A rock stratum fracture seepage velocity detection method and system based on optical fiber sensing

Abstract

The present application relates to the technical field of fluid measurement, and discloses a rock fracture seepage velocity detection method and system based on optical fiber sensing, which comprises the following steps: constructing a sensing detection network based on optical fibers and auxiliary heat sources of a sensing monitoring network; injecting a thermal pulse into rock fractures based on the sensing detection network to obtain a thermal disturbance field; exciting the rock fractures based on the thermal disturbance field; demodulating sensing optical fiber signals to obtain multi-physical field coupling response data; jointly analyzing the multi-physical field coupling response data based on a multi-physical field coupling forward model to obtain seepage characteristic strain components; performing velocity inversion on the seepage characteristic strain components based on the spatial distribution of the multi-physical field coupling forward model to obtain fracture seepage velocity; and performing difference integration on the fracture seepage velocity to generate seepage velocity distribution monitoring results of the rock fractures.

Description

Technical Field

[0001] This invention relates to the field of fluid measurement technology, and in particular to a method and system for detecting seepage velocity in rock fissures based on fiber optic sensing. Background Technology

[0002] In existing technologies, seepage monitoring methods based on distributed optical fiber sensing mainly rely on the measurement and analysis of a single physical quantity, the temperature field. They indirectly infer the presence and approximate intensity of seepage by monitoring temperature anomalies caused by natural seepage or active heating. However, because the rock mass temperature field is easily affected by various non-seepage factors such as changes in ambient temperature and thermal conduction of the surrounding rock, it is difficult to effectively separate the actual thermal disturbances caused by seepage from background noise using only temperature signals. This results in poor specificity and a high false alarm rate in the monitoring results. Furthermore, the single temperature field information dimension is insufficient, making it difficult to establish a direct and robust quantitative mapping relationship with seepage velocity. Typically, only qualitative or semi-quantitative judgments can be achieved, failing to meet the engineering requirements for high-precision quantitative monitoring.

[0003] Furthermore, although a few studies have attempted to introduce fiber optic strain sensing for multi-parameter monitoring, most remain at the level of simple superposition of independently collected parameters such as temperature and strain, with separate threshold alarms. Essentially, this still fails to overcome the shortcomings of insufficient information utilization and weak physical correlation, and cannot achieve enhanced extraction of seepage signals and active suppression of interference signals at the mechanistic level. Therefore, it is difficult to construct a direct and reliable inversion path from the original signal to the key parameter of seepage velocity. Thus, there is an urgent need to develop a monitoring method and system that can deeply integrate multi-physics information, decouple and analyze signals based on a clear physical mechanism, and achieve direct, high-precision, and quantitative inversion of seepage velocity, in order to solve the prominent problems of weak anti-interference ability, low quantitative accuracy, and poor specificity of existing technologies. Summary of the Invention

[0004] This invention provides a method and system for detecting seepage velocity in rock fractures based on optical fiber sensing, the main purpose of which is to address the problems mentioned in the background art above.

[0005] To achieve the above objectives, the present invention provides a method for detecting seepage velocity in rock fractures based on fiber optic sensing, comprising:

[0006] S1: Construct the sensing and detection network for the rock fractures based on optical fibers and an auxiliary heat source in the sensing and monitoring network for rock fractures;

[0007] S2: Based on the sensor detection network, thermal pulses are injected into the rock strata fissures to obtain the thermal disturbance field of the rock strata fissures;

[0008] S3: Based on the thermal disturbance field, thermally excite the rock stratum fissures and demodulate the sensing fiber optic signal of the rock stratum fissures to obtain the multi-physics field coupled response data of the thermal disturbance field;

[0009] S4: Based on the preset multi-physics coupling forward model, the multi-physics coupling response data is jointly analyzed to obtain the seepage characteristic strain components of the rock fracture.

[0010] S5: Based on the spatial distribution of the multi-physics field coupled forward model, the velocity inversion of the seepage characteristic strain components is performed to obtain the fracture seepage velocity of the rock layer fractures;

[0011] S6: Perform differential integration on the seepage velocity of the fracture to generate the monitoring results of the seepage velocity distribution of the rock fracture.

[0012] Preferably, the construction of the sensing and detection network for the rock fractures based on the optical fiber and an auxiliary heat source includes:

[0013] Based on distributed sensing optical fibers, the rock strata fissures are deployed and attached to obtain a preliminary sensing and detection network for the rock strata fissures.

[0014] Stress coupling is applied to the preliminary sensing and detection network to construct the sensing and detection network for the rock strata fractures.

[0015] Preferably, the step of injecting thermal pulses into the rock fractures based on the sensing network to obtain the thermal disturbance field of the rock fractures includes:

[0016] Based on the sensing optical fiber in the sensing network, a rectangular thermal pulse is injected into the rock fissure, and a thermal disturbance field of the rock fissure is generated based on the thermal diffusion effect of the rectangular thermal pulse.

[0017] Preferably, the step of thermally exciting the rock fractures based on the thermal disturbance field and demodulating the sensing fiber optic signal of the rock fractures to obtain the multi-physics coupled response data of the thermal disturbance field includes:

[0018] Based on the thermal pulse action phase and thermal relaxation monitoring phase of the thermal pulse, the Brillouin backscattering spectrum and Raman backscattering spectrum of the sampling points in the sensing fiber are collected simultaneously.

[0019] The frequency shift of the Brillouin backscattering spectrum is calculated to generate the spatiotemporal distribution sequence of strain in the rock strata fractures;

[0020] The intensity ratio of anti-Stokes light to Stokes light in the Raman backscattering spectrum is calculated to generate the spatiotemporal temperature distribution sequence of the rock strata fractures;

[0021] The strain spatiotemporal distribution sequence and the temperature spatiotemporal distribution sequence are spatiotemporally aligned and integrated to generate the multiphysics coupled response data.

[0022] Preferably, the step of jointly analyzing the multi-physics coupling response data based on a preset multi-physics coupling forward model to obtain the seepage characteristic strain components of the rock fracture includes:

[0023] Establish the control relationship between the thermal disturbance field and the heat conduction, convection heat transfer and induced thermoelastic strain in the rock strata fractures;

[0024] Based on the control relationship, a pure heat conduction extrapolation is performed on the rock strata fissures to obtain the theoretical thermal expansion strain distribution of the rock strata fissures;

[0025] Based on the theoretical thermal expansion strain distribution, seepage-related strain anomalies in the rock strata fractures are identified to obtain the residual strain field of the rock strata fractures.

[0026] The spatiotemporal evolution characteristics of the residual strain field are spatiotemporally superimposed with the temperature anomaly region in the multiphysics field coupled response data to obtain the seepage characteristic strain components of the rock fractures.

[0027] Preferably, the step of performing velocity inversion on the seepage characteristic strain components based on the spatial distribution of the multiphysics coupled forward model to obtain the fracture seepage velocity in the rock strata fractures includes:

[0028] Based on the spatial distribution, theoretical thermal expansion strain distribution, and temperature spatiotemporal distribution sequence of the multiphysics coupled forward model, the velocity inversion of the seepage characteristic strain components is performed to obtain the fracture seepage velocity of the rock strata.

[0029] Preferably, the formula for calculating the fissure seepage velocity in the rock strata is as follows:

[0030]

[0031] in, Indicates the optical fiber in the 1st... Axial position at the index The first of the rock strata fissures seepage time at the index For in position Index and time The seepage velocity at the index point is due to the fracture. The rate of change of the seepage characteristic strain component over time. The spatial gradient of the seepage characteristic strain component along the fiber axis. For the axial position is the first At the index, the multiphysics coupled response data, The effective thermal expansion coefficient of the rock strata fracture is given. The equivalent thermal conductivity of the rock strata fracture is given. The width of the rock stratum fracture. The specific heat capacity of the fracture in the rock strata is given. This is the strain space gradient correction coefficient.

[0032] Preferably, after obtaining the fracture seepage velocity in the rock fracture, the method further includes:

[0033] Based on the spatiotemporal distribution field of the fissure seepage velocity, the theoretical temperature response of the sensing network is deduced.

[0034] By comparing the theoretical temperature response with the temperature spatiotemporal distribution sequence, the temperature response difference index of the rock strata fracture is obtained.

[0035] A threshold judgment is made on the temperature response difference index, and the strain space gradient correction coefficient is adjusted based on the threshold judgment.

[0036] Preferably, the step of performing differential integration on the seepage velocity in the fractures to generate the seepage velocity distribution monitoring results of the rock fractures includes:

[0037] The spatiotemporal distribution field of the fissure seepage velocity is integrated by difference to generate a continuous seepage velocity contour map of the rock strata fissures;

[0038] Extract velocity profile data from key monitoring paths in the spatiotemporal distribution field to generate a dynamic evolution diagram of the rock strata fractures;

[0039] The seepage velocity contour map and the dynamic evolution map are fused and labeled to generate the seepage velocity distribution monitoring results of the rock strata fractures.

[0040] A system for detecting seepage velocity in rock fractures based on fiber optic sensing, the system comprising:

[0041] The fiber optic sensor network deployment module is used to construct a sensor detection network for the rock fractures based on the optical fiber and an auxiliary heat source of the sensor monitoring network in the rock fractures.

[0042] A thermal pulse excitation generation module is used to inject thermal pulses into the rock strata fissures based on the sensing and detection network to obtain the thermal disturbance field of the rock strata fissures.

[0043] A multi-physics field coupled response synchronous acquisition module is used to thermally excite the rock stratum fissures based on the thermal disturbance field, and demodulate the sensing fiber optic signal of the rock stratum fissures to obtain the multi-physics field coupled response data of the thermal disturbance field.

[0044] The multi-physics coupling response decoupling analysis module is used to perform joint analysis on the multi-physics coupling response data based on a preset multi-physics coupling forward model, so as to obtain the seepage characteristic strain components of the rock fracture.

[0045] The seepage velocity quantitative inversion module is used to perform velocity inversion on the seepage characteristic strain components based on the spatial distribution of the multi-physics field coupled forward model, so as to obtain the fracture seepage velocity of the rock layer fractures.

[0046] The output module is used to perform differential integration of the seepage velocity in the fractures and generate the monitoring results of the seepage velocity distribution in the rock fractures.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] This invention significantly improves the accuracy, anti-interference capability, and quantitative level of the monitoring system by introducing a novel technical logic of multi-physics field coupling and decoupling analysis and active excitation synchronous sensing. By constructing a physical coupling model, the synchronously acquired temperature and strain response data are jointly analyzed and decoupled causally. A thermal disturbance field is actively generated using controllable thermal pulses, and the spatiotemporal evolution data of temperature and strain are acquired simultaneously, laying a rich and reliable data foundation for quantitative inversion based on physical principles. By extracting the seepage characteristic strain components with clear physical meaning from the decoupled signal, and directly calculating the seepage velocity based on the inversion core formula creatively established in this invention, direct, high-precision, online quantitative monitoring of seepage velocity in rock fractures is achieved, completely breaking through the limitations of traditional methods that rely on indirect inference or threshold comparison.

[0049] The complete technical solution of this invention, from active excitation and simultaneous sensing of dual physical fields to model-driven data decoupling and quantitative parameter inversion, forms a logically rigorous and closed-loop technical system. Driven by the fundamental principles of heat transfer, fluid mechanics, and thermoelasticity, the core inversion process originates from physical derivation rather than pure data fitting, thus possessing good physical interpretability and adaptability to different geological conditions. Combined with the long-distance, continuous spatial coverage advantages brought by distributed fiber optic sensing technology, the system excels in deployment simplicity, intuitive results, and reliable interpretation, thus exhibiting significant advantages in engineering practicality, universality, and long-term monitoring stability. It is easily promoted and applied on a large scale in various geotechnical engineering sites such as slopes, tunnels, and dam foundations. Attached Figure Description

[0050] Figure 1 This is a schematic flowchart of a method for detecting seepage velocity in rock fractures based on fiber optic sensing, provided in an embodiment of the present invention.

[0051] Figure 2 A functional block diagram of a rock fracture seepage velocity detection system based on fiber optic sensing provided in an embodiment of the present invention;

[0052] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0053] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0054] This application provides a method for detecting seepage velocity in rock fissures based on fiber optic sensing. The execution entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for detecting seepage velocity in rock fissures based on fiber optic sensing can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, and big data and artificial intelligence platforms.

[0055] Reference Figure 1 The diagram shown is a flowchart illustrating a method for detecting seepage velocity in rock fissures based on fiber optic sensing, according to an embodiment of the present invention. In this embodiment, the method for detecting seepage velocity in rock fissures based on fiber optic sensing includes:

[0056] In this embodiment of the invention, a sensing and detection network for the rock strata fissures is constructed based on optical fibers and an associated heat source of the sensing and monitoring network in the rock strata fissures.

[0057] The optical fiber and auxiliary heat source used in the rock fracture sensing and monitoring network constitute the rock fracture sensing and detection network, including:

[0058] Based on distributed sensing optical fibers, the rock strata fissures are deployed and attached to obtain a preliminary sensing and detection network for the rock strata fissures.

[0059] Stress coupling is applied to the preliminary sensing and detection network to construct the sensing and detection network for the rock strata fractures.

[0060] Specifically, distributed sensing optical fiber is a special type of optical fiber whose internal structure enables it to sense changes in external physical quantities at any point along its entire length. In the rock fracture seepage monitoring environment of this invention, this optical fiber is used as a core sensor.

[0061] Specifically, rock fissures refer to discontinuities such as cracks, joints, or faults existing in rock strata. In the monitoring environment of this invention, these fissures are the main channels for the seepage of groundwater or other fluids.

[0062] Specifically, the preliminary sensing and detection network refers to the physical structure formed after the distributed sensing optical fibers are initially laid and fixed in the rock fissure area according to a predetermined path.

[0063] Specifically, stress coupling is a mechanical process that describes the formation of a tight and stable mechanical connection between the sensing optical fiber and the rock mass to be monitored. In the context of this invention, the aim is to ensure that any minute deformation of the rock mass can be transmitted to the sensing optical fiber without loss or hysteresis, and thus be accurately sensed by the fiber.

[0064] Specifically, the sensing network is the final, usable front-end part of the monitoring system formed after the stress coupling operation is completed. It consists of distributed sensing optical fibers that have achieved good mechanical coupling with the rock mass, along with necessary fixing and protective accessories.

[0065] Furthermore, firstly, based on the geological survey results, the main development direction of rock fissures and possible seepage paths are determined. Then, operators lay distributed sensing optical fibers, either whole or in segments, along these predetermined paths. During laying, specialized adhesives, clamps, or straps are used to secure the fibers tightly to the rock surface or pre-drilled shallow trenches. For buried installations, holes or trenches are drilled near the fissures, the fibers are inserted, and then backfilled with coupling material.

[0066] Furthermore, after the initial deployment and attachment of the optical fiber, there may be minute gaps or insufficient coupling between it and the rock mass. To achieve stress coupling, a common practice is to apply a small and constant initial tension to the optical fiber, maintain this state, and then completely seal and cure the fiber's fixing points using a curing material. This pre-tensioned state ensures effective strain transfer in the event of changes in ambient temperature or minor deformation of the rock mass.

[0067] Furthermore, another approach is to verify the sensitivity of the optical fiber to vibrations or natural temperature changes in the rock mass environment by monitoring background signals over a period of time, and to optimize this by fine-tuning the tightness of the fixing points. The purpose of this step is to eliminate mechanical hysteresis and ensure that the strain transfer function from the rock mass to the optical fiber is linear and efficient, thereby upgrading the initial sensing network into a reliable sensing network capable of high-precision measurements.

[0068] In summary, distributed sensing optical fibers, as the sensing medium, enable large-scale, continuous spatial information perception, replacing traditional point sensor arrays. Rock fissures are the specific physical environment and monitoring object for this method. Deployment and attachment complete the spatial deployment of sensors in the target monitoring area, which is the basic physical preparation for monitoring to be implemented.

[0069] In summary, stress coupling, a crucial step, enhances and ensures the mechanical transmission performance of the measurement system. It directly determines the fidelity and reliability of subsequently acquired strain data, making it an indispensable step in obtaining high-quality raw data. The performance of the sensing network directly impacts the effectiveness of all subsequent analysis steps.

[0070] In summary, the above steps established a dedicated sensing physical layer for rock strata fractures from scratch, providing a stable and reliable hardware platform for subsequent thermal pulse generation, response data acquisition, and interpretation and inversion speed.

[0071] In this embodiment of the invention, thermal pulses are injected into the rock strata fissures based on the sensing and detection network to obtain the thermal disturbance field of the rock strata fissures;

[0072] The step of injecting thermal pulses into the rock strata fissures based on the sensing network to obtain the thermal disturbance field of the rock strata fissures includes:

[0073] Based on the sensing optical fiber in the sensing network, a rectangular thermal pulse is injected into the rock fissure, and a thermal disturbance field of the rock fissure is generated based on the thermal diffusion effect of the rectangular thermal pulse.

[0074] Specifically, thermal pulse injection is an active energy application process. In the monitoring environment of this invention, it specifically refers to applying a concentrated heat input with specific energy and duration to a rock fracture area where sensing optical fibers have been deployed within a short period of time using certain technical means.

[0075] Specifically, a rectangular thermal pulse is a description of the time-domain waveform characteristics of the injected thermal pulse. In the context of this invention, "rectangular" means that the power or energy output of the thermal pulse remains constant for a set duration, similar to a square wave, where the power instantaneously rises to a set value at the start and remains there, and the power instantaneously drops to zero at the end.

[0076] Specifically, thermal diffusion is a fundamental physical process of heat transfer in a medium. In the rock fracture environment involved in this invention, when a thermal pulse is injected, the heat does not remain at the injection point, but rather spreads and disperses gradually from the injection point to the surrounding rock mass areas with lower temperatures through thermal conduction via the solid framework of the rock mass.

[0077] A thermal disturbance field refers to a dynamically changing temperature field with a spatiotemporal distribution that forms in rock fissures and the surrounding rock mass after a thermal pulse is injected. It is initially determined by the location and time of the rectangular thermal pulse input, and then, under the dominance of the thermal diffusion effect, heat propagates to the surrounding rock mass, forming a region with a temperature higher than the original background temperature, where the temperature distribution changes continuously in space and relaxes over time.

[0078] Furthermore, the sensing optical fiber itself is directly used as a heating element: a high-power modulated laser beam is injected into the distributed optical fiber used for sensing through an optical fiber coupler. By precisely controlling the output power and switching time of the laser, the optical fiber heats up at a constant power for a set duration, thereby linearly heating the rock mass closely attached to the fiber along the optical fiber path and generating rectangular thermal pulses distributed along the direction of the fracture.

[0079] Furthermore, an independent heat source, deployed or integrated in parallel with the sensing fiber, is utilized: a constant voltage or current is applied to this heat source via a programmable power supply for a preset time, generating constant Joule heat, thereby creating a rectangular thermal pulse in the target area, either volumetric or planar. Regardless of the approach, the key action is the timing control of the energy source, achieving instantaneous power activation, constant maintenance, and instantaneous deactivation, thus completing the active thermal excitation conforming to rectangular characteristics.

[0080] Furthermore, after the rectangular thermal pulse is successfully injected into the rock mass, the active control phase ends. Subsequently, under the control of the rock mass's own physical laws, the thermal diffusion effect automatically begins. Heat, originating from the injection point, continuously migrates to the surrounding low-temperature regions through thermal conduction between rock particles. This process is a dynamic and spontaneous physical propagation process. Over time, a thermal disturbance field naturally forms and continuously evolves, with the spatial temperature gradient gradually decreasing from the injection center outwards and the overall temperature gradually approaching equilibrium over time. The distributed temperature sensing system's role is to continuously observe and record the spatiotemporal state of this generated field.

[0081] In summary, the active action of thermal pulse injection transforms the invention from passive monitoring to active detection. This changes the monitoring process from observing weak, noisy natural seepage temperature changes to tracking how a significant, artificially introduced physical marker is affected by seepage, greatly improving the signal-to-noise ratio and detectability.

[0082] In summary, choosing a rectangular thermal pulse simplifies the temporal characteristics of the excitation source, clearly defining its boundary conditions and facilitating accurate determination of the time starting point in subsequent physical modeling and data analysis. The thermal diffusion effect is the core natural physical mechanism connecting excitation and observation, serving as a benchmark for understanding the evolution of thermal disturbance fields without seepage. The entire process of thermal pulse injection and thermal disturbance field generation provides a clear, controllable, and strong signal physical excitation and observation basis for the entire technical solution.

[0083] In this embodiment of the invention, the rock stratum fissures are thermally excited based on the thermal disturbance field, and the sensing fiber optic signal of the rock stratum fissures is demodulated to obtain the multi-physics field coupled response data of the thermal disturbance field.

[0084] The process of thermally exciting the rock strata fractures based on the thermal disturbance field and demodulating the sensing fiber optic signal of the rock strata fractures to obtain multi-physics coupled response data of the thermal disturbance field includes:

[0085] Based on the thermal pulse action phase and thermal relaxation monitoring phase of the thermal pulse, the Brillouin backscattering spectrum and Raman backscattering spectrum of the sampling points in the sensing fiber are collected simultaneously.

[0086] The frequency shift of the Brillouin backscattering spectrum is calculated to generate the spatiotemporal distribution sequence of strain in the rock strata fractures;

[0087] The intensity ratio of anti-Stokes light to Stokes light in the Raman backscattering spectrum is calculated to generate the spatiotemporal temperature distribution sequence of the rock strata fractures;

[0088] The strain spatiotemporal distribution sequence and the temperature spatiotemporal distribution sequence are spatiotemporally aligned and integrated to generate the multiphysics coupled response data.

[0089] Specifically, the thermal pulse action phase is a specific time interval, referring to the entire time period from the moment the rectangular thermal pulse begins to be injected into the rock strata fissures until the moment the thermal pulse ends after being injected for a preset duration.

[0090] Specifically, the thermal relaxation monitoring phase is a longer time interval that follows the thermal pulse phase. During this phase, the external thermal excitation has ceased, but the previously injected heat remains in the rock mass.

[0091] Specifically, Brillouin backscattering is a specific spectral signal generated when light propagates in the sensing optical fiber due to inelastic scattering with acoustic phonons of the fiber material itself. This scattered light travels back in the opposite direction to the incident light.

[0092] Specifically, Raman backscattering spectroscopy is a specific spectral signal generated when light propagates in a sensing optical fiber due to inelastic scattering with the vibrational energy levels of molecules in the fiber material. This scattered light also travels back in the opposite direction.

[0093] Specifically, frequency shift calculation is a signal processing operation that refers to the process of analyzing the acquired Brillouin backscattered spectrum and accurately measuring the shift of the center frequency of the Brillouin scattered light relative to the incident laser frequency.

[0094] Specifically, the strain spatiotemporal distribution sequence refers to a structured dataset formed by continuously calculating the frequency shift at each time sampling moment of each spatial sampling point on the sensing fiber during the thermal pulse action phase and the thermal relaxation monitoring phase, and converting the calculated frequency shift value into a strain value.

[0095] Specifically, reflected Stokes light and anti-Stokes light are two distinct components of the Raman backscattering spectrum. Stokes light refers to the spectral component produced when the energy of the scattered photons is lower than that of the incident photons; while anti-Stokes light refers to the spectral component produced when the energy of the scattered photons is higher than that of the incident photons.

[0096] Specifically, intensity ratio calculation is a signal processing operation, which refers to the process of analyzing the acquired Raman backscattered spectrum, extracting the signal intensities of the anti-Stokes light and the Stokes light respectively, and calculating the ratio between the two.

[0097] Specifically, the temperature spatiotemporal distribution sequence refers to a structured dataset formed by continuously calculating the intensity ratio at each time sampling moment of each spatial sampling point on the sensing fiber during the thermal pulse action phase and the thermal relaxation monitoring phase, and converting the calculated intensity ratio into a temperature value.

[0098] Specifically, spatiotemporal alignment and integration is a data preprocessing and fusion operation. Since the strain spatiotemporal distribution sequence and the temperature spatiotemporal distribution sequence are derived from synchronously acquired data from the same batch of spatial sampling points on the same sensing fiber and on the same time axis, they naturally have the same spatial coordinate system and timestamp.

[0099] Specifically, multiphysics coupled response data is the final dataset formed after spatiotemporal alignment and integration. It includes the temperature field response and strain field response exhibited by the rock mass within the same spatiotemporal range under the same active thermal excitation event.

[0100] Furthermore, the instrument continuously injects probe laser light into the sensing fiber. From the moment the thermal pulse injection begins, the instrument initiates a high-speed, continuous data acquisition mode. Throughout the entire thermal pulse phase and the subsequent thermal relaxation monitoring phase, the demodulator simultaneously activates its internal Brillouin and Raman signal processing channels at a fixed high sampling rate. For each preset spatial sampling point along the fiber path, the instrument synchronously captures the complete backscattered light signal returning from it, containing both Brillouin and Raman scattering components, and generates Brillouin and Raman spectral datasets that continuously vary over time, respectively.

[0101] Furthermore, the demodulator's Brillouin processing channel analyzes the massive amounts of acquired Brillouin spectral data point-by-point and time-by-time. For each spatial-temporal data unit, the channel precisely determines the Brillouin frequency shift peak of the spectrum using its built-in algorithm. Then, the instrument uses preset sensing coefficients to convert this frequency shift into the absolute strain value or strain change value at that point at that time. After traversing all spatial points and all time points, a strain spatiotemporal distribution sequence matrix or data stream with spatial indices and timestamps as coordinates and numerical values ​​as strain values ​​is systematically generated.

[0102] Furthermore, the demodulator's Raman processing channel processes the acquired Raman spectral data point by point and hour by hour. For each data unit, the channel extracts the signal intensity of the anti-Stokes light and the signal intensity of the Stokes light from the spectrum. Then, the intensity ratio between the two is calculated.

[0103] Then, the instrument converts the calculated intensity ratio into the absolute temperature value of that point at that moment, based on a pre-calibrated formula or lookup table describing the relationship between the intensity ratio and temperature. After traversing all spatial points and all time points, it systematically generates a temperature spatiotemporal distribution sequence matrix or data stream with spatial indices and timestamps as coordinates and numerical values ​​as temperature values.

[0104] Furthermore, in the data processing unit, it is first confirmed that the two sequences have identical spatial resolution and time axis. Subsequently, the system establishes a unified data structure, such as a three-dimensional array or a data table containing spatiotemporal coordinates and multiple physical quantity fields. Data from the strain spatiotemporal distribution sequence and the temperature spatiotemporal distribution sequence are then populated into this unified data structure according to their one-to-one spatial and temporal indices. After integration, a complete, integrated multiphysics coupled response data file or database is obtained, which can be directly accessed by subsequent analysis modules.

[0105] In summary, clearly defining the thermal pulse action phase and the thermal relaxation monitoring phase establishes a clear time frame for the entire dynamic observation process. Simultaneous acquisition of Brillouin and Raman spectra is the fundamental technical guarantee for achieving multi-physics coupled observation in this invention.

[0106] In summary, the two core signal processing actions, frequency shift calculation and intensity ratio calculation, are to transform the original optical spectral signal into engineering parameters that can directly reflect the physical state of the rock mass.

[0107] In summary, generating spatiotemporal strain and temperature distribution sequences discretizes and digitizes continuous, simulated physical field changes into structured data that can be stored and analyzed by computers. These sequences comprehensively depict the entire response of the rock mass to thermal excitation.

[0108] In summary, the role of spatiotemporal alignment and integration is to complete the final step of data fusion, seamlessly stitching together two datasets with the same source and consistent goals but different physical meanings into an organic whole—multiphysics coupled response data.

[0109] In this embodiment of the invention, based on a preset multi-physics coupling forward model, the multi-physics coupling response data is jointly analyzed to obtain the seepage characteristic strain components of the rock fracture.

[0110] The method based on a preset multi-physics coupling forward model performs joint analysis on the multi-physics coupling response data to obtain the seepage characteristic strain components of the rock fractures, including:

[0111] Establish the control relationship between the thermal disturbance field and the heat conduction, convection heat transfer and induced thermoelastic strain in the rock strata fractures;

[0112] Based on the control relationship, a pure heat conduction extrapolation is performed on the rock strata fissures to obtain the theoretical thermal expansion strain distribution of the rock strata fissures;

[0113] Based on the theoretical thermal expansion strain distribution, seepage-related strain anomalies in the rock strata fractures are identified to obtain the residual strain field of the rock strata fractures.

[0114] The spatiotemporal evolution characteristics of the residual strain field are spatiotemporally superimposed with the temperature anomaly region in the multiphysics field coupled response data to obtain the seepage characteristic strain components of the rock fractures.

[0115] Specifically, the multiphysics coupled forward model is a conceptual framework or theoretical system used to describe and predict the system behavior under the interaction of multiple physical processes in the fractured rock environment.

[0116] Specifically, the control relationship is a qualitative description of the mutual constraints and influences among the three key physical processes of heat conduction, convection heat transfer, and induced thermoelastic strain within the framework of the multiphysics coupled forward model.

[0117] Specifically, pure heat conduction deduction is a thought experiment or analytical process based on physical assumptions. It refers to, under the guidance of the stated control relationship, artificially setting the influence of seepage to zero, and considering only the single physical mechanism of heat conduction, how the thermal disturbance field will diffuse in the rock mass, and accordingly predicting what kind of rock mass strain distribution it will induce.

[0118] Specifically, theoretical thermal expansion strain distribution refers to the estimated spatial distribution of the strain field formed in the entire monitoring area under ideal conditions assuming no seepage in the rock strata fissures, where heat caused by the injection of the thermal pulse diffuses through pure thermal conduction in the solid skeleton of the rock mass, resulting in uniform or regular thermal expansion and contraction of the rock mass, which is ultimately caused purely by temperature changes.

[0119] Specifically, seepage-related strain anomalies refer to strain deviations or special forms that cannot be explained by pure heat conduction mechanisms and exceed the expected theoretical distribution range when comparing the actual observed strain data with the theoretical thermal expansion strain distribution.

[0120] Specifically, the residual strain field is an intermediate data field obtained through data comparison operations. From the spatiotemporal distribution sequence of strain obtained from actual measurements, at each corresponding spatial and temporal point, the value at the corresponding position in the theoretical thermal expansion strain distribution derived from pure heat conduction is subtracted.

[0121] Specifically, the spatiotemporal evolution characteristics include the distribution pattern of the strain field as it changes with spatial location and its evolution over time.

[0122] Specifically, temperature anomaly regions are special areas identified from the spatiotemporal distribution sequence of temperature in multiphysics coupled response data. They refer to regions where temperature changes exhibit patterns inconsistent with those expected in pure heat conduction, either temporally or spatially.

[0123] Specifically, spatiotemporal overlay analysis refers to comparing and correlating the spatiotemporal evolution characteristics of the residual strain field with the spatiotemporal location information of the temperature anomaly region on the same set of spatial coordinates and time axis.

[0124] Specifically, the seepage characteristic strain components are the target signals that are finally separated, identified and extracted from the original complex strain signals after a series of analyses and verifications.

[0125] Furthermore, firstly, the energy transfer path of the thermal disturbance field in the specific environment of the fractured rock mass is analyzed: heat is conducted through the contact between solid particles of the rock mass, and may also be carried and migrated by the fluid flowing in the fracture.

[0126] Secondly, the mechanical effects triggered by this temperature field change should be clarified: different parts of the rock mass undergo differential thermal expansion and contraction due to uneven temperature, thereby generating internal stress and manifesting as observable strain.

[0127] Finally, the causal chain and mutual influence among these three elements are clearly expressed using logical language. For example, the spatiotemporal evolution of the thermal disturbance field is jointly controlled by heat conduction and convection heat transfer; the evolving temperature field, through thermoelastic constitutive relations, dominates the generation and change of the rock mass strain field. This step establishes the core physical logic guiding subsequent analysis at both the conceptual and document levels.

[0128] Furthermore, based on the control relationship established in the previous step, a key constraint is first applied: assuming the seepage velocity in the fracture is zero, thus eliminating the influence of the convective heat transfer term in the model. Then, considering only the heat conduction mechanism, and combining the initial parameters of the heat pulse and the basic thermophysical parameters of the rock mass, we can qualitatively or semi-quantitatively describe how heat will diffuse symmetrically in the rock mass, either conceptually or through simple physical deductions.

[0129] Next, based on the temperature field prediction under this pure heat conduction, and according to the basic principle of thermal expansion, the overall distribution trend and approximate magnitude of the strain field generated by the rock mass are inferred, forming a theoretical thermal expansion strain distribution map as a comparison benchmark. This step does not require complex numerical solutions, but focuses on establishing reasonable theoretical expectations.

[0130] Furthermore, the spatiotemporal distribution sequence of strain obtained from actual measurements is compared point-by-point with the theoretical thermal expansion strain distribution expected in the previous step. For each data point, the difference between the measured strain value and the theoretical expected value is calculated. Then, the set of these difference data is examined: spatially, attention is paid to which regions have differences that consistently and significantly deviate from zero; temporally, attention is paid to the changing patterns of the difference signals. All regions or data points with significant deviations and whose patterns do not conform to the characteristics of measurement noise are initially marked as possible seepage-related strain anomalies.

[0131] Furthermore, firstly, temperature anomaly regions are independently identified from the spatiotemporal temperature distribution sequence. Then, in the mind of the computer or analyst, the spatial distribution map and temporal evolution curve of the residual strain field are overlaid with the spatial extent and occurrence sequence of the temperature anomaly regions in the same view or coordinate system for comparison. A detailed examination is conducted to determine whether the two anomalies highly overlap spatially and whether they exhibit a covariant relationship temporally.

[0132] In summary, the multiphysics coupling forward model and control relationships provide the correct physical guidance for the entire analytical process. Their role is to ensure that the direction of data analysis conforms to natural laws, avoid confusing signals caused by different physical factors, and serve as a theoretical compass for separating specific components from mixed signals.

[0133] In summary, the key operation of spatiotemporal overlay analysis enables multi-evidence fusion decision-making. Its effect is a significant improvement in the accuracy and reliability of target signal extraction, avoiding misjudgments that may arise from a single indicator criterion. By requiring that strain anomalies and temperature anomalies be spatiotemporally correlated, the reliability of the conclusions is greatly enhanced.

[0134] In this embodiment of the invention, based on the spatial distribution of the multi-physics field coupled forward model, the velocity inversion of the seepage characteristic strain components is performed to obtain the fracture seepage velocity of the rock layer fractures.

[0135] The spatial distribution based on the multi-physics coupled forward model is used to perform velocity inversion on the seepage characteristic strain components to obtain the fracture seepage velocity in the rock strata fractures, including:

[0136] Based on the spatial distribution, theoretical thermal expansion strain distribution, and temperature spatiotemporal distribution sequence of the multiphysics coupled forward model, the velocity inversion of the seepage characteristic strain components is performed to obtain the fracture seepage velocity of the rock strata.

[0137] The formula for calculating the fissure seepage velocity in the rock strata is as follows:

[0138]

[0139] in, Indicates the optical fiber in the 1st... Axial position at the index The first of the rock strata fissures seepage time at the index For in position Index and time The seepage velocity at the index point is due to the fracture. The rate of change of the seepage characteristic strain component over time. The spatial gradient of the seepage characteristic strain component along the fiber axis. For the axial position is the first At the index, the multiphysics coupled response data, The effective thermal expansion coefficient of the rock strata fracture is given. The equivalent thermal conductivity of the rock strata fracture is given. The width of the rock stratum fracture. The specific heat capacity of the fracture in the rock strata is given. This is the strain space gradient correction coefficient.

[0140] After obtaining the fracture seepage velocity in the rock strata fractures, the method further includes:

[0141] Based on the spatiotemporal distribution field of the fissure seepage velocity, the theoretical temperature response of the sensing network is deduced.

[0142] By comparing the theoretical temperature response with the temperature spatiotemporal distribution sequence, the temperature response difference index of the rock strata fracture is obtained.

[0143] A threshold judgment is made on the temperature response difference index, and the strain space gradient correction coefficient is adjusted based on the threshold judgment.

[0144] Specifically, velocity inversion is a goal-oriented data analysis and parameter calculation process. In this invention, it specifically refers to using the seepage characteristic strain components extracted from measured data through multiple steps of analysis as core evidence and input, combined with previously established physical knowledge and known benchmark data, and through physical logic deduction and data correlation, to inversely estimate the root cause of the characteristic strain signal—that is, the rate of fluid movement in rock fissures.

[0145] Specifically, fracture seepage velocity refers to the linear velocity of fluid moving along the fracture channel at a specific spatial location and time point within a rock fracture. Its value directly reflects the strength and speed of seepage, and is a key quantitative indicator for assessing rock mass stability, predicting the risk of sudden water inrush, and analyzing drainage effectiveness—key quantitative indicators for core engineering safety issues.

[0146] Furthermore, firstly, the spatiotemporal characteristics of the strain components of the seepage are analyzed, such as their amplitude, spatial variation gradient along the fracture direction, and rate of evolution over time. These characteristics are direct clues for judging the strength and dynamics of the seepage.

[0147] Secondly, we invoked and examined the physical laws revealed by the spatial distribution of the multiphysics coupling positive model. This model framework indicates the qualitative or semi-quantitative influence of different seepage velocities on the propagation of thermal disturbance field and the strain field induced by it under specific rock mass structure and thermophysical properties.

[0148] Then, by combining this independent observation data, we can verify whether the seepage activity implied by the characteristic strain components of seepage also leaves verifiable anomalous patterns in the temperature field, thereby enhancing the reliability of the inference.

[0149] Meanwhile, by referencing the theoretical thermal expansion strain distribution as a benchmark, the extent to which the background strain caused by non-permeable factors has been completely removed can be assessed, ensuring that the permeable characteristic strain components on which the inversion is based are of high quality.

[0150] Finally, all the above information—the quantitative characteristics of the characteristic strain itself, the effect-cause mapping relationship provided by the physical model, and the supporting information from the temperature field—is integrated. Within this integrated framework, through logical reasoning and correlation analysis based on physical laws, the magnitude of the seepage velocity at each monitoring point at each observation time is estimated and assigned, thereby systematically generating a complete spatiotemporal distribution dataset of fracture seepage velocity.

[0151] Specifically, It is a spatial discrete coordinate. It represents the first spatial coordinate obtained along the length direction of the sensing fiber, starting from the starting end and divided according to a preset spatial sampling interval. The location number of each sampling point. Its physical meaning is to discretize and digitize the continuous spatial location of optical fibers, so that the computer system can independently identify, store, and calculate each specific point.

[0152] Specifically, It is a discrete-time coordinate. It represents the first time interval obtained after dividing the time interval according to a preset time sampling interval, starting from the moment the thermal pulse begins to be injected. The sampling time is numbered. Its physical meaning is to discretize and digitize the continuous monitoring time in order to record and describe the process of physical quantity evolution over time.

[0153] Specifically, This is the calculation result of this formula, and also the core output of the entire method. It represents the spatial location. Place, in time At any given moment, the linear velocity of fluid moving along the fracture channel within the rock strata is measured. Its physical significance lies in being a key indicator for quantitatively characterizing seepage intensity.

[0154] Specifically, The characteristic strain components of seepage are represented at the location. ,time The strain value extracted from the original strain data, which is directly or indirectly caused by seepage activity.

[0155] Specifically, It is the first partial derivative of the time-varying rate of change of the characteristic strain component of seepage, obtained through numerical differentiation. Its physical meaning is to describe the instantaneous rate of change of the characteristic strain component of seepage with time. A large positive value indicates that the strain effect caused by seepage at that point is rapidly increasing, suggesting that seepage may be intensifying or the flow regime may be changing; a rate of change close to zero indicates that the seepage state is stable. It quantifies the temporal intensity of the seepage flux change.

[0156] Specifically, It is the axial spatial gradient of the characteristic strain component of seepage with respect to spatial position. The first partial derivative is obtained through numerical differentiation. Its physical meaning describes the drastic variation of the characteristic strain components of seepage along the fiber axis. A large absolute value indicates a large change in strain value over a very short distance, typically corresponding to the boundary of the main seepage channel, abrupt changes in fracture aperture, or the seepage front; a gentle gradient indicates a wide or uniform seepage influence. It reveals the spatial distribution characteristics and inhomogeneity of seepage activity.

[0157] Specifically, The temperature variation amplitude at the axial position is a scalar field, which is obtained by analyzing the spatiotemporal distribution sequence of temperature at the position. The data is obtained from time-series data analysis. Typically, the temperature value at the end of the thermal pulse phase or the highest temperature reached during the entire monitoring period is taken, minus the initial background temperature before the thermal disturbance.

[0158] Specifically, The effective thermal expansion coefficient of the rock mass is a macroscopic material property constant that characterizes the overall linear expansion capacity of the fractured rock mass under temperature changes.

[0159] Specifically, The equivalent thermal conductivity of the fractured rock mass region is a macroscopic thermophysical constant that characterizes the comprehensive thermal conductivity of the fractured rock mass region as an equivalent continuous medium. This coefficient comprehensively considers the combined effects of the solid skeleton of the rock mass, the fractures, and the fluids within them. It is typically obtained by analyzing the temperature diffusion curve of the temperature spatiotemporal distribution sequence in the early stage after the cessation of the thermal pulse, and then using a one-dimensional heat conduction model for inversion fitting.

[0160] Specifically, is the width of the rock fracture. is a fluid property constant representing the mass density of the fluid filling the rock fracture. Its value is determined by the fluid type and the surrounding temperature and pressure conditions, and can be input as known conditions. Its physical meaning lies in its relationship with the fluid's specific heat capacity. Together, they determine the volumetric heat capacity of a fluid as a heat carrier, which is the amount of heat required to raise the temperature of a unit volume of fluid by 1°C, reflecting the fluid's ability to transport and store heat.

[0161] Specifically, Specific heat capacity is the specific heat capacity of the fluid in the fracture, a fluid property constant. It represents the specific heat capacity of the fluid in the fracture. Its value is known for common fluids. Its physical meaning is the amount of heat absorbed or released per unit mass of fluid when its temperature changes by 1°C.

[0162] Specifically, This is the strain space gradient correction coefficient, a dimensionless empirical or semi-theoretical correction coefficient. Its contribution to velocity inversion is weighted and adjusted. The geometric complexity of actual fractures can lead to deviations between the strain space distribution and the predictions of ideal seepage models.

[0163] Specifically, the spatiotemporal distribution field of fracture seepage velocity is the final output of the previous core step, and is a structured dataset. It contains the seepage velocity value calculated using the inversion formula at each spatial sampling point along the sensing fiber and at each temporal sampling point. Its physical meaning is to fully characterize the spatial distribution of rock fracture seepage velocity within the monitoring area and its dynamic evolution over time. This data field serves as the foundation and input for subsequent verification and optimization.

[0164] Specifically, the theoretical temperature response is a predicted data field obtained through physical deduction that corresponds to the measured data. It refers to the calculation of the temperature change sequence that each point on the sensing network should exhibit, based on the fundamental principles of thermodynamics and heat transfer, namely the combined effects of heat conduction and convection in the seepage-containing fractured medium, under the assumption that the spatiotemporal distribution field of the aforementioned fracture seepage velocity is real and accurate.

[0165] Specifically, the temperature response difference index is a scalar or set of statistics used for quantitative evaluation. It is obtained by comparing theoretical temperature response prediction data with the measured spatiotemporal distribution sequence of temperature extracted from multiphysics coupled response data point-by-point, time-by-time, or globally.

[0166] Specifically, threshold determination is a decision-making logic process. A threshold refers to a pre-set maximum permissible difference limit determined based on the measurement accuracy of the sensing system, the simplification of the model, and the requirements of the engineering application. Threshold determination involves comparing the calculated temperature response difference index with this pre-set permissible threshold.

[0167] Furthermore, in the specific execution, the spatiotemporal distribution field of the fracture seepage velocity obtained from the previous inversion step is used as known input conditions. Simultaneously, known rock mass and fluid thermophysical parameters, fluid density, fluid specific heat capacity, rock mass heat capacity, and initial parameters of the thermal pulse are invoked. In the computer system, based on the law of conservation of energy and the principles of heat transport (conduction and convection), the simulation calculates how the heat generated by the initial thermal pulse is conducted and transported by fluid motion within the rock fracture system under the influence of the aforementioned seepage velocity field. Through this forward simulation, the temperature change value caused by this comprehensive process at each location along the sensing fiber at each moment is calculated, thereby generating a complete theoretical temperature response prediction dataset that perfectly corresponds to the spatiotemporal coordinates of the measured data.

[0168] Furthermore, firstly, it is ensured that the theoretical temperature response data and the measured temperature spatiotemporal distribution sequence data are strictly aligned in terms of spatial grid points and timestamps. Then, for each corresponding data point, the difference between the predicted temperature value and the measured temperature value is calculated.

[0169] Next, statistical analysis is performed on these differences across the entire spatiotemporal region of interest. Commonly used indicators of temperature response difference include the root mean square error, mean absolute error, or the maximum deviation at key feature points. The calculated statistic is the final difference indicator.

[0170] Furthermore, the temperature response difference index calculated in the previous step is compared with a preset tolerance threshold. This threshold is determined based on the absolute temperature measurement accuracy, spatial resolution, and error tolerance of the distributed temperature sensing system for engineering applications.

[0171] If the difference index is less than or equal to the allowable threshold, the spatiotemporal distribution field of the fracture seepage velocity obtained by the current inversion is determined to have sufficient physical consistency. There is no need to adjust the strain space gradient correction coefficient in the inversion formula, and the current result can be used as the final output.

[0172] If the judgment fails: If the difference index exceeds the allowable threshold, it indicates that the temperature effect predicted by the velocity field based on the existing values ​​is significantly inconsistent with the measured temperature, indicating insufficient physical consistency. In this case, an adjustment process needs to be initiated: the system will automatically or with manual intervention change the value of the strain space gradient correction coefficient. Then, all inversion and verification steps after extracting the seepage characteristic strain components will be re-executed. This cycle will continue until the newly calculated difference index drops below the allowable threshold, or the preset maximum number of iterations is reached.

[0173] In this embodiment of the invention, the seepage velocity in the fracture is differentially integrated to generate the monitoring results of the seepage velocity distribution in the rock fracture.

[0174] The step of integrating the differential values ​​of the seepage velocities in the fractures to generate the monitoring results of the seepage velocity distribution in the rock fractures includes:

[0175] The spatiotemporal distribution field of the fissure seepage velocity is integrated by difference to generate a continuous seepage velocity contour map of the rock strata fissures;

[0176] Extract velocity profile data from key monitoring paths in the spatiotemporal distribution field to generate a dynamic evolution diagram of the rock strata fractures;

[0177] The seepage velocity contour map and the dynamic evolution map are fused and labeled to generate the seepage velocity distribution monitoring results of the rock strata fractures.

[0178] Specifically, difference integration refers to a set of technical actions that perform mathematical processing on discrete data points to generate continuous, smooth representations.

[0179] Specifically, the continuous seepage velocity contour map is a visual graphic result generated after interpolation processing. It is a two-dimensional plan view or a three-dimensional spatial profile, in which different colors or shading are used to represent different ranges of seepage velocity values.

[0180] Specifically, the spatiotemporal distribution field refers to the spatiotemporal distribution field of the fracture seepage velocity, which is used as the input data for this step.

[0181] Specifically, a critical monitoring path is a spatial line segment or area of ​​key concern determined based on engineering judgment and geological analysis. It refers to one or more paths within the monitoring area that are specifically designated based on geological survey data, the importance of the engineering structure, or the results of previous data analysis, and which require detailed analysis of seepage dynamics.

[0182] Specifically, velocity profile data parameters are a one-dimensional or two-dimensional data sequence about seepage velocity extracted along a specific path.

[0183] Specifically, a dynamic evolution plot is a dynamic visualization, typically presented as an animation or a sequence of frame charts. It shows how velocity profile data along a key monitoring path changes over time.

[0184] Specifically, fusion annotation is an operational concept of graphic compositing and information enhancement. It refers to the process of overlaying and integrating different visualization results and related engineering information into a single comprehensive drawing or interactive visualization interface.

[0185] Specifically, the results of seepage velocity distribution monitoring are the final deliverable of all the technical steps in this method, and are the culmination of all the aforementioned data processing and graphical work.

[0186] Furthermore, firstly, one or more key monitoring paths are defined on a digital map or model of the monitoring area. Then, from the entire dataset of the spatiotemporal distribution field of fracture seepage velocity, all data points whose spatial locations fall on these paths are selected.

[0187] Next, these data points are organized into a series of time-sorted profile datasets. Finally, using a dynamic visualization tool, an animation is created with these profile datasets as input. In this animation, each frame displays the velocity distribution curve along the path at a specific moment. Frames are played consecutively, clearly showing the evolution of the velocity profile over time, thus generating a dynamic evolution map.

[0188] Furthermore, firstly, a continuous seepage velocity contour map is used as a base map. Then, the locations of key monitoring paths are prominently plotted on the base map. Next, a dynamic evolution diagram is attached to this path as an appendix or interactive component.

[0189] Then, add all necessary annotations in the appropriate places on the drawings: including but not limited to project name, monitoring time, velocity unit, legend, scale, description of velocity range corresponding to different colors / contour lines, and important geological or engineering structure boundaries.

[0190] Finally, all elements are logically and clearly integrated into a single drawing or electronic report page to form a standardized, complete, and directly usable engineering analysis and archiving result document for seepage velocity distribution monitoring.

[0191] like Figure 2 The diagram shown is a functional block diagram of a rock fracture seepage velocity detection system based on optical fiber sensing provided in an embodiment of the present invention.

[0192] The fiber optic sensing-based rock fracture seepage velocity detection system 100 described in this invention can be installed in an electronic device. Depending on the functions implemented, the cloud-based resource scheduling system 100 may include a fiber optic sensor network deployment module 101, a thermal pulse excitation generation module 102, a multi-physics field coupled response synchronous acquisition module 103, a multi-physics field coupled response decoupling analysis module 104, a seepage velocity quantitative inversion module 105, and a results output module 106. The module described in this invention can also be called a unit, referring to a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0193] In this embodiment, the functions of each module / unit are as follows:

[0194] The fiber optic sensor network deployment module 101 is used to construct the sensor detection network of the rock fracture based on the optical fiber and the associated heat source of the sensor monitoring network in the rock fracture.

[0195] The thermal pulse excitation generation module 102 is used to inject thermal pulses into the rock stratum fissures based on the sensing and detection network to obtain the thermal disturbance field of the rock stratum fissures.

[0196] The multi-physics field coupling response synchronous acquisition module 103 is used to thermally excite the rock layer fractures based on the thermal disturbance field and demodulate the sensing fiber optic signal of the rock layer fractures to obtain the multi-physics field coupling response data of the thermal disturbance field.

[0197] The multi-physics coupling response decoupling analysis module 104 is used to perform joint analysis on the multi-physics coupling response data based on a preset multi-physics coupling forward model, so as to obtain the seepage characteristic strain components of the rock fracture.

[0198] The seepage velocity quantitative inversion module 105 is used to perform velocity inversion on the seepage characteristic strain components based on the spatial distribution of the multi-physics field coupled forward model, so as to obtain the fracture seepage velocity of the rock layer fractures.

[0199] The output module 106 is used to perform differential integration on the seepage velocity of the fractures to generate the monitoring results of the seepage velocity distribution of the rock fractures. In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for example, the division of modules is only a logical functional division, and other division methods may exist in actual implementation.

[0200] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0201] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0202] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0203] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0204] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for detecting seepage velocity in rock fractures based on fiber optic sensing, characterized in that, The method includes: S1: Construct the sensing and detection network for the rock fractures based on optical fibers and an auxiliary heat source in the sensing and monitoring network for rock fractures; S2: Based on the sensor detection network, thermal pulses are injected into the rock strata fissures to obtain the thermal disturbance field of the rock strata fissures; S3: Based on the thermal disturbance field, thermally excite the rock stratum fissures and demodulate the sensing fiber optic signal of the rock stratum fissures to obtain the multi-physics field coupled response data of the thermal disturbance field; S4: Based on the preset multi-physics coupling forward model, the multi-physics coupling response data is jointly analyzed to obtain the seepage characteristic strain components of the rock fracture. S5: Based on the spatial distribution of the multi-physics field coupled forward model, the velocity inversion of the seepage characteristic strain components is performed to obtain the fracture seepage velocity of the rock layer fractures; S6: Perform differential integration on the seepage velocity of the fracture to generate the monitoring results of the seepage velocity distribution of the rock fracture.

2. The method for detecting seepage velocity in rock fractures based on fiber optic sensing as described in claim 1, characterized in that, The optical fiber and auxiliary heat source used in the rock fracture sensing and monitoring network constitute the rock fracture sensing and detection network, including: Based on distributed sensing optical fibers, the rock strata fissures are deployed and attached to obtain a preliminary sensing and detection network for the rock strata fissures. Stress coupling is applied to the preliminary sensing and detection network to construct the sensing and detection network for the rock strata fractures.

3. The method for detecting seepage velocity in rock fractures based on fiber optic sensing as described in claim 1, characterized in that, The step of injecting thermal pulses into the rock strata fissures based on the sensing network to obtain the thermal disturbance field of the rock strata fissures includes: Based on the sensing optical fiber in the sensing network, a rectangular thermal pulse is injected into the rock fissure, and a thermal disturbance field of the rock fissure is generated based on the thermal diffusion effect of the rectangular thermal pulse.

4. The method for detecting seepage velocity in rock fractures based on fiber optic sensing as described in claim 1, characterized in that, The process of thermally exciting the rock strata fractures based on the thermal disturbance field and demodulating the sensing fiber optic signal of the rock strata fractures to obtain multi-physics coupled response data of the thermal disturbance field includes: Based on the thermal pulse action phase and thermal relaxation monitoring phase of the thermal pulse, the Brillouin backscattering spectrum and Raman backscattering spectrum of the sampling points in the sensing fiber are collected simultaneously. The frequency shift of the Brillouin backscattering spectrum is calculated to generate the spatiotemporal distribution sequence of strain in the rock strata fractures; The intensity ratio of anti-Stokes light to Stokes light in the Raman backscattering spectrum is calculated to generate the spatiotemporal temperature distribution sequence of the rock strata fractures; The strain spatiotemporal distribution sequence and the temperature spatiotemporal distribution sequence are spatiotemporally aligned and integrated to generate the multiphysics coupled response data.

5. The method for detecting seepage velocity in rock fractures based on fiber optic sensing as described in claim 1, characterized in that, The method based on a preset multi-physics coupling forward model performs joint analysis on the multi-physics coupling response data to obtain the seepage characteristic strain components of the rock fractures, including: Establish the control relationship between the thermal disturbance field and the heat conduction, convection heat transfer and induced thermoelastic strain in the rock strata fractures; Based on the control relationship, a pure heat conduction extrapolation is performed on the rock strata fissures to obtain the theoretical thermal expansion strain distribution of the rock strata fissures; Based on the theoretical thermal expansion strain distribution, seepage-related strain anomalies in the rock strata fractures are identified to obtain the residual strain field of the rock strata fractures. The spatiotemporal evolution characteristics of the residual strain field are spatiotemporally superimposed with the temperature anomaly region in the multiphysics field coupled response data to obtain the seepage characteristic strain components of the rock fractures.

6. The method for detecting seepage velocity in rock fractures based on fiber optic sensing as described in claim 1, characterized in that, The spatial distribution based on the multi-physics coupled forward model is used to perform velocity inversion on the seepage characteristic strain components to obtain the fracture seepage velocity in the rock strata fractures, including: Based on the spatial distribution, theoretical thermal expansion strain distribution, and temperature spatiotemporal distribution sequence of the multiphysics coupled forward model, the velocity inversion of the seepage characteristic strain components is performed to obtain the fracture seepage velocity of the rock strata.

7. The method for detecting seepage velocity in rock fractures based on fiber optic sensing as described in claim 6, characterized in that, The formula for calculating the fissure seepage velocity in the rock strata is as follows: ； in, Indicates the optical fiber in the 1st... Axial position at the index The first of the rock strata fissures seepage time at the index For in position Index and time The seepage velocity at the index point is due to the fracture. The rate of change of the seepage characteristic strain component over time. The spatial gradient of the seepage characteristic strain component along the fiber axis. For the axial position is the first The multiphysics coupling response data mentioned in the index, The effective thermal expansion coefficient of the rock strata fracture is given. The equivalent thermal conductivity of the rock strata fracture is given. The width of the rock stratum fracture. The specific heat capacity of the fracture in the rock strata is given. This is the strain space gradient correction coefficient.

8. The method for detecting seepage velocity in rock fractures based on fiber optic sensing as described in claim 6, characterized in that, After obtaining the fracture seepage velocity in the rock strata fractures, the method further includes: Based on the spatiotemporal distribution field of the fissure seepage velocity, the theoretical temperature response of the sensing network is deduced. By comparing the theoretical temperature response with the temperature spatiotemporal distribution sequence, the temperature response difference index of the rock strata fracture is obtained. A threshold judgment is made on the temperature response difference index, and the strain space gradient correction coefficient is adjusted based on the threshold judgment.

9. The method for detecting seepage velocity in rock fractures based on fiber optic sensing as described in claim 1, characterized in that, The step of integrating the differential values ​​of the seepage velocities in the fractures to generate the monitoring results of the seepage velocity distribution in the rock fractures includes: The spatiotemporal distribution field of the fissure seepage velocity is integrated by difference to generate a continuous seepage velocity contour map of the rock strata fissures; Extract velocity profile data from key monitoring paths in the spatiotemporal distribution field to generate a dynamic evolution diagram of the rock strata fractures; The seepage velocity contour map and the dynamic evolution map are fused and labeled to generate the seepage velocity distribution monitoring results of the rock strata fractures.

10. A rock fracture seepage velocity detection system based on fiber optic sensing, used to implement the rock fracture seepage velocity detection method based on fiber optic sensing as described in any one of claims 1-9, characterized in that, The system includes: The fiber optic sensor network deployment module is used to construct a sensor detection network for the rock fractures based on the optical fiber and an auxiliary heat source of the sensor monitoring network in the rock fractures. A thermal pulse excitation generation module is used to inject thermal pulses into the rock strata fissures based on the sensing and detection network to obtain the thermal disturbance field of the rock strata fissures. A multi-physics field coupled response synchronous acquisition module is used to thermally excite the rock stratum fissures based on the thermal disturbance field, and demodulate the sensing fiber optic signal of the rock stratum fissures to obtain the multi-physics field coupled response data of the thermal disturbance field. The multi-physics coupling response decoupling analysis module is used to perform joint analysis on the multi-physics coupling response data based on a preset multi-physics coupling forward model, so as to obtain the seepage characteristic strain components of the rock fracture. The seepage velocity quantitative inversion module is used to perform velocity inversion on the seepage characteristic strain components based on the spatial distribution of the multi-physics field coupled forward model, so as to obtain the fracture seepage velocity of the rock layer fractures. The output module is used to perform differential integration of the seepage velocity in the fractures and generate the monitoring results of the seepage velocity distribution in the rock fractures.

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