In-situ rock mechanical parameter detection method, device and system

Abstract

The invention provides an in-situ rock mechanical parameter detection method, device and system.The in-situ rock mechanical parameter detection method comprises the steps that an in-situ test point of a to-be-detected rock is determined, and the to-be-detected rock is preprocessed; performing in-situ acoustic detection, in-situ optical detection and in-situ electromagnetic inversion detection on the in-situ test point of the rock to be detected to obtain detection data; the detection data comprises an ultrasonic signal, a hyperspectral image and electromagnetic data; performing deep fusion processing on the detection data to obtain fused data; and calling a pre-trained multi-physics field coupling model, carrying out real-time comprehensive analysis on the fusion data, and determining mechanical parameters of the rock to be tested. By means of the method, information loss caused by sample disturbance or single testing means in a traditional method is avoided, mutual influence and correlation of different physical quantities in the in-situ environment are fully considered, and the problem that an existing rock mechanical parameter detection method is single is solved.

Description

Technical Field

[0001] This invention relates to the field of rock mechanics testing technology, and in particular to an in-situ rock mechanics parameter detection method, device and system. Background Technology

[0002] In numerous rock-related engineering practices, the accurate acquisition of in-situ rock mechanical parameters is crucial. Traditional in-situ testing methods often rely on a single technique, such as using a borehole modulus gauge to measure the rock's elastic modulus. While these methods can obtain rock mechanical properties to some extent, they also have many limitations.

[0003] For example, single ultrasonic in-situ detection is insufficient to comprehensively and accurately depict the complex pore network and microfracture distribution within rocks, especially for rocks with low porosity and fine internal structures, where its detection resolution is inadequate. Simple optical in-situ observation, such as using a conventional microscope to observe the surface of rock outcrops, can present the surface morphology but cannot provide in-depth insights into the potential impact of internal mineral composition and water content on mechanical properties. Electromagnetic-based in-situ measurements, if used alone, are lacking in acquiring information on the dynamic changes in the rock surface strain field and struggle to establish a close and accurate correlation between mineral composition and rock mechanical parameters. These limitations result in incomplete and inaccurate in-situ rock mechanical parameters obtained by traditional methods, failing to meet the urgent need for high-precision parameters in various rock engineering projects under complex geological conditions, and hindering the safe and efficient advancement of these projects.

[0004] There is currently no effective solution to the problem that existing methods for detecting rock mechanical parameters are relatively limited. Summary of the Invention

[0005] This invention provides an in-situ rock mechanical parameter detection method, device, and system to address the shortcomings of existing rock mechanical parameter detection methods, which are relatively singular. It enables the comprehensive, accurate, and real-time acquisition of rock mechanical parameters without damaging the in-situ state of the rock, thus overcoming the inherent limitations of traditional single methods.

[0006] In a first aspect, the present invention provides an in-situ method for detecting rock mechanical parameters, comprising: Determine the in-situ test points of the rock to be tested, and pre-treat the rock to be tested; In-situ acoustic wave detection, in-situ optical detection, and in-situ electromagnetic inversion detection are performed on the in-situ test points of the rock to be tested to obtain detection data; the detection data includes ultrasonic signals, hyperspectral images, and electromagnetic data; The detection data is subjected to deep fusion processing to obtain fused data; The pre-trained multiphysics coupling model is invoked to perform real-time comprehensive analysis on the fused data to determine the mechanical parameters of the rock under test.

[0007] According to the present invention, an in-situ rock mechanical parameter detection method is provided, which determines the in-situ test points of the rock to be tested and preprocesses the rock to be tested, including: Based on the engineering requirements and geological conditions of the area where the rock to be tested is located, in-situ test points are determined; The in-situ test points of the rock to be tested are cleaned; For test scenarios that require drilling, drill holes at the in-situ test points of the delay to be tested, and then clean the area.

[0008] According to the present invention, an in-situ rock mechanical parameter detection method is provided, which involves in-situ acoustic wave detection at in-situ test points of the rock to be tested, including: Ultrasonic waves are emitted according to a preset frequency sequence to detect the in-situ test points of the rock to be tested, and the reflected and transmitted ultrasonic signals are received in real time. The ultrasonic signal is analyzed in real time to determine the internal structure of the rock to be tested.

[0009] According to the present invention, an in-situ rock mechanical parameter detection method is provided, which involves in-situ optical detection of in-situ test points of the rock under test, including: Hyperspectral imaging was performed on the in-situ test points of the rock to be tested to obtain hyperspectral images; The hyperspectral image is processed in real time to extract the spectral features of the rock surface under test, and the morphology of the rock surface and the initial state of the strain field are analyzed.

[0010] According to the present invention, an in-situ rock mechanical parameter detection method is provided, which involves electromagnetic inversion detection of in-situ test points of the rock to be tested, including: Electromagnetic detection was performed on the in-situ test points of the rock to be tested to obtain the microwave dielectric and resistivity of the rock to be tested. Based on the microwave dielectric and resistivity of the rock under test, and combined with a pre-set inversion algorithm, the internal mineral composition and water content of the rock under test are determined.

[0011] According to the in-situ rock mechanical parameter detection method provided by the present invention, training the multiphysics coupling model includes: Obtain rock samples with known mechanical parameters; In-situ acoustic wave detection, in-situ optical detection, and in-situ electromagnetic inversion detection were performed on the rock sample to obtain sample data; A mapping relationship between mechanical parameters and sample data is established for the multiphysics coupling model, and parameter fine-tuning is performed with the aim of minimizing the model loss of the multiphysics coupling model.

[0012] A method for detecting in-situ rock mechanical parameters according to the present invention includes: The rock under test is continuously monitored, and the dynamic changes of its mechanical parameters in the time and space dimensions are continuously tracked to capture abnormal changes in the state of the rock under test.

[0013] A method for detecting in-situ rock mechanical parameters according to the present invention includes: The multiphysics coupling model is periodically calibrated using rock samples with known mechanical parameters. The test results of the multiphysics coupling model are cross-validated by combining other in-situ testing methods, and the model parameters of the multiphysics coupling model are optimized and adjusted based on the validation results; other in-situ testing methods include standard penetration test and static cone penetration test.

[0014] Secondly, the present invention also provides an in-situ rock mechanical parameter detection device, comprising: The processing module is used to determine the in-situ test points of the rock to be tested and to preprocess the rock to be tested. The detection module is used to perform in-situ acoustic wave detection, in-situ optical detection, and in-situ electromagnetic inversion detection on the in-situ test points of the rock to be tested, and to acquire detection data; the detection data includes ultrasonic signals, hyperspectral images, and electromagnetic data; The fusion module is used to perform deep fusion processing on the detection data to obtain fused data; The prediction module is used to call a pre-trained multiphysics coupling model to perform real-time comprehensive analysis of the fused data and determine the mechanical parameters of the rock to be tested.

[0015] Thirdly, the present invention also provides an in-situ rock mechanical parameter detection system, comprising: The in-situ acoustic wave detection subsystem is used to perform in-situ acoustic wave detection on the in-situ test points of the rock to be tested. An in-situ optical characterization subsystem is used to perform in-situ optical detection on in-situ test points of the rock to be tested. The in-situ electromagnetic inversion subsystem is used to perform electromagnetic inversion detection on the in-situ test points of the rock to be tested. The data processing and multiphysics coupling analysis platform is used to perform deep fusion processing on the detection data to obtain fused data, and to call a pre-trained multiphysics coupling model to perform real-time comprehensive analysis on the fused data to determine the mechanical parameters of the rock to be tested.

[0016] In a fourth aspect, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the in-situ rock mechanical parameter detection method as described in the first aspect above.

[0017] Fifthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the in-situ rock mechanical parameter detection method as described in the first aspect above.

[0018] In a sixth aspect, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the in-situ rock mechanical parameter detection method as described in the first aspect above.

[0019] Compared with the prior art, the present invention has the following beneficial effects: The in-situ rock mechanical parameter detection method provided by this invention utilizes acoustic-optical-electrical fusion technology to simultaneously acquire information on the internal structure, surface characteristics, mineral composition, and water content of the rock under test in its in-situ state. This comprehensively reflects the overall characteristics of the rock and provides a rich and accurate data foundation for the accurate calculation of its mechanical parameters, avoiding information loss caused by sample disturbance or limited testing methods in traditional methods. Furthermore, by analyzing the fused data through a multiphysics coupling model, the method fully considers the mutual influence and correlation between different physical quantities in the in-situ environment, thus solving the problem of the relatively limited scope of existing rock mechanical parameter detection methods. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 This is a flowchart of the in-situ rock mechanical parameter detection method provided by the present invention; Figure 2 This is a schematic diagram illustrating the process of mechanical parameter detection in an embodiment of the present invention; Figure 3 This is a structural block diagram of the in-situ rock mechanical parameter detection device provided by the present invention; Figure 4 This is a schematic diagram of the structure of the in-situ rock mechanical parameter detection system provided by the present invention; Figure 5 This is a schematic diagram of mechanical parameter testing in Example 1 of the present invention; Figure 6 This is a schematic diagram of mechanical parameter testing in Example 2 of the present invention; Figure 7 This is a schematic diagram of the structure of the electronic device provided by the present invention.

[0022] Figure label: 1: Data processing and multiphysics coupling analysis platform; 2: In-situ acoustic wave detection subsystem; 3: In-situ optical characterization subsystem; 4: In-situ electromagnetic inversion subsystem. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0024] This invention provides an in-situ method for detecting rock mechanical parameters. Figure 1 This is a flowchart of the in-situ rock mechanical parameter detection method provided by the present invention, as follows: Figure 1 As shown, the method includes the following steps: Step S101: Determine the in-situ test points of the rock to be tested and pre-process the rock to be tested; Step S102: Perform in-situ acoustic wave detection, in-situ optical detection, and in-situ electromagnetic inversion detection on the in-situ test points of the rock to be tested to obtain detection data; the detection data includes ultrasonic signals, hyperspectral images, and electromagnetic data; Step S103: Perform deep fusion processing on the detection data to obtain fused data; Step S104: Call the pre-trained multiphysics coupling model to perform real-time comprehensive analysis of the fused data and determine the mechanical parameters of the rock to be tested.

[0025] In this method, firstly, in-situ test points of the rock to be tested are determined, and the rock is pre-processed to facilitate a better connection between the rock and the testing equipment, thereby improving the detection accuracy. Then, in-situ acoustic wave detection, in-situ optical detection, and in-situ electromagnetic inversion detection are performed on the in-situ test points of the rock to acquire detection data. Next, the detection data acquired from different detection processes are deeply fused to obtain fused data with broader coverage. Finally, the fused data is analyzed using a multiphysics coupling model to obtain the mechanical parameters of the rock to be tested, such as elastic modulus, Poisson's ratio, compressive strength, and shear strength. In the above process, through acoustic-optical-electrical fusion technology, information on the internal structure, surface characteristics, mineral composition, and water content of the rock to be tested can be simultaneously acquired in its in-situ state, comprehensively reflecting the overall characteristics of the rock. This provides a rich and accurate data foundation for accurately calculating the mechanical parameters of the rock, avoiding the information loss caused by sample disturbance or single testing methods in traditional methods. Moreover, by analyzing the fused data through a multiphysics coupling model, the mutual influence and correlation between different physical quantities in the in-situ environment are fully considered, which solves the problem that the existing rock mechanics parameter detection methods are relatively simple.

[0026] Figure 2 This is a schematic diagram illustrating the process of mechanical parameter detection in an embodiment of the present invention, as shown below. Figure 2 As shown, in some embodiments, step S101 involves determining the in-situ test points of the rock to be tested and preprocessing the rock to be tested, including: determining the in-situ test points based on the engineering requirements and geological conditions of the area where the rock to be tested is located; cleaning the in-situ test points of the rock to be tested; and for test scenarios that require drilling, drilling is performed at the in-situ test points of the time delay to be tested, followed by cleaning.

[0027] For example, in the area of ​​rock to be tested, in-situ test points are reasonably selected according to engineering requirements and geological conditions. The surface of the in-situ test points is cleaned of loose soil, impurities, etc., to ensure good contact between the testing equipment and the surface of the rock. For test scenarios requiring drilling, professional drilling equipment is used to drill holes of appropriate depth and diameter, and the holes are cleaned and pre-treated to ensure the installation and measurement accuracy of the testing equipment.

[0028] In some embodiments, step S102, in-situ acoustic detection of the in-situ test point of the rock to be tested, includes: transmitting ultrasonic waves according to a preset frequency sequence to detect the in-situ test point of the rock to be tested, and receiving the reflected and transmitted ultrasonic signals in real time; performing real-time preliminary analysis of the ultrasonic signals to determine the internal structure of the rock to be tested.

[0029] For example, the testing equipment is installed on the surface of the rock to be tested through drilling coupling or surface bonding to ensure good signal transmission. Ultrasonic waves are emitted according to a preset frequency sequence, and reflected and transmitted ultrasonic signals are received in real time. At the same time, the acoustic emission sensor is activated to continuously monitor the acoustic emission signals of the rock under test in its natural state and during possible subsequent loading processes. The collected acoustic wave data is analyzed in real time to quickly obtain general information about the internal structure of the rock under test, such as the preliminary determination of the location and size of cracks and pores.

[0030] In some embodiments, step S102, performing in-situ optical detection on the in-situ test points of the rock to be tested, includes: performing hyperspectral imaging on the in-situ test points of the rock to be tested to obtain a hyperspectral image; performing real-time processing on the hyperspectral image to extract the spectral features of the surface of the rock to be tested, and analyzing the surface morphology and initial state of the strain field of the rock to be tested.

[0031] For example, hyperspectral imaging is performed on the surface of the rock to be tested. Based on the surface condition of the rock to be tested and the test requirements, the angle, focal length and exposure time of the test equipment (such as an imager) are adjusted in real time to ensure that a clear and complete hyperspectral image is obtained. The image data is then processed in real time to extract the spectral characteristics of the surface of the rock to be tested and to analyze the morphology and initial state of the strain field of the surface of the rock to be tested.

[0032] In some embodiments, step S102, performing electromagnetic inversion detection on the in-situ test points of the rock to be tested, includes: performing electromagnetic detection on the in-situ test points of the rock to be tested to obtain the microwave dielectric and resistivity of the rock to be tested; and determining the internal mineral composition and water content of the rock to be tested based on the microwave dielectric and resistivity of the rock to be tested, combined with a pre-set inversion algorithm.

[0033] For example, the testing equipment is connected to the rock to be tested, ensuring good electrical contact and microwave coupling. Microwave dielectric and resistivity measurements are then performed sequentially. Based on the measured data and combined with a corresponding inversion algorithm, the internal mineral composition and water content of the rock are preliminarily determined in real-time on-site. In this embodiment, the inversion algorithm is based on a Physics-Informed Neural Network (PINN) architecture, which embeds physical laws into a deep learning framework. Thanks to this, the inversion algorithm can establish a neural network connection between input and output data based on certain physical equations and limit the data range, thereby quickly and accurately obtaining the required output data from the input data.

[0034] In some embodiments, training the multiphysics coupling model includes: acquiring rock samples with known mechanical parameters; performing in-situ acoustic detection, in-situ optical detection, and in-situ electromagnetic inversion detection on the rock samples to acquire sample data; establishing a mapping relationship between the mechanical parameters and the sample data for the multiphysics coupling model, and fine-tuning the parameters with the aim of minimizing the model loss of the multiphysics coupling model.

[0035] In this embodiment, the multiphysics coupling model employs deep learning technology, utilizing a neural network with hidden layers. Several rock sample data sets are pre-input into the multiphysics coupling model. These data are divided into two categories: one category includes acoustic, optical, and electromagnetic data, and the other category includes basic rock mechanical parameters such as elastic modulus and Poisson's ratio. This allows the multiphysics coupling model to establish a connection and mapping between the two types of data for prediction. When one type of data is input into the multiphysics coupling model, the model uses the hidden layers to map out the other type of data.

[0036] Based on the above embodiments, the detection data obtained from acoustic detection, optical characterization, and electromagnetic inversion are deeply fused to eliminate inconsistencies between different detection data caused by measurement errors, environmental interference, and other factors. Subsequently, a constructed multiphysics coupling model is used to perform real-time comprehensive analysis of the fused data. Through efficient mathematical calculations and model optimization algorithms, various mechanical parameters of the rock are calculated quickly and accurately. In this embodiment, the method of constructing dimensionless numbers is used to achieve multi-dimensional data fusion. Based on the principle of dimensional homogeneity and Buckingham's π theorem, the multi-dimensional problem is simplified to the relationship between a small number of dimensionless numbers, thus completing the multi-dimensional, multi-modal data fusion.

[0037] In some embodiments, the method further includes: continuously monitoring the rock under test, continuously tracking the dynamic changes of the mechanical parameters of the rock under test in the time and space dimensions, and capturing abnormal changes in the state of the rock under test.

[0038] For example, the calculated mechanical parameters of the rock under test can be output in real time in an intuitive and easy-to-understand way, such as generating parameter reports, drawing dynamic change curves and spatial distribution maps of parameters on the on-site display terminal. At the same time, the continuous monitoring function of this method can be used to continuously track the dynamic changes of the mechanical parameters of the rock under test in time and space, promptly detect abnormal changes in the state of the rock under test, and provide real-time early warning for engineering safety.

[0039] In addition, the method also includes: periodically calibrating the multiphysics coupling model using rock samples with known mechanical parameters; cross-validating the test results of the multiphysics coupling model by combining other in-situ testing methods, and optimizing and adjusting the model parameters of the multiphysics coupling model based on the validation results; other in-situ testing methods include standard penetration test and static cone penetration test.

[0040] For example, the testing method is periodically calibrated using rock samples with known mechanical parameters, and the results are cross-validated by combining it with other traditional and reliable in-situ testing methods (such as standard penetration tests and static cone penetration tests). Based on the validation results, the model parameters of this method and the multiphysics coupling model are optimized and adjusted to ensure the accuracy and reliability of the test results.

[0041] The present invention also provides an in-situ rock mechanical parameter detection device. The in-situ rock mechanical parameter detection device provided by the present invention is described below. The in-situ rock mechanical parameter detection device described below and the in-situ rock mechanical parameter detection method described above can be referred to in correspondence. Figure 3 This is a structural block diagram of the in-situ rock mechanical parameter detection device provided by the present invention, as shown below. Figure 3 As shown, the device includes: The processing module 301 is used to determine the in-situ test points of the rock to be tested and to preprocess the rock to be tested. The detection module 302 is used to perform in-situ acoustic wave detection, in-situ optical detection, and in-situ electromagnetic inversion detection at the in-situ test points of the rock to be tested, and to acquire detection data; the detection data includes ultrasonic signals, hyperspectral images, and electromagnetic data; The fusion module 303 is used to perform deep fusion processing on the detection data to obtain fused data; The prediction module 304 is used to call the pre-trained multiphysics coupling model to perform real-time comprehensive analysis of the fused data and determine the mechanical parameters of the rock to be tested.

[0042] In operation, this device first uses the following steps: The processing module 301 determines the in-situ test points of the rock to be tested and preprocesses the rock to facilitate a better connection between the rock and the testing equipment, thereby improving the detection accuracy. Then, the detection module 302 performs in-situ acoustic wave detection, in-situ optical detection, and in-situ electromagnetic inversion detection on the in-situ test points of the rock to acquire detection data. The fusion module 303 then performs deep fusion processing on the detection data acquired from different detection processes to obtain fused data with broader dimensional coverage. Finally, the prediction module 304 analyzes the fused data using a multiphysics coupling model to obtain the mechanical parameters of the rock to be tested, such as elastic modulus, Poisson's ratio, compressive strength, and shear strength. In the aforementioned process, the acoustic-optical-electrical fusion technology enables the simultaneous acquisition of information on the internal structure, surface characteristics, mineral composition, and water content of the rock under test in its in-situ condition. This comprehensively reflects the overall properties of the rock, providing a rich and accurate data foundation for the accurate calculation of its mechanical parameters. This avoids the information loss caused by sample disturbance or limited testing methods in traditional approaches. Furthermore, the analysis of the fused data through a multiphysics coupling model fully considers the mutual influence and correlation between different physical quantities in the in-situ environment, thus addressing the problem of the relatively limited range of existing rock mechanical parameter detection methods.

[0043] This invention also provides an in-situ rock mechanical parameter detection system. Figure 4 This is a schematic diagram of the in-situ rock mechanical parameter detection system provided by the present invention, as shown below. Figure 4 As shown, the system includes: In-situ acoustic wave detection subsystem 2 is used to perform in-situ acoustic wave detection at the in-situ test points of the rock to be tested; In-situ optical characterization subsystem 3 is used for in-situ optical detection of in-situ test points of the rock to be tested; In-situ electromagnetic inversion subsystem 4 is used to perform electromagnetic inversion detection on in-situ test points of the rock under test; The data processing and multiphysics coupling analysis platform 1 is used to perform deep fusion processing on the detection data to obtain fused data, and to call the pre-trained multiphysics coupling model to perform real-time comprehensive analysis on the fused data to determine the mechanical parameters of the rock to be tested.

[0044] Specifically, the in-situ acoustic wave detection subsystem 2 is equipped with a portable ultrasonic transmitter and receiver that can adapt to different geological environments. The transmission frequency can be flexibly adjusted within the range of 50kHz - 20MHz to adapt to the acoustic characteristics of various types of rocks to be tested. At the same time, it integrates a high-sensitivity acoustic emission sensor, which can capture the weak acoustic emission signals generated by the rock under natural conditions and under environmental stress. This enables precise detection of the orientation, width, density of internal cracks, and the size, shape, and connectivity of pores in the rock. It can accurately identify the pore boundaries inside the rock and determine the initiation and expansion location of microcracks.

[0045] The in-situ optical characterization subsystem 3 employs a portable hyperspectral imager with high resolution and a wide spectral range (covering visible and near-infrared light from 350nm to 2500nm). It has a built-in lens assembly with adjustable angle and focal length, which can adapt to the in-situ testing needs of rocks with different shapes and surface conditions. Through a wireless data transmission module, the acquired hyperspectral images can be transmitted to the data processing terminal in real time. Using advanced image analysis algorithms, the hyperspectral images are processed to accurately characterize the mineral distribution, texture features, and changes in the surface strain field during stress on the surface of the rock. It can clearly distinguish various minerals on the surface of the rock and accurately calculate the strain distribution on the surface of the rock using digital image correlation algorithms to obtain strain field information.

[0046] The in-situ electromagnetic inversion subsystem 4 consists of a portable microwave dielectric measuring instrument and a resistivity measuring instrument. The microwave dielectric measuring instrument can transmit microwave signals with frequencies between 0.5 GHz and 15 GHz and accurately measure the dielectric response of the rock to microwaves. The resistivity measuring instrument adopts the four-electrode method and can accurately measure the resistivity of the rock in situ. Equipped with specially designed electrodes and microwave antennas, it can easily achieve good electrical contact and microwave coupling with test points on the rock surface or in the borehole. Furthermore, by establishing the relationship model between microwave dielectric constant and mineral composition, as well as the relationship model between resistivity and rock pore structure and water state, and using the Archie formula, it can invert information such as mineral types, relative contents, and water saturation inside the rock.

[0047] The data processing and multiphysics coupling analysis platform 1 is responsible for real-time acquisition and aggregation of data from the above three subsystems, and performs preliminary data cleaning, filtering and noise reduction to improve data quality. Based on the interdisciplinary theories of rock physics, electromagnetics, optics and mechanics, it constructs a multiphysics coupling model and deeply integrates and analyzes acoustic, optical and electromagnetic data. By establishing quantitative mathematical relationships between the internal structure, surface characteristics, mineral composition, water content and mechanical parameters of the rock under test (such as elastic modulus, Poisson's ratio, compressive strength, shear strength, etc.), it can accurately calculate the mechanical parameters of the rock under test. At the same time, it has data visualization function, which can display the spatial distribution and changes over time of the mechanical parameters of the rock under test in intuitive charts, cloud maps and other forms, providing intuitive basis for engineering decision-making.

[0048] The following examples will illustrate the above systems and methods together: Example 1: Figure 5 This is a schematic diagram of mechanical parameter testing in Example 1 of the present invention, as shown below. Figure 5 As shown, taking the in-situ rock mechanics parameter testing of the surrounding rock in a deep coal mine roadway as an example, the following steps are included: Step 1: Select representative test points on the wall of a deep coal mine roadway. Use a high-pressure water gun to wash the surface of the test points to remove coal dust and loose rocks. Then, lightly sand the surface with sandpaper to ensure it is smooth. For tests that require in-depth exploration of the internal structure, use a special drilling equipment to drill a hole with a diameter of 50mm and a depth of 1m. Clean and dry the hole.

[0049] Step 2: The ultrasonic transmitter and receiver of the in-situ acoustic wave detection subsystem 2 are installed in the borehole using a specially designed drilling coupling device, ensuring a tight fit. The ultrasonic transmitter is set to emit ultrasonic waves at frequencies of 1MHz, 3MHz, and 5MHz, and the transmission and reception operations are performed sequentially. At the same time, the acoustic emission sensor is activated to monitor the acoustic emission signals of the surrounding rock in the natural state. Real-time data analysis on site revealed multiple micro-cracks inside the surrounding rock, with an average width of approximately 0.08mm. These cracks are mainly distributed in the area 0.3-0.8m from the roadway wall, and their orientation forms an angle of 30°-60° with the roadway axis.

[0050] Step 3: Use the in-situ optical characterization subsystem 3 to perform hyperspectral imaging on the test points on the tunnel wall. Based on the on-site lighting conditions, adjust the imager's exposure time to 0.6s and focal length to 10cm to acquire clear hyperspectral images. Perform real-time image analysis. Spectral characteristics identify the main minerals on the rock surface as quartz, feldspar, and clay minerals. Furthermore, near the corners of the tunnel, due to stress concentration, a significant anomaly appears in the surface strain field, with strain values ​​approximately 15% higher than in other areas.

[0051] Step 4: Install the electrodes and microwave antenna of the in-situ electromagnetic inversion subsystem 4 at the test points on the tunnel wall. Microwave dielectric measurement results show that the average dielectric constant of the rock at different frequencies is 5.8. By comparing with a mineral dielectric constant database and combining it with on-site geological analysis, the quartz content is determined to be approximately 35%, the feldspar content approximately 30%, and the clay mineral content approximately 35%. Resistivity measurement results show that the rock's resistivity is 350 Ω·m. Based on a pre-established model relating water content to resistivity, the rock's water saturation is calculated to be approximately 5%.

[0052] Step 5: The above acoustic, optical, and electromagnetic data are transmitted in real time to the in-situ data processing and multiphysics coupling analysis platform 1. The data is filtered and noise reduced, and then a multiphysics coupling model is used for comprehensive analysis. After rapid calculation and model optimization, the elastic modulus of the surrounding rock of the tunnel is 60 GPa, the Poisson's ratio is 0.28, the compressive strength is 180 MPa, and the shear strength is 70 MPa.

[0053] Step 6: The mechanical parameter report is generated in real time on the display terminal at the roadway site, and the curves of parameter changes over time and the spatial distribution map on the roadway wall are plotted. Through continuous dynamic monitoring, it was found that as the roadway is excavated, the elastic modulus and compressive strength of the surrounding rock gradually decrease. At 50m behind the excavation face, the elastic modulus drops to 55GPa and the compressive strength drops to 160MPa, which provides a timely basis for adjusting the support scheme for coal mining.

[0054] Step 7: The testing system was calibrated periodically using standard rock samples and compared with the results of traditional borehole coring laboratory tests. The results showed that the relative error of the elastic modulus was 1.8%, the relative error of Poisson's ratio was 2.5%, the relative error of compressive strength was 3.2%, and the relative error of shear strength was 4.5%, demonstrating the high precision and reliability of the test results.

[0055] Example 2: Figure 6 This is a schematic diagram of mechanical parameter testing in Example 2 of the present invention, as shown below. Figure 6 As shown, taking the in-situ rock mechanics parameter testing of a mountain highway slope as an example, the steps include: Step 1: Select multiple test points on the rock outcrops of the mountain road slope to comprehensively assess the slope stability. Clear the vegetation and weathered layer from the test point surface. For uneven rock surfaces, use small grinding equipment to level them. For some test points where the internal structure needs to be explored, use portable drilling equipment to drill shallow holes with a diameter of 30mm and a depth of 0.5m.

[0056] Step 2: The ultrasonic transmitting and receiving devices of the in-situ acoustic wave detection subsystem 2 are mounted on the rock surface using surface-fitting clamps. For test points with drilled holes, a drilled insertion installation method is used. Ultrasonic waves with transmission frequencies of 200kHz, 500kHz, and 1MHz are used for measurement. The acoustic emission sensor detects a small amount of acoustic emission signals in the slope rock under natural conditions, indicating the presence of some micro-cracks. Data analysis determines that the average width of the micro-cracks is approximately 0.03mm, with an irregular distribution, mainly concentrated in the shallow rock layer within the 0-0.3m range.

[0057] Step 3: Use the in-situ optical characterization subsystem 3 to perform hyperspectral imaging on the slope rock surface. Adjust the imager parameters according to the on-site light intensity and rock color to obtain clear images. Through spectral analysis, the minerals on the rock surface are identified as mainly mica, quartz, and a small amount of amphibole. Near the potential sliding surface of the slope, the surface strain field shows obvious tensile strain characteristics, with strain values ​​reaching 0.05% in local areas.

[0058] Step 4: Connect the electrodes and microwave antenna of the in-situ electromagnetic inversion subsystem 4 to the rock test points on the slope. The microwave dielectric measurement results show that the average dielectric constant is 4.5. The analysis shows that the mica content is about 40%, the quartz content is about 35%, the amphibole content is about 25%, the resistivity measurement shows that the rock resistivity is 200 Ω·m, and the calculated water saturation of the rock is about 7%.

[0059] Step 5: The collected acoustic, optical and electromagnetic data are transmitted in real time to the in-situ data processing and multiphysics coupling analysis platform 1. After the data is fused, the elastic modulus of the slope rock is calculated to be 45 GPa, Poisson's ratio is 0.32, compressive strength is 120 MPa and shear strength is 50 MPa using the multiphysics coupling model.

[0060] Step 6: On-site, mechanical parameter reports and spatial distribution maps are viewed in real time via mobile terminals. Through continuous monitoring, it was found that with increased rainfall, the resistivity of the slope rock decreased, while the water saturation increased. Correspondingly, the compressive strength and shear strength of the rock decreased, providing timely early warning information for the protection and reinforcement of the highway slope.

[0061] Step 7: The testing system is calibrated periodically using standard rock samples and compared with the results of traditional in-situ testing methods (such as point load tests). The results show that the test results are in good agreement with the traditional methods and are more comprehensive and accurate in reflecting the relationship between the internal structure and mechanical properties of the slope rock.

[0062] In summary, this method, through the fusion of acoustic, optical, and electrical technologies, simultaneously acquires information on the internal structure, surface properties, mineral composition, and water content of rocks in their in-situ state. This comprehensively reflects the overall characteristics of the rock, providing a rich and accurate data foundation for the accurate calculation of in-situ rock mechanical parameters, and avoiding the information loss caused by sample disturbance or single testing methods in traditional methods. Furthermore, the multiphysics coupling model fully considers the mutual influence and correlation between different physical quantities in the in-situ environment, effectively eliminating the errors and uncertainties of single methods in in-situ testing. Experimental comparisons show that the in-situ elastic modulus obtained using the method of this invention achieves an accuracy of ±2%, Poisson's ratio accuracy of ±3%, and compressive strength and shear strength accuracy of ±5%, significantly higher than the accuracy of traditional single in-situ testing methods, and can provide more reliable parameter support for complex rock engineering.

[0063] This invention enables real-time in-situ data acquisition and processing of rock, allowing for real-time monitoring of dynamic changes in rock mechanical parameters under natural environmental changes or engineering construction disturbances. Timely updated rock mechanical parameters provide strong support for real-time decision-making in rock engineering. For example, during deep tunnel excavation, real-time monitoring of surrounding rock mechanical parameters allows for timely adjustments to support schemes, ensuring construction safety. Furthermore, this invention is applicable to various types of rocks under complex geological conditions, whether it be hard granite and sandstone buried deep underground, or soft shale and mudstone on the surface. By flexibly adjusting test and model parameters, high-precision in-situ mechanical parameter testing can be achieved, demonstrating broad engineering application prospects.

[0064] Furthermore, the device and system of this invention are portable, with each subsystem easy to install and operate, enabling rapid deployment to in-situ testing sites. Data processing and analysis are automated and real-time, significantly improving the efficiency of in-situ testing and reducing testing time and labor costs, making it particularly suitable for large-scale in-situ rock mechanics parameter testing projects.

[0065] Figure 7 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 7 As shown, the electronic device may include: a processor 701, a communication interface 702, a memory 703, and a communication bus 704. The processor 701, communication interface 702, and memory 703 communicate with each other via the communication bus 704. The processor 701 can call logical instructions from the memory 703 to execute an in-situ rock mechanical parameter detection method, which includes: Determine the in-situ test points of the rock to be tested and pre-treat the rock; In-situ acoustic wave detection, in-situ optical detection, and in-situ electromagnetic inversion detection are performed on the in-situ test points of the rock to be tested to obtain detection data; the detection data includes ultrasonic signals, hyperspectral images, and electromagnetic data; The detection data is subjected to deep fusion processing to obtain fused data; The pre-trained multiphysics coupling model is invoked to perform real-time comprehensive analysis of the fused data and determine the mechanical parameters of the rock to be tested.

[0066] Furthermore, the logical instructions in the aforementioned memory 703 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0067] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to perform the in-situ rock mechanical parameter detection method provided by the above methods, the method comprising: Determine the in-situ test points of the rock to be tested and pre-treat the rock; In-situ acoustic wave detection, in-situ optical detection, and in-situ electromagnetic inversion detection are performed on the in-situ test points of the rock to be tested to obtain detection data; the detection data includes ultrasonic signals, hyperspectral images, and electromagnetic data; The detection data is subjected to deep fusion processing to obtain fused data; The pre-trained multiphysics coupling model is invoked to perform real-time comprehensive analysis of the fused data and determine the mechanical parameters of the rock to be tested.

[0068] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the in-situ rock mechanical parameter detection method provided by the methods described above, the method comprising: Determine the in-situ test points of the rock to be tested and pre-treat the rock; In-situ acoustic wave detection, in-situ optical detection, and in-situ electromagnetic inversion detection are performed on the in-situ test points of the rock to be tested to obtain detection data; the detection data includes ultrasonic signals, hyperspectral images, and electromagnetic data; The detection data is subjected to deep fusion processing to obtain fused data; The pre-trained multiphysics coupling model is invoked to perform real-time comprehensive analysis of the fused data and determine the mechanical parameters of the rock to be tested.

[0069] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units 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. Those skilled in the art can understand and implement this without any creative effort.

[0070] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for in-situ detection of rock mechanical parameters, characterized in that, include: Determine the in-situ test points of the rock to be tested, and pre-treat the rock to be tested; In-situ acoustic wave detection, in-situ optical detection, and in-situ electromagnetic inversion detection are performed on the in-situ test points of the rock to be tested to obtain detection data; the detection data includes ultrasonic signals, hyperspectral images, and electromagnetic data; The detection data is subjected to deep fusion processing to obtain fused data; The pre-trained multiphysics coupling model is invoked to perform real-time comprehensive analysis on the fused data to determine the mechanical parameters of the rock under test.

2. The in-situ rock mechanical parameter detection method according to claim 1, characterized in that, Determine the in-situ test points for the rock to be tested, and preprocess the rock to be tested, including: Based on the engineering requirements and geological conditions of the area where the rock to be tested is located, in-situ test points are determined; The in-situ test points of the rock to be tested are cleaned; For test scenarios that require drilling, drill holes at the in-situ test points of the delay to be tested, and then clean the area.

3. The in-situ rock mechanical parameter detection method according to claim 1, characterized in that, In-situ acoustic wave detection is performed at the in-situ test points of the rock to be tested, including: Ultrasonic waves are emitted according to a preset frequency sequence to detect the in-situ test points of the rock to be tested, and the reflected and transmitted ultrasonic signals are received in real time. The ultrasonic signal is analyzed in real time to determine the internal structure of the rock to be tested.

4. The in-situ rock mechanical parameter detection method according to claim 1, characterized in that, In-situ optical detection is performed on the in-situ test points of the rock to be tested, including: Hyperspectral imaging was performed on the in-situ test points of the rock to be tested to obtain hyperspectral images; The hyperspectral image is processed in real time to extract the spectral features of the rock surface under test, and the morphology of the rock surface and the initial state of the strain field are analyzed.

5. The in-situ rock mechanical parameter detection method according to claim 1, characterized in that, Electromagnetic inversion detection is performed on the in-situ test points of the rock to be tested, including: Electromagnetic detection was performed on the in-situ test points of the rock to be tested to obtain the microwave dielectric and resistivity of the rock to be tested. Based on the microwave dielectric and resistivity of the rock under test, and combined with a pre-set inversion algorithm, the internal mineral composition and water content of the rock under test are determined.

6. The in-situ rock mechanical parameter detection method according to claim 1, characterized in that, Training the multiphysics coupling model includes: Obtain rock samples with known mechanical parameters; In-situ acoustic wave detection, in-situ optical detection, and in-situ electromagnetic inversion detection were performed on the rock sample to obtain sample data; A mapping relationship between mechanical parameters and sample data is established for the multiphysics coupling model, and parameter fine-tuning is performed with the aim of minimizing the model loss of the multiphysics coupling model.

7. The in-situ rock mechanical parameter detection method according to claim 1, characterized in that, include: The rock under test is continuously monitored, and the dynamic changes of its mechanical parameters in the time and space dimensions are continuously tracked to capture abnormal changes in the state of the rock under test.

8. The in-situ rock mechanical parameter detection method according to claim 1, characterized in that, include: The multiphysics coupling model is periodically calibrated using rock samples with known mechanical parameters. The test results of the multiphysics coupling model are cross-validated by combining other in-situ testing methods, and the model parameters of the multiphysics coupling model are optimized and adjusted based on the validation results; other in-situ testing methods include standard penetration test and static cone penetration test.

9. An in-situ rock mechanical parameter testing device, characterized in that, include: The processing module is used to determine the in-situ test points of the rock to be tested and to preprocess the rock to be tested. The detection module is used to perform in-situ acoustic wave detection, in-situ optical detection, and in-situ electromagnetic inversion detection on the in-situ test points of the rock to be tested, and to acquire detection data; the detection data includes ultrasonic signals, hyperspectral images, and electromagnetic data; The fusion module is used to perform deep fusion processing on the detection data to obtain fused data; The prediction module is used to call a pre-trained multiphysics coupling model to perform real-time comprehensive analysis of the fused data and determine the mechanical parameters of the rock to be tested.

10. An in-situ rock mechanical parameter detection system, characterized in that, include: The in-situ acoustic wave detection subsystem is used to perform in-situ acoustic wave detection on the in-situ test points of the rock to be tested. An in-situ optical characterization subsystem is used to perform in-situ optical detection on in-situ test points of the rock to be tested. The in-situ electromagnetic inversion subsystem is used to perform electromagnetic inversion detection on the in-situ test points of the rock to be tested. The data processing and multiphysics coupling analysis platform is used to perform deep fusion processing on the detection data to obtain fused data, and to call a pre-trained multiphysics coupling model to perform real-time comprehensive analysis on the fused data to determine the mechanical parameters of the rock to be tested.

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