference of sound waves according to claim 1, It is characterized in that S1 specifically includes the following steps: S101, performing a preprocessing operation on the acoustic waveform data sampled by the acoustic logging instrument to remove outliers in the acoustic waveform data; S102, segmenting the acoustic waveform data according to the number of receiving channels of the acoustic logging instrument; S103, performing standardization processing on the acoustic waveform data to form acoustic waveform data with a unified mean and a unified standard deviation; S104, interpolating the sound wave waveform data standardized in S103 to form sound wave waveform data with uniform intervals.

3. A method for calculating the time difference of sound waves according to claim 2, It is characterized in that The specific method of step S101 is: replace the outlier value in the sound wave waveform data sampled in step S1 with the average value near the value, where the outlier value is defined as a value exceeding the upper percentile threshold and the lower percentile threshold.

4. A method for calculating the time difference of sound waves according to claim 3, It is characterized in that The upper percentile threshold is set to 97 and the lower percentile threshold is set to 3.

5. A method for calculating the time difference of sound waves according to claim 2, It is characterized in that The specific method of step S103 is: using the normalize function to process the acoustic waveform data after waveform segmentation into acoustic waveform data with a mean of 0 and a standard deviation of 1.

6. A method for calculating the time difference of sound waves according to claim 2, It is characterized in that The interval of the sound wave waveform data formed in step S104 is set to 1 us.

7. A method for calculating the time difference of sound waves according to claim 1, It is characterized in that S2 specifically includes the following steps: S201, calculating the mean and variance of the front noise present in the sound wave waveform data, setting a mean threshold, comparing the mean of the front noise with the set mean threshold, and obtaining corresponding positive peak threshold and negative peak threshold according to different comparison results; S202, setting a waveform amplitude threshold, and setting the position of the cutoff search window to a time series position where the acoustic waveform amplitude is greater than or equal to the set amplitude threshold.

8. A method for calculating the time difference of sound waves according to claim 7, It is characterized in that The specific comparison method of the pre-noise mean and the set mean threshold in step S201 is: (1) When the mean value of the current noise is greater than or equal to the mean threshold, then: Positive Thresholds=abs(mean+3×variance) Negative Thresholds=-abs(3×variance) (2) When the mean of the current noise is less than the mean threshold, then: Positive Thresholds=abs((2×mean+3×variance)×2) Negative Thresholds=-abs(3×variance) In the above formula, Positive Thresholds represents the positive peak threshold, abs represents its absolute value, mean represents the mean, variance represents the variance, and Negative Thresholds represents the negative peak threshold.

9. A method for calculating the time difference of sound waves according to claim 8, It is characterized in that The set mean threshold is 0.

15.

10. A method for calculating the time difference of sound waves according to claim 7, It is characterized in that The waveform amplitude threshold is set to 2.

11. A method for calculating the time difference of sound waves according to claim 1, It is characterized in that There are three kinds of constraints in step S4, namely the first constraint, the second constraint and the third constraint. There are also three corresponding position determination methods. The first constraint corresponds to the first position determination method, the second constraint corresponds to the second position determination method, and the third constraint corresponds to the third position determination method.

12. A method for calculating the time difference of sound waves according to claim 11, It is characterized in that The specific contents of the first constraint, the second constraint, and the third constraint are as follows: (1) The first constraint: 1) The minimum value is after the initial search window; 2) The minimum value is less than the minimum value threshold; 3) The minimum value is before the cutoff search window; 4) The maximum value is after the initial search window; 5) The maximum value is greater than the maximum value threshold; 6) The difference between the two maximum values ​​in the maximum-minimum value is greater than the difference between the two minimum values; 7) The variance value of the maximum-minimum array is greater than the set variance threshold; (2) Second constraint: 1) The maximum value is after the initial search window; 2) The maximum value is greater than the maximum value threshold; 3) The maximum value is before the cutoff search window; 4) The variance of the maximum-minimum array is greater than the set variance threshold; (3) The third constraint: 1) The minimum value is after the initial search window; 2) The minimum value is less than the minimum value threshold; 3) The minimum value is before the cutoff search window; 4) The variance of the maximum-minimum array is greater than the set variance threshold; The specific contents of the first location determination method, the second location determination method, and the third location determination method are as follows: (1) A first position determination method: setting the position of the maximum value in the maximum value-minimum value array to the positive peak position of the first longitudinal wave, and the position of the minimum value to the negative peak position of the first longitudinal wave; (2) A second position determination method: setting the position of the maximum value in the maximum-minimum array as the positive peak position of the first longitudinal wave, and then confirming the negative peak position of the first longitudinal wave according to the positional relationship between the positive peak and the negative peak in the maximum-minimum array; (3) The third position determination method: set the position of the minimum value in the maximum-minimum array to the negative peak position of the first wave of the longitudinal wave, and then confirm the positive peak position of the first wave of the longitudinal wave based on the positional relationship between the positive peak and the negative peak in the maximum-minimum array.

13. A method for calculating the time difference of sound waves according to claim 1, It is characterized in that The final acoustic time difference in step S5 is calculated by the following formula: In the above formula, is the time difference of the ith sound wave in the receiving mode, Indicates the time position of the i+1th peak in the receiving mode, Indicates the time position of the i-th peak in the receiving mode, Indicates the time difference of the i-th sound wave in the transmission mode, Indicates the time position of the i+1th peak in the emission mode, represents the time position of the ith wave peak in the transmission mode, R represents the spacing of the sound wave receivers, Δt i Represents the i-th final sound wave time difference.

14. A method for calculating the time difference of sound waves according to claim 1, It is characterized in that The maximum-minimum array described in step S3 includes two maximum values ​​and two minimum values.

15. A system using the method for calculating the acoustic wave time difference according to any one of claims 1 to 14, It is characterized in that It includes a data processing module, a noise processing module, a peak processing module and a calculation module connected in sequence, the data processing module is used to execute the operation content in step S1, the noise processing module is used to execute the operation content in step S2, the peak processing module is used to execute the operation content in step S3, and the calculation module is used to execute the operation content in S4-S6.

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