4775 results about "Geophysics" patented technology

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

The 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 belongs to the field of geotechnical engineering and engineering geological information modeling, and relates to a stratum-parameter coupling randomness three-dimensional random field modeling and shield construction ground surface settlement rapid prediction method and device based on multi-source data fusion. According to the method, a three-dimensional joint random field acting on stratum types and rock-soil key physical property parameters at the same time can be constructed under the joint constraint of multi-source data such as drilling, geophysical prospecting, geotechnical tests and terrains, and a calibrated, explainable and updatable geological section automatic generation and uncertainty quantification channel is formed; on the premise that geological rationality is guaranteed, section geometry and parameter distribution under the conditions of complex structures and lateral phase change are restored robustly, the accuracy, consistency and interpretability of the three-dimensional geological section are remarkably improved, and a combined uncertainty result which can be directly used for rapid prediction and risk assessment of shield construction settlement is output. The technical problems that a geological section generated by an existing method is not accurate enough, uncertainty is difficult to quantify, and the extrapolation capacity of a complex scene is weak are solved.

The invention relates to the technical field of mining, and discloses a method for determining pre-splitting blasting parameters for mining blasting, which comprises the following steps of: 1, acquiring rock mass structure information of a blasting area through three-dimensional geological radar, laser radar and shallow seismic reflection data, and constructing a structural plane distribution map containing a soft and hard interbed and joint combination mode; 2, initial presplitting blasting parameters are generated based on the structural plane distribution map, wherein the initial presplitting blasting parameters comprise the hole distance, the linear charge density and the charge structure; according to the method, the technical scheme that the structural plane distribution map is constructed through multi-source geological data fusion and digital twinborn simulation dynamic correction is adopted, so that the technical effects that the influence of a complex structure combination on blasting energy transfer is accurately quantified, and the presplitting path control precision is remarkably improved are achieved; compared with the technical scheme depending on a static rock mass model or an empirical formula in the prior art, the defects of presplitting path offset and serious parameter mismatch caused by the fact that modeling is not carried out due to the dynamic effect of the structural plane are overcome.

The invention relates to the technical field of geophysical exploration, in particular to a multichannel deep learning magnetotelluric inversion method based on physical information constraint. The method comprises the following steps: generating a synthetic data set containing a geoelectric model and forward modeling response thereof, and adding a noise simulation actual observation condition; constructing a hybrid network architecture combining Transform and U-Net, taking apparent resistivity and impedance phase as dual-channel input, extracting global features by using an encoder, gradually recovering spatial resolution through a decoder, and outputting an underground resistivity model; network training adopts a composite loss function fusing model loss and data loss, and an inversion process is constrained by introducing a magnetotelluric forward modeling physical rule, so that a result is ensured to fit observation data and conform to a physical mechanism; after training is completed, preprocessed actual measurement data are input into the model, and a resistivity image can be directly obtained. The method is used for geological structure identification and reservoir interpretation, and the inversion precision and reliability are effectively improved.

The invention relates to the technical field of marine surveying and mapping and submarine topography modeling, and discloses a submarine topography and landform construction method based on multi-source water depth data fusion, which comprises a multi-source data acquisition module, a data preprocessing module, a feature extraction module, a fusion algorithm matching module, a three-dimensional modeling module and an uncertainty evaluation module. A historical submarine topographic map is converted into grid data, namely, submarine topography is discretized into digital representation of regular grid units, water depths of other sources are converted into the grid data, the precision of the water depths of different sources is evaluated by adopting a superposition comparison method, and an error distribution model is established to reasonably correct and fuse the water depth data of various sources. The innovation of the method lies in that topographic feature parameters including the gradient, the curvature matrix and the bionic optimization algorithm are introduced, the defect that a single algorithm is preset in a traditional method is overcome, and the precision of coral reef and fault zone complex areas is improved.

The invention discloses an ocean three-dimensional temperature field reconstruction method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-source heterogeneous ocean observation data and a numerical model background field, and generating an input feature group; performing feature extraction on the input feature group by using a double-branch encoder; the extracted features are input to a multi-head space-time channel attention fusion module for dynamic calibration and fusion, and deep fusion hidden variables are obtained; jointly inputting the deeply fused hidden variables and the numerical model background field into a decoder based on a conditional variation auto-encoder, generating high-resolution three-dimensional temperature field grid data, and synchronously outputting a three-dimensional uncertainty field; according to the method, the continuous, complete and high-precision ocean three-dimensional temperature field in the whole research area is reconstructed through a mathematical method and a physical method by utilizing limited, sparse, multi-source and heterogeneous ocean observation data, and the problem that the ocean three-dimensional temperature field generated in the prior art is not accurate enough is solved.

The invention discloses a rock damage assessment method fusing wave velocity and microscopic fracture, and belongs to the technical field of rock damage assessment, and the rock damage assessment method comprises the following steps: arranging a wave velocity testing device on a rock sample, and respectively making an unloaded rock sample and a damaged snapshot rock sample under different stress loading; microcosmic fracture information of the damaged snapshot rock sample is identified, a three-dimensional wave velocity model of the damaged rock sample under different stress loads based on a wave velocity test is constructed, and then a macro-micro scale fusion multi-dimensional data set is constructed; based on the macro-micro scale fusion multi-dimensional data set, constructing and training a rock damage identification model; and inputting the characteristic parameters of the rock sample to be evaluated into the rock damage identification model, identifying the damage distribution characteristics of the rock sample on the spatial scale, and evaluating the rock damage degree. According to the method, accurate prediction and evolution process evaluation of the rock damage degree and spatial distribution can be realized, and a reliable technical means is provided for disaster early warning, stability evaluation and risk prevention and control in complex rock mass projects such as deep mines and tunnels.

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

The invention discloses an InSAR (Interferometric Synthetic Aperture Radar) deformation monitoring system for landslide and a method thereof, and relates to the field of geological disaster monitoring. The method has a high-precision deformation monitoring effect, through multi-source SAR data fusion, interference pair screening optimization and multi-source error correction (such as troposphere and ionosphere delay correction), the fidelity of a deformation phase sequence is remarkably improved, and the precision limitation under a complex terrain is overcome; the method has an intelligent geological constraint inversion capability, introduces geological prior knowledge (such as fault and fracture characteristics), enables a deformation field to better conform to the actual geomechanical law through deep learning semantic segmentation and a Bayesian inversion model, and reduces misinformation and missing report. The method has self-adaptive landslide recognition, adopts deformation gradient field calculation and a dynamic threshold segmentation algorithm, automatically recognizes a potential sliding zone boundary, improves the landslide partitioning efficiency, and is suitable for large-range monitoring.

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 relates to an intelligent soft rock tunnel surrounding rock deformation automatic monitoring and early warning system, and belongs to the field of tunnel construction safety monitoring. According to the system, multi-source data of surrounding rocks are synchronously acquired through a micron-sized laser displacement sensor, a fiber grating strain sensor, a pore water pressure sensor and a three-dimensional micro-seismic sensor array which are distributed; the edge calculation node fuses the data in real time and calculates the displacement rate, the strain gradient and the osmotic pressure coupling coefficient, and local early warning is triggered in combination with a dynamic threshold value; the cloud early warning platform predicts a deformation trend based on an LSTM-Transform fusion model, generates a multi-stage early warning signal through a risk grading engine, and is linked with the supporting equipment to regulate and control pressure; and meanwhile, a self-calibration device, an explosion-proof shell and a piezoelectric-thermal energy self-powered system are integrated. According to the method, closed-loop control from millimeter-level deformation sensing to active supporting can be achieved, and tunnel construction safety is remarkably improved.

The invention relates to a geotechnical engineering geological investigation system and an investigation method thereof, which are used for solving the problems of drilling redundancy, repeated model correction and long period caused by traditional uniform hole distribution. The system comprises a geophysical exploration module, a rock-soil body structure feature extraction module, a geological abnormal area identification module, a survey detection point dynamic optimization module, a drilling sampling analysis module and a geotechnical engineering stability evaluation module. Earth material exploration is carried out on representative points to obtain a comprehensive geophysical prospecting data set, inversion is carried out according to the comprehensive geophysical prospecting data set to obtain a three-dimensional rock-soil structure, an abnormal area is recognized, accordingly, the drilling layout is dynamically optimized, and stability evaluation is completed at a time after a small amount of directional drilling sampling is carried out. By adopting the system, a high-precision geologic model and a stable evaluation result can be obtained with fewer drilled holes in a preliminary exploration stage, the cost is remarkably reduced, and the period is shortened.

The invention relates to the technical field of rock mechanical tests and safety monitoring, and discloses a rock fracture prediction method based on acoustic emission and surface strain cooperative monitoring, and the method comprises the steps: building a monitoring system comprising acoustic emission and an industrial camera array, and building a unified timestamp of each subsystem through a GPS time service module; through load triggering logic, a synchronous trigger is utilized to synchronously acquire an acoustic emission signal and a sample surface image sequence, and evolution characteristics of three-dimensional coordinates of an acoustic emission source and surface full-field strain data are solved. And on the basis, calculating a main strain field variation coefficient, and performing cross-correlation analysis on the resampled energy rate and strain rate. And finally, according to multi-parameter coupling criteria of acoustic emission time sequence parameters, positioning events and surface strain, identifying rock fracture precursor types, and predicting fracture moments and areas by combining a Voight model inverse velocity method and a seismic source projection technology.

The invention provides an ocean live big data fusion method based on deep learning, and relates to the technical field of ocean big data fusion, and the method comprises the steps: obtaining meteorological driving variables, ocean historical reanalysis data, satellite observation data and ocean field observation data, and performing unified format conversion processing on the basis of the acquired meteorological driving variables, the ocean historical reanalysis data, the satellite observation data and the ocean field observation data, and configuring a unified ocean environment feature tensor set. A unified marine environment feature tensor structure is configured, meteorological driving variables, marine historical reanalysis data, satellite remote sensing data and on-site buoy observation data are subjected to space resampling, time interpolation and missing complementation and are unified into a space-time grid under a geographic reference coordinate system, a four-dimensional tensor set is configured, and the four-dimensional tensor set is obtained. The problem of fusion obstacles caused by non-uniform data formats and inconsistent temporal-spatial resolutions in the prior art is effectively solved.

The invention mainly relates to the technical field of tunnel engineering, and provides a tunnel surrounding rock grading dynamic correction method based on image texture features in order to improve the accuracy and real-time performance of surrounding rock grading under complex geological conditions. An improved deep residual network model integrated with an attention mechanism is input and introduced, the model improves the precision of single-point surrounding rock grade identification based on key textures, an initial prediction value of the surrounding rock grade is obtained, and meanwhile a time sequence prediction model is introduced; the whole excavation process is regarded as a continuous geological sequence based on the multi-source surrounding rock feature vectors of the current excavation cycle and at least one previous historical cycle, a time sequence trend value of the surrounding rock grade is obtained, a self-adaptive weight is set, the surrounding rock grade initial prediction value and the time sequence trend value of the surrounding rock grade are smoothed and corrected, and the surrounding rock grade is obtained. Therefore, the accuracy and the stability of the surrounding rock grading result are improved.

The invention discloses a soil constitutive parameter determination system and method based on an in-situ expansion test and numerical inversion, and belongs to the technical field of geotechnical engineering investigation. The system comprises a data interface and preprocessing module, a configurable numerical calculation engine, an intelligent inversion module and a result output module. A pressure-expansion displacement curve of the soil body is obtained in situ through the integrated probe; selecting a target constitutive model and setting a parameter inversion space; an intelligent optimization algorithm is used for driving a numerical calculation engine to conduct iteration forward modeling, and the optimal constitutive parameter set is automatically searched with the purpose of minimizing the difference between a simulation curve and an actual measurement curve. According to the method, in-situ expansion testing and intelligent numerical inversion are deeply fused, in-situ, accurate and automatic calibration of the whole set of parameters of the HSS model is achieved, and dependence on an empirical formula and a disturbed soil sample is abandoned. Reliable technical support is provided for refined design of major projects such as offshore wind power, and the method has great scientific value and engineering application prospects.

The invention is applicable to the technical field of geophysical detection, and provides an underwater target detection method and device based on acoustoelectric joint and width learning, and the method comprises the steps: carrying out the signal collection through an acoustoelectric joint detection network which is disposed in an underwater monitoring region in advance, the method comprises the following steps: acquiring an acoustic signal and an electromagnetic signal synchronously acquired by each detection node in an acoustic-electric joint detection network, performing feature extraction on the acoustic signal and the electromagnetic signal based on a data sliding window mechanism to obtain an acoustic-electric fusion feature matrix, and calculating the acoustic-electric fusion feature matrix according to the acoustic-electric fusion feature matrix. Whether a target object capable of autonomously radiating an acoustic signal and an electromagnetic signal underwater exists in an underwater monitoring area or not is determined through a pre-trained width learning network, so that false alarm interference caused by background noise in an underwater environment is effectively reduced, and the underwater target recognition capability under a weak signal condition is remarkably improved; and the accuracy of underwater target detection is improved.

The invention belongs to the technical field of petroleum and natural gas development engineering, discloses a shale reservoir three-dimensional dessert prediction method based on physical-data dual drive, and solves the defects of an existing shale reservoir dessert evaluation method in the aspect of space continuity. The prediction method comprises the following steps: (1) constructing a three-dimensional attribute field, and forming a multi-channel standardized three-dimensional data volume through voxelization processing; (2) fusing multi-parameter attributes of the shale reservoir and constructing a multi-channel three-dimensional feature body; (3) constructing a dessert prediction model based on the physically constrained space-attribute joint attention three-dimensional convolutional network; and (4) calculating a comprehensive dessert index, performing dessert grading and cause analysis, analyzing and quantifying the contribution degree of each attribute to dessert grading, and analyzing the dessert connectivity and the advantage extension direction. The predicted productivity potential and fracturing potential dessert field keeps reasonable gradient change and continuous distribution in the inter-well area, and the inter-well prediction error is small.

The invention discloses a pre-stack seismic elastic parameter inversion method based on a conditional diffusion model, and the method comprises the steps: obtaining a pre-stack CMP multi-angle seismic trace set and a well data low-frequency trend, mapping seismic data and an elastic parameter model to a potential space, training the conditional diffusion model in the potential space, carrying out the step-by-step denoising in order to recover the distribution of the elastic parameter, and carrying out the inversion of the elastic parameter. In the sampling process, Aki-Richards equation physical constraint is introduced, and uncertainty quantization is realized through transverse constraint and multiple sampling. According to the method, the spatial continuity of an inversion result can be ensured, uncertainty quantification can be realized, and reliable confidence evaluation is provided for geological interpretation. The method is wide in applicability and high in calculation efficiency, and the pre-stack elastic parameter inversion result with higher precision and higher credibility can be quickly obtained in actual three-dimensional seismic data processing.

The present disclosure relates to the technical field of stability control of surrounding rock of tunnelling, and provides an excavation compensation method for tunnelling in deep rock engineering, including: acquiring an engineering geological information of the tunnelling in deep rock engineering; determining an engineering hazard type based on the engineering geological information; determining an excavation compensation support strategy for the surrounding rock of the tunnelling in deep rock engineering based on the engineering hazard type; and performing a supplementary support control on the surrounding rock of the tunnelling in deep rock engineering based on the excavation compensation support strategy. Through the supplementary support strategy, the difference value between a radial stress of the surrounding rock of the tunnelling in deep rock engineering and an initial crustal stress can be reduced to within a preset approximate value range, further effectively preventing a stress concentration phenomenon occurred in a tangential stress.

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

The invention discloses a quick early warning method for regional geological disasters, particularly relates to the field of early warning of geological disasters, and is used for solving the problems of early warning lag and inaccurate positioning caused by dependence on a single monitoring index in the prior art. According to the method, a multi-dimensional monitoring data stream with time-space continuity is constructed by acquiring a slope body deep inclination angle, earth sound event count and earth surface micro-displacement data of a geological early warning area; generating a regional deformation synchronization index based on phase-space reconstruction and correlation dimension analysis, and calculating a system entropy value through density clustering and displacement fluctuation analysis; performing real-time identification on the regional critical state by adopting a critical state judgment model of double-threshold grading; and finally, combining real-time rainfall intensity data to carry out spatial superposition to divide into multiple levels of warning subareas, and outputting corresponding early warning signals. According to the method, pre-disaster megasses can be captured from the aspect of internal structure instability, the accuracy and foresight of regional geological disaster pre-warning are improved, and regional public safety is guaranteed.

The invention relates to the field of roadway surrounding rock deformation analysis, in particular to a roadway surrounding rock deformation intelligent monitoring method and device based on three-dimensional laser scanning. The method comprises the following steps: performing three-dimensional laser scanning on a roadway to generate equalized laser point cloud data; performing point cloud relative deformation detection and surrounding rock deformation dynamic modeling on the equalized laser point cloud data to construct a dynamic surrounding rock deformation model; performing space grid division on the dynamic surrounding rock deformation model, and performing differential change trend analysis to generate a multi-unit deformation trend; performing nonlinear evolution analysis and deformation situation prediction in a future time period on the multi-unit deformation trend to generate a deformation prediction result; and performing deformation situation interference simulation on the deformation prediction result, and extracting various interference simulation results. According to the invention, surrounding rock deformation data is accurately obtained in real time, and the deformation trend is dynamically analyzed and predicted, so that early warning is performed in advance, the safety risk is effectively reduced, and the safety and monitoring efficiency of underground engineering are improved.

The invention discloses a three-dimensional tomography method and system for seismic exploration data migration, and relates to the technical field of geological exploration, and the three-dimensional tomography method comprises the following steps: laying a layered heterogeneous sensing array of adaptive terrain coupling in an exploration target area, the layered heterogeneous sensing array comprises a shallow fiber grating sensing layer, a middle MEMS sensing layer and a deep distributed fiber sensing layer, sensors are all provided with terrain adaptive coupling assemblies, and sensing shafts all point to the radial direction of an underground medium. According to the seismic exploration three-dimensional tomography imaging method, an iterative imaging mechanism of real-time geological constraint feedback is constructed, the limitation of traditional static geological constraint one-time importing is broken through, the imaging process can dynamically fuse newly added local geological data in the exploration process, the imaging model and the actual geological conditions are continuously adapted and optimized, and the imaging efficiency is improved. The integrating degree of the imaging result and the actual geological condition is enhanced, and the high requirement of deep complex area exploration for the imaging accuracy is met.

The invention relates to the field of field engineering characteristic evaluation, in particular to a fracture characteristic calculation method based on resistivity tomography and resistivity static sounding. The method comprises the steps that firstly, in-situ site resistivity parameter calibration is carried out through a resistivity static sounding (RCPTU) test technology, and a resistivity index background value of a target site is obtained; then, acquiring an initial value of the electrical resistivity of the whole area of the target site by adopting a three-dimensional electrical resistivity tomography (ERT) technology; and finally, according to the resistivity-fracture inversion model, identifying the fracture distribution condition of the whole region of the target site, and giving a fracture rate evaluation model of the target site. According to the method, the precision problem of single in-situ test equipment is solved, the test precision of site three-dimensional resistivity parameters is improved, the reliability of three-dimensional inversion calculation is improved, and the three-dimensional space distribution of the soil body fractures can be evaluated quickly, nondestructively and accurately.

The invention discloses a deep rock mass fracture imaging method and system based on micro-seismic monitoring, and belongs to the technical field of geophysical exploration, and the method comprises the steps: collecting multi-channel micro-seismic event waveform data, obtaining a main seismic phase direct waveform and a scattering tail waveform through wave field separation, and carrying out the imaging of a deep rock mass fracture. Determining a three-dimensional space coordinate and an energy distribution form of the seismic source through joint inversion, carrying out clustering analysis, and constructing an initial three-dimensional space distribution form; each seismic source is regarded as an active excitation source, a scattering tail wave waveform is regarded as a reflection signal, scatterer three-dimensional point cloud data is generated through reverse time migration processing, analysis is carried out in combination with an initial three-dimensional space distribution form, and an enhanced fracture structure is formed; and finally, through topology analysis and orientation clustering, discrete scatterers are eliminated, and a fracture development map is generated. According to the method, a technical path of combining a main seismic phase direct wave inversion seismic source and a scattering tail wave inversion scatterer is adopted, high-precision and high-fidelity imaging of a deep rock mass movable fracture can be realized, and physical activity is revealed.

The invention discloses a method for realizing ocean sound field prediction based on a pre-training optimized physical information neural network, which comprises the following steps of: pre-training: constructing a hypothetical ocean environment, generating simulation data in a sound pressure envelope form by using an acoustic numerical calculation tool, and pre-training the physical information neural network to learn a physical rule of sound field propagation; fine tuning: adjusting an energy scale factor according to the proportion of the sound pressure amplitude of the actual measurement point to the sound pressure amplitude of the pre-trained environment, and performing fine tuning by using the envelope amplitude data of the actual measurement sound field for the pre-trained physical information neural network to obtain an ocean sound field prediction model through a pre-training-fine tuning dual-stage strategy; and ocean sound field prediction: according to the ocean sound field prediction model, obtaining a target position envelope from the to-be-predicted range, and then converting the target position envelope into a sound pressure result to realize ocean sound field prediction. The method can improve the high-frequency sound field modeling precision and training efficiency, and can be widely applied to the fields of ocean sound field modeling, sound source localization and environment inversion.

The embodiment of the invention provides a method and a system for processing ocean multi-parameter wide-width imaging data of a Beidou interference altimeter, and relates to the technical field of GNSS-R. The method comprises the following steps: constructing a set of complete and strict data processing chain; through precise calculation of a GNSS-R direct signal and reflection signal delay inequality interference geometric model and correction of actual measurement delay inequality errors, systematic errors in the process of converting original signals into geophysical parameters are remarkably reduced, and the precision and reliability of parameters obtained through final inversion are improved. And on the basis of a self-adaptive data collection strategy of parameter space-time transformation characteristics and a spatial interpolation reconstruction process, discrete strip data is intelligently'woven 'into a continuously-covered wide-swath imaging type data product, so that the data format better meets the analysis requirements in the fields of oceanography, climatics and the like, and the intuition and usability of the product are improved.

The invention relates to the technical field of geological data analysis, discloses a bank slope deep unloading developmental zone identification and evaluation method and system based on a sliding window, and aims at solving the problems that an existing method is low in efficiency and high in subjectivity, and evaluation results are discrete and cannot be quantized. Based on set window parameters, space convolution calculation is carried out through a sliding window, and a continuous feature signal sequence is generated; segmenting the sequence by using a preset background noise threshold value, and identifying candidate sections; matching and calibrating the boundary of the candidate section and the position of a known geological structural plane, and delineating a depth-keeping unloading development zone; the comprehensive development strength index of each deep unloading development zone is calculated, and quantitative evaluation is achieved. According to the method, automatic and continuous space recognition and quantitative representation of the deep unloading development zone are achieved, efficiency and accuracy are improved, and the method is particularly suitable for safe site selection and construction of major projects.