Method, device, equipment and medium for separating gravity data in metal mining area

Abstract

The application discloses a gravity data separation method, device and equipment for a metal mining area and a medium, and relates to the technical field of gravity anomaly data processing. The method comprises the following steps: measuring gravity data of each measuring point in a target area, and preprocessing to obtain Bouguer gravity anomaly data of each measuring point in the target area; calculating the radial logarithmic power spectrum of the Bouguer gravity anomaly data, dividing the radial logarithmic power spectrum into multiple characteristic frequency bands, and extracting the Bouguer gravity anomaly data of each characteristic frequency band; and performing physical property inversion calculation based on the Bouguer gravity anomaly data of each characteristic frequency band to obtain the underground space density distribution result of the target area. In the constraint iteration process, the inversion result is approximated to the prior information, the gravity potential field inversion result of the metal mining area with clear prior information is strictly constrained in the minimum interval of the known prior information, and therefore the accuracy of the underground ore body position inversion is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of gravity anomaly data processing, in particular to a gravity data separation method, device and equipment for a metal mining area and a medium. BACKGROUND

[0002] Gravity anomalies contain the gravity effects caused by all inhomogeneous bodies from the surface to the deep, so in order to study potential ore bodies by using gravity anomalies, the influence of non-potential ore body factors must be eliminated first, and the superimposed coupled geological anomaly signal is decomposed.

[0003] At present, the geological body potential field response extraction method can be mainly divided into two categories of spatial domain and wave number domain methods. The spatial domain method is based on mathematical assumptions or shape approximation of different anomalies to extract target anomalies, and the wave number domain method is based on the difference in spectral characteristics of different anomalies to extract target anomalies. The existing gravity response extraction methods have their own characteristics and advantages.

[0004] However, under such complex geological conditions as metal mining areas, the surface topography is usually rugged, the geological structure is complex, and the size of the ore body is variable. The gravity responses of ore bodies of different scales and interference signals are often highly overlapped, which leads to limited precision of traditional methods and makes it difficult to meet the needs of high-precision exploration.

[0005] The information disclosed in this BACKGROUND section is only intended to increase an understanding of the general background of the application and is not intended to be a recognition or any form of suggestion that this information constitutes prior art. SUMMARY

[0006] The embodiments of the present application provide a gravity data separation method, device, equipment and medium for a metal mining area to solve the defects of the above related technologies. The technical solution is as follows:

[0007] In a first aspect, the embodiments of the present application provide a gravity data separation method for a metal mining area, characterized in that it comprises the following steps:

[0008] Gravity data of each measurement point in the target area is measured;

[0009] The gravity data is preprocessed to obtain the Bouguer gravity anomaly data of each measurement point in the target area;

[0010] The radial log power spectrum of the Bouguer gravity anomaly data is calculated, the radial log power spectrum is divided into a plurality of characteristic frequency bands, and the Bouguer gravity anomaly data of each characteristic frequency band is extracted respectively;

[0011] Based on the Bouguer gravity anomaly data of each characteristic frequency band, the physical property inversion calculation is performed to obtain the underground space density distribution result of the target area.

[0012] In an optional implementation of the first aspect, the pre-processing of the gravity data to obtain the Bouguer gravity anomaly data of each measuring point in the target area comprises:

[0013] performing terrain correction to obtain high-precision terrain data of the target area, and calculating an influence of a terrain parameter at a position of each measuring point on a gravity observation value to obtain a terrain correction term based on the high-precision terrain data;

[0014] performing normal field correction to calculate a theoretical gravity value at the position of each measuring point to obtain a normal field correction term by using an international gravity formula;

[0015] performing Bouguer correction to calculate a Bouguer correction term based on an elevation of each measuring point and an intermediate layer density;

[0016] calculating the Bouguer gravity anomaly data of each measuring point in the target area based on the gravity data, the terrain correction term, the normal field correction term, and the Bouguer correction term.

[0017] In an optional implementation of the first aspect, the calculating of the radial log power spectrum of the Bouguer gravity anomaly data comprises:

[0018] calculating a radial log power spectrum based on the Bouguer gravity anomaly data, and applying a formula:

[0019] ;

[0020] The dividing of the radial log power spectrum into a plurality of characteristic frequency bands comprises:

[0021] determining a power spectrum curve feature of the radial log power spectrum, and dividing the radial log power spectrum into a plurality of characteristic frequency bands in combination with geological data of the target area;

[0022] determining a spatial frequency upper limit and a spatial frequency lower limit of each characteristic frequency band to obtain a characteristic frequency band, and applying a formula:

[0023] ;

[0024] wherein, is the radial log power spectrum, is the Bouguer gravity anomaly data, represents a two-dimensional Fourier transform, is a spatial frequency in the radial log power spectrum, represents an i-th characteristic frequency band, i is a serial number of the characteristic frequency band, and N is a total number of the characteristic frequency bands.

[0025] In an optional implementation of the first aspect, the extracting the Bouguer gravity anomaly data of each characteristic frequency band respectively comprises:

[0026] The characteristic frequency band is extracted through multiple iteration loops, and a corresponding filter operator is defined in each iteration.

[0027] ;

[0028] wherein, is the filter operator of the kth iteration, k represents the iteration number, is the characteristic frequency band selected in the kth iteration;

[0029] In the kth iteration, the characteristic frequency band selected is extracted through the corresponding filter operator to obtain the Bouguer gravity anomaly data of the characteristic frequency band selected.

[0030] The Bouguer gravity anomaly data of each characteristic frequency band is extracted through multiple iteration loops.

[0031] In an optional implementation of the first aspect, the method further comprises:

[0032] In the kth iteration, the serial number of the characteristic frequency band selected is calculated based on the iteration number k and the total number of characteristic frequency bands, so as to dynamically select the characteristic frequency band, and the formula is:

[0033] ;

[0034] wherein, is the serial number of the characteristic frequency band selected, and the mod function is a remainder function for calculating the remainder of two values after division.

[0035] In an optional implementation of the first aspect, the calculating the underground space density distribution result of the target area based on the Bouguer gravity anomaly data of each characteristic frequency band comprises:

[0036] An inversion target equation is constructed, and the inversion target equation is obtained.

[0037] ;

[0038] The Bouguer gravity anomaly data of each characteristic frequency band is substituted into the inversion target equation, and the inversion target equation is solved to obtain the inversion result of each characteristic frequency band.

[0039] The underground space density distribution result of the target area is obtained based on the inversion result.

[0040] wherein,​​ For data fitting terms, For regularization terms, The regularization parameter is used to balance the relative weights of the data fitting term and the regularization term;

[0041] In the data fitting term, It is a data weighted matrix. G is a gravity observation data vector consisting of Bouguer gravity anomaly data for each characteristic frequency band, G is the forward modeling kernel matrix, and m is the target parameter to be solved. Represents the L2 norm;

[0042] In the regularization term, W is the model weighting matrix. It is the prior model vector.

[0043] In one alternative embodiment of the first aspect, after obtaining the inversion results for each characteristic frequency band, the method further includes:

[0044] Calculate the relative error between the inversion result and the prior constraint information for each characteristic frequency band, using the formula:

[0045] ;

[0046] in, The matrix formed by the inversion results of the i-th characteristic frequency band. For prior constraint information, The relative error between the inversion result of the i-th characteristic frequency band and the prior constraint information;

[0047] If relative error If the relative error exceeds a preset threshold, the characteristic frequency band is dynamically adjusted using the following formula:

[0048] ;

[0049] in, Adjust the step size for the preset frequency band. The adjusted characteristic frequency band;

[0050] The filter operator is updated based on the updated feature frequency bands, and the process is then transferred to the calculation of Bouguer gravity anomaly data for each feature frequency band and subsequent steps, until the calculated inversion result converges to the prior information constraint interval.

[0051] Wherein, the prior information constraint interval is ;

[0052] like If the inversion result converges to the prior information constraint interval, the corresponding inversion result is output.

[0053] in, This is the preset deviation parameter.

[0054] Secondly, embodiments of this application also provide a gravity data separation device for metal mining areas, comprising:

[0055] The data measurement unit is used to measure the gravity data at each measurement point within the target area.

[0056] The data preprocessing unit is used to preprocess the gravity data to obtain the Bouguer gravity anomaly data of each measurement point in the target area;

[0057] The calculation unit is used to calculate the radial logarithmic power spectrum of the Bouguer gravity anomaly data, divide the radial logarithmic power spectrum into multiple characteristic frequency bands, and extract the Bouguer gravity anomaly data for each characteristic frequency band respectively.

[0058] The computing unit is also used to perform physical property inversion calculations based on the Bouguer gravity anomaly data of each characteristic frequency band to obtain the underground space density distribution results of the target area.

[0059] Thirdly, embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method provided by the first aspect or any implementation thereof of the embodiments of this application.

[0060] Fourthly, this application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided by the first aspect of the embodiments of this application or any implementation thereof.

[0061] The beneficial effects of the technical solutions provided in some embodiments of this application include at least the following:

[0062] This application provides a method, apparatus, equipment, and medium for gravity data separation in metal mining areas. It innovatively employs nonlinear inversion technology, integrates multi-source geological constraint information to improve solution reliability, and uses a dynamic frequency-selective filtering operator to optimize filtering parameters, thereby achieving high-precision separation and enhancement of the gravity response of geological bodies at both deep and shallow depths in metal mining areas. Furthermore, during the constraint iteration process, the inversion results are approximated to prior information, strictly constraining the gravity potential field inversion results of metal mining areas with well-defined prior information within a very small interval of known prior information. This improves the accuracy of underground ore body location inversion and enables the extraction of anomalies from different sources in underground space. Based on the above decoupled loop framework, effective extraction of the gravity response of specific targets in mining areas under the constraint of geological prior information can be achieved. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 This is a schematic flowchart of a gravity data separation method for metal mining areas provided in an embodiment of this application;

[0065] Figure 2 This is a schematic diagram of a gravity data separation device for a metal mining area provided in an embodiment of this application;

[0066] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0068] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or modules is not limited to the steps or modules listed, but may optionally include steps or modules not listed, or may optionally include other steps or modules inherent to such process, method, product, or apparatus.

[0069] It should be noted that the terms "first" and "second" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects. It is understood that "first" and "second" can be interchanged in a specific order or sequence where permitted. It should be understood that the objects distinguished by "first" and "second" can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in an order other than those described or illustrated herein.

[0070] The present application will now be described in detail with reference to specific embodiments.

[0071] Next, combine Figure 1 This application introduces a gravity data separation method for metal mining areas, provided by an embodiment of the present application.Figure 1 As shown, the method includes the following steps:

[0072] S101, measure the gravity data at each measurement point within the target area;

[0073] S102, preprocess the gravity data to obtain Bouguer gravity anomaly data for each measurement point in the target area;

[0074] S103, calculate the radial logarithmic power spectrum of the Bouguer gravity anomaly data, divide the radial logarithmic power spectrum into multiple characteristic frequency bands, and extract the Bouguer gravity anomaly data for each characteristic frequency band respectively;

[0075] S104, perform physical property inversion calculations based on the Bouguer gravity anomaly data of each characteristic frequency band to obtain the underground space density distribution results of the target area.

[0076] Understandably, the gravity data in S101 can be obtained by measuring instruments such as gravimeters, and this application embodiment does not limit this.

[0077] In some embodiments, S102, the preprocessing of gravity data to obtain Bouguer gravity anomaly data for each measurement point within the target area includes:

[0078] Terrain correction is performed by acquiring high-precision terrain data of the target area, and then calculating the influence of terrain parameters at each measurement point on the gravity observation value based on the high-precision terrain data to obtain the terrain correction term. ,include:

[0079] ;

[0080] in, The gravitational constant is... This is the density of surface rocks (usually taken as 2.67 g / cm³).

[0081] Normal field correction is performed by calculating the theoretical gravity value at each measurement point using the international gravity formula, thus obtaining the normal field correction term. ,include:

[0082] ;

[0083] in, The latitude of the measurement point.

[0084] Bouguer correction is performed, and the Bouguer correction term is calculated based on the elevation and intermediate layer density of each measurement point. ,include:

[0085] ;

[0086] in, For the elevation of the measurement point, This refers to the density of the intermediate layer.

[0087] Based on the gravity data, the terrain correction term, the normal field correction term, and the Bouguer correction term, Bouguer gravity anomaly data for each measurement point within the target area are calculated, including:

[0088] ;

[0089] in, represents the Bouguer gravity anomaly data at the measurement point, and g represents the gravitational acceleration of the target area.

[0090] In some embodiments, S103, calculating the radial logarithmic power spectrum of the Bouguer gravity anomaly data includes:

[0091] The radial logarithmic power spectrum was calculated based on the Bouguer gravity anomaly data, and the formula was applied:

[0092] ;

[0093] The step of dividing the radial logarithmic power spectrum into multiple characteristic frequency bands includes:

[0094] The power spectrum curve characteristics of the radial logarithmic power spectrum are determined, and the radial logarithmic power spectrum is divided into multiple characteristic frequency bands in combination with the geological data of the target area (such as borehole data, seismic exploration results, etc.).

[0095] To determine the spatial frequency upper and lower limits for each characteristic frequency band, the characteristic frequency bands are obtained, and the formula is applied:

[0096] ;

[0097] in, The radial logarithmic power spectrum, This is data on Bouguer gravity anomalies. Represents the two-dimensional Fourier transform. The spatial frequency in the radial logarithmic power spectrum is denoted as . This represents the i-th characteristic frequency band, where i is the index of the characteristic frequency band and N is the total number of characteristic frequency bands.

[0098] In some embodiments, S103, the extraction of Bouguer gravity anomaly data for each characteristic frequency band includes:

[0099] Each feature frequency band is extracted through multiple iterative loops. A corresponding filtering operator is defined for each iteration, and the formula is applied:

[0100] ;

[0101] This formula can be understood as follows: if the corresponding spatial frequency falls within the extracted feature frequency band... Inside, then =1, meaning that the data corresponding to the spatial frequency is processed by applying a bandpass filter.

[0102] in, Let k be the filtering operator for the k-th iteration, where k represents the iteration number. The characteristic frequency band selected for the k-th iteration;

[0103] In the k-th iteration, the corresponding filter operator is used. For the selected characteristic frequency band Feature extraction is performed on each spatial frequency to obtain the selected feature frequency band. Bouguer gravity anomaly data;

[0104] Bouguer gravity anomaly data for each characteristic frequency band were obtained through multiple iterative loops.

[0105] In some embodiments, S103, the method further includes:

[0106] In the k-th iteration, the index of the selected characteristic frequency band is calculated based on the iteration number k and the total number of characteristic frequency bands, so as to dynamically select the characteristic frequency band, using the formula:

[0107] ;

[0108] in, The selected characteristic frequency band is the index. The mod function is the remainder function, which is used to find the remainder after dividing two values.

[0109] In some embodiments, S104, the physical property inversion calculation based on the Bouguer gravity anomaly data of each characteristic frequency band to obtain the underground spatial density distribution result of the target area includes:

[0110] Constructing the inversion objective equation, we obtain:

[0111] ;

[0112] Substitute the Bouguer gravity anomaly data of each characteristic frequency band into the inversion objective equation, solve the inversion objective equation, and obtain the inversion result for each characteristic frequency band;

[0113] Based on the inversion results, the underground space density distribution of the target area is as follows;

[0114] in, For data fitting terms, For regularization terms, The regularization parameter is used to balance the relative weights of the data fitting term and the regularization term;

[0115] In the data fitting term, It is a data weighted matrix. G is a gravity observation data vector consisting of Bouguer gravity anomaly data for each characteristic frequency band, G is the forward modeling kernel matrix, and m is the target parameter to be solved. Represents the L2 norm;

[0116] In the regularization term, W is the model weighting matrix. It is the prior model vector.

[0117] In some embodiments, after obtaining the inversion results for each characteristic frequency band, the method further includes:

[0118] Calculate the relative error between the inversion result and the prior constraint information for each characteristic frequency band, using the formula:

[0119] ;

[0120] in, The matrix formed by the inversion results of the i-th characteristic frequency band. For prior constraint information, The relative error between the inversion result of the i-th characteristic frequency band and the prior constraint information;

[0121] If relative error If the relative error exceeds a preset threshold, the characteristic frequency band is dynamically adjusted using the following formula:

[0122] ;

[0123] in, Adjust the step size for the preset frequency band. If the step size is set small, it is desirable for the result to be close enough to the prior information. Otherwise, it indicates that the accuracy requirement is reduced. The adjusted characteristic frequency band;

[0124] The filter operator is updated based on the updated feature frequency bands, and the process is then transferred to the calculation of Bouguer gravity anomaly data for each feature frequency band and subsequent steps, until the calculated inversion result converges to the prior information constraint interval.

[0125] Wherein, the prior information constraint interval is ;

[0126] like If the inversion result converges to the prior information constraint interval, the corresponding inversion result is output.

[0127] in, This is the preset deviation parameter.

[0128] Finally, based on the inversion results that converge to the prior information constraint interval, the mineral density distribution of the target region can be extracted.

[0129] In this way, the inversion results can be approximated to prior information during the constraint iteration process. The gravity potential field inversion results for metal mining areas with clearly defined prior information are strictly constrained within a very small interval of known prior information, thereby improving the accuracy of underground ore body location inversion and enabling the extraction of anomalies from different sources in underground space. Based on the above decoupled loop framework, the effective extraction of the gravity response of specific targets in mining areas under the constraint of geological prior information can be achieved.

[0130] The following are device embodiments of this application, which can be used to execute the method embodiments of this application. For details not disclosed in the device embodiments of this application, please refer to the method embodiments of this application.

[0131] Please see below. Figure 2 This is a schematic diagram of a gravity data separation device for a metal mining area, provided as an exemplary embodiment of this application. This device can be implemented as all or part of a terminal through software, hardware, or a combination of both, or it can be integrated as an independent module on a server. The gravity data separation device for a metal mining area in this embodiment can be applied to a terminal or the cloud. The device includes a data measurement unit, a data preprocessing unit, and a computing unit, wherein:

[0132] The data measurement unit is used to measure the gravity data at each measurement point within the target area.

[0133] The data preprocessing unit is used to preprocess the gravity data to obtain the Bouguer gravity anomaly data of each measurement point in the target area;

[0134] The calculation unit is used to calculate the radial logarithmic power spectrum of the Bouguer gravity anomaly data, divide the radial logarithmic power spectrum into multiple characteristic frequency bands, and extract the Bouguer gravity anomaly data for each characteristic frequency band respectively.

[0135] The computing unit is also used to perform physical property inversion calculations based on the Bouguer gravity anomaly data of each characteristic frequency band to obtain the underground space density distribution results of the target area.

[0136] It should be noted that the apparatus provided in the above embodiments, when performing the gravity data separation method for metal mining areas, is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the equipment can be divided into different functional modules to complete all or part of the functions described above. Furthermore, the apparatus provided in the above embodiments and the gravity data separation method embodiments for metal mining areas belong to the same concept, and their implementation process is detailed in the method embodiments, which will not be repeated here.

[0137] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of any of the methods described above.

[0138] Please see Figure 3 This is a structural block diagram of an electronic device provided in an embodiment of this application.

[0139] like Figure 3 As shown, the electronic device 300 includes a processor 301 and a memory 302.

[0140] In this embodiment, the processor 301 is the control center of the computer system, and can be a processor of a physical machine or a processor of a virtual machine. The processor 301 may include one or more processing cores, such as a 4-core processor or an 8-core processor. The processor 301 can be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array).

[0141] Processor 301 may also include a main processor and a coprocessor. The main processor is a processor used to process data in the wake-up state, also known as a CPU (Central Processing Unit). The coprocessor is a low-power processor used to process data in the standby state.

[0142] Memory 302 may include one or more computer-readable storage media, which may be non-transitory. Memory 302 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments of this application, the non-transitory computer-readable storage media in memory 302 is used to store at least one instruction, which is executed by processor 301 to implement the method in the embodiments of this application.

[0143] In some embodiments, the electronic device 300 further includes a peripheral device interface 303 and at least one peripheral device 304. The processor 301, memory 302, and peripheral device interface 303 can be connected via a bus or signal line. Each peripheral device 304 can be connected to the peripheral device interface 303 via a bus, signal line, or circuit board. Specifically, the peripheral device 304 includes: a display screen, a camera, and audio circuitry. The peripheral device interface 303 can be used to connect at least one I / O (Input / Output) related peripheral device to the processor 301 and memory 302.

[0144] In some embodiments of this application, the processor 301, memory 302, and peripheral device interface 303 are integrated on the same chip or circuit board; in other embodiments of this application, any one or two of the processor 301, memory 302, and peripheral device interface 303 can be implemented on separate chips or circuit boards. This application does not specifically limit the implementation in this regard.

[0145] The electronic device structural block diagram shown in the embodiments of this application does not constitute a limitation on the electronic device 300. The electronic device 300 may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0146] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the methods in any of the foregoing embodiments. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0147] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of software products. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for separating gravity data in metal mining areas, characterized in that, Includes the following steps: Gravity data were obtained at each measurement point within the target area. The gravity data is preprocessed to obtain Bouguer gravity anomaly data for each measurement point within the target area; Calculate the radial logarithmic power spectrum of the Bouguer gravity anomaly data, divide the radial logarithmic power spectrum into multiple characteristic frequency bands, and extract the Bouguer gravity anomaly data for each characteristic frequency band. Based on the Bouguer gravity anomaly data of each characteristic frequency band, physical property inversion calculations are performed to obtain the underground spatial density distribution results of the target area, including: Constructing the inversion objective equation, we obtain: ； Substitute the Bouguer gravity anomaly data of each characteristic frequency band into the inversion objective equation, solve the inversion objective equation, and obtain the inversion result for each characteristic frequency band; Based on the inversion results, the underground space density distribution of the target area is as follows; in, For data fitting terms, For regularization terms, The regularization parameter is used to balance the relative weights of the data fitting term and the regularization term; In the data fitting term, It is a data weighted matrix. G is a gravity observation data vector consisting of Bouguer gravity anomaly data for each characteristic frequency band, G is the forward modeling kernel matrix, and m is the target parameter to be solved. Represents the L2 norm; In the regularization term, W is the model weighting matrix. It is the prior model vector; After obtaining the inversion results for each characteristic frequency band, the method further includes: Calculate the relative error between the inversion result and the prior constraint information for each characteristic frequency band, using the formula: ； in, The matrix formed by the inversion results of the i-th characteristic frequency band. For prior constraint information, The relative error between the inversion result of the i-th characteristic frequency band and the prior constraint information; If relative error If the relative error exceeds a preset threshold, the characteristic frequency band is dynamically adjusted using the following formula: ; in, Adjust the step size for the preset frequency band. The adjusted characteristic frequency band; The filter operator is updated based on the updated feature frequency bands, and the process is then transferred to the calculation of Bouguer gravity anomaly data for each feature frequency band and subsequent steps, until the calculated inversion result converges to the prior information constraint interval. Wherein, the prior information constraint interval is ; like If the inversion result converges to the prior information constraint interval, the corresponding inversion result is output. in, This is the preset deviation parameter.

2. The gravity data separation method for metal mining areas according to claim 1, characterized in that, The preprocessing of gravity data to obtain Bouguer gravity anomaly data for each measurement point within the target area includes: Perform terrain correction, acquire high-precision terrain data of the target area, calculate the influence of terrain parameters at each measurement point on the gravity observation value based on the high-precision terrain data, and obtain the terrain correction term; Normal field correction is performed by calculating the theoretical gravity value at each measurement point using the international gravity formula, thus obtaining the normal field correction term. Bouguer correction is performed, and the Bouguer correction term is calculated based on the elevation and intermediate layer density of each measurement point. Based on the gravity data, the terrain correction term, the normal field correction term, and the Bouguer correction term, the Bouguer gravity anomaly data for each measurement point within the target area are calculated.

3. The gravity data separation method for metal mining areas according to claim 1, characterized in that, The calculation of the radial logarithmic power spectrum of the Bouguer gravity anomaly data includes: The radial logarithmic power spectrum was calculated based on the Bouguer gravity anomaly data, and the formula was applied: ； The step of dividing the radial logarithmic power spectrum into multiple characteristic frequency bands includes: The power spectrum curve characteristics of the radial logarithmic power spectrum are determined, and the radial logarithmic power spectrum is divided into multiple characteristic frequency bands in combination with the geological data of the target area; To determine the spatial frequency upper and lower limits for each characteristic frequency band, the characteristic frequency bands are obtained, and the formula is applied: ， ； in, The radial logarithmic power spectrum, This is data on Bouguer gravity anomalies. Represents the two-dimensional Fourier transform. The spatial frequency in the radial logarithmic power spectrum is denoted as . This represents the i-th characteristic frequency band, where i is the index of the characteristic frequency band and N is the total number of characteristic frequency bands.

4. The gravity data separation method for metal mining areas according to claim 3, characterized in that, The extraction of Bouguer gravity anomaly data for each characteristic frequency band includes: Each feature frequency band is extracted through multiple iterative loops. A corresponding filtering operator is defined for each iteration, and the formula is applied: ； in, Let k be the filtering operator for the k-th iteration, where k represents the iteration number. The characteristic frequency band selected for the k-th iteration; In the k-th iteration, the corresponding filter operator is used. For the selected characteristic frequency band Feature extraction is performed on each spatial frequency to obtain the selected feature frequency band. Bouguer gravity anomaly data; Bouguer gravity anomaly data for each characteristic frequency band were obtained through multiple iterative loops.

5. A gravity data separation method for metal mining areas according to claim 4, characterized in that, The method further includes: In the k-th iteration, the index of the selected characteristic frequency band is calculated based on the iteration number k and the total number of characteristic frequency bands, so as to dynamically select the characteristic frequency band, using the formula: ; in, The selected characteristic frequency band is the index. The mod function is the remainder function, which is used to find the remainder after dividing two values.

6. A separation apparatus for a gravity data separation method in a metal mining area according to any one of claims 1-5, characterized in that, include: The data measurement unit is used to measure the gravity data at each measurement point within the target area. The data preprocessing unit is used to preprocess the gravity data to obtain the Bouguer gravity anomaly data of each measurement point in the target area; The calculation unit is used to calculate the radial logarithmic power spectrum of the Bouguer gravity anomaly data, divide the radial logarithmic power spectrum into multiple characteristic frequency bands, and extract the Bouguer gravity anomaly data for each characteristic frequency band respectively. The computing unit is also used to perform physical property inversion calculations based on the Bouguer gravity anomaly data of each characteristic frequency band to obtain the underground space density distribution results of the target area.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1 to 5.

8. 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, it implements the steps of the method as described in any one of claims 1 to 5.

Images