CN116466390B - A real-time monitoring and positioning method for earthquakes induced by large reservoirs - Google Patents

A real-time monitoring and positioning method for earthquakes induced by large reservoirs Download PDF

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CN116466390B
CN116466390B CN202310129707.1A CN202310129707A CN116466390B CN 116466390 B CN116466390 B CN 116466390B CN 202310129707 A CN202310129707 A CN 202310129707A CN 116466390 B CN116466390 B CN 116466390B
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CN116466390A (en
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蒋星达
杨华勇
李超
杨得厚
李跃金
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Southern Marine Science and Engineering Guangdong Laboratory Guangzhou
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    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
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    • G01H9/004—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by using radiation-sensitive means, e.g. optical means using fibre optic sensors
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    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
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    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/288—Event detection in seismic signals, e.g. microseismics
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    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
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    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
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Abstract

The application relates to the technical field of reservoir induced earthquake monitoring, and discloses a real-time monitoring and positioning method for large reservoir induced earthquake, which comprises the following steps: an amphibious integrated distributed optical fiber acoustic wave sensing monitoring system is arranged in a multiple reservoir seismic zone, and meter-level space precision continuous observation and seismic signal acquisition are carried out on seismic waves formed by near-surface earthquakes; picking up the relative arrival time of the DAS seismic signals by using a cross-correlation method, and constructing a time difference matrix; correcting the anisotropic VTI speed model by adopting a Bayesian theory and reversible jump Markov chain Monte Carlo algorithm; positioning the natural seismic event by using a double-difference positioning method based on the updated VTI speed model; the method comprises the steps of comprehensive seismic signal acquisition, velocity model correction and double-difference positioning, and the accurate position of the induced earthquake is measured. The application aims to solve the problem of low positioning precision of real-time monitoring of induced seismic events in reservoirs.

Description

Real-time monitoring and positioning method for earthquake induced by large reservoir
Technical Field
The application relates to the technical field of reservoir induced earthquake monitoring, in particular to a real-time monitoring and positioning method for large reservoir induced earthquake.
Background
A large number of reservoirs in China are built on geological fault zones, and the fault zone activities lead to frequent induction of various earthquake events, so that urban drinking water and life and property safety are threatened. Accurate positioning of induced earthquake is helpful to describe a hidden micro fault zone below the reservoir, and dynamic monitoring of reservoir safety is guaranteed. However, the conventional seismograph monitoring method is difficult to arrange the earthquake equipment in the reservoir for a long time, and the earthquake signals cannot be obtained in a short distance. Meanwhile, the earthquake monitoring station has the defect of large interval distance, and the complete earthquake wave field data is difficult to obtain. Various fractures and different lithologies in the formation can also cause the formation velocity to be anisotropic, and the conventional isotropic velocity model can be difficult to accurately locate seismic events. In addition, the seismic event locations obtained with seismic wave travel time are also subject to noise interference. The common influence of observation equipment, speed model errors and earthquake travel time noise causes poor positioning accuracy of reservoir induced earthquakes, and potential micro fault zones are difficult to find. The distributed optical fiber acoustic wave sensing technology (Distributed fiberAcoustic Sensing, DAS) developed in recent years can be distributed in water for a long time, has good environment adaptability, can record a seismic wave field according to meter-level precision, and is beneficial to obtaining the accurate position of a seismic signal through closely monitoring seismic wave inversion. Meanwhile, the anisotropic VTI (Vertical Transverse Isotropy) velocity model is utilized to depict the stratum medium velocity field, so that the real stratum velocity structure can be more approximated. The double-difference positioning method is a relative positioning method, can reduce dependence on a velocity model and the accuracy of the arrival time of the earthquake, and deduces the spatial position of the induced earthquake event by means of the known more accurate main earthquake event position. Therefore, compared with the traditional earthquake positioning method, the combined application of the DAS optical fiber monitoring, the VTI speed model and the double-difference positioning method is more beneficial to obtaining the accurate reservoir induced earthquake event position.
Accordingly, the prior art is still in need of improvement and development.
Disclosure of Invention
Aiming at the defects in the prior art, the application provides a real-time monitoring and positioning method for the induced earthquake of a large reservoir, which aims to solve the problem of low real-time monitoring and positioning precision of the induced earthquake event in the reservoir.
In order to achieve the above purpose, the present application may be performed by the following technical scheme:
a real-time monitoring and positioning method for large reservoir induced earthquake comprises the following steps:
an amphibious integrated distributed optical fiber acoustic wave sensing monitoring system is arranged in a multiple reservoir seismic zone, and meter-level space precision continuous observation and seismic signal acquisition are carried out on seismic waves formed by near-surface earthquakes;
picking up the relative arrival time of the DAS seismic signals by using a cross-correlation method, and constructing a time difference matrix; correcting the anisotropic VTI speed model by adopting a Bayesian theory and reversible jump Markov chain Monte Carlo algorithm;
positioning the natural seismic event by using a double-difference positioning method based on the updated VTI speed model;
the method comprises the steps of comprehensive seismic signal acquisition, velocity model correction and double-difference positioning, and the accurate position of the induced earthquake is measured.
Compared with the prior art, the application has the beneficial effects that: the application provides a novel real-time monitoring and positioning method for large-scale reservoir induced earthquake, which is characterized in that a amphibious distributed optical fiber acoustic wave sensing (Distributed fiber Acoustic Sensing, DAS) monitoring system is arranged in a multiple reservoir seismic zone to continuously observe the meter-level space precision of earthquake waves formed by near-surface earthquake; picking up the relative arrival time of the DAS seismic signals by using a cross-correlation method, and constructing a time difference matrix; correcting the anisotropic VTI speed model by adopting a Bayesian theory and reversible jump Markov chain Monte Carlo (reversible jump Markov Chain Monte Carlo, rjMCMC) algorithm; and positioning the natural seismic event by using a double-difference positioning method based on the updated VTI speed model. The method has the advantages that the continuous wave field of the natural earthquake in the reservoir can be recorded with high spatial resolution, meanwhile, the anisotropic VTI speed model is obtained in an optimized mode, and the accurate reservoir bottom earthquake event position can be obtained by combining the double-difference positioning method with higher positioning precision. The method is beneficial to improving the accuracy of the hidden fault zone characterization below the reservoir and provides a good technical means for reservoir safety assessment.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following description will briefly explain the drawings needed in the embodiments, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for real-time monitoring and positioning of large reservoir induced earthquakes according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a distributed fiber optic sensing device for monitoring reservoir induced seismic signals according to an embodiment of the present application;
FIG. 3 is a flow chart for correcting a VTI media velocity model using the rjMCMC method;
FIG. 4 is a schematic diagram of a dual differential positioning method for obtaining the accurate position of the reservoir induced earthquake.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Examples:
it should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
In the description of the present application, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise. Furthermore, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
The word "exemplary" is used hereinafter to mean "serving as an example, embodiment, or illustration. Any embodiment described as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
The earthquake of the reservoir has important influence on the urban drinking water safety and life and property. Conventional seismic monitoring equipment has difficulty in ensuring that seismic signals are monitored in real time in water for a long time. It is also difficult for a land discretely deployed seismic station to obtain full wavefield information for the seismic. The lack of the traditional method for collecting and processing the reservoir seismic signals causes that the hidden fault zone at the bottom of the reservoir is difficult to find, and the healthy operation of the reservoir is threatened at any time. Therefore, the device capable of recording the information of the full wave field of the earthquake and suitable for underwater long-time monitoring is developed correspondingly to develop an accurate real-time positioning algorithm of the earthquake event, and is beneficial to the fine depiction of reservoir stratum. The application utilizes the amphibious integrated distributed optical fiber acoustic wave sensing monitoring system to continuously observe the meter-scale space precision of the wave field formed by the induced earthquake at the bottom of the reservoir. Meanwhile, a cross-correlation method is used for picking up the relative arrival time of the DAS seismic signals, an arrival time difference matrix is constructed, and the influence of noise on a travel time result is reduced. And correcting the reservoir stratum VTI speed model based on the Bayesian theory and the reversible jump Markov chain Monte Carlo algorithm to obtain a more accurate stratum speed field. The updated VTI speed model is used for positioning the natural seismic event by using a double-difference positioning method, so that the influence of speed errors on positioning results is effectively reduced. Based on the method, the application provides a novel reservoir induced earthquake real-time monitoring and accurate positioning method.
Referring to fig. 1, the method for monitoring and positioning the induced earthquake of the large reservoir in real time can comprise the following steps:
step 1: an amphibious integrated distributed optical fiber acoustic wave sensing monitoring system is arranged in a multiple reservoir seismic zone, and meter-scale space precision continuous observation and seismic signal acquisition are carried out on seismic waves formed by near-surface earthquakes.
Specifically, referring to fig. 2, fig. 2 is a schematic diagram of a distributed optical fiber sensing device for monitoring reservoir induced seismic signals. The earthquake positioning method is based on an amphibious integrated distributed optical fiber sound wave sensing monitoring system, and can be used for continuously observing the meter-scale space precision of earthquake waves formed by near-surface earthquakes. The method comprises the following specific steps:
(1) And (3) arranging communication optical cables on land in the areas where the reservoir induces earthquake, and arranging submarine cables at the bottom of the reservoir. Whether land cable or sea cable, it is desirable to maintain good coupling contact with the formation in order to accept changes in formation stress caused by seismic waves.
(2) And the optical cable signal demodulator is placed in a machine room or other safe positions, so that stable power supply of the demodulator is ensured. The demodulator interface is connected in series with the land cable and the sea cable. The strain caused by the seismic waves felt by the land cable and the sea cable is resolved by the signal demodulator.
(3) The demodulator is used for setting the space monitoring strain sensitivity of the optical cable to be about one meter, the time sampling rate is more than 200Hz, and the continuous wave field of the earthquake transmitted to the ground surface is monitored without interval every day.
Step 2: picking up the relative arrival time of the DAS seismic signals by using a cross-correlation method, and constructing a time difference matrix; and correcting the anisotropic VTI speed model by adopting a Bayesian theory and reversible jump Markov chain Monte Carlo algorithm.
Specifically, the method for picking up the relative arrival time of the DAS seismic signals by using the cross-correlation method constructs a time difference matrix, and specifically comprises the following steps:
first, a long-short window ratio method is used to pick up valid seismic events. The long term window (LTA) is an Average value of energy of a longer time signal sampling length, and the short term window (Short Term Average, STA) is an Average value of energy of a shorter time signal sampling length:
wherein S (N) is the signal amplitude obtained by DAS monitoring, N is the number of long-time window signal samples, M is the number of short-time window signal samples, and N > M. Ratio is the Ratio of the energy of the longer time signal to the shorter time signal obtained by recording. When it is greater than a threshold, the monitored signal may be determined to be caused by a seismic event.
And secondly, accurately picking up the relative arrival time of different optical fiber monitoring channels by using a cross-correlation method, and constructing a time difference matrix. The cross-correlation method formula can be expressed as:
wherein x is 1 And x 2 Monitoring seismic traces for different DAS, R x1x2 Is a similarity coefficient. And precisely obtaining similarity coefficients of the seismic events extracted by Ratio among different optical fiber channels by using a cross-correlation formula, and selecting a time difference with the maximum similarity coefficient of different seismic channels to form a matrix.
Referring to fig. 3, fig. 3 is a flow chart for correcting VTI media velocity model using the rjMCMC method. Correcting the anisotropic VTI speed model by adopting a Bayesian theory and reversible jump Markov chain Monte Carlo algorithm, wherein the specific steps comprise:
the seismic waves may be split into qP, qSV and qSH waves in the VTI medium. From five anisotropic parameters [ alpha ] 0 ,β 0 ,ε,γ,δ]And (5) determining. The velocity model includes a number n of horizons and a horizon depth D. Thus, the inverse VTI velocity model parameter expression is determined as: m= [ n, D, α 0 ,β 0 ,ε,γ,δ]. Wherein alpha is 0 And beta 0 Representing the speed of each layer along the symmetry axis P wave and SV wave; epsilon represents the ratio of the velocity of each layer of qP wave in the horizontal and vertical directions; gamma represents the ratio of the velocity of each layer qSH wave in the horizontal and vertical directions; delta represents the rate of change of the qP wave of each layer in the vertical direction.
The Bayesian theory expression is determined as follows:
where d represents the observed data and m is the model parameter. p (m) is prior model information, p (d|m) is a likelihood function, p (m|d) is posterior model probability, and p (d) is overall probability of observed data in model space and is a constant.
And by utilizing a Bayesian inversion algorithm and combining prior information of each model parameter and likelihood functions, a final speed structure can be obtained by solving posterior probability distribution of the model parameters.
Determining a priori probability distribution of model parameters in bayesian inversion can be expressed as:
p(m)＝p(n)p(D|n)p(α 0 |n)p(β 0 |n)p(ε|n)p(γ|n)p(δ|n)
wherein p (n) represents the probability of the number of horizons; p (D|n) represents the probability distribution per layer depth in case the number of layers is n; p (alpha) 0 I n) represents the anisotropy parameter α per layer in case of the number of layers being n 0 Probability distribution of (2); p (beta) 0 I n) represents the anisotropy parameter β for each layer in the case where the number of layers is n 0 Probability distribution of (2); p (ε|n) represents the probability distribution of the anisotropy parameter ε for each layer with the number of layers n; p (γn) represents a probability distribution of the anisotropy parameter γ for each layer in the case where the number of layers is n; p (δ|n) represents the probability distribution of the anisotropy parameter δ per layer in the case of the number of layers n.
All prior information can be designed into uniform distribution, gaussian distribution, cauchy distribution and the like according to known data and used for solving the final posterior probability distribution.
Likelihood functions of model parameters in Bayesian inversion are determined. The likelihood function represents the distribution of noise in the observed data. In most cases, the likelihood function of the noise contribution can be expressed as a gaussian distribution:
in the method, in the process of the application,mean value of noise and sigma standard deviation of noise.
And combining the established likelihood function with prior probability distribution to construct a Bayesian formula posterior probability density distribution formula.
Iterative generation of posterior probability model by reversible jump Markov chain Monte Carlo algorithm, and random updating of velocity horizon number n, horizon depth D or dissimilarity parameter [ alpha ] in iterative process 0 ,β 0 ,ε,γ,δ]The four options of birth, death, movement and change are included. Wherein,,
"birth": randomly generating a new horizon based on the original horizon, wherein the interface depth obeys the uniform probability distribution:l is the total layer number, n is the current velocity model layer number, D new The velocity model horizon depth is newly generated.
"death": and randomly selecting one horizon of the existing speed model based on the original horizon, and deleting the horizon. The selection probability is as follows:D death the velocity model horizon is deleted.
"move": randomly selecting a horizon from the existing velocity models, randomly perturbing the depth of the horizon according to Gaussian probability, wherein the perturbation probability is as follows:D move for newly generated horizon depth σ 1 Is the standard deviation of the depth disturbance.
"change": randomly selecting a horizon from the existing velocity models, perturbing its anisotropic parameters according to gaussian probabilities, the perturbation probabilities being:v is a variable [ alpha ] 0 ,β 0 ,ε,γ,δ]K is an integer from 1 to 5, σ k Representing the standard deviation of five anisotropic parameters.
And judging whether the update speed model is accepted or not by using the acceptance probability. The probability of acceptance can be expressed as:
wherein m is old M is the original model new Is the updated model. p (m) old ) And p (d|m) old ) Is the prior information and likelihood function of the original model. p (m) new ) And p (d|m) new ) Is the updated model prior information and likelihood function. q (m) new |m old ) Generating transition probabilities of new models for original models, q (m old |m new ) The transition probabilities of the original model are generated for the new model.
The calculated acceptance probability accept_ratio is compared with the random number r between 0, 1. If accept_ratio > r, the updated model will be accepted; if accept_ratio < r, then the original model will go to the next cycle.
And performing posterior probability evaluation on model parameters obtained by Bayesian inversion. The specific process comprises the following steps: selecting a model with the maximum layer number occupation probability in the posterior probability as a final speed model; calculating the average value of each layer depth of the final speed model as the final horizon speed; calculating the anisotropy parameter [ alpha ] of each layer 0 ,β 0 ,ε,γ,δ]As the result of the anisotropic parameter inversion of each layer.
By using the steps, the number n of the layers of the VTI speed model, the depth D of the layers and the anisotropic parameters [ alpha ] of each layer can be finally determined 0 ,β 0 ,ε,γ,δ]。
Step 3: and positioning the natural seismic event by using a double-difference positioning method based on the updated VTI speed model.
Specifically, referring to fig. 4, fig. 4 is a schematic diagram of a dual differential positioning method for obtaining accurate positions of reservoir induced earthquakes. The method for positioning the natural seismic event by utilizing the double-difference positioning method based on the updated VTI speed model comprises the following specific steps:
picking up the arrival information of the induced seismic event, and establishing an objective function between the arrival information and the main event:
where ψ is the objective function. nr represents the number of DAS seismic traces, and N1 and N2 represent the number of main and evoked seismic events. t=o+t, T is the arrival time of DAS recordings, O is the start time of the seismic event, and T is the travel time from the source point to the seismic trace.And->The qP wave travel times of observed seismic traces r to event i and event j are represented. />And->The qSV wave travel times of observed traces r through events i and j are shown. />And->The qSH wave travel times of observed traces r through events i and j are shown. O (O) i And O j Is the actual start time of event i and event j. />And->The qP wave travel times of the detectors r to event i and event j obtained by forward calculation are represented. />And->The qSV wave times for the detectors r through event i and event j obtained by forward calculation are represented. />And->The qSH wave times for the detectors r through event i and event j obtained by forward calculation are represented. The obs and cal represent observed and calculated values.
Iteration through the gauss-newton algorithm, the above objective function linearization can be expressed as:
in the method, in the process of the application,representing the travel time difference of event i and event j to trace k. Deltaτ ij Indicating the difference in the starting moments of event i and event j. V is the background VTI velocity model. ΔH ij And DeltaZ ij Representing the relative distance between the horizontal and vertical positions of event i and event j.
According to the above calculation method, the relative position of the evoked seismic event with respect to the main seismic event can be finally obtained. The extensive situation of the underground fault zone can be finally described by a large number of induced seismic event positioning results, and some tiny hidden fault structures are found.
Step 4: the method comprises the steps of comprehensive seismic signal acquisition, velocity model correction and double-difference positioning, and the accurate position of the induced earthquake is measured.
In summary, according to the method, the amphibious distributed optical fiber acoustic wave sensing (Distributed fiber Acoustic Sensing, DAS) monitoring system is arranged in the multiple reservoir earthquake zones, so that meter-level space precision continuous observation is carried out on earthquake waves formed by near-surface earthquakes; picking up the relative arrival time of the DAS seismic signals by using a cross-correlation method, and constructing a time difference matrix; correcting the anisotropic VTI speed model by adopting a Bayesian theory and reversible jump Markov chain Monte Carlo (reversible jump Markov Chain Monte Carlo, rjMCMC) algorithm; and positioning the natural seismic event by using a double-difference positioning method based on the updated VTI speed model. The method fully utilizes the characteristics of good adaptability and high spatial sampling in the water of the DAS system, optimizes the velocity model by adopting continuous seismic wave field data, introduces rjMCMC theory in the process of correcting the velocity model to obtain a variable-dimension VTI (Vertical Transverse Isotropy) anisotropic velocity structure, and finally positions the seismic event by utilizing a double-difference positioning method, accurately depicts the spatial continuity of the seismic event and accurately depicts a hidden micro fault zone in a reservoir. The method has the advantages that the method can record the continuous wave field of the natural earthquake in the reservoir with high spatial resolution, simultaneously optimize and obtain the anisotropic VTI speed model, and combine with the double-difference positioning method with higher positioning precision, the accurate reservoir bottom earthquake event position can be obtained. The method is beneficial to improving the accuracy of the hidden fault zone characterization below the reservoir and provides a good technical means for reservoir safety assessment.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
The above embodiments are only for illustrating the technical concept and features of the present application, and are intended to enable those skilled in the art to understand the content of the present application and implement the same, and are not intended to limit the scope of the present application. All equivalent changes or modifications made in accordance with the essence of the present application are intended to be included within the scope of the present application.

Claims (10)

1.一种大型水库诱发地震实时监测定位方法，其特征在于，包括步骤：1. A real-time monitoring and positioning method for earthquakes induced by large reservoirs, which is characterized by including the steps: 在水库地震多发地带布设水陆一体分布式光纤声波传感监测系统，对近地表地震形成的地震波进行米级空间精度连续观测和地震信号采集；An amphibious distributed optical fiber acoustic wave sensing monitoring system is deployed in the earthquake-prone area of the reservoir to conduct continuous observation and seismic signal acquisition with meter-level spatial precision of seismic waves generated by near-surface earthquakes; 运用互相关方法拾取DAS地震信号的相对到时，构建到时差矩阵；采用基于贝叶斯理论和可逆跳马尔科夫链蒙特卡罗算法对各向异性VTI速度模型进行校正；The cross-correlation method is used to pick up the relative arrival time of the DAS seismic signal and the arrival time difference matrix is constructed; the anisotropic VTI velocity model is corrected using the Bayesian theory and the reversible jump Markov chain Monte Carlo algorithm; 基于更新的VTI速度模型利用双差定位方法对天然地震事件进行定位；Use the double-difference positioning method to locate natural earthquake events based on the updated VTI velocity model; 综合地震信号采集、速度模型校正和双差定位方法，测定诱发地震的准确位置。Integrate seismic signal acquisition, velocity model correction and double-difference positioning method to determine the exact location of induced earthquakes. 2.根据权利要求1所述的大型水库诱发地震实时监测定位方法，其特征在于，对近地表地震形成的地震波进行米级空间精度连续观测和地震信号采集，具体步骤包括：在水库地震多发地带陆地布设通讯光缆，在水库底部布设海缆；将光缆信号解调器放置于机房，与陆地通讯缆和水库海缆呈串联连接；利用信号解调器解析海陆缆感受到的地震波引起的应变；设置空间监测应变敏感度为一米左右，时间采样率大于200Hz，每天不间隔监测地震传播到地表的连续波场。2. The real-time monitoring and positioning method for earthquakes induced by large reservoirs according to claim 1, characterized in that the seismic waves formed by near-surface earthquakes are continuously observed with meter-level spatial precision and seismic signals are collected. The specific steps include: in reservoir earthquake-prone areas Lay communication optical cables on land and submarine cables at the bottom of the reservoir; place the optical cable signal demodulator in the computer room and connect it in series with the land communication cables and reservoir submarine cables; use the signal demodulator to analyze the strain caused by the seismic waves felt by the submarine and land cables; The spatial monitoring strain sensitivity is set to about one meter, the time sampling rate is greater than 200Hz, and the continuous wave field of earthquake propagation to the surface is monitored at intervals every day. 3.根据权利要求1所述的大型水库诱发地震实时监测定位方法，其特征在于，运用互相关方法拾取DAS地震信号的相对到时，构建到时差矩阵，具体步骤包括：3. The real-time monitoring and positioning method for earthquakes induced by large reservoirs according to claim 1, characterized in that the cross-correlation method is used to pick up the relative arrival times of DAS seismic signals and construct a time difference matrix. The specific steps include: 利用长短时窗比法拾取有效地震事件，其中，长时窗为较长时间信号采样长度的能量平均值，短时窗为较短时间信号采样长度的能量平均值：Use the long and short time window ratio method to pick up effective seismic events, where the long time window is the energy average of the longer signal sampling length, and the short time window is the energy average of the shorter signal sampling length: 式中，S(n)为DAS监测获得的信号幅值，N为长时窗信号采样个数，M为短时窗信号采样个数，N>M，Ratio为记录获得的较短时间信号与较长时间信号平均能量比值；In the formula, S(n) is the signal amplitude obtained by DAS monitoring, N is the number of long time window signal samples, M is the number of short time window signal samples, N>M, Ratio is the shorter time signal obtained by recording and Average energy ratio of longer time signals; 当其大于阈值时，即可判定监测的信号为地震事件引起；When it is greater than the threshold, it can be determined that the monitored signal is caused by an earthquake event; 然后利用互相关方法拾取不同光纤监测道的精确相对地震到时，构建到时差矩阵，互相关方法公式表示为：Then the cross-correlation method is used to pick up the precise relative seismic arrival times of different optical fiber monitoring channels, and a time difference matrix is constructed. The cross-correlation method formula is expressed as: 式中，x1和x2为不同的DAS监测地震道，Rx1x2为相似系数，利用互相关公式精确获得Ratio提取的地震事件在不同光纤道之间相似系数，挑选地震道相似系数最大的时间差构成到时差矩阵。In the formula, x 1 and x 2 are different DAS monitoring seismic channels, R Construct a time difference matrix. 4.根据权利要求3所述的大型水库诱发地震实时监测定位方法，其特征在于，采用基于贝叶斯理论和可逆跳马尔科夫链蒙特卡罗算法对各向异性VTI速度模型进行校正，具体步骤包括：4. The real-time monitoring and positioning method for earthquakes induced by large reservoirs according to claim 3, characterized in that the anisotropic VTI velocity model is corrected using Bayesian theory and a reversible jump Markov chain Monte Carlo algorithm. Specifically Steps include: 地震波在VTI介质中可分裂为qP波、qSV波和qSH波，VTI介质由五个各向异性参数[α0,β0,ε,γ,δ]确定；速度模型包括层位个数n以及层位深度D；Seismic waves can be split into qP waves, qSV waves and qSH waves in the VTI medium. The VTI medium is determined by five anisotropic parameters [α 0 , β 0 , ε, γ, δ]; the velocity model includes the number of layers n and Layer depth D; 因此，确定反演VTI速度模型参数m表达式为：m＝[n,D,α0,β0,ε,γ,δ]Therefore, the expression for determining the inversion VTI velocity model parameter m is: m=[n,D,α 0 ,β 0 ,ε,γ,δ] 式中，α0和β0代表每层沿对称轴qP波和qSV波的速度大小；ε代表每层qP波水平和垂直方向的速度比值；γ代表每层qSH波水平和垂直方向的速度比值；δ代表每层qP波在垂直方向的改变率；In the formula, α 0 and β 0 represent the velocity of qP waves and qSV waves in each layer along the symmetry axis; ε represents the velocity ratio of qP waves in each layer in the horizontal and vertical directions; γ represents the velocity ratio of qSH waves in each layer in the horizontal and vertical directions. ; δ represents the change rate of qP wave in the vertical direction of each layer; 确定贝叶斯理论表达式为：Determine the Bayesian theory expression as: 式中，d表示观测数据，m是模型参数，p(m)是模型先验信息，p(d|m)是似然函数，p(m|d)是模型后验概率，p(d)是观测数据在模型空间中的整体概率，为一常数；In the formula, d represents the observation data, m is the model parameter, p(m) is the model prior information, p(d|m) is the likelihood function, p(m|d) is the model posterior probability, p(d) is the overall probability of observation data in the model space, which is a constant; 利用贝叶斯反演算法，结合各模型参数的先验信息和似然函数，通过求解模型参数后验概率分布获得最终的速度结构。Using the Bayesian inversion algorithm, combined with the prior information and likelihood function of each model parameter, the final velocity structure is obtained by solving the posterior probability distribution of the model parameters. 5.根据权利要求4所述的大型水库诱发地震实时监测定位方法，其特征在于，采用基于贝叶斯理论和可逆跳马尔科夫链蒙特卡罗算法对各向异性VTI速度模型进行校正，具体步骤包括：5. The real-time monitoring and positioning method for earthquakes induced by large reservoirs according to claim 4, characterized in that the anisotropic VTI velocity model is corrected using Bayesian theory and a reversible jump Markov chain Monte Carlo algorithm, specifically Steps include: 确定贝叶斯反演中模型参数的先验概率分布，其先验分布公式表示为：Determine the prior probability distribution of model parameters in Bayesian inversion, and its prior distribution formula is expressed as: p(m)＝p(n)p(D|n)p(α0|n)p(β0|n)p(ε|n)p(γ|n)p(δ|n)p(m)=p(n)p(D|n)p(α 0 |n)p(β 0 |n)p(ε|n)p(γ|n)p(δ|n) 式中，p(n)表示层位个数的概率；p(D|n)表示在层位个数为n的情况下每层深度的概率分布；p(α0|n)表示在层位个数为n的情况下每层各向异性参数α0的概率分布；p(β0|n)表示在层位个数为n的情况下每层各向异性参数β0的概率分布；p(ε|n)表示在层位个数为n的情况下每层各向异性参数ε的概率分布；p(γ|n)表示在层位个数为n的情况下每层各向异性参数γ的概率分布；p(δ|n)表示在层位个数为n的情况下每层各向异性参数δ的概率分布；In the formula, p(n) represents the probability of the number of layers; p(D|n) represents the probability distribution of the depth of each layer when the number of layers is n; p(α 0 |n) represents the probability distribution in the layer The probability distribution of the anisotropy parameter α 0 of each layer when the number of layers is n; p(β 0 |n) represents the probability distribution of the anisotropy parameter β 0 of each layer when the number of layers is n; p (ε|n) represents the probability distribution of the anisotropy parameter ε of each layer when the number of layers is n; p(γ|n) represents the anisotropy parameter of each layer when the number of layers is n. The probability distribution of γ; p(δ|n) represents the probability distribution of the anisotropy parameter δ of each layer when the number of layers is n; 所有先验信息设计成均匀分布、高斯分布、柯西分布，用于求取最终后验概率分布。All prior information is designed into uniform distribution, Gaussian distribution, and Cauchy distribution, which are used to obtain the final posterior probability distribution. 6.根据权利要求5所述的大型水库诱发地震实时监测定位方法，其特征在于，采用基于贝叶斯理论和可逆跳马尔科夫链蒙特卡罗算法对各向异性VTI速度模型进行校正，具体步骤包括：6. The real-time monitoring and positioning method for earthquakes induced by large reservoirs according to claim 5, characterized in that the anisotropic VTI velocity model is corrected using Bayesian theory and a reversible jump Markov chain Monte Carlo algorithm. Specifically, Steps include: 确定贝叶斯反演中模型参数的似然函数，似然函数表示噪声在观测数据中的分布情况，大部分情况下，噪声构成的似然函数表示为高斯分布：Determine the likelihood function of the model parameters in Bayesian inversion. The likelihood function represents the distribution of noise in the observation data. In most cases, the likelihood function composed of noise is expressed as a Gaussian distribution: 表示噪声的平均值，σ表示噪声标准差； represents the average value of noise, and σ represents the standard deviation of noise; 通过建立似然函数，结合先验概率分布，即可构建贝叶斯公式后验概率密度分布公式。By establishing the likelihood function and combining it with the prior probability distribution, the Bayesian formula posterior probability density distribution formula can be constructed. 7.根据权利要求1所述的大型水库诱发地震实时监测定位方法，其特征在于，采用基于贝叶斯理论和可逆跳跃马尔科夫链蒙特卡罗算法对各向异性VTI速度模型进行校正，具体步骤包括：7. The real-time monitoring and positioning method of large reservoir-induced earthquakes according to claim 1, characterized in that the anisotropic VTI velocity model is corrected using Bayesian theory and a reversible jump Markov chain Monte Carlo algorithm, specifically Steps include: 采用可逆跳跃马尔科夫链蒙特卡罗算法迭代产生后验概率模型，迭代过程中，随机更新速度层位个数n、层位深度D或各向异性参数[α0,β0,ε,γ,δ]，共包括出生、死亡、移动及改变四种选择，其中，The reversible jump Markov chain Monte Carlo algorithm is used to iteratively generate a posterior probability model. During the iteration process, the number of velocity layers n, layer depth D or anisotropy parameters [α 0 , β 0 , ε, γ are randomly updated. ,δ], including four options of birth, death, movement and change, among which, “出生”：在原有层位基础上随机的产生一个新的层位，其界面深度服从均匀概率分布：L为总的层位个数，n为目前速度模型层位个数，Dnew为新产生速度模型层位深度；"Birth": Randomly generate a new layer based on the original layer, and its interface depth obeys a uniform probability distribution: L is the total number of layers, n is the number of current velocity model layers, and D new is the depth of the newly generated velocity model layers; “死亡”：在原有层位基础上随机选择现有速度模型的一个层位，删除该层位，选取概率为：Ddeath为删除的速度模型层位；"Death": Randomly select a layer of the existing velocity model based on the original layer and delete the layer. The selection probability is: D death is the deleted velocity model layer; “移动”：在已存在的速度模型中随机选择一个层位，根据高斯概率随机扰动它的深度，扰动概率为：Dmove为新产生的层位深度，σ1为深度扰动标准差；"Move": Randomly select a layer in the existing velocity model and randomly perturb its depth according to Gaussian probability. The perturbation probability is: D move is the newly generated layer depth, σ 1 is the depth disturbance standard deviation; “改变”：在已存在的速度模型中随机选择一个层位，根据高斯概率扰动它的各向异性参数，扰动概率为：V为各向异性参数[α0,β0,ε,γ,δ]，k为1至5的整数，σk代表五个各向异性参数的标准差。"Change": Randomly select a layer in the existing velocity model and perturb its anisotropy parameters according to Gaussian probability. The perturbation probability is: V is the anisotropic parameter [α 0 , β 0 , ε, γ, δ], k is an integer from 1 to 5, and σ k represents the standard deviation of the five anisotropic parameters. 8.根据权利要求1所述的大型水库诱发地震实时监测定位方法，其特征在于，采用基于贝叶斯理论和可逆跳跃马尔科夫链蒙特卡罗算法对各向异性VTI速度模型进行校正，具体步骤包括：8. The real-time monitoring and positioning method for large reservoir-induced earthquakes according to claim 1, characterized in that the anisotropic VTI velocity model is corrected using Bayesian theory and a reversible jump Markov chain Monte Carlo algorithm, specifically Steps include: 用接受概率评判更新速度模型是否接受，接受概率表示为：Use the acceptance probability to judge whether the update speed model is accepted. The acceptance probability is expressed as: 式中，mold为原有的模型，mnew为更新后的模型，p(mold)和p(d|mold)为原有模型的先验信息和似然函数，p(mnew)和p(d|mnew)是更新后的模型先验信息和似然函数，q(mnew|mold)为原有模型生成新模型的转换概率，q(mold|mnew)为新模型产生原有模型的转换概率；In the formula, m old is the original model, m new is the updated model, p(m old ) and p(d|m old ) are the prior information and likelihood function of the original model, p(m new ) and p(d|m new ) are the updated model prior information and likelihood function, q(m new |m old ) is the conversion probability of the original model to generate a new model, q(m old |m new ) is the new The model produces the conversion probability of the original model; 计算接受概率accept_ratio与[0,1]之间的随机数r相比较，如果accept_ratio>r，则更新后的模型将被接受；若accept_ratio<r，则原有模型将会进入下一个循环。Calculate the acceptance probability accept_ratio and compare it with a random number r between [0,1]. If accept_ratio>r, the updated model will be accepted; if accept_ratio<r, the original model will enter the next cycle. 9.根据权利要求1所述的大型水库诱发地震实时监测定位方法，其特征在于，采用基于贝叶斯理论和可逆跳跃马尔科夫链蒙特卡罗算法对各向异性VTI速度模型进行校正，具体步骤包括：9. The real-time monitoring and positioning method for large reservoir-induced earthquakes according to claim 1, characterized in that the anisotropic VTI velocity model is corrected using Bayesian theory and a reversible jump Markov chain Monte Carlo algorithm, specifically Steps include: 对贝叶斯反演获得的模型参数进行后验概率评价，其流程包括：Perform posterior probability evaluation on the model parameters obtained by Bayesian inversion. The process includes: (1)选择后验概率中层位个数占有概率最大的模型作为最终的速度模型；(1) Select the model with the largest occupancy probability of the number of layers in the posterior probability as the final velocity model; (2)计算最终速度模型每层深度的平均值作为最终的层位深度；(2) Calculate the average value of each layer depth of the final velocity model as the final layer depth; (3)计算每层各向异性参数[α0,β0,ε,γ,δ]的平均值作为各层的各向异性参数反演结果；(3) Calculate the average value of the anisotropic parameters [α 0 , β 0 , ε, γ, δ] of each layer as the inversion result of the anisotropic parameters of each layer; 利用以上步骤，即最终确定VTI速度模型的层位个数n，层位深度D，以及各层的各向异性参数[α0,β0,ε,γ,δ]。Using the above steps, the number of layers n, layer depth D, and anisotropy parameters [α 0 , β 0 , ε, γ, δ] of each layer of the VTI velocity model are finally determined. 10.根据权利要求1所述的大型水库诱发地震实时监测定位方法，其特征在于，基于更新的VTI速度模型利用双差定位方法对天然地震事件进行定位，具体步骤包括：10. The real-time monitoring and positioning method for large reservoir-induced earthquakes according to claim 1, characterized in that the double-difference positioning method is used to position natural earthquake events based on the updated VTI velocity model. The specific steps include: 拾取地震事件的到时信息，建立其与主事件的目标函数：Pick up the arrival information of the earthquake event and establish the objective function between it and the main event: 式中，Ψ为目标函数，nr表示DAS地震道的个数，N1、N2表示主地震事件和诱发地震事件个数，t＝O+T，t是DAS记录的达到时刻，O是地震事件的起始时刻，T是震源点到地震道的走时，和/>表示观测得到的地震道r到事件i和事件j的qP波走时，/>和/>表示观测得到的地震道r到事件i和事件j的qSV波走时，/>和/>表示观测得到的地震道r到事件i和事件j的qSH波走时，Oi和Oj是事件i和事件j真正起始时刻，/>和/>表示正演计算获得的检波器r到事件i和事件j的qP波走时，/>和/>表示正演计算获得的检波器r到事件i和事件j的qSV波走时，/>和/>表示正演计算获得的检波器r到事件i和事件j的qSH波走时，obs和cal表示观测和计算获得的值；In the formula, Ψ is the objective function, nr represents the number of DAS seismic channels, N1 and N2 represent the number of main earthquake events and induced earthquake events, t=O+T, t is the arrival time of DAS record, O is the number of earthquake events The starting time, T is the travel time from the earthquake source point to the seismic trace, and/> represents the qP wave travel time from the observed seismic trace r to event i and event j, /> and/> represents the qSV wave travel time from the observed seismic trace r to event i and event j, /> and/> Represents the qSH wave travel time from the observed seismic trace r to event i and event j, O i and O j are the real starting moments of event i and event j,/> and/> Represents the qP wave travel time from detector r to event i and event j obtained by forward calculation,/> and/> Represents the qSV wave travel time from detector r to event i and event j obtained by forward calculation,/> and/> represents the qSH wave travel time from detector r to event i and event j obtained by forward modeling, obs and cal represent the values obtained by observation and calculation; 通过高斯-牛顿算法进行迭代，以上目标函数线性化表示为：Iterating through the Gauss-Newton algorithm, the linearization of the above objective function is expressed as: 式中，表示事件i和事件j到达地震道k的走时差，Δτij表示事件i和事件j的起始时刻差，V是背景VTI速度模型，ΔHij和ΔZij表示事件i和事件j的水平位置和垂直位置相对距离；In the formula, represents the travel time difference between event i and event j when they arrive at seismic trace k, Δτ ij represents the starting time difference between event i and event j, V is the background VTI velocity model, ΔH ij and ΔZ ij represent the sum of the horizontal positions of event i and event j Vertical position relative distance; 根据以上计算方法，最终获得诱发地震事件相对主地震事件的相对位置，大量的诱发地震事件定位结果最终能够刻画地下断层带的延展情况，发现一些微小的隐伏断层结构。Based on the above calculation method, the relative position of induced earthquake events relative to the main earthquake event is finally obtained. A large number of induced earthquake event positioning results can finally describe the extension of underground fault zones and discover some tiny hidden fault structures.
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