Underwater blast sound analysis method and device

Abstract

The present application provides a method and device for analyzing underwater blast noise, relating to the technical field of underwater noise analysis. The method comprises: obtaining multiple acoustic wave signals received at a monitoring point; analyzing the actual propagation time and actual signal strength of each acoustic wave signal; calculating the acoustic wave propagation speed at different depths in a preset water area based on a preset acoustic velocity profile model; performing predicted path analysis based on the acoustic wave propagation speed to obtain multiple predicted paths; performing propagation time matching and signal strength matching on the multiple predicted paths based on the actual propagation time and actual signal strength to determine a target predicted path; calculating the blast source position based on the target predicted path to obtain a predicted blast source position; and determining the target predicted path as the blast sound propagation path if the predicted blast source position is determined to be consistent with the preset blast source position. The present application can analyze the propagation path of underwater blast noise.

Description

Technical Field

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

[0002] A thermocline is a horizontal stratification phenomenon in oceans and large lakes caused by rapid temperature fluctuations. The presence of a thermocline divides the water into distinct vertical layers, characterized by a rapid decrease in water temperature with depth, from the warm surface water to the cooler water below. Thermoclines are of great significance in physical oceanography, particularly in fields such as ocean acoustics, marine biology, and climate research.

[0003] Underwater blasting is a common engineering practice when excavating waterways or canals in lakes. However, the noise generated by underwater blasting can significantly impact the surrounding ecosystem, particularly the survival and behavior of aquatic life. Therefore, conducting a detailed underwater blasting noise analysis before implementing underwater blasting is crucial. Predicting the sound pressure level, propagation range, and frequency characteristics generated during blasting can provide a scientific basis for developing effective noise reduction measures, thereby minimizing negative impacts on the ecological environment and protecting biodiversity and ecosystem balance in lakes.

[0004] Analyzing the propagation path of underwater blasting sound is a key research topic in underwater blasting engineering. Due to the unique underwater environment, the propagation of sound waves in water is affected by a variety of factors, including water depth, water temperature, salinity, bottom sediment characteristics, and suspended particles in the water. In thermoclines, due to changes in water temperature and pressure, the speed of sound varies with depth, causing sound waves to refract in the water. Typically, the sound velocity profile affects the propagation path of sound waves. For example, when the water surface is warmer and the deeper layers are colder, a positive thermocline is formed in the sound velocity profile. Sound waves may propagate in the shallower water layers, while sound waves in deeper layers may be refracted downward. Therefore, a method is needed to analyze the propagation path of underwater blasting sound. Summary of the Invention

[0005] The present application provides a method and device for analyzing underwater plosive sounds, 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 sounds is provided, the method comprising:

[0007] Acquiring multiple acoustic wave signals received by monitoring points;

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

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

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

[0011] 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;

[0012] Calculating the blasting source position based on the target predicted path to obtain a 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 actual signal strength of each of the sound wave signals specifically includes:

[0015] Preprocessing the acoustic 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 transform result to obtain a conjugate result;

[0018] multiplying the conjugated result by the second frequency domain transform result to obtain a correlation result;

[0019] Perform an inverse Fourier transform on the correlation result to obtain a cross-correlation function, which is expressed 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 the initial signal strength;

[0026] The signal attenuation is calculated based on the sound wave propagation distance corresponding to the target predicted path, specifically using 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 further includes:

[0036] Dividing the preset water area into a plurality of depth intervals according to the preset sound velocity profile model;

[0037] Based on the preset sound velocity profile model, the sound wave propagation velocity in each depth interval is calculated, specifically using 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] Wherein, Δt is the interval acoustic wave propagation time of the depth interval [z1, z2], dz is the vertical distance component of the acoustic wave propagation velocity, c0 is the initial acoustic wave propagation velocity of the depth interval, and γ is the acoustic velocity gradient;

[0046] The predicted acoustic wave transmission time of the target predicted path is calculated based on the acoustic wave propagation time in the interval, specifically using the following formula:

[0047]

[0048] Among them, T total is the predicted acoustic 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 calculating of the blasting source position based on the target predicted path to obtain the predicted blasting source position specifically includes:

[0051] For a 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] Where θ1 is the angle of incidence at any depth interval, θ2 is the angle of refraction at any depth interval, c1 is the speed of sound waves entering any depth interval, and c2 is the speed of sound waves leaving 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 velocity profile model, the method further includes:

[0056] Obtaining 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 using an empirical formula based on the processed data;

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

[0060] Based on the sound velocity profile curve, the preset sound velocity 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, a device for analyzing underwater blast sounds is provided. The device includes 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 the preset water area based on the preset sound speed profile model;

[0068] The processing module is configured to perform a predicted path analysis based on the sound wave propagation speed to obtain a plurality of predicted paths;

[0069] The processing module is configured 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, 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 judgment module is configured to determine that the target predicted path is a blast sound propagation path if it is determined 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 configured 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 configured to conjugate the first frequency domain transformation result to obtain a conjugate result;

[0075] The processing module is configured to multiply the conjugation 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 predicted path, specifically using 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 configured to calculate a first signal strength based on the initial signal strength and the signal attenuation;

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

[0087] The processing module is configured to calculate the second signal strength based on 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 configured 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 multiple depth intervals according to the preset sound velocity profile model;

[0092] The processing module is used to calculate the sound wave propagation velocity in each depth interval based on the preset sound velocity profile model, specifically using 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 configured to calculate the interval acoustic wave propagation time for any one of the plurality of depth intervals, specifically by the following formula:

[0099]

[0100] Wherein, Δt is the interval acoustic wave propagation time of the depth interval [z1, z2], dz is the vertical distance component of the acoustic wave propagation velocity, c0 is the initial acoustic wave propagation velocity of the depth interval, and γ is the acoustic velocity gradient;

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

[0102]

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

[0104] The judgment module is configured 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.

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

[0106]

[0107] Where θ1 is the angle of incidence at any depth interval, θ2 is the angle of refraction at any depth interval, c1 is the speed of sound waves entering any depth interval, and c2 is the speed of sound waves leaving 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 position.

[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 using an empirical formula based on the processed data;

[0112] The processing module is used to generate a sound speed profile curve according to the sound speed 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 configured to select a predicted path from the plurality of optional paths based on the sound wave propagation speed, wherein a difference between a 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 performs any 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. When the instructions are executed, any one 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. Because the thermocline causes sound waves to refract in water, resulting in complex propagation paths, the system first acquires the sound wave signal received at the monitoring point, analyzes the actual propagation time and intensity of the signal, and, using a pre-defined sound velocity profile model, calculates the sound wave propagation velocity at different depths and analyzes the possible propagation paths. The predicted path is then matched with the actual propagation time and signal intensity to determine the target predicted path. Finally, this path is used to reversely infer the blast source location and verify its consistency with the pre-determined blast source location, thereby accurately analyzing and locating the propagation path of the underwater blast sound.

[0120] 2. By accurately analyzing the actual propagation time and signal strength of the acoustic wave signal, combined with Fourier transform and cross-correlation function analysis, the propagation path of the plosive sound can be effectively identified. Furthermore, through time-frequency analysis and signal attenuation models, the propagation path and intensity variations of the sound wave are estimated and matched with the actual signal. Finally, multipath analysis identifies the optimal propagation path associated with the blast source, thereby accurately locating the plosive sound propagation path.

[0121] 3. Accurately calculate the sound wave propagation velocity at different depth intervals within a pre-defined water area. Using the ray trajectory equation and integration method, the sound wave propagation path and propagation time within each depth interval are gradually analyzed. 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 effectively identifies the true propagation path of the sound wave, reduces errors, and provides more precise location of the blast source. This improves the accuracy and reliability of underwater blast sound analysis and provides a foundation for subsequent blast sound impact analysis and noise control. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

[0125] Figure 4 This is a structural 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 those skilled in the art 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 this application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0129] In the description of the embodiments of the present application, the term "multiple" means 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 are not to 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 "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0130] Thermoclines, a horizontal stratification phenomenon formed by rapid temperature changes in oceans and lakes, have a significant impact on marine acoustics and the ecological environment. During underwater blasting in lakes, the thermocline can affect the propagation path of sound waves, potentially causing them to refract between different water layers, thus affecting the range and characteristics of the noise. To protect the ecological environment, a detailed noise analysis should be conducted before underwater blasting to predict the propagation path of the blasting sound. This allows for the development of effective noise reduction measures to minimize the impact on aquatic life and ecosystems.

[0131] This embodiment discloses a method for analyzing underwater plosive sound. Figure 1 , including the following steps S110-S170:

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

[0133] In underwater environments, a commonly used acoustic wave monitoring device is the hydrophone. A hydrophone can capture acoustic wave signals in the water and convert them into electrical signals. Highly sensitive, broadband hydrophones are selected to ensure that they can capture blasting sound waves ranging from low to high frequencies. Depending on the size of the blasting area and the needs of the analysis, multiple monitoring points are deployed on the water surface of the blasting area. When an underwater blast occurs, the sound waves generated by the blast propagate through the water and are captured by hydrophones located at different locations. The hydrophones convert the sound waves into electrical signals, recording information such as the amplitude, frequency, and phase of the sound waves in real time.

[0134] The underwater blast sound analysis method disclosed in the embodiments of this application is applied to a server 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 personal computers (PCs), and may also be a backend server. The server may be implemented as a standalone 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 often contains background noise, so the received signal requires preprocessing. Preprocessing typically involves filtering and denoising the received acoustic signal to produce a processed signal. This step aims to remove background noise and other interfering signals while retaining the useful information related to the blast sound wave. Common filtering methods include low-pass filtering, high-pass filtering, and band-pass filtering to eliminate high- and low-frequency noise.

[0137] Perform Fourier transform on the pre-processed signal and convert it into a frequency domain signal. Fourier transform converts the time domain signal into a frequency domain signal, revealing the amplitude and phase information of each frequency component in the signal. Fourier transform converts the time domain signal into a frequency domain signal, and can analyze the components of the signal in the frequency dimension. When propagating underwater, the sound wave signal may be affected by different frequency components, 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, widely used in fields such as signal arrival time estimation, pattern matching, signal detection, and feature extraction. In underwater blast analysis, the cross-correlation method can be used to calculate the time delay between a received acoustic signal and a known reference signal, thereby inferring the propagation time of the acoustic wave. The following section details the theoretical basis, mathematical expression, application steps, and practical considerations of the cross-correlation method.

[0141] In underwater plosive sound analysis, 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 these two signals can be calculated to estimate the signal propagation time.

[0142] In underwater blast acoustic signal analysis, a reference signal is a crucial foundation for localization algorithms such as the cross-correlation method. The reference signal typically represents a known blast acoustic waveform and is used for comparison with the actual received signal to determine the acoustic wave propagation path, time delay, and other parameters. Reference signals 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 through theoretical calculations or simulation models. This approach often relies on an understanding of the physical model and acoustic properties of the blasting process. Based on parameters such as the type of explosive, the amount of explosive, and the blast depth, explosion mechanics and acoustic theory can be used to calculate the acoustic waveform generated by the explosion. A typical blast acoustic signal exhibits a high-intensity impact waveform (impulse signal), characterized by a short rise time, high peak pressure, and a long decay time. Numerical simulation techniques such as computational fluid dynamics (CFD) or finite element analysis (FEA) can then simulate the acoustic wave propagation during the blasting process, generating a theoretical blast acoustic signal. Simulation results typically require correction based on actual underwater acoustic environment parameters (such as the sound velocity profile and absorption coefficient).

[0143] Perform a conjugate operation on the Fourier transform result of the processed signal, that is, take the complex conjugate of each frequency component of the frequency domain signal. The purpose of the conjugate operation is to prepare for the subsequent cross-correlation calculation. Multiply the frequency domain result of the conjugated processed signal 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 conjugate operation and frequency domain multiplication are preparations for the subsequent cross-correlation calculation. The conjugate operation allows us to compare the signals in the frequency domain by multiplication, 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 degree of signal matching can be analyzed in the time domain. By calculating the cross-correlation function, the temporal alignment of different signals 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 optimal match between the reference and processed signals, i.e., the time lag between the acoustic signals. By finding the maximum value of the cross-correlation function, the time difference between the two signals can be determined. The calculated lag, combined with the preset sampling rate, is used to calculate the actual propagation delay. This lag is divided by the sampling rate to obtain the propagation delay. Propagation delay refers to the time required for the acoustic wave to travel from the blast source to the monitoring point.

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

[0151] First, a time-frequency analysis is performed on the reference signal to 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. Based on the results of the time-frequency analysis, the energy distribution of the reference signal is extracted, and its intensity at each frequency and time point is calculated, ultimately obtaining the overall strength of the initial signal.

[0152] When sound waves propagate in water, they 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 predicted 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 predicted 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, reflecting the attenuation of sound wave intensity due to the spatial diffusion of energy during propagation. Assuming that sound waves propagate as spherical waves in water, as the propagation distance increases, the area covered by the sound wave increases, and the sound wave energy density per unit area decreases. This attenuation follows the inverse square law, whereby signal strength is inversely proportional to the square of the distance. Expressed logarithmically, geometric diffusion attenuation is directly proportional to the logarithm of the distance.

[0156] In the above formula, αr represents the attenuation of sound waves due to absorption when propagating through water. α is a parameter related to the physical properties of water (such as temperature, salinity, and pressure) and the frequency of the sound wave, and is expressed in dB / km. Absorption attenuation reflects the energy loss caused by absorption by a medium (such as water) when a sound wave propagates through it. Sound waves of different frequencies absorb energy to varying degrees when propagating through water, with higher frequencies generally experiencing more significant absorption.

[0157] Then, based on the initial signal strength and the calculated signal attenuation, the signal strength of the sound wave reaching the monitoring point is calculated. 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 needs to be compared 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 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 temporal changes of the signal. 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 intensity. By summing the short-time energies in all time windows, the total energy of the processed signal is obtained, which is the second signal intensity. 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 intensity (the signal intensity calculated by the predicted path) is compared with the second signal intensity (the actual received processed signal intensity). 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 intensity and the actual signal intensity match, indicating that the predicted path may be consistent with the actual sound wave propagation path. If the first signal intensity matches the second signal intensity, it is preliminarily determined that the target predicted path is the actual propagation path of the plosive sound.

[0165] S130: Calculate the sound wave propagation speed at different depths in a preset water area based on a preset sound velocity profile model. In one possible embodiment, before calculating the sound wave propagation speed at different depths in the preset water area based on the preset sound velocity profile model, the method further includes: obtaining environmental parameter data for the preset water area; preprocessing the environmental parameter data to obtain processed data; calculating 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 water area. Key environmental parameters include water temperature, salinity, pressure, depth, etc. These parameters 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 in water depends on variations in water temperature, salinity, and pressure (depth). Empirical formulas can be used to convert these environmental parameters into sound speed values. Among the empirical formulas for calculating sound speed values, the Del Grosso formula and the Chen-Millero formula are the most commonly used. Since calculating sound speed values ​​is a standard technique in the relevant technical field, they will not be further elaborated here.

[0168] After calculating the sound velocity values ​​at different depths using an empirical formula, a sound velocity profile curve can be generated. This curve shows the change in sound velocity across the entire water depth range. The sound velocity values ​​at different depths are plotted as a curve, with depth on the vertical axis and sound velocity on the horizontal axis. Generally, sound velocity profile curves exhibit three typical patterns: a positive jump layer, where the surface water temperature is higher. As depth increases, the temperature drops, and the sound velocity also decreases. A negative jump layer, where the deep water temperature is higher and the sound velocity increases with depth. A bi-cline layer, where both the surface and deep layers are high-temperature layers, and a low-temperature layer appears in the middle layer, resulting in a sound velocity profile that forms an "S"-shaped curve.

[0169] Based on the generated sound velocity profile curve, a sound velocity profile model is constructed for subsequent acoustic wave propagation path prediction and blast 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 selected depends on the complexity of the sound velocity profile. The sound velocity profile curve can be fitted to generate a mathematical model that describes the variation of sound velocity with depth. Through polynomial fitting or piecewise linear fitting, a sound velocity function expression for each depth interval is obtained. After the model is constructed, it can be verified by comparing measured data with 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 one 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 the possible propagation paths of sound waves in water. Due to the complexity of the underwater environment, 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, therefore 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 paths of sound waves. These models, based on a sound velocity profile model, consider the effects of environmental parameters such as water temperature, salinity, and depth on sound velocity, and calculate the possible propagation paths of sound waves from the blast source to the monitoring point. By inputting environmental parameter data (such as water temperature, salinity, and depth) for a pre-set water area into the pre-set sound velocity profile model, multiple optional propagation paths are generated.

[0174] After generating multiple possible 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, which is also known as the predicted path. Using the previously constructed sound velocity profile model, the propagation time of the sound wave along different paths is calculated. The propagation time of the sound wave along each path is calculated by integration, 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 locations along the path. Using numerical integration, we can calculate the propagation time of the sound wave along different paths.

[0177] The predicted propagation time for each path is compared with the actual propagation time. The difference is calculated. If the difference is less than a preset threshold (indicating a high degree of match between the predicted and actual paths), the path is selected as the predicted path. In addition to propagation time, signal strength attenuation can also be used 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 possible paths is calculated and compared with the actual propagation time to initially select paths that meet the preset threshold.

[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 preliminarily screening possible propagation paths of the blast sound signal, in addition to further screening possible propagation paths of the sound wave using the sound wave intensity comparison method in S120 , possible propagation paths of the sound wave can also be further screened using the sound wave time comparison method.

[0180] First, based on the change in the sound velocity 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 velocity profile model of the preset water area is input, and the model reflects the change of the sound velocity of the water area with depth through the sound velocity profile curve (indicating the sound velocity at different depths). According to the sound velocity 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 velocity changes at different depths are accurately described. The basis for the division of depth intervals is usually the area with significant changes in sound velocity, such as the thermocline area, near the water surface, etc. The number and division method of the specific depth intervals depend on the complexity of the sound velocity profile.

[0181] The acoustic wave propagation velocity for each depth interval is calculated using the formula c(z) = c0 + γz, where c(z) is the acoustic wave propagation velocity at depth z, c0 is the initial acoustic wave propagation velocity for 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 sound velocity with depth. According to the above formula, by inputting the starting and ending depths of each depth interval, the sound velocity gradient for each interval is determined based on the sound velocity profile model, and the acoustic 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 propagation path of the sound wave is described using ray theory, and the trajectory of the ray is determined by the following analytical equation:

[0183]

[0184] 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.

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

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

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

[0188]

[0189] Wherein, Δt is the interval acoustic wave propagation time of the depth interval [z1, z2], dz is the vertical distance component of the acoustic wave propagation velocity, c0 is the initial acoustic wave propagation velocity of the depth interval, and γ is the acoustic velocity gradient;

[0190] For each depth interval, the above formula is used to integrate and calculate the propagation time of the sound wave within that interval. A numerical integration method (such as the trapezoidal integration method) can be used to solve the problem, accumulating the propagation time of all intervals to obtain the total propagation time. Based on the interval sound wave propagation time, the predicted sound wave transmission time of the target predicted path is calculated using 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] Ultimately, by comparing the predicted propagation time with the actual propagation time, the most likely propagation path for the plosive sound is determined. The difference between the predicted and actual propagation times is calculated. If the time difference is less than a preset threshold, the path is considered the most likely propagation path, thus becoming the target predicted path. In summary, combining a sound velocity profile model, ray theory, and numerical calculation methods, the sound wave propagation path is determined through depth interval division and precise time calculation. By comparing the predicted and actual propagation times, the target predicted path can be effectively selected, thereby accurately determining the propagation path of the plosive sound and ultimately locating the blast source.

[0194] By accurately analyzing the actual propagation time and signal strength of the acoustic wave signal, combined with Fourier transform and cross-correlation function analysis, the propagation path of the plosive sound can be effectively identified. Furthermore, through time-frequency analysis and signal attenuation models, the propagation path and intensity variations of the sound wave are estimated and matched with the actual signal. Ultimately, multipath analysis identifies the optimal propagation path associated with the blast source, enabling precise location of the plosive sound propagation path.

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

[0196] In stratified areas at different depths in water, sound waves will refract due to differences in sound speed. The relationship between the angle of refraction and the angle of incidence 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 the sound wave entering any depth interval, and c2 is the propagation speed of the sound wave 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), the refraction angle of the sound wave in this depth interval is determined. Using Snell's law, the incident angle of the sound wave when it propagates to the previous depth interval is calculated 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 when it travels from the lower layer to the upper layer. The propagation direction and angle of the sound wave in each layer are calculated until it reaches the blasting source position on the water surface. The layer-by-layer reverse calculation combines the path direction and position calculation, and by using Snell's law and the sound speed profile model, the propagation path and refraction behavior of the sound wave are accurately deduced. 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 complex underwater environments, especially when there is a thermocline.

[0200] The system accurately calculates the sound wave propagation velocity at different depth intervals within a pre-defined water area. Using ray-trajectory equations and integration methods, it gradually analyzes the sound wave propagation path and propagation time within 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 effectively identifies the true propagation path of the sound wave, reduces errors, and provides more precise location of the blast source. This improves the accuracy and reliability of underwater blast sound analysis and lays a foundation 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] Because the thermocline causes sound waves to refract in the water, resulting in a complex propagation path, the system first acquires the acoustic signal received at the monitoring point and analyzes its actual propagation time and intensity. Using a pre-defined sound velocity profile model, the system calculates the propagation velocity of the sound wave at different depths and analyzes the potential propagation path. The predicted path is then matched with the actual propagation time and signal intensity to determine the target predicted path. Finally, this path is used to reversely infer the blast source location and verify its consistency with the pre-determined blast source location, thereby accurately analyzing and locating the propagation path of the underwater blast sound.

[0204] This embodiment also discloses an underwater blast 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 configured to perform predicted path analysis based on the sound wave propagation speed to obtain multiple predicted paths.

[0209] The processing module 302 is configured 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 configured to determine the target predicted path as the blast sound propagation path if it is determined that the predicted blast source position is consistent with the preset blast source position.

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

[0213] The processing module 302 is configured 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 configured to conjugate the first frequency domain transformation result to obtain a conjugate result.

[0215] The processing module 302 is configured to multiply the conjugation 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 and obtain the hysteresis.

[0220] The processing module 302 is used to calculate the propagation delay of the acoustic 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 predicted path, specifically using 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 configured to calculate a first signal strength according to the initial signal strength and the signal attenuation.

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

[0227] The processing module 302 is configured to calculate the second signal strength based on the short-time energy, specifically using 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 configured to determine that the second signal strength is the actual signal strength of the sound wave signal and preliminarily determine that the target predicted path is the plosive sound propagation path if it is determined that the difference between the first signal strength and the second signal strength is less than a preset strength threshold.

[0231] In a possible implementation, the processing module 302 is configured 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 the preset sound velocity profile model, specifically using the following formula:

[0233] c(z)=c0+γz

[0234] 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.

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

[0236]

[0237] 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.

[0238] The processing module 302 is configured to calculate the acoustic wave propagation time for any one of the multiple depth intervals, specifically using 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 based on the interval sound wave propagation time, specifically using 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 configured 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 one possible implementation, the processing module 302 is configured to determine, for a segmented interface of any one of the multiple depth intervals, that the refraction angle of the blasting acoustic wave based on the target predicted path satisfies the following law:

[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 the sound wave entering any depth interval, and c2 is the propagation speed of the sound wave 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, and reversely calculate and predict the blasting source location.

[0249] In a possible implementation, the acquisition module 301 is configured 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 using an empirical formula based on 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 configured to generate a plurality of optional paths according to environmental parameter data of a preset water area.

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

[0256] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be 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 embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and 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 implement the connection and communication between these components.

[0259] The user interface 403 may include a display screen (Display) and a camera (Camera). Optionally, the 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] The processor 401 may include one or more processing cores. The processor 401 utilizes various interfaces and lines to connect various parts of the entire server, and executes various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 405, as well as calling data stored in the memory 405. Optionally, the processor 401 may be implemented in the form of at least one hardware component selected from the group consisting of digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). The processor 401 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily 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; and the modem is used to handle wireless communications. It is understood that the modem may not be integrated into the processor 401 and may be implemented separately on a single chip.

[0262] Memory 405 may include random access memory (RAM) or read-only memory (ROM). Optionally, the memory may include non-transitory computer-readable storage medium. Memory 405 may be used to store instructions, programs, codes, code sets, or instruction sets. Memory 405 may include a program storage area and a data storage area. 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 each of the above-mentioned method embodiments, and the data storage area may store data related to each of the above-mentioned method embodiments. Memory 405 may also optionally be at least one storage device located remotely from the processor 401. 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 plosive 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 user input data; and the processor 401 can be used to call an application program storing a method for underwater plosive sound analysis 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 simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this 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 this application.

[0265] In the above embodiments, the description of each embodiment has its own focus. 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 merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, 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 interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

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

[0268] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or 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. The computer software product is stored in a memory 405 and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute 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 storing instructions, which, when executed by one or more processors 401 , enable an electronic device to execute one or more of the methods described in the above embodiments.

[0271] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. 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, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any variation, use or adaptive change of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art that are not recorded in the present disclosure. The description and examples are to be 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 sounds, characterized in that: The method comprises: Acquiring multiple acoustic wave signals received by monitoring points; Analyzing the actual propagation time and actual signal strength of each of the sound wave signals respectively; Calculate the sound wave propagation speed at different depths in the preset water area based on the preset sound velocity profile model; Performing a predicted path analysis based on the sound wave propagation speed to obtain multiple predicted paths; 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; Calculating the blasting source position based on the target predicted path to obtain a 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 actual signal strength of each of the sound wave signals specifically includes: Preprocessing the acoustic 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 transform result to obtain a conjugate result; multiplying the conjugated result by the second frequency domain transform result to obtain a correlation result; Perform an inverse Fourier transform on the correlation result to obtain a cross-correlation function, which is expressed as follows: ； 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 the initial signal strength; The signal attenuation is calculated based on the sound wave propagation distance corresponding to the target predicted path, specifically using the following formula: ； 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 further includes: Dividing the preset water area into a plurality of depth intervals according to the preset sound velocity profile model; Based on the preset sound velocity profile model, the sound wave propagation velocity in each depth interval is calculated, specifically using the following formula: ； 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: ； Wherein, Δt is the interval acoustic wave propagation time of the depth interval [z1, z2], dz is the vertical distance component of the acoustic wave propagation velocity, c0 is the initial acoustic wave propagation velocity of the depth interval, and γ is the acoustic velocity gradient; The predicted acoustic wave transmission time of the target predicted path is calculated based on the acoustic wave propagation time in the interval, specifically using the following formula: ； Among them, T total is the predicted acoustic wave propagation time, ΔTi is the interval acoustic 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 calculation of the blasting source position based on the target predicted path to obtain the predicted blasting source position specifically includes: For a 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: ； Where θ1 is the angle of incidence at any depth interval, θ2 is the angle of refraction at any depth interval, c1 is the speed of sound waves entering any depth interval, and c2 is the speed of sound waves leaving 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: Obtaining 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 according to the sound velocity value; Based on the sound velocity profile curve, the preset sound velocity 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 blast 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 configured 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, and 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 the blast sound propagation path if it is determined 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 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.

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