6239 results about "Earth quake" 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 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 present disclosure discloses a seismic inversion method based on joint constraint of a physical model and priori information. The method includes: extracting seismic wavelets based on seismic data, and determining an amplitude scaling factor of the wavelets; counting priori information of impedance parameters; establishing an initial impedance parameter model by using seismic structural interpretation information and logging data; obtaining a simplified approximate equation based on an interface weak elasticity difference hypothesis, forward modeling a seismic gather by using the simplified equation, and calculating an inversion residual; rewriting an objective function into a function related to the impedance parameters by using a generalized linear inversion idea, solving the impedance parameters by using an iterative reweighted least squares algorithm, and updating the impedance parameters; and repeating the above steps until the inversion residual reaches the requirements or reaches the maximum number of iterations, and outputting a final processing result.

The invention relates to the technical field of petroleum and natural gas engineering, and discloses an oil and gas reservoir optimized mining method based on multi-modal data, which comprises the following steps: constructing an oil and gas reservoir multi-modal data acquisition system, seismic wave field data, logging interpretation data, production dynamic data, micro-seismic monitoring data, underground temperature and pressure time sequence data and shaft structure parameter data are obtained through the acquisition system; and performing space-time alignment processing on the acquired multi-modal data, establishing a unified geological coordinate system and a time reference, and eliminating data isomerism caused by different acquisition frequencies and spatial resolutions. A six-dimensional heterogeneous data acquisition system covering a seismic wave field, well logging interpretation, production dynamics, micro-seismic monitoring, an underground temperature and pressure time sequence and shaft structure parameters is constructed, so that the depiction precision of a reservoir porosity field, a permeability field, a saturation field and a pressure field is essentially improved.

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 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 belongs to the technical field of geophysical monitoring, and relates to an electromagnetic interference detection method for earthquake monitoring, which comprises the following steps: deploying a multi-dimensional electromagnetic field sensing unit and an earthquake signal acquisition unit and realizing high-precision synchronization; synchronously acquiring multi-dimensional electromagnetic field and seismic physical signal data; performing multi-feature and noise feature extraction on the two; inputting the features into a pre-trained machine learning / deep learning model for identification, and outputting information such as interference type, intensity and azimuth angle; and carrying out influence evaluation and quality marking on the seismic data according to the output, and selectively inhibiting or compensating. Through the above scheme, multi-dimensional perception and characterization, high-precision synchronization and correlation analysis, intelligent identification and spatial positioning are realized, and the reliability and accuracy of earthquake monitoring data are significantly improved.

The invention relates to the technical field of bridge structure health monitoring, and particularly provides a bridge earthquake damage rapid assessment method and system, and the method comprises the steps: carrying out the cross-modal coupling calibration of a time-history sudden change feature and a phase of a space energy spectrum, and generating a high-confidence three-dimensional dynamic response field; inputting the spatial gradient distribution characteristics into a nonlinear material state inversion program, performing path integrity verification in combination with an energy dissipation path, and outputting a potential damage topology boundary of the bridge; dividing an output bridge potential damage topological boundary into discretized damage units, and mapping strain constraints of the discretized damage units into triple decision-making indexes including a traffic capacity attenuation coefficient, an emergency disposal aging threshold value and an aftershock risk level through a bridge structure function attenuation rule. The system comprises a mixed field dynamic response reconstruction module, a damage sensitive domain reverse locking module and a function level damage quantitative mapping module. According to the method, deterministic mapping of the bridge structure damage state and the engineering function index is realized.

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 discloses a post-stack seismic data denoising method based on multi-dip-angle structure guided filtering, and belongs to the field of seismic data denoising, and the method comprises the following steps: carrying out the processing of input seismic data through guided filtering, and obtaining a fault likelihood attribute; dividing the seismic data into a fault region and a continuous stratum region based on fault likelihood attributes; in the fault region, performing filtering processing on the seismic data by using an edge-preserving smoothing algorithm to obtain a filtering result of the fault region; in the continuous stratum area, calculating a local inclination angle of the seismic data, and performing filtering processing on the seismic data by adopting a structure guiding filtering algorithm which performs smoothing along the direction of the local inclination angle to obtain a filtering result of the continuous stratum area; and fusing the filtering result of the fault region and the filtering result of the continuous stratum region to obtain final de-noised seismic data. According to the method, efficient noise suppression can be performed on post-stack seismic data, and meanwhile, the protection capability on a complex geological structure is remarkably improved.

The embodiment of the invention discloses a landslide disaster evolution analysis method and system based on a coupling model, and the method comprises the steps: carrying out the multi-source data integration of geological structure monitoring data, landform distribution data and hydrological environment monitoring data of a target seismic region, and generating a comprehensive monitoring data set; based on the data set, establishing an interaction association relationship of the target equation set, and constructing a geology-landform-hydrology coupling model; performing dynamic simulation and quantitative evaluation on the instability probability and the landslide scale of the potential landslide mass by using the coupling model according to a preset scene parameter combination, and generating an image-text annotation quantitative evaluation report; and finally, carrying out vulnerability analysis and risk assessment on the disaster-bearing body based on the report, generating a comprehensive guidance report containing a disaster-bearing body vulnerability spatial distribution diagram, landslide risk prevention and control measure suggestions and a regional space planning optimization scheme, and providing a scientific basis for landslide disaster prevention and control and regional planning.

Disclosed in the present invention is an anti-seismic simulation method for an existing building by considering a real damage state. The method comprises: determining a quantified component damage index on the basis of an observed component damage state; determining a material damage index of a damaged component on the basis of the component damage index; determining key parameters of a constitutive law for a damaged material on the basis of the material damage index; establishing a finite element model of a damaged building on the basis of the constitutive law for the material of the damaged component; and assessing the seismic risk and anti-seismic performance of the damaged building on the basis of the finite element model. In the present invention, a constitutive law for a damaged material is incorporated into a fiber model in OpenSEES, so that rapid modeling of a damaged building can be realized, thereby effectively reproducing the real damage of an existing building; and by using a seismic vulnerability analysis method, rapid assessment of the seismic risk and anti-seismic capability of the existing building can be realized, thereby further providing support for formulating reasonable reinforcement and reconstruction strategies by related departments.

The invention discloses a coal seam passive source seismic survey system and method based on deep learning, and relates to the technical field of coal mining safety monitoring and early warning, and the method comprises the following steps: S1, collecting the passive source seismic data of a coal seam area; s2, deep fusion features are extracted; s3, constructing a semi-supervised self-attention prediction network model; s4, extracting coal seam structure prediction parameters; s5, the corrected coal seam structure parameters are extracted; s6, constructing a fast seismic wave forward model, and calculating and outputting an initial model of the coal seam underground structure; s7, constructing a Transform optimization network, and extracting a depth feature vector; and S8, constructing a coal seam structure inversion model, and training iteration by using the improved batch depth Bayesian optimization algorithm. The method overcomes the limitation of manual analysis and slow response in a traditional coal seam seismic survey method, and provides an efficient and accurate solution for safe production of a coal mine.

The invention discloses a smart bracelet earthquake early warning service system and method, and the method comprises the steps: collecting user physiological parameters and earthquake early warning signals, building a coupling relation between earthquake intensity parameters and user physiological features, and forming an individualized disaster bearing reference; performing gravity sensing detection in combination with the geographic position of the user and the anti-seismic grade of the building, analyzing the spatial relationship between the micro-inclination angle of the building and the position of the user, and generating gravity field risk features; analyzing a group movement mode through relative position changes among multiple bracelets, extracting movement coordination characteristics and direction coordination coefficients, and forming a displacement coordination early warning sequence; a disaster emergency coefficient is generated based on user motion coordination efficiency and protection response behaviors, a sound wave positioning network of a multi-device linkage trigger area is constructed, collaborative alarm and personalized evacuation guidance are realized, differentiated earthquake early warning services can be provided according to individual physiological features and environmental conditions, and the pertinence and effectiveness of early warning are improved.

The invention relates to the technical field of rock mass earthquake dynamic response, and discloses a layered rock mass earthquake dynamic response multi-scale monitoring system and method, and the system comprises a rock mass three-dimensional geological module, a simulation setting module and an earthquake simulation module. The method comprises the following steps: establishing a layered rock mass three-dimensional geologic model; the method comprises the following steps: identifying a dangerous rock mass structural surface, monitoring slope displacement, and installing sound emission monitoring equipment at a key position to form a three-dimensional monitoring network; a layered rock mass and a broken area in the layered rock mass three-dimensional geological model are divided into discrete element areas, and a continuous medium area is divided into finite difference areas; coupling the discrete element region and the finite difference region; inputting the seismic wave as a dynamic boundary condition into the finite difference region, transmitting a response to the discrete element region through a coupling interface, and simulating a rock stratum and rock mass dynamic fracture process; and the crack propagation particle size and the original stress propagation are transmitted to the cloud platform. The geometric morphology and the internal structure of the rock mass can be accurately reflected.

The invention provides an airfield pavement detection method based on an earthquake background noise imaging technology, and relates to the technical field of airfield pavement detection. Comprising the following steps: acquiring seismic background noise data of an airport pavement to be measured; preprocessing the seismic background noise data to obtain preprocessed noise data; carrying out cross-correlation calculation on the preprocessed noise data and carrying out empirical Green function extraction; according to the empirical Green function, a surface wave group velocity frequency dispersion curve is extracted by adopting spatial autocorrelation and multiple filtering methods; inverting shear wave velocity distribution of each layer of the pavement based on the surface wave group velocity dispersion curve, and constructing a three-dimensional velocity model; determining a shear wave velocity distribution diagram according to the three-dimensional velocity model; and determining a detection result of the airfield pavement to be detected according to the transverse wave velocity distribution diagram. According to the invention, the problems of low detection precision and poor real-time performance of a common detection technology in the prior art are solved.

The invention relates to the technical field of computational mechanics and geological disaster simulation, and discloses a full-process numerical simulation method suitable for simulating continuous medium damage to discontinuous medium movement. The invention provides a novel chain-type disaster simulation method for solving the problems that calculation efficiency and large deformation precision are difficult to consider at the same time and the evolution process of a slope from a continuum to a fragmented body cannot be dynamically reflected in the prior art. The core of the method is that a dynamic criterion is set based on a unit real-time damage variable, a failed finite element (FEM) unit is adaptively converted into smoothed particle hydrodynamics (SPH) particles, and the physical state of the particles is accurately mapped; and a virtual particle coupling algorithm is adopted to process a two-domain interface, so that bidirectional transmission of mechanical parameters is realized. The method is executed circularly, the whole process of'continuous deformation-fragmentation flow 'of the side slope is simulated dynamically, high-precision integrated simulation of a'seismic source-propagation-response-movement' disaster chain in the same frame is achieved, and an efficient tool is provided for disaster risk assessment and prevention and control.

The invention discloses a boundary-preserving seismic wave impedance inversion method, device, equipment and medium, and relates to the technical field of geological prospecting. Historical seismic data of a target area and logging curve data including logging impedance parameters are acquired, and seismic wavelet data are extracted from the historical seismic data; carrying out data processing on the seismic wavelet data to obtain target seismic wavelet data, constructing an initial impedance parameter model according to the logging curve data, carrying out forward modeling on the initial impedance parameter model, constructing a composite objective function comprising a boundary preserving constraint term and a prior information constraint term, and carrying out the forward modeling on the initial impedance parameter model based on the composite objective function. And the initial impedance parameter model is updated to obtain the target impedance parameter model, and the output impedance parameter inversion curve is obtained through the target impedance parameter model, so that the inversion precision and efficiency are improved, and the inversion stability and the geological reduction capability are improved.

The invention discloses an optical terminal intensity instrument and an earthquake monitoring service system and method, and the optical terminal intensity instrument comprises an optical fiber modem and an MEMS sensor module. The optical fiber modem comprises an MCU (Microprogrammed Control Unit) and an optical communication module electrically connected with the MCU; the optical communication module is used for realizing network access and data uploading; the MEMS sensor module is electrically connected with the MCU; the MEMS sensor module is used for collecting seismic intensity data in real time; and the optical fiber modem is used for providing a dual data transmission channel comprising broadband communication and the seismic intensity data. According to the technical scheme provided by the invention, the MEMS sensor is embedded in the optical fiber modem, and a more dense, more efficient and more economical solution is provided for earthquake early warning and intensity rapid reporting.

The invention discloses a fault co-seismic sliding surface inversion method and system based on Beidou and artificial intelligence. The method comprises the steps of collecting Beidou data and seismic waveform data of a target area in real time, and performing preprocessing to extract co-seismic displacement and waveform features; fusing the Beidou deformation features and the seismic wave features to form a multi-modal feature vector; a fault prediction model is constructed based on a graph neural network, forward modeling and historical earthquake example data training are utilized, and multi-modal features are input to obtain preliminary fault sliding distribution prediction; rapid optimization under physical constraint is carried out through the elastic dislocation model, and a final fault sliding model conforming to the geophysical law is obtained; finally, uncertainty quantification is carried out, and a visual product of the dynamic process including sliding distribution, seismic moments and fault deformation is generated. According to the system, full-process automatic processing is achieved through cooperation of all the units, the inversion speed, precision and physical credibility are improved, and reliable support is provided for earthquake emergency response and disaster assessment.

The invention discloses a fault slip model smoothing method based on SAR observation data, and the method comprises the steps: firstly, obtaining multi-source observation data (including SAR, GNSS and optical images), and carrying out the joint calculation, and generating a high-resolution earth surface deformation field; the method comprises the following steps: combining a remote sensing image, visually interpreting and drawing a fracture trace, then carrying out data downsampling along an inclination angle direction, and finally constructing an irregular fault geometric model by using a TIN method so as to obtain an initial fault plane model. On this basis, a Laplacian operator suitable for triangular dislocation is defined, a slip inversion model is constructed in combination with an elastic dislocation theory, and a smoothing factor is introduced to balance slip details and calculation efficiency. And finally, optimizing a fault inclination angle and a smoothing factor through a grid search method, evaluating fitting errors and slippage roughness under different combinations, and determining optimal parameters. The final model not only comprises a triangular connection relation, but also provides slip amount distribution and fault dip angle information, and an important basis is provided for seismic activity evaluation and disaster response.

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

The invention relates to a DAS-VSP seismic data reconstruction method based on a multi-scale attention network, and belongs to a reconstruction method based on DAS-VSP missing data in a well. Comprising the following steps: constructing a vertical seismic profile data set, constructing and training a multi-scale attention network, and realizing seismic data reconstruction by using lacked-channel DAS-VSP data. The method has the advantages that a multi-scale network is adopted as a backbone network, potential feature information of different scales is dynamically extracted and fused from DAS-VSP data containing noise and missing, and the accuracy of a reconstruction result is enhanced; a local attention module is introduced, a network is focused on related features of DAS-VSP reconstruction signals through a self-attention mechanism, the reconstruction precision of data is improved, the performance defects of a traditional method are effectively overcome, the reconstruction precision is remarkably improved, a reliable basis is provided for follow-up analysis and explanation work of DAS-VSP data, and the method is suitable for popularization and application. And the recognition degree and the application value of the DAS-VSP technology in the field of oil-gas exploration are improved.

The invention discloses a transmission tower vulnerability analysis method and system based on seismic simulation, and belongs to the technical field of electric power facility disaster prevention, and the method comprises the steps: correcting bedrock seismic oscillation parameters based on equivalent shear wave velocity, and generating a high-precision earth surface PGA distribution diagram; automatically positioning a key node layer through eigenvalue decomposition, reducing the order of the three-dimensional model of the power transmission tower into a two-dimensional series multi-degree-of-freedom model, and outputting a layer mass vector, a condensation stiffness matrix and rod piece bending stiffness; performing time-history analysis by using the corrected PGA-driven reduced-order model to generate an earthquake damage probability; the multi-source remote sensing data and the PGA are fused, the landslide space probability is output through a deep learning model, and the impact strength and the landslide kinetic energy are calculated to judge the landslide damage probability; and based on the dual-threshold condition triggering probability union set, calculating a comprehensive damage probability, and generating a three-dimensional vulnerable curved surface. According to the method, the problems of high missing report rate and low calculation efficiency of single disaster assessment are solved, and minute-level accurate early warning of the composite disaster risk of the power transmission tower is realized.

The invention provides a karst collapse early warning method and device, electronic equipment and a storage medium. The method provided by the invention comprises the steps of determining a first time sequence feature of surface displacement data, a second time sequence feature of groundwater dynamic data, a spatial feature of geological radar image data and a vibration feature of microseismic signal data, and generating a multi-modal fusion feature vector according to the first time sequence feature, the second time sequence feature, the spatial feature and the vibration feature; according to the multi-modal fusion feature vector and an early warning model, an early warning signal is obtained, and the early warning model is obtained by updating an early warning threshold value through a historical multi-modal fusion feature vector and a Transform-LSTM hybrid network by using a time sequence difference learning strategy; and performing validity judgment on the early warning signal according to the surface displacement data, the groundwater dynamic data and the micro-seismic signal data, and determining whether to perform early warning according to a judgment result. According to the method, the accuracy of karst collapse early warning can be improved.

The invention provides a three-dimensional visualization analysis method for a real scene of an ancient earthquake exploring groove, which belongs to the technical field of seismic geological survey and comprises a data acquisition module used for acquiring point cloud data of target exploring grooves in different regions. And the model building module is used for carrying out scene format conversion on the point cloud data of the target exploring grooves in different regions, and carrying out exploring groove three-dimensional model building by utilizing the converted SLPK file. And the visualization module is used for carrying out live-action three-dimensional display on the three-dimensional models of the target probe grooves in different regions through a three-dimensional scene Web server based on the SLPK files in different regions, and rendering node data of different detail levels of the probe grooves according to user requirements. The ancient earthquake event can be better understood, and a reliable three-dimensional digital file and a new shared analysis normal form are provided for the ancient earthquake.

The invention belongs to the technical field of seismic exploration, and discloses a robust full-waveform inversion method, system and device based on generative modeling, and a medium, and the method comprises the steps: obtaining seismic observation data; the method comprises the following steps: starting from random Gaussian noise, establishing a model space comprising a plurality of initial velocity models through an unconditional score model, and determining a global optimal initial velocity model of the model space by adopting a strategy search algorithm; de-noising is carried out on the global optimal initial velocity model based on back diffusion, seismological observation data are introduced as observation constraints, the velocity model is updated by calculating the mismatch gradient of forward modeling data of the current velocity model and the seismological observation data, and an implicit condition sample is obtained; performing forward diffusion processing on the implicit condition sample to obtain a speed model for a subsequent annealing time step; and when the annealing process reaches a preset condition, outputting a final speed model. According to the method, the robustness and accuracy of full-waveform inversion are improved, and the bottleneck problem of traditional full-waveform inversion is solved.

The invention discloses a three-dimensional geological model dynamic updating system and method based on multi-modal data fusion, and belongs to the technical field of geological engineering and information. According to the method, multi-source data of geological radar, earthquake, drilling, remote sensing and the like are integrated, and after unified formatting and quality optimization preprocessing, geological features of a stratum interface, a fault zone and the like are automatically extracted by using a deep learning network; multi-source feature data fusion is realized through space-time registration and attention; constructing a three-dimensional geologic model by adopting a TIN and Octree mixed structure based on a fusion result; dynamic updating of the model is realized through incremental detection and a local reconstruction mechanism, and the boundary of the model is optimized by adopting a radial basis function interpolation. According to the method, the problems of single data, updating lag and the like of a traditional method are solved, the modeling precision and efficiency are remarkably improved, and reliable support is provided for geological engineering decision making.

The invention belongs to the technical field of geological research, and discloses a multi-scale lithofacies identification and sedimentary microfacies reconstruction method and system, and the method comprises the steps: data collection and preprocessing: collecting core data, slice data, logging data and seismic data of a research region; multi-scale lithofacies feature extraction, wherein the multi-scale lithofacies feature extraction comprises micro-scale feature extraction, medium-scale feature extraction and macro-scale feature extraction; constructing and training a multi-scale lithofacies recognition model, constructing the multi-scale lithofacies recognition model by adopting a deep learning algorithm, and taking the extracted microscopic, mesoscopic and macroscopic lithofacies features as input data; multi-scale lithofacies identification: extracting multi-scale lithofacies features from the preprocessed multi-source data; and sedimentary microfacies reconstruction: establishing a lithofacies-sedimentary microfacies conversion relation in combination with a sedimentology principle and a geological knowledge base. According to the method, multi-source data such as rock cores, slices, logging and earthquakes are integrated, lithofacies features are extracted from microcosmic, mesoscopic and macroscopic multiple scales, the features and changes of lithofacies can be comprehensively and accurately reflected, and compared with a traditional single-scale method, the accuracy and reliability of lithofacies recognition are improved.

The invention relates to the technical field of earthquake prediction, in particular to a three-dimensional earthquake motion field rapid prediction method based on deep learning and physical constraint, and the method comprises the following steps: S1, data collection: obtaining original data related to an earthquake event in real time; s2, data preprocessing and multi-modal data fusion: preprocessing the original data, and fusing the preprocessed original data into multi-modal data; s3, spatial interpolation: realizing seismic oscillation feature interpolation prediction of the epicentral region, and outputting spatial spectrum data; s4, time sequence prediction and physical information optimization: generating a seismic oscillation time domain prediction sequence, and correcting errors by using an SVM model; s5, real-time reasoning and emergency response: realizing real-time minute-level response under the GPU architecture, and outputting three-dimensional seismic motion field data; according to the method, high-precision minute-level prediction of the three-dimensional seismic motion field is realized, and the efficiency and reliability of post-earthquake emergency response are remarkably improved.