281 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 belongs to the technical field of geophysics, and particularly discloses a magnetization intensity vector sparse inversion method under a strong residual magnetism condition. The method comprises the following steps: firstly, subdividing an underground space, and simulating an underground magnetic anomalous body; obtaining a sensitivity matrix according to partial derivatives of observed magnetic anomaly values of the magnetic anomaly body in each unit magnetization direction, and constructing a total objective function of magnetization intensity vector inversion based on the sensitivity matrix; taking magnetization intensity amplitude as a parameter to introduce sparse constraint for the total objective function; converting the total target function into a standard L2 regularization form, and calculating a sensitivity matrix by using a singular value analysis method so as to obtain target parameters of the total target function; and finally, obtaining three components of the magnetization intensity vector from the target parameters, and further solving the magnetization intensity amplitude and the total magnetization direction. According to the scheme, the magnetization intensity inversion precision under the influence of strong residual magnetization is effectively improved.

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 discloses a multi-metal mineral exploration method and system, and relates to the field of multi-metal mineral exploration, and the system comprises a data acquisition module, a structure analysis module, a geophysical exploration module and a mineral evaluation module. Comprising the following steps: collecting wrinkle information, fault information and geophysical prospecting information, carrying out wrinkle state and fault state analysis on the wrinkle information and the fault information to obtain a structural shape prospecting value of each region, comparing the structural shape prospecting value of each region with a set comparison threshold to obtain a judgment signal of each region, receiving a geophysical prospecting signal, and carrying out geophysical prospecting. And performing gravity anomaly, magnetic anomaly and electrical difference analysis on the high-possibility mineral product existence area to obtain a physical exploration shape value of each geophysical exploration area, and performing comparative analysis on the physical exploration shape value and a set reference standard interval to obtain a set of mineral product areas of different grades corresponding to different geophysical exploration areas, so that the multi-metal mineral product areas can be graded according to mineral product reserves. And a basis is provided for making multi-metal mineral exploration and mine development design.

The invention discloses a reweighted inversion method and system for a magnetic anomaly modulus under a residual magnetism condition and a storage medium, and belongs to the technical field of magnetic exploration, and the method comprises the following steps: converting the magnetic anomaly modulus to obtain the magnetic anomaly modulus; carrying out nonlinear forward modeling on the magnetic anomaly modulus; and linearizing a forward modeling formula of the magnetic anomaly modulus, constructing a reweighted minimum model objective function, solving the reweighted minimum model objective function, finally obtaining the magnetization intensity of an inversion space, and delineating a target geologic body. According to the scheme, the nonlinear forward modeling formula of the magnetic anomaly modulus is subjected to linear representation, and the sensitivity matrix serving as depth weighting is further updated by utilizing the magnetic anomaly modulus kernel function matrix updated along with iteration, so that the reweighting minimum model objective function is constructed, the precision of magnetic anomaly modulus inversion under the residual magnetism condition is improved, and the accuracy of the magnetic anomaly modulus inversion under the residual magnetism condition is improved. And a more reliable inversion result is provided for more accurate depiction of a deep magnetic structure, mineral resource exploration and the like.

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 path planning, in particular to a path planning and optimizing system for offshore wind power mine sweeping operation, which comprises a magnetic anomaly data processing module, a target identification and classification module, a path planning and optimizing module and a path simulation verification module. According to the method, accurate magnetic field data processing and spatial correlation analysis are realized by detecting the magnetic field intensity of the operation area in real time and eliminating interference of environmental noise, abnormal points are identified through a gradient analysis method, and magnetic dipole feature extraction and physical property comparison are performed, so that the target identification accuracy is improved, the classification process is optimized, and the classification efficiency is improved. On the aspect of path planning optimization, by analyzing the spatial characteristics of the metal mine explosion device and combining real-time environment data such as the water flow speed and the wind speed, path planning is dynamically adjusted, dual optimization of cost and efficiency is achieved, and by means of the method of comprehensively considering data analysis and environment factors, the efficiency of the metal mine explosion device is improved. And the operation risk is effectively reduced, and the mine clearance operation efficiency is improved.

The invention relates to the technical field of magnetic exploration, in particular to a deep learning network structure and method for magnetic anomaly inversion, and solves the problems that a traditional linear inversion method is slow in calculation stable speed and relatively low in precision, and a traditional ground and aeromagnetic data inversion method is insufficient in vertical resolution and relatively low in precision. A traditional linear inversion method is always encountered with the problems of low calculation efficiency and poor stability due to the local minimum value problem, and comprises an inversion network encoder, and a residual block and down-sampling method is adopted to realize the characteristics of magnetic anomaly data to extract an encoding network unit in a two-dimensional form. According to the method, feature extraction of magnetic anomaly data is realized, a residual module is added in an encoder to fuse feature maps from different sources, a three-dimensional magnetic susceptibility structure is reconstructed from two-dimensional observation magnetic field data, and in order to solve the problem that a sample library of deep learning inversion needs enough diversity and complexity, the method has high robustness. The generalization ability of the magnetic method inversion neural network is fully improved, and the inversion precision is further improved.

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 relates to the technical field of drilling tool attitude dynamic measurement, and discloses a dynamic measurement method and system for attitude parameters of a rotary steering tool, and the method comprises the steps: obtaining a triaxial angular rate, a triaxial earth gravitational field component and a triaxial earth magnetic field component of the rotary steering tool in real time; determining an attitude angle priori estimated value according to the triaxial angular rate; determining an attitude angle posterior observation value according to the three-axis earth gravitational field component and the three-axis earth magnetic field component; fusing the attitude angle priori estimation value and the attitude angle posteriori observation value through iterative extended Kalman filtering to obtain a posteriori optimal attitude angle estimation result; and calculating attitude parameters according to a posterior optimal attitude angle estimation result. According to the method, adaptive fusion is carried out on the dynamic observed quantities measured by the three-axis gyroscope, the three-axis accelerometer and the three-axis magnetometer through the iterative extended Kalman filter, so that noise interference of the external environment, such as high rotating speed, strong vibration and magnetic anomaly, in the dynamic measurement process is eliminated, and the control and measurement precision of the rotary steering tool is improved.

The invention discloses a magnetic gradient tensor-based magnetic anomaly boundary information identification method and system, and relates to the technical field of magnetic anomaly data processing, and the method comprises the steps: collecting actual measurement magnetic anomaly data, and carrying out the preprocessing of the data, and obtaining the grid magnetic anomaly data after the pole transformation; carrying out calculation on the basis of the grid magnetic anomaly data after pole conversion to obtain magnetic gradient tensor component data; performing boundary identification calculation on the magnetic gradient tensor component data to obtain a boundary identification calculation result; and identifying geologic body boundary information by using the boundary identification calculation result. By introducing the magnetic gradient tensor data, the suppression effect on noise interference is stronger, so that the influence of oblique magnetization on the geologic body boundary identification result is small, and the boundary information of the geologic body can be identified more accurately; the geologic body boundary is identified by using the extreme value information of the boundary identification calculation result, the boundary information is highlighted, the accuracy of the identification result is ensured, and the lateral resolution of the identification result is improved.

The present invention relates to the technical field of dynamic measurement of drill tool attitude, and discloses a dynamic measurement method and system for attitude parameters of a rotary steering tool. The method includes: obtaining in real time the three-axis angular rate, the three-axis earth gravity field components, and the three-axis geomagnetic field components of the rotary steering tool; determining a prior estimate value of the attitude angle according to the three-axis angular rate; determining a posteriori observation values of the attitude angle according to the three-axis earth gravity field components and the three-axis geomagnetic field components; obtaining a posteriori optimal attitude angle estimation result by fusing the prior estimate value of the attitude angle and the posteriori observation values of the attitude angle through iterative extended Kalman filtering; and calculating the attitude parameters according to the posteriori optimal attitude angle estimation result. In this application, an iterative extended Kalman filter is used to adaptively fuse the dynamic observables measured by a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer, so as to eliminate the noise interference of external environments such as high rotational speed, strong vibration, and magnetic anomaly faced during the dynamic measurement process, thereby improving the control and measurement accuracy of the rotary steering tool.

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 self-constraint magnetic anomaly physical property inversion method and system based on correlation coefficients, and relates to the field of geophysical data inversion interpretation, and the method comprises the steps: carrying out the grid processing of actual measurement data, and obtaining actual measurement two-dimensional data D; dividing the underground space into uniform grid units; correlation imaging calculation is carried out on the actually measured data, and a correlation imaging coefficient matrix C is calculated; obtaining a conversion coefficient matrix K according to the prior geological data, and calculating an inversion initial model M0 according to the conversion coefficient matrix K and the related imaging coefficient matrix C; and performing physical property inversion calculation on the inversion initial model M0 by using a conjugate gradient algorithm to obtain inversion magnetic information of the underground space. According to the method, the observation data correlation coefficient matrix is introduced as a self-constraint condition, and the fitting degree and roughness of the conjugate gradient inversion model are self-constrained through the correlation coefficient matrix, so that the multiplicity of solutions of the inversion result can be reduced, the stability of the inversion result can be enhanced, and the resolution of the inversion result can be improved.

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 geomagnetic anomaly data stable downward continuation method based on an SRGAN deep learning network, and belongs to the field of geophysical magnetic field data continuation. According to the invention, in combination with the continuation characteristic of geomagnetic anomaly data, a CBAM space attention mechanism module is added to improve the spatial perception capability of a network model to the magnetic anomaly data, and in combination with the characteristic that the downward continuation of potential field data can amplify the noise generated by a high-frequency signal; and the deep learning model is combined with adaptive threshold multiple wavelet transform to suppress noise data so as to further improve the continuation precision. In order to enable a model to fully learn detailed information of geomagnetic anomaly data, a training data set construction method combining a Kriging super-resolution algorithm with a sliding subarea mode is provided. By designing and synthesizing a simulation model experiment and comparing actual measurement data with other three algorithms, the model is highest in calculation precision and best in magnetic anomaly detail information recovery.

The invention relates to the technical field of geomagnetic vector measurement, and discloses an error correction method of a cross-shaped magnetic gradient tensor measurement system. According to the method, a coupling model containing a sensitivity error, a non-orthogonal error, a zero offset error and an array non-alignment error of a single fluxgate sensor is established, and a fitness function is designed to solve error parameters by utilizing the characteristics of high global search capability and high convergence speed of a firefly optimization algorithm (FA). Compared with the existing particle swarm optimization and the least square method, the method has the advantages that the correction precision of zero offset and non-alignment errors is remarkably improved, the single-sensor zero offset error estimation deviation is smaller than 2%, the array non-alignment angle error deviation is smaller than 1.5%, expensive equipment support is not needed, and real-time on-site implementation can be achieved. The method is suitable for the fields of magnetic anomaly detection, geophysical exploration and the like, and has the advantages of low cost, simplicity and convenience in operation and remarkable correction effect.

The invention discloses a marine noise online learning magnetic anomaly detection method, and relates to the technical field of signal processing, and the method comprises the following steps: constructing a Transform denoising network with a self-attention mechanism; the method comprises the following steps of: training a Transform denoising network; testing the geomagnetic field reconstruction model, and determining a detection threshold according to a reconstruction error; actually-measured ocean magnetic field data are collected, and magnetic anomaly detection is carried out; carrying out online learning training on the geomagnetic field reconstruction model by adopting actually measured ocean magnetic field data; and repeating the steps from testing to online learning training. According to the method, the Transform denoising network with the self-attention mechanism is trained, and magnetic anomaly detection is performed according to the reconstruction error, so that the accuracy of magnetic anomaly detection in a complex noise environment is improved; on-line learning training is introduced, so that the real-time performance and the dynamic adaptability of magnetic anomaly detection are improved.

The invention discloses a deep learning-based interior inversion method and a related device, and relates to the technical field of geophysical geological resource exploration, and the method comprises the steps: inputting actual measurement magnetic anomaly data into a trained interior prediction model, and obtaining interior fluctuation data, the interior prediction model is a deep learning network model obtained by training with interior fluctuation simulation data as feature data and magnetic anomaly simulation data as label data. Compared with a traditional Parker-Oldenburg interface inversion method, a deep learning method is adopted to invert the interior, calculation is convenient and fast, high calculation precision can be obtained, and the interior with the high precision value can be rapidly obtained by conducting interior inversion through the method.

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 discloses a method, a system and equipment for calculating a three-dimensional form of an iron ore body. The method comprises the following steps: acquiring magnetic anomaly data and a magnetic anomaly region of a known hidden magnetite body in a target region; the first top surface burial depth of the magnetite body is calculated or obtained through drilling data based on the magnetic anomaly area, and magnetization intensity three-dimensional distribution of the magnetic anomaly area is obtained; delineating a magnetic body range smaller than preset magnetization intensity according to the first top surface burial depth of the magnetite body and the magnetization intensity three-dimensional distribution; calculating the second top surface depth of the iron ore body according to the preset magnetization intensity, and counting the minimum value of the difference between the second top surface depth and the first top surface burial depth; and extracting a magnetization intensity distribution range which is greater than the inversion of the minimum value of the depth difference in the magnetization intensity inversion result, and calculating the form numerical value of the iron ore body according to the magnetization intensity distribution range.

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.