A method, apparatus, and equipment for determining the micromechanical properties of rocks.

Abstract

This specification relates to the field of rock mechanics, and more particularly to a method, apparatus, and equipment for determining the micromechanical properties of rocks. The method includes: performing indentation testing on a target area of ​​a rock sample surface to obtain micromechanical response data; the micromechanical response data representing the relationship between the micromechanical response and time; scanning the target area of ​​the rock sample surface during the indentation test to obtain microstructure evolution data corresponding to the micromechanical response data; the microstructure evolution data representing the relationship between the microstructure and time; and determining the micromechanical properties of the rock sample based on the micromechanical response data and the corresponding microstructure evolution data. This specification allows for the accurate characterization of the heterogeneous micromechanical properties of rocks based on the synergistic analysis of the micromechanical response and microstructure of rock samples at multiple time points, achieving dynamic, in-situ, and quantitative characterization of the micromechanical properties of rocks.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of rock mechanics technology, specifically to a method, apparatus, and equipment for determining the micromechanical properties of rocks. Background Technology

[0002] As the main constituent material of the Earth's crust, rocks exhibit highly complex microscopic pore structures and compositions. Taking sedimentary rocks as an example, shale is rich in organic matter and clay minerals, developing nanoscale bedding pores; coal rocks have obvious cleavage systems and matrix pores; carbonate rocks commonly contain dissolution cavities and microfractures. In igneous rocks, granite is composed of interlocking mineral grains such as quartz, feldspar, and mica, while basalt exhibits a typical cryptocrystalline-glassy structure. This multi-mineral composition and multi-scale pore microscopic characteristic leads to significant mechanical heterogeneity in rocks at the microscale, manifested as spatial heterogeneity in parameters such as elastic modulus, hardness, and fracture toughness at the micrometer and even nanometer scales.

[0003] Traditional triaxial stress experiments can simulate the triaxial stress state in formations by applying confining pressure and axial stress, thereby obtaining macroscopic stress-strain curves, peak intensity, and other parameters. However, the experimental sample size is usually on the centimeter scale, which cannot reveal microscopic mechanical behavior. Existing nanoindentation technology can obtain the statistical distribution of parameters such as hardness and modulus through gridded testing, but it is essentially a static representation of the microscopic mechanical properties of rocks based on statistical principles, and cannot accurately characterize the microscopic mechanical properties of rocks based on their dynamic changes.

[0004] Therefore, the key issues that urgently need to be addressed are how to overcome the problem that existing methods can only use statistical principles to achieve a static representation of the micromechanical properties of rocks, and how to accurately characterize the micromechanical properties of rocks based on their dynamic changes. Summary of the Invention

[0005] The purpose of the embodiments in this specification is to provide a method, apparatus, and equipment for determining the micromechanical properties of rocks, so as to overcome the problem that existing methods for determining the micromechanical properties of rocks can only use statistical principles to achieve a static representation of the micromechanical properties of rocks.

[0006] To solve the above-mentioned technical problems, the specific technical solutions of the embodiments in this specification are as follows:

[0007] On the one hand, embodiments of this specification provide a method for determining the micromechanical properties of rocks, the method comprising:

[0008] Indentation tests were performed on the target area of ​​the rock sample surface to obtain the micromechanical response data of the rock sample; the micromechanical response data represents the relationship between the micromechanical response of the rock sample and time.

[0009] During the indentation test, the target area on the surface of the rock sample is scanned to obtain the microstructure evolution data corresponding to the micromechanical response data; the microstructure evolution data represents the relationship between the microstructure of the rock sample and time.

[0010] The micromechanical properties of the rock samples were determined based on their micromechanical response data and corresponding microstructure evolution data.

[0011] Furthermore, the method also includes:

[0012] Based on energy dispersive spectroscopy analysis, scanning electron microscopy was used to obtain chemical composition data and physical structure data of the target area on the surface of the rock sample.

[0013] Based on the chemical composition data and physical structure data, the target indenter of the scanning electron microscope cavity indenter and the tilt angle of the scanning electron microscope cavity indenter are determined.

[0014] The indentation test on the target area of ​​the rock sample surface includes:

[0015] At the tilt angle, the target indenter of the scanning electron microscope cavity indenter is used to perform indentation tests on the target area of ​​the rock sample surface.

[0016] Furthermore, the method also includes:

[0017] Based on the chemical composition data and physical structure data of the target region, determine the temperature field and electric field corresponding to the target region;

[0018] Based on the temperature and electric fields corresponding to the target area, the geological environment of the rock sample is simulated;

[0019] The indentation test on the target area of ​​the rock sample surface includes:

[0020] In a simulated geological environment, indentation tests were performed on the target area of ​​the rock sample surface.

[0021] Furthermore, the indentation test performed on the target area of ​​the rock sample surface to obtain the micromechanical response data of the rock sample includes:

[0022] Based on the geological data of the rock sample, determine the loading mode of the indentation test;

[0023] In the load mode, indentation tests were performed on the target area of ​​the rock sample surface to obtain load and displacement change data at multiple time points.

[0024] Furthermore, the indentation test includes a loading phase, a constant load phase, and an unloading phase;

[0025] Determining the loading mode of the indentation test based on the geological data of the rock sample includes:

[0026] Based on the geological data of the rock samples, determine the loading mode, constant load mode, and unloading mode corresponding to the loading stage, constant load stage, and unloading stage.

[0027] Further, the step of scanning the target region during the indentation test to obtain the microstructure evolution data corresponding to the micromechanical response data includes:

[0028] During the indentation test, the target area is scanned using a scanning electron microscope to obtain rock sample crack propagation data and rock sample particle migration data corresponding to the micromechanical response data; the rock sample crack propagation data represents the relationship between the rock sample crack morphology and time; the rock sample particle migration data represents the relationship between the rock sample particle coordinates and time.

[0029] Furthermore, determining the micromechanical properties of the rock sample based on its micromechanical response data and corresponding microstructure evolution data includes:

[0030] The micromechanical response and microstructure at multiple time points during the indentation test are spatiotemporally coupled.

[0031] The micromechanical properties of the rock sample are determined based on the micromechanical response and microstructure at multiple time points after the spatiotemporal coupling.

[0032] Furthermore, the method also includes:

[0033] Based on the microstructure at multiple time points, the micromechanical response at multiple time points is modified.

[0034] On the other hand, embodiments of this specification also provide an apparatus for determining the micromechanical properties of rocks, the apparatus comprising:

[0035] The testing module is used to perform indentation tests on the target area of ​​the rock sample surface to obtain the micromechanical response data of the rock sample; the micromechanical response data represents the relationship between the micromechanical response of the rock sample and time.

[0036] The scanning module is used to scan the target area on the surface of the rock sample during the indentation test to obtain the microstructure evolution data corresponding to the micromechanical response data; the microstructure evolution data represents the relationship between the microstructure of the rock sample and time.

[0037] The determination module is used to determine the micromechanical properties of the rock sample based on the micromechanical response data and the corresponding microstructure evolution data of the rock sample.

[0038] Furthermore, embodiments of this specification also provide a computer device, including:

[0039] Memory, used to store computer programs;

[0040] A processor for executing the computer program to implement any of the methods for determining the micromechanical properties of rocks described above.

[0041] Furthermore, embodiments of this specification also provide a computer program product, which, when run by the processor of a computer device, executes instructions for any of the methods described above.

[0042] As can be seen from the technical solutions provided in the embodiments of this specification above, these embodiments can perform indentation tests on target areas of rock sample surfaces to obtain micromechanical response data of the rock sample; the micromechanical response data represents the relationship between the micromechanical response of the rock sample and time; during the indentation test, the target area of ​​the rock sample surface is scanned to obtain microstructural evolution data corresponding to the micromechanical response data; the microstructural evolution data represents the relationship between the microstructure of the rock sample and time; based on the micromechanical response data and the corresponding microstructural evolution data, the micromechanical properties of the rock sample are determined. Compared with existing methods, these embodiments can scan the target area of ​​the rock sample surface while performing indentation tests, thereby obtaining the micromechanical response and microstructure of the rock sample at multiple time points. Based on the synergistic analysis of the micromechanical response and microstructure of the rock sample at multiple time points, the intrinsic correlation between micromechanical behavior and microstructural evolution can be revealed, thus more accurately characterizing the heterogeneous micromechanical properties of rocks and achieving dynamic, in-situ, and quantitative characterization of the micromechanical properties of rocks.

[0043] The above description is merely an overview of some embodiments of the technical solutions in this specification. In order to better understand the technical means of some embodiments of this specification and to implement them in accordance with the content of the specification, and to make the above and other objects, features and advantages of the embodiments of this specification more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the accompanying drawings used in the description of the embodiments or prior art will be briefly introduced below.

[0045] Figure 1 This is a flowchart illustrating a method for determining the micromechanical properties of rocks, as provided in the embodiments of this specification.

[0046] Figure 2This is a schematic diagram of the overall process logic of a method for determining the micromechanical properties of rocks provided in the embodiments of this specification;

[0047] Figure 3 This is a microstructure image of the rock sample surface indentation test before the test provided in the embodiments of this specification;

[0048] Figure 4 This is a magnified microstructure image of the target area before the surface indentation test of the rock sample in area A, provided in the embodiments of this specification.

[0049] Figure 5 These are microstructure images of rock samples from area A after surface indentation testing, provided in the embodiments of this specification.

[0050] Figure 6 This is a magnified microstructure image of the target area after surface indentation testing of a rock sample in region A, provided in the embodiments of this specification.

[0051] Figure 7 This is a schematic diagram of the pressure-load curve recorded during the indentation test of the rock sample target area in region A, as provided in the embodiments of this specification;

[0052] Figure 8 This is a schematic diagram of the changes in indenter load, indenter displacement, and indentation depth of rock sample target area in area A over time during the indentation test of the target area in area A provided in the embodiments of this specification;

[0053] Figure 9 This is a schematic diagram of the structural composition of a device for determining the micromechanical properties of rocks provided in the embodiments of this specification;

[0054] Figure 10 This is a schematic diagram of the structural composition of the computer device provided in the embodiments of this specification. Detailed Implementation

[0055] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0056] Figure 1 This is a flowchart illustrating a method for determining the micromechanical properties of rocks, as provided in the embodiments of this specification. Figure 2 This is an overall logical flowchart of a method for determining the micromechanical properties of rocks provided in the embodiments of this specification. In specific implementation, it includes the following steps:

[0057] S101: Indentation test is performed on the target area of ​​the rock sample surface to obtain the micromechanical response data of the rock sample; the micromechanical response data represents the relationship between the micromechanical response of the rock sample and time.

[0058] In some embodiments, the loading mode of the indentation test can be determined based on the geological data of the rock sample; the indentation test can be performed on the target area of ​​the rock sample surface under the loading mode to obtain load and displacement change data at multiple time points.

[0059] Determining the loading mode of indentation testing using geological data helps to accurately characterize the micromechanical properties of rocks. The time-series load-displacement data obtained from indentation testing not only contains static mechanical parameters but also records the dynamic response process, providing a good data foundation for studying the dynamic rheological properties and damage accumulation mechanisms of rocks.

[0060] Indentation testing utilizes a nanoindenter to apply a controlled mechanical load to the material surface using a rigid indenter, simultaneously recording the mechanical load-material displacement change process to invert the material / rock mechanical properties. For heterogeneous multiphase composite materials such as rocks, the interaction between the indenter and mineral particles, cement, and pore structure during testing exhibits significant spatiotemporal heterogeneity. A nanoindenter can consist of a precision loading unit, a mechanical sensing module, a three-dimensional displacement sensing module, and a data acquisition system, with a load resolution set to the order of 0.1 mN and a displacement measurement accuracy set to 1 nm.

[0061] Based on geological data such as the mineral composition, microstructure, porosity distribution, and bedding structure of rock samples, the corresponding loading mode can be determined. Loading modes can include stepped loading, cyclic loading, and monotonic loading. In monotonic loading, the load increases steadily and continuously, visually representing the entire process of rock deformation from its initial state to failure, and can be used to investigate the mechanical response of rock under its first load. In cyclic loading, the load increases and decreases periodically, simulating repeated stress on rock in actual engineering, which helps to study the fatigue characteristics of rock and observe changes in the internal structure and damage accumulation of rock under multiple loading and unloading cycles. Stepped loading gradually increases the load in specific steps, clearly analyzing the changes in the mechanical properties of rock at different stress stages. The loading rate can be adjusted within the range of 0.01 mN / s–10 mN / s; low-speed loading can more meticulously capture the mechanical behavior of rock during slow deformation, while high-speed loading can simulate rapid stress scenarios such as earthquakes, studying the mechanical response of rock under impact. Specifically, taking shale as an example, when the clay mineral content exceeds 40%, a stepped loading strategy is required to avoid sudden indentation failure; while for granite containing brittle quartz particles, a quasi-static continuous loading mode is suitable. Specific parameters of the loading mode include peak load (typically 50-500 mN), loading rate (0.05-5 mN / s), holding time (10-600 s), and number of cycles. These parameters can be dynamically adjusted based on the estimated Young's modulus and hardness range of the rock.

[0062] Before the experiment, the rock sample surface can be calibrated and pre-treated. A scanning electron microscope (SEM) can be used to perform three-dimensional morphological scanning of the rock sample surface, identifying mineral phase boundaries and microfracture distribution, and selecting representative target areas. The surface roughness of the rock sample can be controlled to Ra < 50 nm, and an atomically smooth surface can be obtained through ion beam polishing. The type of indenter can be selected based on a comprehensive consideration of the test target; for example, a Vickers pyramid indenter is suitable for anisotropic analysis, a Boehringer indenter is beneficial for measuring elastic recovery, and a spherical indenter is more suitable for studying creep characteristics. During the indentation test, the indenter can perform indentation testing on the target area along a preset trajectory.

[0063] The data acquisition system can synchronously record time-series load data, as well as micromechanical response data including time-series data of displacement and contact stiffness. The sampling frequency can meet the Nyquist criterion and is no less than 1kHz. Multiple preprocessing steps can be performed on the time-series load, displacement, and contact stiffness data acquired by the data acquisition system: first, baseline correction can be performed to eliminate the influence of equipment flexibility; second, a digital filter (Butterworth low-pass, cutoff frequency 1kHz) is applied to suppress high-frequency noise; and finally, a thermal drift compensation algorithm is used to eliminate displacement drift caused by environmental temperature fluctuations.

[0064] Based on time-series data such as load, displacement, and contact stiffness acquired by the data acquisition system, load-displacement curves corresponding to the indentation test process can be constructed. These curves contain data on the changes in indenter load and rock sample displacement over time. The load-displacement curves can be divided into five characteristic segments: the nonlinear response in the initial contact stage reflects the surface adsorption layer effect; the slope of the elastic deformation segment corresponds to the contact stiffness; the load abrupt change at the plastic yield point indicates dislocation initiation; the creep curve in the load-holding stage reflects the time-dependent deformation mechanism; and the elastic recovery rate of the unloading curve characterizes energy dissipation properties. For layered rocks, the curves can exhibit multi-stage yielding characteristics, which are closely related to interlayer slip and mineral phase transformation.

[0065] S102: During the indentation test, the target area on the surface of the rock sample is scanned to obtain the microstructure evolution data corresponding to the micromechanical response data; the microstructure evolution data represents the relationship between the microstructure of the rock sample and the change over time.

[0066] In some embodiments, the target area can be scanned during the indentation test to obtain rock sample crack propagation data and rock sample particle migration data corresponding to the micromechanical response data; the rock sample crack propagation data represents the relationship between the rock sample crack morphology and time; the rock sample particle migration data represents the relationship between the rock sample particle coordinates and time.

[0067] By simultaneously applying mechanical loads and performing spatiotemporal analysis of the fracture network development trajectory and particle motion behavior in the target region, a leap from static parameter testing to dynamic process monitoring has been achieved. This provides a novel experimental paradigm and precise experimental data for studying the brittle-ductile transition of rocks, localized failure, and multiphase coupling effects. Furthermore, simultaneously acquiring microscopic mechanical responses and microstructural evolution helps reveal transient processes and nonlinear effects that are unobservable by traditional experimental methods. This provides an experimental foundation for analyzing the co-evolutionary laws governing fracture propagation and particle migration.

[0068] During indentation testing, a scanning electron microscope (SEM) can be used to scan the target area on the rock sample surface in real time, obtaining microstructural images of the target area at each time point during the indentation test. Based on the microstructural images of the target area at each time point during the indentation test, the relationship between the morphology of rock sample cracks and time, as well as the relationship between the coordinates of rock sample particles and time, can be obtained.

[0069] Specifically, regarding the relationship between the morphology and temporal variation of rock sample cracks in the target area, a dynamic digital image correlation (DIC) algorithm can be developed to perform sub-pixel-level displacement field calculations on continuously acquired microstructural image sequences. Combined with a crack tip stress intensity factor model, the crack propagation rate and path deflection can be quantitatively characterized. For example, edge detection technology based on phase consistency can be used to automatically identify crack contours in each frame of the image. The watershed algorithm can be used to segment the primary crack and secondary crack network, and then the temporal evolution of parameters such as crack length, aperture, and bifurcation angle can be calculated. For complex three-dimensional crack systems, stereo imaging technology can be combined with a dual-probe secondary electron detector to acquire parallax images, and a three-dimensional reconstruction algorithm can be used to establish a model of the evolution of the crack spatial topology over time.

[0070] Regarding the relationship between the coordinates and time of rock sample particles in the target area, SEM observations can enhance atomic number contrast using backscattered electron (BSE) mode, and combined with energy dispersive spectral analysis, the chemical composition of different mineral particles can be distinguished. An improved scale-invariant feature transformation algorithm can be used to extract particle feature points, and the displacement vector between adjacent frames can be calculated using optical flow, enabling automatic tracking of multi-particle trajectories. Furthermore, to improve recognition accuracy in complex backgrounds, a deep learning framework can be introduced. For example, a U-Net neural network can be trained using a deep learning framework to perform pixel-level segmentation of particle boundaries, and a Kalman filter algorithm can be used to predict motion trajectories, effectively overcoming the interference of electron beam scanning distortion and image noise. The final output particle transport database contains time series of parameters such as the three-dimensional coordinates (X, Y, Z), velocity vectors (Vx, Vy, Vz), and rotation angles of each particle, providing experimental validation data for establishing a discrete element model.

[0071] After obtaining the temporal and structural relationships of rock sample fracture morphology and rock sample grain coordinates in the target area, time series analysis algorithms can be used to analyze the continuously acquired microstructural image sequences. For example, the long-range correlation of fracture growth can be determined by calculating the Hurst index, and the Lévy flight theory can be used to describe the jump-like propagation behavior of fracture tips. In particle transport analysis, complex network theory can be applied to construct particle contact force chain evolution models, and the time-varying characteristics of node degree distribution and clustering coefficients can reveal the force chain reorganization mechanism.

[0072] Furthermore, machine learning algorithms can be introduced to achieve efficient processing of massive microstructure image sequences. For example, convolutional neural networks (CNNs) can be used to automatically classify crack propagation patterns (such as transgranular propagation, intergranular propagation, or bifurcation propagation), long short-term memory (LSTM) networks can be used to predict particle migration trajectories, and SHAP value analysis can be combined to reveal key factors controlling the migration direction (such as local stress gradients, particle shape anisotropy, etc.).

[0073] In some embodiments, pulsed electron beam technology can be used to improve the imaging temporal resolution to the nanosecond level, enabling the capture of transient fracture processes induced by stress wave propagation. Combining SEM with focused ion beam (FIB) milling technology allows for in-situ cross-sectional observation and 3D reconstruction of three-dimensional crack networks. Furthermore, integrating cathodoluminescence (CL) spectroscopy and electron backscatter diffraction (EBSD) techniques allows for the simultaneous acquisition of mineral phase transformation information and crystal orientation evolution data during mechanical testing, establishing multi-field coupled constitutive relations of mechanics, chemistry, and structure.

[0074] S103: Determine the micromechanical properties of the rock sample based on the micromechanical response data and the corresponding microstructure evolution data of the rock sample.

[0075] In some embodiments, the micromechanical responses and microstructures at multiple time points during the indentation test can be spatiotemporally coupled; the micromechanical properties of the rock sample can be determined based on the spatiotemporally coupled micromechanical responses and microstructures at multiple time points.

[0076] By establishing a spatiotemporal coupled model that dynamically correlates micromechanical response and structural evolution, this approach overcomes the limitations of traditional methods that rely on fragmented analysis across time and space dimensions. By integrating high spatiotemporal resolution mechanical test data with multimodal microscopic observation information, a complete analytical chain from transient mechanical behavior to long-term structural evolution is constructed. This reveals transient processes and nonlinear effects that are unobservable by traditional experimental methods, facilitating a deeper analysis of the co-evolutionary laws governing crack propagation and particle migration, and ultimately enabling precise determination of the micromechanical properties of rocks.

[0077] To address the temporal coupling between the micromechanical response and microstructure at multiple time points during indentation testing, an FPGA-based hardware-level synchronization controller can be used to precisely align the load signal of the nanoindenter (e.g., sampling rate of 100kHz), the SEM electron beam scanning signal (e.g., frame synchronization accuracy of ±10ns), and the Raman spectroscopy acquisition trigger signal (timing jitter <1μs). This ensures that each mechanical data point (load P, displacement h) has a defined timestamp with the corresponding structural image (crack length L, particle coordinates (x,y) etc.).

[0078] To address the spatial coupling of micromechanical responses and microstructures at multiple time points during indentation testing, a micrometer-scale positioning marker array (such as a cross-shaped marker grid prepared by photolithography) can be used, combined with an affine transformation algorithm to eliminate coordinate system deviations between different observation devices (SEM, AFM, X-ray CT), achieving sub-pixel-level (<50nm) spatial registration accuracy in three-dimensional space. For coordinate system drift during dynamic deformation, a real-time correction algorithm based on feature point tracking can be used to optimize dynamic registration through maximum mutual information (MI) optimization between adjacent frames.

[0079] After completing the spatiotemporal coupling of the micromechanical response and microstructure at multiple time points during the indentation test, a unified data model can be established to integrate mechanical time series data (P(t)-h(t) curve), structural evolution data (e.g., crack density D(x,y,t)), and compositional distribution data (mineral phase diagram M(x,y)). Furthermore, the spatiotemporal voxelization method can be used to interpolate discrete sampling points into a continuous four-dimensional data field, forming a spatiotemporal hypercube data structure capable of tensor operations.

[0080] Based on the micromechanical response and microstructure at multiple time points after spatiotemporal coupling, the micromechanical properties of the rock sample can be determined, including:

[0081] Transient features of the load-displacement curve (such as the moment of crack nucleation corresponding to the sudden drop point) can be extracted by wavelet transform, and time-varying parameters such as creep compliance J(t) and relaxation modulus E(t) can be calculated.

[0082] Computer vision algorithms (such as the image segmentation model SAM) can be used to quantify spatiotemporal evolution parameters such as the fractal dimension DF(t), particle transport and diffusion coefficient D(t), and pore topological connectivity index C(t) of the crack network;

[0083] Based on spatiotemporal cross-correlation analysis, the hysteresis correlation between mechanical parameters and structural parameters can be calculated, for example, to determine the time hysteresis relationship between crack density D(t+Δt) and local stress concentration factor K(t);

[0084] Dynamic mode decomposition can be used to identify the key modes that dominate the spatiotemporal evolution during indentation testing, and to extract the key feature frequencies and spatial patterns that control damage evolution.

[0085] A data-driven constitutive neural network can be established, with the spatiotemporal structural parameters corresponding to the indentation test process as input and the mechanical response as output. The implicit constitutive relationship between the rock sample structure and the rock sample mechanical parameters can be obtained through deep reinforcement learning training.

[0086] It is possible to develop a micromechanical model of multiphase media, and simulate the dynamic interaction of particle-matrix-pore system based on the discrete element-finite element coupling (DEM-FEM) method. The micromechanical parameters of rock samples, such as interface strength and friction coefficient, can be determined through parameter inversion.

[0087] In some embodiments, based on energy dispersive spectroscopy (EDS) analysis, scanning electron microscopy (SEM) is used to acquire chemical composition data and physical structure data of the target area on the surface of a rock sample; based on the chemical composition data and physical structure data, the target indenter of the SEM cavity indenter and the tilt angle of the SEM cavity indenter are determined; at the tilt angle, the target indenter of the SEM cavity indenter is used to perform indentation testing on the target area on the surface of the rock sample.

[0088] The synergistic application of scanning electron microscopy and energy dispersive spectroscopy provides multi-physics coupled characterization capabilities for rock micromechanical testing. Specifically, based on high spatial resolution microscopic observations, combined with elemental distribution and mineral phase identification results, the indenter type, loading direction, and contact mode can be dynamically adjusted, significantly improving the spatial targeting and data reliability of micromechanical testing.

[0089] In the chemical composition data acquisition stage, the energy dispersive spectroscopy (EDS) system can achieve qualitative and quantitative analysis of the elemental composition of the target region through the acquisition and interpretation of characteristic X-ray energy spectra. Characteristic X-rays excited by the interaction between the electron beam and the sample are received by a silicon drift detector and converted into EDS signals by a pulse processor. By identifying peak positions (e.g., Si-Kα peaks correspond to quartz, Ca-Kα peaks correspond to calcite) and calculating peak area integration, the weight percentage and atomic percentage of each element can be obtained. For complex mineral assemblages, a surface distribution analysis (mapping) mode can be used to obtain a two-dimensional elemental distribution map in pixels, combined with an automatic phase recognition algorithm (e.g., based on a random forest classifier) ​​to delineate mineral phase boundaries. For example, in shale samples, it can accurately distinguish kerogen-rich areas (C element content > 70%), clay mineral areas (Al, Si, O element combination), and pyrite particles (Fe, S element characteristic peaks), providing a chemical basis for targeted localization in subsequent mechanical testing.

[0090] The acquisition of physical structure data can rely on the high-resolution imaging capabilities of scanning electron microscopy (SEM). Through the coordinated analysis of secondary electron and backscattered electron signals, three-dimensional morphology and density contrast information of the target region can be constructed. Secondary electron imaging is sensitive to surface morphology and can clearly present topological features such as micron-scale pores and nanoscale layering; backscattered electron imaging reflects differences in atomic number and can distinguish grain boundaries and inclusions of different mineral phases. Stereoscopic imaging technology and digital elevation model (DEM) reconstruction algorithms can be combined to quantitatively calculate surface roughness parameters, pore geometry (pore size distribution, aspect ratio), and mineral grain orientation distribution. For layered rocks (such as shale and gneiss), electron backscattered diffraction (ESD) technology can be used to further obtain crystal orientation data and establish a correlation model between preferred orientation and mechanical anisotropy of mineral grains.

[0091] After obtaining the chemical composition and physical structure data of the target area on the rock sample surface, the target indenter can be selected based on the dual principles of material matching and geometric adaptation. Specifically, in terms of material matching, the indenter material can be selected according to the hardness range of the mineral being tested. For example, for ultrahard minerals (such as diamond indenters used for quartz testing, with a Vickers hardness >10 GPa), single-crystal diamond indenters can be used to avoid deformation; for medium- and low-hardness minerals (such as calcite and clay minerals), sapphire or tungsten carbide indenters can be selected to reduce testing costs. In terms of geometric adaptation, the indenter tip shape can be selected based on the size and morphological characteristics of the mineral phase: Berkovich triangular pyramidal indenters (tip curvature radius 100 nm) are suitable for hardness and modulus testing in homogeneous regions at the micrometer scale; spherical indenters (radius 5-50 μm) are more suitable for analyzing elastoplastic transition behavior and creep characteristics; for layered structures or pore edge regions, flat punches are used to avoid abnormal fractures caused by stress concentration. During the selection of the indenter, chemical composition data (to avoid chemical reactions between the indenter material and the sample) and physical structure data (such as the influence of pore density on the effective contact area) can be comprehensively evaluated. The stress field distribution under different indenters can be pre-simulated through finite element simulation to finally determine the optimal indenter configuration scheme.

[0092] After acquiring chemical composition and physical structure data of the target area on the rock sample surface, the tilt angle of the indenter inside the SEM cavity can be optimized by comprehensively considering the SEM observation angle, sample surface morphology, mineral crystal orientation, and test target. Specifically, for rough surfaces with significant undulations (Ra>1μm), the local normal direction can be calculated using DEM data, and the sample stage tilt angle (range 10°-45°) can be dynamically adjusted to ensure that the indenter axis is perpendicular to the test point surface, ensuring that the load application direction is consistent with the theoretical calculation. For anisotropic minerals (such as the layered structure of mica and the columnar cleavage of pyroxene), specific loading directions can be set in conjunction with EBSD lattice orientation data to study the influence of crystallographic orientation on mechanical response: for example, applying a load perpendicular to the (001) plane of mica to test the interlayer bonding strength, or loading along the c-axis of quartz to study the anisotropic elastic modulus. In multilayer composite structure regions, a multi-angle continuous testing strategy is adopted, and the mechanical parameter matrix under different loading directions is obtained by changing the tilt angle at 5° intervals (0°, 5°, 10°), establishing a direction-dependent model of mineral interface strength.

[0093] In some embodiments, the determination of the target indenter and the tilt angle of the indenter in a scanning electron microscope (SEM) cavity indenter can be based on machine learning algorithms. Specifically, a mineral feature database can be constructed first, containing the energy dispersive spectral fingerprints, grayscale threshold ranges, and surface morphology features of typical rocks and minerals. During testing, real-time acquired chemical-physical data can be used to classify mineral phases through a convolutional neural network and generate initial parameter suggestions by calling a preset indenter selection rule base (If-Then rules). Furthermore, reinforcement learning algorithms can be used to dynamically optimize the indenter type and tilt angle parameters based on indicators such as the test success rate and data stability of different parameter combinations in historical test data. For example, when a high porosity region is detected, the algorithm automatically increases the selection weight of the spherical indenter to avoid stress concentration; when a layered structure is identified, the tilt angle compensation strategy is preferentially recommended. This data-driven parameter optimization method transforms human experience into a quantifiable decision model, significantly improving testing efficiency and result repeatability.

[0094] In some embodiments, the tilt angle of the indenter inside the scanning electron microscope cavity can be determined based on the chemical composition data, physical structure data, and the observation angle of the scanning electron microscope.

[0095] By precisely adjusting the geometric parameters, the interaction between the electron beam and the sample surface is ensured to be in the optimal state, thereby obtaining real-time observation images with high signal-to-noise ratio and low distortion, while maintaining the mechanical loading accuracy of the indentation test.

[0096] The imaging quality of a scanning electron microscope (SEM) is influenced by the combined effects of the electron beam incident angle and the detector receiving angle. By adjusting the stage tilt angle to align the electron beam axis with the surface normal direction at the test point, the deviation in electron scattering direction caused by local topography can be minimized. For rough surfaces (Ra>0.5μm), real-time surface topography reconstruction techniques can be used to dynamically calculate the local normal direction based on DEM data, driving the stage to compensate for the tilt angle with an accuracy of 0.001°. The optimal receiving angle for the secondary electron detector is typically 35°-45°, which can be achieved by jointly tilting the indenter and the stage to ensure that the surface normal direction of the test area forms the optimal receiving angle with the detector axis. For multi-detector systems (such as In-lens SE and BSE combinations), an angle optimization matrix can be established to balance the acquisition efficiency of different signal sources.

[0097] During loading, the indenter geometry may obstruct the electron beam path or the detector's field of view. Finite element ray tracing simulations can be used to pre-simulate the indenter shadow area at different tilt angles, allowing for the selection of unobstructed or minimally obstructed angle configurations. For Berkovich indenters, the major axis of the indenter can be set at a 45° angle to the detector axis to avoid the main shadow area. To ensure that the indenter-sample contact point is always located in the center of the SEM field of view (covering at least 30% of the field of view), the indenter trajectory can be calculated using a preset kinematic model. This allows for simultaneous compensation of the sample stage translation during tilt angle adjustments, maintaining the spatial stability of the contact point within the imaging field of view (drift <0.1 μm / min). Furthermore, tilt loading can introduce asymmetric contact stress distribution. Hertz contact theory corrections can be used to calculate the stress field shift caused by the tilt angle. When the angle exceeds 5°, a contact mechanics compensation algorithm is activated to correct the anisotropic deviation of the load-displacement curve.

[0098] Furthermore, during continuous loading, sample surface deformation may alter local geometry. A real-time feedback adjustment mechanism can be established: 1) Analyze real-time SEM image sequences using the DIC algorithm to calculate the surface displacement gradient tensor. When local deformation causes a change in the normal direction exceeding 0.5°, micro-angle compensation of the sample stage (step size 0.01°) is triggered to maintain orthogonal electron beam incidence. 2) Changes in surface height (such as indentation formation) can alter the optimal focusing position. An autofocus algorithm can be used to adjust the objective current in real-time through edge sharpness analysis, combined with dynamic astigmatism correction (30ms refresh rate per frame) to maintain image clarity. 3) A closed-loop control system of tilt angle, load, displacement, and focal length can be constructed, employing a PID algorithm to coordinate the response speed of each actuator, ensuring that mechanical vibrations caused by angle adjustment decay to nanometer-level amplitude (<2nm) before continuing loading.

[0099] Through the above multi-dimensional optimization strategies, the tilt angle configuration of the indenter in the scanning electron microscope cavity can maintain the best observation angle throughout the entire mechanical testing cycle, providing high-fidelity visualization data support for in-situ dynamic research on the micromechanical behavior of rocks.

[0100] In some embodiments, the temperature field and electric field corresponding to the target region are determined based on the chemical composition data and physical structure data of the target region; the geological environment of the rock sample is simulated based on the temperature field and electric field corresponding to the target region; and indentation tests are performed on the target region on the surface of the rock sample under the simulated geological environment of the rock sample.

[0101] By deeply integrating chemical composition and physical structure data, a model of temperature and electric field distribution that reflects actual geological conditions can be constructed, thereby enabling accurate reproduction of complex geological environments and providing a precise environment for studying the long-term evolution behavior of rocks under thermo-electric-mechanical coupling.

[0102] In the chemical composition data analysis stage, thermophysical parameter models can be established based on quantitative elemental distribution and mineral phase information obtained from energy dispersive spectroscopy (EDS) and X-ray diffraction (XRD). Specifically, parameters such as thermal conductivity, specific heat capacity, and coefficient of thermal expansion of each mineral phase can be matched using mineral thermal databases (such as Thermocalc and HPx), and the effective thermal conductivity of the composite rock can be calculated using the mixing law. Electrical parameters can be determined based on mineral resistivity databases (such as the CIPW standard). For example, the resistivity of hydrous rocks can be calculated using the Archie formula, while for dry rocks, the effective medium theory (EMT) can be used to simulate the electrical conductivity behavior of the mineral-pore composite system.

[0103] Furthermore, the spatiotemporal evolution of geological environment simulations can be achieved through time-stepping. Specifically, initial conditions can be set using current geological data, while boundary conditions are dynamically adjusted based on tectonic history. Within each time step, the temperature field (affecting rock thermal fracturing and mineral phase transformation), electric field (controlling ion migration and alteration reactions), mechanical field (inducing stress redistribution), and chemical field (changing mineral composition and pore structure) are updated sequentially, forming a positive feedback loop. For simulations on a million-year scale, a strategy combining explicit time integration and adaptive mesh refinement (AMR) is employed to improve efficiency while ensuring computational accuracy.

[0104] In some embodiments, the indentation test includes a loading phase, a constant load phase, and an unloading phase; based on the geological data of the rock sample, the loading mode, constant load mode, and unloading mode corresponding to the loading phase, constant load phase, and unloading phase are determined.

[0105] The phased control of rock indentation testing is a core technical aspect of micromechanical characterization. The scientific setting of loading, constant load, and unloading modes directly determines the reliability and engineering applicability of the test data. This technical system, based on multi-dimensional analysis of rock geological data, establishes differentiated loading strategies to accurately match testing requirements under different lithologies, structures, and environmental conditions, providing methodological support for revealing the spatiotemporal evolution of rock micromechanical behavior.

[0106] Loading modes can include monotonic loading, cyclic loading, and high-speed loading. For example, in monotonic loading, the load increases steadily and continuously, visually demonstrating the entire process of rock deformation from its initial state to failure, which can be used to investigate the mechanical response of rock under its first load. In cyclic loading, the load increases and decreases periodically, simulating repeated stress on rock in actual engineering, which helps to study the fatigue characteristics of rock and observe the changes in the internal structure and damage accumulation of rock under multiple loading and unloading cycles. Stepped loading increases the load gradually in specific steps, which can clearly analyze the changes in the mechanical properties of rock at different stress stages. The loading rate can be adjusted within the range of 0.01 mN / s – 10 mN / s. Low-speed loading can capture the mechanical behavior of rock during slow deformation in greater detail; high-speed loading can simulate rapid stress scenarios such as earthquakes and study the mechanical response of rock under impact.

[0107] The loading mode for each stage of loading can be configured by comprehensively considering the brittle-ductile characteristics, mineral hardness distribution, and microstructure of the rock. For layered shale or gneiss, a differentiated loading rate can be set according to the angle θ between the bedding direction and the loading direction: when θ = 0° (parallel bedding), a stepped loading method (0.5 mN per step, held for 10 s) is used to avoid sudden fractures caused by interlayer slip; when θ = 90° (perpendicular bedding), cyclic loading (rate 1 mN / s) is used to fully stimulate the interlayer bonding strength. For porous sandstone or carbonate rocks, a porosity-loading rate correlation model can be introduced to dynamically adjust the loading rate based on the porosity obtained from CT scans. For composite rocks containing weak minerals (such as clay and gypsum), a pre-loading-relaxation cyclic strategy can be adopted: after an initial loading of 0.5 mN, it is held for 30 s to allow the weak phases to undergo plastic adjustment, and then the main loading process continues to obtain a stable mechanical response.

[0108] The constant load mode can be set by quantifying time-dependent deformation (creep, stress relaxation). Specifically, based on mineral composition data, rheological models for different phase components can be established. Based on the rheological model, different constant load times can be set. For example, for elastic phases such as quartz, the constant load time can be set to 10-30 s to detect its elastic recovery rate; for viscoelastic phases such as clay, the constant load time can be set to 300-600 s to record creep displacement curves; for viscoplastic phases such as organic matter, the constant load time can be set to greater than 1800 s to obtain the steady-state creep rate.

[0109] The choice of unloading phase mode directly affects the measurement accuracy of residual deformation and elastic parameters. Therefore, for brittle rocks (such as granite and basalt), instantaneous unloading (rate 10 mN / s) can be used to capture the transient response of elastic rebound; for ductile rocks (such as salt rock and organic shale), stepped unloading (0.2 mN per stage, 5 s interval) can be used to separate the viscoelastic recovery component.

[0110] Furthermore, based on the mineral hardness ratio, differential unloading paths can be set. For example, when the hardness ratio is >3, a spherical indenter combined with a logarithmic unloading curve can be used; when the hardness ratio is 1-3, a Berkovich indenter combined with linear unloading can be used; and when the hardness ratio is <1, a Flat Punch indenter combined with exponential unloading can be used.

[0111] In some embodiments, the micromechanical response at the plurality of time points can be modified based on the microstructure at the plurality of time points.

[0112] By using time-series microstructure observation data as constraints to correct the micromechanical response at multiple time points, it is possible to approximate the original mechanical response to the true intrinsic behavior based on the spatiotemporal causal relationship between microstructure evolution and mechanical behavior. This helps to reveal key mechanisms such as rock damage accumulation, energy dissipation, and instability precursors.

[0113] From a microstructural perspective, changes in grain size significantly impact micromechanical response. According to the Hall-Petch relation, grain refinement typically increases material strength because grain boundaries impede dislocation movement, leading to dislocation pile-up and increased deformation resistance. Therefore, if an abnormal increase in grain size is observed at a certain time point, but the yield strength of the rock sample grains in the micromechanical response does not decrease accordingly, then the micromechanical response at that time point is abnormal. Grain boundary characteristics also play a crucial role in micromechanical behavior. High-angle grain boundaries possess higher energy and lower slip transfer efficiency, effectively hindering dislocation movement; while low-angle grain boundaries have a relatively weaker resistance to deformation. When the type and distribution of grain boundaries change abruptly, such as a shift from a structure dominated by high-angle grain boundaries to one dominated by low-angle grain boundaries, the plastic deformation mechanism of the rock sample / particles may alter, thus affecting the normal performance of the micromechanical response. Phase distribution and phase transformation processes are also important criteria for judging micromechanical responses. Different phases in rock samples exhibit significant differences in mechanical properties, and the precipitation, dissolution, or growth of second-phase particles can alter the material's load-bearing capacity and deformation patterns. For example, in aluminum alloys, the fine strengthening phases precipitated during aging can pin dislocations, significantly improving the material's strength. However, if at a certain point in time the strengthening phase exhibits abnormal coarsening, but the hardness in the micromechanical response does not decrease significantly, this clearly does not conform to normal mechanical behavior. Furthermore, the morphology and evolution of defects (such as pores, cracks, and dislocations) have a decisive influence on the micromechanical response. The growth and aggregation of pores may lead to a decrease in the material's toughness, the initiation and propagation of cracks directly threaten the structural integrity of the material, and the proliferation, movement, and interaction of dislocations are the core mechanisms of plastic deformation in materials. Therefore, by comparing and analyzing the defect characteristics in the microstructure at different time points, it is possible to determine whether the micromechanical response conforms to the expected defect evolution patterns.

[0114] To accurately assess the micromechanical response, quantitative indicators and standards can be established. Specifically, based on the constitutive model of the rock sample and micromechanical theory, combined with extensive experimental data and simulation results, the range of correspondence between microstructure parameters and micromechanical response parameters under normal conditions can be determined. For example, by testing the microstructure and mechanical properties of a large number of similar material samples, empirical formulas for grain size and yield strength can be statistically derived, and their reasonable fluctuation range can be determined. Using the finite element method, stress-strain curves of materials with different phase distributions and defect morphologies can be calculated, establishing a standard mechanical response curve library. In the process of correcting the corresponding micromechanical response based on the microstructure at each time point, the acquired microstructure data from multiple time points can be substituted into relevant models and formulas to calculate the corresponding predicted micromechanical response values, which can then be compared with the actual measured micromechanical response data. If the actual value exceeds the reasonable fluctuation range of the predicted value, it can be preliminarily determined that the micromechanical response at that time point is abnormal. When conducting comparative analysis, slight differences in measurement errors and experimental conditions can also be considered, and statistical methods can be used to process the data to improve the accuracy and reliability of the judgment results.

[0115] When anomalies are detected in the micromechanical response at multiple time points, corrections can be made based on the microstructure at those times. For example, if microstructure analysis indicates that the abnormal micromechanical response is caused by abnormal changes in grain size (rock particles in the target area of ​​the rock sample surface), the yield strength, hardness, and other mechanical property parameters corresponding to the current grain size can be recalculated based on the Hall-Petch relationship, and the original micromechanical response data can be adjusted. The influence of grain size changes on other mechanical properties (such as plasticity and toughness) can also be considered to comprehensively correct various aspects of the micromechanical response. If the anomaly is caused by phase distribution or phase transformation anomalies, thermodynamic and kinetic theories can be used to analyze the driving force, resistance, and transformation rate during the phase transformation process. Combined with information on the phase morphology and content in the microstructure, a more accurate phase transformation model can be established, thereby correcting the phase transformation-related mechanical property parameters in the micromechanical response, such as elastic modulus and Poisson's ratio. For micromechanical response anomalies caused by defect morphology and evolution anomalies, fracture mechanics and damage mechanics theories can be used to model and analyze the defect propagation behavior and damage accumulation process. By calculating parameters such as the stress intensity factor and damage variable at the defect tip, the degree of influence of the defect on the mechanical properties of the material can be predicted, and key indicators such as strength and toughness in the micromechanical response can be corrected accordingly. This will not be elaborated further here.

[0116] In some embodiments, microstructure data can be input into a numerical simulation model to simulate the micromechanical response under normal conditions. This simulation is then compared with the actual abnormal mechanical response to verify the rationality and effectiveness of the correction strategy. The numerical simulation model can be either the finite element method (FEM) or molecular dynamics simulation. The FEM can discretize complex microstructure models and calculate the stress and strain distribution of materials under different loads and boundary conditions by solving mechanical equilibrium equations, thereby simulating the change process of the micromechanical response. Molecular dynamics simulation can study the mechanical behavior of materials at the atomic scale, revealing the influence of microscopic mechanisms such as dislocation motion and atomic diffusion on mechanical properties. During the simulation process, sensitivity analysis can also be performed on different correction parameters to identify key influencing factors, optimize the correction scheme, and improve the accuracy of the correction results.

[0117] The following is a specific embodiment of this specification:

[0118] 1. Select a blocky rock sample from location A, and process the blocky rock sample into a cuboid thin slice of the target size using a wire cutting process. Then, perform argon ion polishing on the upper surface of the cuboid thin slice to obtain the rock sample after sample preparation.

[0119] 1.1 First, based on the research objectives and needs, accurately determine the target strata and carefully select representative massive rock samples from the strata of area A. During the selection process, the integrity of the rocks should be fully considered, avoiding the selection of parts with obvious cracks, fractures, or other defects, ensuring that the selected samples accurately reflect the characteristics of the rocks in that stratum. During this process, a preliminary examination of the selected massive rock samples can be conducted, recording their appearance characteristics, such as color and texture.

[0120] 1.2 Using professional wire EDM equipment, the blocky rock sample is cut into rectangular thin slices of the target size according to precise dimensional requirements. During the wire EDM process, the cutting speed and wire tension are strictly controlled to ensure the flatness and accuracy of the cut surface. Excessive cutting speed may result in a rough cut surface, while excessively slow speed may affect work efficiency and cause unnecessary thermal impact on the sample. After cutting, the dimensions of the resulting rectangular thin slices are measured to ensure that their length, width, and height match the target dimensions, such as a target size of 3mm × 3mm × 2mm.

[0121] 1.3 The cut cuboid slices are placed in a specialized argon ion polishing apparatus. During polishing, the upper surface of the slices is finely polished by controlling parameters such as the energy, flow rate, and polishing time of the argon ions. Argon ions, under a certain energy, bombard the sample surface, removing minute protrusions and uneven parts to obtain an ideal smooth surface. The polishing process needs to be carried out in a high vacuum environment to reduce interference from impurities and ensure polishing effect. At the same time, the polishing process is monitored in real time to avoid over-polishing, which could damage the sample surface or cause it to lose its original microstructure characteristics. After polishing, the sample surface is inspected again. High-precision testing equipment such as atomic force microscopes can be used to measure the surface roughness to ensure that the surface reaches the ideal smoothness and meets the surface quality requirements of subsequent experiments, ultimately obtaining the prepared rock sample.

[0122] 2. Using scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS) analysis, the chemical composition and physical structure of the target area on the surface of the rock sample after sample preparation were identified.

[0123] 2.1 Perform comprehensive debugging and calibration of the scanning electron microscope (SEM) to ensure that all performance indicators are at their optimal state. Check parameters such as the electron gun's emission current and accelerating voltage to ensure stable generation of a high-energy electron beam. Adjust the scanning coil current to ensure accurate grating scanning of the electron beam on the sample surface. Then, clean the sample chamber to remove any possible impurities and contaminants to prevent interference with the rock sample or test results. Securely install the sample stage and ensure it can move and position accurately in three dimensions to observe and analyze different areas of the rock sample surface. Simultaneously, select appropriate detectors, such as secondary electron detectors and backscattered electron detectors, and calibrate their sensitivity. Secondary electron detectors are mainly used to acquire morphological information of the sample surface, while backscattered electron detectors are more sensitive to differences in sample composition and can provide preliminary information about elemental composition.

[0124] 2.2 Carefully place the prepared rock sample on the sample stage, ensuring its stability and preventing displacement during observation. Use the sample stage's movement control device to precisely align the target area on the rock sample surface with the electron beam's scanning range.

[0125] 2.3 Record the initial position of the sample to ensure accurate return to the same area for comparative observation or further analysis during subsequent analysis. Then, start the scanning electron microscope (SEM) and set an appropriate accelerating voltage and working distance. The accelerating voltage is generally selected between 5 kV and 30 kV, depending on the properties of the rock sample and the research objective, while the working distance is controlled between a few millimeters and tens of millimeters to obtain clear images. Then, electron beam scanning begins. The electron beam scans the surface of the prepared rock sample row by row, interacting with the atoms on the sample surface to generate secondary electrons, backscattered electrons, and other signals. These signals are collected by the detector and converted into electrical signals. After amplification and processing, an image of the rock sample surface is formed on the monitor.

[0126] 3. Vertically load the prepared rock sample into the nanoindenter, equip the nanoindenter with the target type indenter, place the nanoindenter with the sample loaded inside the scanning electron microscope cavity, and adjust it to the preset tilt angle.

[0127] 3.1 First, prepare the sample stage of the nanoindenter, ensuring its surface is clean and free of impurities to avoid affecting sample placement and test results. Carefully pick up the pre-treated rock sample using specialized tools (such as tweezers or sample holders) to avoid damaging the sample surface.

[0128] 3.2 After sample preparation, slowly place the rock sample vertically onto the sample stage of the nanoindenter, ensuring that the bottom surface of the sample is in close contact with the sample stage and that the long axis of the sample is aligned with the loading direction of the nanoindenter. After placing the sample, use the sample stage's fixing device (such as a clamp or magnetic suction device) to firmly secure the rock sample to the sample stage to prevent the sample from moving or shaking during the test.

[0129] 3.3 Select the appropriate target type of indenter. Select an indenter based on the mechanical property parameters to be measured, and carefully install the selected indenter on the indenter loading device of the nanoindenter, ensuring that the indenter is securely installed and concentric with the loading axis.

[0130] 3.4 Place the nanoindenter inside the scanning electron microscope (SEM) chamber and tilt it. First, move the nanoindenter, loaded with the prepared rock sample and indenter, to the vicinity of the SEM chamber. During the movement, keep the nanoindenter level and stable, avoiding collisions or violent vibrations to prevent damage to the loaded sample and indenter. Open the sealed door of the SEM chamber and slowly place the nanoindenter in the pre-designed position inside the chamber. This position should ensure the stability of the nanoindenter inside the chamber and facilitate connection and coordination with the chamber's internal temperature control, pressure control, and voltage control systems. Using a specialized adjustment device (such as a tilting bracket or rotating base), tilt the nanoindenter at a certain angle inside the chamber to ensure that the electron beam of the SEM can monitor the morphological changes of the target area on the rock surface in real time and clearly during the nanoindentation test.

[0131] 3.5 After tilting the nanoindenter, recheck its position and stability within the chamber to ensure no displacement occurs during the entire testing process. Simultaneously, check the connections between the nanoindenter and all systems within the scanning electron microscope chamber, including signal transmission lines, power lines, and interfaces with temperature, pressure, and voltage control systems, to ensure all equipment functions correctly and to adequately prepare for subsequent in-situ nanoindentation testing.

[0132] 4. The temperature field is regulated by the temperature control module of the scanning electron microscope, and the voltage field is regulated by the integrated electric field control module. The temperature regulation range is from room temperature to 1000ºC, and the voltage regulation range is from open circuit voltage to several kilovolts.

[0133] 4.1 Temperature control is achieved using a dedicated temperature control system integrated into the scanning electron microscope (SEM). This system mainly consists of heating elements and temperature sensors. The heating elements are resistance heaters, evenly distributed within the microscope cavity to ensure uniform heating of the internal space. The temperature sensors monitor the cavity temperature in real time. During experiments, a target temperature is set according to research needs, such as simulating temperature environments at different depths underground. The temperature control system adjusts the temperature by controlling the power of the heating elements. When the sensor detects that the current temperature is lower than the target temperature, the power of the heating elements is increased to raise the temperature; conversely, the power is decreased. A closed-loop feedback mechanism is used during adjustment, continuously adjusting the heating power until a stable target temperature is reached, thus achieving precise control of the temperature field. The control range is from room temperature to 1000℃, ensuring the simulation of various real-world temperature conditions.

[0134] 4.2 For electric field control, a suitable electrode layout can be set inside the scanning electron microscope cavity according to experimental requirements. Electrodes of specific materials and shapes can be selected to ensure a uniform and controllable electric field is generated in the sample area. The power supply provides an adjustable voltage output, and the target voltage value is set before the experiment. After starting the electric field control simulation, the power supply applies voltage to the electrodes to form an electric field. The electric field strength monitoring device monitors the electric field strength in the sample area in real time and feeds the data back to the control system. If the actual electric field strength deviates from the target value, the control system adjusts the output voltage of the power supply to bring the electric field strength to the set value. At the same time, the current monitoring device (such as an ammeter) monitors the current change, and the voltage adjustment is further optimized based on the current change to achieve precise control of the electric field strength and current. This meets the requirements for studying electric field factors under multi-field coupling conditions, placing the rock sample in an accurate multi-field coupling environment and providing experimental conditions for subsequent in-situ nanoindentation testing.

[0135] 5. Inside the scanning electron microscope, under different temperature and electric field coupling conditions, in-situ nanoindentation tests were conducted using a loading-constant-unloading mode. The dynamic evolution of nano- and micro-sized cracks and particle migration behavior in the target area of ​​the rock sample were simultaneously observed using real-time electron microscopy imaging technology. The load-displacement relationship curve was recorded, enabling a visualized, dynamic, and quantitative characterization of the micromechanical properties of rocks under multi-field coupling.

[0136] 5.1 During the loading stage, the indenter gradually applies pressure to the target area on the rock sample surface at a rate of 0.01 mN / s–10 mN / s, allowing observation of the initial deformation of the rock. During the constant load stage, the pressure is kept stable, enabling the study of the rock's performance changes under continuous stress. The unloading stage allows analysis of the rock's elastic recovery and residual deformation. Different loading modes, such as monotonic loading, cyclic loading, and stepped loading, can simulate the stress state of the rock under different engineering scenarios, thus providing a comprehensive understanding of the rock's mechanical response.

[0137] 5.2 Simultaneously with nanoindentation testing, real-time electron microscopy imaging technology can be used to capture images of the target area of ​​the rock sample at high resolution and high frame rate. This allows for clear and synchronous observation of the dynamic evolution of nano- and micro-sized cracks, including the crack initiation location, propagation direction, propagation rate, and changes in crack morphology during loading, constant loading, and unloading processes. It also allows for precise observation of particle migration behavior, i.e., the movement, rotation, and rearrangement of mineral particles relative to surrounding materials under load. These records of dynamic processes can provide intuitive evidence for studying the micromechanical behavior of rocks. For example... Figure 3 As shown, from Figure 1 The microscopic surface structure of the rock sample in area A can be observed, as well as an indenter approaching or contacting the sample surface. Scanning electron microscopy allows for real-time observation of changes on the rock surface during the indentation process, such as the initiation and propagation of cracks.

[0138] 5.3 Under different multi-field coupling conditions, in-situ nanoindentation tests were conducted using various external loading modes, and the load-displacement relationship and dynamic processes of the rock target region were recorded simultaneously. Specifically, Figure 3 and Figure 4 The image shows the microstructure of the rock sample target area before indentation testing in region A. Figure 5 and Figure 6 The image shows the microstructure of the rock sample target area after indentation testing in region A. Figure 7 The diagram shows a load-displacement curve recorded during the indentation test of the target area rock sample in region A. Figure 8 The diagram shows the curves of the indenter load, indenter displacement, and indentation depth of the rock sample target area in area A changing over time during the indentation test.

[0139] 5.4 From a time perspective, Figure 7 Each data point on the load-displacement curve corresponds to a scanning electron microscope (SEM) image frame at the same moment, forming a time series of "mechanical response-structural evolution." Spatially, the indentation location is strictly aligned with the SEM observation area, achieving precise "point-to-point" correlation. In the elastic stage, the curve slope corresponds to the elastic modulus, and the SEM image shows no visible cracks, only lattice elastic deformation. In the yielding stage, the curve grows nonlinearly, corresponding to the initiation of microcracks. Figures 4 to 6 During the transition, mineral grain boundary slippage or initial cracks can be seen in the scanning electron microscope (SEM) images. In the failure stage, a sudden drop in load is accompanied by a sharp increase in displacement. The SEM images capture the main crack propagation path and grain detachment. Figure 6 (Radial cracks around the indentation) During the unloading stage, the springback slope of the curve reflects the residual deformation, and the scanning electron microscope image shows the solidification of an irreversible crack network. By linking the curve with the image, the differences in mineral grain strength can be quantitatively analyzed.

[0140] The method for determining the micromechanical properties of rocks provided in this specification involves indentation testing of a target area on the surface of a rock sample to obtain micromechanical response data. This micromechanical response data represents the relationship between the micromechanical response of the rock sample and time. During the indentation test, the target area on the surface of the rock sample is scanned to obtain microstructural evolution data corresponding to the micromechanical response data. This microstructural evolution data represents the relationship between the microstructure of the rock sample and time. Based on the micromechanical response data and the corresponding microstructural evolution data, the micromechanical properties of the rock sample are determined. Compared with existing methods, this specification allows for simultaneous indentation testing and scanning of the target area on the rock sample surface, thereby obtaining the micromechanical response and microstructure of the rock sample at multiple time points. Through synergistic analysis of the micromechanical response and microstructure of the rock sample at multiple time points, the intrinsic correlation between micromechanical behavior and microstructural evolution can be revealed, thus more accurately characterizing the heterogeneous micromechanical properties of rocks and achieving dynamic, in-situ, and quantitative characterization of the micromechanical properties of rocks.

[0141] Based on the above-described method for determining the micromechanical properties of rocks, this specification also provides embodiments of a device for determining the micromechanical properties of rocks. For example... Figure 9 As shown, the device 900 for determining the micromechanical properties of rocks may specifically include the following modules:

[0142] The test module 901 can be used to perform indentation tests on the target area of ​​the rock sample surface to obtain the micromechanical response data of the rock sample; the micromechanical response data represents the relationship between the micromechanical response of the rock sample and time.

[0143] The scanning module 902 can be used to scan the target area on the surface of the rock sample during the indentation test to obtain the microstructure evolution data corresponding to the micromechanical response data; the microstructure evolution data represents the relationship between the microstructure of the rock sample and time.

[0144] The determination module 903 can be used to determine the micromechanical properties of the rock sample based on the micromechanical response data and the corresponding microstructure evolution data of the rock sample.

[0145] In some embodiments, the above-mentioned test module 901 can be specifically used to acquire chemical composition data and physical structure data of the target area on the surface of a rock sample using a scanning electron microscope based on energy dispersive spectroscopy (EDS) analysis; determine the target indenter and the tilt angle of the scanning electron microscope indenter based on the chemical composition data and physical structure data; and perform indentation testing on the target area on the surface of the rock sample using the target indenter of the scanning electron microscope indenter at the tilt angle.

[0146] In some embodiments, the test module 901 can also be used to determine the temperature field and electric field corresponding to the target area based on the chemical composition data and physical structure data of the target area; simulate the geological environment of the rock sample based on the temperature field and electric field corresponding to the target area; and perform indentation test on the target area on the surface of the rock sample under the simulated geological environment of the rock sample.

[0147] In some embodiments, the test module 901 can also be used to determine the load mode of the indentation test based on the geological data of the rock sample; and to perform an indentation test on the target area of ​​the rock sample surface under the load mode to obtain load and displacement change data at multiple time points.

[0148] In some embodiments, the test module 901 can also be used to determine the loading mode, constant load mode and unloading mode corresponding to the loading stage, constant load stage and unloading stage based on the geological data of the rock sample.

[0149] In some embodiments, the scanning module 902 can be specifically used to scan the target area using a scanning electron microscope during the indentation test to obtain rock sample crack propagation data and rock sample particle migration data corresponding to the micromechanical response data; the rock sample crack propagation data represents the relationship between the rock sample crack morphology and time; the rock sample particle migration data represents the relationship between the rock sample particle coordinates and time.

[0150] In some embodiments, the determining module 903 can be specifically used to spatiotemporally couple the micromechanical response and microstructure at multiple time points during the indentation test; and determine the micromechanical properties of the rock sample based on the spatiotemporally coupled micromechanical response and microstructure at multiple time points.

[0151] In some embodiments, the determining module 903 may also be used to correct the micromechanical response at multiple time points based on the microstructure at multiple time points.

[0152] The device for determining the micromechanical properties of rocks provided in this specification can perform indentation testing on a target area of ​​a rock sample surface to obtain micromechanical response data of the rock sample. This micromechanical response data represents the relationship between the micromechanical response of the rock sample and time. During the indentation test, the target area of ​​the rock sample surface is scanned to obtain microstructural evolution data corresponding to the micromechanical response data. This microstructural evolution data represents the relationship between the microstructure of the rock sample and time. Based on the micromechanical response data and the corresponding microstructural evolution data, the micromechanical properties of the rock sample are determined. Compared with existing methods, this specification embodiment can scan the target area of ​​the rock sample surface while performing indentation testing, thereby obtaining the micromechanical response and microstructure of the rock sample at multiple time points. Based on the synergistic analysis of the micromechanical response and microstructure of the rock sample at multiple time points, the intrinsic correlation between micromechanical behavior and microstructural evolution can be revealed, thus more accurately characterizing the heterogeneous micromechanical properties of rocks and achieving dynamic, in-situ, and quantitative characterization of the micromechanical properties of rocks.

[0153] It should be noted that the units, devices, or modules described in the above embodiments can be implemented by computer chips or physical entities, or by products with certain functions. For ease of description, the above devices are described by dividing them into various modules according to their functions. Of course, in implementing this specification, the functions of each module can be implemented in one or more software and / or hardware, or the module that implements the same function can be implemented by a combination of multiple sub-modules or sub-units, etc. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection between the devices or units shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0154] This specification also provides a computer device for determining the micromechanical properties of rocks, including a processor and a memory for storing processor-executable instructions. Specifically, the processor can execute the following steps according to the instructions: performing an indentation test on a target area of ​​the rock sample surface to obtain micromechanical response data of the rock sample; the micromechanical response data represents the relationship between the micromechanical response of the rock sample and time; scanning the target area of ​​the rock sample surface during the indentation test to obtain microstructure evolution data corresponding to the micromechanical response data; the microstructure evolution data represents the relationship between the microstructure of the rock sample and time; and determining the micromechanical properties of the rock sample based on the micromechanical response data and the corresponding microstructure evolution data.

[0155] To execute the above instructions more accurately, please refer to... Figure 10 As shown in the embodiments of this specification, another specific computer device 1000 is also provided, wherein the computer device 1000 includes a network communication port 1001, a processor 1002 and a memory 1003, and the above structures are connected by internal cables so that the various structures can perform specific data interaction.

[0156] The processor 1002 is specifically used to perform indentation testing on a target area of ​​the rock sample surface to obtain micromechanical response data of the rock sample; the micromechanical response data represents the relationship between the micromechanical response of the rock sample and time; during the indentation test, the processor scans the target area of ​​the rock sample surface to obtain microstructure evolution data corresponding to the micromechanical response data; the microstructure evolution data represents the relationship between the microstructure of the rock sample and time; and based on the micromechanical response data and the corresponding microstructure evolution data, the micromechanical properties of the rock sample are determined.

[0157] The memory 1003 can be used to store the corresponding instruction program.

[0158] In this embodiment, the network communication port 1001 can be a virtual port bound to different communication protocols, thereby enabling the sending or receiving of different data. For example, the network communication port can be a port responsible for web data communication, a port responsible for FTP data communication, or a port responsible for email data communication. Furthermore, the network communication port can also be a physical communication interface or communication chip. For example, it can be a wireless mobile network communication chip, such as GSM or CDMA; it can also be a Wi-Fi chip; or it can be a Bluetooth chip.

[0159] In this embodiment, the processor 1002 can be implemented in any suitable manner. For example, the processor can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers, etc. This specification is not limiting.

[0160] In this embodiment, the memory 1003 includes volatile memory and non-volatile memory. The memory 1003 can include multiple layers. In digital systems, anything that can store binary data can be a memory; in integrated circuits, a circuit with storage function but no physical form is also called a memory, such as RAM, FIFO, etc.; in a system, a storage device with a physical form is also called a memory, such as a memory stick, TF card, etc.

[0161] This specification also provides a computer program product, including at least one instruction or at least one program segment, wherein the at least one instruction or the at least one program segment is loaded and executed by a processor to achieve the following: Figure 1 The method shown.

[0162] Those skilled in the art should understand that, in the various embodiments of this specification, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this specification.

[0163] Those skilled in the art should also understand that, in the embodiments of this specification, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, the character " / " in this specification generally indicates that the preceding and following related objects have an "or" relationship.

[0164] Those skilled in the art will also understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0165] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0166] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0167] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0168] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for determining the micromechanical properties of rocks, characterized in that, The method includes: Indentation tests were performed on the target area of ​​the rock sample surface to obtain the micromechanical response data of the rock sample; the micromechanical response data represents the relationship between the micromechanical response of the rock sample and time; the micromechanical properties of the rock sample are heterogeneous; During the indentation test, the target area on the surface of the rock sample is scanned to obtain microstructural evolution data corresponding to the micromechanical response data. The microstructural evolution data represents the relationship between the microstructure of the rock sample and time. This includes: during the indentation test, scanning the target area with a scanning electron microscope to obtain rock sample crack propagation data and rock sample particle migration data corresponding to the micromechanical response data. The rock sample crack propagation data represents the relationship between the crack morphology and time; the rock sample particle migration data represents the relationship between the coordinates of the rock sample particles and time. Based on the micromechanical response data and corresponding microstructure evolution data of the rock sample, the micromechanical properties of the rock sample are determined, including: temporal and spatial coupling of the micromechanical response and microstructure at multiple time points during the indentation test; and determining the micromechanical properties of the rock sample based on the micromechanical response and microstructure at multiple time points after temporal and spatial coupling; the micromechanical properties of the rock sample include the strength of mineral grains. The time coupling includes: an FPGA-based hardware-level synchronization controller that aligns the load signal of the nanoindenter and the Raman spectroscopy acquisition trigger signal to ensure that each mechanical data point has a defined timestamp with the corresponding structural image; The spatial coupling includes: using a micron-level positioning marker array combined with an affine transformation algorithm to eliminate coordinate system deviations between different observation devices, performing sub-pixel-level spatial registration in three-dimensional space, and using a real-time correction algorithm based on feature point tracking to correct coordinate system drift during dynamic deformation, and achieving dynamic registration by optimizing the maximum mutual information between adjacent frames. The method further includes: modifying the micromechanical response at multiple time points based on the microstructure at multiple time points.

2. The method according to claim 1, characterized in that, The method further includes: Based on energy dispersive spectroscopy analysis, scanning electron microscopy was used to obtain chemical composition data and physical structure data of the target area on the surface of the rock sample. Based on the chemical composition data and physical structure data, the target indenter of the scanning electron microscope cavity indenter and the tilt angle of the scanning electron microscope cavity indenter are determined. The indentation test on the target area of ​​the rock sample surface includes: At the tilt angle, the target indenter of the scanning electron microscope cavity indenter is used to perform indentation tests on the target area of ​​the rock sample surface.

3. The method according to claim 2, characterized in that, The method further includes: Based on the chemical composition data and physical structure data of the target region, determine the temperature field and electric field corresponding to the target region; Based on the temperature and electric fields corresponding to the target area, the geological environment of the rock sample is simulated; The indentation test on the target area of ​​the rock sample surface includes: In a simulated geological environment, indentation tests were performed on the target area of ​​the rock sample surface.

4. The method according to claim 1, characterized in that, The indentation test performed on the target area of ​​the rock sample surface to obtain the micromechanical response data of the rock sample includes: Based on the geological data of the rock sample, determine the loading mode of the indentation test; In the load mode, indentation tests were performed on the target area of ​​the rock sample surface to obtain load and displacement change data at multiple time points.

5. The method according to claim 4, characterized in that, The indentation test includes a loading phase, a constant load phase, and an unloading phase; Determining the loading mode of the indentation test based on the geological data of the rock sample includes: Based on the geological data of the rock samples, determine the loading mode, constant load mode, and unloading mode corresponding to the loading stage, constant load stage, and unloading stage.

6. A device for determining the microscopic mechanical properties of rocks, characterized in that, The device includes: The testing module is used to perform indentation tests on the target area of ​​the rock sample surface to obtain the micromechanical response data of the rock sample; the micromechanical response data represents the relationship between the micromechanical response of the rock sample and time; the micromechanical properties of the rock sample are heterogeneous. A scanning module is used to scan a target area on the surface of the rock sample during the indentation test to obtain microstructure evolution data corresponding to the micromechanical response data. The microstructure evolution data represents the relationship between the microstructure of the rock sample and time. This includes: scanning the target area using a scanning electron microscope during the indentation test to obtain rock sample crack propagation data and rock sample particle migration data corresponding to the micromechanical response data; the rock sample crack propagation data represents the relationship between the crack morphology of the rock sample and time; and the rock sample particle migration data represents the relationship between the coordinates of the rock sample particles and time. The determination module is used to determine the micromechanical properties of the rock sample based on its micromechanical response data and corresponding microstructure evolution data. This includes: temporally and spatially coupling the micromechanical responses and microstructures at multiple time points during the indentation test; and determining the micromechanical properties of the rock sample based on the spatiotemporally coupled micromechanical responses and microstructures at these multiple time points. The micromechanical properties of the rock sample include the strength of the mineral grains. The time coupling includes: an FPGA-based hardware-level synchronization controller that aligns the load signal of the nanoindenter and the Raman spectroscopy acquisition trigger signal to ensure that each mechanical data point has a defined timestamp with the corresponding structural image; The spatial coupling includes: using a micron-level positioning marker array combined with an affine transformation algorithm to eliminate coordinate system deviations between different observation devices, performing sub-pixel-level spatial registration in three-dimensional space, and using a real-time correction algorithm based on feature point tracking to correct coordinate system drift during dynamic deformation, and achieving dynamic registration by optimizing the maximum mutual information between adjacent frames. The device is also used to correct the micromechanical response at multiple time points based on the microstructure at multiple time points.

7. A computer device, characterized in that, include: memory for storing computer programs; A processor for executing the computer program to implement the method of any one of claims 1-5.

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