329 results about "Geophysical prospection" patented technology

The invention relates to the technical field of mine safety monitoring and geophysical exploration, in particular to a mine water disaster intelligent alarm system responding to multi-dimensional physical field parameter abnormity, which comprises a full-space data acquisition module for acquiring monitoring area observation data and calling a background geological physical model; the ideal reference reconstruction module is used for constructing an ideal physical field reference in a non-abnormal state; the double difference extraction module is used for calculating an observation residual error of observation data relative to a reference and a theoretical residual error of simulation response relative to the reference; the inversion coupling verification module is used for driving the theoretical residual error to approach to the observation residual error and extracting parameter sensitivity characteristics; the intelligent alarm judgment module is used for analyzing sensitivity characteristics and convergence states; generating an alarm instruction if the feature is determined to be the entity fluid abnormal feature; if it is determined that the target abnormal interference feature is not the target abnormal interference feature, signal suppression processing is executed; according to the invention, the false alarm rate of mine water disaster detection is greatly reduced.

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 discloses an underground physical characteristic prediction method and system combining seismic waves and drilling data, and relates to the technical field of geophysical exploration, and the method comprises the steps: collecting original seismic records, carrying out the preprocessing, obtaining a three-dimensional seismic data body, carrying out the complex channel analysis of the three-dimensional seismic data body, extracting the instantaneous amplitude, instantaneous phase and instantaneous frequency attributes, and carrying out the calculation of the instantaneous amplitude, instantaneous phase and instantaneous frequency attributes. Fault features are identified through a coherent body algorithm, an earthquake feature matrix is obtained, depth alignment and correction are carried out on density logging, interval transit time and neutron porosity curves of drill holes, and a standardized drill hole data table is generated; and based on the seismic feature matrix and the standardized drilling data table. According to the method, deep complementation of the earthquake macroscopic information and the borehole local truth value is realized through the geometric perception multi-modal graph neural network and the dynamic gating fusion mechanism, the problems of insufficient cross-modal feature fusion and misalignment of physical rules in the prior art are solved, and the prediction precision and reliability under complex geological conditions are remarkably improved.

The invention relates to a transient electromagnetic and seismic wave multi-mode joint inversion imaging method based on physical information Transform, and belongs to the crossing field of geophysical exploration and artificial intelligence. Comprising the following steps: carrying out anomaly detection, interpolation, filtering and normalization preprocessing on transient electromagnetic and seismic wave original data; extracting features representing electrical property, elasticity and cross physical significance; serializing the spatial data through a gridding and alternating fusion strategy, and constructing an enhanced code fusing absolute and relative positions and physical attributes; a physically constrained encoder-decoder architecture is designed to carry out multi-scale forward modeling-inversion; and quantizing the uncertainty of an inversion result by adopting a Bayesian Monte Carlo method, and generating a confidence map. According to the method, through physical rule driving and multi-modal depth complementary fusion, the fine recognition capability and interpretation reliability of hidden disaster-causing geologic bodies such as underground goaf and collapse columns are effectively improved while the physical consistency of data is kept.

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 seismic wave velocity-resistivity joint inversion method based on a time dimension, belongs to the technical field of geophysical exploration, and can solve the problem that the dynamic change of a geological structure cannot be determined in the prior art. The method comprises the following steps: constructing a seismic wave velocity distribution model according to seismic exploration data of a target area, and constructing a resistivity distribution model according to electromagnetic exploration data of the target area; determining an objective function of seismic wave velocity-resistivity joint inversion according to the seismic wave velocity distribution model and the resistivity distribution model; updating and iterating the seismic wave velocity distribution model and the resistivity distribution model according to the seismic exploration data and the electromagnetic exploration data of the plurality of periods until the objective function is minimized; and according to the corresponding seismic wave velocity distribution model and the resistivity distribution model when the target function is minimized, respectively determining seismic wave velocity inversion results and resistivity inversion results of the target area in multiple periods. The method is used for inverting the seismic wave velocity and the resistivity.

The invention belongs to the technical field of geophysical exploration, and particularly relates to a method and a system for quantitatively measuring intrinsic parameters of bedrock fractures based on all-angle domain amplitude and phase joint inversion. The method comprises the following steps: acquiring geological radar reflection data of a target fracture at a plurality of incident angles; synchronously extracting data sequences of amplitude change along with angles and phase change along with angles; establishing a fracture reflection theoretical model; constructing a target function jointly constrained by the amplitude residual and the phase residual; and adopting a global optimization algorithm to minimize the target function, and synchronously inverting to obtain the fracture opening, the filler dielectric constant and the occurrence parameter. According to the invention, through the combined constraint of the amplitude and the phase, the inversion multiplicity is effectively inhibited; accurate system effect correction is adopted, so that the reliability of large-angle data is ensured; according to the method, multi-parameter synchronous inversion without prior hypothesis is realized, the inversion precision is small in relative error, the method is obviously superior to a traditional method, and the method has important engineering application value.

The invention discloses a hidden ore body multi-source data fusion prediction method and system, and belongs to the technical field of geophysical exploration data processing, and the method comprises the steps: carrying out the standardization of obtained multi-source exploration data, and generating standardized data; quantifying errors of the standardized data in spatial positions and attribute values to generate a multi-source data set; constraining the multi-source data set according to a physical rule to obtain physical constraint embedded data; performing multi-scale feature extraction on the physical constraint embedded data to generate a multi-source feature map; performing cross-source and cross-scale association on the multi-source feature map to generate a fusion feature vector; and based on the fusion feature vector, predicting the mineralization probability, the physical property parameters and the uncertainty distribution, and generating a prediction result map. According to the method, multi-source data standardization, physical law constraint embedding, multi-scale feature fusion and uncertainty quantification technologies are adopted, and a three-dimensional mineralization prediction result with reasonable geology and controllable risk can be generated.

The invention discloses a stratigraphic water content layering inversion method based on a shallow seismic full waveform, and relates to the technical field of geophysical exploration data processing, and the method comprises the following steps: 1, under the same survey line condition, synchronously obtaining a seismic wave full waveform, complex impedance and an induced polarization frequency spectrum; 2, constructing a layered initial model containing a layer boundary line and an in-layer water content initial value; based on the layered initial model, executing dual-domain quantile alignment interface keeping iteration until convergence, and obtaining a final layer boundary line and an in-layer water content field; the double-domain quantile alignment interface maintaining iteration comprises the following steps: updating an in-layer moisture content field and a layer boundary line according to a seismic wave full waveform; and performing total variation regularization processing on the updated in-layer water content field to maintain the transition characteristic of the layer boundary line. According to the method, an efficient and reliable solution with actual guidance value is provided for mineral resource exploration, geological structure detection, groundwater survey and engineering geology evaluation.

The invention discloses a magnetic method data physical property inversion method and system based on a deep learning terrain disturbance layer, and relates to the technical field of geophysical exploration, and the method comprises the steps: building a magnetic anomaly data set, generating an underground three-dimensional magnetic anomaly model through a mixed hexahedron model and a terrain disturbance function, and obtaining corresponding magnetic anomaly data through forward modeling, each group of magnetic anomaly data corresponds to different terrain parameter combinations; constructing a deep learning inversion network model based on UNet; and training the deep learning inversion network model based on the magnetic anomaly data, taking the trained training set parameters as the weight of the deep learning inversion network, verifying the effect by using the verification set after the training is finished, and predicting the effect according to the test set. According to the method, a traditional same-dimensional random projection inversion method is combined with a deep learning network structure, and dynamic transformation of terrain parameters is realized by using a terrain disturbance layer, so that the network keeps high inversion precision and stability under different terrain fluctuation conditions.

The invention discloses a magnetic data boundary identification method and system based on a physical prior selection module, and relates to the technical field of geophysical exploration, and the method comprises the steps: building a magnetic anomaly data set through underground target magnetic anomaly data with different parameters; generating magnetic measurement data through a magnetic anomaly forward modeling calculation formula, and forming a magnetic measurement data set; constructing a boundary recognition network model based on UNet + +, and integrating the physical prior selection module to the boundary recognition network model; inputting the training set sample, the verification set sample and the test set sample into a physical prior selection module for operator transformation, and generating a corresponding transformation result graph; and calculating the structural similarity index of each transformation result graph and the corresponding sample label, and splicing the transformation result graph with the maximum structural similarity index and the corresponding sample according to the channel dimension to generate a corresponding multi-channel feature. According to the method, the boundary recognition network model is established and the physical prior selection module is added, so that the robustness of the boundary recognition network model to complex noise is improved.

The invention provides an electromagnetic detection system based on a sea-bottom crawler, belonging to the field of geophysical exploration technology, comprising: a survey vessel, a sea-bottom crawler, a transmitting antenna, a receiving node array, a control chip, and a power supply; the survey vessel is connected to the sea-bottom crawler through a traction rope, and the tail of the sea-bottom crawler is provided with a drag cable, on which the transmitting antenna and the receiving node array are mounted, and both the control chip and the power supply are configured in the integrated electronic cabin of the sea-bottom crawler; the power supply provides electrical energy to the sea-bottom crawler, transmitting antenna, receiving node array, and control chip, and the control chip is electrically connected to the transmitting antenna, the receiving node array, and the power system of the sea-bottom crawler; the control chip is configured to have an electromagnetic detection interference compensation module for performing interference elimination and compensation on reflected waves received by the receiving node array. It solves the technical problem of insufficient detection accuracy in existing technologies.

The invention discloses a magnetic target boundary recognition method and system based on a magnetic gradient tensor, and relates to the technical field of geophysical exploration and industrial nondestructive testing, and the method comprises the following steps: collecting spatial magnetic gradient tensor data through a magnetic gradient sensor array, and obtaining nine components of the magnetic gradient tensor; calculating the vertical guide modulus of the vertical component of the magnetic gradient tensor; calculating a Z-direction analysis signal ASAz and a vertical guide thereof; a boundary recognition amount is generated by a ratio operation, and a boundary position of the magnetic target is determined based on a maximum value position of the boundary recognition amount. According to the method, a vertical component derivative and analytic signal derivative dual-derivative coupling mechanism is combined for the first time, the limitation that a traditional method depends on a single parameter is overcome, the vertical component derivative accurately captures the change rate of the boundary in the vertical direction, and the analytic signal derivative effectively suppresses background noise.

The invention belongs to the technical field of seismic data processing in geophysical exploration, and relates to a Marchenko multiple suppression method for correcting wave field mismatch based on Wasserstein distance, which comprises the following steps: acquiring a pulse reflection sequence through preprocessing and deconvolution; a pulse reflection sequence is input, and Marchenko iteration initialization is carried out; and remodeled Marchenko iteration for correcting the wave field mismatch based on the Wasserstein distance is carried out. According to the method, the optimal transmission theory is systematically introduced into the Marchenko multiple wave suppression field for the first time, the adaptive capacity of the Marchenko method to actual data noise, defect and amplitude distortion is remarkably improved while the advantage that the Marchenko method does not need a speed model is kept, and therefore higher-precision multiple wave suppression and more reliable primary wave recovery are achieved, and the method is suitable for popularization and application. The method is used for high-precision seismic imaging in a complex land exploration environment. The method has remarkable advantages in the aspects of constraint mechanism, error control and global feature protection.

The invention discloses a method for data inversion by utilizing a multipoint geological statistical structure constraint direct current electric method, which belongs to the field of geophysical exploration, and comprises the following steps: based on geological drilling data, establishing a model containing geological structure information by adopting a multipoint geological statistical algorithm, and constructing an underground stratum structure operator by utilizing the model; a structure operator is added to a regularization term, an initial resistivity model is given to calculate a data residual term, and an inversion objective function with structure constraint is formed; in the inversion process, a finite memory quasi-Newton optimization algorithm is adopted to carry out iterative optimization on a target function until the difference between forward modeling simulation data and actually measured data reaches a set threshold value, and constraint inversion is completed; and finally, outputting the resistivity inversion model. According to the method, the problems of fuzzy stratum boundary, reduced resolution and the like caused by regularization smooth constraint in a traditional method are reduced, and the method is suitable for subsurface stratum structure detection and fine imaging in a complex geological environment and has wide application value.

The invention discloses a seismic strong background noise removal method based on a two-channel network, and relates to the field of geophysical exploration data processing, and the method comprises the steps: selecting a noiseless signal and background noise when the noiseless signal and the background noise are not excited from a single-channel seismic record after a seismic source is excited, carrying out the superposition, generating noisy data, and carrying out the normalization processing; constructing a dual-channel constraint denoising network composed of a global constraint sub-network and a denoising sub-network; optimizing network parameters through the training sample set; and performing global constraint denoising and slice local denoising on the noisy seismic data by using the trained model, and finally splicing to obtain a complete denoising result. According to the method, the splicing effect problem caused by traditional blocking processing is effectively solved, the strong background noise removal effect is remarkably improved, and seamless high-quality denoising with the complete edge is achieved.

The invention discloses a gravity anomalous body inversion method based on a PMU-Net deep learning network, and belongs to the field of gravity inversion, and the method comprises the steps: increasing the number of observation equation sets through the joint inversion of two different types of gravity anomalous data, and comprehensively presenting an underground geologic structure. Data are processed by means of two independent channels, one channel uses a Mama module to capture dependency between observation points, and the other channel uses a spatial pre-training model to capture position association between the observation points and underground density. And the processed features are fused and input into the U-shaped neural network constructed by one-dimensional convolution, so that the calculation amount is reduced and the efficiency is improved. The system further comprises a simulation data generation module, and the problem of weak interpretability of deep learning in gravity inversion is solved by embedding priori knowledge. According to the invention, the accuracy and reliability of inversion under complex geological conditions are effectively improved, and an efficient and accurate new method is provided for geophysical exploration.

The invention relates to the technical field of geophysical exploration, and discloses a three-dimensional fine electromagnetic detection method for ore control fracture of a deep gold ore. The method comprises the following steps: arranging a measuring point array to emit a coding artificial electromagnetic pulse signal, and synchronously receiving a plurality of induced magnetic field responses to form an original data volume; estimating a noise baseline based on statistical distribution and adaptively calibrating data; constructing a spatial correlation network by using the time sequence correlation of adjacent measuring points, and identifying a high-connectivity cluster as a suspected fracture region; extracting a regional time sequence to perform multi-level clustering, and screening a typical fracture response mode; based on the mode set global matching enhancement response, generating an enhancement data volume; multi-scale three-dimensional tensor decomposition is adopted to extract a tensor kernel component, and an electrical structure is reconstructed according to the spatial compactness and energy attenuation characteristics of the tensor kernel component; and finally, iteratively fusing with a geological constraint model to output a three-dimensional ore control fracture fine detection result. According to the method, intelligent identification and high-precision imaging of the deep fracture are realized.

The invention relates to the technical field of geological exploration, and discloses an exploration method and device for intelligent fusion of multi-geophysical field data. The method comprises the steps of signal source excitation, data acquisition and synchronization, data recording and transmission, preprocessing, feature value extraction, AI model fusion processing, joint inversion, imaging and interpretation and the like. According to the invention, multi-physics field data is synchronously acquired, and a multi-source heterogeneous data intelligent preprocessing and feature extraction method and a multi-physics field data fusion and joint inversion method based on artificial intelligence are provided for data such as earthquake, electromagnetism and gravity in a unified signal analysis framework. Through the steps, the problems of data isomerism, space-time inconsistency, inversion multiplicity and the like in geophysical exploration are solved.

The invention relates to the technical field of crossing of geophysical exploration and artificial intelligence, and discloses a three-dimensional VSP data reconstruction method based on a three-dimensional blind sidewalk time frequency-space multi-domain interpolation network model, and the method comprises the steps: obtaining a to-be-reconstructed three-dimensional VSP data volume containing an irregular missing sidewalk; inputting the three-dimensional VSP data volume into a trained three-dimensional space-time-frequency mixed domain convolutional network model; performing down-sampling on the input data through an encoder to extract initial spatial-temporal characteristics; inputting the initial spatiotemporal features into a residual spatiotemporal-frequency wavenumber domain joint convolution feature fusion module to obtain fusion features; and inputting the fusion features into a decoder, and reconstructing a complete three-dimensional VSP data volume through up-sampling operation. According to the invention, through joint learning of ground seismic data (providing a macrostructure) and illuminance data (providing a physical propagation path), a reconstruction result with a reasonable geologic structure and extremely high fidelity of wave field features can be generated.

A method for evaluating geological hydrogen source rock in a region is provided. The method includes: collecting magnetic susceptibility data from a plurality of subsurface locations in the region; receiving geophysical survey data of the region, wherein the geophysical survey data comprises geophysical data collected from above ground; tying the magnetic susceptibility data of the plurality of subsurface locations to the geophysical survey data; generating a geologic map of igneous rock, metamorphic rock, or mafic mineral- or iron-rich sedimentary rock within the region based on the magnetic susceptibility data using a model; and determining, based on the geologic map, a target zone of the geological hydrogen source rock with a potential for hydrogen production or with the potential for hydrogen accumulation.

The invention relates to the technical field of geophysical exploration, in particular to a three-dimensional resistivity overwater detection method and system based on cooperation of an unmanned aerial vehicle and an unmanned ship, and the system comprises a main unmanned ship, a plurality of auxiliary unmanned ships, an unmanned aerial vehicle auxiliary platform and a control terminal. The main unmanned ship is used for providing an electric field source; the auxiliary unmanned surface vehicle is used for dragging a floating cable in water along a sailing path, and a measuring module is arranged on the auxiliary unmanned surface vehicle and used for measuring the potential difference between power supply electrodes in the floating cable; the unmanned aerial vehicle auxiliary platform is used for providing air guidance and obstacle avoidance navigation for the main unmanned ship and the auxiliary unmanned ship; and the control terminal is in communication connection with the positioning module and the measurement module and is used for receiving the potential difference information and the electrode positioning information and obtaining a resistivity space distribution image of the target water area by adopting a three-dimensional forward and reverse modeling and visualization algorithm. According to the invention, the operation safety, the measurement efficiency and the space coverage range are obviously improved, and the method is suitable for rapid three-dimensional detection of complex water areas such as rivers and lakes.

The invention is suitable for the technical field of geophysical exploration, and provides a rock physics-driven time-frequency domain longitudinal wave velocity dispersion attribute prediction method, which comprises the following steps of: establishing a rock physics model; under the driving of the rock physical model, inverting the porosity of the microfractures in the well, calculating the skeleton elastic modulus of the rock containing the fractures, and predicting a frequency-dependent longitudinal wave velocity logging curve; constructing a time-frequency domain longitudinal wave velocity seismic inversion initial model; constructing a TF-AVO theoretical formula, developing a time-frequency domain longitudinal wave velocity seismic inversion method based on an L-BFGS algorithm, and performing longitudinal wave velocity inversion by taking the initial model as prior information; analyzing the relative change of the frequency-varying longitudinal wave speed under different frequencies, and quantitatively predicting the frequency dispersion attribute of the longitudinal wave speed; and on the basis of logging data, calibrating a longitudinal wave velocity dispersion attribute prediction result, and carrying out reservoir oil and gas detection. The method has higher precision and resolution, the stability and reliability of longitudinal wave velocity dispersion attribute prediction are remarkably improved, and the dependence on strong amplitude response is overcome.

The invention belongs to the technical field of geophysical exploration, and particularly relates to a two-section type phase control high-precision speed field building method for a complex structure area. A two-section type phase control high-precision speed field building method for a complex structure area comprises the following steps that a shallow surface layer area marker bed in a research area is determined, drilled wells evenly distributed in the research area are screened out, and according to the shallow surface layer area marker bed and well logging data of the drilled wells, the shallow surface layer area marker bed and the well logging data of the drilled wells are determined; establishing a three-dimensional structure model and determining a shallow surface layer velocity field of the research area; determining a geological anomalous body and a marker bed of a middle-deep layer area in the research area through seismic geology combination, establishing a middle-deep layer structure model, and determining a middle-deep layer velocity field of the research area; a shallow surface layer velocity field and a middle-deep layer velocity field are fused, a velocity field of a research area is established, time-depth conversion of a time-domain seismic data volume is completed, and a depth-domain seismic data volume is obtained, so that the structural interpretation precision in the research area is improved, and support is provided for well position deployment and storage increase and production.

The invention relates to the technical field of oil and gas geophysical exploration, and discloses a seismic imaging method and system based on a frequency-frequency convolutional neural network. The amplitude fidelity, structural continuity and physical rationality of seismic data are cooperatively controlled by using a multi-task learning mechanism, and meanwhile, an F2F-CNN network structure integrating attention gating and multi-scale feature fusion is constructed, an encoder-decoder symmetric design is adopted, an attention mechanism is embedded in jump connection, and a multi-scale feature fusion algorithm is established. The method comprises the following steps: adaptively enhancing response to key geological features such as a thin layer, and inhibiting background noise interference, on this basis, establishing a dynamic weighted fusion strategy based on a local signal-to-noise ratio, and adaptively adjusting a fusion weight according to the signal-to-noise ratio levels of predicted high-frequency data and original high-frequency data in different regions, so as to obtain a high-frequency high-frequency data fusion model. The reliable components in the original data are reserved while high-frequency effective signals of network reconstruction are emphasized.

The invention discloses a low-noise, high-precision and low-power-consumption tunnel magnetoresistance array type magnetic anomaly detection device, and relates to the field of geomagnetic field magnetic anomaly detection. In order to overcome the defects that in the prior art, the detection precision is insufficient, the noise level is high, power consumption is large, and long-time stable operation is difficult to consider, the technical scheme provided by the invention is that a signal acquisition and amplification circuit is composed of a plurality of triaxial tunnel magnetoresistive sensors, an isolation circuit, an instrument amplification circuit and a passive filter circuit; the master control and signal processing circuit is composed of a high-precision analog-to-digital conversion circuit, a clock circuit and a microprocessor circuit. The signal transmission circuit is composed of an Ethernet module and is used for transmitting the processed data to an upper computer; the power supply module is composed of a low-noise low-dropout voltage stabilizing circuit and a reference voltage circuit, and is used for providing a stable power supply and a unified reference voltage. The method is suitable for long-time high-precision geomagnetic anomaly detection work in geophysical exploration, national defense safety monitoring and complex environments.

The invention provides a gold mine prospecting target area intelligent delineation method and system based on multi-source geological data, and relates to the technical field of mineral exploration. Remote sensing images, geophysical exploration and earth surface rock chemical data associated with gold mine mineralization are collected, and according to the gold mine mineralization characteristics, the gold mine prospecting target area intelligent delineation method and system based on the multi-source geological data are obtained; correcting gold element space distribution difference, decomposing mineral spectrum mixing information, and strengthening mineralization related characteristic information. Then mining a data feature association relationship based on the feature information, the physical exploration data and the geochemical data, constructing a metallogenic association network, and inputting the feature information to obtain metallogenic association parameters; calculating mineralization probability values point by point in combination with spatial distribution characteristics of the multi-source geological data, generating a continuous mineralization probability curved surface, finally screening areas with probability values higher than a preset threshold value, locking a conforming closed area in combination with a typical gold ore deposit model, and recording coordinate boundary information to generate a gold ore prospecting target area map. The intelligent and accurate delineation of the gold ore prospecting target area can be realized.

The invention relates to the technical field of geophysical exploration, and discloses a common station pair coherent superposition enhancement method for active source seismic surface waves with a low signal-to-noise ratio. Comprising the following steps: generating a Green function according to a relative relationship between a seismic source position and a station pair: when the station pair is located at the same side of a seismic source, calculating the cross-correlation of seismic records of two stations in the station pair, and generating a cross-correlation Green function; when the station pair is located on the different side of the seismic source, the cross correlation between the seismic records of the two stations in the station pair and the seismic record of the reference station at the seismic source is calculated, then convolution calculation is carried out, and a convolution Green function is generated; frequency dispersion energy diagrams are respectively calculated based on the superposed cross-correlation Green function and convolution Green function; and superposing the two types of frequency dispersion energy diagrams to generate a final frequency dispersion diagram for extracting a frequency dispersion curve. According to the active source seismic surface wave signal co-station pair coherent superposition enhancement method provided by the invention, the surface wave signal-to-noise ratio and the frequency dispersion diagram quality are effectively improved on the premise that the data acquisition cost is not increased.

The invention relates to the technical field of geological disaster prevention and control, and discloses a mountain and valley area landslide hazard source identification and dynamic risk assessment method, which is characterized by comprising the following steps: S1, regional disaster-pregnant mode analysis and identification target region delineating: constructing a comprehensive susceptibility evaluation model, and delineating investigation target regions with different priorities in combination with InSAR deformation monitoring; s2, fine identification of a potential sliding source: accurately identifying a landslide boundary and a deep structure by adopting a'satellite-aircraft-ground 'cooperative technology combining multi-stage LiDAR topographic change detection, unmanned aerial vehicle multispectral and thermal infrared detection and geophysical exploration; and S3, dynamic risk assessment: establishing a dynamic and static load coupling stability model considering rainstorm and earthquake working conditions, calculating an instability probability by adopting Monte Carlo simulation, and realizing quantitative risk classification in combination with vulnerability analysis of a disaster-bearing body. According to the invention, early identification, accurate positioning and dynamic risk quantification of landslide hazard sources are realized, and the prevention and control capability of landslide disasters in alpine and valley areas is improved.

This invention belongs to the fields of rock mechanics, geological engineering, and geophysical exploration technology. Specifically, it discloses a method and system for constructing a mapping relationship between acoustic wave velocity and surrounding rock stress. The method includes: performing uniaxial graded loading on a standard sandstone sample and simultaneously measuring the P-wave velocity and S-wave velocity under each stress level; then fitting a linear mapping relationship between the P-wave / S-wave velocity ratio and the axial stress of the surrounding rock; introducing joint correction coefficients and water content correction coefficients to optimize the linear mapping relationship and constructing an acoustoelastic model; and using the acoustoelastic model and field-measured wave velocity data to invert the stress state of the actual surrounding rock, thus completing the construction of the acoustic wave velocity-surround rock stress mapping relationship. This invention solves the problems of traditional wave velocity-stress relationships, which, when describing the nonlinear response of rocks at different stress stages, are not sensitive enough to subtle stress changes and lack a complete, high-precision, and interference-resistant stress inversion calculation method, making them difficult to directly apply to complex actual engineering environments.