378 results about "Geological formation" patented technology

The embodiment of the invention provides a mineral target area intelligent delineation method and system based on multi-source heterogeneous geoscience data, and relates to the technical field of mineral resource exploration, and the method comprises the steps: dividing a multi-source heterogeneous geoscience data set comprising geological structure, geochemistry, geophysics and remote sensing image data into a plurality of data modules according to a geological action influence range; each data module records spatial distribution and attribute information, then a dynamic mapping relation between the data modules and a mineral product mineralization mechanism is constructed, a mineralization association chain between the data modules is generated based on the dynamic mapping relation, a cooperative change rule of nodes and connection paths is defined, then a target region feature set conforming to the mineralization association chain is screened, and a target region feature set is obtained; and finally, according to a collaborative change rule, judging the integrating degree of a target region feature set space unit and a mineralization condition, dynamically adjusting a space boundary range, and generating a mineral target region delineation result, thereby comprehensively utilizing multi-source data, deeply mining a relationship with a mineralization mechanism, and improving the accuracy and reliability of mineral target region delineation.

The invention discloses a geological structure three-dimensional reconstruction and analysis system based on multi-modal data fusion, and relates to the technical field of geological exploration information. The method comprises the following steps: acquiring and normalizing multi-source heterogeneous geological observation data through a preprocessing module, and generating basic data and a data coverage density map; the adaptive fusion module performs analysis to generate a dynamic adaptive weight field, and performs weighted fusion on the data to obtain a primary three-dimensional geological data volume; the construction and optimization module constructs a three-dimensional space residual field in a vector form based on the difference between the fusion body and the original data, and deduces the geometric morphology of the geological interface; the reconstruction module ensures the consistency between model layers through hierarchical bidirectional belief propagation processing; the quantification module calculates an uncertainty index; and the visualization module finally generates a three-dimensional geological scene. According to the method, the problems of conflict resolution and reliability evaluation in multi-source geological data fusion are effectively solved, and the precision of the three-dimensional geological model and the engineering decision support capability are improved.

The invention discloses a metal ore body identification method based on remote sensing interpretation, relates to the technical field of mineral resource exploration, and aims to solve the problems of low identification precision, neglect of geological laws and poor generalization caused by dependence on single remote sensing data in the prior art. After standardization processing, spectrum and texture features are fused to generate a composite vector; extracting a multi-scale mineralization signal by using a convolutional neural network, and interpreting and constructing an ore control area in combination with a terrain gradient and a curvature; alteration zoning is identified through spectrum angle matching, and a favorable mineralization area is determined by combining lithology combination analysis; and dynamically weighting geological elements by adopting a deep learning fusion network, outputting an ore body position according to a comprehensive score, and finally optimizing a boundary through spatial clustering and morphological filtering. The method significantly improves the automation degree and reliability of ore body identification in a complex environment, and is suitable for metal mineral exploration target area delineation.

The invention relates to the technical field of geological exploration, and particularly discloses a geological structure automatic identification and interpretation method based on deep learning, and the method comprises the steps: obtaining seismic data, logging data, gravity data and / or magnetic data, and generating a fusion tensor through space-time alignment and dynamic weight distribution; performing space-frequency domain joint coding on the fusion tensor by using an improved Transform encoder, and generating multi-scale geologic feature representation; a generative adversarial network is constructed, a generator of the generative adversarial network outputs a geological structure pseudo label, and a discriminator and a geological expert rule base collaboratively optimize a prediction result; and quantizing a confidence interval of a prediction result based on Bayesian deep learning, and combining geological priori knowledge to generate a dynamic interpretation scheme. Through multi-source data fusion, an innovative algorithm and model collaboration, accurate recognition and scientific interpretation of the geological structure are achieved, the accuracy and efficiency are improved, the cost is reduced, and the method has remarkable advantages in complex geological exploration.

The invention belongs to the technical field of mineral resource exploration, and provides a multi-source remote sensing data and AI combined copper ore exploration method and system, and the method comprises the following steps: obtaining multi-source remote sensing data of a target region through a plurality of remote sensing devices; preprocessing the multi-source remote sensing data to obtain fusion data; the fusion data are analyzed through an AI algorithm, and mineralization alteration areas and geological structure information are obtained through recognition; performing overlay analysis on the mineralization alteration area and the known geochemical anomaly area, analyzing the spatial relationship between the mineralization area and the structure in combination with the geological structure information, and judging the favorable mineralization part of the copper ore to obtain an analysis result; performing field investigation on the potential copper ore area determined by the analysis result, collecting rock samples for laboratory analysis, and verifying the accuracy of the analysis result; according to the method, the multi-source remote sensing data and the AI technology are comprehensively utilized, the target area is rapidly analyzed, the potential copper ore area is positioned, the workload of blind exploration is reduced, and the exploration efficiency is improved.

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.

Provided is a method for judging geology of a time-delayed extremely intense rockburst as to deep tunnel engineering. Information about geological structures is recorded according to methods such as geological mapping and advanced geological prediction of the tunnel face. Geometric characteristics of burst craters formed during the rockburst are recorded by utilizing advanced geological drilling and profile scanning technologies in a rockburst-damaged zone. 3D geostress in the rockburst-damaged zone is measured by using a stress relief method or a hydraulic fracturing method to determine the orientation of the maximum principal stress. A 3D numerical calculation model is established based on initial geostress testing results and physical and mechanical parameters of rock and structural discontinuities obtained through on-site geological investigations. By conducting excavation simulations under different combinations of structural discontinuities and performing numerical calculation, the risk of time-delayed extremely intense rockburst disasters at current locations after tunnel excavation is judged.

The invention discloses an intelligent prospecting prediction method based on a three-in-one metallogenic prediction theory, and relates to the technical field of prospecting prediction, and the method comprises the steps: determining various geological elements formed by a regional control ore deposit, obtaining data related to the elements formed by the regional control ore deposit, and storing the data in a database; comprising geological survey, engineering exploration, geophysics, geochemistry and remote sensing data; performing data preprocessing on the geological element data and dividing the geological element data into a plurality of categories; unsupervised learning is carried out on the geological data according to a deep learning algorithm, and the geological structure of the mining area is described in detail; in the areas with relatively high reconstruction errors, selecting the areas with relatively high geochemical anomaly and geophysical anomaly indication variables or reconstruction error values, comparing the selected areas with known typical ore deposits to determine the correlation of space structures, and extracting ore-forming favorable anomaly feature information from the selected areas; and the extracted abnormal information is matched with various geological elements to delineate the target region, so that the accuracy and efficiency of prospecting prediction are improved.

The invention belongs to the technical field of seismic exploration, and discloses a pre-stack gather optimization method based on multi-dimensional constraint dynamic time warping, which remarkably improves the space continuity and stability of a time shift field. According to the method, the time shift field which is smoother and more continuous in a three-dimensional space can be generated, and the common abnormal jump and unreasonable jitter of the time shift field in a conventional DTW method are effectively inhibited. This better conforms to the gradient characteristics of the geologic structure. Mismatching and processing illusion are effectively avoided, the adaptability of a complex structure area is improved, the global optimization capacity of the DTW algorithm is enhanced through multi-dimensional constraint, and the phenomenon that different horizons or effective waves are wrongly matched with interference waves due to local waveform complexity or noise interference is reduced. Therefore, common processing illusions such as event dislocation and local distortion in a conventional DTW method can be remarkably reduced.

The invention relates to the technical field of three-dimensional modeling, in particular to a dynamic modeling method for a geological structure three-dimensional model. The method comprises the steps that multi-source data are acquired and preprocessed, a voxel semantic fusion algorithm based on variational optimization is introduced, after semantic information is extracted from the preprocessed multi-source data, the preprocessed multi-source data are mapped to a target three-dimensional space grid, and an optimal semantic fusion vector is obtained; based on the optimal semantic fusion vector, generating a standard voxel data pool, and constructing a geological structure three-dimensional model; monitoring data change, calculating the position of a newly added data point, combining the standard voxel data pool to obtain a space updating area, and modeling the space updating area to realize model updating; and after the modeling of the space updating region is completed, optimizing the boundary continuity. The problems that multi-source geological data cannot be directly used for structure construction and semantic fusion of a three-dimensional model, dynamic response to newly-added data is lacked, and geometric discontinuity and structural logic discontinuity exist at the boundary are solved.

The invention discloses a method and a system for constructing a multi-level geologic structure model based on electromagnetic detection, and the method comprises the steps: transmitting electromagnetic signals with different frequencies at all measurement points through an electromagnetic detector, carrying out the resistivity inversion through apparent resistance and impedance phase, obtaining a geologic electrical structure, dividing non-uniform and uniform electrical geology based on electromagnetic static displacement, and obtaining a multi-level geologic structure model. Analyzing an orthogonal component magnetic field to obtain an electromagnetic response parameter and a polarization ellipse parameter; determining an electric field diffusion curve; obtaining an electric field distribution model through finite difference method inversion; deducing underground geological spatial form and distribution law; and finally, calculating an electromagnetic response curved surface to obtain a two-dimensional profile curve, and performing three-dimensional reconstruction to obtain a multi-level geologic structure model. The method not only can improve the precision and reliability of the structure model, but also has good interpretability, and can be directly applied to a construction system of a multilevel geological structure model based on electromagnetic detection.

The invention provides a geological three-dimensional science popularization system based on a VR technology, and the system comprises an AI-driven geological data intelligent processing and three-dimensional scene dynamic construction module which is configured to intelligently extract key geological elements from one or more geological data sources through employing an artificial intelligence algorithm, and a high-precision and interactive three-dimensional geological virtual reality scene is constructed based on the geological elements, and the module can dynamically optimize the scene and load the scene as required. According to the system, the understandability and science popularization interestingness of geological phenomena are remarkably improved, learning interests are stimulated, complex geological structures, geomorphic forms and ancient environments can realistically reappear through an AI-enhanced high-precision three-dimensional scene construction technology, the cognitive threshold of geological knowledge is greatly reduced through an interactive geological process simulation function, and the system is suitable for popularization and application. Abstract is changed into concrete, and boring is changed into vivid, so that the user can be effectively stimulated.

The invention provides a three-dimensional geological structure modeling method, device and system based on drilling information. The method comprises the following steps: S1, collecting drilling data, seismic exploration data and remote sensing image data of a target area; s2, constructing a neural network model composed of a simple full-connection module, a complex convolution residual module and a hierarchical integration module which are connected in sequence; s3, training the initial three-dimensional geologic model by adopting a joint loss function; and S4, in the network training process, a plurality of geological scene models are generated, and each scene model corresponds to a group of different network parameter combinations. According to the three-dimensional geologic structure modeling method, device and system based on drilling information, a spatial receptive field for feature extraction is expanded through a three-dimensional cavity convolution residual block of a complex convolution residual module according to a symmetric expansion rate, input features and deep convolution features are added, processing is carried out through a hierarchical integration module, and the three-dimensional geologic structure modeling method, device and system based on drilling information are obtained. And geologic structure correlation characteristics across drill holes can be effectively captured.

The invention discloses a multi-source information real-time identification and positioning method for a coal mine rock burst risk area. The method comprises the following steps: integrating a micro-seismic system, a rock noise system, a stress system and a geological system to construct an acquisition network; extracting multi-source characteristic indexes and carrying out space-time unification; establishing a dynamic weighted risk fusion model to calculate a comprehensive risk index RI of a three-dimensional grid unit, wherein the weight of the comprehensive risk index RI is dynamically and adaptively adjusted along with mining; based on the RI value, a high-risk area is identified and positioned through three-dimensional visualization, and graded early warning information is generated; according to the invention, a time-space synchronous multi-source information acquisition network is constructed, and uniform preprocessing and feature extraction are carried out on four types of data of microseism, rock noise, stress and geologic structure, so that deep fusion and collaborative analysis of multi-source information are realized, and an information island phenomenon existing in traditional monitoring is overcome; and the judgment on the rock burst risk is more comprehensive.

The invention provides a mineral resource overburden risk assessment method and system, and the method comprises the steps: obtaining multi-source heterogeneous geological data, carrying out the deep fusion of the multi-source heterogeneous geological data through employing a game theory gradient control robust neural network training method, and obtaining a fusion data set; performing deep mining and reasoning on high-dimensional geological data by utilizing the pattern recognition and correlation analysis capability of a large model and combining a manifold perception regularization technology, and recognizing geological structure features; establishing a dynamic safety mining depth evaluation model by using a fractional peak differential equation neural network with efficient accompanying parameter training, and calculating safety mining depth intervals and risk probabilities under different confidence degrees by integrating geological structure risks, mining disturbance effects and engineering safety thresholds; and constructing an assessment report generation module including core risk point analysis, key technology demonstration and prospective risk early warning, and generating an intelligent overburden risk assessment report. According to the invention, the accuracy and reliability of the pressing and covering risk assessment can be improved.

The invention relates to the technical field of tunnel construction geological disaster early warning, and discloses a tunnel construction geological disaster early warning method and system. According to the method, multi-source geological monitoring data and environment dynamic parameters of a tunnel construction area are collected; and then through data fusion and feature engineering processing, data contradiction and redundancy are eliminated, and a standardized training data set is formed. And then training and verifying a disaster risk prediction model by using the data set and adopting a machine learning algorithm so as to obtain an optimized model to accurately predict the disaster risk. And performing risk value prediction on different geological structure units by applying the optimization model, generating a risk distribution map, and determining an optimal geological structure unit through a search algorithm. Based on this, numerical simulation of geological disaster triggering and propagation is executed, a disaster evolution rule is analyzed, and a path simulation data set is output. And finally, performing multi-objective optimization on the data set according to a safety early warning objective, generating a geological disaster early warning scheme, and performing iterative calibration.

The invention discloses a geological exploration data intelligent cloud management and analysis system. The system comprises a data access module which is responsible for supporting automatic or manual importing of geological exploration data from multiple sources; the data management and integration module is used for receiving the data transmitted from the data acquisition and uploading module, performing version control and integrating the data; the data processing module receives the data provided by the data management and integration module, is responsible for data cleaning, format conversion and normalization processing, introduces an adversarial training technology and generates an adversarial sample; the intelligent analysis module is used for constructing a prediction model, the prediction model is used for modeling time series geological data by using LSTM, capturing a long-term dependency relationship in the data and identifying a geological change trend, then the output of the LSTM is used as a feature to be input into an SVM classifier, classification or regression prediction is carried out on a geological structure, and the geological process and a disaster occurrence mechanism are simulated to obtain a geological model; and generating more synthetic data similar to real data and used for training a prediction model.

The invention discloses a geological structure recognition method based on deep learning, and relates to the technical field of geological recognition, and the method comprises the steps: inputting enhanced multi-modal data into a multi-scale visual Transform network to generate a depth feature map, extracting a local structure feature map through a geological prior function and a pyramid structure, and obtaining a local structure feature map; fusing the local construction feature map with the depth feature map to generate a multi-modal feature map, establishing a spatial attention weight map, calculating the regional correlation between the multi-modal feature map and the spatial attention weight map, and performing regional adaptive weighting on the multi-modal feature map according to the regional correlation to obtain a weighted feature map; according to the method, global feature modeling is realized through the multi-scale visual Transform network, the cross-regional continuity and the wrinkle macroform of the fault are captured, and the processing capability of high-resolution geological data is improved.

The invention relates to a tunneling action generation method and system based on time-space depth fusion multi-task prediction in the technical field of shield engineering data processing, and the method and system integrate local and global time sequence information through a dynamic depth fusion network, achieve the fusion of multiple time-space scales, improve the perception capability of complex working conditions, and improve the efficiency of shield engineering data processing. Meanwhile, future state prediction and control candidates are output, action fusion is carried out on a strategy layer, performance loss caused by prediction-control splitting is reduced, prediction-control integration is achieved, an uncertainty head is introduced, explicit constraint is carried out on a loss function and strategy fusion layer, the risk under stratum sudden change or sensing noise is effectively restrained, and the prediction-control performance is improved. Uncertainty constraint security is realized; on the basis of experience playback, weight self-adaption and noise removal, online self-adaption updating requirements of different stratums and tunneling stages are met, online self-adaption updating can be achieved, and the action generation capacity of tunneling stability control under the complex stratums and noise conditions is remarkably improved.

The invention belongs to the technical field of geophysical exploration transient electromagnetic detection, particularly relates to an ultra-wide coal face roof electrical tomography system and method, and is suitable for high-precision detection of a hidden geological structure of a working face roof with the width exceeding 300 meters and the depth within 100 meters in an underground coal mine. A transient electromagnetic perspective detection technology is adopted, and full-coverage imaging of an electrical structure of a working face roof is realized through alternate arrangement of transmitting-receiving roadways and dynamic data fusion; a time-space mapping relation is established in combination with a smoke ring effect diffusion model, and omnibearing imaging of the top plate is achieved; aiming at the refined detection of the hidden geologic structure of the top plate of the ultra-wide coal face (the width is greater than or equal to 300 meters), bidirectional detection and grid subdivision are adopted, and a single-side detection blind area is eliminated through roadway transposition of a transmitting end and a receiving end, so that grid full-coverage imaging in a detection range is realized.

The invention discloses a karst region land degradation degree evaluation method. The method comprises the steps of collecting karst multi-source data; constructing a soil quality index, a climate quality index, a vegetation quality index, a terrain quality index, a human activity index and an underground leakage index as a karst region MEDALUS-Karst index system; carrying out standardization and comprehensive score calculation on each index to realize comparability among indexes of different dimensions, and reflecting the land degradation degree of the karst region through a land degradation comprehensive index; the method solves the problems that an MEDALUS model is adopted for land degradation evaluation of the karst region, due to the fact that unique geological structures, hydrological processes and degradation characteristics of the karst region conflict with core assumptions and index systems of the MEDALUS model, key driving factors and spatial distribution characteristics of land degradation are difficult to accurately recognize, and the evaluation accuracy of the land degradation is affected. And thus, the technical problems of lack of pertinence of ecological management measures are solved.

The present application relates to new systems and methods of quantifying hydrocarbon saturation using measured acoustic logs. The methods and systems herein can accurately quantify the water saturation and hydrocarbon percentage in a low resistivity low contrast shaly sand reservoir where previous methods would indicate the reservoir was wet. In an embodiment, the disclosed system utilizes acoustic logging equipment or tools and techniques to transmit and / or record acoustic signals. Acoustic logging equipment operates by generating acoustic signals and detecting the acoustic signals after the acoustic signals pass through one or more geologic formations.

The present application relates to the technical field of geological model, especially to a three-dimensional geological model dynamic visualization method, system, equipment and storage medium for underground engineering. The technical scheme comprises the following steps: data acquisition and integration: obtaining geological data through various exploration methods, and performing standardized and fusion processing to obtain unified three-dimensional coordinate geological data; three-dimensional geological model initialization construction: constructing a stratigraphic framework based on the integrated data, and accurately embedding geological structures therein to form a complete three-dimensional geological model framework; model dynamic visualization rendering: setting specific lighting and material properties for the constructed three-dimensional geological model. The present application constructs a high-precision model through multi-source data fusion, optimizes the visualization effect and interactivity, realizes real-time monitoring and continuous optimization of the model, provides accurate geological basis for underground engineering, enhances decision accuracy, ensures safety, improves construction quality and economic benefits.

The invention provides a dynamic blasting parameter design system and method for an upper-soft and lower-hard stratum of a large-span tunnel, and relates to the technical field of tunnel engineering. The system comprises a data collection module used for collecting while-drilling parameter information and geological exploration information, the while-drilling parameter information comprises a drilling rate and single-hole energy consumption of a drilling machine, and the geological exploration information comprises geological structures, rock mechanical properties and hydrogeological data; the data preprocessing module is used for carrying out cleaning, denoising, mode conversion and standardization processing on the data collected by the data collection module; the data analysis module is used for performing data analysis on the data processed by the data preprocessing module to obtain optimal blasting parameters; and the data storage module is used for storing data of the data collection module, the data preprocessing module and the data analysis module. The accuracy of the blasting parameters can be improved.

The invention relates to the technical field of data analysis, in particular to an artificial ore pillar replacement system and method based on mechanical property optimization, and the method specifically comprises the steps: obtaining an ore top three-dimensional stress distribution cloud picture and geological structure data of a target ore pillar area; carrying out bearing effect evolution analysis to obtain a bearing effect evaluation value; performing stability effect evolution analysis to obtain a stability effect evaluation value; obtaining a mechanical demand comparison value according to a mechanical demand comparison strategy; and carrying out optimal parameter analysis on the replacement pillar by utilizing the replacement pillar parameter decision strategy, and generating an artificial pillar replacement scheme in real time. The method solves the problems that in the prior art, the top plate control qualification rate is low after ore pillar replacement, and the comprehensive cost of ore pillars is high.

The techniques as described herein enhance the accuracy and precision of mineralogy analysis in elemental spectroscopy logging by utilizing oxide standards and / or rock-forming mineral compounds as reference materials that provide more representative and realistic signatures of geological formations. A method comprises logging a wellbore with a tool and fitting reference spectra from oxide compounds and / or rock-forming mineral compounds against measured gamma spectra obtained with the tool, to characterize a subterranean formation that the wellbore extends through.

The invention discloses a mineral resource reserve estimation method and system based on satellite data, and the method comprises the steps: S1, obtaining multispectral satellite remote sensing data, extracting surface mineral alteration information and geological structure features through a spectral feature recognition algorithm, and generating a surface feature data set; s2, based on the earth surface feature data set, establishing a spatial correlation model of earth surface anomaly and deep mineralization by adopting a geostatistics method, and obtaining a three-dimensional probability distribution matrix of deep mineralization intensity based on the spatial correlation model; s3, dividing a to-be-estimated region into three-dimensional grid units based on the three-dimensional probability distribution matrix; and S4, calculating an in-mineral resource reserve estimation value of each grid unit in the three-dimensional grid units, and completing mineral resource reserve estimation based on satellite data based on the in-mineral resource reserve estimation value.

The invention relates to the technical field of geological exploration, in particular to an X-ray fluorescence spectrometer and geological radar combined gold vein rapid positioning method, which comprises the following steps: S1, arranging an exploration survey line in a pre-selected exploration area; s2, along the same measuring line, measuring the content of the gold mineralization indication element in the earth surface or the shallow medium by using an X-ray fluorescence spectrometer, synchronously recording the position information of a measuring point, and generating element content spatial distribution data; the gold mineralization indicating element comprises at least one of arsenic As, antimony Sb and mercury Hg; s3, carrying out detection by using a geological radar along the same measuring line, obtaining a reflected wave signal of an underground medium, synchronously recording position information of a detection point, identifying fault zone and joint zone geological structure anomalous bodies through data processing, and generating geological radar space distribution data; through XRF and GPR data fusion, a CAI quantitative model is established to precisely delineate an ore prospecting target area, a hidden ore body is effectively identified, the exploration precision and efficiency are greatly improved, and the exploration cost is reduced.

Provided herein are methods of determining the reactivity of one or more minerals present within a geological formation within a target zone in response to the injection of CO2 into the target zone. For example, the methods may comprise one or more of the following steps: (1) determining one or more mineral and fluid characteristics of a geological formation comprising one or more minerals; (2) using a reaction rate model to characterize the chemical reactivity of one or more minerals present in a target zone of the geological formation in response to injection of CO2 into the target zone; (3) using a reactivity index model to estimate the amount of one or more minerals in the target zone that would be modified between a first time point and a second point during a CO2 injection and sequestration operation; and (4) injecting an amount of CO2 into the target zone based on the estimated reactivity of the one or more minerals present in the target zone.

The invention relates to the technical field of geological exploration and three-dimensional modeling, and discloses a geological structure three-dimensional measurement system based on inertial navigation. According to the system, an inertial measurement data flow is processed through a resolving module, the instantaneous space attitude and the relative displacement vector of a measurement carrier are obtained, and a basic three-dimensional geologic structure model is constructed. The quantization module calculates the confidence coefficient of a resolving result in real time and generates a space-time uncertainty distribution field; and the model correction module performs error propagation analysis by using the distribution field, and performs iterative optimization of geometric morphology on a low-confidence region in the model. The system imports lithology, formation and structure interpretation data from the outside, and associates the data as attributes to the model. And the result packaging module packages the optimized model, the association attribute and the uncertainty distribution field into a composite three-dimensional geological survey result file. According to the method, dynamic quantification and visualization of measurement uncertainty are realized, the model can be autonomously optimized, and the reliability and precision of inertial navigation three-dimensional geological modeling are improved.