A method for real-time monitoring of marine strata activity

Abstract

The present application relates to the technical field of marine stratum activity monitoring, and particularly relates to a real-time monitoring method for marine stratum activity, which mainly comprises the following steps: connecting a communication cable already existing at the bottom of the sea with a land demodulator to form a distributed optical fiber acoustic wave sensing monitoring system for continuously observing marine stratum activity signals in space and time; training a deep learning algorithm to realize efficient and accurate pickup of marine stratum activity DAS monitoring signals; developing a DAS positioning algorithm to accurately obtain the spatial accurate position of underwater vibration signals; combining time-frequency analysis technology to construct a time-space-frequency-intensity correlation relationship of marine stratum activity and explore the evolution law of marine stratum activity. The method improves the reliability and decision-making ability of marine geological disaster risk assessment, and provides important support for sustainable development and utilization of marine resources in the future.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of marine stratum activity monitoring, and particularly relates to a real-time marine stratum activity monitoring method. BACKGROUND

[0002] Monitoring marine stratum activity and ensuring economic and personnel health development is one of the important tasks faced by China in the 21st century. However, the traditional marine stratum activity monitoring method cannot obtain the seabed stratum activity status in real time: reflection seismic, seabed seismograph, well logging, gravity inversion and other means can only obtain the structure of the marine stratum at different spatial scales, but cannot monitor the stratum activity in real time. The distributed fiber acoustic sensing (DAS) monitoring technology developed in recent years provides a new idea for marine bottom stratum activity monitoring. The DAS technology is a new vibration signal detection method developed in recent years, which has the advantages of low comprehensive cost, good real-time transmission, continuous spatial signal acquisition, strong environmental adaptability, wide frequency spectrum and high sensitivity, and is suitable for various extreme environments such as deep sea, deep earth and deep space. Based on the DAS technology, the signal acquisition, detection and positioning of marine stratum activity can be carried out, so that the marine stratum activity can be understood in time, and the influence of seabed natural disasters on the economy and personnel health of coastal cities can be prevented. SUMMARY

[0003] In view of at least one deficiency in the prior art, the present application provides a real-time marine stratum activity monitoring method, which aims to solve the problem of low timeliness of real-time marine stratum activity monitoring and positioning.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical scheme:

[0005] A real-time marine stratum activity monitoring method, comprising the steps of:

[0006] connecting the communication optical cable at the bottom of the sea and the land demodulator to form a distributed fiber acoustic sensing marine stratum activity monitoring system;

[0007] constructing a vibration signal acquisition unit in the distributed fiber acoustic sensing marine stratum activity monitoring system, wherein the vibration signal acquisition unit trains a pre-constructed neural network model according to public data; and using the trained neural network model to perform spatio-temporal continuous observation on the vibration signal formed by the marine bottom stratum activity to obtain vibration signal detection data, wherein the observation data includes the vibration signal formed by the marine bottom stratum activity;

[0008] According to the distributed optical fiber acoustic wave sensing marine stratum activity monitoring system, a vibration signal positioning unit is constructed, and according to the observation data and a pre-constructed signal positioning algorithm, stratum activity space-time distribution data is obtained, the stratum activity space-time distribution data including time, spatial position and corresponding probability of the stratum activity.

[0009] According to the distributed optical fiber acoustic wave sensing marine stratum activity monitoring system, a time-frequency characteristic analysis unit is constructed, and according to the observation data and stratum activity data, time-frequency analysis is performed to obtain a vibration signal space-time evolution rule.

[0010] The marine stratum activity real-time monitoring method described above further includes training a pre-constructed neural network model, specifically including:

[0011] A training data set is obtained, the training data set containing vibration signals from multiple public data sources, and the training data set is pre-processed to obtain a training database;

[0012] A neural network model is constructed based on PhaseNet-DAS, and the neural network model is trained using the training database;

[0013] The trained neural network model and a traditional signal detection algorithm are used to test pool signals to obtain a comparison result, and according to the comparison result, the network structure of the neural network model is iteratively optimized and the parameters of the neural network model are updated.

[0014] The marine stratum activity real-time monitoring method described above further includes constructing a signal positioning algorithm, specifically including:

[0015] A target function is constructed using Bayesian probability inversion theory, and a DAS monitoring vibration event positioning algorithm is constructed;

[0016] The most likely position of the predicted vibration event is taken as an initial position, and a Markov chain Monte Carlo algorithm is used to solve the DAS monitoring vibration event positioning algorithm to obtain accurate positioning of the vibration event.

[0017] The marine stratum activity real-time monitoring method described above further includes constructing a target function using Bayesian probability inversion theory, specifically including:

[0018] The Bayesian formula is expressed as:

[0019]

[0020] In the formula, d represents the arrival time data of the observed vibration signal, m represents the position parameter of the vibration signal, p(m) represents the prior probability distribution of the spatial position of the vibration signal, p(d|m) represents the conditional probability of the arrival time data d of the observed vibration signal obtained when the spatial position m of the vibration event is given, p(d) is the probability of obtaining the observed data d, which is set as a normalization constant, and p(m|d) is the posterior probability, which is the possible position of the spatial position m of the vibration event given the arrival time data d of the observed vibration signal.

[0021] Since the spatial location of the vibration event and the probability of various observation data occurrences are the same, the prior probability p(m) and the observation data probability p(d) are set to constants.

[0022] Then the maximum a posteriori probability p max (m|d) depends only on the value of the likelihood function p(d|m):

[0023] p max (m|d)∝p(d|m),(2)

[0024] Assuming the picking error follows a Gaussian distribution, the likelihood function is expressed in Gaussian probability form:

[0025]

[0026] In the formula, Here, d represents the actual arrival time data of the vibration signal, σ represents the arrival time data calculated based on the location of the vibration event, and σ represents the standard deviation. This represents the time-lapse mismatch function.

[0027] The above-described method for real-time monitoring of marine stratigraphic activity further includes constructing a mismatch function, specifically comprising:

[0028] Considering the spatial continuity of seismic records in a distributed fiber optic acoustic sensing marine stratigraphic activity monitoring system, its mismatch function Ψ1 is expressed as:

[0029]

[0030] nr represents the number of detector channels, ns1 represents the number of vibration events; t = O + T is the arrival time of the record, O is the start time of the event, and T is the travel time from the source point to the detector channel; and Let i and j represent the travel times from the observed events i and j to the detector channel r, respectively. and This indicates the calculated P-wave and S-wave travel times from events i and j to detector channel r; obs and cal represent the observed and calculated values;

[0031] The mismatch function Ψ1 uses the distributed fiber optic acoustic sensing ocean stratum activity monitoring system to constrain the absolute spatial position of the microseismic event, and the spatial likelihood function of the vibration event is expressed as

[0032]

[0033] The spatial position corresponding to the maximum likelihood function is the most likely position of the vibration event.

[0034] The real-time monitoring method of the ocean stratum activity as described above further takes the most likely position of the predicted vibration event as the initial position, and performs accurate positioning of the vibration event through the Markov chain Monte Carlo algorithm:

[0035] ① Initialization: input the initial model m containing the spatial position of the noise and vibration signal 0，0 =[σ0,x0,y0,z0], calculate the posterior probability size p(d|m 0，0 ) at this time;

[0036] ② Generate Markov Chain, outer loop for N times, and only the arrival time error σ changes each time; inner loop for M times, and only the seismic position parameters are updated each time;

[0037] Assuming that the arrival time error of the i-th outer loop is σ i , at this time, the position parameter [x j , y j , z j ] of the j-th inner loop is updated as follows:

[0038] [1] On the basis of the model parameter m i,j-1 =[σ i ,x j-1 ,y j-1 ,z j-1 ] of the previous step, randomly disturb to generate a new model m i,j =[σ i ,x j ,y j ,z j ];

[0039] [2] Calculate the posterior probability p(d|m i,j ) of the model m i,j ;

[0040] [3] Calculate the acceptance probability

[0041] [4] Compare the acceptance probability α with a random number u between 0 and 1; α≥u, accept the new model parameter; α<u, then m i,j =m i,j-1 ;

[0042] [5] go back to [1] for next iteration;

[0043] After the inner and outer loop, the final posterior probability distribution is generated by the subsequent stable part statistics, and the maximum a posteriori solution is the most likely position of the vibration event.

[0044] The real-time monitoring method of marine stratum activity as described above, further, time-frequency analysis is performed according to the observation data and stratum activity data to obtain the vibration signal evolution law, specifically comprising:

[0045] The observation data and stratum activity data are subjected to time-frequency analysis by wavelet transform, Hilbert-Huang transform or generalized S transform,

[0046] The wavelet transform formula is:

[0047]

[0048] x(t) is the original signal, ψ(t) is the mother wavelet function, and a and b are the scaling and translation, respectively;

[0049] The Hilbert-Huang transform formula is:

[0050]

[0051] x(t) is the original signal;

[0052] The generalized S transform formula is:

[0053]

[0054] x(t) is the original signal, and λ and p are the control parameters of the Gaussian window function;

[0055] The vibration signal evolution law includes the evolution law of stratum activity occurrence time, spatial position, signal time-frequency parameter and waveform intensity.

[0056] The real-time monitoring method of marine stratum activity as described above, further, a communication optical cable connected to the bottom of the sea and a land demodulator are connected to form a distributed optical fiber acoustic wave sensing marine stratum activity monitoring system, specifically comprising:

[0057] Select a suitable communication optical cable to be laid on the bottom of the sea, and accurately measure its length while determining its spatial position;

[0058] Select a suitable position on land to install the optical fiber vibration signal demodulator, ensure that it is placed in a machine room with waterproof and moisture-proof function, and provide continuous 24-hour power supply;

[0059] The demodulator on land is connected with the submarine communication optical cable, so that the vibration signals of different parts of the optical cable are monitored in real time.

[0060] The real-time monitoring method of marine stratum activity as described above, further, the vibration signals formed by the marine bottom stratum activity are observed continuously in space and time, specifically comprising:

[0061] The spatial sampling interval is set to 100 meters, the time sampling rate is more than 500Hz, and the vibration signals around the communication optical cable of the marine bottom are measured continuously every day.

[0062] Compared with the prior art, the present application has the beneficial effects that: the present application connects the land demodulator with the submarine communication optical cable, constructs a distributed fiber acoustic sensing (DAS) monitoring system, realizes the continuous observation of the space-time of the marine bottom stratum activity signal, trains the deep learning algorithm, efficiently and accurately picks up the vibration signal in the marine stratum DAS monitoring, develops the marine vibration signal DAS positioning algorithm, accurately determines the spatial position of the submarine vibration signal, combines the time-frequency analysis technology, establishes the space-time-frequency-intensity correlation model between the marine stratum vibration signal and the stratum activity, and reveals the evolution law of the marine stratum activity. The advantage of the method is that it can record the continuous vibration signal of the marine stratum with high space-time resolution, and can accurately locate the spatial position of the vibration signal, and deeply explore the relationship between the DAS monitored vibration signal and the stratum activity intensity. The results of the project are expected to improve the accuracy and decision efficiency of marine geological disaster risk assessment, and provide key technical support for the sustainable development and utilization of the marine bottom. BRIEF DESCRIPTION OF DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0064] Figure 1 The flow chart of the real-time monitoring method of marine stratum activity of the present application.

[0065] Figure 2 The DAS real-time monitoring signal diagram of marine stratum activity of the present application.

[0066] Figure 3 The schematic diagram of efficiently picking up DAS vibration signal by using deep learning algorithm.

[0067] Figure 4A flowchart for locating a seabed stratum activity position based on a DAS monitoring signal. DETAILED DESCRIPTION

[0068] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0069] Embodiment:

[0070] The terms "comprising" and "having" and any variations thereof in the embodiments of the present application are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to the clearly listed steps or units, but can include other steps or units that are not clearly listed or inherent to the process, method, product or device.

[0071] Ocean stratum activity has a significant impact on the economic development of coastal areas and the safety of urban residents' life and property. Traditional stratum activity monitoring technology is difficult to ensure real-time acquisition of accurate ocean stratum activity data. Even on land, the seismic station is also difficult to accurately capture the full wave field information of the underwater vibration signal. The traditional method lacks timeliness in collecting and processing ocean stratum activity data, and often faces the problem of insufficient spatial resolution. Therefore, developing a technology that can monitor ocean stratum vibration signals in real time has great significance for preventing ocean geological disasters.

[0072] The present application utilizes a land demodulator connected with a seabed communication optical cable to construct a distributed fiber acoustic sensing (DAS) monitoring system, realizes the spatio-temporal continuous observation of the ocean bottom stratum activity signal, improves the pickup efficiency and accuracy of the ocean stratum DAS monitoring vibration signal by training a deep learning algorithm, develops a DAS positioning algorithm for ocean vibration signals to accurately determine the spatial position of the seabed vibration signal, and establishes a time-space-frequency-intensity correlation model between the ocean stratum vibration signal and the stratum activity to reveal the evolution law of the ocean stratum activity in combination with the time-frequency analysis technology.

[0073] Referring to Figures 1 to 4 The embodiment of the present application provides a real-time monitoring method for ocean stratum activity, which can specifically include the following steps:

[0074] Step 101: connecting the communication optical cable at the bottom of the ocean and the land demodulator to form a distributed fiber acoustic sensing ocean stratum activity monitoring system.

[0075] In this step, the communication cable suitable for laying on the ocean floor is selected and its length is accurately measured, and its spatial position is determined. The appropriate location on land is selected to install the optical fiber vibration signal demodulator, which is placed in a machine room with waterproof and moisture-proof function, and provides continuous 24-hour power supply. The demodulator on land is connected with the submarine communication cable to monitor the vibration signals of different parts of the cable in real time. The spatial sampling interval is set to 100 meters, and the time sampling rate is more than 500Hz. Ensure continuous measurement of the vibration signals around the ocean cable every day.

[0076] In particular implementation, Figure 2 The schematic diagram of monitoring the activity signal of the ocean stratum by the distributed optical fiber sensing device (DAS). It is mainly composed of several parts and components:

[0077] The optical fiber demodulator is laid on the land near the coastline and connected with the communication cable on the ocean floor. For both land and submarine cables, it is necessary to ensure close coupling with the stratum and to ensure that the optical fiber does not break or have other abnormal conditions in the transmission path. The cable signal demodulator is placed in a land machine room or other safe location to ensure that the demodulator has continuous 24-hour stable power supply. The demodulator is connected with the submarine cable in series. The signal demodulator is used to analyze the submarine vibration signals detected by the submarine cable. The interval of the cable space monitoring is set to 100 meters by the demodulator, and the time sampling rate is more than 500Hz, so as to realize continuous monitoring of the submarine stratum vibration signal and ensure 24-hour uninterrupted monitoring every day.

[0078] Step 102: constructing a vibration signal acquisition unit in the distributed optical fiber acoustic wave sensing ocean stratum activity monitoring system, training a pre-constructed neural network model according to public data; using the trained neural network model to conduct spatio-temporal continuous detection on the vibration signals formed by the activity of the ocean floor, obtaining vibration signal detection data, and the observation data containing the vibration signals formed by the activity of the ocean floor.

[0079] In this step, the pre-constructed neural network model is trained, which specifically includes: obtaining a training data set containing vibration signals from multiple public data sources, preprocessing the training data set to obtain a training database; constructing a neural network model based on PhaseNet-DAS, training the neural network model using the training database; using the trained neural network model and the traditional neural network model to test the pool signal, obtaining comparison data, and according to the comparison data, iteratively optimizing the network structure of the neural network model and updating the parameters of the neural network model.

[0080] In specific implementation, ① a training database is constructed. International published marine and terrestrial distributed acoustic sensing (DAS) seismic signals and theoretical DAS seismic signals generated based on forward modeling are collected. The diversity and generalization ability of the training dataset are improved by performing preprocessing steps such as filtering, smoothing, normalization, and adding noise. ② a neural network model is trained. The PhaseNet-DAS deep learning architecture is used to select appropriate neural network parameters and layers to train a neural network model suitable for monitoring marine stratum vibration activities using DAS technology. ③ a pool signal test is performed. The well-trained neural network model is applied to underwater DAS monitoring data and compared with traditional detection techniques to further optimize the network structure. The prediction accuracy and efficiency of the model under different vibration positions, different magnitude sizes, and different signal-to-noise ratios are quantitatively analyzed to determine the application range and applicable conditions of the algorithm.

[0081] In specific implementation, Figure 3 The deep learning algorithm is used to efficiently pick up DAS vibration signals. The basic principle is:

[0082] ① A variety of training samples need to be provided. Training samples are composed of multiple sources, including: published marine and terrestrial DAS data; DAS data obtained by numerical simulation methods; and data obtained by filtering, smoothing, normalization, and adding noise to increase the richness and generalization of the training data. ② A suitable neural network structure needs to be trained. Based on the U-shaped neural network structure of the PhaseNet-DAS two-dimensional convolution layer, the network model parameters (such as the number of layers, the number of neurons, the filter size, the activation function, and the learning rate) are tested and optimized by increasing the training samples. In the optimization process, the Gaussian Mixture Model Associator (GaMMA) is used to filter the phase information, and the sample label categories are increased by means of superposition, inversion, stretching, and adding noise. Through multiple cycles of iteration, a suitable neural network structure is trained. ③ A pool experiment is performed. After performing necessary preprocessing of the original data (filtering, smoothing, and normalization), the trained deep learning algorithm is applied to actual pool experiment DAS signals that have not been tested. The detected DAS vibration signals are compared with the seismograph results, and the unrecognized DAS vibration signals are separated to retrain the neural network structure. At the same time, the actual DAS monitoring seabed vibration signals are changed in signal-to-noise ratio, and the vibration events are detected based on the trained neural network. The influence of different noise on the prediction results is analyzed to obtain the effective range and applicable conditions of the deep learning algorithm.

[0083] Step 103: Construct a vibration signal positioning unit in the distributed optical fiber acoustic wave sensing seabed stratum activity monitoring system, and obtain stratum activity space-time distribution data according to the observation data and a pre-constructed signal positioning algorithm, wherein the stratum activity space-time distribution data includes time, position and corresponding probability of the stratum activity.

[0084] In this step, a target function is constructed by using the Bayesian probability inversion theory, and then a DAS monitoring vibration event positioning algorithm is constructed; the most probable position of the predicted vibration event is taken as an initial position, and the Markov chain Monte Carlo algorithm is used to solve the DAS monitoring vibration event positioning algorithm to obtain accurate positioning of the vibration event.

[0085] In specific implementation, by constructing an accurate target function and using the Markov chain Monte Carlo (MCMC) algorithm, the potential position of the stratum activity and its probability distribution can be inferred, so as to quantitatively evaluate the possibility of different spatial distribution of the stratum activity.

[0086] Figure 4 A flowchart for positioning the seabed stratum activity position based on the DAS monitoring signal is shown in FIG. 1. The basic principle is as follows:

[0087] (1) Construct a target function. DAS (Distributed Acoustic Sensing) can collect a spatially continuous vibration wave field, but its signal-to-noise ratio is low, and the pickup time delay accuracy of different channels is different. In order to avoid the interference of different noises on the positioning result, the Bayesian probability inversion theory is used to construct the target function in this embodiment.

[0088] The Bayesian formula can be expressed as:

[0089]

[0090] In the formula, d is the observation vibration signal data, and m is the vibration signal position parameter. p(m) represents the prior probability distribution of the vibration signal spatial position, p(d|m) represents the conditional probability of the observation data d obtained under the given vibration event position m, which is called the likelihood function. p(d) is the probability of obtaining the observation data d, which can be regarded as a normalization constant. p(m|d) is the posterior probability, which is the possible position of the vibration event spatial position m under the given observation data d.

[0091] Since the spatial position of the vibration event and the probability of various cases of the observation data are the same, the prior probability p(m) and the observation data probability p(d) are often calculated as constants. The maximum posterior probability p(m|d) is only related to the value of the likelihood function p(d|m): max

[0092] p max ​(m|d)∝p(d|m),(2)

[0093] Under the general condition of timing pick-up error, the likelihood function can be expressed in the form of Gaussian probability:

[0094]

[0095] In the embodiment is the true timing data of the vibration signal, d is the timing data calculated according to the position of the vibration event, and σ is the standard deviation. Different standard deviation sizes are related to the signal-to-noise ratio and represent different pick-up accuracies. represents the travel time mismatch function.

[0096] Considering the spatial continuity of the DAS seismic record, the mismatch function Ψ1 of the DAS seismic record can be expressed as:

[0097]

[0098] nr represents the number of detection channels, and ns1 represents the number of vibration events. t = O + T is the arrival time of the record, O is the starting time of the event, and T is the travel time from the source point to the detection channel. and and and indicate the calculated P-wave and S-wave travel times of events i and j to detection channel r. obs and cal represent the observed and calculated values.

[0099] Ψ1 uses the arc shape of the DAS timing connection curve to constrain the absolute spatial position of the microseismic event. The spatial likelihood function of the vibration event can be expressed as

[0100]

[0101] The spatial position corresponding to the maximum likelihood function is the most likely position of the vibration event.

[0102] (2) Selection of inversion algorithm. In this embodiment, a grid search algorithm is first used to construct an initial vibration signal source spatial position dictionary model based on a wide search grid, and the possible geographical position of the vibration event is predicted through comparison and analysis with the actual data. The predicted geographical position is used as the starting point for inversion analysis, and then the precise positioning of the vibration event is realized through the inversion process.

[0103] To assess the impact of noise error on positioning accuracy, this case study also simulates positioning results under different noise standard deviations σ. Furthermore, this embodiment employs the classic MH MCMC (Metropolis-Hastings Markov Chain Monte Carlo) algorithm to generate a posterior probability model for inverting and inferring the exact location of microseismic events. The Markov Chain in its inversion step can be represented as:

[0104] ① Initialization: Input an initial model m containing the spatial locations of noise and vibration signals. 0，0 =[σ0,x0,y0,z0], calculate the posterior probability p(d|m) at this time. 0，0 );

[0105] ② Generate a Markov Chain. The outer loop is performed N times, with only the time error σ changing each time. The inner loop is performed M times, with only the seismic location parameters being updated each time.

[0106] Assume the time error of the i-th iteration of the outer loop is σ. i At this point, update the position parameter [x] in the j-th iteration of the inner loop. j ,y j ,z j The steps are as follows:

[0107] [1] In the previous step, the model parameter m i,j-1 =[σ i ,x j-1 ,y j-1 ,z j-1 Based on this, a new model m is generated by random perturbation. i,j =[σ i ,x j ,y j ,z j ];

[0108] [2] Calculation model m i,j The posterior probability p(d|m) i,j );

[0109] [3] Calculate the receiving probability

[0110] [4] Compare the acceptance probability α with a random number u between [0,1]. If α ≥ u, accept the new model parameters; if α < u, then m i,j =m i,j-1 ;

[0111] [5] Return to [1] for the next iteration;

[0112] ③After the internal and external circulation ends, the initial "preheating" part of the Markov Chain is discarded, and the final posterior probability distribution is calculated based on the subsequent stable part, and the maximum a posteriori solution is the most likely location of the microseismic event.

[0113] Based on the idea of Bayesian posterior probability evaluation, it will help to quantitatively evaluate the influence of the time pickup error of the vibration signal on the positioning result, and to speculate the accurate spatial position of the vibration event.

[0114] Step 104: According to the time-frequency feature analysis unit constructed in the distributed optical fiber acoustic wave sensing marine stratum activity monitoring system, the time-frequency feature analysis unit carries out time-frequency analysis according to the observation data and stratum activity data, and obtains the spatio-temporal evolution law of the vibration signal.

[0115] In this step, by analyzing the spatio-temporal distribution characteristics of the marine bottom vibration event, the frequency and spatial density of the marine stratum activity can be determined. In addition, this embodiment will also use the noise and vibration waveform data obtained by the distributed acoustic sensing (DAS) to perform f-k transform, wavelet transform, Hilbert-Huang transform, generalized S transform and other methods to extract the time-frequency characteristics of the data, and analyze the variation law of these time-frequency parameters with time in the stratum activity process. Among them,

[0116] The wavelet transform formula is:

[0117]

[0118] x(t) is the original signal, ψ(t) is the mother wavelet function, and a and b are the scaling and translation, respectively.

[0119] Hilbert-Huang transform is to perform Hilbert transform on a single frequency function based on empirical mode decomposition of the signal, and its formula can be expressed as:

[0120]

[0121] x(t) is the original signal.

[0122] The generalized S transform uses a Gaussian window function to perform time-frequency analysis on the original signal, and its formula can be expressed as:

[0123]

[0124] x(t) is the original signal, and λ and p are the control parameters of the Gaussian window function.

[0125] By analyzing the time and location results of the detected vibration events, the source depth, direction, occurrence time, duration, sequence interval, frequency distribution and energy size of the vibration signal can be determined by using statistical methods. This helps to construct the time-space-frequency-energy relationship model, so as to identify the evolution law of stratum activity and quantitatively evaluate the activity and evolution characteristics of the seabed stratum.

[0126] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples without contradiction.

[0127] The above embodiments are only for the purpose of illustrating the technical concept and characteristics of the present application, and the purpose is to enable those skilled in the art to understand the content of the present application and to implement it, and cannot limit the protection scope of the present application. Any equivalent changes or modifications made in accordance with the essence of the present application shall be covered within the protection scope of the present application.

Claims

1. A method for real-time monitoring of marine stratigraphic activity, characterized in that, Including the following steps: The communication optical cable connecting the ocean floor and the land demodulator constitutes a distributed fiber optic acoustic sensing system for monitoring ocean strata activity. A vibration signal acquisition unit is constructed within the distributed fiber optic acoustic sensing marine strata activity monitoring system. The vibration signal acquisition unit trains a pre-constructed neural network model based on publicly available data. The trained neural network model is then used to perform spatiotemporal continuous detection of vibration signals generated by marine strata activity to obtain observation data of effective abnormal vibrations. The observation data includes vibration signals generated by marine strata activity. A vibration signal localization unit is constructed based on the distributed fiber optic acoustic sensing marine strata activity monitoring system. This unit obtains spatiotemporal distribution data of strata activity based on the observed data and a pre-constructed signal localization algorithm. This data includes the time, location, and corresponding probability of the strata activity. The pre-constructed signal localization algorithm involves: constructing an objective function using Bayesian probability inversion theory, and then constructing a DAS-monitored vibration event localization algorithm; using the most probable predicted location of the vibration event as the initial location, and employing a Markov chain Monte Carlo algorithm to invert and solve the DAS-monitored vibration event localization algorithm to obtain the precise location of the vibration event. Based on the distributed optical fiber acoustic sensing marine stratigraphic activity monitoring system, a time-frequency characteristic analysis unit is constructed. The time-frequency characteristic analysis unit performs time-frequency analysis based on the observation data and stratigraphic activity data to obtain the spatiotemporal evolution law of vibration signal.

2. The method for real-time monitoring of marine stratigraphic activity according to claim 1, characterized in that, Training a pre-built neural network model specifically includes: A training dataset is obtained, which contains vibration signals from multiple publicly available data sources. The training dataset is preprocessed to obtain a training database. A neural network model was constructed based on PhaseNet-DAS, and the neural network model was trained using a training database. The trained neural network model was compared with a traditional signal detection algorithm to test the signal in a water tank. The comparison results were obtained, and the network structure and parameters of the neural network model were iteratively optimized based on the comparison results.

3. The method for real-time monitoring of marine stratigraphic activity according to claim 2, characterized in that, The objective function is constructed using Bayesian probability inversion theory, specifically including: Bayes' theorem is expressed as: In the formula, d represents the arrival time data of the observed vibration signal, m represents the position parameter of the vibration signal, p(m) represents the prior probability distribution of the spatial position of the vibration signal, p(d|m) is the likelihood function, which represents the conditional probability of obtaining the arrival time data d of the observed vibration signal given the spatial position m of the vibration event, p(d) is the probability of obtaining the observed data d, which is set as a normalization constant, and p(m|d) is the posterior probability, which is the possible position of the spatial position m of the vibration event given the arrival time data d of the observed vibration signal. Since the spatial location of the vibration event and the probability of various observation data occurrences are the same, the prior probability p(m) and the observation data probability p(d) are set to constants. Then the maximum a posteriori probability p max (m|d) depends only on the value of the likelihood function p(d|m): p max (m|d)∝p(d|m), (2) Assuming the picking error follows a Gaussian distribution, the likelihood function is expressed in Gaussian probability form: In the formula, Here, d represents the actual arrival time data of the vibration signal, σ represents the arrival time data calculated based on the location of the vibration event, and σ represents the standard deviation. This represents the time-lapse mismatch function.

4. The method for real-time monitoring of marine stratigraphic activity according to claim 3, characterized in that, Constructing the mismatch function specifically includes: Considering the spatial continuity of seismic records in a distributed fiber optic acoustic sensing marine stratigraphic activity monitoring system, its mismatch function Ψ1 is expressed as: nr represents the number of detector channels, ns1 represents the number of vibration events; t = O + T is the arrival time of the record, O is the start time of the event, and T is the travel time from the source point to the detector channel; and Let i and j represent the travel times from the observed events i and j to the detector channel r, respectively. and This indicates the calculated P-wave and S-wave travel times from events i and j to detector channel r; obs and cal represent the observed and calculated values; The mismatch function Ψ1 utilizes the arc shape of the arrival-time curve of the distributed fiber optic acoustic sensing marine stratigraphic activity monitoring system to constrain the absolute spatial location of microseismic events. The spatial likelihood function of the vibration event is expressed as follows: The spatial location corresponding to the maximum likelihood function is the most likely location of the vibration event.

5. The method for real-time monitoring of marine stratigraphic activity according to claim 4, characterized in that, Using the most probable location of the predicted vibration event as the initial location, the vibration event is precisely located using the Markov chain Monte Carlo algorithm: ① Initialization: Input an initial model m containing the spatial locations of noise and vibration signals. 0，0 =[σ0,x0,y0,z0], calculate the posterior probability p(d|m) at this time. 0，0 ); ② Generate a Markov Chain. The outer loop is executed N times, with only the time error σ changing each time. The inner loop is executed M times, with only the seismic location parameters being updated each time. Assume the time error of the i-th iteration of the outer loop is σ. i At this point, update the position parameter [x] in the j-th iteration of the inner loop. j ,y j ,z j The steps are as follows: [1] In the previous step, the model parameter m i,j-1 =[σ i ,x j-1 ,y j-1 ,z j-1 Based on this, a new model m is generated by random perturbation. i,j =[σ i ,x j ,y j ,z j ]; [2] Calculation model m i,j The posterior probability p(d|m) i,j ); [3] Calculate the receiving probability [4] Compare the acceptance probability α with a random number u between [0,1]; if α≥u, accept the new model parameters; if α<u, then m i,j =m i,j-1 ; [5] Return to [1] for the next iteration; ③ After the inner and outer cycles are completed, the subsequent stable part is statistically generated to produce the final posterior probability distribution, and its maximum posterior solution is the most likely location of the vibration event.

6. The method for real-time monitoring of marine stratigraphic activity according to claim 1, characterized in that, Based on the aforementioned observation data and stratigraphic activity data, time-frequency analysis was performed to obtain the evolution law of the vibration signal, specifically including: Time-frequency analysis was performed on the observation data and stratigraphic activity data using wavelet transform, Hilbert-Huang transform, or generalized S-transform. The wavelet transform formula is: x(t) is the original signal, ψ(t) is the mother wavelet function, and a and b are the scaling and translation amounts, respectively. The Hilbert-Huang transform formula is: x(t) is the original signal; The generalized S-transform formula is: x(t) is the original signal, and λ and p are the control parameters of the Gaussian window function; The evolution law of the vibration signal includes the evolution law of the occurrence time, spatial location, signal time and frequency parameters and waveform intensity of the stratum activity.

7. The method for real-time monitoring of marine stratigraphic activity according to claim 1, characterized in that, The communication fiber optic cable connecting the ocean floor and the land-based demodulator constitutes a distributed fiber optic acoustic sensing system for monitoring oceanographic activity, specifically including: Select suitable communication optical cables for deployment on the ocean floor, accurately measure their length, and determine their spatial location. Select a suitable location on land to install the fiber optic vibration signal demodulator, ensuring that it is placed in a waterproof and moisture-proof equipment room and provided with continuous 24-hour power supply. Connect the land-based demodulator to the submarine communication fiber optic cable to monitor vibration signals at different parts of the cable in real time.

8. The method for real-time monitoring of marine stratigraphic activity according to claim 1, characterized in that, Continuous spatiotemporal observation of vibration signals generated by ocean floor strata activity, specifically including: The spatial sampling interval is set to 100 meters, the time sampling rate exceeds 500 Hz, and continuous daily measurement of vibration signals around the communication optical cable on the ocean floor is ensured.

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