949 results about "Lithology" patented technology

The invention provides a rock image classification method based on edge enhancement and multi-scale feature fusion. By combining the edge enhancement and multi-scale feature fusion technology, the accuracy of mineral classification in the rock slice image is remarkably improved. According to the method, rock slice image interference is eliminated through filtering and denoising, and mineral particle edges are enhanced by fusing morphological top-hat transformation and a Laplace operator; and performing multi-scale pyramid decomposition on the enhanced image to extract high-frequency information, performing multi-direction response enhancement to generate a direction feature map, and performing channel-level fusion on the original image, the edge enhanced image and the direction feature map. And finally, inputting the fused image into a convolutional neural network to complete rock type classification. According to the method, mineral boundary expression is effectively enhanced, the classification accuracy is improved, and a reliable technical scheme is provided for geological analysis and lithology identification.

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 a reservoir porosity and permeability prediction method based on a dynamic committee integration model, and the method comprises the following steps: obtaining an original data set which comprises shale content, porosity, permeability, GR, AC, CNL, DEN, RT, RXO, SP, CALC, CALI, depth and lithologic labels; performing data enhancement on the original data set; determining main control factors influencing the porosity and the permeability; constructing a dynamic committee integration model; inputting the main control factors into a dynamic committee integration model; and utilizing the trained dynamic committee integration model to respectively predict the porosity and the permeability. According to the method, the logging data is processed by adopting a machine learning method, reservoir parameters can be efficiently and accurately predicted, and favorable support can be provided for oil-gas exploration and development; the dynamic committee integrated model constructed by the invention can dynamically adjust the weight of each model according to different geological conditions and data features, can more flexibly adapt to the geological condition of a research area compared with a single model, and improves the prediction precision and generalization of the model.

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 is suitable for the technical field of logging lithology identification, and provides a logging lithology identification method based on physical information constraint, which comprises the following steps: step 1, data processing; 2, constructing a graph; step 3, constructing a graph attention neural network; 4, constructing a mixed loss function; and 5, lithology prediction. According to the method, through the synergistic effect of data driving and physical information, the performance is obviously superior to that of various pure data driving models. Benefited from the strong regularization effect of physical constraints, the method has stronger generalization ability and prediction stability on new data. The problem that a traditional black box model may generate a result violating physical common knowledge is fundamentally solved, and the geologic rationality of an output result is ensured.

The invention discloses a skarn type deposit three-dimensional metallogenic prediction method, system and equipment and a medium, and relates to the technical field of mineral exploration. The method comprises the following steps: collecting and sorting geological information of a to-be-studied area and a surrounding area; according to the collected information, performing gravity-magnetic three-dimensional physical property inversion and lithology mapping; carrying out gravity-magnetic three-dimensional comprehensive geological modeling by adopting a discrete body inversion method; on the basis of the constructed model, performing three-dimensional metallogenic favorable contrast prediction; constructing a three-dimensional mineralization comprehensive prediction model based on the mineralization favorable degree value; and carrying out target region delineation and verification by using the three-dimensional metallogenic comprehensive prediction model to complete three-dimensional metallogenic prediction under the support of regional gravity and magnetic data. The method has the advantages of wider prediction range, high prediction reliability and the like.

The invention belongs to the technical field of carbonate rock oil-gas exploration, and particularly discloses a complex carbonate rock logging lithology identification method based on a diffusion model, and the method comprises the following steps: determining the lithology types of a plurality of observation wells based on the on-site rock core observation and slice analysis, and synchronously obtaining the logging data of the corresponding observation wells, constructing a lithology training data set in combination with a depth corresponding relationship between the lithology category and the logging data; a diffusion model is adopted to generate and supplement lithology categories with insufficient samples; logging data is adopted as an input feature, the lithology category is adopted as an output label, and a convolutional neural network fused with a Bayesian optimization algorithm and a Transformer hybrid model are utilized to train a lithology identification model; and inputting to-be-identified logging data into the trained lithology identification model, and outputting a lithology identification result. According to the method, high-precision lithology identification can be realized under the conditions of deep layers and complex stratums.

The invention discloses a geological modeling method for fusing remote sensing and seismic data based on a multi-mode auto-encoder, and the method comprises the steps: collecting a remote sensing image covering a target area and seismic exploration data, and converting the attribute of the seismic exploration data into a resolution consistent with the remote sensing image; respectively extracting remote sensing image features and seismic attribute features by using a parallel double-branch coding structure of a multi-mode encoder; a fusion module is arranged in the middle layer of the remote sensing image encoder and the seismic attribute encoder, fusion is carried out in the fusion module, remote sensing images and seismic data are reconstructed by using a decoder based on fusion features, and difference data between reconstructed data and the remote sensing images and the seismic data are calculated based on a combined loss function introducing regular terms. Training a multi-modal encoder by using the difference data to obtain an encoding feature and a difference heat map; and on the basis of the coding features and the difference heat map, key geological structure region identification and visual display are carried out, and key geological structures comprise faults and lithology.

The invention provides a tunnel three-dimensional geologic body modeling method and system based on in-hole camera shooting and while-drilling lithology perception, and relates to the technical field of tunnel and underground engineering and intelligent geological information processing.The method comprises the steps that firstly, a drill hole wall optical image sequence and while-drilling engineering parameters are synchronously collected and preprocessed respectively; then establishing a depth-time mapping relation to realize accurate alignment of the multi-source data in a depth domain; then fracture intelligent identification and parameter extraction are realized through an improved U-Net network, and lithology classification is completed by using an XGBoost model; feature level and decision level fusion is carried out on the fracture features and the lithologic features, and rock mass quality grades are generated; and finally, constructing an integrated geologic model fusing lithologic distribution and a three-dimensional fracture network by adopting an implicit modeling and discrete fracture network technology. According to the method, the problem of splitting of in-hole visual information and while-drilling physical information is solved, automation and precision of geological recognition are improved, and reliable geological information support is provided for tunnel construction safety and support design.

The invention discloses a geological vision large model-based drilling camera intelligent interpretation method, which comprises the following steps of: 1) acquiring and preprocessing a drilling image, and constructing a sample library containing geological labels; 2) based on the existing general visual large model, embedding a geological feature attention module and a lithology classification adapter, performing special optimization in combination with deep coal mine geological features, and constructing a geological visual large model; 3) constructing a geological algorithm dictionary to convert geology knowledge experience into a computable algorithm module, and embedding the algorithm module into a geological vision large model; and 4) realizing sample adaptive diagnosis and repeated learning through an AI module. According to the method, the geologic vision large model is constructed, the geologic feature attention module and the geologic algorithm dictionary are embedded in the model, and a sample adaptive diagnosis and repeated learning mechanism is fused, so that the accuracy of geologic feature recognition in the borehole camera image can be remarkably improved, and efficient and accurate interpretation of the borehole image is realized; and accurate geological data support is provided for underground operation.

The invention relates to the technical field of geological survey, in particular to an unmanned aerial vehicle multispectral geological survey method and system, and the method comprises the steps: fusing a multispectral image, a digital elevation model, geophysics and historical geological data, systematically constructing a geological feature priori knowledge model, including lithology, construction and alteration feature libraries, and mapping with multispectral data; multi-scale geologic features are extracted through adaptive wavelet transform and morphological analysis, and feature weight adaptive adjustment is achieved; geological units are accurately divided by adopting geological scene perception superpixel segmentation and combining geological boundary constraint and similarity recursion combination; identifying an interference mode, generating an adaptive filtering matrix, and enhancing image quality; cooperatively interpreting multi-source information by using a deep auto-encoder network to generate a high-precision geological interpretation map and a confidence map; geological professional knowledge is introduced, so that the geologic body recognition accuracy is remarkably improved; the adaptive flight control strategy ensures the consistency of complex terrain data, and improves the precision and efficiency of geological survey.

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 lithology generality joint feature classification model construction method, which relates to the technical field of intelligent geological prospecting, introduces a cross-scale feature alignment mechanism, and utilizes scale transformation factors to dynamically match the resolutions of macroscopic, mesoscopic and microscopic features, thereby avoiding feature distortion caused by direct fusion; for example, in a volcanic rock and sedimentary rock mixed area, rough texture of a remote sensing image originally conflicts with smooth response of a logging curve, but through alignment processing, the model can automatically balance credibility of different data sources, and misjudgment is reduced; inter-scale dependence modeling is further enhanced through introduction of the feature association graph, vertical sequence association of a thin interbed is captured through a graph structure, the boundary of a millimeter-level thin layer is made clear, and the problem of fuzziness caused by neglecting of interlayer interaction in an existing scheme is solved.

The invention discloses a roadway roof damage time prediction method and system, and belongs to the technical field of mine rock mechanics and safety engineering. Comprising the following steps: recognizing a roof rock stratum structure and lithology of each layer through drilling peeping, and drilling a representative rock sample; determining the crack initiation strength and peak strength of the rock sample through a uniaxial compression test; selecting a plurality of stress levels between the axial strain and the acoustic emission signal to carry out a creep test, synchronously monitoring the axial strain and the acoustic emission signal, and determining an accelerated creep starting moment as the damage time under the stress according to the abrupt change characteristics of the axial strain and the acoustic emission signal; establishing a stress-failure time relation model of each lithology; finally, selecting a corresponding model to predict the damage time of rocks at different layers of the roof according to the actual rock stratum structure and ground stress state of the roadway; according to the method, accurate and reliable time prediction of the progressive damage process of the roadway roof is realized.

The invention relates to the technical field of geological informatization, in particular to an artificial intelligence modeling analysis method for a hydrate pilot production data set, which comprises the following steps of: acquiring logging data, lithology data, stratum physical property parameters and natural gas hydrate production dynamic data; screening, cleaning, complementing, de-noising and standardizing are carried out in sequence to obtain an artificial intelligence modeling data set; and establishing a stratum lithology machine learning recognition model, a stratum physical property machine learning recognition model and a natural gas hydrate artificial intelligence historical fitting model through a support vector machine SVM, a random forest RF and a neural network DNN. According to the method, a serial modeling architecture of lithology identification, physical property prediction and production history fitting is created, and the prediction output of the upstream model is used as the optimization input of the downstream model, so that the downstream production prediction model can learn physical property parameters which are recalculated based on machine learning and have higher precision; and the accuracy of final production prediction is improved from the data source.

The application discloses a lithology identification method, device and system based on a vibration signal and a storage medium. The method comprises the following steps: acquiring a vibration signal sample of a drill bit when the drill bit is used to drill a rock sample in a work area, and extracting a signal characteristic parameter from the vibration signal sample; establishing a probability distribution relationship model between the lithology of the rock sample and the signal characteristic parameter of the vibration signal of the drill bit according to a corresponding relationship between the lithology of the rock sample and the signal characteristic parameter of the vibration signal sample; acquiring a vibration signal generated when the drill bit breaks the rock during drilling in the work area, and extracting a signal characteristic parameter from the vibration signal; and according to the signal characteristic parameter of the vibration signal, the lithology of the stratum in the work area drilled by the drill bit is inferred by using the probability distribution relationship model between the lithology of the rock in the work area and the signal characteristic parameter of the vibration signal of the drill bit.

The invention discloses a key layer presplitting settlement control method based on overlying strata fracture angle arrangement. The method comprises the steps that geological mining parameters of a target mine are collected and determined; based on the physical and mechanical parameters of each rock stratum, determining the horizon height of the key stratum in combination with a thick and hard rock stratum judgment criterion; according to coal seam mining parameters and overlying strata lithology, the height of a water flowing fractured zone is judged, and high and low horizon classification is conducted on the water flowing fractured zone by combining the horizon of the key horizon; according to the development law of overlying strata damage along the fracture angle from bottom to top, the presplitting position in the horizontal direction of each layer key layer in the presplitting range is calculated and determined, and full-section directional presplitting cutting is carried out; based on thick and hard rock stratum mining-induced stress distribution characteristics and a damage deformation transmission mechanism, a working face moving boundary and a reduction distance of a building (structure) protection coal pillar after key stratum presplitting are calculated and determined. According to the method, presplitting implementation is guided based on the key layer fracture rule, and a new technical approach is provided for subsidence control of the mining area.

The invention provides a stratigraphic structure and ground pressure distribution detection method based on wave velocity tomography, and relates to the technical field of wave velocity tomography. The method comprises the following steps: firstly, arranging dense linear detector arrays in a monitoring area at an interval of 5m, and collecting the distribution condition of an aquifer, a lithology detection result and a drill core diagram of the monitoring area; recording seismic wave data, carrying out noise reduction processing on the seismic wave data, picking up arrival time, and associating the arrival time with a waveform event; performing path tracking on an observation result by adopting a wave velocity inversion algorithm, generating a wave velocity tomography image and substituting the wave velocity tomography image into the wave velocity-ground pressure evaluation model; and finally generating a ground pressure distribution diagram.

The invention provides a real-time early warning method and system for the jamming risk of an open-type TBM crossing fault fracture zone, and relates to the technical field of data processing.The method comprises the steps that an analysis area is constructed with three fixed stress monitoring points as vertexes, and a circumcircle of the analysis area is calculated; defining a circular analysis area according to the circumcircle, and performing fan-shaped partitioning on the circular analysis area to obtain a plurality of fan-shaped analysis units; according to the stress change rate data of each fan-shaped analysis unit, combining the corresponding fan-shaped area to determine a weight coefficient, and determining a dynamic adjustment value; calibrating the tunneling parameter by using the dynamic adjustment value to obtain a calibrated tunneling parameter; and according to the calibrated tunneling parameters, risk grade comprehensive evaluation is carried out in combination with surrounding rock lithology, joint development conditions and water inflow data revealed by excavation, and grading early warning signals are generated. According to the invention, early warning of the fault fracture zone jamming risk can be realized.

The invention discloses a lithology analysis and identification method and system based on imaging logging, and the method comprises the steps: collecting imaging logging image data of different reservoir types, and constructing a lithology-imaging feature original data set; performing feature space transformation on the lithology-imaging feature original data set to generate enhanced feature representation in a manifold space, and taking the enhanced feature representation as a lithology-imaging feature enhanced data set; training an adaptive noise injection network by using the lithology-imaging feature enhanced data set to obtain a preliminary lithology classification model; judging a confidence index according to lithological characters output by the initial lithological character classification model to obtain a final lithological character classification model; and acquiring imaging logging image data of a to-be-identified well section, generating corresponding enhanced feature representation, inputting the enhanced feature representation into the final lithology classification model, and outputting a corresponding lithology identification result. The lithology identification accuracy and stability can be improved.

The invention provides a lithology identification method, system and device based on multi-modal data, and belongs to the technical field of lithology identification. The method comprises the following steps: carrying out anti-interference processing on visible light image data, and carrying out feature enhancement and dimension reduction processing on hyperspectral image data so as to align and encode the processed two types of image data in a time dimension and a feature vector dimension. After encoding, inputting the image data into a deformable convolutional network to perform spatial position correction on the encoded two types of image data, fusing the corrected two types of image data according to an interactive attention mechanism to obtain multi-modal interactive fusion data, and training a preset classification network model; and performing lithology identification on the to-be-identified data based on the trained preset classification network model, determining a corresponding lithology identification result, obtaining regional priori knowledge from a historical database based on a GPS position to deduce lithology, comparing the lithology with an actual lithology identification result, and if the result is inconsistent, generating an early warning and sending the early warning to a user terminal.

The invention provides a productivity evaluation method for a tight gas reservoir horizontal well, and relates to the technical field of tight gas reservoirs, and the method comprises the following steps: obtaining well logging sensitive parameters and rock mechanical parameters of the tight gas reservoir horizontal well; based on the logging sensitive parameters and a comprehensive index model, comprehensive indexes corresponding to different reservoirs in the horizontal well are calculated; based on the comprehensive index, the logging sensitive parameter and the rock mechanical parameter, determining a geological dessert level and an engineering dessert level corresponding to the reservoir; and carrying out productivity evaluation on the horizontal well based on the geological dessert level and the engineering dessert level corresponding to the reservoir. Therefore, the geological sweet spots can be accurately identified based on different data dimensions through the comprehensive indexes determined by the logging sensitive parameters having great influence on lithology and the logging sensitive parameters, collaborative evaluation is performed through the geological sweet spots and the engineering sweet spots, the reservoir capacity and the transformation capacity of the reservoir capacity are considered at the same time, and the accuracy of capacity prediction is remarkably improved.

The invention provides a double-criterion active learning carbonate rock lithology classification method and system. The method comprises the steps of obtaining conventional logging information of multiple wells; in the data preprocessing step, a logging curve is intercepted into sample segments, and data are made to conform to the input shape of the model; building a hybrid model Bi-LSTM-DANN combining a bidirectional long-short term memory neural network and a domain adversarial neural network, and inputting the intercepted samples into the network; domain adversarial pre-training: defining a loss function to determine hyper-parameters and training the network to obtain a pre-training model; the active learning fine tuning adopts a dual-criterion active learning method combining KMeans and a minimum confidence method, and the network is further fine-tuned to obtain a final model; and testing and applying the model. According to the method, two sample query strategies are fused, so that the prediction performance of the model is greatly improved; the performance of the model is further improved through domain confrontation, accurate identification of the lithology of the carbonate rock is achieved, and the system has good mobility and robustness.

The invention discloses an intrusive rock alteration zone reservoir prediction method comprising the following steps: S1, analyzing and identifying sensitive logging parameters of an intrusive rock alteration zone, and establishing a dominant lithology identification combination chart; s2, establishing a model, and determining seismic response characteristics of intrusive rocks; s3, quantitative evaluation is carried out on the intrusive rock; s4, reservoir prediction of the intrusive rock alteration zone is carried out; and S5, quantitatively predicting the reservoir distribution and thickness of the intrusive rock alteration zone. According to the method, through different lithologic logging response characteristics, a multi-parameter intersection advantage lithologic chart is established, wave group characteristics of intrusive rocks are determined, and the distribution range and thickness of the intrusive rocks are identified by utilizing wave impedance inversion and optimizing attributes of a product of a reflection intensity alternating current component and a phase cosine; and waveform indication inversion of the invaded rock mud shale alteration zone is carried out by using natural potential as a characteristic curve, and reservoir distribution and thickness of the invaded rock alteration zone are predicted and are basically consistent with actual drilling thickness.

The invention discloses a rock sample bedding identification method and device based on machine learning, and the method comprises the steps: collecting and processing original rock sample bedding images with category information labels, and constructing a rock sample bedding image training data set; based on the training data set, a multi-feature fusion recognition model based on a convolutional neural network is constructed and trained, and the multi-feature fusion recognition model fuses rock sample posture features and multi-dimensional bedding features; and inputting a to-be-recognized target rock sample bedding image into the trained multi-feature fusion recognition model, and outputting a corresponding rock sample bedding recognition result. The method aims to focus on the characteristics of various different rock sample bedding, including the characteristics of diversity and high similarity of rock lithology, porosity and permeability, and a multi-feature fusion recognition model is constructed; according to the model, from the bedding of a rock sample, the posture characteristics and the multi-dimensional bedding characteristics of the rock sample are calculated and utilized respectively, so that high-precision extraction and recognition of different types of rock characteristics are ensured.

The invention discloses a batch modeling method for slope excavation support discontinuous numerical calculation. The batch modeling method comprises the steps that a lithologic model reflecting material partitions is established according to terrain and stratum lithologic characters contained in a slope design result; according to the fracture and joint distribution characteristics, a fracture model and a joint model are established in combination with the calculation purpose, and the inheritance relation among the fracture model, the joint model and the fracture model is established; selecting one of the three models as a parent model, setting an excavation sequence of each bench according to a design result, and constructing a step-by-step excavation model; according to the relative relation between the supporting opportunity and the excavation sequence, corresponding supporting models are created on the basis of the excavation models of all the stair flights in sequence; and according to the inheritance relationship, automatically generating general command stream files containing excavation information, support information and parent model information in batches, and converting the general command stream files into command stream files meeting calculation requirements, so as to establish slope excavation support discontinuous numerical calculation models in batches.

The invention provides a method for identifying a middle-level boundary of a geological envelope based on artificial intelligence. The method comprises the following steps: determining seismic exploration data and logging data of a target work area; extracting seismic reflection features from the seismic exploration data based on the trained first neural network model, and extracting logging sequence features from the logging data by using the trained second neural network model; according to the seismic reflection characteristics and the logging sequence characteristics, geological characteristic representation of the target work area is generated; based on formation contact relation constraint and lithologic sequence constraint which are constructed for a target work area, according to geological characteristic representation, determining implicit topological structure representation of a representation geological envelope body in the target work area; and processing the implicit topological structure representation based on the trained third neural network model to predict the horizon boundary of the geological envelope in the target work area. According to the invention, the prediction result of the horizon boundary of the geological envelope body better conforms to the actual geological law.

The invention discloses a meizoseismal region co-seismic landslide prediction method and device based on a terrain amplification coefficient, and the method comprises the steps: dividing a meizoseismal region into a plurality of grid units, and determining the initial peak acceleration of each grid unit; then determining a corresponding terrain amplification coefficient based on the terrain gradient and the lithologic parameter of the grid unit; determining the peak acceleration of the corresponding grid unit according to the initial peak acceleration and the terrain amplification coefficient; and finally, inputting the influence factor and the peak acceleration into a landslide prediction model to obtain a prediction result, the reliability of the scheme is superior to that of a traditional scheme, the method has high scientificity, and the co-seismic landslide can be predicted more accurately, so that the disaster prevention and reduction capability is improved.

The invention relates to the technical field of geological modeling, and discloses an underground reservoir three-dimensional geological model construction method and system. The method comprises the following steps: screening drilling data through a preset precision threshold value, correcting topographic data by using a first precision drilling data set to generate a topographic surface model, reversely calibrating a second precision drilling hole, and establishing a multivariate data fusion mechanism; layering standardization is achieved in combination with sedimentary cycle characteristics, aquifer units are automatically divided, and stratum lithology codes are compiled; a three-dimensional model is generated by adopting an implicit modeling method based on modeling boundary constraints, and the model precision is verified through a profile comparison method and a leave-one-out method; and converting the adjusted geological section into a three-dimensional constraint surface to drive the generation of a virtual drill hole so as to realize incremental updating of the three-dimensional geological model. According to the method, the spatial precision of the terrain surface model is remarkably improved, the problem of non-uniform reference of multivariate heterogeneous data is solved, the reliability and accuracy of model construction are improved, the virtual drilling data set is generated, the data are dynamically modeled, and the dynamic updating of the model is realized.

The invention provides an auxiliary rock core geological logging method based on drilling parameters, and relates to the technical field of geological investigation, the method is applied to a logging control terminal, and the method mainly comprises the following steps: obtaining regional typical rock core samples and five types of drilling parameter signals, classifying and extracting static characteristics of the rock core samples, and carrying out parallel noise reduction analysis on the parameter signals; obtaining a matching relationship between the rock core and the parameter signal, and constructing a lithologic parameter bidirectional association reference library; on the basis of the matching relation of the lithologic parameter bidirectional association reference library, the model is embedded into an incremental learning module to update the weight, and a subsection lithologic pre-judgment result is output; and calling a preset catalog template to load a pre-judgment result, generating a catalog report after man-machine interaction recheck and correction, collecting correction data, reversely transmitting the correction data back to the reference library to update a matching relationship, and triggering incremental learning of the model to complete a closed loop. The technical problems that in traditional rock core logging, regional lithology adaptation is poor, parameter interference is large, a model is not dynamically optimized, the deep operation risk is high, and efficiency is low are effectively solved.