5002results about "Geological measurements" patented technology

The invention relates to the technical field of data processing, and discloses a deep learning-based mineral resource prediction method and device, and a storage medium. The method comprises the steps of performing data correction and normalization processing on multi-source geological data to obtain a standardized data set, extracting features by using a deep learning model and fusing the features, constructing a metallogenic knowledge graph to obtain knowledge vectors, constructing a prediction model based on a Transform network and a Bayesian framework to obtain a prediction result, and obtaining a prediction result. And after space optimization, constructing a three-dimensional visual model by using the depth generation model, and performing interpretive analysis to obtain a prediction verification result. According to the method, multi-source geological data can be effectively fused, multiple prediction tasks can be completed at the same time, and the prediction result is made to conform to the geological law and have high accuracy.

The invention relates to the field of shale oil and gas, and discloses a method for identifying a shale oil and gas reservoir dessert, which comprises the following steps: acquiring a core CT image, three-dimensional seismic data and production dynamic data, and carrying out cross-scale preprocessing; constructing a fractional-order non-local seepage field model to represent nano-to-kilometer-level flow characteristics; predicting seepage field parameters through a Lie group symmetry constrained neural network; establishing a cross-scale coupling model of the quantum adsorption effect and the macroscopic seepage law; dynamically updating model parameters based on real-time monitoring data; and executing multi-target collaborative optimization to generate a sweet spot three-dimensional distribution and development scheme. According to the method, a fractal dimension dynamic constraint cross-scale data fusion technology is adopted, the effect of accurate mapping of nanopore and macroscopic fracture network parameters is achieved, and the problem of misalignment of CT scanning and seismic inversion data space registration is solved through pore communication fractal analysis.

The invention provides an adaptive multi-modal feature fusion well logging interpretation method based on multi-task joint learning, and the model comprehensively employs a graph convolution network, a multi-head attention mechanism, a gating mechanism and an adaptive loss weight adjustment algorithm, and effectively integrates multi-well group well logging data and geological structure information. And the complex spatial relationship and geological similarity between the wells are fully captured. The model can process multi-source data in a unified manner, extracts high-level feature representation containing spatio-temporal correlation, avoids repeated feature learning, reduces calculation complexity, and ensures the consistency and complementarity of prediction task features at the same time. The self-adaptive loss weight adjustment mechanism dynamically optimizes weight distribution according to the loss change of each task, realizes balance optimization among different tasks, and comprehensively improves the prediction precision of each task. The physical constraint regularization module ensures that the prediction result not only has statistical accuracy, but also conforms to the actual geological physical law, so that the reasonability and reliability of the prediction result are improved.

According to the goaf detection method based on transient electromagnetic and seismic wave field joint inversion, data acquisition and joint inversion are carried out on the goaf through the combination of the transient electromagnetic detection system and the reflection seismic detection system, the complementary advantages of the two detection methods are played, the bottlenecks of a single method in the aspects of resolution and signal-to-noise ratio are broken through, and the goaf detection method based on transient electromagnetic and seismic wave field joint inversion is realized. Fine characterization of the structure, the physical property and the stability of the goaf is realized; a machine learning classification algorithm is introduced to analyze a goaf multi-attribute feature data set, and intelligent discrimination of the type, scale and stability of the goaf under complex geological conditions is realized; establishing a quantitative response relationship between goaf physical property parameters and goaf electrical and elastic difference characteristic parameters, constructing a goaf transient electromagnetic and seismic wave field joint forward modeling numerical model, and constructing an electromagnetic-seismic joint inversion objective function; and calculating and solving to obtain goaf resistivity, seismic wave velocity three-dimensional distribution and uncertainty quantitative evaluation results, and reducing blind areas of goaf judgment.

The invention relates to the technical field of geological disaster prevention and control, in particular to a rock engineering slope stability classification method. According to the technical scheme, the method comprises the following steps: obtaining spatial distribution characteristics of a slope structural plane through combined detection of three-dimensional laser scanning and a geological radar, and establishing a quantitative index system containing occurrence, density and connectivity of the structural plane; the method comprises the following steps: acquiring a fracture signal in a slope in real time by adopting a micro-seismic monitoring system, and extracting a micro-seismic event energy release rate, a dominant frequency offset and a seismic source mechanism parameter through time-frequency analysis; and constructing a geologic structure evolution inversion model, reconstructing a geologic structure evolution process based on the regional geologic database and the field drilling data, and calculating the distribution characteristics of the residual stress field of the structure. According to the method, comprehensive, accurate and real-time monitoring and classified evaluation of the rock engineering slope stability are realized, potential risks can be effectively predicted, a scientific and accurate decision basis is provided for slope support and treatment, and the safety and stability of the rock engineering slope are greatly guaranteed.

The invention provides an innovative deep concealed ore deposit detection method, which comprehensively utilizes geology, geophysics, geochemistry and remote sensing technologies, and realizes effective integration of multi-source geological data by developing an advanced data fusion algorithm. According to the method, data collected by geological survey, wide-area electromagnetic method exploration, hydrocarbon-mercury superposition halo analysis and aviation hyperspectral remote sensing are subjected to standardized preprocessing, so that the consistency and accuracy of the data are ensured; and then, deep analysis is carried out on the fused data by utilizing machine learning and statistical analysis technologies so as to identify and predict mineralization modes and abnormal regions. Finally, a three-dimensional geologic model is constructed, the form and distribution of an ore deposit are visually displayed, and a scientific basis is provided for geological exploration. The method is characterized in that a multi-source data fusion technology and mineralization mode identification automation and precision are realized, so that the efficiency and the accuracy of deep hidden ore deposit detection are remarkably improved.

The invention discloses a mining rock mass structure degradation monitoring method and system based on deep learning, particularly relates to the technical field of mine safety monitoring, and is used for solving the problems of monitoring lag and misjudgment caused by the fact that an existing static model cannot automatically adapt to rock mass damage dynamic evolution. The method comprises the following steps: constructing a dynamic damage incremental data set by collecting multi-source heterogeneous monitoring data under mining disturbance in real time, extracting newly-added damage features, and calculating a distribution offset degree between the newly-added damage features and historical features; when the deviation degree exceeds the limit, verifying and screening a damage feature subset according with a mechanical law based on a rock constitutive equation, and blocking non-physical noise interference; a geological structure evolution mode is matched through damage path dependence modeling, and a potential damage path thermodynamic diagram is generated in combination with a space gradient; performing cross-stage association on the thermodynamic diagram and a historical feature library by adopting a knowledge distillation mechanism to generate a dynamic weight matrix fused with a mining time sequence; and finally, updating the damage assessment model through a parameter reweighting mechanism to realize adaptive assessment of the rock mass degradation risk.

The invention relates to the field of geological exploration, in particular to an intelligent prospecting prediction method and system for an ore deposit three-dimensional geological model, a terminal and a medium. The method comprises the steps of obtaining multi-source geological data of a target area to construct an ore deposit three-dimensional geological model, inputting the ore deposit three-dimensional geological model into a trained intelligent prospecting prediction model for ore-forming potential analysis, and optimizing the model or generating an intelligent prospecting prediction scheme according to a predicted resource quantity confidence degree condition; when the model is constructed, three-dimensional inversion calculation, element anomaly field construction and the like are carried out, the model can be optimized through transfer learning, exploration data can be accessed in real time to realize dynamic updating, and a multi-target optimization model is established to output an exploration scheme; the invention also relates to a corresponding system, a terminal and a storage medium. The method achieves the technical effects of improving the accuracy and efficiency of prospecting prediction, dynamically optimizing the model according to the actual situation, reasonably planning the exploration scheme, and reducing the exploration cost and risk.

The invention discloses a radioactive measurement data processing method for improving uranium exploration efficiency. The method comprises the following steps: standardized data acquisition: integrating a multi-parameter module, carrying out time-space synchronous acquisition of geological geophysical environment parameters, standardizing protocol alignment data, and supporting three-dimensional modeling; a three-dimensional coupling model is used for processing interference in a sub-module mode, LiDAR-DEM is used for correcting gamma attenuation in the terrain, an optical fiber thermopermeability instrument is used for correcting daughter errors in the hydrology, signal attenuation of a borehole is compensated through a drilling fluid chart, and a space-time continuous interference field is formed; performing dynamic equilibrium coefficient inversion: constructing equilibrium coefficient isoparametric mapping, and inputting rock core, logging and geochemical data into a random forest model; the transfer learning is trained by using historical data, and dynamic inversion and updating of a new area are carried out; intelligent data processing three-dimensional visualization: self-adaptive denoising and spectral shape matching reinforcement abnormity are carried out; constructing a three-dimensional model, and carrying out transfer learning to optimize the precision; the WebGL platform integrates multi-parameter display, virtual drilling functions and high-dimensional data intuitive interpretation.

The invention provides a while-drilling formation pressure early warning method and device based on model prediction, equipment and a medium, and relates to the technical field of low-carbon mining, and the method comprises the steps: in a pre-drilling stage, based on seismic data, adjacent well historical data and design data, analyzing and generating prediction risk data of a target monitoring well; in the drilling process, actual formation pressure data are calculated in real time by using drilling data collected for a target monitoring well in real time, and the actual formation pressure data are compared with the predicted risk data; in the drilling stage, a pressure safety window is depicted based on the predicted risk data, the cycle equivalent density of the target monitoring well calculated in real time is compared with the safety window, and the drilling safety risk is recognized and analyzed; and after drilling is finished, summarizing the predicted risk data, the actual formation pressure data and the drilling safety risk, and updating and iterating the model algorithm through the summarized data. The method has the effect of improving the accuracy of formation pressure early warning while drilling in the petroleum drilling process.

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 invention discloses a rock burst prediction large model construction method based on multi-modal data, and the method comprises the steps: constructing a multi-modal data set through collecting data of different modals; preprocessing the multi-modal data set, and constructing a precursor mode sequence of model training; according to the characteristics of different mining areas, the precursor mode sequence is converted into a corresponding grade form, and corresponding rock burst danger grade labels are divided; transform is adopted as a core framework, the precursor mode sequence after grade division is processed, and finally prediction of the rock burst grade occurrence probability is achieved. A comprehensive index method is used for independently evaluating the danger degree of the mining information data and the geological structure data, and the overall danger level of the rock burst is comprehensively evaluated in combination with a danger probability result output by the rock burst prediction module; according to the method, the applicability and prediction precision of the model under different mining area conditions can be improved, and accurate prediction of the rock burst risk is realized.

The invention discloses a seismic data constrained direct current method three-dimensional inversion method, which relates to the field of geophysical exploration and comprises the steps of processing seismic data and performing inversion processing on resistivity data. The seismic wave layer velocity information is used for constraining high-density electrical method inversion, so that a supplement effect to a certain degree can be achieved, three-dimensional seismic image information can be used for supplementing the resolution ratio of resistivity inversion, the boundary of an underground electrical anomalous body is reflected, and meanwhile, through the comprehensive geological-geophysical information, the electrical anomalous body can be accurately determined. And boundary features of the anomalous body can be further depicted. Seismic data constraints are applied to three-dimensional resistivity inversion, numerical simulation and inversion processing are performed on electrical method data under rugged topography according to finite volume method numerical simulation and a Gaussian-Newton inversion method, and meanwhile, seismic anisotropic velocity tensor constraints are introduced to perform inversion, so that the problem of multiplicity of solutions of inversion of a single method is reduced, and the inversion accuracy is improved. A more accurate three-dimensional underground electrical structure is obtained, and the resolution of inversion to a longitudinal boundary is improved.

The invention relates to the technical field of unmanned aerial vehicle detection and identification, in particular to an unmanned aerial vehicle detection method and system based on visible light polarization imaging. According to the method, a channel state sequence with aligned time sequences is constructed by obtaining multi-source environment sensing data, cross-layer optimization parameters are dynamically calculated and decomposed into calculation and transmission characteristic quantities, and edge nodes are scheduled to cooperatively generate resource scheduling instructions; physical resources are matched to construct a virtual instance topology, a control instruction is generated through online reasoning of a dual-channel graph neural network training model, and finally the control instruction is fed back to the polarization sensor to achieve link calibration. According to the system, through fusion of polarization imaging and cross-layer optimization technologies, the accuracy and real-time performance of unmanned aerial vehicle detection in a complex environment are remarkably improved, meanwhile, computing resource consumption is reduced, and efficient spectrum multiplexing and power adaptive adjustment in a dynamic environment are achieved.

The invention relates to the field of oil and gas field exploration and development, in particular to an oil and gas reservoir four-dimensional stress field quantitative prediction and integrated modeling method. Establishing finite element geological geometric models in different periods by recovering a paleostructure form in a key structure transformation period; predicting three-dimensional distribution of rock mechanics parameters by combining a rock mechanics experiment and adopting a well-seismic combination method; burial depth changes of rocks in different periods are determined through ancient and modern structure fusion, three-dimensional distribution of mechanical parameters of the ancient rocks is predicted, geomechanical heterogeneous models of oil and gas reservoirs in different periods are established, and numerical simulation of four-dimensional stress fields in different periods is achieved; and a multi-period stress unit cyclic coupling algorithm is adopted to realize coupling of multi-period stress field prediction data into one model. The invention provides a quantitative prediction and integrated modeling method for a four-dimensional stress field of an oil and gas reservoir. The method has practical significance in multiple aspects of ancient stress field numerical simulation, tectonic fracture distribution prediction, 'geology and engineering dessert 'evaluation and the like.

A pore pressure prediction method for fine-grained hybrid sedimentary rock based on variable P-wave velocity includes: carrying out a P-wave velocity test of rock under different effective stresses, determining a function form of the relationship between acoustic velocity and effective stress, fitting the parameters in the function by optimization, and establishing an evaluation model of effective stress influence term by lithology. According to the measured formation pressure data and the effective stress influence term model, the acoustic velocity background value is deduced, the logging curve is preferred, and the acoustic velocity background value logging evaluation model is established. According to the logging curves of DT and DEN, combined with the lithology identification results, the acoustic velocity background value is calculated, and then the effective stress of the formation is obtained, and then the pore pressure is determined according to the overburden pressure.

The invention discloses a geotechnical engineering intelligent investigation system and method based on big data. The system comprises a field sensing module, a multi-source fusion module, an intelligent analysis module, a modeling early warning module and a report generation module. The field sensing module collects drilling parameters, in-situ test data and geological images in real time; the multi-source fusion module receives an original investigation signal and accesses a historical engineering investigation database and a regional geology database; the intelligent analysis module receives the fused data signal, constructs a knowledge graph containing a rock-soil entity relationship, predicts rock-soil parameters and stratum distribution of an unexplored area based on a machine learning model, and generates a parameter prediction signal; the modeling early warning module receives the parameter prediction signal; and the report generation module receives the parameter prediction signal and the risk early warning signal. The geotechnical engineering intelligent investigation system and method based on big data can solve the problems of discrete investigation data, strong experience dependence and low intelligent degree.

The invention discloses a deep coal rock in-situ gas content and adsorption / free gas proportion calculation method and device, and relates to the field of reservoir resource potential evaluation.The deep coal rock in-situ gas content and adsorption / free gas proportion calculation method comprises the steps that an isotope fractionation model coupled with a double-dispersion pore structure (matrix pores and kerogen pores) and a multi-gas transmission mechanism (viscous flow, Knudsen diffusion and surface diffusion) is constructed; the accumulated analytic gas quantity and the apparent isotope value of the produced gas obtained through solving are fitted with the actually measured accumulated analytic gas quantity and the apparent isotope value of the produced gas, and key parameters corresponding to the optimal fitting result are obtained; and determining the in-situ gas content, the in-situ adsorption gas proportion and the in-situ free gas proportion according to the productivity evaluation parameters at the initial moment corresponding to the optimal key parameters. According to the method, the in-situ gas content, the in-situ adsorbed gas proportion and the in-situ free gas proportion of the deep coal rock are calculated on the basis of an isotope fractionation model coupled with a double-dispersion pore structure and a multi-gas transmission mechanism, so that the gas resource content of the deep coal rock is predicted.

The invention relates to the technical field of data processing, and discloses an unmanned aerial vehicle detection method and system based on radio intelligent fusion. The method comprises the following steps: acquiring a radio signal to obtain I / Q data; performing time-frequency conversion and feature extraction; inputting convolutional neural network identification; obtaining a type by using a clustering joint recognition algorithm; constructing relay network fusion processing to determine a position identity; and calculating a threat index to execute early warning grading to form a defense strategy. According to the invention, cooperative sensing and intelligent fusion of multi-source information can be realized through a multi-stage relay network in a complex electromagnetic environment, the accuracy, coverage and real-time performance of unmanned aerial vehicle detection are improved, and adaptive early warning and defense response are realized based on threat intelligent evaluation.

The invention relates to the technical field of construction surrounding rock stability evaluation, discloses a tunnel surrounding rock stability evaluation method and system based on multi-physics field parameter inversion, and aims to solve the problems that an existing method is insufficient in data collaboration, high in parameter inversion multiplicity and poor in surrounding rock stability evaluation accuracy and timeliness. According to the scheme, the method mainly comprises the steps that an acousto-optic electromagnetic vibration drill multi-mode sensing array is arranged, and time-space synchronization is implemented; establishing a mutual interference entropy spectral density model to realize multi-physics field collaborative excitation and acquisition; a unified feature vector is obtained through data correction, feature extraction and weighted fusion; a joint inversion objective function embedded with rock physical constraints is constructed, a three-dimensional physical property parameter field is obtained through inversion, and a dynamic permeability field is calculated in combination with acoustic emission energy; and finally, dynamically updating the model by utilizing ensemble Kalman filtering, and obtaining a final risk probability based on updated parameters and seepage-uncertainty coupling correction. According to the method, the accuracy, the real-time performance and the reliability of the stability evaluation of the surrounding rock of the deep-buried tunnel are improved.

The invention relates to the technical field of unconventional oil-gas exploration, and discloses a well-seismic integrated horizontal well geosteering risk evaluation method, which comprises the following steps: S1, acquiring three-dimensional seismic data, measurement while drilling data and geological logging data through a well-seismic integrated data acquisition platform; s2, constructing a horizontal well geosteering dynamic model based on multi-source heterogeneous data, and integrating a seismic inversion result and real-time drilling parameters; by establishing a well-seismic data dynamic fusion mechanism and a multi-source risk quantification model, the problem of space-time dislocation of seismic inversion and measurement-while-drilling data is solved, the recognition precision of a fault boundary, a lithologic interface and a pressure abnormal zone is ensured to be matched with drilling position changes in real time, risk misjudgment caused by model updating lag in a traditional method is eliminated, and the accuracy of the method is improved. The accuracy and timeliness of geosteering decision making of the complex structure area are improved; by monitoring well track deviation and geological model prediction deviation in real time, the risks of well wall instability and target deviation are avoided, and the operation safety of the horizontal well in the full life cycle is guaranteed.

The invention relates to the technical field of geological exploration, and discloses a water-rich sandy stratum tunnel base settlement prediction method and system, and the method comprises the steps: constructing a multi-source cooperative exploration system; deploying a sanding-seepage-stress coupling model at an edge computing node, and mining the relevance of data of different dimensions through a space-time attention fusion network; based on the prediction matrix and mineral exploitation working condition parameters; when the predicted settlement exceeds a safety threshold value or the sanding expansion rate is abnormal, a high-precision remeasurement mechanism is triggered, and a cross-hole radar and acoustic logging combined verification module is activated; and iteratively optimizing coupling model parameters by adopting a transfer learning algorithm through deviation analysis of measured data and a prediction result. According to the method, a scientific basis is provided for stratum stability evaluation and prevention and control measure formulation in the mineral exploitation process, and the probability of engineering risks caused by base settlement of the water-rich sandy stratum is effectively reduced.

The invention provides a polar region sea ice thickness inversion method based on multi-source remote sensing data fusion, and the method comprises the steps: carrying out the feature extraction of SAR data, MODIS data, AMSR2 data and MIRAS data in a target region, constructing an image and sequence feature data set, carrying out the fusion of the image and sequence feature data set through employing a designed multi-modal data fusion network, and obtaining a polar region sea ice thickness inversion result. A new sequence feature data set is obtained, multiple machine learning regression models are used for training the sequence feature data set, the machine learning regression model with the optimal index is selected for sea ice thickness inversion, optical data are deleted, and a sea ice thickness inversion model is obtained through re-training; and dividing the target area into a clouded area and a cloudless area by using the cloud mask data, and applying corresponding inversion models to the clouded area and the cloudless area respectively. According to the method, the advantages of various remote sensing data in sea ice thickness inversion are fully fused, continuous large-area ice thickness information extraction of sea ice can be realized, and the method has better universality.

The invention belongs to the technical field of underground engineering unfavorable geological disaster prediction and intelligent control, and provides a tunnel unfavorable geological physical field-hydrological field fusion holographic detection method and system. Detection information of various physical fields such as an induced electric field, a seismic electric field, a natural electric field and a hydrological field is used as a fusion data source; establishing a coupling objective function taking cross gradient inversion as a physical constraint condition; a CNN-GNN-Transform hybrid network is constructed, and multi-level feature fusion is carried out; through contribution of physical constraint conditions in a self-adaptive weight dynamic balance coupling objective function and deep learning data driving feature learning capability of a hybrid network, multi-field holographic interpretation with a hybrid deep learning model as a carrier is realized, and the fusion degree of multi-source heterogeneous data is improved. Three-dimensional holographic imaging and water gushing prediction of a water gushing disaster source can be achieved, and the accuracy of unfavorable geological disaster detection and the reliability of intelligent decision making are improved.

The present disclosure discloses an early warning method for a shallow soil landslide based on rainfall and soil moisture content, relating to the field of soil landslides prevention and control. The early warning method comprises the following steps: a. determining whether a location is a humid region; b. calculating soil moisture content of the humid region in a rainy season; c. monitoring rainfall amounts in real time by sensors; d. calculating the soil moisture content S by step b; e. calculating rainfall duration on an hourly basis, calculating a total hourly rainfall amount by superposition from an initial 1-hour rainfall amount, and dividing the total hourly rainfall amount by the rainfall duration to obtain hourly rainfall intensity; f. calculating a critical value Cr of a shallow soil landslide; and g. determining a landslide grade and giving an early warning signal.

The invention relates to the technical field, in particular to a geothermal resource favorable area prediction method and system, and the method comprises the following steps: collecting electromagnetic and seismic wave data of a plurality of positions of a target area through a sensor based on regional position data, carrying out the correlation of the data and position information, and generating a geological exploration data set through the combination of data cleaning and standardization. According to the method, electromagnetic and seismic wave data of multiple positions are collected and associated with geographic position information, the practicability and reliability of the data are improved, the data quality is optimized in combination with data filtering, the accuracy of stratum structure prediction is improved, a three-dimensional geologic model is constructed and combined with actual drilling data, and the practicability and accuracy of the model are verified; the method improves the accuracy and credibility of model prediction, optimizes the precision and practicability of resource prediction by analyzing the correlation between a geologic structure and geothermal resources, provides scientific decision support for geothermal development, and supports the effective utilization of renewable energy sources and the realization of a sustainable development target.

The present disclosure relates to the field of CO2 geological sequestration, and provides a whole-process multi-parameter monitoring device and method for simulating CO2 geological sequestration and use thereof. A large-scale reactor body is developed, and a geological reservoir at a depth of 800 to 3000 meters is simulated. A modular built-in model device is researched and developed, a multi-type and variable-structure reservoir remodeling technology is developed, and mutual coupling of modules can be achieved. The design idea of separating the pressure reactor from the built-in sample model is adopted in the device. The confining pressure, the pore pressure, the temperature gradient, and the axial load of the device can be independently controlled through a temperature control mechanism and a loading mechanism respectively, so that the restoration of the state of in-situ stresses in the reservoir is realized.

In general, in one aspect, embodiments relate to a method that includes selecting one or more stratigraphic forward models from a digital analogue library, generating one or more k-layers based at least in part on the one or more selected stratigraphic forward models and one or more generative machine learning models, and predicting thicknesses of one or more geological properties based at least in part on the one or more k-layers.

The invention provides a method and device for determining the horizontal stress difference ratio of a deep coalbed methane reservoir, and relates to the technical field of deep coalbed methane exploration, and the method comprises the steps: obtaining the logging data and azimuth seismic observation data of the deep coalbed methane reservoir, building a reservoir parameter initial model of the deep coalbed methane reservoir based on the logging data, and obtaining the horizontal stress difference ratio of the deep coalbed methane reservoir; constructing a target functional for inverting deep coal bed gas reservoir parameters on the basis of azimuth seismic observation data and an azimuth seismic reflection coefficient equation, and solving the target functional on the basis of a generalized linear inversion theory and an iterative reweighted least square joint algorithm under the constraint of the initial model to obtain a reservoir parameter target model; and calculating the horizontal stress difference ratio of each sampling point in the deep coal bed gas reservoir according to the target model. According to the method, under the constraint of the accurate initial model, accurate estimation of the horizontal stress difference ratio of the deep coal bed gas reservoir is achieved, and the technical problem that an existing horizontal stress difference estimation method is low in accuracy is solved.

The invention relates to a multi-source data volume fused coal reservoir fracture prediction model construction method, which comprises the following steps of: acquiring a coal reservoir coal sample of a well in a certain research area, and adopting a higher fracture strength value in core fracture strength and imaging logging information for a fracture surface strength non-overlapping part obtained by artificially describing the core fracture strength and the imaging logging information; generating a coal sample crack strength curve; establishing a fractal dimension curve; carrying out full-diameter CT (Computed Tomography) scanning on the coal sample, and carrying out quantitative calculation on the single-layer fracture rate to obtain a single-layer fracture rate curve; fusing the obtained three types of curves, and establishing a comprehensive indication curve; constructing a fault distance attribute body; extracting a seismic data body, simulating the correlation between the seismic data body and the comprehensive indication curve, and selecting a sensitive seismic body; constructing a crack comprehensive discrimination function body; and a coal reservoir fracture prediction model is constructed, and coal reservoir fracture prediction is carried out. According to the method, coring data, conventional logging, imaging logging, CT scanning and earthquake crack prediction data are integrated, and the prediction precision of the coal reservoir crack is improved.