A method for optimizing mineral resource exploration based on gravity forward and inversion

Abstract

The invention discloses a mineral resource exploration optimization method based on gravity forward and inversion, which relates to the technical field of Internet of Things. Multi-source conversion is performed on target gravity data to obtain a target gravity factor, a target optimization coefficient is set to perform parameter change and series division to obtain a target coefficient slice, and the target gravity factor is optimized in detail through the target coefficient slice to obtain an optimized gravity coefficient; an optimization coefficient spectrum is generated according to the optimized gravity coefficient, and a gravity coefficient curve is adjusted and determined according to a dynamic decision axis to obtain a section range coefficient; range distinction and same-level linking are performed on virtual space coordinates according to the section range coefficient to obtain a same-level coefficient linking circle, and an abnormal survey is performed on a virtual target area according to the same-level coefficient linking circle to obtain an abnormal gravity area; the accuracy of the gravity inversion result is greatly improved, and by improving the exploration efficiency, the resources and time required for exploration are reduced, and the exploration cost is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of Internet of Things, and in particular to a mineral resource exploration optimization method based on gravity forward and inversion. Background Art

[0002] Gravity forward and inversion is a technical method in geophysical exploration that involves using gravity data to infer the distribution and properties of underground materials. Specifically, gravity forward modeling refers to the establishment of a mathematical model based on known geological structures and density distributions to predict the behavior of the gravity field. Gravity inversion, on the other hand, is the analysis of actual measured gravity data to infer the density distribution and geological model of underground structures.

[0003] Mineral resource exploration is the basis of resource development and utilization. Traditional mineral resource exploration methods rely on geologists to conduct ground exploration based on experience. This method is inefficient and its accuracy is limited by the experience of geologists and field conditions. In recent years, geophysical exploration technology, especially gravity exploration technology, has been widely used in mineral resource exploration. Gravity forward and inversion technology can infer the density distribution of underground geological structures by analyzing gravity anomaly data, providing important information for mineral resource exploration. However, the existing gravity forward and inversion methods have problems such as low computational efficiency and the accuracy of the results is limited by the initial model. For this reason, a mineral resource exploration optimization method based on gravity forward and inversion is provided. Summary of the invention

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] A method for optimizing mineral resource exploration based on gravity forward and inversion includes the following steps:

[0006] Step S1: Setting the gravity collection terminal to collect target gravity data;

[0007] Step S2: Perform multi-source conversion on the target gravity data to obtain the target gravity factor, set the target optimization coefficient to perform parameter changes and adjustments, obtain the optimization series, divide the target optimization coefficient into series according to the optimization series, obtain the target coefficient slices, optimize the target gravity factor in detail through the target coefficient slices, and obtain the optimized gravity coefficient;

[0008] Step S3: Generate an optimization coefficient spectrum according to the optimized gravity coefficient based on the predicted virtual block, set the activity interval to adjust the regional decision axis, obtain the dynamic decision axis, adjust and determine the gravity coefficient curve according to the dynamic decision axis, and obtain the section range coefficient;

[0009] Step S4: range-differentiate the virtual space coordinates according to the segment range coefficients to obtain the coordinate range coefficients, perform same-level linking on the coordinate range coefficients to obtain same-level coefficient linking circles, perform anomaly survey on the virtual target area according to the same-level coefficient linking circles to obtain abnormal gravity areas.

[0010] The process of setting up the gravity acquisition terminal to collect target gravity data includes:

[0011] Conduct measurement planning for the target area, obtain a preliminary measurement route, divide the target area into blocks according to the preliminary measurement route, and obtain predicted target blocks;

[0012] A gravity acquisition terminal is set up according to the obtained preliminary measurement route, and information of the predicted target block is collected through the gravity acquisition terminal to obtain target gravity data, the time of collecting the target gravity data is marked to obtain the target timestamp, the position of collecting the target gravity data is marked to obtain spatial coordinate data.

[0013] The process of obtaining the optimal level includes:

[0014] Perform multi-source conversion on the target gravity data to obtain the target gravity factor, and set the planning period according to the obtained target timestamp;

[0015] Frequency interception of the planning cycle is performed according to the target timestamp to obtain the target frequency;

[0016] Set the target optimization coefficient, change the parameters of the target optimization coefficient, and obtain the optimal parameters;

[0017] According to the optimal variable parameter, the target optimization coefficient is adjusted to obtain the variable parameter spacing;

[0018] The optimization level is obtained according to the obtained optimal variable parameters, variable parameter spacing and target optimization coefficient.

[0019] The process of obtaining the optimized gravity coefficient includes:

[0020] Factor-divide the target gravity factor according to the target frequency to obtain the target factor node;

[0021] Divide the target optimization coefficient into series according to the optimization series to obtain target coefficient slices;

[0022] The obtained target coefficient slice is uploaded to the target factor node, and the target gravity factor is optimized in detail through the target coefficient slice to obtain the optimized gravity coefficient.

[0023] The process of generating an optimized coefficient spectrum according to the optimized gravity coefficient based on the predicted virtual block includes:

[0024] Performing geological virtualization on the obtained target area to obtain a virtual target area, and performing block docking on the virtual target area according to the predicted target block to obtain a predicted virtual block;

[0025] The optimized gravity coefficient corresponding to the predicted virtual block is obtained, an optimized coefficient spectrum is generated according to the obtained optimized gravity coefficient, and the obtained optimized coefficient spectrum is curve-marked to obtain a gravity coefficient curve.

[0026] The process of obtaining the segment range coefficient includes:

[0027] The regional decision axis is set according to the obtained optimization coefficient spectrum, and the obtained dynamic decision axis is uploaded to the optimization coefficient spectrum;

[0028] Setting an activity interval, and adjusting the regional decision axis according to the obtained activity interval to obtain a dynamic decision axis;

[0029] The gravity coefficient curve is adjusted and determined according to the obtained dynamic judgment axis to obtain the section range coefficient.

[0030] The process of obtaining an anomalous gravity area includes:

[0031] According to the spatial coordinate data, the predicted virtual block is positioned to obtain the virtual spatial coordinates, the optimized gravity coefficient is positionally matched according to the virtual spatial coordinates, and the successfully matched optimized gravity coefficient is uploaded to the corresponding virtual spatial coordinates;

[0032] According to the section range coefficient, the optimized gravity coefficient at the virtual space coordinate is range-differentiated to obtain the coordinate range coefficient;

[0033] The obtained coordinate range coefficients are linked at the same level to obtain a link circle of coefficients at the same level, and an abnormal survey is performed on the virtual target area according to the link circle of coefficients at the same level to obtain an abnormal gravity area.

[0034] The process of performing anomaly survey on the virtual target area based on the same-level coefficient link circle includes:

[0035] Based on the virtual target area, the color of the same-level coefficient link circles of the predicted virtual block is compared to obtain the block color circle sequence;

[0036] Set a virtual block for comparison, and obtain a block color circle sequence corresponding to all adjacent predicted virtual blocks of the virtual block for comparison;

[0037] According to the block color circle sequence of the compared virtual block, the block color circle sequences corresponding to the adjacent predicted virtual blocks are compared to obtain the arrangement coincidence, and a safety judgment is made on the arrangement coincidence to obtain the abnormal color block area, and the predicted target block corresponding to the abnormal color block area is obtained, which is recorded as the abnormal gravity area.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] Perform multi-source conversion on target gravity data to obtain target gravity factors, set target optimization coefficients to optimize target gravity factors in detail, obtain optimized gravity coefficients, generate optimization coefficient spectra based on optimized gravity coefficients, and determine the optimization coefficient spectra to obtain section range coefficients; converting gravity data from different sources into a standardized unified form is conducive to improving data processing efficiency and increasing geological exploration efficiency;

[0040] According to the section range coefficient, the virtual space coordinates are range-differentiated and linked at the same level to obtain the link circle of the same level coefficient. According to the link circle of the same level coefficient, the virtual target area is surveyed for anomalies to obtain the abnormal gravity area; the accuracy of the gravity inversion results is greatly improved, the resources and time required for exploration are reduced, and the exploration cost is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 It is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0043] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] like Figure 1 As shown, a method for optimizing mineral resource exploration based on gravity forward inversion includes the following steps:

[0045] Step S1: Setting the gravity collection terminal to collect target gravity data;

[0046] Step S2: Perform multi-source conversion on the target gravity data to obtain the target gravity factor, set the target optimization coefficient to perform parameter changes and adjustments, obtain the optimization series, divide the target optimization coefficient into series according to the optimization series, obtain the target coefficient slices, optimize the target gravity factor in detail through the target coefficient slices, and obtain the optimized gravity coefficient;

[0047] Step S3: Generate an optimization coefficient spectrum according to the optimized gravity coefficient based on the predicted virtual block, set the activity interval to adjust the regional decision axis, obtain the dynamic decision axis, adjust and determine the gravity coefficient curve according to the dynamic decision axis, and obtain the section range coefficient;

[0048] Step S4: range-differentiate the virtual space coordinates according to the segment range coefficients to obtain the coordinate range coefficients, perform same-level linking on the coordinate range coefficients to obtain same-level coefficient linking circles, perform anomaly survey on the virtual target area according to the same-level coefficient linking circles to obtain abnormal gravity areas.

[0049] It should be further explained that, in the specific implementation process, the process of collecting target gravity data includes:

[0050] Conduct measurement planning for the target area and obtain the preliminary measurement route;

[0051] Furthermore, the target area refers to a geological area for mineral resource exploration, and the prepared survey route refers to planning a survey route based on the topography and geological conditions of the target area to ensure coverage of the area to be explored;

[0052] Divide the target area into blocks according to the obtained preliminary measurement route to obtain predicted target blocks;

[0053] Furthermore, the block division means dividing the area passed by the prepared measurement route into a prediction target block according to the range passed by the prepared measurement route;

[0054] Setting a gravity collection terminal according to the obtained preliminary measurement route;

[0055] The gravity acquisition end refers to a gravity measuring instrument for collecting information on the target area, such as a gravimeter or a gradiometer. The gravity acquisition end set is related to the measurement range and accuracy requirements, and each predicted target block has a corresponding gravity acquisition end for collecting all data information within the predicted target block;

[0056] The target block is collected through the gravity collection end to obtain target gravity data, the time of collecting the target gravity data is marked to obtain the target timestamp, the location of collecting the target gravity data is marked to obtain the spatial coordinate data, and the spatial coordinate data represents the latitude, longitude and elevation data of the location collected by the gravity collection end;

[0057] Associating the obtained target gravity data with the corresponding predicted target block;

[0058] The information collection means collecting information along the route according to the obtained track of the prepared measurement route to obtain gravity data, and the target gravity data includes gravity field data, gravity gradient data, and gravity reference data, wherein the gravity field data represents the gravity effect on each point on the earth's surface at a specific point in time.

[0059] Perform multi-source conversion on the obtained target gravity data to obtain the target gravity factor;

[0060] Furthermore, the multi-source conversion means converting the target gravity data into a signal form, namely, a target gravity factor;

[0061] The planning period is set according to the obtained target timestamp, and the obtained planning period is marked as T 规 ;

[0062] According to the obtained target timestamp, the frequency of the planning cycle is intercepted to obtain the target frequency, and the obtained target frequency is marked as f 目 ,in, Δa represents the time interval between two adjacent target timestamps;

[0063] Setting a target optimization coefficient, wherein the target optimization coefficient is expressed in a function form;

[0064] Perform parameter changes on the obtained target optimization coefficients to obtain optimal parameters;

[0065] Furthermore, the parameter change represents the scaling and translation transformation of the adjustment target optimization coefficient in the time dimension and the frequency dimension, and the distance of the scaling and translation change is recorded to obtain the optimal parameter;

[0066] According to the obtained optimal variable parameters, the target optimization coefficients are adjusted in interval to obtain the variable parameter interval;

[0067] Furthermore, the spacing adjustment means that the distance between two adjacent optimal variable parameters is counted in the target optimization coefficient to obtain the variable parameter spacing;

[0068] The optimization level is obtained according to the obtained optimal variable parameters, variable parameter spacing and target optimization coefficient, and the obtained optimization level is marked as Y, where: M represents the coefficient length of the target optimization coefficient, B represents the variable parameter spacing, i represents the number of variable parameter spacings in the target optimization coefficient, and c represents the optimal variable parameter. Representation calculation Then round down;

[0069] Factor-divide the target gravity factor according to the obtained target frequency to obtain the target factor node;

[0070] Further, the factor division means dividing the target gravity factor according to the target frequency, and marking the node to be divided as the target factor node;

[0071] According to the obtained optimization series, the target optimization coefficient is divided into series to obtain target coefficient slices;

[0072] Furthermore, the level division means that the target optimization coefficients are equally divided according to the number of optimization levels to obtain target coefficient slices of equal length;

[0073] The obtained target coefficient slice is uploaded to the target factor node, and the target gravity factor is optimized in detail through the target coefficient slice to obtain the optimized gravity coefficient;

[0074] It should be further explained that, in the specific implementation process, the process of detail optimization includes:

[0075] According to the order of the series division, the obtained target coefficient slices are sorted to obtain a target coefficient slice sequence;

[0076] According to the obtained target coefficient slice sequence, the first target coefficient slice is uploaded to the first target factor node of the target gravity factor, and the target coefficient slice is optimized and convolved at the target factor node to obtain the target node coefficient, that is, the target coefficient slice is convolved with the target factor node;

[0077] Upload the target coefficient slice to the next target factor node and perform optimized convolution with the target factor node until all target factor nodes of the target gravity factor are covered, and sum the obtained target node coefficients to obtain the slice target coefficient;

[0078] Uploading the next target coefficient slice to the target gravity factor according to the target coefficient slice sequence, and repeating the process of obtaining the slice target coefficient until all target coefficient slices in the target coefficient slice sequence are uploaded to the target gravity factor;

[0079] Performing discrete Fourier transformation on the obtained slice target coefficients to obtain discrete slice coefficients, summing the obtained discrete slice coefficients to obtain comprehensive target coefficients, performing exponential transformation on the obtained comprehensive target coefficients to obtain exponential target coefficients, and performing inverse discrete cosine transformation on the obtained exponential target coefficients to obtain optimized gravity coefficients;

[0080] Further, according to the gravity field data, gravity gradient data, gravity reference data and space coordinate data included in the target gravity data, the optimized gravity coefficient includes the gravity field coefficient, the gravity gradient coefficient and the gravity reference coefficient.

[0081] Performing geological virtualization on the obtained target area to obtain a virtual target area;

[0082] Perform block docking on the virtual target area according to the obtained predicted target block to obtain a predicted virtual block;

[0083] Furthermore, the geological virtual representation converts the target area into a three-dimensional virtual space, namely, a virtual target area, and marks a corresponding range in the virtual target area according to the area range of the predicted target block;

[0084] Obtaining an optimized gravity coefficient corresponding to the predicted virtual block, generating an optimized coefficient spectrum according to the obtained optimized gravity coefficient, marking the obtained optimized coefficient spectrum with a curve, and obtaining a gravity coefficient curve;

[0085] Furthermore, the generated optimization coefficient spectrum is a two-dimensional rectangular coordinate system with the target timestamp as the horizontal axis, and the obtained optimized gravity coefficient is uploaded to the two-dimensional rectangular coordinate system to generate a gravity coefficient curve to obtain the optimization coefficient spectrum;

[0086] The regional decision axis is set according to the obtained optimization coefficient spectrum, and the obtained dynamic decision axis is uploaded to the optimization coefficient spectrum;

[0087] Furthermore, the set area decision axis is set corresponding to each gravity coefficient curve, and the area decision axis is a straight line parallel to the horizontal axis;

[0088] Setting an activity interval, performing activity adjustment on the regional decision axis according to the obtained activity interval, obtaining a dynamic decision axis, adjusting and determining the gravity coefficient curve according to the obtained dynamic decision axis, and obtaining a section range coefficient, wherein the section range coefficient includes a first range coefficient, a second range coefficient, ..., an Nth range coefficient;

[0089] It should be further explained that, in the specific implementation process, the activity interval represents the interval distance of the regional decision axis moving up and down in the optimization coefficient spectrum. In order to better classify the gravity data to obtain the abnormal range, the set activity interval needs to match the corresponding characteristics of the gravity data to obtain the most appropriate activity interval;

[0090] The regional decision axis is translated upward from the horizontal axis, and the translation distance each time is an activity interval. The optimized gravity coefficient falling between two adjacent dynamic decision axes is marked as the gravity axis coefficient, that is, the gravity axis coefficient between each activity interval is the coefficient range of the same level, recorded as the first range coefficient, and the regional decision axis is continued to be translated according to an activity interval to obtain the gravity axis coefficient of the next range, recorded as the second range coefficient, until no gravity coefficient curve reaches this range after translation, and the adjustment judgment is completed.

[0091] The predicted virtual block is positioned according to the obtained spatial coordinate data to obtain the virtual spatial coordinates, the optimized gravity coefficient is positionally matched according to the obtained virtual spatial coordinates, and the successfully matched optimized gravity coefficient is uploaded to the corresponding virtual spatial coordinates;

[0092] According to the obtained section range coefficient, the optimized gravity coefficient at the virtual space coordinate is range-differentiated to obtain the coordinate range coefficient;

[0093] Further, the range distinction indicates that the optimized gravity coefficient is range matched at the virtual space coordinates, and the optimized gravity coefficients of the same range coefficients are marked at the same level according to the segment range coefficients obtained by adjusting the gravity coefficient curve generated by the optimized gravity coefficients, that is, the optimized gravity coefficients of the same segment range coefficients are recorded as coefficients of the same level, marked with the same color, that is, the coordinate range coefficients, and one segment range coefficient is marked with one color; for example, the optimized gravity coefficient at point P1 is matched to the second range coefficient, the optimized gravity coefficient at point P2 is matched to the second range coefficient, the optimized gravity coefficient at point P6 is matched to the second range coefficient, the optimized gravity coefficient at point P3 is matched to the fourth range coefficient, and the optimized gravity coefficient at point P4 is matched to the fourth range coefficient, then P1, P2, and P6 are the same range coefficients, and the virtual space coordinates are marked with color g1, P3 and P4 are the same range coefficients, and the virtual space coordinates are marked with color g2;

[0094] Perform same-level linking on the obtained coordinate range coefficients to obtain a same-level coefficient linking circle;

[0095] Further, the same-level link means linking the virtual space coordinates corresponding to the coordinate range coefficients of the same range coefficients in the predicted virtual block, that is, linking the virtual space coordinates of the same color, wherein the linking order is to select any virtual space coordinate as the starting point, link all virtual space coordinates of the same color, and finally link the virtual space coordinates at the starting point again, so that the linked virtual space coordinates are a closed irregular circle, that is, the same-level coefficient link circle;

[0096] In particular, all predicted virtual blocks in the virtual target area are statistically linked to obtain the same-level coefficient link circles corresponding to all predicted virtual blocks;

[0097] Anomaly survey is performed on the virtual target area according to the obtained same-level coefficient linking circles to obtain an abnormal gravity area;

[0098] It should be further explained that, in the specific implementation process, the abnormal survey process includes:

[0099] Based on the virtual target area, the color of the same-level coefficient link circles of the predicted virtual block is compared to obtain the block color circle sequence;

[0100] Furthermore, the color comparison means arranging the colors of the same-level coefficient link circles in an order from inside to outside to obtain a block color circle sequence, wherein the order from inside to outside is based on the center of the same-level coefficient link circle closest to the center position of the predicted virtual block as the starting point;

[0101] Taking a predicted virtual block as the center, recorded as the compared virtual block, obtain the block color circle sequence corresponding to all adjacent predicted virtual blocks of the compared virtual block;

[0102] According to the block color circle sequence of the compared virtual block, the block color circle sequences corresponding to the adjacent predicted virtual blocks are compared for coincidence to obtain the arrangement coincidence;

[0103] Furthermore, the arrangement coincidence degree represents the color arrangement difference between the block color circle sequence of the compared virtual block and the block color circle sequence corresponding to the adjacent predicted virtual block, that is, the repetition degree of the same arrangement sequence. The arrangement coincidence degree obtained is the coincidence degree between the compared virtual block and any one of all the adjacent predicted virtual blocks, and the coincidence degree of all adjacent blocks is calculated;

[0104] Based on the virtual target area, one of the remaining predicted virtual blocks is selected as a comparison virtual block, and the process of obtaining the arrangement overlap is repeated until the virtual target area is covered;

[0105] Perform safety judgment on the obtained arrangement overlap to obtain the abnormal color block area;

[0106] Furthermore, the safety determination indicates that the balanced overlap is obtained by comparing all the arrangement overlaps of the virtual blocks, and the obtained balanced overlap is marked as Z. 均 , where Z 均 =β1*L1+β2*L2+β3*L3+……+β j *L j , β1, β2, β3...β j is the weight factor, and β1+β2+β3+…+β j =1,L j is the arrangement overlap corresponding to adjacent predicted virtual blocks;

[0107] If the balance coincidence degree is less than the determination threshold, the compared virtual block is recorded as an abnormal color block area, wherein the determination threshold is a preset threshold;

[0108] The predicted target block corresponding to the color block abnormal area is obtained, and the obtained predicted target block is marked as an abnormal gravity area. According to the obtained abnormal gravity area, the location of the area to be surveyed can be obtained, that is, mineral resource survey is carried out on the abnormal gravity area.

[0109] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for optimizing mineral resource exploration based on gravity forward and inversion, characterized in that: The following steps are involved: Step S1: Setting the gravity collection terminal to collect target gravity data; Step S2: performing multi-source conversion on the target gravity data, obtaining the target gravity factor, setting the target optimization coefficient to perform parameter changes and adjustments, and obtaining the optimization level includes: performing multi-source conversion on the target gravity data, obtaining the target gravity factor, and setting the planning period according to the obtained target timestamp; Frequency interception of the planning cycle is performed according to the target timestamp to obtain the target frequency; Set the target optimization coefficient, change the parameters of the target optimization coefficient, and obtain the optimal parameters; According to the optimal variable parameter, the target optimization coefficient is adjusted to obtain the variable parameter spacing; The optimization series is obtained according to the obtained optimal variable parameters, variable parameter spacing and target optimization coefficient. The target optimization coefficient is divided into series according to the optimization series to obtain target coefficient slices. The target gravity factor is optimized in detail through the target coefficient slices to obtain the process of optimizing the gravity coefficient: Factor-divide the target gravity factor according to the target frequency to obtain the target factor node; Divide the target optimization coefficient into series according to the optimization series to obtain target coefficient slices; The obtained target coefficient slice is uploaded to the target factor node, and the target gravity factor is optimized in detail through the target coefficient slice to obtain the optimized gravity coefficient; Step S3: Generate an optimization coefficient spectrum according to the optimized gravity coefficient based on the predicted virtual block, set the activity interval to adjust the regional decision axis, obtain the dynamic decision axis, adjust and determine the gravity coefficient curve according to the dynamic decision axis, and obtain the section range coefficient; Step S4: range-differentiate the virtual space coordinates according to the segment range coefficients to obtain the coordinate range coefficients, perform same-level linking on the coordinate range coefficients to obtain same-level coefficient linking circles, perform anomaly survey on the virtual target area according to the same-level coefficient linking circles to obtain abnormal gravity areas.

2. The method for optimizing mineral resource exploration based on gravity forward and inversion according to claim 1 is characterized in that: The process of setting up the gravity acquisition terminal to collect target gravity data includes: Conduct measurement planning for the target area, obtain a preliminary measurement route, divide the target area into blocks according to the preliminary measurement route, and obtain predicted target blocks; A gravity acquisition terminal is set up according to the obtained preliminary measurement route, and information of the predicted target block is collected through the gravity acquisition terminal to obtain target gravity data, the time of collecting the target gravity data is marked to obtain the target timestamp, the position of collecting the target gravity data is marked to obtain spatial coordinate data.

3. The method for optimizing mineral resource exploration based on gravity forward and inversion according to claim 2 is characterized in that: The process of generating an optimized coefficient spectrum according to the optimized gravity coefficient based on the predicted virtual block includes: Performing geological virtualization on the obtained target area to obtain a virtual target area, and performing block docking on the virtual target area according to the predicted target block to obtain a predicted virtual block; The optimized gravity coefficient corresponding to the predicted virtual block is obtained, an optimized coefficient spectrum is generated according to the obtained optimized gravity coefficient, and the obtained optimized coefficient spectrum is curve-marked to obtain a gravity coefficient curve.

4. The method for optimizing mineral resource exploration based on gravity forward and inversion according to claim 3 is characterized in that: The process of obtaining the segment range coefficient includes: The regional decision axis is set according to the obtained optimization coefficient spectrum, and the obtained dynamic decision axis is uploaded to the optimization coefficient spectrum; Setting an activity interval, and adjusting the regional decision axis according to the obtained activity interval to obtain a dynamic decision axis; The gravity coefficient curve is adjusted and determined according to the obtained dynamic judgment axis to obtain the section range coefficient.

5. The method for optimizing mineral resource exploration based on gravity forward and inversion according to claim 4 is characterized in that: The process of obtaining an anomalous gravity area includes: According to the spatial coordinate data, the predicted virtual block is positioned to obtain the virtual spatial coordinates, the optimized gravity coefficient is positionally matched according to the virtual spatial coordinates, and the successfully matched optimized gravity coefficient is uploaded to the corresponding virtual spatial coordinates; According to the section range coefficient, the optimized gravity coefficient at the virtual space coordinate is range-differentiated to obtain the coordinate range coefficient; The obtained coordinate range coefficients are linked at the same level to obtain a link circle of coefficients at the same level, and an abnormal survey is performed on the virtual target area according to the link circle of coefficients at the same level to obtain an abnormal gravity area.

6. The method for optimizing mineral resource exploration based on gravity forward and inversion according to claim 5, characterized in that: The process of performing anomaly survey on the virtual target area based on the same-level coefficient link circle includes: Based on the virtual target area, the color of the same-level coefficient link circles of the predicted virtual block is compared to obtain the block color circle sequence; Set a virtual block for comparison, and obtain a block color circle sequence corresponding to all adjacent predicted virtual blocks of the virtual block for comparison; According to the block color circle sequence of the compared virtual block, the block color circle sequences corresponding to the adjacent predicted virtual blocks are compared to obtain the arrangement coincidence, and a safety judgment is made on the arrangement coincidence to obtain the abnormal color block area, and the predicted target block corresponding to the abnormal color block area is obtained, which is recorded as the abnormal gravity area.

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