167 results about "Magnetic anomaly" patented technology

The invention provides a geophysical gravity and magnetic anomaly source model construction and rapid forward and reverse modeling method, and relates to the technical field of intelligent three-dimensional geological modeling and simulation. According to geological information of a research area, source body modeling parameters of a gravity and magnetic anomalous field are set for the research area, and a three-dimensional model matrix M capable of describing a plurality of underground anomalous bodies is obtained; constructing a fast forward modeling network, establishing nonlinear mapping from the three-dimensional model matrix to the simulated abnormal data through the fast forward modeling network, and calculating forward modeling response of the three-dimensional model matrix; and constructing an inversion network based on a concurrent module CFTBlock and combining a CNN and a Transform, and establishing nonlinear mapping from the gravity and magnetic abnormal data to a three-dimensional model matrix. The method has the advantages of fast and accurate forward modeling, high-resolution inversion and the like, and is suitable for better interpretation of actually measured gravity and magnetic data.

The invention discloses a self-adaptive octree three-dimensional magnetic method inversion method fused with abnormal region recognition, and belongs to the technical field of three-dimensional magnetic method data inversion processing. Comprising the steps that initial inversion is rapidly completed on a coarse grid, the overall contour of an underground structure is obtained, a fuzzy c-means clustering algorithm is introduced, and a target area with significant magnetic anomaly is automatically extracted from a coarse solution; and constructing a multi-level octree grid in the extracted abnormal region to realize fine subdivision of the complex geological boundary, and further executing high-precision inversion by using a fine model as a new starting point. The adaptive octree grid has the characteristics of high efficiency, small grid number and small data fitting difference. The process of coarse inversion, intelligent identification, local refinement and fine inversion can be circularly executed as required, and computing resources are dynamically allocated. Compared with a traditional global fine grid scheme, the inversion process has the advantages that the inversion precision is maintained and even improved, and meanwhile, the number of grid units is remarkably reduced.

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 a multi-source geophysical prospecting data fused Zenzenite rock mass contact zone anomaly method and system, and relates to the technical field of geophysical prospecting, the method comprises the following steps: executing joint denoising processing and time-frequency feature enhancement processing on a TFEM data set in a three-dimensional fused data volume, and outputting the optimized three-dimensional fused data volume; and based on the optimized three-dimensional fusion data body, by establishing a mapping relationship between magnetic, electric and polarization anomalies and a geologic body, quantifying the correlation between a multi-parameter combination and a mineralization index and screening an up-to-standard abnormal region, and finally delineating a magnetic anomaly gradient zone, a low-resistance region and a high-polarization anomaly co-occurrence ore prospecting target region three-dimensional distribution diagram. Through multi-source geophysical prospecting method collaboration, data deep fusion, quality optimization and accurate mapping, full-chain technology upgrading from data acquisition to target area delineation is realized, and the high efficiency of mineral exploration is improved.

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 relates to the technical field of magnetic detection, in particular to a target positioning method based on geomagnetic anomaly and an axis frequency magnetic field. Comprising the following steps: S1, preparing before entering water; s2, approaching a target; s3, target detection: when no target appears, an observation signal is composed of an environment magnetic field, and the energy value of the part is very small; once the target appears, the energy of the observation signal can be continuously and obviously increased, when the energy value is greater than a preset judgment threshold, the target point is judged as a suspected target point, and if the suspected point is continuously detected for a specified time, the existence of the target is confirmed; s4, combined positioning: under a Kalman filtering framework, enabling the target to be equivalent to a magnetic dipole and an electric dipole, and correcting a position predicted by a target state equation by combining measurement equations of the two models, thereby realizing combined positioning of the target and obtaining more accurate target position information; and S5, verifying and outputting a positioning result. The method has the advantages of improving the positioning precision, enhancing the anti-interference capability and the like.

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 an underwater acoustic-magnetic multi-modal fusion processing method, which comprises the following steps: carrying out preprocessing and time-space alignment on collected sonar and magnetic data, and extracting a morphological feature vector and a magnetic feature vector of a pipeline; based on a pipeline route trend, calculating an optimal matching path of a sonar contour and a magnetic force abnormal zone through dynamic time warping, establishing acoustic-magnetic mapping, and fusing features to generate a joint vector; finally, the type and state of the pipeline are judged according to the combined characteristics, and the problem that in a near-shore port and an industrial area water area, due to the interference of construction waste and the influence of a pipeline coating layer, a single sonar is difficult to accurately detect the pollution discharge pipeline and the cable is solved. According to the method, the complementarity of acoustic and magnetic characteristics is utilized, the accuracy of pipeline detection in a complex scene is improved, and reliable data support is provided for operation and maintenance of near-shore water area pipelines.

The invention belongs to the technical field of gravity and magnetic method exploration data processing, and relates to a source body positioning method based on intelligent gravity and magnetic field separation and Euler deconvolution, which comprises the following steps: setting gravity and magnetic abnormal field modeling parameters according to geological and physical property data of a research area; generating an initial seed point in the underground three-dimensional grid and expanding and growing the initial seed point to generate Q independent abnormal field models; mapping the position of the model to a real coordinate, and calculating to obtain a forward modeling abnormal field data set; extracting the maximum value of the global absolute value of the data set and sorting in an ascending order, superposing the first p absolute values to generate a composite field, and constructing a field separation data set by taking the p absolute value as a target field and the composite field; using the field separation data set to train the field separation network until convergence; inputting measured data score departure field and residual field data, and selecting data for iterative separation according to requirements to obtain a field separation result; and obtaining a preliminary positioning result by using a field separation result and combining with an Euler deconvolution method, and determining final source body positioning after screening. The method has the beneficial effects that multi-scale geological target field separation can be realized, and source body positioning can be determined.

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 belongs to the field of magnetic target body positioning, and relates to a measuring line vertical gradient-based magnetic multi-target positioning and magnetic moment inversion method, which comprises the following steps of: obtaining a unit orthogonal primary function and a primary function coefficient corresponding to each component of magnetic anomaly gradient data vertical to a measuring line, and calculating an energy curved surface of the magnetic anomaly gradient data vertical to the measuring line; detecting a local energy peak value of the energy curved surface, obtaining an energy peak value ratio sequence, obtaining a target number and horizontal position estimation corresponding to the target according to an energy sudden change value in the energy ratio sequence, and calculating a target depth corresponding to a measuring line vertical magnetic anomaly gradient energy extreme value by using a dichotomy method, and constructing a linear equation set about the target magnetic moment by using the measuring line vertical magnetic anomaly gradient data and the unit orthogonal basis function, and obtaining target magnetic moment parameter estimation through a least square method. According to the invention, the anisotropy difference of the estimation precision of the horizontal position of the target is effectively reduced, and the problem of target detection omission caused by large difference between the target burial depth and the magnetic moment module value in multi-target positioning is improved.

The invention provides a uranium ore in-situ leaching solute transport regulation and control method and system based on three-dimensional geological modeling. According to the method, the high-frequency pressure data of the liquid injection well are obtained and combined with the three-dimensional geologic model to construct the permeability distribution field, and the abnormal high-pressure blocking coordinate set is extracted. Magnetic anomaly signals caused by iron ion concentration changes of the leaching agent are synchronously collected, the space gradient of the magnetic anomaly signals is converted into a solute transport trajectory diagram, and a solute diffusion lag map is constructed based on the trajectory diagram. And further extracting a lagging boundary coordinate set, and performing space matching with the abnormal high-voltage blocking coordinate set to generate a blocking retention incidence matrix. And dynamically adjusting a liquid injection well pressure gradient distribution strategy according to the matrix, and generating a pumping and injection flow adjusting instruction to inhibit a leaching dead angle. According to the technical scheme provided by the invention, the efficiency and precision of uranium ore in-situ leaching solute transport regulation and control can be improved.

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 relates to the technical field of mineralization analysis, in particular to a coverage area iron-rich ore information analysis method, device and equipment and a storage medium, and the method integrates multi-dimensional information such as gravity data, magnetic aviation delta T data, remote sensing images and geological data of a to-be-predicted area, and constructs a feature system capable of comprehensively reflecting mineralization conditions. The limitation of single data source analysis is broken through, a rich and accurate data basis is provided for intelligent division of the metallogenic target area, and the accuracy of metallogenic prediction is remarkably improved; furthermore, according to the method, a fracture speculation diagram, a regional magnetic anomaly plane diagram, a magnetic body three-dimensional probability model and a hydroxyl anomaly distribution vector diagram are adopted for collaborative analysis, so that a rich and accurate data basis is provided for intelligent division of a metallogenic target region, and the accuracy of metallogenic prediction is further improved; the recognition systematicness of the hidden ore body is obviously enhanced; compared with a traditional method depending on artificial experience, the method has the advantage that the objectivity of metallogenic prediction is effectively improved.

The invention provides a three-dimensional magnetic anomaly feature extraction method and system applied to unexploded ordnance identification, and the method comprises the steps: rotating the angle of an unexploded ordnance, and adjusting the position of a first magnetometer relative to the unexploded ordnance; the data recording instrument records unexploded ordnance identification information and position data and angle data of the unexploded ordnance relative to the first magnetometer, and original magnetic anomaly data of the unexploded ordnance is obtained by combining the first magnetic data and a difference value of synchronously collected second magnetic data; performing inversion based on the original magnetic anomaly data and the position data of a plurality of positions corresponding to the same angle so as to form a three-dimensional point cloud model at the angle; and performing inversion based on the three-dimensional point cloud model of the multiple angles to form three-dimensional magnetic anomaly features. Multiple groups of original magnetic anomaly data of the unexploded ordnance are collected and inverted in the outdoor environment to form three-dimensional magnetic anomaly features capable of being compared and matched with actually measured data, and the efficiency, accuracy and convenience of unexploded ordnance detection are improved.

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 discloses a deep ore body three-dimensional positioning method and a deep ore body three-dimensional positioning system, belongs to the technical field of ore body detection, and aims to solve the problems of low resolution and large positioning error in traditional single physical field inversion. Comprising the following steps: collecting broadband waveform data acquired by an underground microseismic sensor and high-precision magnetic field data of a ground quantum magnetometer; respectively extracting travel time characteristics from the micro-seismic waveform and extracting magnetic anomaly gradient characteristics from the magnetic field data; inputting the two physical field features into a graph convolutional network, and generating a three-dimensional velocity field model through cross-modal coupling; inputting the model into a generative adversarial network to generate synthetic vein topological structure data; and finally, jointly transmitting the velocity field model and the synthetic data to a wave-magnetic fusion inversion engine, calculating an ore body three-dimensional structure through a joint optimization algorithm, carrying out dynamic matching verification by utilizing real-time micro-seismic travel time data, and outputting a final ore body three-dimensional structure model when the matching degree meets a preset threshold value.

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 present application discloses a method, device, equipment, medium, and product for digitally reconstructing a gravity and magnetic anomaly color image, relating to the field of geophysical exploration technology. The method comprises: obtaining a gravity and magnetic anomaly color image; determining an actual geographic coordinate system based on projection parameters; converting the gravity and magnetic anomaly color image into an actual geographic coordinate system using pixel points to obtain anomaly grid data; determining discrete sample points based on the mapping scale of the gravity and magnetic anomaly color image, and obtaining sample grid data of the discrete sample points using the discrete sample points and the anomaly grid data; editing the sample grid data based on the anomaly grid data to obtain initial contour lines of the gravity and magnetic anomaly color image; and reconstructing the anomaly grid data based on the actual anomaly values ​​and initial contour lines of the gravity and magnetic anomaly color image to complete the reconstruction of the gravity and magnetic anomaly color image. The present application can reconstruct a gravity and magnetic anomaly color image, and the reconstructed gravity and magnetic anomaly color image can directly extract gravity and magnetic anomaly data.

The invention discloses a submarine cable magnetic anomaly detection method, device and equipment based on deep learning, and relates to the technical field of artificial intelligence geophysics. The method comprises the following steps: firstly, carrying out magnetic field forward modeling simulation through a magnetic anomaly theory to obtain magnetic anomalies of the submarine cable with different geometric parameters and physical property parameters; generating a background magnetic field of the seabed environment; based on the magnetic anomaly and the background magnetic field of the submarine cable, constructing a sample data set, and training a deep learning neural network model; and then submarine cable magnetic anomaly detection is carried out based on the trained deep learning neural network model. The neural network of the deep learning architecture is applied to submarine cable magnetic anomaly detection, feature representation of submarine cable magnetic anomaly is automatically learned from complex magnetic field data based on the deep learning method, automatic detection of submarine cable magnetic signals is achieved, the horizontal position and the vertical distance of the submarine cable are output, and the submarine cable magnetic anomaly detection accuracy is improved. And the accuracy of submarine cable magnetic anomaly detection is 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 invention provides an aeromagnetic data-based interior high-precision inversion method, and the method comprises the steps: carrying out the preprocessing of the aeromagnetic data of a target region, eliminating the noise and interference, and extracting the region magnetic anomaly information related to a deep interior; constructing a remanence intensity model based on a depth attenuation function, and obtaining the remanence intensity of each grid point of the target area according to the target area and the remanence intensity model; based on the residual magnetism intensity model, establishing a non-linear attenuation model of the residual magnetism intensity along with the depth, and obtaining the magnetic anomaly intensity generated by each depth layer in the target area on the earth surface by using the non-linear attenuation model; and inputting the regional magnetic anomaly information, the residual magnetism probability parameter, the geological constraint and the magnetic anomaly intensity into the interior inversion model to obtain an interior depth prediction value of the target region. According to the method, the influence of residual magnetism on the magnetization direction is considered, and the precision of interior inversion is improved.

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.