Underwater plosive analysis method and device

Abstract

The invention provides an underwater plosive sound analysis method and device, and relates to the technical field of underwater noise analysis, and the method comprises the steps: obtaining a plurality of sound wave signals received by a monitoring point; respectively analyzing the actual propagation time and the actual signal intensity of each sound wave signal; based on a preset sound velocity profile model, calculating sound wave propagation velocities of different depths of a preset water area; performing prediction path analysis based on the sound wave propagation speed to obtain a plurality of prediction paths; according to the actual propagation time and the actual signal intensity, performing propagation time matching and signal intensity matching on the plurality of prediction paths, and determining a target prediction path; based on the target prediction path, performing blasting source position calculation to obtain a predicted blasting source position; and if it is judged that the predicted blasting source position is consistent with the preset blasting source position, determining that the target predicted path is a blasting sound propagation path. According to the method, the propagation path of the underwater plosive sound can be analyzed.

Description

Technical Field

[0001] The present application relates to the technical field of underwater noise analysis, and in particular to an underwater blast sound analysis method and device. Background Art

[0002] Thermocline is a horizontal stratification phenomenon formed in the ocean or large lakes due to the rapid change of water temperature. The existence of thermocline makes the water body divided into different layers in the vertical direction. Its characteristic is that the water temperature drops rapidly with depth from the warm water body on the surface to the cold water body below. Thermocline is of great significance in physical oceanography, especially in the fields of ocean acoustics, marine biology and climate research.

[0003] Underwater blasting is one of the commonly used engineering methods when excavating waterways or canals in lakes. However, the noise generated by underwater blasting may have a significant impact on the surrounding ecological environment, especially the survival and behavior of aquatic organisms. Therefore, it is crucial to conduct a detailed underwater blasting noise analysis before implementing underwater blasting. By predicting the sound pressure level, sound wave propagation range and frequency characteristics generated during the blasting process, a scientific basis can be provided for the formulation of effective noise reduction measures, thereby minimizing the negative impact on the ecological environment and protecting the biodiversity and ecosystem balance in the lake.

[0004] The propagation path analysis of underwater blasting sound is a key research topic in underwater blasting engineering. Due to the uniqueness of the underwater environment, the propagation of sound waves in water is affected by many factors, including water depth, water temperature, salinity, bottom characteristics, and suspended particles in the water. For thermoclines, due to changes in water temperature and pressure, the speed of sound changes with depth, causing sound waves to refract in the water. Generally, the sound velocity profile affects the propagation path of sound waves. For example, when the water surface is warmer and the deep layer is colder, the sound velocity profile forms a positive thermocline, and sound waves may propagate in shallower water layers, while sound waves in deeper layers may refract downward. Therefore, a method is needed to analyze the propagation path of underwater blasting sounds. Summary of the invention

[0005] The present application provides an underwater plosive sound analysis method and device, which can analyze the propagation path of underwater plosive sounds.

[0006] In a first aspect of the present application, a method for analyzing underwater plosive sound is provided, the method comprising:

[0007] Acquire multiple sound wave signals received by the monitoring point;

[0008] Respectively analyzing the actual propagation time and actual signal strength of each of the sound wave signals;

[0009] Based on the preset sound velocity profile model, calculate the sound wave propagation speed at different depths in the preset water area;

[0010] Performing predicted path analysis based on the sound wave propagation speed to obtain multiple predicted paths;

[0011] According to the actual propagation time and the actual signal strength, performing propagation time matching and signal strength matching on the plurality of predicted paths to determine a target predicted path;

[0012] Based on the target predicted path, the blasting source position is calculated to obtain the predicted blasting source position;

[0013] If it is determined that the predicted blast source position is consistent with the preset blast source position, the target predicted path is determined to be the blast sound propagation path.

[0014] Optionally, respectively analyzing the actual propagation time and the actual signal strength of each of the sound wave signals specifically includes:

[0015] Preprocessing the sound wave signal to obtain a processed signal;

[0016] Performing Fourier transform on the processed signal to obtain a first frequency domain transform result, and performing Fourier transform on a reference signal of the processed signal to obtain a second frequency domain transform result;

[0017] Taking a conjugate of the first frequency domain transformation result to obtain a conjugate result;

[0018] multiplying the conjugation result and the second frequency domain transformation result to obtain a correlation result;

[0019] The correlation result is subjected to inverse Fourier transform to obtain a cross-correlation function, the expression of which is as follows:

[0020] R xy (t) = ∫x(τ)y(τ+t)dτ

[0021] Among them, R xy (t) is the cross-correlation function, x(τ) is the reference signal, and y(τ+t) is the processed signal;

[0022] Querying the maximum value of the cross-correlation function to obtain a hysteresis;

[0023] The propagation delay of the acoustic wave signal is calculated according to the lag amount and the preset sampling rate to obtain the actual propagation time.

[0024] Optionally, performing propagation time matching and signal strength matching on the plurality of predicted paths according to the actual propagation time and the actual signal strength to determine a target predicted path specifically includes:

[0025] Performing time-frequency analysis on the reference signal to calculate initial signal strength;

[0026] Based on the sound wave propagation distance corresponding to the target predicted path, the signal attenuation is calculated, which is specifically calculated by the following formula:

[0027] L total =20log 10 (r)+αr

[0028] Among them, L total is the signal attenuation, r is the sound wave propagation distance, and α is the sound wave energy absorption coefficient;

[0029] Calculating a first signal strength according to the initial signal strength and the signal attenuation;

[0030] Performing short-time energy extraction on the processed signal to obtain short-time energy in multiple time windows;

[0031] The second signal strength is calculated according to the short-time energy, specifically by the following formula:

[0032]

[0033] Among them, E total is the second signal strength, M is the window size, and x(nm) is the signal sample in the time window;

[0034] If it is determined that the difference between the first signal strength and the second signal strength is less than a preset strength threshold, the second signal strength is determined to be the actual signal strength of the sound wave signal, and the target predicted path is preliminarily determined to be a plosive sound propagation path.

[0035] Optionally, performing propagation time matching and signal strength matching on the plurality of predicted paths according to the actual propagation time and the actual signal strength to determine a target predicted path specifically further includes:

[0036] According to the preset sound velocity profile model, the preset water area is divided into a plurality of depth intervals;

[0037] Based on the preset sound velocity profile model, the sound wave propagation velocity in each depth interval is calculated, specifically by the following formula:

[0038] c(z)=c0+γz

[0039] Where c(z) is the sound wave propagation velocity at depth z, c0 is the initial sound wave propagation velocity in the depth interval, and γ is the sound velocity gradient;

[0040] Analyze the ray trajectory equation of the target prediction path:

[0041]

[0042] Where c(z) is the sound wave propagation velocity at depth z, dx is the sound wave propagation velocity along the horizontal distance component, and dz is the sound wave propagation velocity along the vertical distance component;

[0043] For any one of the multiple depth intervals, the interval sound wave propagation time is calculated, specifically by the following formula:

[0044]

[0045] Among them, Δt is the interval sound wave propagation time in the depth interval [z1, z2], dz is the sound wave propagation velocity along the vertical distance component, c0 is the initial sound wave propagation velocity in the depth interval, and γ is the sound velocity gradient;

[0046] The predicted sound wave transmission time of the target predicted path is calculated according to the interval sound wave propagation time, which is specifically calculated by the following formula:

[0047]

[0048] Among them, T total is the predicted sound wave transmission time, Δt i is the interval sound wave propagation time of the i-th depth interval;

[0049] If it is determined that the time difference between the predicted sound wave transmission time and the actual transmission time is less than a preset difference, the target predicted path is determined to be a plosive sound transmission path.

[0050] Optionally, the step of calculating the blasting source position based on the target predicted path to obtain the predicted blasting source position specifically includes:

[0051] For the segmented interface of any one of the plurality of depth intervals, the refraction angle of the blasting sound wave based on the target predicted path satisfies the following law:

[0052]

[0053] Wherein, θ1 is the angle of incidence at any depth interval, θ2 is the angle of refraction at any depth interval, c1 is the propagation speed of the sound wave entering any depth interval, and c2 is the propagation speed of the sound wave out of any depth interval;

[0054] According to the position of the monitoring point, the preset water area and the refraction angle, starting from the monitoring point and according to the target predicted path, the propagation direction and angle of the sound wave in each depth interval are calculated layer by layer, and the predicted blasting source position is reversely calculated.

[0055] Optionally, before calculating the sound wave propagation speeds at different depths in a preset water area based on a preset sound speed profile model, the method further includes:

[0056] Acquire environmental parameter data of the preset water area;

[0057] Preprocessing the environmental parameter data to obtain processed data;

[0058] Calculating the sound velocity values ​​at different depths by using an empirical formula according to the processed data;

[0059] Generating a sound velocity profile curve according to the sound velocity value;

[0060] Based on the sound speed profile curve, the preset sound speed profile model is constructed.

[0061] Optionally, performing predicted path analysis based on the sound wave propagation speed to obtain multiple predicted paths specifically includes:

[0062] Generating a plurality of optional paths according to the environmental parameter data of the preset water area;

[0063] Based on the sound wave propagation speed, a predicted path is screened out from the multiple optional paths, wherein the difference between the predicted sound wave propagation time corresponding to the predicted path and the actual propagation time is less than a preset threshold.

[0064] In a second aspect of the present application, an underwater blast sound analysis device is provided, the device comprising an acquisition module, a processing module and a judgment module, wherein:

[0065] The acquisition module is used to acquire multiple sound wave signals received by the monitoring point;

[0066] The processing module is used to analyze the actual propagation time and actual signal strength of each of the sound wave signals respectively;

[0067] The processing module is used to calculate the sound wave propagation speed at different depths in a preset water area based on a preset sound speed profile model;

[0068] The processing module is used to perform predicted path analysis based on the sound wave propagation speed to obtain multiple predicted paths;

[0069] The processing module is used to perform propagation time matching and signal strength matching on the plurality of predicted paths according to the actual propagation time and the actual signal strength, so as to determine a target predicted path;

[0070] The processing module is used to calculate the blasting source position based on the target predicted path to obtain the predicted blasting source position;

[0071] The judging module is used to determine that the target predicted path is a blast sound propagation path if it is judged that the predicted blast source position is consistent with the preset blast source position.

[0072] Optionally, the processing module is used to pre-process the sound wave signal to obtain a processed signal;

[0073] The processing module is used to perform Fourier transform on the processed signal to obtain a first frequency domain transform result, and perform Fourier transform on a reference signal of the processed signal to obtain a second frequency domain transform result;

[0074] The processing module is used to conjugate the first frequency domain transformation result to obtain a conjugate result;

[0075] The processing module is used to multiply the conjugate result and the second frequency domain transformation result to obtain a correlation result;

[0076] The processing module is used to perform an inverse Fourier transform on the correlation result to obtain a cross-correlation function, and the expression of the cross-correlation function is as follows:

[0077] R xy (t) = ∫x(τ)y(τ+t)dτ

[0078] Among them, R xy (t) is the cross-correlation function, x(τ) is the reference signal, and y(τ+t) is the processed signal;

[0079] The acquisition module is used to query the maximum value of the cross-correlation function to obtain the hysteresis;

[0080] The processing module is used to calculate the propagation delay of the sound wave signal according to the lag amount and the preset sampling rate to obtain the actual propagation time.

[0081] Optionally, the processing module is used to perform time-frequency analysis on the reference signal to calculate the initial signal strength;

[0082] The processing module is used to calculate the signal attenuation based on the sound wave propagation distance corresponding to the target prediction path, specifically by the following formula:

[0083] L total=20log 10 (r)+αr

[0084] Among them, L total is the signal attenuation, r is the sound wave propagation distance, and α is the sound wave energy absorption coefficient;

[0085] The processing module is used to calculate the first signal strength according to the initial signal strength and the signal attenuation;

[0086] The processing module is used to extract short-time energy from the processed signal to obtain short-time energy in multiple time windows;

[0087] The processing module is used to calculate the second signal strength according to the short-time energy, specifically by the following formula:

[0088]

[0089] Among them, E total is the second signal strength, M is the window size, and x(nm) is the signal sample in the time window;

[0090] The judgment module is used to determine that the second signal strength is the actual signal strength of the sound wave signal if it is determined that the difference between the first signal strength and the second signal strength is less than a preset strength threshold, and preliminarily determine that the target predicted path is a plosive sound propagation path.

[0091] Optionally, the processing module is used to divide the preset water area into a plurality of depth intervals according to the preset sound velocity profile model;

[0092] The processing module is used to calculate the sound wave propagation speed in each depth interval based on the preset sound speed profile model, specifically by the following formula:

[0093] c(z)=c0+γz

[0094] Where c(z) is the sound wave propagation velocity at depth z, c0 is the initial sound wave propagation velocity in the depth interval, and γ is the sound velocity gradient;

[0095] The processing module is used to analyze the ray trajectory equation of the target prediction path:

[0096]

[0097] Where c(z) is the sound wave propagation velocity at depth z, dx is the sound wave propagation velocity along the horizontal distance component, and dz is the sound wave propagation velocity along the vertical distance component;

[0098] The processing module is used to calculate the interval sound wave propagation time for any one of the multiple depth intervals, specifically by the following formula:

[0099]

[0100] Among them, Δt is the interval sound wave propagation time in the depth interval [z1, z2], dz is the sound wave propagation velocity along the vertical distance component, c0 is the initial sound wave propagation velocity in the depth interval, and γ is the sound velocity gradient;

[0101] The processing module is used to calculate the predicted sound wave transmission time of the target predicted path according to the interval sound wave propagation time, specifically by the following formula:

[0102]

[0103] Among them, T total is the predicted sound wave transmission time, Δt i is the interval sound wave propagation time of the i-th depth interval;

[0104] The judging module is configured to determine that the target predicted path is a plosive sound propagation path if it is judged that the time difference between the predicted sound wave transmission time and the actual propagation time is less than a preset difference.

[0105] Optionally, the processing module is configured to satisfy the following law for a segmented interface of any one of the plurality of depth intervals, based on a refraction angle of the blasting sound wave of the target predicted path:

[0106]

[0107] Wherein, θ1 is the angle of incidence at any depth interval, θ2 is the angle of refraction at any depth interval, c1 is the propagation speed of the sound wave entering any depth interval, and c2 is the propagation speed of the sound wave out of any depth interval;

[0108] The processing module is used to calculate the propagation direction and angle of the sound wave in each depth interval layer by layer based on the location of the monitoring point, the preset water area and the refraction angle, starting from the monitoring point and according to the target predicted path, and reversely calculate the predicted blasting source location.

[0109] Optionally, the acquisition module is used to acquire environmental parameter data of the preset water area;

[0110] The processing module is used to pre-process the environmental parameter data to obtain processed data;

[0111] The processing module is used to calculate the sound velocity values ​​at different depths through an empirical formula according to the processed data;

[0112] The processing module is used to generate a sound velocity profile curve according to the sound velocity value;

[0113] The processing module is used to construct the preset sound speed profile model based on the sound speed profile curve.

[0114] Optionally, the processing module is used to generate a plurality of optional paths according to the environmental parameter data of the preset water area;

[0115] The processing module is used to select a predicted path from the multiple optional paths based on the sound wave propagation speed, wherein the difference between the predicted sound wave propagation time corresponding to the predicted path and the actual propagation time is less than a preset threshold.

[0116] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods described above.

[0117] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any of the methods described above is executed.

[0118] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0119] 1. Since the thermocline causes sound waves to refract in the water, the propagation path of the sound waves is complicated. Therefore, the sound wave signal received by the monitoring point is first obtained, the actual propagation time and intensity of the signal are analyzed, and the sound wave propagation speed at different depths of the water is calculated in combination with the preset sound velocity profile model, and the possible propagation path of the sound wave is analyzed. Then, the predicted path is matched with the actual propagation time and signal intensity to determine the target predicted path. Finally, the path is used to reversely calculate the blasting source position and verify its consistency with the preset blasting source position, thereby realizing the accurate analysis and positioning of the propagation path of the underwater blasting sound.

[0120] 2. By accurately analyzing the actual propagation time and actual signal strength of the sound wave signal, combined with Fourier transform and cross-correlation function analysis, the propagation path of the plosive sound can be effectively identified. On this basis, through time-frequency analysis and signal attenuation model, the propagation path and intensity change of the sound wave are estimated and matched with the actual signal. Finally, the optimal propagation path related to the blast source position is identified through multi-path analysis, thereby achieving accurate positioning of the plosive sound propagation path.

[0121] 3. Accurately calculate the sound wave propagation speed in different depth intervals of the preset water area, and use the ray trajectory equation and integration method to gradually analyze the propagation path and propagation time of the sound wave in each depth interval. By matching the predicted sound wave propagation time with the actual propagation time and combining it with signal strength analysis, the propagation path of the blast sound is determined. This method can effectively identify the true propagation path of the sound wave, reduce errors, provide more accurate blast source location, improve the accuracy and reliability of underwater blast sound analysis, and provide a basis for subsequent blast sound impact analysis and noise control. BRIEF DESCRIPTION OF THE DRAWINGS

[0122] Figure 1 It is a flow chart of an underwater blast sound analysis method disclosed in an embodiment of the present application;

[0123] Figure 2 It is a schematic diagram of the propagation path of an underwater blasting sound disclosed in an embodiment of the present application;

[0124] Figure 3 It is a module schematic diagram of an underwater blasting sound analysis device disclosed in an embodiment of the present application;

[0125] Figure 4 It is a structural schematic diagram of an electronic device disclosed in an embodiment of the present application.

[0126] Explanation of the reference numerals: 301, acquisition module; 302, processing module; 303, judgment module; 401, processor; 402, communication bus; 403, user interface; 404, network interface; 405, memory. DETAILED DESCRIPTION

[0127] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0128] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0129] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0130] Thermocline is a horizontal stratification phenomenon formed by rapid changes in water temperature in the ocean or lake, which has an important impact on marine acoustics and the ecological environment. When conducting underwater blasting projects in lakes, the thermocline will affect the propagation path of sound waves, which may cause sound waves to refract in different water layers, thereby affecting the propagation range and characteristics of the noise. In order to protect the ecological environment, a detailed analysis of the noise should be conducted before underwater blasting to predict the propagation path of the blasting sound, so as to formulate effective noise reduction measures and minimize the impact on aquatic organisms and ecosystems.

[0131] This embodiment discloses an underwater plosive sound analysis method, referring to Figure 1 , including the following steps S110-S170:

[0132] S110, obtaining multiple sound wave signals received by the monitoring point.

[0133] In underwater environments, the commonly used acoustic wave monitoring equipment is the hydrophone. The hydrophone can capture acoustic wave signals in the water and convert them into electrical signals. Select a highly sensitive, wide-band hydrophone to ensure that the blasting sound waves in the low-frequency to high-frequency range can be captured. According to the scale of the blasting area and the needs of analysis, multiple monitoring points are deployed on the water surface of the blasting area. When an underwater blasting occurs, the sound waves generated by the blasting will propagate in the water and be captured by hydrophones distributed at different locations. The hydrophone converts the sound waves into electrical signals and records the amplitude, frequency, phase and other information of the sound waves in real time.

[0134] The underwater blast sound analysis method disclosed in the embodiment of the present application is applied to a server, which is connected to a hydrophone at a monitoring point. The server includes but is not limited to electronic devices such as mobile phones, tablet computers, wearable devices, and PCs (Personal Computers), and may also be a background server running XX. The server may be implemented as an independent server or a server cluster consisting of multiple servers.

[0135] S120, respectively analyzing the actual propagation time and actual signal strength of each sound wave signal.

[0136] The original signal usually contains background noise, so the received signal needs to be preprocessed. The received sound wave signal is preprocessed, usually including filtering and denoising, to obtain a processed signal. The purpose of this step is to remove background noise and other interfering signals and retain useful information related to the blasting sound wave. Commonly used filtering methods include low-pass filtering, high-pass filtering and band-pass filtering to eliminate high-frequency or low-frequency noise.

[0137] Perform Fourier transform on the preprocessed signal and convert it into frequency domain signal. Fourier transform converts time domain signal into frequency domain signal, revealing the amplitude and phase information of each frequency component in the signal. Fourier transform converts time domain signal into frequency domain signal, and can analyze the components of the signal in the frequency dimension. Sound wave signals may be affected by different frequency components during underwater propagation, so decomposing the signal into frequency components can help better understand the propagation characteristics of the signal. Perform Fourier transform on both the processed signal and the reference signal so that they can be compared and analyzed in the frequency domain. Mathematically, Fourier transform can be expressed as:

[0138]

[0139] Among them, x(t) is the time domain signal and X(f) is the corresponding frequency domain signal.

[0140] The cross-correlation method is a commonly used signal processing technique, which is widely used in signal arrival time estimation, pattern matching, signal detection, feature extraction and other fields. In underwater blast analysis, the cross-correlation method can be used to calculate the time delay between the received sound wave signal and the known reference signal, thereby inferring the propagation time of the sound wave. The following will introduce in detail the theoretical basis, mathematical expression, application steps and precautions of the cross-correlation method in practical applications.

[0141] In the analysis of underwater plosive sound, it is assumed that there is a known plosive sound signal (reference signal) and a received plosive sound signal. Through cross-correlation analysis, the time difference between the two signals can be calculated to estimate the propagation time of the signal.

[0142] In the analysis of underwater blasting sound signals, the reference signal is an important basis for positioning algorithms such as the cross-correlation method. The reference signal usually represents a known blasting sound waveform and is used to compare with the actual received signal to determine the propagation path, time delay, etc. of the sound wave. The reference signal can be obtained in a variety of ways, depending on the application scenario and actual technical conditions. In some cases, the reference signal can be generated by theoretical calculation or simulation model. This method usually relies on the understanding of the physical model of the blasting process and the acoustic characteristics. According to parameters such as the type of explosive, the amount of explosion, and the depth of explosion, the explosion mechanics and acoustic theory can be used to calculate the sound wave waveform generated by the explosion. A typical blasting sound signal will show a high-intensity impact waveform (pulse signal), which is characterized by a short rise time, a high peak pressure, and a long fall time. Then, through numerical simulation techniques such as computational fluid dynamics (CFD) or finite element analysis (FEA), the sound wave propagation during the blasting process can be simulated to generate a theoretical blasting sound signal. The simulation results usually need to be corrected in combination with the actual underwater acoustic environment parameters (such as sound velocity profile, absorption coefficient, etc.).

[0143] The Fourier transform result of the processed signal is conjugated, that is, each frequency component of the frequency domain signal is complex conjugated. The purpose of the conjugation operation is to prepare for the subsequent cross-correlation calculation. The frequency domain result of the processed signal after conjugation is multiplied with the frequency domain result of the reference signal to obtain the frequency domain correlation result. This step corresponds to phase matching in the frequency domain. The conjugation operation and frequency domain multiplication are preparations for the subsequent cross-correlation calculation. The conjugation operation allows us to compare the signals in the frequency domain by multiplying them, thereby matching the signals in frequency. The result of the multiplication actually represents the similarity of the two signals in the frequency domain. This step detects the degree of matching between the two signals in different frequency components by comparing the phase information. Mathematically, frequency domain multiplication is expressed as:

[0144] Z(f)=X * (f)·Y(f)

[0145] Among them, X * (f) is the conjugate result, Y(f) is the second frequency domain transform result, and Z(f) is the correlation result after multiplication.

[0146] Perform an inverse Fourier transform on the frequency domain correlation results, convert the frequency domain signal back to the time domain, and obtain the cross-correlation function. The cross-correlation function reflects the degree of temporal matching between the processed signal and the reference signal, and its peak position corresponds to the time lag between the signals. The inverse Fourier transform converts the correlation results in the frequency domain back to the time domain, so that the matching degree of the signals can be analyzed in the time domain. By calculating the cross-correlation function, the alignment of different signals in time can be clarified and the time difference between the signals can be found. This step converts the frequency domain information into more intuitive time delay information. The expression of the cross-correlation function is as follows:

[0147] R xy (t) = ∫x(τ)y(τ+t)dτ

[0148] Among them, R xy (t) is the cross-correlation function, x(τ) is the reference signal, and y(τ+t) is the processed signal.

[0149] The peak of the cross-correlation function represents the best match between the reference signal and the processed signal, that is, the time lag of the acoustic signal. By finding the maximum position of the cross-correlation function, the time difference between the two signals can be determined. Based on the calculated lag, combined with the preset sampling rate, the actual propagation delay is calculated, and the lag is divided by the sampling rate to get the propagation delay. The propagation delay refers to the time required for the sound wave to propagate from the blast source to the monitoring point.

[0150] Furthermore, the actual strength of the sound wave signal is determined by means of time-frequency analysis, signal attenuation calculation, short-time energy extraction and signal strength matching.

[0151] First, perform time-frequency analysis on the reference signal and calculate the initial signal strength. Time-frequency analysis is used to represent the reference signal in two dimensions: time and frequency. Common time-frequency analysis methods include short-time Fourier transform (STFT) and wavelet transform. Through time-frequency analysis, the temporal trend of the signal and the distribution of frequency components can be observed. The signal is divided into multiple small time windows, and a Fourier transform is performed on each window to obtain a joint representation of the signal in time and frequency. The signal is decomposed into components of different scales and frequencies, so that the transient characteristics of the signal can be captured. According to the results of the time-frequency analysis, the energy distribution of the reference signal is extracted, and its strength at each frequency and time point is calculated, and finally the overall strength of the initial signal is obtained.

[0152] When sound waves propagate in water, they will attenuate due to geometric diffusion and energy absorption. Therefore, the signal attenuation can be calculated based on the sound wave propagation distance corresponding to the target prediction path. The specific calculation is based on the following formula:

[0153] L total =20log 10(r)+αr

[0154] Among them, L total is the signal attenuation, r is the sound wave propagation distance, and α is the sound wave energy absorption coefficient.

[0155] Based on the geometric information of the target prediction path that was initially screened, the actual distance that the sound wave propagates in the water is calculated. This distance is the total distance that the blasting sound wave propagates from the blasting source to the monitoring point. In the above calculation formula, 20log 10 (r) represents geometric diffusion attenuation, which reflects the intensity attenuation caused by the diffusion of energy in space during the propagation of sound waves. Assuming that sound waves propagate in the water in the form of spherical waves, as the propagation distance increases, the area covered by the sound waves increases, and the sound wave energy density per unit area decreases. This attenuation follows the inverse square law, that is, the signal strength is inversely proportional to the square of the distance. When expressed in logarithmic expressions, the geometric diffusion attenuation is proportional to the logarithm of the distance.

[0156] In the above calculation formula, αr represents the attenuation of sound waves due to medium absorption when propagating in water, and α is a parameter related to the physical properties of water (such as temperature, salinity, pressure) and the frequency of sound waves, and the unit is dB / km. Absorption attenuation reflects the energy loss caused by the absorption of the medium when sound waves propagate in a medium (such as water). When sound waves of different frequencies propagate in water, the degree of energy absorption is different, and usually the absorption of high-frequency sound waves is more significant.

[0157] Then, the signal strength of the sound wave reaching the monitoring point is calculated based on the initial signal strength and the calculated signal attenuation. The specific calculation formula is:

[0158] I first =I initial -L total

[0159] Among them, I first is the first signal strength, I initial is the initial signal strength of the reference signal, L total is the signal attenuation.

[0160] The result calculated by the initial signal strength and signal attenuation is the sound wave signal intensity value after the blasting sound is transmitted to the monitoring point through the target predicted path. Therefore, it is necessary to compare it with the actual sound wave intensity corresponding to the blasting point. If the intensities of the two are basically the same, it can be preliminarily determined that the target predicted path is the propagation path of the blasting sound.

[0161] First, short-time energy extraction is performed on the processed signal to obtain the short-time energy in multiple time windows. The received processed signal is divided into multiple short-time windows, and the signal energy in each time window is calculated. Short-time energy extraction is used to capture the changing characteristics of the signal in a short period of time, and is particularly suitable for the analysis of non-stationary signals. Short-time energy analysis is a commonly used signal processing technology that is suitable for the analysis of non-stationary signals. It divides the signal into multiple small time windows and calculates the signal energy in each window, thereby capturing the changes in the signal over time. For each time window, the energy calculation formula is:

[0162]

[0163] Among them, E total is the second signal strength, M is the window size, and x(nm) is the signal sample in the time window.

[0164] The short-time energies in multiple time windows are summed to obtain the total energy of the entire processed signal. This total energy is the second signal strength. By summing the short-time energies in all time windows, the total energy of the processed signal is obtained, which is the second signal strength. The purpose of this step is to extract the actual energy level of the received processed signal from the time dimension. Finally, the first signal strength (the signal strength calculated by the predicted path) is compared with the second signal strength (the actual received processed signal strength). The specific judgment criterion is whether the difference between the two is less than the preset intensity threshold. If the difference is small enough, it can be considered that the predicted signal strength and the actual signal strength match, indicating that the predicted path may be consistent with the actual sound wave propagation path. If the first signal strength matches the second signal strength, it is preliminarily determined that the target predicted path is the actual propagation path of the plosive sound.

[0165] S130, based on a preset sound velocity profile model, calculating the sound wave propagation speed at different depths in a preset water area. In a possible implementation, before calculating the sound wave propagation speed at different depths in a preset water area based on a preset sound velocity profile model, the method further includes: acquiring environmental parameter data of the preset water area; preprocessing the environmental parameter data to obtain processed data; calculating the sound velocity values ​​at different depths using an empirical formula based on the processed data; generating a sound velocity profile curve based on the sound velocity values; and constructing a preset sound velocity profile model based on the sound velocity profile curve.

[0166] Specifically, before constructing the sound velocity profile model, it is necessary to collect environmental parameter data of the preset waters. Key environmental parameters include water temperature, salinity, pressure, depth, etc., which are crucial for calculating the sound velocity values ​​at different depths. The acquired environmental parameter data often contain noise or incomplete information, so data preprocessing is required to ensure that the calculated sound velocity values ​​are more accurate and reliable. This includes excluding abnormal data points (such as data loss, erroneous values, etc.), filling in missing data, removing noise, and ensuring the integrity and accuracy of the data. If data at certain depths is missing, the missing data can be supplemented by an interpolation algorithm. Common methods include linear interpolation, spline interpolation, etc. For data such as water temperature and salinity, smoothing can be performed to eliminate the impact of small measurement errors on subsequent calculations.

[0167] The speed of sound propagation in water depends on the changes in water temperature, salinity and pressure (depth). Empirical formulas can convert these environmental parameters into sound speed values. Among the empirical formulas for calculating the speed of sound, the Del Grosso formula and the Chen-Millero formula are more commonly used. Since the calculation of the speed of sound is only a conventional technical means in the relevant technical field, no further details will be given here.

[0168] After calculating the sound velocity values ​​at different depths through empirical formulas, a sound velocity profile curve can be generated. This curve shows the change in sound velocity within the entire water depth range. The sound velocity values ​​at different depths are plotted as a curve, with the vertical axis being the depth and the horizontal axis being the sound velocity. Usually, the sound velocity profile curve presents three typical patterns: positive jump layer, where the surface water temperature is higher, and as the depth increases, the temperature drops, and the sound velocity also decreases. Negative jump layer, where the deep water temperature is higher, and the sound velocity increases with increasing depth. Double jump layer, where both the surface and deep layers are high temperature layers, and a low temperature layer appears in the middle layer, and the sound velocity profile forms a curve similar to an "S" shape.

[0169] Based on the generated sound velocity profile curve, a sound velocity profile model is constructed for subsequent prediction of the sound wave propagation path and blasting source location analysis. The sound velocity profile model can be constructed using a piecewise linear model, a polynomial fitting model or a numerical model. The specific model selection depends on the complexity of the sound velocity profile. The sound velocity profile curve can be fitted to generate a mathematical model to describe the trend of sound velocity with depth. The sound velocity function expression in each depth interval is obtained through polynomial fitting or piecewise linear fitting. After the model is built, it can be verified by comparing the measured data with the model prediction data to ensure the accuracy and reliability of the model.

[0170] S140, performing predicted path analysis based on the sound wave propagation speed to obtain multiple predicted paths

[0171] In a possible implementation, a predicted path analysis is performed based on the sound wave propagation speed to obtain multiple predicted paths, specifically including: generating multiple optional paths based on environmental parameter data of a preset water area; and screening out a predicted path from the multiple optional paths based on the sound wave propagation speed, wherein the difference between the predicted sound wave propagation time corresponding to the predicted path and the actual propagation time is less than a preset threshold.

[0172] Specifically, the purpose of generating optional paths is to explore possible propagation paths of sound waves in water. Figure 2 ,The existence of the thermocline will cause the sound waves to be refracted along different paths and thus transmitted to the monitoring point, so it is necessary to consider a variety of possible sound wave propagation paths.

[0173] Ray Theory or numerical acoustic models are used to simulate the propagation path of sound waves. These models are based on the sound velocity profile model, taking into account the influence of environmental parameters such as water temperature, salinity, and depth on the sound velocity, and calculate the possible propagation path of the sound wave from the blasting source to the monitoring point. The environmental parameter data of the preset water area (such as water temperature, salinity, depth, etc.) is input into the preset sound velocity profile model to generate multiple optional propagation paths.

[0174] After generating multiple optional paths, the next step is to calculate the propagation time of the sound wave along these paths and match it with the actual propagation time received at the monitoring point to screen out the most likely propagation path, that is, the predicted path. The sound velocity profile model constructed earlier is used to calculate the propagation time of the sound wave on different paths. The sound wave propagation time on each path is obtained by integral calculation, and the specific expression is:

[0175]

[0176] Where T is the propagation time, r is the propagation distance, and c(x) is the speed of sound at different positions on the path. Through the numerical integration method, the propagation time of the sound wave along different paths can be obtained.

[0177] Compare the predicted propagation time of each path with the actual propagation time. By calculating the difference, if the difference is less than the preset threshold (indicating that the predicted path and the actual path have a high degree of match), the path is selected as the predicted path. In addition to the propagation time, the attenuation of the signal strength can also be combined for screening. If the predicted signal strength on a path matches the actual signal strength received at the monitoring point, the possibility of the path is further confirmed. The propagation time of all optional paths is traversed and calculated, and compared with the actual propagation time, and the paths that meet the preset threshold conditions are preliminarily selected.

[0178] S150, performing propagation time matching and signal strength matching on multiple predicted paths according to the actual propagation time and the actual signal strength, and determining a target predicted path.

[0179] After the possible propagation paths of the blasting sound signal are preliminarily screened, in addition to further screening the possible propagation paths of the sound wave by using the sound wave intensity comparison method in S120, the possible propagation paths of the sound wave can also be further screened by using the sound wave time comparison method.

[0180] First, based on the change of the sound speed profile, the water area is divided into several depth intervals in order to accurately calculate the propagation time of the sound wave in each interval. The sound speed profile model of the preset water area is input, and the model reflects the change of the sound speed of the water area with depth through the sound speed profile curve (indicating the sound speed at different depths). According to the sound speed profile model, the water area is divided into multiple depth intervals. For example, the thermocline area can be refined into several small intervals to ensure that the sound speed changes at different depths are accurately described. The basis for the division of depth intervals is usually the area with significant changes in sound speed, such as the thermocline area, near the water surface, etc. The number and division method of specific depth intervals depend on the complexity of the sound speed profile.

[0181] The sound wave propagation velocity of each depth interval is calculated by the formula c(z)=c0+γz, where c(z) is the sound wave propagation velocity at depth z, c0 is the initial sound wave propagation velocity of the depth interval (usually the sound velocity at the surface or reference depth), and γ is the sound velocity gradient, which represents the rate of change of the sound velocity with depth. According to the above formula, by inputting the starting depth and ending depth of each depth interval, the sound velocity gradient of each interval is determined according to the sound velocity profile model, and the sound wave propagation velocity at each depth is calculated.

[0182] Then the propagation trajectory of the sound wave in different depth intervals is analyzed to determine the actual propagation path of the sound wave. Specifically, the ray theory is used to describe the propagation path of the sound wave, and the trajectory of the ray is determined by the following analytical equation:

[0183]

[0184] Among them, c(z) is the sound wave propagation velocity at depth z, dx is the sound wave propagation velocity along the horizontal distance component, and dz is the sound wave propagation velocity along the vertical distance component.

[0185] This equation describes the ray path of a sound wave as it propagates through water. Based on Fermat's principle (i.e., the path that a sound wave takes in a medium is the path that takes the shortest time), this equation can be used to derive the path that the sound wave takes, especially when the speed of sound is unevenly distributed. The path that a sound wave takes at different depths is affected by the speed gradient. Sound waves travel along refracted paths, that is, they bend in areas of low speed. This equation expresses the relationship between the change in speed of a sound wave as it propagates along a path and the direction of propagation. By solving this equation, the path that the sound wave takes can be found, especially in thermoclines, where sound waves may bend or reflect.

[0186] In each depth interval, the propagation speed of sound waves is different, so it is necessary to calculate the propagation time of each interval separately and add them up to get the total propagation time.

[0187] For any depth interval of multiple depth intervals, calculate the interval sound wave propagation time, which is specifically calculated by the following formula:

[0188]

[0189] Among them, Δt is the interval sound wave propagation time in the depth interval [z1, z2], dz is the sound wave propagation velocity along the vertical distance component, c0 is the initial sound wave propagation velocity in the depth interval, and γ is the sound velocity gradient;

[0190] For each depth interval, the above formula is used for integration to calculate the propagation time of the sound wave in the interval. A numerical integration method (such as the trapezoidal integration method) can be used to solve it, and the propagation time of all intervals is accumulated to obtain the total propagation time. According to the interval sound wave propagation time, the predicted sound wave transmission time of the target prediction path is calculated, which is specifically calculated by the following formula:

[0191]

[0192] Among them, T total To predict the sound wave transmission time, Δt i is the interval sound wave propagation time of the i-th depth interval.

[0193] Finally, by comparing the propagation time of the predicted path with the actual propagation time, we can determine which path is the most likely propagation path of the blast. Calculate the difference between the predicted propagation time and the actual propagation time. If the time difference is less than the preset threshold, the path is considered to be the most likely propagation path, that is, the target predicted path. In summary, the sound velocity profile model, ray theory and numerical calculation method are combined to determine the propagation path of the sound wave through depth interval division and precise time calculation. By comparing the predicted propagation time with the actual propagation time, the target predicted path can be effectively screened out, thereby accurately determining the propagation path of the blast and ultimately locating the blast source.

[0194] By accurately analyzing the actual propagation time and actual signal strength of the sound wave signal, combined with Fourier transform and cross-correlation function analysis, the propagation path of the plosive sound can be effectively identified. On this basis, the propagation path and intensity change of the sound wave are estimated through time-frequency analysis and signal attenuation model, and matched with the actual signal. Finally, the optimal propagation path related to the blast source position is identified through multipath analysis, thereby achieving accurate positioning of the plosive sound propagation path.

[0195] S160, calculating the blasting source position based on the target predicted path to obtain the predicted blasting source position.

[0196] In the stratified areas of different depths in water, sound waves will be refracted due to the difference in sound speed. The relationship between the refraction angle and the incident angle can be described by Snell's Law:

[0197]

[0198] Among them, θ1 is the angle of incidence in any depth interval, θ2 is the angle of refraction in any depth interval, c1 is the propagation speed of sound waves entering any depth interval, and c2 is the propagation speed of sound waves out of any depth interval.

[0199] Then, starting from the last interval of the target predicted path (i.e., the depth interval where the monitoring point is located), determine the refraction angle of the sound wave in this depth interval. Using Snell's law, the incident angle of the sound wave when it propagates to the previous depth interval is calculated by using the known sound speed and refraction angle. Starting from the monitoring point position, the sound wave propagation path is calculated in reverse. For each depth interval, the law of refraction is used to calculate the incident angle and refraction angle of the sound wave from the lower layer to the upper layer. The propagation direction and angle of the sound wave in each layer are calculated until the blasting source position on the water surface is reached. The layer-by-layer reverse calculation combines the path direction and position calculation, and accurately derives the propagation path and refraction behavior of the sound wave by using Snell's law and the sound speed profile model. By accumulating the path calculation layer by layer, the blasting source position is finally obtained, and the accuracy of the calculation results is ensured by error correction. This process can effectively analyze the sound wave propagation path and locate the blasting source in a complex underwater environment, especially in the presence of a thermocline.

[0200] The sound wave propagation speed in different depth intervals of the preset water area is accurately calculated, and the propagation path and propagation time of the sound wave in each depth interval are gradually analyzed using the ray trajectory equation and the integration method. The propagation path of the blast sound is determined by matching the predicted sound wave propagation time with the actual propagation time and combining it with the signal strength analysis. This method can effectively identify the true propagation path of the sound wave, reduce errors, provide more accurate blast source location, improve the accuracy and reliability of underwater blast sound analysis, and provide a basis for subsequent blast sound impact analysis and noise control.

[0201] S170: If it is determined that the predicted blast source position is consistent with the preset blast source position, the target predicted path is determined to be the blast sound propagation path.

[0202] Finally, the predicted blasting source position obtained by reverse calculation is compared with the preset blasting source position. If the error is within an acceptable range, the selected target predicted path is confirmed to be the true sound wave propagation path, and the blasting source position is determined.

[0203] Since the thermocline causes sound waves to refract in the water, and the propagation path of the sound waves is complex, the sound wave signals received at the monitoring point are first obtained, the actual propagation time and intensity of the signals are analyzed, and the sound wave propagation speeds at different depths of the water area are calculated in combination with the preset sound velocity profile model, and the possible propagation paths of the sound waves are analyzed. Then, the predicted path is matched with the actual propagation time and signal intensity to determine the target predicted path. Finally, the path is used to reversely calculate the blasting source position, and its consistency with the preset blasting source position is verified, thereby realizing the accurate analysis and positioning of the propagation path of the underwater blasting sound.

[0204] This embodiment also discloses an underwater blasting sound analysis device, referring to Figure 3 The device includes an acquisition module 301, a processing module 302 and a judgment module 303, wherein:

[0205] The acquisition module 301 is used to acquire multiple sound wave signals received by the monitoring point.

[0206] The processing module 302 is used to analyze the actual propagation time and actual signal strength of each sound wave signal.

[0207] The processing module 302 is used to calculate the sound wave propagation speed at different depths in a preset water area based on a preset sound speed profile model.

[0208] The processing module 302 is used to perform predicted path analysis based on the sound wave propagation speed to obtain multiple predicted paths.

[0209] The processing module 302 is used to perform propagation time matching and signal strength matching on multiple predicted paths according to the actual propagation time and the actual signal strength, and determine a target predicted path.

[0210] The processing module 302 is used to calculate the blasting source position based on the target predicted path to obtain the predicted blasting source position.

[0211] The judgment module 303 is used to determine the target predicted path as the blast sound propagation path if it is judged that the predicted blast source position is consistent with the preset blast source position.

[0212] In a possible implementation, the processing module 302 is used to pre-process the sound wave signal to obtain a processed signal.

[0213] The processing module 302 is used to perform Fourier transform on the processed signal to obtain a first frequency domain transform result, and perform Fourier transform on a reference signal of the processed signal to obtain a second frequency domain transform result.

[0214] The processing module 302 is used to conjugate the first frequency domain transformation result to obtain a conjugate result.

[0215] The processing module 302 is used to multiply the conjugate result and the second frequency domain transformation result to obtain a correlation result.

[0216] The processing module 302 is used to perform an inverse Fourier transform on the correlation result to obtain a cross-correlation function. The expression of the cross-correlation function is as follows:

[0217] R xy (t) = ∫x(τ)y(τ+t)dτ

[0218] Among them, R xy (t) is the cross-correlation function, x(τ) is the reference signal, and y(τ+t) is the processed signal.

[0219] The acquisition module 301 is used to query the maximum value of the cross-correlation function to obtain the hysteresis.

[0220] The processing module 302 is used to calculate the propagation delay of the sound wave signal according to the lag amount and the preset sampling rate to obtain the actual propagation time.

[0221] In a possible implementation, the processing module 302 is configured to perform time-frequency analysis on the reference signal to calculate the initial signal strength.

[0222] The processing module 302 is used to calculate the signal attenuation based on the sound wave propagation distance corresponding to the target prediction path, which is specifically calculated by the following formula:

[0223] L total =20log 10 (r)+αr

[0224] Among them, L totalis the signal attenuation, r is the sound wave propagation distance, and α is the sound wave energy absorption coefficient.

[0225] The processing module 302 is used to calculate the first signal strength according to the initial signal strength and the signal attenuation.

[0226] The processing module 302 is used to perform short-time energy extraction on the processed signal to obtain short-time energy in multiple time windows.

[0227] The processing module 302 is used to calculate the second signal strength according to the short-time energy, specifically by the following formula:

[0228]

[0229] Among them, E total is the second signal strength, M is the window size, and x(nm) is the signal sample in the time window.

[0230] The judgment module 303 is used to determine that the second signal strength is the actual signal strength of the sound wave signal if it is determined that the difference between the first signal strength and the second signal strength is less than a preset strength threshold, and preliminarily determine that the target predicted path is the plosive sound propagation path.

[0231] In a possible implementation, the processing module 302 is used to divide the preset water area into a plurality of depth intervals according to a preset sound velocity profile model.

[0232] The processing module 302 is used to calculate the sound wave propagation speed in each depth interval based on a preset sound velocity profile model, specifically by the following formula:

[0233] c(z)=c0+γz

[0234] Among them, c(z) is the sound wave propagation velocity at depth z, c0 is the initial sound wave propagation velocity in the depth interval, and γ is the sound velocity gradient.

[0235] Processing module 302 is used to analyze the ray trajectory equation of the target prediction path:

[0236]

[0237] Among them, c(z) is the sound wave propagation velocity at depth z, dx is the sound wave propagation velocity along the horizontal distance component, and dz is the sound wave propagation velocity along the vertical distance component.

[0238] The processing module 302 is used to calculate the interval sound wave propagation time for any depth interval of the multiple depth intervals, specifically by the following formula:

[0239]

[0240] Among them, Δt is the interval sound wave propagation time in the depth interval [z1, z2], dz is the vertical distance component of the sound wave propagation velocity, c0 is the initial sound wave propagation velocity in the depth interval, and γ is the sound velocity gradient.

[0241] The processing module 302 is used to calculate the predicted sound wave transmission time of the target predicted path according to the interval sound wave propagation time, which is specifically calculated by the following formula:

[0242]

[0243] Among them, T total To predict the sound wave transmission time, Δt i is the interval sound wave propagation time of the i-th depth interval.

[0244] The judgment module 303 is used to determine that the target predicted path is a plosive sound propagation path if it is determined that the time difference between the predicted sound wave transmission time and the actual propagation time is less than a preset difference.

[0245] In a possible implementation, the processing module 302 is configured to satisfy the following law for the refraction angle of the blasting sound wave based on the target prediction path for the segmented interface of any one of the multiple depth intervals:

[0246]

[0247] Among them, θ1 is the angle of incidence in any depth interval, θ2 is the angle of refraction in any depth interval, c1 is the propagation speed of sound waves entering any depth interval, and c2 is the propagation speed of sound waves out of any depth interval.

[0248] The processing module 302 is used to calculate the propagation direction and angle of the sound wave in each depth interval layer by layer based on the location of the monitoring point, the preset water area and the refraction angle, starting from the monitoring point and according to the target prediction path, and reversely calculate and predict the blasting source location.

[0249] In a possible implementation, the acquisition module 301 is used to acquire environmental parameter data of a preset water area.

[0250] The processing module 302 is used to pre-process the environmental parameter data to obtain processed data.

[0251] The processing module 302 is used to calculate the sound velocity values ​​at different depths through an empirical formula according to the processed data.

[0252] The processing module 302 is used to generate a sound speed profile curve according to the sound speed value.

[0253] The processing module 302 is used to construct a preset sound speed profile model based on the sound speed profile curve.

[0254] In a possible implementation, the processing module 302 is used to generate a plurality of optional paths according to environmental parameter data of a preset water area.

[0255] The processing module 302 is used to select a predicted path from multiple optional paths based on the sound wave propagation speed, wherein the difference between the predicted sound wave propagation time corresponding to the predicted path and the actual propagation time is less than a preset threshold.

[0256] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0257] This embodiment also discloses an electronic device, referring to Figure 4 The electronic device may include: at least one processor 401 , at least one communication bus 402 , a user interface 403 , a network interface 404 , and at least one memory 405 .

[0258] The communication bus 402 is used to realize the connection and communication between these components.

[0259] The user interface 403 may include a display screen (Display) and a camera (Camera), and the optional user interface 403 may also include a standard wired interface and a wireless interface.

[0260] The network interface 404 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0261] Among them, the processor 401 may include one or more processing cores. The processor 401 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 405, and calling data stored in the memory 405. Optionally, the processor 401 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 401 can integrate one or more combinations of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 401, and it can be implemented by a single chip.

[0262] The memory 405 may include a random access memory (RAM) or a read-only memory (ROM). Optionally, the memory includes a non-transitory computer-readable storage medium. The memory 405 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 405 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 405 may also be at least one storage device located away from the aforementioned processor 401. The memory 405 as a computer storage medium may include an operating system, a network communication module, a user interface 403 module, and an application for an underwater blast sound analysis method.

[0263] exist Figure 4In the electronic device shown, the user interface 403 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 401 can be used to call an application program storing an underwater blast sound analysis method in the memory 405. When executed by one or more processors 401, the electronic device executes one or more methods in the above-mentioned embodiments.

[0264] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0265] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0266] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0267] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0268] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0269] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory 405, including a number of instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory 405 includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0270] The present application also discloses a computer-readable storage medium, wherein the computer-readable storage medium stores instructions. When executed by one or more processors 401, the electronic device executes one or more of the methods described in the above embodiments.

[0271] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, it will be easy for those skilled in the art to think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for analyzing underwater plosive sound, characterized in that: The method comprises: Acquire multiple sound wave signals received by the monitoring point; Respectively analyzing the actual propagation time and actual signal strength of each of the sound wave signals; Based on the preset sound velocity profile model, calculate the sound wave propagation speed at different depths in the preset water area; Performing predicted path analysis based on the sound wave propagation speed to obtain multiple predicted paths; According to the actual propagation time and the actual signal strength, performing propagation time matching and signal strength matching on the plurality of predicted paths to determine a target predicted path; Based on the target predicted path, the blasting source position is calculated to obtain the predicted blasting source position; If it is determined that the predicted blast source position is consistent with the preset blast source position, the target predicted path is determined to be the blast sound propagation path.

2. The underwater plosive sound analysis method according to claim 1, characterized in that: The respectively analyzing the actual propagation time and the actual signal strength of each of the sound wave signals specifically includes: Preprocessing the sound wave signal to obtain a processed signal; Performing Fourier transform on the processed signal to obtain a first frequency domain transform result, and performing Fourier transform on a reference signal of the processed signal to obtain a second frequency domain transform result; Taking a conjugate of the first frequency domain transformation result to obtain a conjugate result; multiplying the conjugation result and the second frequency domain transformation result to obtain a correlation result; The correlation result is subjected to inverse Fourier transform to obtain a cross-correlation function, the expression of which is as follows: R xy (t)=∫x(τ)y(τ+t)dτ Among them, R xy (t) is the cross-correlation function, x(τ) is the reference signal, and y(τ+t) is the processed signal; Querying the maximum value of the cross-correlation function to obtain a hysteresis; The propagation delay of the acoustic wave signal is calculated according to the lag amount and the preset sampling rate to obtain the actual propagation time.

3. The underwater plosive sound analysis method according to claim 2, characterized in that: The performing propagation time matching and signal strength matching on the plurality of predicted paths according to the actual propagation time and the actual signal strength to determine a target predicted path specifically includes: Performing time-frequency analysis on the reference signal to calculate initial signal strength; Based on the sound wave propagation distance corresponding to the target predicted path, the signal attenuation is calculated, which is specifically calculated by the following formula: L total =20log 10 (r)+αr Among them, L total is the signal attenuation, r is the sound wave propagation distance, and α is the sound wave energy absorption coefficient; Calculating a first signal strength according to the initial signal strength and the signal attenuation; Performing short-time energy extraction on the processed signal to obtain short-time energy in multiple time windows; The second signal strength is calculated according to the short-time energy, specifically by the following formula: Among them, E total is the second signal strength, M is the window size, and x(nm) is the signal sample in the time window; If it is determined that the difference between the first signal strength and the second signal strength is less than a preset strength threshold, the second signal strength is determined to be the actual signal strength of the sound wave signal, and the target predicted path is preliminarily determined to be a plosive sound propagation path.

4. The underwater plosive sound analysis method according to claim 2, characterized in that: The performing propagation time matching and signal strength matching on the plurality of predicted paths according to the actual propagation time and the actual signal strength to determine a target predicted path specifically includes: According to the preset sound velocity profile model, the preset water area is divided into a plurality of depth intervals; Based on the preset sound velocity profile model, the sound wave propagation velocity in each depth interval is calculated, specifically by the following formula: c(z)=c0+γz Where c(z) is the sound wave propagation velocity at depth z, c0 is the initial sound wave propagation velocity in the depth interval, and γ is the sound velocity gradient; Analyze the ray trajectory equation of the target prediction path: Where c(z) is the sound wave propagation velocity at depth z, dx is the sound wave propagation velocity along the horizontal distance component, and dz is the sound wave propagation velocity along the vertical distance component; For any one of the multiple depth intervals, the interval sound wave propagation time is calculated, specifically by the following formula: Among them, Δt is the interval sound wave propagation time in the depth interval [z1, z2], dz is the sound wave propagation velocity along the vertical distance component, c0 is the initial sound wave propagation velocity in the depth interval, and γ is the sound velocity gradient; The predicted sound wave transmission time of the target predicted path is calculated according to the interval sound wave propagation time, which is specifically calculated by the following formula: Among them, T total is the predicted sound wave transmission time, Δt i is the interval sound wave propagation time of the i-th depth interval; If it is determined that the time difference between the predicted sound wave transmission time and the actual transmission time is less than a preset difference, the target predicted path is determined to be a plosive sound transmission path.

5. The underwater plosive sound analysis method according to claim 4, characterized in that: The step of calculating the blasting source position based on the target predicted path to obtain the predicted blasting source position specifically includes: For the segmented interface of any one of the plurality of depth intervals, the refraction angle of the blasting sound wave based on the target predicted path satisfies the following law: Wherein, θ1 is the angle of incidence at any depth interval, θ2 is the angle of refraction at any depth interval, c1 is the propagation speed of the sound wave entering any depth interval, and c2 is the propagation speed of the sound wave out of any depth interval; According to the position of the monitoring point, the preset water area and the refraction angle, starting from the monitoring point and according to the target predicted path, the propagation direction and angle of the sound wave in each depth interval are calculated layer by layer, and the predicted blasting source position is reversely calculated.

6. The underwater plosive sound analysis method according to claim 1, characterized in that: Before calculating the sound wave propagation speeds at different depths in the preset water area based on the preset sound speed profile model, the method further includes: Acquire environmental parameter data of the preset water area; Preprocessing the environmental parameter data to obtain processed data; Calculating the sound velocity values ​​at different depths by using an empirical formula according to the processed data; Generating a sound velocity profile curve according to the sound velocity value; Based on the sound speed profile curve, the preset sound speed profile model is constructed.

7. The underwater plosive sound analysis method according to claim 1, characterized in that: The predicted path analysis based on the sound wave propagation speed is performed to obtain multiple predicted paths, specifically including: Generating a plurality of optional paths according to the environmental parameter data of the preset water area; Based on the sound wave propagation speed, a predicted path is screened out from the multiple optional paths, wherein the difference between the predicted sound wave propagation time corresponding to the predicted path and the actual propagation time is less than a preset threshold.

8. An underwater blasting sound analysis device, characterized in that: The device comprises an acquisition module (301), a processing module (302) and a judgment module (303), wherein: The acquisition module (301) is used to acquire multiple sound wave signals received by the monitoring point; The processing module (302) is used to analyze the actual propagation time and actual signal strength of each of the sound wave signals respectively; The processing module (302) is used to calculate the sound wave propagation speed at different depths in a preset water area based on a preset sound speed profile model; The processing module (302) is used to perform predicted path analysis based on the sound wave propagation speed to obtain multiple predicted paths; The processing module (302) is used to perform propagation time matching and signal strength matching on the plurality of predicted paths according to the actual propagation time and the actual signal strength, so as to determine a target predicted path; The processing module (302) is used to calculate the blasting source position based on the target predicted path to obtain the predicted blasting source position; The judgment module (303) is used to determine that the target predicted path is a blast sound propagation path if it is judged that the predicted blast source position is consistent with the preset blast source position.

9. An electronic device, characterized in that: The electronic device comprises a processor (401), a communication bus (402), a memory (405), a user interface (403), a network interface (404) and a memory (405), wherein the memory (405) is used to store instructions, the user interface (403) and the network interface (404) are both used to communicate with other devices, and the processor (401) is used to execute the instructions stored in the memory (405) so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

Images