107 results about "Magnetic anomaly" patented technology

The invention belongs to the technical field of data processing, and particularly relates to a hidden ore body evaluation and positioning method based on multi-source data processing. The method mainly aims at the problems of incompleteness and isomerism of multi-source geological data in acquisition, fusion and modeling. Comprising the following steps: acquiring hyperspectral, geochemical and magnetic anomaly multi-source data of an evaluation area; intelligently complementing missing modal data by using a generative adversarial network based on geological constraints and modal outburst to form a complete multi-source data set; an unsupervised clustering algorithm combining geological correlation and entropy weight analysis is adopted to construct high-confidence-coefficient pseudo-label data, and knowledge mining of unlabeled samples is achieved; feature purification and dimension reduction are carried out through multi-modal feature fusion and hierarchical principal component analysis, and key feature vectors representing the existence of the ore body are extracted; and finally realizing space prediction of the concealed ore body by utilizing the classification model. According to the method, a high-quality data basis and a unified processing framework are provided for intelligent recognition of the hidden ore body, and efficient and accurate positioning of the hidden ore body is achieved.

The invention discloses a detection method of an ocean magnetic field sensor based on a diamond NV color center. The detection method comprises the following steps: S1, calibrating a base line; s2, alternately measuring fluorescence between two single quantum resonance frequencies, and obtaining a difference to realize DQ reading; s3, three-axis compensation current is adjusted based on closed-loop control, and the NV working point is maintained at zero magnetization bias; s4, obtaining background magnetic field change through digital demodulation and adaptive filtering; and S5, calculating a gradient based on the spatial difference of the multi-point light spots, judging the existence and trajectory of the underwater vehicle in combination with a threshold value and a spectrum matching method, calibrating the threshold value according to the magnetic field characteristics of the existing underwater vehicle, adopting short-time Fourier transform signal spectrum, comparing the threshold value with known submarine characteristic spectrum data, and prompting if the similarity exceeds a set threshold value. According to the detection method, the measurement precision and the anti-interference capability can be improved, and the method is suitable for real-time monitoring of weak magnetic anomalies (such as magnetic moment disturbance of an underwater vehicle).

The invention provides an underwater target multi-sensor fusion magnetic field measurement system in a complex magnetic environment, which relates to the technical field of marine equipment and comprises a multi-mode magnetic field sensing module, an attitude correlation correction module, a layered fusion processing module and an environment self-adaption and magnetic anomaly detection module. The multi-modal magnetic field sensing module is responsible for collecting multi-dimensional and multi-type original magnetic field data around the underwater vehicle and constructing a complete magnetic field observation basis, and the attitude correlation correction module is used for eliminating the influence of the motion attitude change of the underwater vehicle on magnetic field measurement. The hierarchical fusion processing module performs hierarchical fusion on multi-sensor data; the environment self-adaption and magnetic anomaly detection module realizes background magnetic field separation, interference suppression and magnetic anomaly target identification; according to the system, the magnetic field measurement precision and robustness of the underwater vehicle in a complex magnetic environment can be improved, and high-reliability magnetic field feature extraction and situation awareness functions are realized.

The invention discloses a tungsten deposit deep edge metallogenic prediction method based on machine learning, and particularly relates to the technical field of geological prediction and data processing. The method comprises the following steps: acquiring multi-source geological data, constructing a multi-dimensional space grid, and dividing candidate prediction units; quartz vein density, alteration indexes, geochemical combination anomaly weights and high-frequency magnetic anomaly waveform features are extracted, and an ore control feature tensor matrix is constructed; extracting a spatial dependency relationship among the candidate units based on a graph neural network, constructing a metallogenic potential prediction model, and outputting a metallogenic probability graph and a metallogenic hot spot graph; model parameters and feature weights are dynamically optimized through spatial superposition analysis and an abnormal response recognition mechanism; according to the method, the precision and efficiency of deep edge tungsten ore prospecting can be remarkably improved, and good self-adaptability and popularization and application value are achieved.

The invention discloses a magnetic anomaly data de-noising method and de-noising system based on step-by-step U-Net, and relates to the technical field of geophysical exploration, and the method comprises the steps: carrying out the forward modeling calculation based on a magnetic anomaly model in a simulation region, and obtaining a synthetic magnetic anomaly data set; a U-Net step-by-step denoising network model is constructed; designing a self-adaptive multi-stage training mechanism based on mean square error minimization; training the U-Net step-by-step denoising network model through the training set, taking parameters of the U-Net step-by-step denoising network model as initial weights, and performing fine tuning on the U-Net step-by-step denoising network model by utilizing supervised learning; the verification set is used for verifying the effect, and the effectiveness of the whole step-by-step denoising network model is verified according to the prediction effect of the test set; a forward noise adding process and a reverse noise removing process are designed, network behaviors are automatically adjusted according to noise steps, the problems of high training cost and poor performance when a deep learning model processes colored noise are effectively solved, and the problem of low signal-to-noise ratio is better solved.

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 drill rod signal transmission monitoring control method, which comprises the following steps: capturing drill rod axial vibration waveform and peripheral magnetic field vector change data in real time, analyzing and marking potential resonance frequency points, calculating magnetic field intensity break variable, and judging a geomagnetic anomaly region; determining the relevance between a resonance frequency point and geomagnetic mutation through time alignment processing, and separating and coupling interference signals; generating an anti-phase damping wave according to the resonance frequency point, dynamically adjusting the amplitude, and injecting a pre-compensation phase to counteract carrier offset; the signal-to-noise ratio and the phase error after suppression are calculated in real time, and a parameter readjustment process is triggered to ensure the suppression effect; when the rock drilling vehicle enters a new rock stratum, parameters are automatically recorded and updated to adapt to different rock stratum interference characteristics; the drilling stability and the signal quality are obviously improved, and the equipment loss and the maintenance cost are reduced.

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 discloses a magnetic source target positioning method, which belongs to the technical field of geophysical exploration, is used for positioning a magnetic source target, and comprises the following steps: collecting and preprocessing plane grid magnetic anomaly data, constructing a simplified Euler deconvolution model under the constraint of derivative features, segmenting observation data by using a local sliding window, and resolving to generate an Euler solution point cloud. The method comprises the following steps: converting a selection problem of an equivalent scale factor into a one-dimensional global optimization problem, adaptively searching an optimal solution through a Bayesian optimization method, identifying a magnetic source high-probability solution point and a noise solution point by using a density-based spatial clustering method, introducing a density penalty term to strengthen a high-probability solution constraint, and constructing a final positioning target function. And outputting the optimal position solution of the magnetic source. According to the method, a traditional Euler deconvolution model is simplified, noise interference is effectively suppressed, the stability and robustness of the inversion process are improved, uncertainty caused by manual parameter selection is avoided, and the positioning precision is improved.

The invention belongs to the technical field of degaussing, and relates to a ship winding degaussing current solving method based on a gradient tensor, which comprises the following steps: acquiring the magnetic gradient tensor of a plane ship when coils are not electrified; respectively performing simulation electrification on the winding coils to obtain the magnetic gradient tensor of the electrified measurement plane; and calculating an efficiency matrix of the magnetic gradient tensor excited by the unit current. The gradient of the geomagnetic field is extremely small, direct detection of the magnetic gradient tensor of the ship and the electrified coil can better reflect the magnetic characteristics of the ship and the coil compared with detection of a magnetic vector, and better resolution is achieved for magnetic field changes caused by magnetic anomalies; under the motion platform, the magnetic gradient tensor is insensitive to the attitude angle error, and more accurate detection data can be obtained; in addition, the gradient tensor is weak in response to time-varying geomagnetic disturbance, and has higher anti-interference capability. Therefore, according to the method, the accuracy of ship magnetic field modeling and degaussing current calculation is improved, and the magnetic stealth performance of a ship facing high-sensitivity magnetic gradient detection equipment is enhanced.

The invention relates to the field of geophysics, and provides a magnetic anomaly inversion method, device, equipment and medium, and the method comprises the steps: obtaining magnetic anomaly observation data corresponding to an underground target region, and dividing an inversion region of the underground target region; calculating a smooth inversion result corresponding to the inversion region through a pre-constructed inversion expression based on the magnetic anomaly observation data, and calculating a sparse inversion result corresponding to the inversion region based on the smooth inversion result; updating the inversion model in the inversion expression based on the difference value between the sparse inversion result and the L0 norm of the preset smooth approximation function, and returning to the step of calculating the smooth inversion result and the sparse inversion result corresponding to the inversion region until the condition of stopping updating is reached; and predicting the magnetic body distribution of the to-be-observed area based on the updated inversion model. According to the scheme disclosed by the invention, accurate underground magnetic field intensity distribution and a clear boundary inversion result can be obtained, and the stability and the accuracy of a magnetic anomaly inversion result are improved.

The invention belongs to the technical field of geophysical exploration, and discloses a mine transient electromagnetic anomaly boundary identification method based on DBSCAN clustering. The method comprises the following steps: step 1, establishing a three-dimensional geophysical model; 2, calculating apparent resistivity data by using a small loop transient electromagnetic method; 3, drawing a k-distance map, and obtaining an optimal neighborhood radius according to the k-distance map; step 4, performing DBSCAN clustering on the apparent resistivity data to obtain a clustering result; 5, replacing the intra-cluster apparent resistivity value with a cluster center value to obtain replaced apparent resistivity data; step 6, imaging the replaced apparent resistivity data and identifying an abnormal body; 7, comparing the anomalous body boundary with the anomalous body boundary in the model, and if the difference is too large, adjusting the optimal neighborhood radius and returning to the step 4; and otherwise, taking the current abnormal body as a final identified abnormal body. The method can effectively identify irregular anomalous body boundaries of non-uniform complex stratums, and can adapt to real-time identification requirements of underground anomalous bodies.

The application discloses a small sample magnetic anomaly data enhancement and boundary identification method and system based on StyleGAN2-ADA, and relates to the field of geophysical exploration.The method comprises the following steps: establishing an initial small sample set; constructing a generative adversarial network based on adaptive discriminator augmentation (ADA), training a generator by using the initial small sample set, and generating a large amount of high-fidelity magnetic anomaly data; calculating boundary labels for the generated data by using a physical operator fusion strategy, and constructing a large-scale labeled training set; constructing a U-Net boundary identification network, pre-training the U-Net boundary identification network by using the large-scale labeled training set, fine-tuning the U-Net boundary identification network by using measured data, and finally outputting the magnetic anomaly boundary position of a target to-be-measured area.The application solves the problems of a lack of measured samples and high labeling costs in deep learning in magnetic exploration, realizes high-quality expansion of data under small samples, and improves the generalization ability and noise resistance of the boundary identification network to complex geological features.

The application discloses a three-dimensional positioning method and device based on scalar magnetic anomaly and related equipment, and the method comprises the following steps: acquiring two-dimensional scalar magnetic anomaly data, and the record points of the two-dimensional scalar magnetic anomaly data are uniformly distributed in a detection area; based on the two-dimensional scalar magnetic anomaly data, acquiring energy level distribution data of two-dimensional scalar magnetic anomaly in a two-dimensional space formed by each group of unit orthogonal bases, wherein the unit orthogonal bases are obtained by decomposing two-dimensional scalar magnetic anomaly data generated by a magnetic dipole of a target body at an arbitrary height; based on the energy level distribution data, determining a horizontal positioning result and a vertical positioning result of the target body. The application converts two-dimensional scalar magnetic anomaly distribution into two-dimensional energy distribution, improves the signal-to-noise ratio of data, and does not involve the vector magnetic moment information of the target body in the three-dimensional positioning process of the target body, is not affected by the magnetic moment, and can improve the positioning accuracy in the vertical direction.

The application discloses a magnetic data physical property inversion method and system based on a deep learning terrain disturbance layer, relates to the technical field of geophysical exploration, and comprises the following steps: establishing a magnetic anomaly data set, generating an underground three-dimensional magnetic anomaly model by mixing a hexahedron model and a terrain disturbance function, and obtaining corresponding magnetic anomaly data by forward calculation, wherein each group of magnetic anomaly data corresponds to different terrain parameter combinations; a deep learning inversion network model based on UNet is constructed; the deep learning inversion network model is trained based on the magnetic anomaly data, the trained training set parameters are used as the deep learning inversion network weight, the effect is verified by using a verification set after the training is completed, and the prediction effect is predicted according to a test set. The application combines a traditional same-dimension random projection inversion method and a deep learning network structure, realizes dynamic transformation of terrain parameters by using a terrain disturbance layer, and enables the network to maintain high inversion precision and stability under different terrain undulation conditions.

The application discloses a kind of based on aeromagnetic three-component polarizability method and system, it is related to the field of magnetic prospecting, including: obtaining target area aeromagnetic three-component data and carrying out anomaly extraction;The three-component anomaly data extracted is grid processing, and equivalent rectangular body data is constructed;Based on the equivalent rectangular body data, inversion is carried out using pre-trained inversion model, and the total magnetization intensity three-component data of each rectangular body is obtained;According to the total magnetization intensity three-component data, aeromagnetic three-component polar calculation is carried out, and the final polar result is obtained.The application greatly improves the reliability and engineering applicability of magnetic anomaly polarizability, and provides more efficient and stable technical support for mineral exploration, geological structure analysis and deep geological exploration.

This invention discloses a three-dimensional modeling and visualization method based on gravity and magnetic data, relating to the field of geophysical exploration technology. The method includes the following steps: performing multi-scale decomposition processing on gravity and magnetic anomaly data to weaken the volume field superposition effect; constructing a three-dimensional grid model of the study area based on the spatial distribution of surface observation data, using the density and magnetic susceptibility of each grid cell as model variables to be inverted, and reducing the impact of superposition effect on subsurface exploration modeling through multiple gravity and magnetic field components with different depth sensitivities, providing high-quality input for the constructed three-dimensional grid model; then inverting the three-dimensional grid model, and introducing differentiated weight constraints during the inversion process to improve the resolution and stability of the subsurface physical property distribution inversion, and enhance the accuracy of inferring subsurface geological properties.

The application discloses a kind of well-ground empty multi-dimensional magnetic anomaly joint inversion method and system, system includes data acquisition module, grid division module, matrix generation module, matrix combination module, equation construction module and equation solution module.First, the total magnetization direction of inversion region, geomagnetic direction and observation data are acquired by data acquisition module.Then, grid division module carries out cuboid grid division to underground space.Afterwards, matrix generation module calculates ground data kernel matrix, aviation data kernel matrix, well three-component data kernel matrix and pre-optimization matrix.Then, matrix combination module combines all well three-component data kernel matrix, ground data kernel matrix and aviation data kernel matrix into total kernel matrix, and observation data is combined into total observation data matrix.Afterwards, equation construction module constructs joint inversion target equation based on the foregoing matrix.Last, solve joint inversion target equation by equation solution module.

The application provides an arbitrary dimension magnetic anomaly gradient positioning method for buried target detection, comprising the following steps: an optimization function modeling step: constructing an arbitrary dimension magnetic anomaly gradient function based on a magnetic dipole model; an optimization solving step: obtaining the position and magnetic moment module value of a target object through a particle swarm optimization algorithm based on the arbitrary dimension magnetic anomaly gradient function; and a data fusion step: fusing multiple sensor information based on the position and magnetic moment module value of the target object to obtain the buried depth of the target object under cross-medium conditions.

This invention relates to the field of geophysical exploration, specifically disclosing a method for identifying geological body boundaries based on adaptive matching of multi-source gravity and magnetic data. The method includes: acquiring gravity and magnetic anomaly data; estimating the equivalent source depth; constructing a depth-response mismatch correction field based on the depth attenuation difference between gravity and magnetic potential fields; compensating for and registering the magnetic anomaly with remanent magnetization and demagnetization offset; constructing an adaptive coupling direction tensor based on the consistency of the local gradient structures of the two fields; performing anisotropic boundary enhancement on the two fields using the principal direction of the adaptive coupling direction tensor as a constraint and the coupling strength as a weight; and iteratively fusing the two fields through mutual verification using the adaptive coupling direction tensor as a constraint and the depth-response mismatch correction field as a weight, outputting the fine spatial location of the geological body boundary and an adaptive confidence evaluation result. This invention achieves adaptive registration and direction constraint enhancement in the depth domain of gravity and magnetic data, effectively improving the accuracy and reliability of geological body boundary identification under complex geological conditions.

The invention provides a sub-meter buried unexploded ordnance detection and identification method based on correlation three-dimensional magnetic imaging, and the method comprises the steps: obtaining ground surface magnetic anomaly measured data and corresponding trajectory data of a detection region, and dividing a local anomaly region according to a magnetic anomaly change rate; dividing the underground space corresponding to the local abnormal region into three-dimensional grids based on the local abnormal region, calculating the theoretical magnetic anomaly of each grid node based on a magnetic dipole model, obtaining a normalized local correlation coefficient point by point with the measured data of the magnetic anomaly, and reserving nodes higher than a preset threshold as candidate nodes; taking error minimization between theoretical magnetic anomaly of the candidate node and magnetic anomaly measured data as a fitness function, and obtaining an optimal magnetization parameter and an optimal local correlation imaging coefficient based on particle swarm optimization iterative optimization; and based on the optimal local correlation imaging coefficient, carrying out integration according to a spatial position, generating a three-dimensional magnetic imaging result of the detection area, and obtaining a contour parameter and a spatial position of the sub-meter buried unexploded ordnance.

The invention discloses an unmanned aerial vehicle magnetic detection efficiency evaluation method based on an underwater target composite magnetic signal, and the method comprises the steps: constructing a comprehensive magnetic signal fusing a target magnetic anomaly field and a wake flow magnetic field, designing a plurality of typical unmanned aerial vehicle detection paths, and carrying out the detection of the unmanned aerial vehicle under the conditions of ideal Gaussian noise and wave magnetic noise. The detection probability of the unmanned aerial vehicle is simulated and analyzed, and scientific prediction and quantitative evaluation of the magnetic detection efficiency of the unmanned aerial vehicle are realized. According to the method, the target comprehensive magnetic signal model in the marine environment is constructed, multiple typical unmanned aerial vehicle detection paths are designed, and a systematized solution thought is provided for systematized and quantitative analysis of the efficiency of an unmanned aerial vehicle magnetic detection scheme.

The application relates to the technical field of in-situ testing of geotechnical engineering unsaturated soil, and discloses an in-situ testing device and method for thermal-water-force characteristics of soil body, which comprises a flying dynamic control module and a drill rod; the device can synchronously acquire the three-dimensional terrain features, surface material composition distribution and underground magnetic anomaly information of the operation area during the flight. Therefore, the device has the surveying capability for the complex operation environment and does not need to rely on artificial pre-surveying; meanwhile, the application combines the flying module, field surveying technique, static sounding, temperature sensing, resistivity detection, water injection detection, and analyzes and evaluates the statics properties, strength characteristics, fracture development characteristics, hydrological properties and three-dimensional temperature field of the unsaturated soil; and the in-situ testing problem of the unsaturated soil is solved.

The invention relates to the technical field of data processing, in particular to a mining area geological information comprehensive analysis method and system based on data integration, and the method comprises the steps: obtaining and preprocessing multi-source geological data, carrying out the feature correlation analysis and normalization, and obtaining a standardized data set; classifying the magnetic anomaly and gravity anomaly data through a support vector machine to generate a fluid dynamic classification result; performing interpolation processing on the classification result to generate continuous fluid dynamic data, and analyzing the speed and direction change of the fluid to generate a permeability distribution diagram; extracting an abnormal region according to the dynamic change trend, matching the abnormal region with historical data, marking geological abnormal points and generating an abnormal distribution diagram; according to the geological flow monitoring report, the underground water body type or the change of the oil-gas mixing proportion is judged, a pressure fluctuation mode is recognized, and a refined geological abnormal point distribution diagram is generated. According to the invention, mining area resource management and potential risk early warning can be effectively supported.

A method comprises selecting a first data set including a first magnetic anomaly map of a given area, the first map having a first accuracy; selecting a second data set including geological data for the given area; sending the first and second data sets to a machine learning model; and generating a second magnetic anomaly map of the given area based on the first and second data sets, the second map having a second accuracy higher than the first accuracy. The method further comprises comparing the second map with a ground truth map to train the machine learning model; and performing a validation test of the trained machine learning model by sending an additional data set including held-out magnetic anomaly map data to the machine learning model. In response to a validation threshold being met, the trained machine learning model is used to generate higher accuracy magnetic anomaly maps.

The application belongs to the technical field of geophysical magnetic exploration, and specifically discloses a method and device for reconstructing crust temperature distribution based on magnetic anomaly, which comprises the following steps: obtaining a magnetic susceptibility-temperature curve based on rock physical experiments, combining a preset temperature-depth relationship to construct a depth-dependent magnetic susceptibility function, and taking the depth-dependent magnetic susceptibility function as a magnetic susceptibility structure constraint for magnetic anomaly forward modeling and inversion; inputting magnetic anomaly grid data and the depth-dependent magnetic susceptibility function into a spatial domain forward modeling and inversion framework established based on a Cauchy-type curved area integral, constructing a target functional, iteratively solving by using a reweighted regularization conjugate gradient algorithm, and obtaining an equivalent intracrust depth distribution; taking the equivalent intracrust depth distribution as a first isothermic surface, combining a surface boundary temperature and a temperature boundary taking the Moho as a second isothermic surface, reconstructing a regional three-dimensional crust temperature structure, and outputting a temperature distribution result. The application can improve the calculation efficiency of intracrust inversion and the geological reliability of the result.

The application discloses a kind of estimation method and system based on high frequency information gravity-magnetic anomaly lateral resolution, comprising the following steps: obtaining gravity-magnetic anomaly data, griding gravity-magnetic anomaly data, obtaining griding gravity-magnetic anomaly data;Low-pass filtering is carried out to the griding gravity-magnetic anomaly data, and regional gravity-magnetic anomaly data are obtained;Based on the griding gravity-magnetic anomaly data and regional gravity-magnetic anomaly data, subtract to obtain residual gravity-magnetic anomaly data;Change geometric scale factor, obtain residual gravity-magnetic anomaly data of different scales;The residual gravity-magnetic anomaly data of different scales are compared and analyzed, and the preset scale that presents residual gravity-magnetic anomaly distribution characteristics is obtained, and the estimation of gravity-magnetic anomaly lateral resolution is realized.The lateral resolution of gravity anomaly or magnetic anomaly estimated by the method can evaluate the related geological problems that can be solved by existing gravity-magnetic data, and can improve the efficiency of gravity-magnetic data processing and interpretation.

This invention discloses a comprehensive prediction method for oil-uranium-hot dry rock-shale oil in sedimentary basins, belonging to the field of geological exploration technology. This method involves using effective geophysical methods to extract basic geological background information, constructing a three-dimensional spatial framework of the basin, clarifying the correlation between structures, strata, and igneous rocks, calculating the correlation coefficient and intercept of airborne gravity and magnetic anomalies, initially screening key target areas, extracting key information on energy resource accumulation / mineralization, performing two-dimensional or three-dimensional characterization of target geological bodies, combining magnetotelluric sounding inversion profiles with seismic and drilling data, adjusting core parameter settings, and overlaying oil-uranium-hot dry rock-shale oil resource accumulation / mineralization evaluation information to obtain comprehensive energy resource prediction results. This method can provide scientific and technical support for basic geological research on oil-uranium-hot dry rock-shale oil in sedimentary basins and provide comprehensive and reliable prediction of resources.

A system comprises a vehicle; an onboard navigation processing unit including an Earth magnetic model, a sensor compensation module, a navigation filter, and a magnetic anomaly map storage; and a plurality of onboard magnetometers in communication with the sensor compensation module and spatially separated from each other. The navigation filter hosts one or more magnetic anomaly navigation algorithms. The sensor compensation module has program instructions for performing a method to provide enhanced magnetic anomaly navigation, comprising performing data acquisition by recording temporally synchronized magnetometer measurements; and performing blind source separation with a set of constraints including a far-field assumption, and spatial coherence. The method further comprises performing interference source elimination to zero out or reduce irrelevant interference sources; reconstructing the signal of interest to generate enhanced magnetic field measurements; and feeding the enhanced magnetic field measurements to the one or more magnetic anomaly navigation algorithms in the navigation filter.

The invention provides a buried magnetic target detection and positioning method based on a self-adaptive multi-dipole model. The method comprises the following steps: collecting magnetic field scalar data of a target area; processing defective pixels of the magnetic field scalar data; performing filtering and linear interpolation calculation on the magnetic field scalar data; drawing a magnetic field contour map through the calculated data, and marking positive and negative extremum blocks in the contour map; constructing a self-adaptive multi-dipole model according to the category of the extreme value block; and optimizing the physical parameters of the inversion target through the rule of the adaptive multi-dipole model. Through three-stage data cleaning of'dead pixel processing + filtering + linear interpolation ', environmental noise, sensor abnormal values and geological background interference are effectively eliminated, real magnetic abnormal signals are reserved, and the signal-to-noise ratio of the data is improved. The extreme value block automatic marking function of the magnetic field contour map can visually recognize the magnetic anomaly center position, and manual interpretation errors are avoided.