3430results about "Geological measurements" patented technology

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 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 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 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 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 invention relates to the technical field of coal mine goaf exploration, in particular to a goaf combined detection and identification method fusing gravity, earthquake and transient electromagnetism, which comprises the following steps of: (1) arranging an earthquake, transient electromagnetism and gravity method combined observation system according to a detection geological task and a field condition of a coal goaf; (2) seismic data, transient electromagnetic data and gravity data are collected through the combined observation system; (3) performing independent inversion on the gravity data, the transient electromagnetic data and the seismic data to obtain density distribution, resistivity distribution and wave velocity distribution of the goaf; (4) carrying out a joint inversion method based on improved cross gradient seismic data, transient electromagnetic data and gravity data; (5) obtaining a joint inversion resistivity profile map, a joint inversion wave velocity profile map and a joint inversion density profile map of the goaf through joint inversion of the seismic data, the transient electromagnetic data and the gravity data of the goaf; and identifying development characteristics of the goaf.

The invention provides a road underground disease detection model based on medium inversion and deep learning. A generation method of the model comprises the following steps: 1) collecting road underground radar data by using a ground penetrating radar; 2) obtaining the distribution condition of the underground dielectric constant corresponding to the B-Scan, and constructing a B-Scan-dielectric constant distribution data set; 3) constructing a deep learning medium dielectric property inversion model, and performing training by optimizing a comprehensive loss function; 4) utilizing the trained dielectric dielectric property inversion model to perform inversion on the actually measured radar map to generate an underground dielectric property distribution map; 5) performing disease classification marking according to the underground dielectric property distribution diagram, and constructing and training a disease target identification model; and 6) inputting the to-be-detected dielectric property distribution data into the disease target identification model, and outputting a prediction result of the underground hidden disease. Compared with the prior art, the method provided by the invention can realize accurate disease identification and positioning under complex underground medium distribution, and has better accuracy advantage and stronger generalization ability.

The invention relates to the technical field of mineral resource exploration. The prospecting method, device and equipment based on the multi-source geological relation data and the medium are provided, the method comprises the steps that space-time alignment and standardization processing are conducted on obtained geological historical evolution data, geophysical field data, geochemical migration data and remote sensing alteration information, and a dynamic knowledge graph is generated; performing mineralization stage division processing on the geological historical evolution data to obtain a mineralization stage division result; integrating the fluid migration space-time path network, performing three-dimensional dynamic modeling processing, and generating a staged three-dimensional metallogenic evolution model; extracting a stage-specific geological mark combination from the staged three-dimensional metallogenic evolution model, performing knowledge reasoning processing based on a preset historical ore deposit rule base, and outputting an ore prospecting target region and a metallogenic process constraint basis which meet a preset confidence coefficient condition, so as to improve the precision of a structural model, generate a staged fluid dynamic field, and improve the precision of the metallogenic process. And the target region prediction confidence is enhanced.

The invention discloses a tunnel rockburst type prediction method and system based on multi-dimensional mechanism fusion and a medium. Relates to the technical field of tunnel rockburst. Dynamically acquiring multi-dimensional basic information in a tunnel construction process; inverting first proportions of different fracture modes of the micro-seismic event according to the micro-seismic information fusion moment tensor, and performing energy evolution on the acoustic emission information to obtain second proportions of different fracture modes of each acoustic emission event; the dynamic failure weights of different fracture modes are comprehensively obtained; spatial clustering analysis is carried out based on the dynamic damage weight and the multi-dimensional basic information, rockburst type probability models of different rockburst types are constructed, and the probabilities of different rockburst types are predicted; according to the scheme, the rockburst type probability model is constructed in combination with real-time multi-dimensional basic information, dynamic judgment and prediction of rockburst types are achieved, and technical guarantee is provided for deep tunnel construction safety.

An integrated system for detecting dam breaches, as an early warning system and for evacuation assistance, consisting of: A geoinformatics analysis module comprising a modeling processor with a graphics processing unit, configured to: analyze spatial, hydrological, and topographic data of a dam and its downstream area; generate hydrological models using probable maximum rainfall (PMP) data to determine probable maximum flood discharge (PMF); generate hydraulic models to simulate flood propagation and determine flood depth, flood extent, flow velocity, and flood arrival time; create flood vulnerability models, vulnerability models, and flood hotspot models using a multi-criteria decision-making (MCDM) framework; and generate evacuation plans for high-risk zones based on the models. A sensor monitoring subsystem includes: a distributed fiber optic sensor (DFOS) configured to monitor the structural load and deformation of a dam, and a radar level gauge configured to measure the water level in the reservoir; a microcontroller unit configured to receive and process sensor data from the sensor monitoring subsystem, to detect dam breaches based on abnormal structural movements detected by the DFOS or sudden drops in water level below predefined thresholds detected by the radar level gauge; a communication module configured to transmit warning messages when dam breaches are detected; a warning system configured to: send SMS notifications to residents in downstream high-risk zones via a GSM communication module, generate real-time alerts via an IoT platform on user devices, and activate siren systems in villages to issue loud alarms and voice announcements; and an evacuation assistance platform configured to provide real-time evacuation assistance in emergencies due to dam breaks, displaying site maps with nearby emergency shelters, evacuation routes, hospitals, government buildings and aid centers, showing hydrological layers including flood extent, water depth, flow velocity and arrival time of the water, and enabling real-time location tracking in relation to safety zones and flood-prone areas.

The invention provides a karst area pile foundation intelligent positioning and precise construction method which comprises the steps that a karst development characteristic data set of a target engineering area is obtained, and the karst development characteristic data set comprises a three-dimensional geological exploration data set and a karst attribute data set; and preprocessing the data set to obtain a karst preprocessed data set. And based on a feature extraction model, performing feature extraction on the preprocessed three-dimensional geological exploration data set to generate a three-dimensional geological feature map. And identifying the map through a karst cavity identification model to generate a karst cavity distribution thermodynamic diagram. And karst cave feature conversion processing is conducted on the karst preprocessing data set, and a pile foundation construction feature data set is obtained. And according to the karst cavity distribution thermodynamic diagram, constraint processing is conducted on the pile foundation construction characteristic data set, and a pile foundation constraint boundary diagram is generated. And outputting a pile foundation end hole positioning coordinate set according to a pile foundation positioning optimization algorithm and the pile foundation constraint boundary diagram. The construction safety and accuracy of the pile foundation in the karst area can be improved, and the construction risk and cost are reduced.

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 relates to the technical field of distributed target detection, in particular to a distributed target detection fusion system and method based on cooperation of multiple unmanned aerial vehicles, and the system comprises a data collection module, a coordinate system conversion module, a time registration module, a track association module and a filtering estimation module. The method comprises the following steps: collecting detection target data reported by each slave platform through a central platform, and sequentially performing coordinate system conversion, time registration, track association and filtering and state estimation on the detection target data to generate a global unified situation.

The invention belongs to the technical field of mineral exploration, and particularly discloses a copper ore prospecting method based on three-dimensional geological modeling. The method comprises the following steps: acquiring geological data of a target area; after extreme values in the geological data are recognized and removed through a box plot, the geological data are mapped to an interval of [0, 1], and the geological data are divided into terrain data, stratum data and mineral resource data; adopting Kalman filtering to enable the data to form fusion data, and generating a thematic map comprising a geological map, a mineralization anomaly map and a topographic slope map; based on the data and the thematic map, constructing a three-dimensional geologic model by a finite element method, and performing mining path planning by adopting a specific path planning algorithm formula; exploring by the mining equipment according to the planned path, acquiring geological data, comparing the geological data with the predicted data, re-planning the path and continuing exploring if the path is blocked, the resource recovery is out of tolerance and new mineralization is abnormal, otherwise continuing exploring according to the planned path. The method has the advantages of being high in exploration efficiency and data precision, low in comprehensive cost and high in adaptability.

The invention relates to the technical field of roadway surrounding rock danger identification, and discloses a roadway surrounding rock danger identification model construction method, which comprises the steps of collecting multi-modal data, and generating preprocessed data through synchronous calibration and denoising; extracting a seismic wave frequency domain and image texture features, and generating a multi-modal feature matrix; in combination with a geological prior clustering mining abnormal mode, generating a labeled sample data set; generating a danger identification model based on a transfer learning and feature fusion training network; and the edge deployment model performs real-time reasoning, and generates an early warning result through an adaptive algorithm. According to the method, the frequency domain features of the seismic fluctuation signals and the depth texture features of the surrounding rock images are fused, the multi-modal feature matrix is constructed, abnormal mode mining is carried out in combination with geological prior knowledge, and early weak abnormal signals such as hidden fault slippage or asymmetric microfracture extension which are difficult to find by a single monitoring means can be effectively recognized.

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 invention discloses a geological monitoring data processing method and system based on multiple neural networks, and relates to the technical field of geological monitoring data processing, and the method comprises the steps: employing a dynamic heterogeneous neural network fusion architecture, and carrying out the classification routing processing of various types of geological monitoring input data, obtaining a corresponding first local space feature, a second fault topological structure feature and a third time sequence feature vector; performing optimization training on the multi-task prediction network by using a joint loss function with geological physical constraints, the main task network being used for predicting an earth crust displacement field, and the auxiliary task network being used for predicting an earth crust stress field to obtain an optimized fusion model; based on the optimized fusion model, performing reasoning prediction on the new geological monitoring input data, and outputting a comprehensive geological risk assessment result; through the dynamic heterogeneous neural network fusion architecture, various geological monitoring input data are classified and routed, and the problem that multi-source heterogeneous geological data cannot be effectively processed through a traditional method is solved.

The invention discloses an offshore oil and gas resource efficient evaluation method based on a well-free / few-well condition, and the method comprises the steps: S10, carrying out the collection of seabed multi-field coupling data, and obtaining an original data flow; s20, performing multi-source data intelligent fusion processing: performing data space-time alignment, performing electromagnetic seismic joint inversion by using a joint objective function, performing leakage path analysis, and extracting reservoir physical property parameters and a leakage network; s30, dynamic geological knowledge graph construction: constructing a graph neural network and establishing a cross-regional reservoir parameter prediction model through transfer learning to obtain a dynamic geological model and transfer learning parameters; s40, virtual well intelligent generation: guiding virtual well generation to obtain a virtual well data set; and S50, reservoir modeling evaluation: training reservoir modeling together with the virtual well data and the real seismic attributes to obtain resource probability distribution and sweet spot division. According to the method, the dependence on dense drilling is reduced, the defect of multiplicity of solutions of geophysical data is overcome, and the technical bottleneck of modeling under the condition of less wells / no wells is broken through.

The invention relates to the field of submarine structure detection, in particular to a submarine water intake structure hidden danger detection method based on acousto-optic joint detection. According to the method, through a multi-source data acquisition and intelligent fusion technology, including centimeter-level high-resolution sonar sounding, water body layer structure acoustic imaging, ROV optical camera shooting observation and laser calibration, scouring, crack and blockage information of the seabed grating is comprehensively obtained, and the seabed grating is comprehensively identified by adopting adaptive enhancement, threshold segmentation and spatial interpolation algorithms. The problems that a traditional sonar imaging blind area exists, the target boundary is fuzzy and the biological adhesion thickness is difficult to quantify are solved, and high-precision and multi-dimensional detection of seabed structure damage, blockage and scour forms is achieved. According to the overall scheme, the detection integrity and accuracy are improved, the submarine environment safety monitoring capacity is enhanced, and reliable technical support is provided for operation and maintenance of submarine facilities.

The invention discloses a satellite-air-ground-well multi-method collaborative shallow coverage area copper-gold ore exploration method, and relates to the technical field of mineral resource exploration, regional alteration mineral information is extracted in a shallow coverage area, and a porphyry type copper-gold ore prospecting prospective area is determined according to alteration mineral space distribution and zoning characteristics thereof; in the prospecting prospective area, a key exploration area is selected according to metallogenic geological conditions, aviation geophysical prospecting and aviation hyperspectral remote sensing investigation are carried out, and a prospecting favorable area is delineated; performing prospecting prediction on related ore types in the favorable prospecting area, and delineating a prospecting target area; determining a new drilling position in the target area; drilling verification and well logging are carried out, ground-well geophysical prospecting is carried out to describe the spatial form of the metallogenic geologic body, and the prospecting effect is expanded. According to the method, the favorable prospecting area and the prospecting target area can be more accurately delineated, the spatial form of the ore body is finely delineated, a reliable basis is provided for prospecting of the porphyry type copper-gold ore, and the prospecting success rate and efficiency of the porphyry type copper-gold ore in the shallow coverage area are greatly improved.

The invention provides a mine water prevention and control risk early warning method and system based on earthquake and geology fusion, and belongs to the field of soil layer or rock drilling, the method comprises the steps that real-time earthquake data of an earthquake monitoring module and geological exploration data of a geological exploration module are received, and the geological exploration module comprises a drilling detection module and a hydrogeological analysis module; based on the real-time seismic data, the geological exploration data and a preset water prevention and control risk early warning model, obtaining a risk level including a risk level and a corresponding water prevention and control execution measure; triggering target execution equipment such as drilling equipment to perform prevention and treatment according to execution measures; drilling a monitoring hole and arranging an optical fiber osmometer to collect osmotic pressure data at a first risk grade; at the second risk level, grouting plugging is conducted on the crack drilling grouting holes; constructing a water guide hole in the aquifer at the third risk level, and guiding underground water to a safe area through a drainage pump group. According to the method, multi-source data are fused to realize graded accurate early warning and prevention.

The invention discloses an orthotropic rock physical modeling method. The method comprises the following steps: S1, obtaining tight reservoir parameters; s2, estimating the elastic modulus of the mixed minerals; s3, enabling the mixed mineral to be equivalent to a VTI background rock matrix caused by a thin layer; s4, matrix pores are added into a VTI background rock matrix; s5, embedding the vertical crack into the VTI background'dry 'rock skeleton to obtain an OA medium'dry' rock skeleton; s6, calculating the bulk modulus and saturated rock density of the mixed fluid; s7, adding the mixed fluid into the OA medium'dry 'rock skeleton to obtain OA medium saturated rock; s8, calculating longitudinal and transverse wave velocities and anisotropy parameters of OA medium saturated rocks; and S9, calculating OA medium saturated rock. According to the method, the tight reservoir with the orthotropic characteristic can be simulated, the Thomsen parameter and the fracture weakness parameter of the tight reservoir can be obtained, and data support is provided for fracture evaluation, anisotropic parameter inversion, permeability prediction and the like of the tight reservoir.