262 results about "System" patented technology

The invention provides a tunnel disaster source multi-attribute joint inversion method and system based on physical property constraint, and relates to the technical field of tunnel engineering and joint inversion, and the method comprises the steps: constructing a tunnel face three-dimensional geologic model, applying controlled disturbance to surrounding rock physical property parameters, analyzing the change characteristics of earthquake and electromagnetic observation response, and obtaining a tunnel face three-dimensional geologic model; establishing a quantitative mapping relation between the surrounding rock physical property parameter disturbance quantity and the multi-field response signal variable quantity; constructing a multi-dimensional sensitivity evaluation index, and generating a joint sensitivity result; the surrounding rock units are identified and divided, differentiated partition rock physical relationship constraints are constructed, and a physical multi-attribute inversion target of joint inversion is determined based on the partition rock physical relationship constraints; and on the basis of a physical multi-attribute inversion target, a multi-source joint inversion target function is constructed in combination with the joint sensitivity result, joint solving and iterative updating are carried out, and a tunnel face advanced inversion result is output. According to the invention, calamity source fine identification and risk quantitative evaluation in front of the tunnel face during the tunnel construction period are realized.

The embodiment of the invention discloses a mineral resource exploration data analysis method and device based on three-dimensional space modeling. The method comprises the steps that back-flushing pushing-covering structure feature information is extracted through structure interface occurrence measurement, lithology combination analysis and structure movement trace recognition and converted into geologic structure feature parameters; stratigraphic age data and lithology distribution information are integrated, and a three-dimensional stratigraphic model is constructed with a stratigraphic superposition relation as a constraint; geophysical data are imported to extract geophysical response characteristics of stratums with different depths, and lithologic boundary correction is combined to obtain deep fracture characteristic parameters; sedimentary system division is carried out on lithofacies paleogeographic information, and deep fracture characteristic parameters are fused to establish a fracture system three-dimensional model; the multi-feature association relationship is identified, the ore body prediction model is established, the spatial data is input to obtain the three-dimensional visual prediction result of the hidden ore body spatial distribution, and the exploration efficiency and accuracy in the deep phosphorite exploration process are improved.

The invention relates to the technical field of rock mass earthquake dynamic response, and discloses a layered rock mass earthquake dynamic response multi-scale monitoring system and method, and the system comprises a rock mass three-dimensional geological module, a simulation setting module and an earthquake simulation module. The method comprises the following steps: establishing a layered rock mass three-dimensional geologic model; the method comprises the following steps: identifying a dangerous rock mass structural surface, monitoring slope displacement, and installing sound emission monitoring equipment at a key position to form a three-dimensional monitoring network; a layered rock mass and a broken area in the layered rock mass three-dimensional geological model are divided into discrete element areas, and a continuous medium area is divided into finite difference areas; coupling the discrete element region and the finite difference region; inputting the seismic wave as a dynamic boundary condition into the finite difference region, transmitting a response to the discrete element region through a coupling interface, and simulating a rock stratum and rock mass dynamic fracture process; and the crack propagation particle size and the original stress propagation are transmitted to the cloud platform. The geometric morphology and the internal structure of the rock mass can be accurately reflected.

The invention relates to the technical field of data processing, in particular to an engineering geological survey data processing method and system, and the method comprises the following steps: obtaining hole site space positioning and lithologic boundary information, collecting a structure segment sequence, extracting resistance change direction classification disturbance behaviors, recognizing node sequence conflicts, and judging hydrological turning positions. Identifying the corresponding relation between the structural section and the water level track, and generating a structural section water level merging mapping table. According to the method, the spatial information, the lithology identification, the sampling level and the penetration resistance of the drilling structure sections are collected and serialized, disturbance identification and non-sampling section classification are performed based on the resistance change trend between the sections, and node conflicts are identified in combination with the numbering relation and the lithology combination; continuous modeling and classified merging of geological information on the spatial level are achieved, the linkage adaptability of data in a multi-dimensional analysis scene is enhanced, misjudgment interference caused by information isolation is avoided, and dynamic recognition and hydrological response analysis of a geological structure are supported.

The invention discloses a three-dimensional geological model dynamic updating system and method based on multi-modal data fusion, and belongs to the technical field of geological engineering and information. According to the method, multi-source data of geological radar, earthquake, drilling, remote sensing and the like are integrated, and after unified formatting and quality optimization preprocessing, geological features of a stratum interface, a fault zone and the like are automatically extracted by using a deep learning network; multi-source feature data fusion is realized through space-time registration and attention; constructing a three-dimensional geologic model by adopting a TIN and Octree mixed structure based on a fusion result; dynamic updating of the model is realized through incremental detection and a local reconstruction mechanism, and the boundary of the model is optimized by adopting a radial basis function interpolation. According to the method, the problems of single data, updating lag and the like of a traditional method are solved, the modeling precision and efficiency are remarkably improved, and reliable support is provided for geological engineering decision making.

The invention relates to the technical field of big data processing, and discloses a data extraction method and system for geological mineral exploration, and the method comprises the steps: carrying out the standardization preprocessing of original data containing space coordinates, lithology texts, mineral components, logging curves and mineralization labels; spatial, semantic, concentration and time sequence deep representations are extracted in parallel through a multi-modal geologic feature encoder and fused into high-dimensional vectors; applying structured sparse constraint to the features by using a graph attention mechanism guided by an expert knowledge graph, and strengthening a mineralization association dimension; a dynamic incremental learning engine is combined with an elastic weight solidification mechanism to realize local fine tuning of model parameters and historical knowledge retention; and finally outputting a mineralization potential score, a mineralization factor sequence and an abnormal element combination. According to the method, through triple mechanisms of multi-modal fusion, knowledge embedding and incremental evolution, the accuracy, interpretability and timeliness of data extraction are improved, and the real-time analysis requirement of large-scale mineral exploration is met.

The invention discloses a method and a system for identifying a micro-seismic signal mode of a surrounding rock rockburst risk, particularly relates to the technical field of deep underground engineering rockburst monitoring, and is used for solving the problem that an existing micro-seismic early warning model lags in identifying a rock mass damage evolution critical state. The method comprises the following steps: collecting a surrounding rock micro-seismic event signal in real time, intercepting an event sequence based on a sliding time window, and calculating a space-time fractal dimension; extracting an event space density field in the window to calculate an energy gradient, and marking a strong interaction cluster of which the energy gradient exceeds a threshold value; storing the fractal dimension change sequence in real time; performing characteristic frequency band filtering on the signals in the window, calculating a ratio of an energy ratio entropy descent rate to a fractal dimension descent rate, and determining a damage acceleration state when the ratio is a supercritical coefficient; generating a rockburst high-risk instruction when the strong interaction cluster and the damage acceleration state coexist and the fractal dimension descent rate is supercritical threshold; the multi-dimensional collaborative identification of the rockburst precursor is realized, and the early warning accuracy and timeliness are remarkably improved.

The invention relates to the technical field of tunnel engineering, in particular to a method and a system for optimizing design parameters based on unsymmetrical pressure tunnel blasting excavation dynamic response. The method comprises the following steps of obtaining geological parameters and construction parameters of surrounding rock of the bias tunnel, constructing a discontinuous structure equivalent damage model, and obtaining initial stress field distribution of a rock mass in combination with the discontinuous structure equivalent damage model, the geological parameters and the construction parameters; analyzing the time-varying modified dynamic mechanical parameters, and calculating through a stress wave propagation path and an energy dissipation rule to obtain a time travel curve of dynamic stress and vibration speed of each point of the surrounding rock in the blasting process; correcting a dynamic response index by using a self-adaptive artificial boundary condition to obtain a dynamic response state characteristic; and on the basis of the initial stress field distribution of the rock mass, the time history curve and the dynamic response state characteristics, optimal design parameters are obtained according to optimal design indexes. According to the method, the balance between the blasting vibration strength and the construction efficiency is realized by optimizing and adjusting the design parameters.

The invention discloses a tunnel precise drilling and blasting design method integrating multi-source intelligence and closed-loop optimization. The method comprises the steps that geological parameters are collected through geological detection equipment; based on fracture mechanics and rock mass damage theories, constructing a quantitative relation model reflecting joint network distribution, stress wave propagation characteristics and a rock mass crushing effect coupling mechanism; inputting the geological parameters into a pre-constructed multi-dimensional knowledge base, integrating historical engineering data association rules by the multi-dimensional knowledge base by using a graph database, and extracting geological features; based on a deep learning model, analyzing the mapping relation between the geological features and the blasting parameters, and generating a preliminary blasting scheme including the blast hole distance, the charging structure and the detonating mode; according to the method, a closed-loop optimization system of geologic features-blasting parameters-effect evaluation is constructed, so that dynamic intelligent decision-making and construction self-adaptive regulation and control of tunnel drilling and blasting design are realized, and the contour forming precision and the blasting efficiency are remarkably improved.

The invention discloses a metamorphic rock P-T-t trajectory reconstruction method and system based on multi-source data fusion, and relates to the field of metamorphic rock P-T-t trajectory reconstruction.The method comprises the steps that electronic probe microcell map data and internal standard sample data of a metamorphic rock sample are obtained, and multi-stage data correction is conducted; carrying out mineral phase identification and chemical component quantitative interpretation to generate a mineral classification map, an element concentration distribution map and a total rock main component data set; thermodynamic phase equilibrium simulation is carried out, a P-T view profile map is generated, and a P-T track is extracted; performing microcell in-situ chronological analysis on selected minerals in the mineral classification map to obtain chronological data; and carrying out space-time coupling on the P-T trajectory and chronology data, constructing a P-T-t three-dimensional trajectory model of the metamorphic rock, and outputting a visual reconstruction report. According to the method, automation and standardization of the whole process of metamorphic rock P-T-t trajectory from data acquisition and processing to model reconstruction are realized, and the precision, efficiency and reliability of trajectory analysis are improved.

The invention discloses a foundation pit seepage-deformation space-time coupling early warning method and system based on a large geological model, and belongs to the technical field of geotechnical engineering intelligent monitoring, and the method comprises the steps: processing a regional geological survey report through the large geological model based on a Transform architecture, and generating a high-precision three-dimensional stratum parameter field; further, an improved particle finite element method is adopted to carry out dynamic coupling solution of a seepage field and a stress field, a displacement rate vector is obtained, a hybrid network space-time prediction model fused with LSTM-Transform is constructed, a composite early warning index is calculated in combination with the real-time displacement rate vector, and through multi-source data fusion and space-time coupling analysis, an early warning result is obtained. Accurate evaluation and advanced early warning of the seepage stability and the deformation state of the deep foundation pit project are achieved, the accuracy and reliability of early warning signals are remarkably improved, and the method is suitable for foundation pit safety control under complex geological conditions.

The invention relates to a tunnel surrounding rock grading method and system based on multi-source data fusion, and the method comprises the following steps: obtaining image data and point cloud data of a tunnel face, carrying out the preprocessing and data enhancement, obtaining image features and point cloud features reflecting the shape, structure, texture and roughness of the tunnel face, and carrying out the classification of the image features and the point cloud features; carrying out fusion processing on the obtained features; during feature fusion, according to the joint density in the image, determining the image block granularity, and projecting the image block granularity to the image features; determining a geological weight according to geological information acquired in advance, and embedding the geological weight into the image features; fusing the surrounding rock roughness and the rock quality index in the point cloud features with the image features through a cross-modal attention mechanism to obtain fused features; and the fused features learn geological structure laws through a pre-trained main network and a pre-trained slave network to obtain a comprehensive score, and a surrounding rock grading result is determined in combination with a threshold value of a set geological parameter.

The invention relates to the technical field of underground geotechnical engineering and tunnel construction monitoring, in particular to a shield tunneling rock stratum real-time judgment method. The method comprises the steps that according to signals of neutron radiation characteristics released when different lithologic samples are fractured under a shield tunneling stress path simulated in a laboratory, a knowledge base of the neutron radiation characteristics of a rock stratum is constructed; deploying an on-site multi-source data acquisition system, and acquiring data of neutron radiation characteristics and shield tunneling parameters; jointly forming a fusion feature vector according to the data of the neutron radiation features and the shield tunneling parameters; the method has the advantages that signals of neutron radiation characteristics released in the rock fracture process are directly monitored, the signals are fused with shield tunneling parameters in a multi-source mode, a machine learning model is combined, and the lithology classification probability and the rock mass integrity index are obtained. Real-time and accurate discrimination of lithology and integrity of a front rock stratum is realized, physical characteristics of a rock mass are reflected, and a discrimination effect is continuously optimized.

The invention provides a sandstone-type uranium deposit sedimentary facies identification method, system and device and a medium, and belongs to the technical field of sedimentary facies identification, and the method comprises the steps: obtaining sandstone-type uranium deposit sedimentary facies multi-source data; time-frequency domain feature data, texture feature and waveform feature data and element ratio feature and statistical feature data related to sedimentary facies in the sedimentary facies multi-source data are extracted; time sequence features, three-dimensional structure features and metallogenic environment features are extracted from the feature data and weighted fusion is carried out to obtain the sedimentary facies category recognition probability. Multi-dimensional information complementation is realized by combining logging, earthquake and geochemical data, covering vertical lithology change, three-dimensional sand body distribution and a sedimentary facies state in a redox environment scene, and the focusing capability and the recognition precision of the model are effectively improved.

A method for evaluating geological hydrogen source rock in a region is provided. The method includes: collecting magnetic susceptibility data from a plurality of subsurface locations in the region; receiving geophysical survey data of the region, wherein the geophysical survey data comprises geophysical data collected from above ground; tying the magnetic susceptibility data of the plurality of subsurface locations to the geophysical survey data; generating a geologic map of igneous rock, metamorphic rock, or mafic mineral- or iron-rich sedimentary rock within the region based on the magnetic susceptibility data using a model; and determining, based on the geologic map, a target zone of the geological hydrogen source rock with a potential for hydrogen production or with the potential for hydrogen accumulation.

The invention discloses a geological rock rare earth element quantitative analysis method and system based on laser-induced breakdown spectroscopy, and belongs to the technical field of element spectral analysis, and the geological rock rare earth element quantitative analysis method comprises the following steps: constructing a dual-acquisition system to obtain a spectral signal and a plasma image of a rock sample, and preprocessing the spectral signal and the plasma image; determining rock lithology through a classification model, calculating plasma temperature based on the spectral signal, calculating brightness based on the plasma image, and correcting the plasma temperature; and constructing a multi-category bimodal quantitative analysis model, and inputting the lithology, the corrected temperature and the preprocessed image to complete quantitative analysis. The system comprises a data acquisition subsystem and a quantitative analysis subsystem, and data acquisition time sequence consistency and analysis reliability are guaranteed. According to the method, a bimodal data architecture is adopted, temperature correction and an advanced feature fusion network are combined, the limitation of single spectral data is broken through, the analysis precision and stability are improved, and popularization and application in geological exploration scenes are facilitated.

The invention discloses a multi-modal geologic feature fusion method and a multi-modal geologic feature fusion system based on a discrete wavelet transform (CLIP)-Stable Diffusion model. The method comprises the following steps: reconstructing multi-modal geological data through differential wavelet transformation, extracting a low-frequency trend from earthquake and deposition data by adopting a Daubechies wavelet, capturing high-frequency details from deposition numerical simulation and logging data by adopting a Coiflets wavelet, and vectorizing a geological text through an orthogonal basis matrix; a CLIP-Stable Diffusion fusion model is constructed, a text encoder is utilized to analyze geological semantic features, an image encoder is utilized to extract spatial features, and a U-net diffusion generator realizes cross-modal alignment under the guidance of text conditions through a cross attention mechanism; and adopting a loss threshold termination mechanism constrained by a geological law in diffusion training, and finally outputting fusion data through wavelet inverse transformation. According to the method, cross-scale correlation deficiency and semantic segmentation limitation of a traditional method are broken through, and reservoir modeling precision and exploration efficiency are remarkably improved.

The invention relates to the technical field of underground engineering monitoring, in particular to an underground cavern monitoring and early warning method and system based on machine learning. The method comprises the following steps: acquiring stratum rock mass parameters and geological structure labels of a target cavern; generating a virtual geological disaster scene data set by using the generative adversarial network; a combined feature tensor of InSAR point cloud phase fluctuation, anchor rod stress meter reading and composite electrode potential gradient is extracted through a time sequence convolution layer; the fusion weight is optimized, and a pre-training strategy network is output; a model independent element learning adapter is adopted, and actual cavern monitoring data are utilized to carry out fine tuning on the pre-training network to obtain a disaster response model; and finally, the potential gradient of the composite electrode is monitored in real time, a displacement and stress cooperative control instruction is generated through the model, and a rockburst risk grade instruction and a target reinforcement area coordinate are issued. According to the method, the physical rationality of the virtual data and the geological adaptation capability and interpretability of the model are effectively improved.

The invention provides a rock mechanics dynamic analysis method, system and equipment based on numerical simulation, and relates to the technical field of rock mechanics analys.The method comprises the steps that a three-dimensional geologic model and a three-dimensional geomechanical attribute model of a target sequestration body are constructed; based on the three-dimensional geologic model, establishing an oil reservoir numerical simulation model of the CO2 injection and storage process, and simulating to obtain a reservoir pressure field and a CO2 saturation field under different time steps; carrying out dynamic rock mechanics calculation to obtain dynamic fracture pressure fields of the cover layer under different time steps; and coupling the reservoir pressure field, the CO2 saturation field and the dynamic fracture pressure field, and carrying out transverse and longitudinal leakage risk dynamic evaluation. The technical problems that in the prior art, a simplified geologic model or a coarse mesh model is generally adopted, complex geologic and mechanical characteristics cannot be accurately described, the long-term stability of the storage body cannot be accurately evaluated, and the safety and long-term stability of storage are affected are solved.

The invention provides a geological exploration data visualization dynamic processing method and system, and relates to the technical field of intelligent geological exploration, and the method comprises the steps: constructing a prior three-dimensional geological structure model and a forward substitution model; constructing a multi-source joint loss function based on forward modeling output of the forward modeling substitution model and geological survey data; based on a multi-source joint loss function, performing layer-by-layer reasoning reconstruction on geological point cloud parameters in the prior three-dimensional geological structure model to obtain a dynamic three-dimensional geological parameter set; performing fusion rendering and interaction analysis on the dynamic three-dimensional geological parameter set to obtain a visualization result of a fusion geological structure and multi-field evolution information, and constructing a scene deduction component based on the dynamic three-dimensional geological parameter set and a forward substitution model; according to the geological situation sensing and deduction early warning method and device, high-precision geological situation sensing and deduction early warning can be achieved through deep fusion of the forward modeling model constrained by physical information and the layered dynamic inversion algorithm.

The invention relates to the technical field of petroleum geology experiments, in particular to an experimental simulation and quantitative evaluation method suitable for collaborative evolution of carbonate rock apertures. The method comprises the steps that paleo-temperature, paleo-confining pressure, paleo-axial pressure and paleo-pore pressure evolution paths of a target reservoir are obtained through geological process inversion and converted into experiment loading parameters; preparing a plurality of parallel samples and carrying out initial state multi-scale characterization; in the high-temperature and high-pressure triaxial experiment system, the same temperature-pressure cooperative loading path is applied to the samples under different pore pressure working conditions; carrying out fine comparison analysis on the sample after the experiment; and finally, establishing a reservoir quality evaluation chart by calculating original parameters such as a compaction resistance coefficient, a pore-crack synergistic interaction index and crack communication efficiency. The problems that a traditional experiment is disjointed with the geological process, and multi-field coupling simulation is insufficient are solved, and accurate revelation and quantitative evaluation of the ultra-deep carbonate rock aperture co-evolution mechanism are achieved.

The invention discloses an intelligent grading method and system for tunnel face surrounding rock. According to the method, a two-dimensional image, a three-dimensional point cloud and environmental mechanics data of a tunnel face are synchronously collected through a sensor array integrated on tunneling equipment; after space-time registration fusion is carried out on the multi-source data, pixel-level identification and quantitative extraction of geological features are carried out by using a deep learning model, and three-dimensional reconstruction is carried out based on a motion recovery structure algorithm to obtain rock mass structural plane occurrence parameters; visual, geometric and environmental characteristics are fused, and accurate prediction of the surrounding rock grade is realized through an integrated learning model; and finally, dynamic excavation numerical simulation is carried out by combining the three-dimensional geologic model and a prediction result, the surrounding rock stability is analyzed, and safety early warning is generated. According to the method, automation and quantification of the whole process of surrounding rock grading are achieved, and the accuracy and timeliness of tunnel construction safety decision making are effectively improved.

An image spectrum technology-based geological sketching method, including: based on collected image-spectrum data information of a tunnel face, extracting mineral end members and spectrum to determine types and contents of minerals, extracting texture features and feature waveband spectrums to determine stratum lithology, extracting crack features and identifying crack fillers to obtain a crack identification result; obtaining degrees of crushing of surrounding rocks according to numbers and relative areas of cracks; analyzing weathering variation proportions of minerals according to stratum lithology and mineral analysis result for surrounding rocks, acquiring color differences between different degrees of weathering according to spectral color difference identification results, obtaining degrees of weathering of the surrounding rocks; identifying water outflow form and water outflow position of tunnel face to be detected; and obtaining a geological sketch map for the tunnel face by performing labeling and summarization by using different labeling symbols according to the results.

This invention relates to the field of geophysical exploration and oil and gas geological analysis technology, and discloses a method for predicting fractured reservoirs in carbonate buried hills based on geological guidance and multi-scale fusion. The method includes: constructing a three-dimensional geological guidance constraint body using paleogeography and fault systems as geological priors; characterizing the fracture response mechanism using Gabor wavelet frequency division and structural tensor; constructing sensitive attributes and predicting the fracture body using a multi-branch convolutional neural network that fuses multi-source seismic attributes; and calculating the fracture complexity index based on the predicted fracture body to classify and zone the buried hill fractures. This invention introduces geological guidance constraints to improve the geological consistency of the prediction results; utilizes multi-scale Gabor frequency division and structural tensor analysis to achieve multi-scale fracture characterization; enhances model interpretability and prediction accuracy by fusing multi-source attributes through a multi-branch convolutional neural network; and achieves quantitative reservoir classification through the fracture complexity index, effectively guiding well location deployment and drilling decisions.

The invention relates to the technical field of petroleum geophysical exploration, and provides a reservoir forming period hydrocarbon source rock paleo-pressure quantitative prediction method and system, and the method comprises the steps: solving the stratum denudation thickness based on a mudstone compaction curve extrapolation method; obtaining the thickness of a terrain skeleton, constructing a binary function based on the stratum denudation thickness and the thickness of the terrain skeleton to analyze an approximate solution, and inverting the stratum paleo-burial depth for the logging paleo-burial depth; obtaining the total porosity of the hydrocarbon source rock according to the stratum paleo-burial depth; quantitatively predicting the single well paleo pressure of the hydrocarbon source rock according to the total porosity; the multi-well paleo pressure is obtained based on the single well paleo pressure, interpolation is performed according to the multi-well paleo pressure to obtain the plane paleo pressure, in this way, the accurate well logging compaction trend line is established, the accuracy and reliability of the prediction result are improved by considering the influence of the shear wave speed, and therefore the accuracy of predicting the paleo pressure of the hydrocarbon source rock in the reservoir forming period is improved.

The application discloses a kind of based on sequence stratigraphy and seismic sedimentology's braided river channel reservoir fine characterization method, comprising the following steps: obtaining target area core, well logging and high-resolution three-dimensional seismic data;By analyzing the sedimentology mark and logging facies mark in the target area, to determine the sedimentary facies type and sedimentary microfacies division;Using lithology and well logging curve, using the theory of sedimentology, high-resolution sequence stratigraphy identifies sequence interface type and feature, and carries out high-resolution sequence stratigraphic sequence division;Division reservoir architecture interface;According to the internal structure of sedimentary system, sedimentary facies distribution, focus on high-frequency sequence and sedimentary body scale, generate the braided river channel reservoir fine characterization result of target area architecture unit.The present application effectively solves the deficiencies of prior art in reservoir fine description, boundary identification and parameter inversion, improves the efficiency and success rate of oil and gas reservoir development.

This invention belongs to the field of oil and gas production enhancement and stimulation technology. It provides a FDEM simulation method and system for mineral-scale shale hydration-induced fracturing. The method includes the following steps: obtaining target reservoir stress, rock porosity, and permeability; preparing rock samples from the target stratigraphic layer and obtaining the mineral composition and content through X-ray diffraction experiments; constructing a polycrystalline geometric model of the minerals using random Voronoi mosaic technology; conducting core immersion hydration experiments to obtain the relationship between core water content and time; setting initial and boundary conditions for the model and establishing a numerical model of clay mineral hydration expansion-induced fracturing based on the finite discrete element method; and conducting numerical experiments on shale hydration-induced fracturing to obtain the micro-fracture propagation morphology induced by clay mineral hydration expansion. This invention considers the non-uniform mosaic distribution of multiple minerals in the reservoir rock, simulates the non-uniform stress distribution induced by clay mineral water absorption and expansion, and realizes numerical simulation of the mineral-scale shale hydration-induced fracturing process.

The present application belongs to the field of geological exploration and artificial intelligence technology, and discloses a mineral resource prediction method and system based on a Bayesian physical information neural network. The method comprises the following steps: obtaining and preprocessing multi-source geological data of a study area; constructing a Bayesian neural network based on variational inference; constructing a physical constraint loss with a geological source term; performing three-stage progressive training based on a multi-objective loss function; and performing Monte Carlo sampling prediction and uncertainty quantification. The present application has strong physical interpretability: by embedding a steady-state diffusion equation with a geological source term, the model prediction result conforms to the geological law of ore-forming element migration and enrichment, and the source term clearly corresponds to two geological actions of fracture channel and ore-forming parent rock. The present application realizes the organic combination of data driving and physical driving by using multi-source information such as geochemical data, fracture structure, rock mass distribution and known mine point labels.

The invention discloses a karst ancient landform recovery method and system based on the real thickness of a stratum, and belongs to the technical field of rock stratum ancient landform recovery. The real stratum thickness of the first algorithm is calculated through drilling data when the upper interface and the lower interface of the stratum are inclined and not parallel; the real stratum thickness of a second algorithm is obtained through calculation according to the seismic data; counting the thickness difference between the real stratum thickness of the first algorithm and the real stratum thickness of the second algorithm in the research area at the well point, and performing difference gridding interpolation by adopting a cubic spline interpolation method; and merging the difference gridding interpolation data and the stratum real thickness gridding data of the second algorithm to obtain an ancient landform map. According to the method, the drilling data, the seismic data and other data are combined, ancient landform recovery errors caused by the difference between the stratum apparent thickness and the real thickness due to stratum inclination are eliminated by recovering the real thickness of the stratum, and the ancient landform recovery precision and the exploration and development success rate can be effectively improved.