A method for monitoring seafloor crustal activity based on differential phase-sensitive optical time-domain reflectometry

Abstract

The present invention relates to the technical field of optical signal processing and analysis, and specifically provides a method for monitoring seabed crust activity based on a differential phase-sensitive optical time domain reflectometer, comprising: differentially processing an echo optical pulse signal to obtain a first differential echo matrix. Setting a dynamic window constraint to obtain a second differential echo matrix. The second differential echo matrix includes low-rank terms, sparse terms, and noise terms. A global optimization problem is constructed based on the second differential echo matrix, and the sparse term estimation matrix corresponding to the sparse terms is solved. The irrelevant activities in the sparse term estimation matrix are separated to obtain a target activity matrix, and then the monitoring results are obtained. The above method converts non-low-rank trend features such as slowly varying interference into low-rank features through differential processing, so that the periodic and regular components in the received signal are clearer, and can separate periodic events and sudden events through a low-rank sparse decomposition model, and further separate irrelevant human activities, thereby realizing accurate monitoring of sudden activities in the seabed crust.

Description

Technical Field

[0001] The invention relates to the technical field of optical signal processing and analysis, and in particular to a method for monitoring seafloor crust activity based on a differential phase-sensitive optical time-domain reflectometer. Background Art

[0002] When external events act on optical fibers, they cause minute changes in their refractive index, length, and other parameters, which in turn alter their phase. Phase-sensitive optical time domain reflectometer (OTDR) technology can measure and locate external events based on signal phase changes, and is therefore widely used in geological monitoring.

[0003] Phase OTDR technology is commonly used to monitor submarine plate activity. This activity can occur both periodically and suddenly. Sudden events often trigger severe natural disasters such as earthquakes and tsunamis, necessitating close attention. However, existing monitoring methods are hampered by trends such as slowly varying interference in the marine environment, making it difficult to isolate periodic events and obtaining accurate disaster warnings. This leads to high false alarm rates and limited reliability. Summary of the Invention

[0004] The method for monitoring seabed crustal activity based on differential phase-sensitive optical time-domain reflectometry provided by the embodiments of the present invention can eliminate slowly varying interference and separate irrelevant noise, at least solving the problem of insufficient accuracy in monitoring seabed crustal activity.

[0005] In a first aspect, the present invention provides a method for monitoring seafloor crustal activity based on differential phase-sensitive optical time-domain reflectometry, the method comprising:

[0006] Acquire an echo optical pulse signal, and perform differential processing on the echo optical pulse signal to obtain a first differential echo matrix;

[0007] Setting a dynamic window to obtain a second differential echo matrix of the first differential echo matrix under the constraints of the dynamic window; wherein the second differential echo matrix includes a low-rank term, a sparse term, and a noise term;

[0008] Constructing a global optimization problem according to the second differential echo matrix, and solving a sparse item estimation matrix corresponding to the sparse item in the global optimization problem;

[0009] Separating irrelevant activities in the sparse item estimation matrix to obtain a target activity matrix;

[0010] A monitoring result of the target activity is obtained according to the target activity matrix.

[0011] The present invention provides a method for monitoring seafloor crust activity based on a differential phase-sensitive optical time-domain reflectometer, which obtains an echo light pulse signal and performs differential processing on the echo light pulse signal to obtain a first differential echo matrix, including:

[0012] receiving the echo light pulse signal through a spectrum analyzer, and obtaining a sampled Rayleigh curve corresponding to each echo light pulse signal;

[0013] Performing differential processing on two adjacent sampled Rayleigh curves to obtain a first differential signal;

[0014] The first differential signals constitute the first differential echo matrix.

[0015] The present invention provides a method for monitoring seafloor crust activity based on a differential phase-sensitive optical time-domain reflectometer, wherein a dynamic window is set to obtain a second differential echo matrix constrained by the dynamic window from the first differential echo matrix, including:

[0016] Obtaining the corresponding signal main frequency according to the sampled Rayleigh curve of the echo optical pulse signal;

[0017] Setting the window length of the dynamic window according to the main frequency of the signal; wherein the dynamic window, the main frequency of the signal and the sampled Rayleigh curve are in one-to-one correspondence;

[0018] Calculating the differential signal components of the first differential signal at each sampling point one by one; wherein the differential signal components are calculated within the length range of the dynamic window;

[0019] Arranging the calculated differential signal components to obtain the second differential signal;

[0020] The second differential signals constitute the second differential echo matrix.

[0021] The method for monitoring seafloor crust activity based on differential phase-sensitive optical time-domain reflectometry provided by an embodiment of the present invention further includes, before constructing a global optimization problem based on the second differential echo matrix:

[0022] Setting a weight coefficient of the low-rank term according to the dynamic window;

[0023] The weight coefficient of the sparse term is calculated based on the weight coefficient of the low-rank term; wherein the sum of the weight coefficient of the low-rank term and the weight coefficient of the sparse term is 1.

[0024] The method for monitoring seafloor crust activity based on differential phase-sensitive optical time-domain reflectometry provided by the embodiment of the present invention includes setting the weight coefficient of the low-rank term according to the dynamic window:

[0025] Calculating a mean value of the window lengths of the dynamic windows;

[0026] Calculate the standard deviation of the window length of the dynamic window according to the mean;

[0027] Creating a local weight function according to the mean and the standard deviation; wherein the local weight function is set as a piecewise function, and the piecewise points of the local weight function are determined according to the mean and the standard deviation;

[0028] Obtaining a local weight corresponding to each of the dynamic windows according to the local weight function;

[0029] An average value of the local weights is taken as the weight coefficient of the low-rank term.

[0030] The method for monitoring seafloor crust activity based on differential phase-sensitive optical time-domain reflectometry provided by the embodiment of the present invention comprises constructing a global optimization problem based on the second differential echo matrix, including:

[0031] Performing singular value decomposition on the low-rank term and summing the singular values ​​to obtain a nuclear norm of the low-rank term;

[0032] Taking the 1-norm of the elements in the sparse item;

[0033] A weighted sum of the nuclear norm of the low-rank term and the 1-norm of the sparse term is performed to obtain an objective function of the global optimization problem;

[0034] Calculating the F-norm of the noise term and squaring it, and constraining the squared result to be less than or equal to a noise suppression threshold as a constraint condition of the global optimization problem; wherein the noise suppression threshold is preset;

[0035] The global optimization problem is obtained according to the objective function and the constraint conditions.

[0036] The present invention provides a method for monitoring seafloor crust activity based on differential phase-sensitive optical time-domain reflectometry, wherein the method separates irrelevant activities in the sparse item estimation matrix to obtain a target activity matrix, including:

[0037] Setting a time domain target activity set, performing time dimension extraction on the sparse item estimation matrix, and classifying the columns of the sparse item estimation matrix corresponding to the time domain target activity into the time domain target activity set;

[0038] Setting a spatial target activity set, performing spatial dimension extraction on the sparse item estimation matrix, and classifying the columns of the sparse item estimation matrix corresponding to the spatial target activity into the spatial target activity set;

[0039] The columns corresponding to irrelevant activities in the sparse item estimation matrix are set to zero to obtain the target activity matrix; wherein the columns corresponding to irrelevant activities in the sparse item estimation matrix are columns that are neither included in the time domain target activity set nor in the spatial domain target activity set.

[0040] The method for monitoring seafloor crust activity based on differential phase-sensitive optical time-domain reflectometry provided by an embodiment of the present invention extracts the time dimension of the sparse item estimation matrix, and classifies the columns of the sparse item estimation matrix corresponding to the time domain target activity into the time domain target activity set, including:

[0041] Setting a sliding window for an element of the sparse term estimation matrix; wherein the sliding window and the element are in the same row of the sparse term estimation matrix, the sliding window is centered on the element, and the length of the sliding window is preset;

[0042] Calculating the average value of all elements in the sliding window;

[0043] For the element at the center of the sliding window, subtract the average value of all elements in the sliding window and take the second norm to obtain the smoothing error of the element;

[0044] Comparing the smoothing error of each element of the sparse term estimation matrix with a smoothness threshold value respectively; wherein the smoothness threshold value is preset;

[0045] When the smoothing error of any element is smaller than the smoothness threshold, the column where the element is located is included in the time-domain target activity set.

[0046] The method for monitoring seafloor crust activity based on differential phase-sensitive optical time-domain reflectometry provided by an embodiment of the present invention extracts the spatial dimension of the sparse item estimation matrix, and classifies the columns of the sparse item estimation matrix corresponding to the spatial target activity into the spatial target activity set, including:

[0047] For each column of the sparse item estimation matrix, the sum of the absolute values ​​of the column elements is calculated as a sparsity parameter;

[0048] Comparing the sparsity parameter of each column of the sparse item estimation matrix with a sparsity threshold value respectively; wherein the sparsity threshold value is preset;

[0049] When the sparsity parameter of any column is greater than the sparsity threshold, the corresponding column is included in the spatial target activity set.

[0050] In a second aspect, an embodiment of the present invention further provides an electronic device comprising: a processor, and a memory for storing a program, wherein the program comprises instructions, and when the instructions are executed by the processor, the processor executes the method for monitoring seabed crust activity based on differential phase-sensitive optical time-domain reflectometry provided in any of the above embodiments.

[0051] The present invention provides a method for monitoring seafloor crustal activity based on differential phase-sensitive optical time-domain reflectometry. This method converts non-low-rank features such as slowly varying interference, high-dimensional complex structures, and long-term trends in received signals into low-rank features through differential signal processing. This makes the periodic and regular components of the received signals clearer, and can separate periodic events from sudden events through a low-rank sparse decomposition model. Furthermore, it can separate unrelated human activities from sudden events, achieving precise monitoring of sudden seafloor crustal activity. The monitoring results are highly accurate and robust. Further analysis shows that the false alarm rate of natural disaster warnings is lower and more reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these drawings without inventive effort.

[0053] Figure 1 This is a flow chart of a method for monitoring seafloor crust activity based on differential phase-sensitive optical time-domain reflectometry in an embodiment of the present invention.

[0054] Figure 2 It is a structural schematic diagram of the electronic device created by the present invention. DETAILED DESCRIPTION

[0055] The following describes embodiments of the present invention in more detail with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0056] Optical Time Domain Reflectometer (OTDR) technology can be categorized by measurement principle and application scenario. Phase-sensitive Optical Time Domain Reflectometer (Phase OTDR) technology is a distributed fiber optic sensing technology based on Rayleigh scattering. When factors such as vibration, strain, and temperature act on an optical fiber, they cause subtle changes in the fiber's refractive index and length, which in turn alter its phase. Phase OTDR technology detects phase changes to obtain parameter variations along the fiber and detect the location of vibration or strain. Phase OTDR technology also offers advantages such as long-distance and distributed monitoring, making it widely used in geological monitoring.

[0057] Seafloor crustal movement can trigger natural disasters such as earthquakes and tsunamis, as well as a range of environmental changes. It is also interconnected with the continental crust. Therefore, monitoring subtle seafloor crustal movement is crucial. Phase OTDR technology can detect phase shifts in optical fiber echo signals, capturing subtle geological deformations and monitoring seafloor crustal movement, providing early warning of natural disasters. However, echo signals contain significant amounts of noise and slowly varying interference, which can affect monitoring and lead to a high rate of false alarms.

[0058] To this end, embodiments of the present invention provide a method for monitoring seafloor crustal activity based on differential phase-sensitive optical time-domain reflectometry. This method analyzes echoed optical pulse signals using phase OTDR technology. Through differential signal processing and global optimization problem formulation and solution, it separates a large amount of irrelevant impurities from the signal, accurately monitoring sudden seafloor crustal activity and providing early warning for natural disasters such as tsunamis and earthquakes.

[0059] It's important to note that, based on the monitoring principles of phase OTDR technology, any event that could cause optical fiber phase shifts in the submarine environment will be present in the echoed optical pulse signal. These events, which can cause optical fiber phase shifts due to vibration, strain, or temperature changes, include periodic events, sudden events, and interference events.

[0060] Periodic events, for example, recur at regular intervals and exhibit relatively stable cycles and patterns, such as the periodic activity of marine life and tidal-induced movement of the seafloor crust. Unlike periodic events, sudden events lack stable patterns and are unpredictable, characterized by suddenness and uncertainty. Sudden events can be divided into both human-caused and natural events. Human-caused events include signal changes caused by human factors, such as failures in submarine communication systems, fishing activities, and seabed excavation. Natural events involve the sudden movement of submarine plates, including fractures and shifts in the seafloor crust and changes in seafloor topography, which can trigger natural disasters such as tsunamis and earthquakes.

[0061] The monitoring method provided by the embodiments of the present invention focuses on the natural activities involved in sudden submarine events, specifically the sudden movements of submarine crustal plates, targeting natural activities that could trigger natural disasters. This monitoring method eliminates interference, separates periodic events from sudden events, and further isolates the human activities involved in sudden events, ultimately providing accurate and reliable early warnings of potential natural disasters.

[0062] Specifically, the monitoring method provided by the embodiment of the present invention includes the following steps.

[0063] Step S100: Acquire an echo optical pulse signal, and perform differential processing on the echo optical pulse signal to obtain a first differential echo matrix.

[0064] Step S100 includes:

[0065] Step S110: In a distributed optical fiber sensing system based on a phase OTDR, an optical spectrum analyzer is used to receive echo optical pulse signals, and a sampled Rayleigh curve corresponding to each echo optical pulse signal is obtained.

[0066] In the embodiment of the present invention, a total of P+1 echo light pulse signals are received during monitoring. Each echo light pulse signal corresponds to a monitoring moment. The initial monitoring moment is set to t0. When the light source emits light pulses, the emission interval between adjacent light pulses is For the pth received echo optical pulse signal, the corresponding monitoring time is t0+ Where p is an integer and 1≤p≤P+1.

[0067] Each echo light pulse signal corresponds to a sampling Rayleigh curve. The sampling Rayleigh curve contains several different positions x on the optical fiber, that is, different sampling points. The sampling Rayleigh curve corresponding to the pth echo light pulse signal is .

[0068] The matrix formed by the sampled Rayleigh curves corresponding to the received P+1 echo optical pulse signals is the echo optical pulse matrix R. The echo optical pulse matrix R is expressed as:

[0069] .

[0070] Step S120: Perform differential processing on two adjacent sampled Rayleigh curves in the echo optical pulse matrix R to obtain a first differential signal. The differential processing process is calculated with reference to the following formula:

[0071] .

[0072] Where, is the pth first differential signal.

[0073] Step S130: The first differential signals in step S102 are combined into a first differential echo matrix D. The first differential echo matrix D is represented by the following formula:

[0074] .

[0075] As previously mentioned, events that may cause optical fiber phase changes in marine environments include periodic events, sudden events, and interference events. Periodic events, in particular, cause the data corresponding to the received signal to have a low-rank structure. In related geological monitoring, a low-rank sparse decomposition model is established based on the low-rank nature of periodic events to separate periodic events and identify sudden events.

[0076] However, in the marine environment, interference events include a large amount of noise and slowly varying interference. Slowly varying interference includes temperature changes and crustal trends. Unlike the regular variations of periodic events or the sporadic nature of sudden events, slow-varying interference typically evolves slowly over time, forming a trend rather than a simple periodic component. Therefore, slow-varying interference such as temperature changes does not exhibit low-rank characteristics in the received signal and actually obscures the low-rank nature of the received signal. This in turn makes it difficult for the low-rank sparse decomposition model to effectively separate and identify these interferences, resulting in a high false positive rate.

[0077] The monitoring method provided by the embodiment of the present invention can eliminate various types of slow-varying interference, typified by temperature drift, by differential processing of the sampled Rayleigh curve. Non-low-rank features such as slow-varying interference, high-dimensional complex structures, and long-term trends in the received echo optical pulse signal are converted into low-rank features. The non-low-rank features that originally changed over time no longer dominate the echo optical pulse matrix R. After the differential operation, the periodic components with low-rank characteristics dominate the first differential echo matrix D. Differential processing can better present the periodic structure of the received signal and eliminate the ambiguity and influence of trend components such as slow-varying interference on the data structure.

[0078] Furthermore, in an embodiment of the present invention, referring to the following formula, the first differential echo matrix D is decomposed into a low-rank term L, a sparse term S, and a noise term N.

[0079] .

[0080] In the formula, the low-rank term L corresponds to the periodic component of the received signal after removing trend features such as slowly varying interference. This corresponds to periodic events in the marine environment. The sparse term S corresponds to sudden events, including both human and natural activities. After separating human activities from the sparse term S, the target activity for monitoring is obtained. This target activity can reflect sudden activity in the seafloor crust and is important for natural disaster early warning. The noise term N corresponds to other noise.

[0081] Step S200: setting a dynamic window and adopting a dynamic window alignment method to obtain a second differential echo matrix of the first differential echo matrix D under the dynamic window constraint.

[0082] As an implementable method, step S200 includes the following steps:

[0083] Step S210: Use frequency analysis to estimate the frequency characteristics of the echo light pulse signal. Obtain the corresponding signal main frequency through the sampling Rayleigh curve of the echo light pulse signal. The echo optical pulse signal, the corresponding signal main frequency is . Signal main frequency Refer to the following formula for calculation:

[0084] .

[0085] Where, is the sampling Rayleigh curve corresponding to the p-th echo optical pulse signal. express The Fourier transform of . Represents the energy distribution of the spectrum.

[0086] By obtaining the main frequency of the signal , can be based on the main frequency of the signal By comparing the magnitude of the signal with an empirical threshold, the researchers can determine whether the echoed optical pulse signal corresponds to low-frequency or high-frequency burst crustal activity. By adjusting the dynamic window based on the dominant signal frequency, the constrained second differential echo matrix can adapt to crustal activity at different frequencies, thereby improving monitoring accuracy, sensitivity, reliability, and stability.

[0087] Step S220: According to the main frequency of the signal obtained in step S210 Set the window length of the dynamic window. The window length of the dynamic window is calculated according to the following formula:

[0088] .

[0089] Where, The window length after dynamic window adjustment; is the first adjustment parameter; is the second adjustment parameter. If confirmed, the first adjustment parameter and the second tuning parameter Used to comprehensively control the window length range of the dynamic window.

[0090] First adjustment parameter Used to control the window length At the same signal main frequency The first adjustment parameter is within the range The larger the dynamic window is, the longer the window The larger the range of change, the greater the first adjustment parameter. The smaller the dynamic window length In the embodiment of the present invention, the first adjustment parameter The range is set to [10, 1000].

[0091] At the main signal frequency When it is higher than the empirical threshold, the corresponding echo light pulse signal corresponds to high-frequency crustal activity. The value is small, in order to avoid the window length Too small, set the second adjustment parameter Ensure the minimum length of the dynamic window. In the embodiment of the present invention, the second adjustment parameter The range is set to [100, 5000].

[0092] Signal main frequency and the pth echo optical pulse signal and the sampled Rayleigh curve There is a one-to-one correspondence, and the main frequency of each signal It also corresponds to a dynamic window, the window length of the dynamic window Based on the main frequency of the signal Dynamically adjusted.

[0093] Step S230 , calculating the differential signal components of the first differential signal at each sampling point one by one; wherein the differential signal components are calculated within the length range of the dynamic window.

[0094] Under the dynamic window constraint, the differential signal component of the first differential signal at position x is calculated according to the following formula:

[0095] .

[0096] In the formula, x is a sampling position on the optical fiber, that is, a sampling point. Limiting the range of i in the formula makes the operation within the window length of the dynamic window Perform pulse alignment differential processing to obtain is the differential signal component corresponding to the sampling point x.

[0097] Step S240, perform window constraint differential adjustment on all sampling points on the optical fiber as in step S230. The differential signal component obtained in step S230 is a single point value corresponding to the sampling position x. After arranging the differential signal components corresponding to each sampling position in the order of the original sampling position, the second differential signal is obtained. .

[0098] Step S250: Repeat the dynamic window setting and constrained difference adjustment in steps S210 to S240 for each first differential signal. Each first differential signal obtains a corresponding second differential signal, and the corresponding second differential signals form a second differential echo matrix. The second differential echo matrix Expressed as:

[0099] .

[0100] Second differential echo matrix It is the result of processing the first differential echo matrix D under the dynamic window constraint, and is used to dynamically adjust and adapt to different frequencies for monitoring crustal activity. It is also decomposed into: including low-rank term L, sparse term S and noise term N.

[0101] Step S300: According to the second differential echo matrix A global optimization problem is constructed, and the sparse term estimation matrix corresponding to the sparse term S is solved in the global optimization problem.

[0102] Before constructing the global optimization problem in step S300, the monitoring method provided in the embodiment of the present invention further includes setting weight coefficients for the low-rank term L and the sparse term S. The weight coefficients are used to weight the low-rank term L and the sparse term S respectively when constructing the global optimization problem. This can prevent overfitting, facilitate fine-tuning of the global optimization problem, improve flexibility, and balance the contributions of the low-rank and sparse characteristics of the data in the global optimization problem.

[0103] The weight coefficients of the low-rank term L and the sparse term S are based on the dynamic window length in step S200 In the embodiment of the present invention, first according to the window length of the dynamic window Calculate the weight coefficient of the low-rank term L, and then calculate the weight coefficient of the sparse term S based on the weight coefficient of the low-rank term L.

[0104] Specifically, firstly, the mean value of the window length is calculated according to the dynamic window in step S200.

[0105] Receive P+1 echo optical pulse signals, corresponding to P+1 sampled Rayleigh curves, and then corresponding to P+1 dynamic windows The average window length of P+1 dynamic windows for:

[0106] .

[0107] Calculate P+1 dynamic windows based on the mean The variance of the window length is:

[0108] .

[0109] The standard deviation is σ W .

[0110] Continue, according to the mean and standard deviation σ W Creating a local weight function Among them, the local weight function Set as a piecewise function, the segmentation points of the piecewise function are based on the mean and standard deviation σ W OK. Local weight function Refer to the following formula:

[0111] .

[0112] Set the window length of the dynamic window Enter the local weight function Then, the local weight corresponding to the dynamic window is output.

[0113] Referring to the following formula, the average of the local weights corresponding to the outputs of P+1 dynamic windows is taken as the weight coefficient of the low-rank term L .

[0114] .

[0115] It should be noted that the local weight function The segmentation points are based on the mean and standard deviation σ W Set the interval. For a dynamic window corresponding to an echo optical pulse signal, the length of the dynamic window When it is larger and exceeds this range, the local weight function The local weight of the output is 1, which means that the echo optical pulse signal corresponding to the dynamic window is more inclined to the periodic event dominated by the low-rank characteristic. Increase the local weight, thereby increasing the weight coefficient of the low-rank term L .

[0116] For a dynamic window corresponding to an echo optical pulse signal, the length of the dynamic window is When it is smaller and exceeds this range, the local weight function The local weight of the output is 0, which means that the echo light pulse signal corresponding to the dynamic window is more inclined to the sudden event dominated by the sparse characteristics. Therefore, through the local weight function Reduce local weights to control the weight coefficient of the low-rank term L Lower.

[0117] Set the local weight function Calculate the weight coefficient of the low-rank term L , which can integrate the window length of each dynamic window , which reflects the global distribution of the dynamic window as a whole, rationalizes the low-rank and sparse characteristics in the data, balances the low-rank and sparse characteristics, and obtains a more accurate weight coefficient of the low-rank term L .

[0118] Then, according to the weight coefficient of the low-rank term L obtained Calculating sparse terms The weight coefficient Among them, the weight coefficient of the low-rank term L is and sparse terms The weight coefficient The sum is 1. Sparse items The weight coefficient Refer to the following formula for calculation:

[0119] .

[0120] Ideally, when classifying events, the noise-free assumption is used, and the presence of noise is not considered. Instead, only periodic events dominated by low-rank characteristics and sudden events dominated by sparse characteristics are considered. During weighted processing, all items other than the low-rank item L are considered to be sparse items S, and all items other than the sparse item S are considered to be low-rank items L. Therefore, in this embodiment of the present invention, the sum of the weight coefficients of the two is set to 1 to balance the sparse and low-rank characteristics.

[0121] In calculating the weight coefficient of the low-rank term L and sparse terms The weight coefficient Then, as an implementation method, step S300 specifically includes:

[0122] Step S310: perform singular value decomposition on the low-rank term L and sum the singular values ​​to obtain the nuclear norm of the low-rank term L. The nuclear norm of the low-rank term L Used to characterize the low-rank characteristics of the low-rank term L.

[0123] Take the 1 norm of the elements in the sparse item S The 1-norm is the maximum value of the sum of the absolute values ​​of the elements in each column of the matrix. 1-norm of Used to characterize the sparse characteristics of the sparse item S.

[0124] Step S320: The nuclear norm of the low-rank term L is and the 1-norm of the sparse term S The objective function of the global optimization problem is obtained by weighted summation. The weights in the weighted summation are the weight coefficients of the low-rank term L obtained by the above calculation. and the weight coefficient of the sparse term S .

[0125] Calculate the F norm of the noise term N and square it, limiting the square result to be less than or equal to the noise suppression threshold As the constraint condition of the global optimization problem. Among them, the noise suppression threshold It is preset.

[0126] According to the objective function and constraints, the global optimization problem is shown in the following formula:

[0127] .

[0128] Where, is the F norm, also known as the Frobenius norm, which is used to measure the reconstruction error.

[0129] Step S330 , solving the sparse item estimation matrix corresponding to the sparse item S in the global optimization problem obtained in step S320 .

[0130] Preferably, in an embodiment of the present invention, the global optimization problem is solved using the Alternating Direction Method of Multipliers (ADMM) to obtain a sparse term estimation matrix corresponding to the sparse term S. The ADMM algorithm is an optimization algorithm that combines the principles of the augmented Lagrangian function and the dual ascent algorithm and has been widely used in many fields, including machine learning, signal processing, image processing, and statistical analysis.

[0131] Step S400 , separating irrelevant activities in the sparse item estimation matrix to obtain a target activity matrix.

[0132] The sudden events corresponding to the sparse terms include both human and natural activities that can cause optical fiber phase changes. Human activities are irrelevant and not a target for monitoring seafloor crustal activity. Natural activities are target activities that need to be monitored and can reflect sudden seafloor crustal activity, which is of great significance for natural disaster early warning. Step S400 is used to separate human and natural activities in the sparse term estimation matrix.

[0133] As an implementable method, step S400 includes:

[0134] Step S410: Setting a time domain target activity set: Extracting the time dimension of the sparse item estimation matrix obtained by step S330, and assigning the columns of the sparse item estimation matrix corresponding to the time domain target activity to the time domain target activity set.

[0135] The time domain target activity refers to the target activity obtained based on the time domain judgment condition when judging the target activity from the time domain condition. The time domain target activity set refers to the sparse Rayleigh spectrum set corresponding to the time domain target activity.

[0136] Before the time dimension is extracted, the time domain target activity set is an empty set. Define the time domain target activity set , after classifying all time domain target activities into the time domain target activity set, the setting of the time domain target activity set is completed.

[0137] Specifically, the elements of the matrix are estimated for the sparse terms Set the sliding window. Among them, the element is the element on the xth row and pth column in the sparse item estimation matrix, where the xth row is the optical fiber sampling position, and the pth column corresponds to the pth echo optical pulse signal, which is associated with the sampling time.

[0138] Sliding windows and elements Within the same row of the sparse term estimation matrix, the sliding window is spaced by elements The sliding window is centered and the length of the sliding window is preset. The smoothing error of the row. The calculation of the smoothing error is shown in the following formula:

[0139] .

[0140] In the formula, S(x, p+j) is the element For other elements in the same row, define S(x, p+j) as elements by setting the value of j. Elements in the sliding window of the same row. t is the preset length, and the sliding window is filled with elements 2u as the center t +1 element.

[0141] Calculate the average value of all elements in the sliding window. For the element at the center of the sliding window , and the average value of all elements in the sliding window is subtracted and the two norm is taken to obtain the element Smoothing error .

[0142] Compare the smoothing error and smoothness threshold of each element of the sparse term estimation matrix separately Among them, the smoothness threshold It is preset.

[0143] When the smoothing error of any element is less than the smoothness threshold , the column where the element is located is included in the time domain target activity set. For example, for the element Smoothing error , if exists , then the sparse component in the p-th sampled Rayleigh curve, that is, the p-th column in the sparse term estimation matrix Identified as time domain target activity , and classified into the time domain target activity set .

[0144] Step S420 , setting a spatial target activity set, extracting the spatial dimension of the sparse item estimation matrix obtained by solving step S330 , and classifying the columns of the sparse item estimation matrix corresponding to the spatial target activity into the spatial target activity set.

[0145] Airspace target activity refers to the target activity obtained based on the airspace judgment conditions when judging the target activity from the airspace conditions. Airspace target activity set refers to the set corresponding to airspace target activities.

[0146] Before spatial dimension extraction, the spatial target activity set is an empty set. Define the spatial target activity set After all spatial target activities are grouped into the spatial target activity set, the setting of the spatial target activity set is completed. Steps S410 and S420 determine target activities from the time domain and spatial domain dimensions, respectively. The time domain target activity set can be set first and the time dimension extracted, followed by the spatial target activity set and the spatial dimension extracted. Alternatively, the spatial target activity set can be set first and the spatial dimension extracted, followed by the time domain target activity set and the time dimension extracted. The execution order is not limited to this.

[0147] Specifically, for each column vector of the sparse term estimation matrix Calculate the 1 norm, that is, the sum of the absolute values ​​of the elements in each column, as the sparsity parameter Sparsity parameter Used to characterize sparsity, refer to the following formula for calculation:

[0148] .

[0149] Compare the sparsity parameters of each column of the sparse term estimation matrix separately and sparsity threshold Among them, the sparsity threshold It is preset.

[0150] When the sparsity parameter of any column is greater than the sparsity threshold, that is, , the corresponding columns Identified as airspace target activity , and classified into the airspace target activity set .

[0151] Step S430, reconstruct the sparse item estimation matrix based on the time domain target activity set in step S410 and the spatial domain target activity set in step S420. Specifically, the columns corresponding to irrelevant activities are set to zero to obtain the target activity matrix The reconstruction process refers to the following formula:

[0152] .

[0153] Keep the columns that fall into the temporal target activity set and the columns that fall into the spatial target activity set. Set the columns that fall into neither the temporal nor the spatial target activity set to zero. The columns that fall into neither the temporal nor the spatial target activity set are the columns in the sparse term estimation matrix that correspond to irrelevant activities.

[0154] Step S400 uses a combined analysis of the temporal and spatial domains to separate the target sudden seafloor crustal activity from irrelevant human activities based on the temporal and spatial dimensions. The fusion of temporal and spatial decision results can improve the accuracy and robustness of target activity monitoring.

[0155] Step S500: According to the target activity matrix Get the monitoring results of the target activity.

[0156] Preferably, in the embodiment of the present invention, the target activity matrix can be trained by sample or by building an experience dictionary. The target activities in the monitoring are further classified, the monitoring results are output, and the natural disaster warning is issued based on the further analysis of the monitoring results.

[0157] Sample training involves pre-collecting a large amount of sample data related to the target activity. For sudden crustal movement on the seafloor, the sample data can include seafloor topography and geomorphology data, geothermal flow data, and data on the physical and chemical properties of rock samples. This large amount of collected data is cleaned and corrected before feature extraction. For example, for submarine earthquakes, features such as amplitude, frequency, and waveform are extracted. Certain frequencies of seismic waves may be associated with the type of crustal activity.

[0158] Further classify the target activities and select the appropriate model, use the sample data of the extracted features to train the selected model, evaluate and optimize the model, and use the final model optimization results to evaluate the target activity matrix. Conduct further analysis.

[0159] Establishing an experience dictionary is to collect data on sudden crustal activity on the seabed, extract typical characteristic patterns and description information, and digitally represent them. Match and further classify with experience dictionaries, and evaluate and screen the dictionaries regularly.

[0160] Target Activity Matrix When further classifying and outputting monitoring results, in addition to sample training and establishing an experience dictionary, other methods can be selected according to the needs of outputting monitoring results, but are not limited to these.

[0161] The method for monitoring seabed crustal activity based on differential phase-sensitive optical time-domain reflectometry provided in an embodiment of the present invention converts non-low-rank features such as slowly varying interference, high-dimensional complex structures, and long-term trends in the received signal into low-rank features through differential signal processing, thereby solving the technical problem that the identification of target activities by related models cannot be applied to scenarios with slowly varying interference. Differential signal processing makes the periodic and regular components in the received signal clearer, and a low-rank sparse decomposition model can be used to separate periodic events and sudden events, and further separate unrelated human activities in sudden events, thereby achieving accurate monitoring of sudden activities in the seabed crust, and the monitoring results have good accuracy and robustness. Further analysis shows that the false alarm rate of natural disaster warnings is lower and the reliability is higher.

[0162] An embodiment of the present invention also provides a non-transitory machine-readable medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to enable the computer to execute a method for monitoring seafloor crust activity based on differential phase-sensitive optical time-domain reflectometry.

[0163] An embodiment of the present invention further provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor of a computer, the computer is used to enable the computer to execute a method for monitoring seafloor crust activity based on differential phase-sensitive optical time-domain reflectometry.

[0164] An embodiment of the present invention further provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, wherein the computer program, when executed by the at least one processor, causes the electronic device to perform a method for monitoring seafloor crustal activity using differential phase-sensitive optical time-domain reflectometry.

[0165] refer to Figure 2 , a structural block diagram of an electronic device that can be used as a server or client of an embodiment of the present invention will now be described, which is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0166] like Figure 2 As shown, the electronic device includes a computing unit 101, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 102 or a computer program loaded from a storage unit 108 into a random access memory (RAM) 103. Various programs and data required for the operation of the electronic device can also be stored in the RAM 103. The computing unit 101, ROM 102, and RAM 103 are connected to each other via a bus 104. An input / output (I / O) interface 105 is also connected to the bus 104.

[0167] Multiple components in the electronic device are connected to the I / O interface 105, including: an input unit 106, an output unit 107, a storage unit 108, and a communication unit 109. The input unit 106 can be any type of device capable of inputting information into the electronic device. The input unit 106 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 107 can be any type of device capable of presenting information and can include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 108 can include, but is not limited to, a magnetic disk and an optical disk. The communication unit 109 allows the electronic device to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks, and can include, but is not limited to, a modem, a network card, an infrared communication device, and / or a wireless communication transceiver, such as a Bluetooth device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0168] The computing unit 101 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 101 include, but are not limited to, a CPU, a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing units, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 101 performs the various methods and processes described above. For example, in some embodiments, the method embodiments of the present invention may be implemented as a computer program tangibly embodied in a machine-readable medium, such as a storage unit 108. In some embodiments, part or all of the computer program may be loaded and / or installed onto the electronic device via the ROM 102 and / or the communication unit 109. In some embodiments, the computing unit 101 may be configured to perform the above-described methods by any other suitable means (e.g., via firmware).

[0169] The computer programs for implementing the methods of the embodiments of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0170] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable signal medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, or infrared system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0171] It should be noted that the term "including" and its variations used in the embodiments of the present invention are open inclusions, that is, "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". The modifications of "one" and "multiple" mentioned in the embodiments of the present invention are illustrative and not restrictive. Those skilled in the art should understand that unless the context clearly indicates otherwise, they should be understood as "one or more".

[0172] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and corresponding operation entrances shall be provided for users to choose to authorize or refuse.

[0173] The various steps described in the method implementation methods provided by the embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method implementation methods may include additional steps and / or omit the steps shown. The scope of protection of the present invention is not limited in this respect.

[0174] The term "embodiment" in this specification refers to specific features, structures or characteristics described in conjunction with the embodiment that can be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily mean the same embodiment, nor does it mean that it is mutually exclusive with other embodiments and is independent or optional. The various embodiments in this specification are described in a related manner, and the same or similar parts between the various embodiments are referenced to each other. In particular, for the device, equipment, and system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts refer to the partial description of the method embodiment.

[0175] The above-described embodiments merely represent several implementation methods of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of protection. It should be noted that a person of ordinary skill in the art would be able to make various modifications and improvements without departing from the scope of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for monitoring seafloor crustal activity based on differential phase-sensitive optical time-domain reflectometry, characterized in that: The method comprises: Acquire an echo optical pulse signal, and perform differential processing on the echo optical pulse signal to obtain a first differential echo matrix; Setting a dynamic window to obtain a second differential echo matrix of the first differential echo matrix under the constraints of the dynamic window; wherein the second differential echo matrix includes a low-rank term, a sparse term, and a noise term; Constructing a global optimization problem based on the second differential echo matrix, and solving a sparse item estimation matrix corresponding to the sparse item in the global optimization problem; wherein, an objective function of the global optimization problem is constructed based on the low-rank item and the sparse item of the second differential echo matrix, and a constraint condition of the global optimization problem is constructed based on the noise item of the second differential echo matrix; Separating irrelevant activities in the sparse item estimation matrix to obtain a target activity matrix; A monitoring result of the target activity is obtained according to the target activity matrix.

2. The method for monitoring seafloor crustal activity based on differential phase-sensitive optical time-domain reflectometry according to claim 1, characterized in that: Acquiring an echo optical pulse signal and performing differential processing on the echo optical pulse signal to obtain a first differential echo matrix includes: receiving the echo light pulse signal through a spectrum analyzer, and obtaining a sampled Rayleigh curve corresponding to each echo light pulse signal; Performing differential processing on two adjacent sampled Rayleigh curves to obtain a first differential signal; The first differential signals constitute the first differential echo matrix.

3. The method for monitoring seafloor crustal activity based on differential phase-sensitive optical time-domain reflectometry according to claim 2, characterized in that: Setting a dynamic window to obtain a second differential echo matrix of the first differential echo matrix under the constraint of the dynamic window includes: Obtaining the corresponding signal main frequency according to the sampled Rayleigh curve of the echo optical pulse signal; Setting the window length of the dynamic window according to the main frequency of the signal; wherein the dynamic window, the main frequency of the signal and the sampled Rayleigh curve are in one-to-one correspondence; Calculating the differential signal components of the first differential signal at each sampling point one by one; wherein the differential signal components are calculated within the length range of the dynamic window; Arranging the calculated differential signal components to obtain a second differential signal; The second differential signals constitute the second differential echo matrix.

4. The method for monitoring seafloor crustal activity based on differential phase-sensitive optical time-domain reflectometry according to claim 1, wherein: Before constructing the global optimization problem according to the second differential echo matrix, the method further includes: Setting a weight coefficient of the low-rank term according to the dynamic window; The weight coefficient of the sparse term is calculated based on the weight coefficient of the low-rank term; wherein the sum of the weight coefficient of the low-rank term and the weight coefficient of the sparse term is 1.

5. The method for monitoring seafloor crustal activity based on differential phase-sensitive optical time-domain reflectometry according to claim 4, characterized in that: Setting the weight coefficient of the low-rank term according to the dynamic window includes: Calculating a mean value of the window lengths of the dynamic windows; Calculate the standard deviation of the window length of the dynamic window according to the mean; Creating a local weight function according to the mean and the standard deviation; wherein the local weight function is set as a piecewise function, and the piecewise points of the local weight function are determined according to the mean and the standard deviation; Obtaining a local weight corresponding to each of the dynamic windows according to the local weight function; An average value of the local weights is taken as the weight coefficient of the low-rank term.

6. The method for monitoring seafloor crustal activity based on differential phase-sensitive optical time-domain reflectometry according to claim 1, characterized in that: Constructing a global optimization problem based on the second differential echo matrix includes: Performing singular value decomposition on the low-rank term and summing the singular values ​​to obtain a nuclear norm of the low-rank term; Taking the 1-norm of the elements in the sparse item; A weighted sum of the nuclear norm of the low-rank term and the 1-norm of the sparse term is performed to obtain an objective function of the global optimization problem; Calculating the F-norm of the noise term and squaring it, and constraining the squared result to be less than or equal to a noise suppression threshold as a constraint condition of the global optimization problem; wherein the noise suppression threshold is preset; The global optimization problem is obtained according to the objective function and the constraint conditions.

7. The method for monitoring seafloor crustal activity based on differential phase-sensitive optical time-domain reflectometry according to claim 1, characterized in that: Separating irrelevant activities in the sparse item estimation matrix to obtain a target activity matrix includes: Setting a time domain target activity set, performing time dimension extraction on the sparse item estimation matrix, and classifying the columns of the sparse item estimation matrix corresponding to the time domain target activity into the time domain target activity set; Setting a spatial target activity set, performing spatial dimension extraction on the sparse item estimation matrix, and classifying the columns of the sparse item estimation matrix corresponding to the spatial target activity into the spatial target activity set; The columns corresponding to irrelevant activities in the sparse item estimation matrix are set to zero to obtain the target activity matrix; wherein the columns corresponding to irrelevant activities in the sparse item estimation matrix are columns that are neither included in the time domain target activity set nor in the spatial domain target activity set.

8. The method for monitoring seafloor crustal activity based on differential phase-sensitive optical time-domain reflectometry according to claim 7, characterized in that: Extracting the time dimension of the sparse item estimation matrix and classifying the columns of the sparse item estimation matrix corresponding to the time domain target activity into the time domain target activity set includes: Setting a sliding window for an element of the sparse term estimation matrix; wherein the sliding window and the element are in the same row of the sparse term estimation matrix, the sliding window is centered on the element, and the length of the sliding window is preset; Calculating the average value of all elements in the sliding window; For the element at the center of the sliding window, subtract the average value of all elements in the sliding window and take the second norm to obtain the smoothing error of the element; Comparing the smoothing error of each element of the sparse term estimation matrix with a smoothness threshold value respectively; wherein the smoothness threshold value is preset; When the smoothing error of any element is smaller than the smoothness threshold, the column where the element is located is included in the time-domain target activity set.

9. The method for monitoring seafloor crustal activity based on differential phase-sensitive optical time-domain reflectometry according to claim 7, characterized in that: Extracting the spatial dimension of the sparse item estimation matrix and classifying the columns of the sparse item estimation matrix corresponding to the spatial target activity into the spatial target activity set includes: For each column of the sparse item estimation matrix, the sum of the absolute values ​​of the column elements is calculated as a sparsity parameter; Comparing the sparsity parameter of each column of the sparse item estimation matrix with a sparsity threshold value respectively; wherein the sparsity threshold value is preset; When the sparsity parameter of any column is greater than the sparsity threshold, the corresponding column is included in the spatial target activity set.

10. An electronic device comprising: A processor, and a memory storing a program, characterized in that the program includes instructions, which, when executed by the processor, cause the processor to execute the method for monitoring seafloor crust activity based on differential phase-sensitive optical time-domain reflectometry according to any one of claims 1 to 9.

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