Method and device for generating correlation model of geomagnetic field anomaly and mineral resource distribution
By using quantum sensors and quantum neural network models to process geomagnetic field data, the problem of insufficient accuracy and anti-interference ability in deep and hidden ore body detection is solved, and more efficient and accurate mineral resource positioning is achieved.
Patent Information
- Application Number
- CN202510349416.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-24
- Publication Date
- 2025-05-13
AI Technical Summary
When traditional geomagnetic field measurement methods detect deep and hidden ore bodies, the measurement accuracy, resolution and anti-interference ability are insufficient, resulting in low exploration efficiency and accuracy.
Quantum sensors are used to collect geomagnetic field data and obtain preprocessed data through filtering, denoising and normalization. Then, based on the quantum neural network model, the geomagnetic field anomaly region data and geomagnetic field characteristics related to mineral resources are extracted, and the correlation model is constructed to locate mineral resources.
It improves the accuracy and anti-interference ability of geomagnetic field detection, enhances the efficiency, reliability and accuracy of mineral resource positioning, and solves the problem of low exploration efficiency and accuracy in traditional methods.
Smart Images
Figure CN119986829A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of geophysical exploration technology, and in particular to a method and device for generating a correlation model between geomagnetic field anomaly and mineral resource distribution. Background Art
[0002] Traditional geomagnetic field measurement methods mainly rely on fluxgate magnetometers, proton magnetometers and other equipment. Although these devices can provide certain geomagnetic field information, they have limitations in measurement accuracy, resolution and anti-interference ability, especially when detecting deep and hidden ore bodies, and often cannot meet the needs of high-precision exploration. In addition, traditional methods are greatly affected by environmental noise and instrument errors, resulting in low exploration efficiency and accuracy, which in turn leads to high uncertainty in measurement results. Summary of the invention
[0003] In view of this, it is necessary to provide a method and device for generating a correlation model between geomagnetic field anomaly and mineral resource distribution, so as to solve the technical problems of low exploration efficiency and accuracy in existing geomagnetic field anomaly detection and mineral location methods.
[0004] In order to solve the above problems, the present invention provides a method for generating a correlation model between geomagnetic field anomaly and mineral resource distribution, comprising: Filtering, denoising and normalizing the geomagnetic field data of the target area collected by the quantum sensor to obtain preprocessed data; Extracting geomagnetic field anomaly area data and geomagnetic field characteristics related to mineral resources from the preprocessed data; Based on the geomagnetic field anomaly area data and geomagnetic field characteristics related to mineral resources, a quantum neural network model is trained to obtain a correlation model between geomagnetic field anomaly and mineral resource distribution; The association model is used to identify the geomagnetic field data to be measured in the target area to determine the mineral resource positioning information of the target area.
[0005] In a possible implementation, the geomagnetic field data of the target area collected by the quantum sensor is filtered, denoised and normalized to obtain preprocessed data, including: Decomposing the geomagnetic field data based on a wavelet transform method to remove high-frequency noise, and suppressing dynamic noise based on a Kalman filter algorithm to obtain filtered data; Performing denoising processing on the filtered data to obtain denoised data; The denoised data is normalized to obtain preprocessed data.
[0006] In a possible implementation, performing denoising processing on the filtered data to obtain denoised data includes: Environmental noise in the filtered data is eliminated based on an adaptive filtering method, and quantum sensor system errors in the filtered data are eliminated based on an instrument error correction algorithm to obtain denoised data.
[0007] In a possible implementation, normalizing the denoised data to obtain preprocessed data includes: Based on the minimum-maximum normalization or Z-score normalization method, the denoised data is normalized to obtain preprocessed data.
[0008] In a possible implementation, extracting geomagnetic field anomaly area data and geomagnetic field characteristics related to mineral resources from the preprocessed data includes: Based on the quantum support vector machine, the geomagnetic field anomaly area data and the geomagnetic field characteristics related to mineral resources are extracted from the preprocessed data.
[0009] In a possible implementation, based on the geomagnetic field anomaly area data and geomagnetic field characteristics related to mineral resources, a quantum neural network model is trained to obtain a correlation model between geomagnetic field anomaly and mineral resource distribution, including: Acquire geological data of the target area, and spatially match the geological data with the data of the geomagnetic field anomaly area to achieve data fusion and obtain fused data; constructing a training set based on the fused data and geomagnetic field characteristics related to mineral resources; Based on the training set, a quantum neural network model is trained to obtain a correlation model between geomagnetic field anomalies and mineral resource distribution.
[0010] In a possible implementation manner, the quantum sensor is a quantum magnetometer based on atomic spin effect, and there are multiple quantum sensors.
[0011] In a second aspect, the present invention further provides a method for locating mineral resources, comprising: Filtering, denoising and normalizing the geomagnetic field data to be measured in the target area collected by the quantum sensor to obtain pre-processed data to be measured; The pre-processed data to be tested is input into the association model obtained by the above method to obtain the mineral resource positioning information of the target area.
[0012] In a third aspect, the present invention further provides a device for generating a correlation model between geomagnetic field anomalies and mineral resource distribution, comprising: A preprocessing module is used to filter, denoise and normalize the geomagnetic field data of the target area collected by the quantum sensor to obtain preprocessed data; An extraction module, used to extract geomagnetic field anomaly area data and geomagnetic field characteristics related to mineral resources from the preprocessed data; A training module is used to train a quantum neural network model based on the geomagnetic field anomaly area data and geomagnetic field characteristics related to mineral resources to obtain a correlation model between geomagnetic field anomaly and mineral resource distribution; The association model is used to identify the geomagnetic field data to be measured in the target area to determine the mineral resource positioning information of the target area.
[0013] In a fourth aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a method for generating a correlation model between geomagnetic field anomalies and mineral resource distribution as described in any one of the above items.
[0014] The beneficial effects of adopting the above implementation mode are as follows: the method and device for generating the association model of geomagnetic field anomaly and mineral resource distribution provided by the present invention filter, denoise and normalize the geomagnetic field data of the target area collected by the quantum sensor to obtain preprocessed data. The quantum sensor has ultra-high sensitivity and resolution, and can detect weak geomagnetic field anomalies, thereby improving the detection accuracy, which helps to improve the accuracy of geomagnetic field detection and exploration efficiency; based on the geomagnetic field anomaly area data and the geomagnetic field characteristics related to mineral resources, the quantum neural network model is trained to obtain the association model of geomagnetic field anomaly and mineral resource distribution to determine the mineral resource positioning information of the target area. The combination of data preprocessing and quantum neural network model helps to eliminate the influence of environmental noise and quantum sensor errors, has strong anti-interference ability, and can improve the efficiency, reliability and accuracy of mineral resource positioning results. Therefore, the present invention can solve the technical problems of low exploration efficiency and accuracy in the existing geomagnetic field anomaly detection and mineral positioning methods. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.
[0016] Figure 1 A flow chart of an embodiment of a method for generating a correlation model between geomagnetic field anomaly and mineral resource distribution provided by the present invention; Figure 2 A flow chart of an embodiment of the mineral resource method provided by the present invention; Figure 3A principle block diagram of an embodiment of a device for generating a correlation model between geomagnetic field anomaly and mineral resource distribution provided by the present invention; Figure 4 A schematic structural diagram of an embodiment of an electronic device provided by the present invention. DETAILED DESCRIPTION
[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.
[0018] In the description of the embodiments of the present application, unless otherwise specified, “plurality” means two or more than two.
[0019] The terms "including" and "having" and any variations thereof in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product or equipment comprising a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, methods, products or equipment.
[0020] The naming or numbering of the steps in the embodiments of the present invention does not mean that the steps in the method flow must be executed in the time / logical sequence indicated by the naming or numbering. The execution order of the named or numbered process steps can be changed according to the technical purpose to be achieved, as long as the same or similar technical effects can be achieved.
[0021] Reference to an "embodiment" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.
[0022] The present invention provides a method and device for generating a correlation model between geomagnetic field anomaly and mineral resource distribution, which are respectively described below.
[0023] In recent years, the rapid development of quantum precision measurement technology has provided new solutions for geomagnetic field measurement. Quantum sensors are based on principles such as atomic spin effects and quantum entanglement, and can achieve ultra-high-precision magnetic field measurements, with sensitivity and resolution far exceeding traditional equipment. Applying quantum precision measurement technology to geomagnetic field anomaly detection and mineral location can significantly improve exploration efficiency and accuracy, and provide technical support for the efficient development of mineral resources.
[0024] In combination with quantum precision measurement technology, the present invention provides a method for generating a correlation model between geomagnetic field anomalies and mineral resource distribution, such as Figure 1 As shown, the method includes: S101, filtering, denoising and normalizing the geomagnetic field data of the target area collected by the quantum sensor to obtain preprocessed data.
[0025] It can be understood that the data preprocessing process, that is, filtering, denoising and normalizing the measured geomagnetic field data, eliminates the influence of environmental noise and instrument errors.
[0026] The working principle of quantum sensor: It uses the Larmor precession effect of atomic spin in an external magnetic field to measure the strength and direction of the geomagnetic field, that is, to obtain geomagnetic field data. Through laser pumping and detection technology, high-sensitivity magnetic field measurement is achieved, which can detect weak geomagnetic field anomalies and is suitable for the detection of deep and hidden ore bodies.
[0027] The quantum sensor collects geomagnetic field data of the target area. The collected geomagnetic field data can be stored in the database first. When needed, the original geomagnetic field data can be obtained from the database. Technical parameters of quantum sensors: sensitivity reaches fT / √Hz level. The measurement frequency range is DC to hundreds of Hz. It is suitable for geomagnetic field measurement in complex outdoor environments.
[0028] S102, extracting geomagnetic field anomaly area data and geomagnetic field characteristics related to mineral resources from the preprocessed data.
[0029] It can be understood that the pre-processed geomagnetic field data can be analyzed by quantum computing algorithms to extract geomagnetic field anomaly information.
[0030] S103, based on the geomagnetic field anomaly area data and geomagnetic field characteristics related to mineral resources, training a quantum neural network model to obtain a correlation model between geomagnetic field anomaly and mineral resource distribution; The association model is used to identify the geomagnetic field data to be measured in the target area to determine the mineral resource positioning information of the target area.
[0031] It is understandable that quantum neural network (QNN) is used to construct the association model between geomagnetic field anomaly and mineral resource distribution, and quantum support vector machine (QSVM) is used for geomagnetic field anomaly detection.
[0032] Algorithm implementation: Run the algorithm on a quantum computer or quantum simulator, that is, run the above-mentioned correlation model and quantum support vector machine. Use the superposition and entanglement characteristics of quantum bits to accelerate data processing and model training.
[0033] The method provided by the present invention can be executed by an application in a terminal or a server, the terminal can be a mobile phone, a tablet computer or a computer, and the server can be a cloud server or an edge server. The terminal or the server is connected to multiple quantum sensors distributed in the target area and receives data collected by the quantum sensors.
[0034] The purpose of the present invention is to provide a method for geomagnetic field anomaly detection and mineral location based on quantum precision measurement, so as to solve the problems of insufficient accuracy and poor anti-interference ability of traditional geomagnetic measurement methods, and realize high-precision detection and location of deep and hidden mineral resources. Compared with traditional machine learning algorithms, the method provided by the present invention has higher computational efficiency and accuracy, and can process large-scale, high-dimensional geomagnetic field data. The present invention is suitable for the exploration of various mineral resources such as metal mines, non-metallic mines, oil and gas resources, and has broad application prospects.
[0035] In some embodiments, a method for detecting geomagnetic field anomalies and locating mineral resources based on quantum precision measurement comprises the following steps: Use quantum sensors to measure the geomagnetic field of the target area with high precision and obtain geomagnetic field data; deploy multiple quantum magnetometers in the target area and use quantum sensors based on atomic spin effects to measure the geomagnetic field with high precision. The sensitivity of quantum sensors reaches the fT / √Hz level and can detect weak changes in the geomagnetic field. The measurement data includes geomagnetic field strength, direction and gradient information.
[0036] The measured geomagnetic field data is filtered and high-frequency noise is removed using wavelet transform or Kalman filter algorithm. De-noising is performed to eliminate environmental noise (such as power line interference, vehicle interference, etc.) and instrument errors. The data is then normalized to obtain pre-processed data for subsequent analysis.
[0037] Combine geological data and quantum computing algorithms to build a correlation model between geomagnetic field anomalies and mineral resource distribution; use quantum computing algorithms (such as quantum support vector machines) to analyze preprocessed geomagnetic field data. Extract geomagnetic field anomaly area data and identify geomagnetic field characteristics related to mineral resources.
[0038] Combining geological data (such as rock properties, geological structure, etc.) with quantum machine learning algorithms, a correlation model between geomagnetic field anomalies and mineral resource distribution is constructed to predict the location, scale and type of mineral resources in the target area.
[0039] In some embodiments, the geomagnetic field data of the target area collected by the quantum sensor is filtered, denoised and normalized to obtain pre-processed data, including: Decomposing the geomagnetic field data based on a wavelet transform method to remove high-frequency noise, and suppressing dynamic noise based on a Kalman filter algorithm to obtain filtered data; Performing denoising processing on the filtered data to obtain denoised data; The denoised data is normalized to obtain preprocessed data.
[0040] It is understandable that the wavelet transform algorithm can be used to decompose the original geomagnetic field data and remove high-frequency noise during filtering, and the Kalman filter algorithm can be used to suppress dynamic noise.
[0041] In some embodiments, performing denoising on the filtered data to obtain denoised data includes: Environmental noise in the filtered data is eliminated based on an adaptive filtering method, and quantum sensor system errors in the filtered data are eliminated based on an instrument error correction algorithm to obtain denoised data.
[0042] It can be understood that the denoising process is to eliminate environmental noise (such as power line interference, vehicle interference, etc.) through adaptive filtering technology, and use instrument error correction algorithms to eliminate the system errors of the sensor itself.
[0043] In some embodiments, normalizing the denoised data to obtain preprocessed data includes: Based on the minimum-maximum normalization or Z-score normalization method, the denoised data is normalized to obtain preprocessed data.
[0044] It is understandable that the filtered and denoised data are normalized to conform to a uniform dimension and range.
[0045] In some embodiments, extracting geomagnetic field anomaly area data and geomagnetic field characteristics related to mineral resources from the preprocessed data includes: Based on the quantum support vector machine, the geomagnetic field anomaly area data and the geomagnetic field characteristics related to mineral resources are extracted from the preprocessed data.
[0046] It can be understood that through data preprocessing and quantum computing algorithms, the influence of environmental noise and instrument errors can be effectively eliminated, the reliability of measurement results can be improved, and it has strong anti-interference ability.
[0047] In some embodiments, based on the geomagnetic field anomaly area data and geomagnetic field characteristics related to mineral resources, a quantum neural network model is trained to obtain a correlation model between geomagnetic field anomaly and mineral resource distribution, including: Acquire geological data of the target area, and spatially match the geological data with the data of the geomagnetic field anomaly area to achieve data fusion and obtain fused data; constructing a training set based on the fused data and geomagnetic field characteristics related to mineral resources; Based on the training set, a quantum neural network model is trained to obtain a correlation model between geomagnetic field anomalies and mineral resource distribution.
[0048] It is understandable that the geological data in this embodiment can be rock properties, geological structures, etc., and the geomagnetic field anomaly information is integrated and analyzed with the geological structure and rock physical property data. The geomagnetic field anomaly information is integrated and analyzed with the geological structure and rock physical property data to improve the accuracy of mineral location. Combining geological data and quantum machine learning algorithms can quickly and accurately predict the distribution of mineral resources and improve exploration efficiency.
[0049] Among them, the geological data sources are: geological structure data: including faults, folds, strata and other information. Rock physical property data: including density, magnetism, electrical properties and other parameters.
[0050] Data fusion method: Use multi-source data fusion technology to spatially match geomagnetic field anomaly information with geological data. Use a weighted fusion algorithm to assign different weights based on data reliability.
[0051] Advantages: Improve the accuracy and reliability of mineral location. Able to identify the distribution of mineral resources under complex geological conditions.
[0052] In some embodiments, the quantum sensor is a quantum magnetometer based on atomic spin effect, and the number of the quantum sensors is multiple.
[0053] It is understandable that the quantum sensor used in the present invention is a quantum magnetometer based on the atomic spin effect, which has high sensitivity and high resolution. Compared with traditional magnetometers, it has higher accuracy and resolution, and has strong anti-interference ability and can work stably in a strong noise environment.
[0054] It can be understood that, in some embodiments, the system for implementing the above method includes the following modules: Quantum sensor module: used to measure the geomagnetic field of the target area with high precision.
[0055] Data preprocessing module: used to filter, denoise and normalize the measurement data.
[0056] Quantum computing module: used to run quantum computing algorithms and extract geomagnetic field anomaly information.
[0057] Mineral resource positioning module: used to combine geological data and quantum machine learning algorithms to predict the distribution of mineral resources.
[0058] Result output module: used to generate mineral distribution maps, three-dimensional models and positioning reports.
[0059] like Figure 2 The present invention also provides a method for locating mineral resources, comprising: S201, filtering, denoising and normalizing the geomagnetic field data to be measured in the target area collected by the quantum sensor to obtain pre-processed data to be measured; S202, inputting the pre-processed data to be tested into the association model obtained by the above method to obtain the mineral resource positioning information of the target area.
[0060] In some embodiments, the method provided by the present invention is applied to metal mine exploration, specifically comprising: Measurement phase: Multiple quantum magnetometers are deployed in the target area to perform high-precision measurements of the geomagnetic field and obtain geomagnetic field data.
[0061] Preprocessing stage: Filter and denoise the measurement data to eliminate the influence of environmental noise (such as power line interference, vehicle interference, etc.).
[0062] Anomaly detection stage: Use quantum computing algorithms to analyze preprocessed data and extract abnormal areas of the geomagnetic field.
[0063] Mineral positioning stage: Combining geological data and quantum machine learning algorithms, a correlation model between geomagnetic field anomalies and metal mineral distribution is constructed to predict the location and scale of mineral resources.
[0064] In some embodiments, the method provided by the present invention is applied to oil and gas resource exploration, specifically comprising: Measurement phase: Deploy quantum magnetometers in the oil and gas exploration area to measure the geomagnetic field.
[0065] Preprocessing stage: Normalize the measurement data to eliminate instrument errors.
[0066] Anomaly detection phase: Identify abnormal areas of the geomagnetic field through quantum computing algorithms.
[0067] Mineral positioning stage: Combine geological structure data and quantum machine learning algorithms to predict the distribution of oil and gas resources.
[0068] In some embodiments, the method provided by the present invention is applied to the detection of concealed ore bodies, specifically comprising: Measurement phase: Conduct high-density geomagnetic field measurements in areas where concealed ore bodies may exist.
[0069] Preprocessing stage: filter and denoise the data to improve the signal-to-noise ratio.
[0070] Anomaly detection stage: Use quantum computing algorithms to extract weak geomagnetic field anomaly signals.
[0071] Mineral positioning stage: Combine rock physical property data and quantum machine learning algorithms to accurately locate the location of hidden ore bodies.
[0072] like Figure 3 As shown, the present invention also provides a device 300 for generating a correlation model between geomagnetic field anomaly and mineral resource distribution, comprising: A preprocessing module 301 is used to filter, denoise and normalize the geomagnetic field data of the target area collected by the quantum sensor to obtain preprocessed data; An extraction module 302 is used to extract geomagnetic field anomaly area data and geomagnetic field characteristics related to mineral resources from the preprocessed data; A training module is used to train a quantum neural network model based on the geomagnetic field anomaly area data and geomagnetic field characteristics related to mineral resources to obtain a correlation model between geomagnetic field anomaly and mineral resource distribution; The association model is used to identify the geomagnetic field data to be measured in the target area to determine the mineral resource positioning information of the target area.
[0073] The device for generating the correlation model between geomagnetic field anomalies and mineral resource distribution provided in the above embodiment can realize the technical solution described in the above embodiment of the method for generating the correlation model between geomagnetic field anomalies and mineral resource distribution. The specific implementation principles of the above modules or units can refer to the corresponding contents in the above embodiment of the method for generating the correlation model between geomagnetic field anomalies and mineral resource distribution, which will not be repeated here.
[0074] like Figure 4 As shown, the present invention also provides an electronic device 400. The electronic device 400 includes a processor 401, a memory 402 and a display 403. Figure 4 Only some components of the electronic device 400 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.
[0075] In some embodiments, the memory 402 may be an internal storage unit of the electronic device 400, such as a hard disk or memory of the electronic device 400. In other embodiments, the memory 402 may also be an external storage device of the electronic device 400, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the electronic device 400.
[0076] Furthermore, the memory 402 may include both an internal storage unit of the electronic device 400 and an external storage device. The memory 402 is used to store application software installed in the electronic device 400 and various data.
[0077] In some embodiments, the processor 401 can be a central processing unit (CPU), a microprocessor or other data processing chip, which is used to run the program code or process data stored in the memory 402, such as the method for generating the correlation model between the geomagnetic field anomaly and the mineral resource distribution in the present invention.
[0078] In some embodiments, the display 403 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 403 is used to display information on the electronic device 400 and to display a visual user interface. The components 401-403 of the electronic device 400 communicate with each other via a system bus.
[0079] In some embodiments of the present invention, when the processor 401 executes the program for generating the correlation model between geomagnetic field anomaly and mineral resource distribution in the memory 402, the following steps may be implemented: Filtering, denoising and normalizing the geomagnetic field data of the target area collected by the quantum sensor to obtain preprocessed data; Extracting geomagnetic field anomaly area data and geomagnetic field characteristics related to mineral resources from the preprocessed data; Based on the geomagnetic field anomaly area data and geomagnetic field characteristics related to mineral resources, a quantum neural network model is trained to obtain a correlation model between geomagnetic field anomaly and mineral resource distribution; The association model is used to identify the geomagnetic field data to be measured in the target area to determine the mineral resource positioning information of the target area.
[0080] It should be understood that: when the processor 401 executes the program for generating the correlation model between geomagnetic field anomalies and mineral resource distribution in the memory 402, in addition to the above functions, other functions can also be realized. For details, please refer to the description of the corresponding method embodiment above.
[0081] Furthermore, the embodiment of the present invention does not specifically limit the type of the electronic device 400 mentioned, and the electronic device 400 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, etc. Exemplary embodiments of portable electronic devices include but are not limited to portable electronic devices equipped with IOS, Android, Microsoft or other operating systems. The above-mentioned portable electronic device may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 400 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).
[0082] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to execute the method for generating a correlation model between geomagnetic field anomaly and mineral resource distribution provided by the above methods, the method comprising: Filtering, denoising and normalizing the geomagnetic field data of the target area collected by the quantum sensor to obtain preprocessed data; Extracting geomagnetic field anomaly area data and geomagnetic field characteristics related to mineral resources from the preprocessed data; Based on the geomagnetic field anomaly area data and geomagnetic field characteristics related to mineral resources, a quantum neural network model is trained to obtain a correlation model between geomagnetic field anomaly and mineral resource distribution; The association model is used to identify the geomagnetic field data to be measured in the target area to determine the mineral resource positioning information of the target area.
[0083] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.
[0084] The above is a detailed introduction to the method and device for generating the correlation model between the geomagnetic field anomaly and the mineral resource distribution provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for technical personnel in this field, according to the idea of the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.
Claims
1. A method for generating a correlation model between geomagnetic field anomaly and mineral resource distribution, characterized in that: include: Filtering, denoising and normalizing the geomagnetic field data of the target area collected by the quantum sensor to obtain preprocessed data; Extracting geomagnetic field anomaly area data and geomagnetic field characteristics related to mineral resources from the preprocessed data; Based on the geomagnetic field anomaly area data and geomagnetic field characteristics related to mineral resources, a quantum neural network model is trained to obtain a correlation model between geomagnetic field anomaly and mineral resource distribution; The association model is used to identify the geomagnetic field data to be measured in the target area to determine the mineral resource positioning information of the target area.
2. The method for generating a correlation model between geomagnetic field anomaly and mineral resource distribution according to claim 1, characterized in that: The geomagnetic field data of the target area collected by the quantum sensor is filtered, denoised and normalized to obtain pre-processed data, including: Decomposing the geomagnetic field data based on a wavelet transform method to remove high-frequency noise, and suppressing dynamic noise based on a Kalman filter algorithm to obtain filtered data; Performing denoising processing on the filtered data to obtain denoised data; The denoised data is normalized to obtain preprocessed data.
3. The method for generating a correlation model between geomagnetic field anomaly and mineral resource distribution according to claim 2, characterized in that: Performing denoising processing on the filtered data to obtain denoised data includes: Environmental noise in the filtered data is eliminated based on an adaptive filtering method, and quantum sensor system errors in the filtered data are eliminated based on an instrument error correction algorithm to obtain denoised data.
4. The method for generating a correlation model between geomagnetic field anomaly and mineral resource distribution according to claim 2, characterized in that: The denoised data is normalized to obtain preprocessed data, including: Based on the minimum-maximum normalization or Z-score normalization method, the denoised data is normalized to obtain preprocessed data.
5. The method for generating a correlation model between geomagnetic field anomaly and mineral resource distribution according to claim 1, characterized in that: Extracting geomagnetic field anomaly area data and geomagnetic field characteristics related to mineral resources from the preprocessed data includes: Based on the quantum support vector machine, the geomagnetic field anomaly area data and the geomagnetic field characteristics related to mineral resources are extracted from the preprocessed data.
6. The method for generating a correlation model between geomagnetic field anomaly and mineral resource distribution according to claim 1, characterized in that: Based on the geomagnetic field anomaly area data and geomagnetic field characteristics related to mineral resources, a quantum neural network model is trained to obtain a correlation model between geomagnetic field anomaly and mineral resource distribution, including: Acquire geological data of the target area, and spatially match the geological data with the data of the geomagnetic field anomaly area to achieve data fusion and obtain fused data; constructing a training set based on the fused data and geomagnetic field characteristics related to mineral resources; Based on the training set, a quantum neural network model is trained to obtain a correlation model between geomagnetic field anomalies and mineral resource distribution.
7. The method for generating a correlation model between geomagnetic field anomaly and mineral resource distribution according to any one of claims 1 to 6, characterized in that: The quantum sensor is a quantum magnetometer based on atomic spin effect, and the number of the quantum sensors is plural.
8. A method for locating mineral resources, characterized in that: include: Filtering, denoising and normalizing the geomagnetic field data to be measured in the target area collected by the quantum sensor to obtain pre-processed data to be measured; The pre-processed data to be tested is input into the association model obtained by the method according to any one of claims 1 to 7 to obtain the mineral resource positioning information of the target area.
9. A device for generating a correlation model between geomagnetic field anomaly and mineral resource distribution, characterized in that: include: A preprocessing module is used to filter, denoise and normalize the geomagnetic field data of the target area collected by the quantum sensor to obtain preprocessed data; An extraction module, used to extract geomagnetic field anomaly area data and geomagnetic field characteristics related to mineral resources from the preprocessed data; A training module is used to train a quantum neural network model based on the geomagnetic field anomaly area data and geomagnetic field characteristics related to mineral resources to obtain a correlation model between geomagnetic field anomaly and mineral resource distribution; The association model is used to identify the geomagnetic field data to be measured in the target area to determine the mineral resource positioning information of the target area.
10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for generating a correlation model between geomagnetic field anomalies and mineral resource distribution as described in any one of claims 1 to 7 are implemented.