Rock multi-parameter detection method, device and system

Abstract

The invention provides a rock multi-parameter detection method, device and system, and the method comprises the steps: obtaining a to-be-detected rock, and carrying out the preprocessing of the to-be-detected rock; performing parameter detection on the to-be-detected rock to obtain detection data of the to-be-detected rock; the parameter detection comprises wave velocity measurement, dielectric measurement and spectral measurement; the detection data comprises wave velocity data, dielectric data and spectral data; and carrying out standardization processing on the detection data of the rock to be detected, and carrying out iterative optimization through a pre-trained data association model to invert physical parameters of the rock to be detected. According to the invention, the internal relation and mutual influence among different physical quantities are fully considered, the error and uncertainty of a single test method are effectively eliminated, the comprehensive characteristics of the rock to be tested can be comprehensively reflected, and the problems of incomplete detection and poor accuracy in the prior art are solved.

Description

Technical Field

[0001] This invention relates to the field of rock detection technology, and in particular to a method, apparatus and system for multi-parameter rock detection. Background Technology

[0002] In all engineering activities involving rocks, accurately obtaining the physical parameters of rocks plays a decisive role in engineering design, construction safety, and resource assessment. Traditionally, the testing of rock parameters often relies on the measurement of a single physical quantity, such as inferring the elastic properties of rocks solely through ultrasonic wave velocity measurement, assessing the water content of rocks solely based on dielectric constant measurement, or simply relying on spectral analysis to determine the mineral composition of rocks.

[0003] However, these isolated testing methods have significant drawbacks. Relying solely on wave velocity measurements makes it difficult to fully understand the complex mineral composition and microstructure within rocks, especially for rocks containing multiple minerals and with heterogeneous structures. The information reflected by wave velocity is limited and cannot be accurately correlated with other key rock parameters. While simple dielectric constant measurements can reflect the water-bearing characteristics of rocks to some extent, they cannot reveal in-depth details of the rock's mechanical properties and mineral distribution, and are easily affected by the complex pore structure and impurities within the rock, leading to inaccurate results. Although single spectral analysis can identify the types of minerals on the rock surface, it lacks an effective means of detecting the quantitative distribution of minerals within the rock and their relationship with the overall physical properties of the rock. These limitations make traditional methods unable to meet the urgent needs of modern complex rock engineering for high-precision, comprehensive rock parameter information.

[0004] There is currently no effective solution to the problems of insufficient detection and poor accuracy in existing related technologies. Summary of the Invention

[0005] This invention provides a method, apparatus, and system for multi-parameter detection of rocks, which addresses the shortcomings of existing related technologies, such as insufficient detection coverage and poor accuracy.

[0006] In a first aspect, the present invention provides a method for multi-parameter detection of rocks, comprising: Obtain the rock to be tested and preprocess the rock to be tested; The rock to be tested is subjected to parameter detection to obtain the detection data of the rock to be tested; the parameter detection includes wave velocity measurement, dielectric measurement and spectral measurement; the detection data includes wave velocity data, dielectric data and spectral data; The detection data of the rock to be tested are standardized and iteratively optimized using a pre-trained data association model to deduce the physical parameters of the rock to be tested.

[0007] According to a method for multi-parameter detection of rocks provided by the present invention, the method involves acquiring a rock to be tested and preprocessing the rock, including: For blocky rocks to be tested, the surface of the rocks is polished to remove impurities and weathering layers. For core samples of the rock to be tested, the rock is cut and processed according to the testing requirements.

[0008] According to a multi-parameter rock detection method provided by the present invention, wave velocity measurement is performed on the rock to be tested to obtain wave velocity data of the rock to be tested, including: The ultrasonic emission frequency is determined based on the rock type of the rock to be tested and the estimated wave velocity range. Longitudinal wave signals and transverse wave signals are emitted sequentially, and the propagation time of ultrasound waves in the rock is recorded. Based on the propagation time of the ultrasonic waves in the rock, the longitudinal wave velocity and the transverse wave velocity are determined.

[0009] According to a multi-parameter rock detection method provided by the present invention, dielectric measurement is performed on the rock to be tested to obtain dielectric data of the rock to be tested, including: Based on the shape and properties of the rock to be tested, the measurement method for dielectric measurement is determined; the measurement method includes contact measurement and non-contact measurement. According to the measurement method described, the rock under test is tested at different microwave frequencies to obtain the dielectric constant and dielectric loss.

[0010] According to the present invention, a multi-parameter detection method for rocks is provided, wherein the rock to be tested is detected at different microwave frequencies according to the measurement method, including: For the rock to be tested that has a regular shape, a contact electrode is used to connect to the rock to be tested and the rock is tested. For irregular rocks to be tested, a non-contact sensor probe is brought close to the surface of the rock, and the position and angle of the non-contact sensor probe are adjusted to detect the rock.

[0011] According to a multi-parameter rock detection method provided by the present invention, the method involves performing spectral measurements on the rock to be tested to obtain spectral data of the rock, including: Based on the size of the rock to be tested and the required testing accuracy, determine the focal length and angle for the spectral measurement; The spectral reflectance and absorptivity of the rock under test are obtained by collecting spectra within different wavelength ranges.

[0012] According to a rock multi-parameter detection method provided by the present invention, training the data association model includes: Obtain the test data and physical parameters of the sample rock blocks; The detection data and physical parameters of the sample rock block are input into the data association model, and the data association model is fine-tuned to construct a mapping between the detection data and the physical parameters.

[0013] According to the multi-parameter detection method for rocks provided by the present invention, after reversing the physical parameters of the rock to be tested, the method includes: The physical parameters of the rock to be tested are processed to generate data reports or data charts; The test data and physical parameters of the sample rock blocks are obtained, and the test data and physical parameters of the sample rock blocks are compared and verified.

[0014] Secondly, the present invention also provides a rock multi-parameter detection device, comprising: An acquisition module is used to acquire the rock to be tested and to preprocess the rock to be tested; The detection module is used to perform parameter detection on the rock to be tested and obtain detection data of the rock to be tested; the parameter detection includes wave velocity measurement, dielectric measurement and spectral measurement; the detection data includes wave velocity data, dielectric data and spectral data; The processing module is used to standardize the detection data of the rock to be tested and iteratively optimize it through a pre-trained data association model to deduce the physical parameters of the rock to be tested.

[0015] Thirdly, the present invention also provides a multi-parameter rock detection system, comprising: The wave velocity measurement subsystem is used to measure the wave velocity of the rock under test and obtain the wave velocity data of the rock under test. A dielectric measurement subsystem is used to perform dielectric measurements on the rock under test and obtain dielectric data of the rock under test. A spectral measurement subsystem is used to perform spectral measurements on the rock to be tested and obtain the spectral data of the rock to be tested; The data processing and association subsystem is used to standardize the detection data of the rock to be tested and iteratively optimize it through a pre-trained data association model to deduce the physical 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 rock multi-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 rock multi-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 rock multi-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 multi-parameter rock detection method provided by this invention fully considers the inherent relationships and mutual influences between different physical quantities, effectively eliminating the errors and uncertainties of single testing methods. It comprehensively reflects the overall characteristics of the rock under test, providing richer and more comprehensive data support for rock-related engineering projects, and solving the problems of insufficient detection and poor accuracy in existing related technologies. Furthermore, this method is applicable to various types of rocks, whether hard granite and basalt, relatively soft shale and mudstone, or complex metamorphic rocks. High-precision physical parameter testing can be achieved by adjusting the test parameters and the correlation coefficient of the data association model, demonstrating broad application prospects. The entire testing process achieves automated data acquisition and rapid calculation processing, greatly shortening the testing time and improving work efficiency. Compared with the traditional combination of multiple testing methods, it can obtain comprehensive physical parameters of the rock under test in a shorter time, meeting the needs of rapid decision-making in engineering sites. 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 rock multi-parameter detection method provided by the present invention; Figure 2 This is a schematic diagram illustrating the process of processing and inverting detection data in an embodiment of the present invention; Figure 3 This is a schematic diagram of the detection equipment in embodiment 1 of the present invention; Figure 4 This is a schematic diagram of the detection equipment in embodiment 2 of the present invention; Figure 5 This is a structural block diagram of the rock multi-parameter detection device provided by the present invention; Figure 6This is a structural block diagram of the rock multi-parameter detection system provided by 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: Wave velocity measurement subsystem; 2: High-speed channel; 3: Spectral measurement subsystem; 4: Data processing and correlation subsystem; 5: Dielectric measurement 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 a method for detecting multiple parameters of rocks. Figure 1 This is a flowchart of the multi-parameter rock detection method provided by the present invention, as follows: Figure 1 As shown, the method includes the following steps: Step S101: Obtain the rock to be tested and preprocess it. Step S102: Perform parameter detection on the rock to be tested to obtain the detection data of the rock to be tested; the parameter detection includes wave velocity measurement, dielectric measurement and spectral measurement; the detection data includes wave velocity data, dielectric data and spectral data; Step S103: Standardize the detection data of the rock to be tested, and iteratively optimize it through a pre-trained data association model to retrieve the physical parameters of the rock to be tested.

[0025] In this method, the rock to be tested is first pre-treated to remove impurities from its surface, allowing for better contact or connection with the testing equipment and thus improving the testing accuracy. Then, parameter detection is performed on the rock to obtain detection data, including wave velocity, dielectric, and spectral data. By performing different types or dimensions of detection on the rock, a richer and more comprehensive understanding of its internal structure can be obtained. Finally, the detection data is standardized to eliminate the influence of different measurement units and magnitudes on the results. The trained data association model is then iteratively optimized to retrieve the physical parameters of the rock. This data association model integrates wave velocity, dielectric, and spectral data, simultaneously acquiring multiple physical parameters such as mineral composition, pore structure, water content, and mechanical properties. Therefore, the data association model can also be referred to as the wave velocity-dielectric-spectral correlation model.

[0026] Compared to traditional single-method testing, the above process fully considers the inherent relationships and mutual influences between different physical quantities, effectively eliminating the errors and uncertainties of single-method testing. It comprehensively reflects the overall characteristics of the rock under test, providing richer and more comprehensive data support for rock-related engineering projects, and solving the problems of insufficient detection and poor accuracy in existing related technologies. Furthermore, this method is applicable to various types of rocks, whether hard granite and basalt, relatively soft shale and mudstone, or complex metamorphic rocks. High-precision physical parameter testing can be achieved by adjusting the test parameters and the correlation coefficient of the data association model, demonstrating broad application prospects. The entire testing process achieves automated data acquisition and rapid calculation processing, greatly shortening the testing time and improving work efficiency. Compared to traditional combinations of multiple testing methods, it can obtain comprehensive physical parameters of the rock under test in a shorter time, meeting the needs of rapid decision-making in engineering sites.

[0027] In some embodiments, step S101 involves obtaining the rock to be tested and pre-processing it, including: for blocky rocks, grinding the surface of the rock to remove impurities and weathering layers; for core-type rocks, cutting and processing them according to testing requirements.

[0028] In this embodiment, representative rocks are selected for testing. For blocky rocks, their surfaces are ground smooth to remove surface impurities and weathering layers, ensuring good contact and signal transmission between the testing equipment and the rock. Core samples are cut and processed according to testing requirements to ensure the size and shape of the rocks meet the testing specifications.

[0029] In some embodiments, wave velocity measurement is performed on the rock to be tested to obtain wave velocity data of the rock, including: determining the ultrasonic emission frequency according to the rock type and the estimated wave velocity range; sequentially transmitting longitudinal wave signals and transverse wave signals, and recording the propagation time of the ultrasonic waves in the rock; and determining the longitudinal wave velocity and transverse wave velocity based on the propagation time of the ultrasonic waves in the rock.

[0030] In this embodiment, the ultrasonic transmitting and receiving device is tightly coupled to the rock to be tested. According to the type of rock to be tested and the estimated wave velocity range, an appropriate transmission frequency is selected, and longitudinal wave and transverse wave signals are transmitted in sequence. The propagation time of the ultrasonic wave in the rock is recorded, and the longitudinal wave velocity and transverse wave velocity are calculated. The measurement data are collected and averaged multiple times to improve the accuracy of wave velocity measurement.

[0031] Dielectric measurement is performed on the rock to obtain its dielectric data, including: determining the measurement method based on the shape and properties of the rock; the measurement method includes contact measurement and non-contact measurement; and according to the measurement method, the rock is tested at different microwave frequencies to obtain the dielectric constant and dielectric loss.

[0032] Specifically, the test is performed on the rock under different microwave frequencies, including: for regularly shaped rocks, a contact electrode is used to connect to the rock and the rock is tested; for irregularly shaped rocks, a non-contact induction probe is brought close to the surface of the rock and the position and angle of the non-contact induction probe are adjusted to test the rock.

[0033] In this embodiment, a suitable dielectric measurement method is selected based on the shape and properties of the rock to be tested. For regular-shaped rocks, a contact electrode is used to connect to a specific part of the rock to ensure good contact between the electrode and the rock. For irregular-shaped rocks, a non-contact induction probe is brought close to the surface of the rock, and the probe position and angle are adjusted to optimize the measurement signal. The dielectric constant and dielectric loss are measured at different microwave frequencies, and the measurement data are recorded.

[0034] The spectral measurement of the rock to be tested is performed to obtain its spectral data, including: determining the focal length and angle of the spectral measurement based on the size of the rock and the required testing accuracy; and acquiring the spectral reflectance and absorptivity of the rock in different wavelength ranges.

[0035] In this embodiment, an optical lens is aimed at the surface of the rock to be tested. The focal length and angle of the optical lens are adjusted according to the size of the rock and the required testing accuracy. Spectral data are acquired within different wavelength ranges to obtain the spectral reflectance and absorptivity data of the rock. Furthermore, for situations requiring a deeper understanding of the internal spectral characteristics of the rock, spectral transmission technology combined with optical tomography algorithms can be used to obtain spectral information at a certain depth within the rock.

[0036] Based on the above embodiments, the data association model is trained, including: acquiring the detection data and physical parameters of the sample rock blocks; inputting the detection data and physical parameters of the sample rock blocks into the data association model; fine-tuning the data association model; and constructing a mapping between the detection data and the physical parameters.

[0037] Figure 2 This is a schematic diagram illustrating the process of processing and inverting detection data in an embodiment of the present invention, as shown below. Figure 2 As shown, in this embodiment, the detection data obtained from wave velocity measurement, dielectric measurement, and spectral measurement are subjected to noise reduction filtering and standardization to eliminate the influence of different measurement units and magnitudes on the calculation results. Then, using the constructed data association model, through complex numerical calculations and iterative optimization processes, various physical parameters of the rock, such as mineral composition, porosity, water saturation, elastic modulus, and Poisson's ratio, are calculated inversely.

[0038] In this embodiment, the inversion algorithm is based on the Physics-Informed Neural Networks (PINN) architecture, which embeds physical laws into a deep learning framework. Thanks to this, the 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, achieving inversion and iteration. In other words, normally, the range of data in dataset B (dielectric constant, wave velocity, and spectrum) can be derived from dataset A (containing various physical parameters such as mineral composition, porosity, water saturation, elastic modulus, and Poisson's ratio of rocks). However, by using this algorithm to establish a connection between A and B and perform inversion and iteration, the range of A can be deduced from B.

[0039] In some embodiments, after step S103, which involves retrieving the physical parameters of the rock to be tested, the steps include: processing the physical parameters of the rock to be tested to generate a data report or data chart; acquiring the test data and physical parameters of the sample rock block; and comparing and verifying the test data and physical parameters of the sample rock block.

[0040] In this embodiment, the calculated physical parameters of the rock to be tested are output in the form of intuitive reports, charts, etc., which is convenient for users to view and use. At the same time, the accuracy and reliability of the test results of this invention are ensured by comparing and verifying the physical parameters with those of known standard rock samples, as well as by cross-validating with other traditional and reliable testing methods.

[0041] The above method is described below with a specific implementation plan to demonstrate its effectiveness.

[0042] Implementation Plan 1 – Taking granite as an example for testing, Figure 3 This is a schematic diagram of the detection equipment in embodiment 1 of the present invention, as shown below. Figure 3 As shown: Step 1: Select a piece of granite measuring 10cm×10cm×10cm, and use sandpaper to smooth its six surfaces, removing dust and impurities to ensure that the surface finish meets the test requirements.

[0043] Step 2: The ultrasonic transmitter and receiver are tightly attached to opposite sides of the granite using a special coupling agent. The transmission frequencies are set to 1MHz, 3MHz, and 5MHz to transmit longitudinal and transverse wave signals, respectively. After multiple measurements and data averaging, the longitudinal wave velocity V is obtained. p The shear wave velocity is 5800 m / s, V. s It is 3200m / s.

[0044] Step 3: Using contact electrodes, the electrodes were connected to specific parts of the granite, and the dielectric constant and dielectric loss were measured at multiple microwave frequencies, including 0.5 GHz, 2 GHz, 5 GHz, and 10 GHz. The results showed that the dielectric constant at different frequencies... The average value is 6.8, dielectric loss The average value is 0.02.

[0045] Step 4: Aim the optical lens at the granite surface, adjust the focal length and angle to ensure a clear spectral image is obtained, and collect spectra in the visible, near-infrared and mid-infrared ranges. Through spectral analysis algorithms, identify the main minerals on the rock surface as quartz, feldspar and mica.

[0046] Step 5: After data preprocessing and data association model calculation, the detection data of wave velocity, dielectric constant, and spectral density are obtained to determine the mineral composition of the granite: quartz content is approximately 42%, feldspar content is approximately 35%, and mica content is approximately 23%; porosity... It is 2.5%; water saturation S w The value is 1.8%; the elastic modulus E is 75 GPa, and the Poisson's ratio v is 0.26.

[0047] Step 6: Generate a report of the calculated physical parameters and compare it with the physical parameters of a known standard granite sample. The results show that the relative error of the mineral composition obtained by this method is within ±3%, the porosity error is ±2%, the water saturation error is ±3%, the relative error of the elastic modulus is 2.5%, and the relative error of Poisson's ratio is 3.5%.

[0048] Implementation Plan 2 – Taking shale as an example for testing, Figure 4 This is a schematic diagram of the detection equipment in embodiment 2 of the present invention, as shown below. Figure 4 As shown: Step 1: Obtain a piece of shale measuring 8cm×8cm×8cm, clean and lightly polish its surface to remove clay impurities and uneven parts.

[0049] Step 2: Install the ultrasonic transmitter and receiver on the shale sample, using low-frequency ultrasonic waves, setting the transmission frequency to 200kHz and 500kHz. Measure the longitudinal wave velocity V. p The shear wave velocity is 2800 m / s, V. s It is 1500 m / s.

[0050] Step 3: Due to the irregular shape of the shale sample, a non-contact inductive probe was used to perform dielectric measurements at frequencies of 1 GHz, 3 GHz, 6 GHz, and 9 GHz. The results showed that the dielectric constant... The average value is 4.5, dielectric loss The average value is 0.05.

[0051] Step 4: Spectral acquisition of shale samples. Spectral analysis revealed obvious layering structure on the shale surface and unique spectral characteristics at specific wavelengths. By comparing with a standard mineral spectral library, it was determined that the shale contained a high content of clay minerals.

[0052] Step 5: The test data is processed using a data association model to calculate the mineral composition of the shale: clay mineral content is approximately 65%, and other mineral content is approximately 35%; porosity... It is 15%; water saturation S w The value is 10%; the elastic modulus E is 35 GPa, and the Poisson's ratio v is 0.32.

[0053] Step 6: Output the test results report and compare it with the results of traditional test methods. The results show that the test results are in good agreement with the traditional methods, and are more accurate and comprehensive in reflecting the complex internal structure and physical properties of shale.

[0054] The above experimental verification shows that the relative error of mineral composition obtained by this method can be controlled within ±5%, the porosity measurement accuracy can reach ±3%, the water saturation measurement error is within ±4%, and the accuracy of elastic modulus and Poisson's ratio can reach ±3% and ±4% respectively, which are far higher than the accuracy of traditional single test methods.

[0055] The present invention also provides a rock multi-parameter detection device. The rock multi-parameter detection device provided by the present invention will be described below. The rock multi-parameter detection device described below can be referred to in correspondence with the rock multi-parameter detection method described above. Figure 5 This is a structural block diagram of the multi-parameter rock detection device provided by the present invention, as shown below. Figure 5 As shown, the device includes: The acquisition module 501 is used to acquire the rock to be tested and to preprocess the rock to be tested; The detection module 502 is used to perform parameter detection on the rock to be tested and obtain the detection data of the rock to be tested; the parameter detection includes wave velocity measurement, dielectric measurement and spectral measurement; the detection data includes wave velocity data, dielectric data and spectral data; The processing module 503 is used to standardize the detection data of the rock to be tested and to iteratively optimize it through a pre-trained data association model to retrieve the physical parameters of the rock to be tested.

[0056] In use, this device first preprocesses the rock under test using the acquisition module 501, removing impurities from the rock surface to improve contact and connection with the detection equipment, thereby enhancing detection accuracy. Then, the detection module 502 performs parameter detection on the rock, obtaining detection data including wave velocity, dielectric, and spectral data. By performing different types or dimensions of detection, a richer and more comprehensive understanding of the rock's internal structure can be obtained. Finally, the processing module 503 standardizes the detection data to eliminate the influence of different measurement units and magnitudes on the results. Iterative optimization is then performed using a trained data association model to retrieve the physical parameters of the rock. This data association model integrates wave velocity, dielectric, and spectral data, simultaneously acquiring multiple physical parameters such as mineral composition, pore structure, water content, and mechanical properties. Therefore, the data association model can also be referred to as the wave velocity-dielectric-spectral association model.

[0057] Compared to traditional single-method testing, the above process fully considers the inherent relationships and mutual influences between different physical quantities, effectively eliminating the errors and uncertainties of single-method testing. It comprehensively reflects the overall characteristics of the rock under test, providing richer and more comprehensive data support for rock-related engineering projects, and solving the problems of insufficient detection and poor accuracy in existing related technologies. Furthermore, this device is applicable to various types of rocks, whether hard granite and basalt, softer shale and mudstone, or complex metamorphic rocks. By adjusting the test parameters and the correlation coefficient of the data association model, high-precision physical parameter testing can be achieved, demonstrating broad application prospects. The entire testing process achieves automated data acquisition and rapid calculation processing, greatly shortening testing time and improving work efficiency. Compared to traditional combinations of multiple testing methods, it can obtain comprehensive physical parameters of the rock under test in a shorter time, meeting the needs of rapid decision-making in engineering sites.

[0058] This invention also provides a multi-parameter rock detection system. Figure 6 This is a structural block diagram of the multi-parameter rock detection system provided by the present invention, as shown below. Figure 6 As shown, the system includes: Wave velocity measurement subsystem 1 is used to measure the wave velocity of the rock under test and obtain the wave velocity data of the rock under test; Dielectric measurement subsystem 5 is used to perform dielectric measurements on the rock under test and obtain the dielectric data of the rock under test; The spectral measurement subsystem 3 is used to perform spectral measurements on the rock to be tested and obtain the spectral data of the rock. The data processing and association subsystem 4 is used to standardize the detection data of the rock under test and to iteratively optimize it through a pre-trained data association model to retrieve the physical parameters of the rock under test.

[0059] In this system, the wave velocity measurement subsystem 1 is connected to the spectral measurement subsystem 3, the wave velocity measurement subsystem 1 is connected to the dielectric measurement subsystem 5, and the wave velocity measurement subsystem 1 is connected to the data processing and correlation subsystem 4 through the high-speed channel 2.

[0060] Specifically, the wave velocity measurement subsystem 1 is equipped with a highly stable ultrasonic transmitter and receiver. The transmission frequency can be flexibly adjusted within the range of 50 kHz to 15 MHz to adapt to the wave velocity testing requirements of different types of rocks. At the same time, it adopts advanced signal processing technology to accurately measure the longitudinal wave velocity V of ultrasonic waves in the rock under test. p and transverse wave velocity V sThe wave velocity measurement subsystem 1 also has a built-in high-precision time measurement module to ensure the accuracy of wave velocity measurement. In addition, the wave velocity measurement subsystem 1 also has an automatic calibration function, which can periodically compare with standard wave velocity rock samples to ensure measurement accuracy. For example, by measuring the time difference of ultrasonic wave propagation in the rock and the known propagation distance, the wave velocity can be accurately calculated using the formula V = L / t (V is wave velocity, L is propagation distance, and t is propagation time).

[0061] The dielectric measurement subsystem 5 includes a dielectric constant measuring instrument with a microwave frequency range of 0.5 GHz - 12 GHz, which can accurately measure the dielectric constant of the rock under test at different frequencies. and dielectric loss The measuring instrument combines non-contact sensing technology with contact electrode measurement, making it adaptable to rock samples of different shapes and properties. For irregular rocks, a non-contact sensing probe can be used to obtain dielectric information by detecting changes in the electromagnetic field around the rock. For regular rocks, a contact electrode is used to ensure the stability and accuracy of the measurement. Furthermore, by establishing a database of dielectric responses under different mineral and water-bearing conditions, it provides a data foundation for subsequent rock parameter inversion.

[0062] The spectral measurement subsystem 3 employs a high-resolution spectrometer with a spectral range covering the visible light (350 nm - 760 nm) and near-infrared light (760 nm - 2500 nm) regions. It can acquire the spectral reflectance R and absorptivity A of the rock surface and a certain depth inside. Equipped with an adjustable optical lens with adjustable angle and focal length, it can accurately image and acquire spectra of rocks of different sizes and shapes. Through advanced spectral analysis algorithms, it can identify the mineral types in the rocks and preliminarily determine their relative contents.

[0063] The data processing and correlation subsystem 4 is responsible for collecting detection data from the wave velocity measurement subsystem 1, dielectric measurement subsystem 5, and spectral measurement subsystem 3. It performs preprocessing on the raw detection data, such as filtering and noise reduction, to remove interference signals during the measurement process and improve data quality. It can also construct data correlation models based on theories from rock physics, electromagnetics, and optics, establishing relationships between wave velocity, dielectric constant, spectral characteristics, and the mineral composition M of the rock. i (i represents different mineral types), porosity Water saturation S w The quantitative mathematical relationship between physical parameters such as elastic modulus E and Poisson's ratio v is established to realize the comprehensive inversion calculation of multiple physical parameters of the rock under test and to establish correlation equations.

[0064] Figure 7 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 7As shown, the electronic device may include: a processor 701, a communication interface 702, a memory 703, and a communication bus 704, wherein the processor 701, the communication interface 702, and the memory 703 communicate with each other via the communication bus 704. The processor 701 can call logical instructions in the memory 703 to execute a multi-parameter rock detection method, which includes: Obtain the rock to be tested and preprocess it; The parameters of the rock to be tested are measured to obtain the test data; the parameter measurement includes wave velocity measurement, dielectric measurement and spectral measurement; the test data includes wave velocity data, dielectric data and spectral data. The test data of the rock to be tested are standardized and iteratively optimized through a pre-trained data association model to deduce the physical parameters of the rock to be tested.

[0065] 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.

[0066] 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 execute the rock multi-parameter detection method provided by the above methods, the method comprising: Obtain the rock to be tested and preprocess it; The parameters of the rock to be tested are measured to obtain the test data; the parameter measurement includes wave velocity measurement, dielectric measurement and spectral measurement; the test data includes wave velocity data, dielectric data and spectral data. The test data of the rock to be tested are standardized and iteratively optimized through a pre-trained data association model to deduce the physical parameters of the rock to be tested.

[0067] 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 rock multi-parameter detection method provided by the methods described above, the method comprising: Obtain the rock to be tested and preprocess it; The parameters of the rock to be tested are measured to obtain the test data; the parameter measurement includes wave velocity measurement, dielectric measurement and spectral measurement; the test data includes wave velocity data, dielectric data and spectral data. The test data of the rock to be tested are standardized and iteratively optimized through a pre-trained data association model to deduce the physical parameters of the rock to be tested.

[0068] 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.

[0069] 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.

[0070] 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 multi-parameter detection of rocks, characterized in that, include: Obtain the rock to be tested and preprocess the rock to be tested; The rock to be tested is subjected to parameter detection to obtain the detection data of the rock to be tested; the parameter detection includes wave velocity measurement, dielectric measurement and spectral measurement; the detection data includes wave velocity data, dielectric data and spectral data; The detection data of the rock to be tested are standardized and iteratively optimized using a pre-trained data association model to deduce the physical parameters of the rock to be tested.

2. The rock multi-parameter detection method according to claim 1, characterized in that, Obtain the rock to be tested and preprocess the rock, including: For blocky rocks to be tested, the surface of the rocks is polished to remove impurities and weathering layers. For core samples of the rock to be tested, the rock is cut and processed according to the testing requirements.

3. The rock multi-parameter detection method according to claim 1, characterized in that, Wave velocity measurements are performed on the rock to be tested to obtain wave velocity data of the rock, including: The ultrasonic emission frequency is determined based on the rock type of the rock to be tested and the estimated wave velocity range. Longitudinal wave signals and transverse wave signals are emitted sequentially, and the propagation time of ultrasound waves in the rock is recorded. Based on the propagation time of the ultrasonic waves in the rock, the longitudinal wave velocity and the transverse wave velocity are determined.

4. The rock multi-parameter detection method according to claim 1, characterized in that, Dielectric measurements are performed on the rock to be tested to obtain its dielectric data, including: Based on the shape and properties of the rock to be tested, the measurement method for dielectric measurement is determined; the measurement method includes contact measurement and non-contact measurement. According to the measurement method described, the rock under test is tested at different microwave frequencies to obtain the dielectric constant and dielectric loss.

5. The rock multi-parameter detection method according to claim 4, characterized in that, The rock to be tested is examined at different microwave frequencies according to the measurement method described above, including: For the rock to be tested that has a regular shape, a contact electrode is used to connect to the rock to be tested and the rock is tested. For irregular rocks to be tested, a non-contact sensor probe is brought close to the surface of the rock, and the position and angle of the non-contact sensor probe are adjusted to detect the rock.

6. The rock multi-parameter detection method according to claim 1, characterized in that, The rock to be tested is subjected to spectral measurements to obtain spectral data of the rock, including: Based on the size of the rock to be tested and the required testing accuracy, determine the focal length and angle for the spectral measurement; The spectral reflectance and absorptivity of the rock under test are obtained by collecting spectra within different wavelength ranges.

7. The rock multi-parameter detection method according to claim 1, characterized in that, Training the data association model includes: Obtain the test data and physical parameters of the sample rock blocks; The detection data and physical parameters of the sample rock block are input into the data association model, and the data association model is fine-tuned to construct a mapping between the detection data and the physical parameters.

8. The rock multi-parameter detection method according to claim 1, characterized in that, After reversing the physical parameters of the rock to be tested, the following steps are taken: The physical parameters of the rock to be tested are processed to generate data reports or data charts; The test data and physical parameters of the sample rock blocks are obtained, and the test data and physical parameters of the sample rock blocks are compared and verified.

9. A multi-parameter rock detection device, characterized in that, include: An acquisition module is used to acquire the rock to be tested and to preprocess the rock to be tested; The detection module is used to perform parameter detection on the rock to be tested and obtain detection data of the rock to be tested; the parameter detection includes wave velocity measurement, dielectric measurement and spectral measurement; the detection data includes wave velocity data, dielectric data and spectral data; The processing module is used to standardize the detection data of the rock to be tested and iteratively optimize it through a pre-trained data association model to deduce the physical parameters of the rock to be tested.

10. A multi-parameter rock detection system, characterized in that, include: The wave velocity measurement subsystem is used to measure the wave velocity of the rock under test and obtain the wave velocity data of the rock under test. A dielectric measurement subsystem is used to perform dielectric measurements on the rock under test and obtain dielectric data of the rock under test. A spectral measurement subsystem is used to perform spectral measurements on the rock to be tested and obtain the spectral data of the rock to be tested; The data processing and association subsystem is used to standardize the detection data of the rock to be tested and iteratively optimize it through a pre-trained data association model to deduce the physical parameters of the rock to be tested.

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