Magnetic interface inversion method and equipment based on magnetic anomaly modulus

Abstract

The invention discloses a magnetic interface inversion method and device based on a magnetic anomaly modulus, and the method comprises the steps: constructing a plurality of magnetic interfaces, setting a reference surface, corresponding to the magnetic interfaces, of the earth surface, and enabling the magnetic interfaces to fluctuate up and down around the reference surface; setting a random magnetic dip angle, a random magnetic declination angle and magnetization intensity for the magnetic interface, calculating to obtain simulated magnetic anomaly moduli of the magnetic interface at each observation point of the earth surface, and repeatedly calculating to obtain simulated magnetic anomaly moduli of all the magnetic interfaces at each observation point of the earth surface; and applying the simulated magnetic anomaly modulus of the magnetic interface to a neural network for magnetic interface inversion to predict indwelling depth distribution. According to the method, the influence of residual magnetism is effectively considered, the accuracy of an inversion result is improved, and relatively accurate interface depth information can be obtained.

Description

Technical Field

[0001] The present invention belongs to the field of geospace technology, and more specifically, relates to a magnetic interface inversion method and device based on magnetic anomaly modulus. Background Art

[0002] The depth distribution of Curie point can be delineated by inversion of magnetic data, and further applied to geothermal resource assessment, crustal structure research, geodynamic analysis, etc. The magnetization intensity in the geomagnetic field is composed of induced magnetization intensity and residual magnetization intensity. The conventional Curie point depth inversion assumes that the parameters of the geomagnetic field in the study area (intensity, inclination, declination) are all current geomagnetic field parameters, that is, only induced magnetization is considered without the influence of residual magnetization. However, in practical applications, residual magnetism is widely present, which leads to the unpredictability of geomagnetic field parameters and enhances the unreliability of inversion results. Existing inversion methods face the following bottleneck problems when considering residual magnetism: the direction and intensity of residual magnetism are unknown, making it difficult to accurately set the geomagnetic field parameters in the inversion, which greatly affects the reliability of the inversion results.

[0003] In view of this, overcoming the technical defects of the above-mentioned prior art is an urgent problem to be solved in this technical field. Summary of the invention

[0004] In view of the above defects or improvement needs of the prior art, the present invention provides a magnetic interface inversion method and device based on magnetic anomaly modulus, which aims to solve the technical problem of inaccurate inversion results caused by failure to consider residual magnetism in current magnetic interface inversion methods.

[0005] To achieve the above object, according to one aspect of the present invention, a magnetic interface inversion method based on magnetic anomaly modulus is provided, the method comprising:

[0006] Constructing a plurality of magnetic interfaces, setting a reference plane corresponding to the surface of the earth and the magnetic interface, wherein the magnetic interface fluctuates up and down around the reference plane;

[0007] Setting a random magnetic inclination, a random magnetic declination and a magnetization intensity for the magnetic interface, calculating a simulated magnetic anomaly modulus of the magnetic interface at each observation point on the surface, and repeatedly calculating to obtain the simulated magnetic anomaly modulus of all the magnetic interfaces at each observation point on the surface;

[0008] The simulated magnetic anomaly modulus of the magnetic interface is used in a neural network for magnetic interface inversion to predict the Curie point depth distribution.

[0009] As a further improvement and supplement to the above solution, the present invention also includes the following additional technical features.

[0010] Preferably, the method for calculating the simulated magnetic anomaly modulus of the magnetic interface at each observation point on the surface comprises:

[0011] Uniformly divide the portion of the magnetic interface surrounding the reference plane into a plurality of prisms, project the magnetization intensity of the prism at the observation point to three directions, and obtain the magnetization intensity components of the prism in the three directions at the observation point according to the random magnetic inclination and the random magnetic declination;

[0012] The three components of the magnetic anomaly response generated by the prism at the observation point are calculated based on the magnetization intensity components, the three components of the magnetic anomaly of all the prisms in the same direction are added together at the observation point, and the simulated magnetic anomaly moduli of all the prisms at the observation point are calculated;

[0013] Repeat the calculation to obtain the simulated magnetic anomaly modulus of all the prisms at each of the observation points, that is, to obtain the simulated magnetic anomaly modulus of the magnetic interface at each of the observation points, and repeat the calculation to obtain the simulated magnetic anomaly modulus of all the magnetic interfaces at each of the observation points.

[0014] Preferably, the method of repeatedly calculating and obtaining the simulated magnetic anomaly modulus of all the magnetic interfaces at each observation point on the surface comprises:

[0015] The simulated magnetic anomaly moduli of the magnetic interface at each observation point on the surface are combined into a simulated magnetic anomaly modulus matrix according to the order of the observation points, that is, the simulated magnetic anomaly modulus of the magnetic interface.

[0016] Preferably, the random magnetic inclination angle ranges from -90° to 90°, and the random magnetic declination angle ranges from -180° to 180°.

[0017] Preferably, the magnetization intensity of the prism is projected to three directions, which include a north-south direction, an east-west direction, and a vertical direction.

[0018] Preferably, the calculation method for obtaining the magnetization intensity components of the prism in three directions at a certain observation point according to the random magnetic inclination and the random magnetic declination is:

[0019]

[0020] Among them, (M x ,M y ,M z ) are the components of the magnetization intensity M in the north-south direction, east-west direction and vertical direction, the random magnetic inclination angle is I1, and the random declination angle is D1.

[0021] Preferably, the method of calculating the three components of the magnetic anomaly response generated by the prism at the observation point based on the magnetization intensity components comprises:

[0022] The north-south component of the magnetic anomaly generated by the prism at the observation point is H ax :

[0023]

[0024] The east-west component of the magnetic anomaly generated by the prism at the observation point is H ay :

[0025]

[0026] The vertical component of the magnetic anomaly generated by the prism at the observation point is Z a :

[0027]

[0028] Wherein, μ0 represents the vacuum permeability, (ξ,η,ζ) is the coordinate of any point on the prism, and the distance between the coordinate of any point on the prism and the coordinate of the observation point (x,y,z) is r=[(ξ-x) 2 +(η-y) 2 +(ζ-z) 2 ] 1 / 2 .

[0029] Preferably, the method for calculating the simulated magnetic anomaly modulus of all the prisms at the observation point comprises:

[0030] The simulated magnetic anomaly modulus of all the prisms at the observation point coordinates (x, y, z) is T a (x,y,z):

[0031]

[0032] Among them, H' ax is the sum of the north-south components of the magnetic anomalies of all the prisms at the observation point; H' ay is the sum of the east-west components of the magnetic anomalies of all the prisms at the observation point; Z' a is the sum of the vertical direction components of the magnetic anomalies of all the prisms at the observation point; Ta is the simulated magnetic anomaly modulus of all the prisms at the observation point, that is, the simulated magnetic anomaly modulus of the magnetic interface at the observation point;

[0033] The simulated magnetic anomaly modulus of the magnetic interface at each of the observation points is calculated in sequence to obtain the simulated magnetic anomaly modulus of the magnetic interface.

[0034] Preferably, the method of using the simulated magnetic anomaly modulus of the magnetic interface to predict Curie point depth distribution in a neural network for magnetic interface inversion comprises:

[0035] The input magnetic anomaly modulus data with the Curie depth value as the label is used for neural network training;

[0036] All simulated magnetic anomaly modulus and real magnetic anomaly modulus data are integrated into the training set of the neural network.

[0037] According to another aspect of the present invention, a magnetic interface inversion device based on magnetic anomaly modulus is provided, the device comprising:

[0038] one or more processors;

[0039] A storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the magnetic interface inversion method based on magnetic anomaly modulus as described above.

[0040] In general, the above technical solution conceived by the present invention has the following beneficial effects compared with the prior art:

[0041] 1. Effectively consider the influence of residual magnetism and improve the accuracy of inversion results;

[0042] 2. Obtain more accurate interface depth information. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 This embodiment 1 provides a flowchart of the steps of a magnetic interface inversion method based on simulated magnetic anomaly modulus;

[0045] Figure 2 is a schematic diagram of the magnetic interface provided in the first embodiment;

[0046] Figure 3 is a schematic diagram of a prism provided in the first embodiment;

[0047] Figure 4 is a schematic diagram of the neural network architecture provided in the first embodiment;

[0048] Figure 5 This is a schematic diagram of a magnetic interface inversion device based on magnetic anomaly modulus provided in the second embodiment. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0050] Embodiment 1

[0051] This embodiment provides a magnetic interface inversion method based on simulated magnetic anomaly modulus, the method comprising the following steps: Figure 1 As shown:

[0052] S101: constructing a plurality of magnetic interfaces, setting a reference plane corresponding to the surface of the earth and the magnetic interface, and the magnetic interface fluctuates up and down around the reference plane.

[0053] Before constructing several magnetic interfaces, fully collect the existing Curie point depth data in the study area, preprocess the data, including clipping, filling, and coordinate conversion, and analyze the data characteristics to obtain the approximate range of interface depth parameters and the actual magnetic anomaly modulus measured on the surface. If there is no available data in the study area, find existing literature that is close or similar to the study area, extract the relevant Curie point depth range and magnetic parameters, and use them as references to assist in the construction of the interface model. Based on the results of the data analysis, build and generate complex and diverse interface models.

[0054] S201: setting a random magnetic inclination, a random magnetic declination and a magnetization intensity for the magnetic interface, calculating a simulated magnetic anomaly modulus of the magnetic interface at each observation point on the surface, and repeating the calculation to obtain the simulated magnetic anomaly modulus of all the magnetic interfaces.

[0055] A random magnetic inclination and random declination are given to each interface to simulate the influence of remanent magnetism, and then the simulated magnetic anomaly modulus T generated on the surface is calculated. a Specifically, the construction of the magnetic interface can be as follows Figure 2 As shown, it is assumed that Z = 0 is the surface, there is an interface underground, the average depth of the interface is h, and Z = h is taken as the reference plane. The interface fluctuates up and down around the reference plane. A random magnetic inclination I1 and a random declination D1 are given to the interface, and it is evenly divided into several prisms. The height of each prism is its corresponding △h, and △h is randomly set.

[0056] At this time, the forward calculation of the magnetic interface can be regarded as a combined calculation of multiple prisms, such as Figure 2As shown, X is the north-south component, Y is the east-west component, and Z is the vertical component. Assume that there is a prism underground with side lengths (a, b, Δh) and its upper left vertex coordinates (x0, y0, z0), random magnetic inclination angle I1, and random declination angle D1. At this time, the magnetization intensity M of the prism can be projected into three directions according to the following formula, (M x ,M y ,M z ) are the components of the magnetization intensity M in the north-south direction, the east-west direction and the vertical direction respectively. The magnetization intensity is set according to the geomagnetic field data provided by the international geomagnetic field model, which is expressed by formula 1:

[0057]

[0058] Assuming that there is an observation point grid on the surface, the number of grid points is N, the following formula 2 can express the magnetization intensity of the observation point grid:

[0059]

[0060] Each row in the matrix represents the magnetization intensity vector of an observation point. The magnetization intensity of the i-th point is M i =(M xi ,M yi ,M zi ).

[0061] If there is an observation point (x, y, z) on the surface at this time, the corresponding magnetization intensity can be brought in, and the three components H of the magnetic anomaly response generated by the prism at the surface measurement point can be calculated according to formulas 3, 4, and 5 respectively: ax , H ay and z a μ0 represents the vacuum permeability, (ξ,η,ζ) are the coordinates of any point on the block. Define r=[(ξ-x) 2 +(η-y) 2 +(ζ-z) 2 ] 1 / 2 .

[0062] The north-south component of the magnetic anomaly generated by the prism at the observation point is H ax :

[0063]

[0064] The east-west component of the magnetic anomaly generated by the prism at the observation point is H ay :

[0065]

[0066] The vertical component of the magnetic anomaly generated by the prism at the observation point is Z a :

[0067]

[0068] After calculating the three-component magnetic anomaly based on the above formula, the combined response of all prisms in each interface to the ground measuring point can be calculated separately, which is the magnetic anomaly response generated by the interface on the surface. Then, the superimposed simulated magnetic anomaly modulus of each measuring point can be calculated using Formula 6:

[0069]

[0070] Among them, H' ax is the sum of the north-south components of the magnetic anomalies of all the prisms at the observation point; H' ay is the sum of the east-west components of the magnetic anomalies of all the prisms at the observation point; Z' a is the sum of the vertical components of the magnetic anomalies of all the prisms at the observation point; Ta is the simulated magnetic anomaly modulus of the prism at the observation point.

[0071] The simulated magnetic anomaly modulus of the magnetic interface at each of the observation points is calculated in sequence to obtain the simulated magnetic anomaly modulus of the magnetic interface.

[0072] The order of the observation points is combined into a simulated magnetic anomaly modulus matrix. The calculated Ta is a matrix, which corresponds to the simulated magnetic anomaly modulus of an interface. Each value in Ta corresponds to the simulated magnetic anomaly modulus measured at each measuring point. This Ta is then saved as a set of simulated magnetic anomaly moduli.

[0073] S301: Inputting the simulated magnetic anomaly moduli of all the magnetic interfaces into a neural network for magnetic interface inversion for training, so as to predict the Curie point depth distribution.

[0074] Build a neural network that integrates magnetic anomaly modulus and interface depth, integrate all simulated magnetic anomaly modulus and real magnetic anomaly modulus data in the neural network to construct a training set, integrate and analyze their distribution range characteristics and statistical properties, so as to subsequently identify possible singular values ​​or data quality problems, and globally normalize the fused data, namely the depth data and modulus data, so that the data range is controlled in [0,1], so that the network can better extract data features.

[0075] Design a three-dimensional intelligent U-Net structure that can integrate interface depth data and magnetic anomaly modulus data. Figure 4As shown (the black numbers represent the data volume size, and the red numbers represent the number of channels). By applying multi-scale convolution modules in the encoding and decoder parts of the network, the capture of different spatial frequencies of the modulus data is enhanced, thereby improving the response to different depth features. The jump connection module is improved, and the attention mechanism is introduced to enhance the network model's attention to the edge features of the modulus data, so that the model has a more detailed boundary feature expression capability in the mapping of modulus data to depth data. There are some high-intensity peak areas in the modulus data. The existence of these areas may cause overfitting of network training. To address this problem, adaptive regularization processing is added to the attention mechanism, that is, smoothing the strong peak areas to limit the network model's excessive dependence on specific strong signal areas. By integrating the above modules, the neural network model can further improve the ability to capture modulus data, thereby improving the prediction accuracy of the interface depth and enhancing the expression of boundary details.

[0076] In combination with the embodiment of the present invention, there is also a preferred implementation scheme. Specifically, the method for calculating the magnetic anomaly modulus of the magnetic interface at each observation point on the surface includes:

[0077] Uniformly divide the portion of the magnetic interface surrounding the reference plane into a plurality of prisms, project the magnetization intensity of the prism at the observation point to three directions, and obtain the magnetization intensity components of the prism in the three directions at the observation point according to the random magnetic inclination and the random magnetic declination;

[0078] The three components of the magnetic anomaly response generated by the prism at the observation point are calculated based on the magnetization intensity components, the three components of the magnetic anomaly of all the prisms in the same direction are added together at the observation point, and the magnetic anomaly modulus of the prism at the observation point is calculated;

[0079] The magnetic anomaly modulus of all the prisms at each of the observation points is obtained by repeated calculation, that is, the magnetic anomaly modulus of the magnetic interface at each of the observation points is obtained, and the magnetic anomaly modulus of all the magnetic interfaces at each of the observation points is obtained by repeated calculation.

[0080] Assume that interface 1 is divided into 10 prisms 1, 2, 3... Calculate the three components of prism 1 at measuring point 1, calculate the three components of prism 2 at measuring point 1... Calculate the three components of the 10 prisms at measuring point 1 in sequence, and convert the 10 H ax Add the components together, 10 H ay Add the components together, 10 Z a The three components are added together to get the three components of interface 1. Then, the T of interface 1 at measuring point 1 can be calculated by the formula on the left. a1, and then calculate the measuring points 2, 3, 4, etc. in turn until N measuring points are calculated. In this way, each measuring point has a corresponding simulated magnetic anomaly modulus T a i, this T a i is the magnetic anomaly modulus generated by the magnetic interface at measuring point i.

[0081] The calculated T a is a matrix, corresponding to the magnetic anomaly modulus of an interface, T a Each value in it corresponds to the simulated magnetic anomaly modulus measured at each measuring point. Then put this T a Save as a set of data.

[0082] Then calculate the T of each interface in turn a , calculate 10,000 times, and you can get 10,000 sets of data.

[0083] In combination with the embodiment of the present invention, there is also a preferred implementation scheme. Specifically, the method of repeatedly calculating the magnetic anomaly modulus of all the magnetic interfaces at each observation point on the surface includes:

[0084] The magnetic anomaly moduli of the magnetic interface at each observation point on the surface are combined into a magnetic anomaly modulus matrix according to the order of the observation points, that is, the magnetic anomaly modulus of the magnetic interface.

[0085] Assuming that there is an observation point grid on the surface, the number of grid points is N, the following formula 2 can express the magnetization intensity of the observation point grid:

[0086]

[0087] Each row in the matrix represents the magnetization intensity vector of a grid point, and the magnetization intensity of the i-th point is M i =(M xi ,M yi ,M zi ).

[0088] In combination with the embodiment of the present invention, there is also a preferred implementation scheme, specifically, the range of the random magnetic inclination angle is -90° to 90°, and the range of the random magnetic declination angle is -180° to 180°.

[0089] In combination with the embodiment of the present invention, there is also a preferred implementation scheme, specifically, the magnetization intensity of the prism is projected to three directions, and the three directions include north-south direction, east-west direction and vertical direction.

[0090] In combination with the embodiment of the present invention, there is also a preferred implementation scheme. Specifically, the calculation method of obtaining the magnetization intensity components of the prism in three directions of a certain observation point according to the random magnetic inclination and the random magnetic declination is:

[0091]

[0092] Among them, (M x ,M y ,M z ) are the components of the magnetization intensity M in the north-south direction, east-west direction and vertical direction, the random magnetic inclination angle is I1, and the random declination angle is D1.

[0093] In combination with the embodiment of the present invention, there is also a preferred implementation scheme. Specifically, the method of calculating the three components of the magnetic anomaly response generated by the prism at the observation point according to the magnetization intensity components includes:

[0094] The north-south component of the magnetic anomaly generated by the prism at the observation point i is H ax :

[0095]

[0096] The east-west component of the magnetic anomaly generated by the prism at the observation point i is H ay :

[0097]

[0098] The vertical component of the magnetic anomaly generated by the prism at the observation point i is Z a :

[0099]

[0100] Wherein, μ0 represents the vacuum permeability, (ξ,η,ζ) is the coordinate of any point on the prism, and the distance between the coordinate of any point on the prism and the coordinate of the observation point (x,y,z) is r=[(ξ-x) 2 +(η-y) 2 +(ζ-z) 2 ] 1 / 2 .

[0101] In combination with the embodiment of the present invention, there is also a preferred implementation scheme. Specifically, the method for calculating the simulated magnetic anomaly modulus of all the prisms at the observation point includes:

[0102] The simulated magnetic anomaly modulus of all the prisms at the observation point coordinates (x, y, z) is T a (x,y,z):

[0103]

[0104] Among them, H' ax is the sum of the north-south components of the magnetic anomalies of all the prisms at the observation point; H'ay is the sum of the east-west components of the magnetic anomalies of all the prisms at the observation point; Z' a is the sum of the vertical direction components of the magnetic anomalies of all the prisms at the observation point; Ta is the simulated magnetic anomaly modulus of all the prisms at the observation point, that is, the simulated magnetic anomaly modulus of the magnetic interface at the observation point;

[0105] The magnetic anomaly modulus of the magnetic interface at each of the observation points is calculated in sequence to obtain the magnetic anomaly modulus of the magnetic interface.

[0106] In combination with the embodiment of the present invention, there is also a preferred implementation scheme. Specifically, the method of using the magnetic anomaly modulus of the magnetic interface to predict the Curie point depth distribution in the neural network of magnetic interface inversion includes:

[0107] The input magnetic anomaly modulus data with the Curie depth value as the label is used for neural network training;

[0108] All simulated magnetic anomaly modulus and real magnetic anomaly modulus data are integrated into the training set of the neural network.

[0109] During the training process, the following formulas 7 and 8 are used to monitor and calculate the loss value L, accuracy A and other indicators in real time, and the following formula 9 is used to calculate the change rate L after every 10 trainings. r With A r :

[0110]

[0111]

[0112] In formula 7, n is the number of samples, i.e. the number of training data sets, and y i is the true value of the i-th sample, is the model prediction value obtained through neural network training. p is the number of correctly predicted samples. In formula 9, E represents the number of training times.

[0113] If the calculated loss change rate and accuracy change rate change by more than 10% compared with the last calculated value, incremental learning and reinforcement learning strategies are used to delete poorly performing data, continuously integrate and input new data to optimize the training set, and adaptively adjust network parameters to continuously improve the performance of the model obtained through neural network training. When the change rate changes by less than 10% compared with the last calculated value and this state is maintained for more than five times, the network training is terminated and the model is saved.

[0114] By using the method of the first embodiment, the Curie point depth distribution in a specific research area can be predicted more accurately in the presence of remanent magnetization.

[0115] Embodiment 2:

[0116] The second embodiment provides a magnetic interface inversion device based on magnetic anomaly modulus, the device comprising:

[0117] one or more processors;

[0118] A storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the magnetic interface inversion method based on magnetic anomaly modulus as described in Example 1.

[0119] Figure 5 This is a schematic diagram of the structure of the magnetic interface inversion equipment based on magnetic anomaly modulus provided in the second embodiment. Figure 5 A block diagram of an exemplary magnetic interface inversion apparatus based on magnetic anomaly modulus suitable for implementing embodiments of the present invention is shown. Figure 5 The magnetic interface inversion device based on magnetic anomaly modulus shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0120] like Figure 5 As shown, the magnetic interface inversion device based on magnetic anomaly modulus is presented in the form of a general device. The components of the magnetic interface inversion device based on magnetic anomaly modulus may include but are not limited to: one or more processors or processing units, memory, and a bus connecting different system components (including memory and processing unit).

[0121] The term "bus" refers to one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0122] The magnetic interface inversion device based on magnetic anomaly modulus typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the device for correcting the intelligent logging interpretation model, including volatile and non-volatile media, removable and non-removable media.

[0123] The memory may include a computer system readable medium in the form of a volatile memory, such as a random access memory (RAM) 30 and / or a cache memory. The magnetic interface inversion device based on magnetic anomaly modulus may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system may be used to read and write non-removable, non-volatile magnetic media ( Figure 5 not shown, usually called a "hard drive"). Although Figure 5 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical medium) may be provided. In these cases, each drive may be connected to the bus via one or more data medium interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the embodiments of the present invention.

[0124] A program / utility having a set (at least one) of program modules may be stored, for example, in a memory, such program modules including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules generally perform the functions and / or methods of the embodiments described herein.

[0125] The magnetic interface inversion device based on magnetic anomaly modulus can also communicate with one or more external devices (such as keyboards, pointing devices, displays, etc.), and can also communicate with one or more devices that enable users to interact with the magnetic interface inversion device based on magnetic anomaly modulus, and / or communicate with any device that enables the magnetic interface inversion device based on magnetic anomaly modulus to communicate with one or more other devices (such as network cards, modems, etc.). Such communication can be carried out through an input / output (I / O) interface. In addition, the device for correcting the intelligent logging interpretation model can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN) and / or a public network, such as the Internet) through a network adapter. Figure 5 As shown, the network adapter communicates with other modules of the magnetic interface inversion device based on magnetic anomaly modulus through the bus. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the magnetic interface inversion device based on magnetic anomaly modulus, including but not limited to: microcode, device driver, redundant processing unit, external disk drive array, RAID system, tape drive, and data backup storage system, etc.

[0126] The processing unit executes various functional applications and data processing by running the program stored in the memory, for example, realizing the magnetic interface inversion method based on magnetic anomaly modulus provided in any embodiment of the present invention. That is: constructing a number of magnetic interfaces, setting a reference plane corresponding to the surface of the magnetic interface, and the magnetic interface fluctuates up and down around the reference plane; setting a random magnetic inclination, random magnetic declination and magnetization intensity for the magnetic interface, calculating the simulated magnetic anomaly modulus of the magnetic interface at each observation point on the surface, and repeatedly calculating to obtain the simulated magnetic anomaly modulus of all the magnetic interfaces at each observation point on the surface; using the simulated magnetic anomaly modulus of the magnetic interface in the neural network of magnetic interface inversion to predict the Curie point depth distribution.

[0127] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A magnetic interface inversion method based on magnetic anomaly modulus, characterized in that: The method comprises: Constructing a plurality of magnetic interfaces, setting a reference plane corresponding to the surface of the earth and the magnetic interface, wherein the magnetic interface fluctuates up and down around the reference plane; Setting a random magnetic inclination, a random magnetic declination and a magnetization intensity for the magnetic interface, calculating a simulated magnetic anomaly modulus of the magnetic interface at each observation point on the surface, and repeatedly calculating to obtain the simulated magnetic anomaly modulus of all the magnetic interfaces at each observation point on the surface; The simulated magnetic anomaly modulus of the magnetic interface is used in a neural network for magnetic interface inversion to predict the Curie point depth distribution.

2. The magnetic interface inversion method based on magnetic anomaly modulus according to claim 1, characterized in that: The method for calculating the simulated magnetic anomaly modulus of the magnetic interface at each observation point on the surface includes: Uniformly divide the portion of the magnetic interface surrounding the reference plane into a plurality of prisms, project the magnetization intensity of the prism at the observation point to three directions, and obtain the magnetization intensity components of the prism in the three directions at the observation point according to the random magnetic inclination and the random magnetic declination; The three components of the magnetic anomaly response generated by the prism at the observation point are calculated based on the magnetization intensity components, the three components of the magnetic anomaly of all the prisms in the same direction are added together at the observation point, and the simulated magnetic anomaly moduli of all the prisms at the observation point are calculated; Repeat the calculation to obtain the simulated magnetic anomaly modulus of all the prisms at each of the observation points, that is, to obtain the simulated magnetic anomaly modulus of the magnetic interface at each of the observation points, and repeat the calculation to obtain the simulated magnetic anomaly modulus of all the magnetic interfaces at each of the observation points.

3. The magnetic interface inversion method based on magnetic anomaly modulus according to claim 1, characterized in that: The method of repeatedly calculating and obtaining the simulated magnetic anomaly modulus of all the magnetic interfaces at each observation point on the surface includes: The simulated magnetic anomaly moduli of the magnetic interface at each observation point on the surface are combined into a simulated magnetic anomaly modulus matrix according to the order of the observation points, that is, the simulated magnetic anomaly modulus of the magnetic interface.

4. The magnetic interface inversion method based on magnetic anomaly modulus according to claim 1, characterized in that: The random magnetic inclination angle ranges from -90° to 90°, and the random magnetic declination angle ranges from -180° to 180°.

5. The magnetic interface inversion method based on magnetic anomaly modulus according to claim 2, characterized in that: The magnetization intensity of the prism is projected to three directions, which include north-south direction, east-west direction and vertical direction.

6. The magnetic interface inversion method based on magnetic anomaly modulus according to claim 2, characterized in that: The calculation method of obtaining the magnetization intensity components of the prism in three directions at a certain observation point according to the random magnetic inclination and the random magnetic declination is: Among them, (M x ,M y ,M z ) are the components of the magnetization intensity M in the north-south direction, east-west direction and vertical direction, the random magnetic inclination angle is I1, and the random declination angle is D1.

7. The magnetic interface inversion method based on magnetic anomaly modulus according to claim 2, characterized in that: The method of calculating the three components of the magnetic anomaly response generated by the prism at the observation point based on the magnetization intensity components includes: The north-south component of the magnetic anomaly generated by the prism at the observation point is H ax : The east-west component of the magnetic anomaly generated by the prism at the observation point is H ay : The vertical component of the magnetic anomaly generated by the prism at the observation point is Z a : Wherein, μ0 represents the vacuum permeability, (ξ,η,ζ) is the coordinate of any point on the prism, and the distance between the coordinate of any point on the prism and the coordinate of the observation point (x,y,z) is r=[(ξ-x) 2 +(η-y) 2 +(ζ-z) 2 ] 1 / 2 .

8. The magnetic interface inversion method based on magnetic anomaly modulus according to claim 2 is characterized in that: The method for calculating the simulated magnetic anomaly modulus of all the prisms at the observation point includes: The simulated magnetic anomaly modulus of all the prisms at the observation point coordinates (x, y, z) is T a (x,y,z): Among them, H' ax is the sum of the north-south components of the magnetic anomalies of all the prisms at the observation point; H' ay is the sum of the east-west components of the magnetic anomalies of all the prisms at the observation point; Z' a is the sum of the vertical direction components of the magnetic anomalies of all the prisms at the observation point; Ta is the simulated magnetic anomaly modulus of all the prisms at the observation point, that is, the simulated magnetic anomaly modulus of the magnetic interface at the observation point; The simulated magnetic anomaly modulus of the magnetic interface at each of the observation points is calculated in sequence to obtain the simulated magnetic anomaly modulus of the magnetic interface.

9. The magnetic interface inversion method based on magnetic anomaly modulus according to claim 2, characterized in that: The method of using the simulated magnetic anomaly modulus of the magnetic interface to predict Curie point depth distribution in a neural network for magnetic interface inversion comprises: The input magnetic anomaly modulus data with the Curie depth value as the label is used for neural network training; All simulated magnetic anomaly modulus and real magnetic anomaly modulus data are integrated into the training set of the neural network.

10. A magnetic interface inversion device based on magnetic anomaly modulus, characterized in that the device include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the magnetic interface inversion method based on magnetic anomaly modulus as described in any one of claims 1-9.

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