Hard and brittle rock local instability identification method based on distributed optical fiber technology

Abstract

The invention provides a hard and brittle rock local instability identification method based on a distributed optical fiber technology, and relates to the technical field of rock instability identification. The method comprises the following steps: firstly, connecting a rock sample with a distributed optical fiber data monitoring system, carrying out data acquisition, carrying out noise reduction processing on the collected data, carrying out data correction according to a precision requirement, then carrying out post-processing on the data by selecting an analysis method based on statistics or a deformation localization analysis method based on coordinates, rock local instability identification is carried out through changes of corresponding indexes, and the rock deformation localization degree is continuously and rapidly increased to indicate the local instability of the rock.

Description

Technical Field

[0001] The present invention relates to the technical field of rock instability identification, and in particular to a method for identifying local instability of hard and brittle rocks based on distributed optical fiber technology. Background Art

[0002] During the construction of major deep-level projects in my country, hard and brittle rocks such as granite are often encountered, such as the granite tunnels on the Sichuan-Tibet Railway. During construction, the energy contained in the hard and brittle rocks can be suddenly released, causing engineering disasters such as rockbursts. Accurately predicting localized rock instability and failure is crucial to ensuring the safe construction of projects and the safety of workers and property. Traditional displacement sensors primarily rely on single-point displacement monitoring, and the prediction of localized rock instability through single-point displacement monitoring often relies on manual experience. When the rock deformation detected by the displacement sensor increases significantly, rock instability and failure may occur. However, the instability and failure of hard and brittle rock occurs through the accumulation and sudden release of elastic energy within the rock. Because hard rock generally has a high elastic modulus, the small deformations that occur during the accumulation of elastic energy are unlikely to provide sufficient early warning information. Furthermore, the heterogeneity of rock means that localized instability and deformation can occur at any location, making it difficult for conventional displacement sensors to monitor deformation over a large area. Furthermore, the failure of hard and brittle rocks is characterized by sudden energy release and instability. Once the deformation or stress of the rock reaches a critical level, the rock undergoes a sudden energy release and instability. This requires sensors with high-frequency dynamic monitoring capabilities. Traditional intermittent monitoring methods cannot achieve stable, long-term, high-frequency dynamic displacement monitoring of the rock. Distributed optical fiber, as a novel monitoring technology, allows contact strain measurement by attaching it to the measured object, offering advantages such as high accuracy and high frequency. Strategically deploying optical fibers allows for deformation monitoring over a large area. Acoustic emission, digital image processing, computed tomography, and polarizing microscopy have demonstrated that the deformation and failure of hard and brittle rocks are primarily controlled by internal crack propagation. Prior to peak strength, a distinct crack initiation-propagation-coalescing-into-macrocrack process occurs, accompanied by significant deformation localization. Therefore, with the rapid development of full-field deformation monitoring technology, capturing and quantitatively analyzing the localized deformation characteristics of rock has become an effective method for predicting localized rock instability. Summary of the Invention

[0003] In response to the shortcomings of the existing technology, the present invention provides a method for identifying local instability of hard and brittle rocks based on distributed optical fiber technology. Distributed optical fiber technology is applied to the identification of local instability of hard and brittle rocks. The full-field deformation data collected by the distributed optical fiber is post-processed to solve the problem that it is difficult to predict instability of hard and brittle rocks through deformation. This method realizes the prediction of instability and damage of test pieces through the small deformation data of the rock surface collected by optical fiber.

[0004] A method for identifying local instability of hard and brittle rocks based on distributed optical fiber technology comprises the following steps:

[0005] Step 1: Couple the rock sample with the distributed optical fiber;

[0006] Specifically, the rock sample is processed into a cylindrical shape, wherein the cross-sectional radius of the cylindrical rock sample is larger than the minimum curvature radius of the optical fiber;

[0007] A distributed optical fiber is wound on the surface of the rock sample. The winding methods of the distributed optical fiber include horizontal winding and spiral winding. The spirally wound distributed optical fiber forms a spiral optical fiber. The set prestress is maintained during the winding process. The distributed optical fiber is adhered to the rock surface using quick-drying glue as a coupling agent.

[0008] The tilt angle of the spiral fiber tends to 0, and the maximum spiral fiber tilt angle is less than where v is Poisson's ratio.

[0009] Step 2: Connect the rock sample to the distributed fiber optic data monitoring system for data acquisition;

[0010] The distributed optical fiber monitoring system includes an optical fiber, an optical fiber data demodulator and a computer host;

[0011] Step 2.1: Place the rock sample in a mechanical loading device, and lead one end of the distributed optical fiber out of the mechanical loading device as the input end for the optical signal. The other end of the distributed optical fiber serves as the output end of the optical signal, and the distributed optical fiber at the output end is knotted.

[0012] Step 2.2: Splice the inlet end with the optical fiber of the fiber optic data demodulator and use a laser pointer or fiber optic data demodulator to detect the distributed optical fiber.

[0013] Step 2.3: Set the data parameters of the distributed optical fiber monitoring system on the host computer, including the strain transfer coefficient and temperature transfer coefficient of the distributed optical fiber, the spatial resolution and temporal resolution of data acquisition;

[0014] Step 2.4: Detect the optical loss in the distributed optical fiber again and perform position calibration by pressing the distributed optical fiber to determine the starting and ending points of data collection.

[0015] Step 2.5: Mechanically preload the rock, and after the preload is completed, reset the axial force and displacement data in the mechanical loading device and the surface strain data collected by the distributed optical fiber;

[0016] Step 2.6: Perform mechanical loading to apply compressive force to the rock sample wrapped with the distributed optical fiber, and simultaneously start collecting axial stress, axial strain, and optical fiber strain data.

[0017] Step 3: Fiber strain data correction;

[0018] The fiber strain data is denoised. The noise types include values ​​exceeding the strain range and sudden changes in the data during continuous changes. The fiber strain data after denoising meets the requirements of continuous strain changes and has no sudden changes.

[0019] The inclination angle of the spiral fiber determines whether the fiber strain data obtained from the spiral fiber needs to be corrected. The judgment formula is:

[0020]

[0021] Among them, ε f is the strain data obtained from the helical fiber, θ is the fiber inclination angle, v is the Poisson's ratio, ε c is the hoop strain value of the specimen. When θ is less than 5°, the theoretical error between the optical fiber strain data and the true hoop strain value is less than 1%, and no correction is required.

[0022] If the direct error between the measured strain value caused by the helical fiber inclination and the true hoop strain value is greater than the set error, the hoop strain correction formula is used to calculate the corrected hoop strain distribution. The hoop strain correction formula is:

[0023]

[0024] Among them, θ1 and θ2 are the two inclination angles of the helical fiber, and ε1 and ε2 are the corresponding fiber strain values;

[0025] Step 4: Extract the precursory features of local rock instability and failure based on the optical fiber data deformation localization analysis method based on statistical distribution;

[0026] The hoop strain values ​​acquired by the distributed optical fiber are arranged in time series, with each time point as a group. The concentration of rock deformation at each time point is calculated using the discrete Gini coefficient calculation formula. When the strain value in a certain area within the monitoring range increases rapidly, the Gini coefficient increases, indicating that large deformation has occurred in that area, that is, deformation localization, which is a precursor to local failure.

[0027] The rapid increase in strain is determined by the following method: the strain data collected at two time points are normalized according to the following formula: the normalized strain result at the next time point is subtracted from the normalized strain result at the previous time point, and the monitoring range of the monitoring point with a positive subtraction is the area with increased strain value, i.e., ε t,i -ε t-1,i >0,ε t,i -ε t-1,i The larger it is, the faster the strain is considered to increase;

[0028]

[0029] Among them, ε i is the strain value of the i-th monitoring point, and σ are the mean and standard deviation of the data set, ε t,i is the standardized result of the i-th monitoring point at time point t;

[0030] The calculation formula of the Gini coefficient is as follows:

[0031]

[0032] Among them, G is the Gini coefficient, W i and P i Sort all optical fiber strain data from low to high, and calculate the proportion of the strain of the i-th monitoring point to the total strain value of the whole field, W i and the ratio of the area monitored by the i-th monitoring point to the total monitoring area P i , considering that the distributed optical fiber is a linear sensor, the monitoring length of each monitoring point is the same, so P i =1 / N, N is the number of measuring points within the monitoring range;

[0033] The value range of the Gini coefficient is (0,1). When the strain is evenly distributed at each monitoring point, G = 0. The more concentrated the high strain values ​​are, the higher the degree of deformation localization is, and the larger the Gini coefficient is.

[0034] For hard and brittle rocks, the abnormally high strain values ​​caused by surface cracks are captured to assist in identifying the precursors of local rock instability. The calculation formula of the skewness coefficient is as follows:

[0035]

[0036] S is the skewness coefficient, ε i is the strain value of each monitoring point, is the mean value of a set of strain data, and σ is the standard deviation of a set of data;

[0037] In the process of local failure prediction of hard and brittle rocks, the Gini coefficient and skewness coefficient joint determination method can be used to determine the simultaneous and continuous increase of the Gini coefficient and skewness coefficient as a precursor to rock instability and failure.

[0038] Step 5: Extract the precursory features of local rock instability and failure based on the optical fiber data deformation localization analysis method based on the monitoring point coordinates;

[0039] When the monitoring data corresponds one-to-one to the coordinates of the monitoring points, the coordinate-related characterization method can also be used to perform quantitative analysis of deformation localization:

[0040] First, the degree of spatial deformation localization is calculated using the global Moran index. When strain values ​​of similar magnitude cluster in space, the spatial correlation of monitoring points with similar strain values ​​increases, and the Moran index increases. Conversely, when the spatial correlation of monitoring points with similar strain values ​​weakens, the Moran index decreases.

[0041] The specific calculation formula of the Moran Index is as follows:

[0042]

[0043] I is the Moran index, N is the number of monitoring points in the considered spatial range, X i and is the strain value of the i-th monitoring point and the average strain value of N monitoring points, w ij is the weight matrix;

[0044] The Moran index ranges from (-1, 1). When the high strain value monitoring points are clustered in space, the Moran index is positive and increases continuously, showing the characteristics of strain localization.

[0045] When hard and brittle rock is close to instability and failure, the deformation near the crack increases significantly, showing a localized deformation feature. When the Moran index begins to increase continuously, it proves that the rock begins to have obvious localized deformation.

[0046] The beneficial effects of adopting the above technical solution are:

[0047] The present invention provides a method for identifying local instability of hard and brittle rocks based on distributed optical fiber technology, which has the following beneficial effects:

[0048] 1. A statistically based deformation localization quantification method is provided, which solves the problem that traditional methods must rely on coordinates to evaluate the degree of deformation localization;

[0049] 2. A method for quantitatively predicting local instability of hard and brittle rocks based on deformation localization is provided. Compared with traditional methods that can only judge rock damage and predict failure based on axial, circumferential and volumetric stress-strain curves, the method proposed in this invention fully considers the heterogeneous deformation during the rock compression failure process and is applicable to data obtained by full-field deformation monitoring means such as distributed optical fiber. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is an overall flow chart of the method for identifying local instability of hard and brittle rocks in an embodiment of the present invention;

[0051] Figure 2 A distributed optical fiber data monitoring system and optical fiber layout path during a triaxial high-voltage test in an embodiment of the present invention;

[0052] Figure 3 The results of the Gini coefficient and skewness coefficient analysis during the graded creep of granite under a confining pressure of 60 MPa in the embodiment of the present invention are as follows;

[0053] Figure 4 Graph showing the normalized probability distribution of creep strains obtained at all monitoring points under different stress levels in an embodiment of the present invention;

[0054] Figure 5 The Moran index analysis results of the granite graded creep process under 60 MPa confining pressure in the embodiment of the present invention are shown;

[0055] Figure 6 The visualization results of strain distribution under different stresses in the embodiment of the present invention are shown;

[0056] Among them, (a) - rock surface strain distribution at 26% peak stress in the early loading stage; (b) - rock surface strain distribution at the mid-loading cracking stress (60% peak stress); (c) - rock surface strain distribution at 98% peak stress in the late loading stage. DETAILED DESCRIPTION

[0057] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0058] A method for identifying local instability of hard and brittle rocks based on distributed optical fiber technology, such as Figure 1 As shown, the following steps are included:

[0059] Step 1: Couple the rock sample with the distributed optical fiber;

[0060] Coupled rock and distributed fiber optic monitoring system:

[0061] Specifically, the rock sample is processed into a standard cylindrical shape, wherein the cross-sectional radius of the cylindrical rock sample is larger than the minimum curvature radius of the optical fiber to ensure that the optical fiber can normally transmit the optical signal;

[0062] Densely wind distributed optical fibers on the surface of the rock sample to obtain as much rock surface deformation data as possible. Distributed optical fiber winding methods include horizontal winding and spiral winding. Spirally wound distributed optical fibers form spiral fibers. During the winding process, a set prestress is maintained to ensure full contact between the distributed optical fibers and the measured rock surface. High-elastic modulus quick-drying adhesive is used as a coupling agent to adhere the distributed optical fibers to the rock surface. Ensure that the optical fibers are not damaged during the pasting process.

[0063] The tilt angle of the spiral fiber tends to 0 to keep the fiber in tension. The maximum spiral fiber tilt angle should be less than where v is Poisson's ratio.

[0064] Step 2: Connect the rock sample to the distributed fiber optic data monitoring system for data acquisition;

[0065] The distributed optical fiber monitoring system includes an optical fiber data demodulator and a computer host; in this embodiment, Figure 2 As shown, the distributed optical fiber selects G652b single-mode optical fiber, and the surface coating layer of the distributed optical fiber should be as thin as possible to reduce strain hysteresis and obtain more accurate strain data;

[0066] Step 2.1: Place the rock sample in a mechanical loading device and lead one end of the distributed optical fiber out of the mechanical loading device as the input end for the optical signal. The other end of the distributed optical fiber serves as the output end for the optical signal. Tie a knot at the output end of the distributed optical fiber to minimize the impact of optical loss at the fiber break on the test results.

[0067] Step 2.2: Splice the fiber at the inlet end with the fiber optic data demodulator to ensure that the optical loss is less than 0.01dB. Use a laser pointer or fiber optic data demodulator to inspect the distributed optical fiber to ensure that the optical fiber can transmit signals normally.

[0068] Step 2.3: Set the data parameters of the distributed fiber optic monitoring system on the host computer, including the strain transfer coefficient and temperature transfer coefficient of the distributed fiber, the spatial resolution and temporal resolution of the data acquisition; the equipment used in this invention is based on Optical Frequency Domain Reflectometry (OFDR), and the data sampling accuracy is 1με;

[0069] Step 2.4: Check the optical loss in the distributed optical fiber again to ensure that the distributed optical fiber can transmit the strain signal normally. Press the distributed optical fiber to perform position calibration and determine the starting and ending points of data collection.

[0070] Step 2.5: Mechanically preload the rock, and after the preload is completed, reset the axial force and displacement data in the mechanical loading device and the surface strain data collected by the distributed optical fiber;

[0071] Step 2.6: Perform mechanical loading to apply compressive force to the rock sample wrapped with the distributed optical fiber, and simultaneously start collecting axial stress, axial strain, and optical fiber strain data.

[0072] Step 3: Fiber strain data correction;

[0073] The fiber optic strain data is subjected to data denoising. The noise types included include values ​​exceeding the strain range and sudden changes in the data during continuous changes. The denoised fiber optic strain data meets the requirements of continuous strain changes and no sudden changes. The data must be completely denoised, as excessive or insufficient noise data will seriously affect the results of subsequent rock deformation localization analysis.

[0074] According to the inclination angle of the spiral fiber, it is determined whether the fiber strain data obtained by the spiral fiber needs to be corrected to obtain a more accurate hoop strain. The judgment formula is:

[0075]

[0076] Among them, ε f is the strain data obtained from the helical fiber, θ is the fiber inclination angle, v is the Poisson's ratio, ε c is the hoop strain value of the specimen. When θ is less than 5°, the theoretical error between the optical fiber strain data and the true hoop strain value is less than 1%, and no correction is required.

[0077] If the direct error between the measured strain value caused by the helical fiber inclination and the true hoop strain value is greater than the set error, the hoop strain correction formula is used to calculate the corrected hoop strain distribution. The hoop strain correction formula is:

[0078]

[0079] Among them, θ1 and θ2 are the two inclination angles of the helical fiber, and ε1 and ε2 are the corresponding fiber strain values. For the non-uniform deformation field of rock, it is best to use the average strain value of the adjacent area when correcting the strain in the above formula.

[0080] Step 4: Extract the precursory features of local rock instability and failure based on the optical fiber data deformation localization analysis method based on statistical distribution;

[0081] The hoop strain values ​​acquired by the distributed optical fiber are arranged in time series, with each time point as a group. The concentration of rock deformation at each time point is calculated using the discrete Gini coefficient formula. The Gini coefficient reflects the distribution characteristics of the data. When the strain value in a certain area within the monitoring range increases rapidly, the Gini coefficient increases, indicating that large deformation has occurred in that area, that is, deformation localization, which is a precursor to local failure.

[0082] The rapid increase is determined by the following method: the strain data collected at two time points are normalized according to the following formula: the normalized strain result of the later time point is subtracted from the normalized strain result of the previous time point at the same monitoring point, and the monitoring range of the monitoring point with a positive subtraction is the area with increased strain value, that is, ε t,i -ε t-1,i >0,ε t,i-ε t-1,i The larger it is, the faster the strain is considered to increase;

[0083]

[0084] Among them, ε i is the strain value of the i-th monitoring point, and σ are the mean and standard deviation of the data set, ε t,i is the standardized result of the i-th monitoring point at time point t;

[0085] The calculation formula of the Gini coefficient is as follows:

[0086]

[0087] Among them, G is the Gini coefficient, W i and P i Sort all optical fiber strain data from low to high, and calculate the proportion of the strain of the i-th monitoring point to the total strain value of the whole field, W i and the ratio of the area monitored by the i-th monitoring point to the total monitoring area P i , considering that the distributed optical fiber is a linear sensor, the monitoring length of each monitoring point is the same, so P i =1 / N, N is the number of measuring points within the monitoring range;

[0088] The value range of the Gini coefficient is (0,1). When the strain is evenly distributed at each monitoring point, G = 0. The more concentrated the high strain values ​​are, the higher the degree of deformation localization is, and the larger the Gini coefficient is.

[0089] It should be noted that the Gini coefficient measures the relative difference in strain distribution. If the strain data of each monitoring point doubles, although the absolute value of the deformation increase at the relatively weak position of the rock mass is much larger than that at the relatively hard position of the rock mass, because the increase ratio of each position is the same, the relative difference remains the same, and the calculated Gini coefficient is also the same. Only when an abnormally large deformation occurs at a certain position will the Gini coefficient continue to increase. In this embodiment, Figure 3 As shown;

[0090] For hard and brittle rocks, progressive failure of rocks is caused by the initiation, expansion, and penetration of cracks. Abnormally high strain values ​​caused by surface cracks can be used to assist in identifying the precursors of local rock instability. Abnormally high strain values ​​can lead to severe uneven data distribution. This non-uniform strain data distribution caused by abnormally high strain values ​​can be identified by the skewness coefficient. The skewness coefficient is calculated as follows:

[0091]

[0092] S is the skewness coefficient, εi is the strain value of each monitoring point, is the mean value of a set of strain data, and σ is the standard deviation of a set of data;

[0093] The skewness coefficient measures the degree of skew distribution of data. When the data presents a symmetrical normal distribution, the skewness coefficient is close to 0. When there are a few large strain values, the right tail of the probability distribution function curve of the data is very long, that is, it presents a positive skewed distribution. The longer and thicker the right tail is, the larger the skewness coefficient is. In this embodiment, Figure 4 As shown, the calculated result of the skewness coefficient can be positive or negative. When the distribution is positively skewed, the skewness coefficient is positive.

[0094] When a crack appears at the optical fiber monitoring position, the abnormally high strain value causes the data probability density curve to show an obvious positive skewed distribution. The positive skewed distribution becomes more obvious as the crack expands, and the skewness coefficient increases continuously.

[0095] In the process of local failure prediction of hard and brittle rocks, the Gini coefficient and skewness coefficient are jointly determined to avoid the error caused by a single calculation method. The simultaneous and continuous increase of the Gini coefficient and skewness coefficient is considered as a precursor to rock instability and failure.

[0096] Step 5: Extract the precursory features of local rock instability and failure based on the optical fiber data deformation localization analysis method based on the monitoring point coordinates;

[0097] The above-mentioned method of using the Gini coefficient and skewness coefficient to characterize strain localization is applicable to complex situations where the coordinates of the monitoring points cannot be determined. When the monitoring data correspond to the coordinates of the monitoring points one-to-one, the characterization method related to the coordinates can also be used to perform quantitative analysis of deformation localization:

[0098] First, the degree of spatial deformation localization is calculated using the global Moran index. When strain values ​​of similar magnitude cluster in space, the spatial correlation of monitoring points with similar strain values ​​increases, and the Moran index increases. Conversely, when the spatial correlation of monitoring points with similar strain values ​​weakens, the Moran index decreases.

[0099] The specific calculation formula of the Moran Index is as follows:

[0100]

[0101] I is the Moran index, N is the number of monitoring points in the considered spatial range, X i and is the strain value of the i-th monitoring point and the average strain value of N monitoring points, w ijThe weight matrix is ​​selected according to the needs, such as the weight is inversely proportional to the Euclidean distance from the strain center point, or a binary weight matrix is ​​adopted, that is, the weight value within a certain threshold range from the center point is 1, and the weight value outside the range is 0. The distance threshold range can be adjusted according to different site conditions;

[0102] The Moran index ranges from (-1, 1). When the high strain value monitoring points are clustered in space, the Moran index is positive and increases continuously, showing the strain localization feature. In this embodiment, Figure 5 As shown in Figure 3, the global Moran index takes into account the distribution of monitoring points with high and low strain values. The aggregation of monitoring points with low strain values ​​will also increase the Moran index.

[0103] When hard and brittle rocks are close to failure, the deformation near the crack increases significantly, showing localized deformation. When the Moran index begins to increase continuously, it indicates that the rock has begun to have obvious localized deformation. It should be noted that the Moran index is also affected by the distribution of low strain values. When low strain values ​​are widely dispersed and high strain values ​​are highly concentrated, the Moran index may decrease slightly.

[0104] It is worth noting that the analysis method provided above is applicable to the strain localization analysis of different rocks. However, only hard and brittle rocks with strong correlation between deformation and cracks will show obvious strain localization phenomenon before failure due to the generation and expansion of macro cracks. The deformation localization characteristics of other types of rocks before failure may be different. For example, marble shows obvious strain delocalization characteristics before instability failure. After detailed research, the above analysis method can also be applied to other rocks for failure precursor identification. In this embodiment, if Figure 6 The following are visualization results of strain distribution under different stresses, where (a) is the strain distribution on the rock surface at 26% of the peak stress in the early stage of loading, (b) is the strain distribution on the rock surface at the cracking stress (60% of the peak stress) in the middle stage of loading, and (c) is the strain distribution on the rock surface at 98% of the peak stress in the late stage of loading.

[0105] The above description is merely a preferred embodiment of the present disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also encompass other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned inventive concept. For example, a technical solution formed by mutually replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A method for identifying local instability of hard and brittle rocks based on distributed optical fiber technology, characterized in that: The following steps are involved: Step 1: Couple the rock sample with the distributed optical fiber; Step 2: Connect the rock sample to the distributed fiber optic data monitoring system for data acquisition; The distributed optical fiber monitoring system includes an optical fiber, an optical fiber data demodulator and a computer host; Step 3: Fiber strain data correction; The fiber strain data is denoised. The noise types include values ​​exceeding the strain range and sudden changes in the data during continuous changes. The fiber strain data after denoising meets the requirements of continuous strain changes and has no sudden changes. Step 4: Extract the precursory features of local rock instability and failure based on the optical fiber data deformation localization analysis method based on statistical distribution; Step 5: Extract the precursory features of local rock instability and failure based on the optical fiber data deformation localization analysis method of the monitoring point coordinates.

2. The method for identifying local instability of hard and brittle rocks based on distributed optical fiber technology according to claim 1 is characterized in that: The step 1 specifically comprises: processing the rock sample into a cylindrical shape, wherein the cross-sectional radius of the cylindrical rock sample is greater than the minimum curvature radius of the optical fiber; A distributed optical fiber is wound on the surface of the rock sample. The winding methods of the distributed optical fiber include horizontal winding and spiral winding. The spirally wound distributed optical fiber forms a spiral optical fiber. The set prestress is maintained during the winding process. Quick-drying glue is used as a coupling agent to stick the distributed optical fiber to the rock surface.

3. The method for identifying local instability of hard and brittle rocks based on distributed optical fiber technology according to claim 2, characterized in that: The tilt angle of the spiral fiber tends to 0, and the maximum spiral fiber tilt angle is less than where v is Poisson's ratio.

4. The method for identifying local instability of hard and brittle rocks based on distributed optical fiber technology according to claim 1 is characterized in that: The step 2 specifically includes the following steps: Step 2.1: Place the rock sample in a mechanical loading device, and lead one end of the distributed optical fiber out of the mechanical loading device as the input end for the optical signal. The other end of the distributed optical fiber serves as the output end of the optical signal, and the distributed optical fiber at the output end is knotted. Step 2.2: Splice the inlet end with the optical fiber of the fiber optic data demodulator and use a laser pointer or fiber optic data demodulator to detect the distributed optical fiber. Step 2.3: Set the data parameters of the distributed optical fiber monitoring system on the host computer, including the strain transfer coefficient and temperature transfer coefficient of the distributed optical fiber, the spatial resolution and temporal resolution of data acquisition; Step 2.4: Detect the optical loss in the distributed optical fiber again and perform position calibration by pressing the distributed optical fiber to determine the starting and ending points of data collection. Step 2.5: Mechanically preload the rock, and after the preload is completed, reset the axial force and displacement data in the mechanical loading device and the surface strain data collected by the distributed optical fiber; Step 2.6: Perform mechanical loading to apply compressive force to the rock sample wrapped with the distributed optical fiber, and simultaneously start collecting axial stress, axial strain, and optical fiber strain data.

5. The method for identifying local instability of hard and brittle rocks based on distributed optical fiber technology according to claim 1 is characterized in that: The step 3 is specifically as follows: The inclination angle of the spiral fiber determines whether the fiber strain data obtained from the spiral fiber needs to be corrected. The judgment formula is: Among them, ε f is the strain data obtained from the helical fiber, θ is the fiber inclination angle, v is the Poisson's ratio, ε c is the hoop strain value of the specimen. When θ is less than 5°, the theoretical error between the optical fiber strain data and the true hoop strain value is less than 1%, and no correction is required. If the direct error between the measured strain value caused by the helical fiber inclination and the true hoop strain value is greater than the set error, the hoop strain correction formula is used to calculate the corrected hoop strain distribution. The hoop strain correction formula is: Among them, θ1 and θ2 are the two inclination angles of the helical fiber, and ε1 and ε2 are the corresponding fiber strain values.

6. The method for identifying local instability of hard and brittle rocks based on distributed optical fiber technology according to claim 1, characterized in that: Specifically, step 4 comprises: arranging the hoop strain values ​​acquired by the distributed optical fiber in a time series, with each time point as a group, and calculating the concentration of rock deformation at each time point according to the calculation formula of the discrete Gini coefficient. When the strain value in a certain area within the monitoring range increases rapidly, the Gini coefficient increases, indicating that a large deformation has occurred in the area, i.e., deformation localization, which is a precursor to local failure; The rapid increase in strain is determined by the following method: the strain data collected at two time points are normalized according to the following formula: the normalized strain result at the next time point is subtracted from the normalized strain result at the previous time point, and the monitoring range of the monitoring point with a positive subtraction is the area with increased strain value, i.e., ε t,i -ε t-1,i >0,ε t,i -ε t-1,i The larger it is, the faster the strain is considered to increase; Among them, ε i is the strain value of the i-th monitoring point, and σ are the mean and standard deviation of the data set, ε t,i is the standardized result of the i-th monitoring point at time point t; The calculation formula of the Gini coefficient is as follows: Among them, G is the Gini coefficient, W i and P i Sort all optical fiber strain data from low to high, and calculate the proportion of the strain of the i-th monitoring point to the total strain value of the whole field, W i and the ratio of the area monitored by the i-th monitoring point to the total monitoring area P i , considering that the distributed optical fiber is a linear sensor, the monitoring length of each monitoring point is the same, so P i =1 / N, N is the number of measuring points within the monitoring range; The value range of the Gini coefficient is (0,1). When the strain is evenly distributed at each monitoring point, G = 0. The more concentrated the high strain values ​​are, the higher the degree of deformation localization is, and the larger the Gini coefficient is. For hard and brittle rocks, the abnormally high strain values ​​caused by surface cracks are captured to assist in identifying the precursors of local rock instability. The calculation formula of the skewness coefficient is as follows: S is the skewness coefficient, ε i is the strain value of each monitoring point, is the mean value of a set of strain data, and σ is the standard deviation of a set of data; In the process of local failure prediction of hard and brittle rocks, the Gini coefficient and skewness coefficient joint determination method is adopted, and the simultaneous and continuous increase of the Gini coefficient and the skewness coefficient is determined as a precursor to rock instability and failure.

7. The method for identifying local instability of hard and brittle rocks based on distributed optical fiber technology according to claim 1, characterized in that: The step 5 is specifically as follows: when the monitoring data corresponds to the coordinates of the monitoring points one by one, a coordinate-related characterization method can also be used to perform localized quantitative analysis of deformation: First, the degree of spatial deformation localization is calculated using the global Moran index. When strain values ​​of similar magnitude cluster in space, the spatial correlation of monitoring points with similar strain values ​​increases, and the Moran index increases. Conversely, when the spatial correlation of monitoring points with similar strain values ​​weakens, the Moran index decreases. The specific calculation formula of the Moran Index is as follows: I is the Moran index, N is the number of monitoring points in the considered spatial range, X i and is the strain value of the i-th monitoring point and the average strain value of N monitoring points, w ij is the weight matrix; The Moran index ranges from (-1, 1). When the high strain value monitoring points are clustered in space, the Moran index is positive and increases continuously, showing the characteristics of strain localization. When hard and brittle rock is close to instability and failure, the deformation near the crack increases significantly, showing a localized deformation feature. When the Moran index begins to increase continuously, it proves that the rock begins to have obvious localized deformation.

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