Iron-rich ore exploration method and system with mutual constraint of multiple exploration means

Abstract

The invention belongs to the technical field of iron-rich ore exploration, and discloses an iron-rich ore exploration method and system with mutual constraint of multiple exploration means. The method comprises the steps of information integration and anomaly primary selection, and completing aeronautical geophysical prospecting anomaly selection; completing ground geophysical prospecting abnormal section selection; geophysical prospecting section abnormal point selection is completed; checking on a drilling logging point is completed; and iron ore resource comprehensive evaluation is carried out based on drilling and logging abnormity verification results. According to the method, fusion comparative analysis is carried out by utilizing aviation magnetic method measurement and aviation gravity measurement results, gravity and magnetic homology is analyzed, regional geology, geophysical prospecting, mineral product and other result data are compared, skarn type rich iron ore exploration and area selection are carried out, and then the success rate of exploration work area selection is increased. By analyzing the physical property characteristics of the skarn type iron-rich ore and summarizing the exploration work experience of the type of iron ore in recent years, the skarn type iron-rich ore multi-element exploration means are optimized, and the multi-element exploration means optimization combination in the iron-rich ore exploration work in a certain area is extracted.

Description

Technical Field

[0001] The present invention belongs to the technical field of rich iron ore exploration, and particularly relates to a rich iron ore exploration method and system with mutual constraints among multiple exploration means. Background Technique

[0002] With the in-depth advancement of mineral exploration work, most exploration work has shifted from the previous exposed areas and shallowly covered areas of ore deposits to deeply covered areas (even ultra-deeply covered areas), resulting in increasing difficulty in prospecting and higher exploration costs. The main genetic type of iron ore is sedimentary metamorphic iron ore (total iron grade 25% - 30%, belonging to lean iron ore), accounting for about 60%. The main genetic type of rich iron ore (total iron grade greater than 50%, belonging to rich iron ore) is skarn type (also called contact metasomatic type), accounting for about 15%. Therefore, the reserves of this type of iron ore account for a very small proportion and are extremely limited in distribution. In previous exploration work, magnetic method and gravity method were mostly used for inversion analysis of skarn-type rich iron ore, and the exploration effect was better in the exposed areas and shallowly covered areas of the ore deposit, but the exploration effect in (ultra-)deeply covered areas was greatly reduced, and the ore discovery rate was extremely low. Summary of the Invention

[0003] To overcome the problems existing in the related technologies, the disclosed embodiments of the present invention provide a rich iron ore exploration method and system with mutual constraints among multiple exploration means.

[0004] The technical solution is as follows: A rich iron ore exploration method with mutual constraints among multiple exploration means, including the following steps:

[0005] S1, Information integration and preliminary anomaly selection. By collecting geological minerals, geophysical and geochemical exploration, drilling, and scientific research data, analyze the hidden deep geological unit information of the iron ore deposit in the target area, and conduct preliminary anomaly selection for the hidden deep geological unit information of the iron ore deposit in the target area;

[0006] S2, Based on the information integration and preliminary anomaly selection results, determine the key areas of geophysical anomalies through areal airborne gravity survey and areal airborne magnetic survey, and complete the selection of areas for airborne geophysical anomalies;

[0007] S3, Based on the determined key areas of geophysical anomalies, use areal ground magnetic survey and areal ground gravity survey to determine the iron ore prospecting target areas, and complete the selection of sections for ground geophysical anomalies;

[0008] S4, Based on the determined iron ore prospecting target areas, conduct large-scale gravity profile survey, large-scale magnetic profile survey, large-scale electrical profile survey, and two-dimensional seismic profile survey to locate the favorable positions where iron ore bodies occur, and complete the selection of points for geophysical profile anomalies;

[0009] S5. Based on the favorable occurrence parts of iron ore bodies determined by positioning, carry out drilling and verification of logging anomalies, complete the verification at the borehole logging points, and conduct a comprehensive evaluation of iron ore resources based on the results of drilling and logging anomaly verification; among them, the constraints for drilling are: accurately detect deep geological units and clarify deep alteration characteristics; the constraints for logging are: detect abnormal parts deep in the borehole and beside the borehole.

[0010] In step S3, complete the selection of sections for surface geophysical anomalies, including:

[0011] After providing areal airborne gravity measurement and areal airborne magnetic measurement, conduct bedrock geological mapping, and then extract ore-controlling elements to determine iron ore prospecting target areas.

[0012] In step S4, the constraints for large-scale gravity profile measurement are all: delineate high-value parts of gravity anomalies and high-value areas of residual gravity anomalies;

[0013] The constraints for large-scale magnetic profile measurement are all: delineate high magnetic anomaly and high-value areas of reduced-to-pole anomalies, high values and conversion parts of the first and second vertical derivatives of reduced-to-pole;

[0014] The constraints for large-scale electrical profile measurement are: delineate the conversion parts of deep high-low group anomalies; the constraints for two-dimensional seismic profile measurement include delineating the contact zone between Ordovician strata and intermediate-basic intrusive rocks;

[0015] Complete the selection of points for geophysical profile anomalies, including: by fusing the information of large-scale gravity profile measurement, large-scale magnetic profile measurement, large-scale electrical profile measurement, and two-dimensional seismic profile measurement, obtain the positioning information of deep high-magnetic, high-density, low-resistance, and high-polarization iron ore bodies.

[0016] Furthermore, obtaining the positioning information of deep high-magnetic, high-density, low-resistance, and high-polarization iron ore bodies includes:

[0017] Step 1. Obtain the multi-mode profile measurement fusion network topology and the information of gravity profile, magnetic profile, electrical profile, and two-dimensional seismic profile measurement;

[0018] Step 2. Construct an extended positioning map G T (V,A) of the multi-mode profile measurement fusion network; where V represents one of the high-magnetic, high-density, low-resistance, and high-polarization characteristics in the attributes of deep iron ore bodies, and A represents one of the high-magnetic, high-density, low-resistance, and high-polarization characteristics in the set of virtual mapping distances of the extended positioning map of the attributes of deep iron ore bodies;

[0019] Step 3. Calculate the virtual source vertex in the extended positioning map of the attributes of deep iron ore bodies to the virtual destination vertex The minimum mapping distance segmentation set R;

[0020] Step 4, calculate the diffuse scattering path distance set of each mapping distance in the minimum mapping distance segmentation set R;

[0021] Step 5, according to the diffuse scattering path distance set path(i), r i ∈R, calculate the extended characterization value t i of the deep iron-rich ore body attribute feature positioning index that characterizes the key degree of the mapping distance r c (i), the smaller t c (i), the more critical the mapping distance r i ;

[0022] Step 6, according to the extended characterization value of the mapping distance, calculate the key deep iron-rich ore body attribute feature positioning path set S.

[0023] In step 1, obtain the multi-mode profile measurement fusion network topology and gravity profile, magnetic method profile, electrical method profile, and two-dimensional seismic profile measurement information, including:

[0024] (1) Initialize the multi-mode profile measurement fusion network multi-mode profile measurement V1 = {v1, v2…v n} of the gravity profile, magnetic method profile, electrical method profile, and two-dimensional seismic profile measurement source nodes v 1s , v 2s , v 3s , v 4s ; Initialize the destination nodes v of the gravity profile, magnetic method profile, electrical method profile, and two-dimensional seismic profile measurement 1d , v 2d , v 3d , v 4d The number K of diffuse scattering paths;

[0025] (2) Divide the measurement period [0, t total of the multi-mode profile measurement fusion network into T equal-area size interval zones, and the area of each size interval is τ = t total / T;

[0026] (3) According to the measurement trajectories of the multi-mode profile measurement, calculate the superposition of different profile measurement mode nodes in each size interval zone.

[0027] In step 2, construct a deep iron-rich ore body attribute feature extended positioning map G T (V, A) to model the multi-mode profile measurement fusion network, including:

[0028] (1) Initialize a blank T-layer directed graph, where the kth layer represents the kth size interval zone of the multi-mode profile measurement fusion network;

[0029] (2) Traverse all the multi-modal profile measurements in the multi-modal profile measurement set V1, and for each observed multi-modal profile measurement v n ∈ V1, add a vertex to the extended localization map of the deep iron-rich ore body attribute characteristics Among them, the vertex is located in the k-th layer of the extended localization map of the deep iron-rich ore body attribute characteristics, indicating the multi-modal profile measurement v in the k-th size interval area n , and denote the set of multi-modal profile measurement vertices in the extended localization map of the deep iron-rich ore body attribute characteristics as

[0030] (3) Add mapping distance: For any pair of multi-modal profile measurement nodes v i and v j , if two multi-modal profile measurements overlap in the k-th size interval area, then add a mapping distance in the extended localization map of the deep iron-rich ore body attribute characteristics Denote the set of mapping distances in the extended localization map of the deep iron-rich ore body attribute characteristics as For a certain profile measurement method v in the multi-modal profile measurement i and another profile measurement method v j are superimposed in the size interval area k, 1 ≤ k ≤ T;

[0031] (4) Add storage mapping distance: For the multi-modal profile measurement v n ∈ V1, add a storage mapping distance in the extended localization map of the deep iron-rich ore body attribute characteristics Denote the set of storage mapping distances in the extended localization map of the deep iron-rich ore body attribute characteristics as

[0032] (5) Add virtual source vertex Virtual destination vertex As the source and destination vertices on the extended localization map of the deep iron-rich ore body attribute characteristics, add virtual mapping distances and to connect the virtual source and destination vertices to the source and destination nodes in the network, and denote the set of virtual mapping distances in the extended localization map of the deep iron-rich ore body attribute characteristics as

[0033] (6) Let the extended localization map of the deep iron-rich ore body attribute characteristics be G T (V, A), where A = A t ∪ A s ∪ A vir , Among them, serves as the source vertex, As the target vertex.

[0034] In step three, calculate the virtual source vertex in the extended positioning map of the deep iron-rich ore body attribute characteristics to the virtual target vertex of the minimum mapping distance segmentation set R, including:

[0035] 1) Set the mapping distance capacity of all in the extended positioning map of the deep iron-rich ore body attribute characteristics to 1, for let c(u, v) = 1;

[0036] 2) Set the storage mapping distance and virtual mapping distance capacity in the extended positioning map of the deep iron-rich ore body attribute characteristics to infinity, for let c(u, v) = ∞;

[0037] 3) Use the Ford–Fulkerson algorithm to calculate the minimum cut set between the virtual source vertex and the virtual target vertex in the extended positioning map of the deep iron-rich ore body attribute characteristics, and name this set the minimum mapping distance segmentation set R.

[0038] In step four, calculate the diffused path distance set of each mapping distance in the minimum mapping distance segmentation set R, including:

[0039] (a) Initialize the diffused path distance set of each mapping distance in the minimum mapping distance segmentation set R, that is, for let where r i is the i-th mapping distance in the minimum mapping distance segmentation set R, and path(i) is the diffused path distance set of r i ;

[0040] (b) Calculate the residual graph G s of the extended positioning map of the deep iron-rich ore body attribute characteristics excluding the minimum mapping distance segmentation set, let G s = GT / R;

[0041] (c) For perform the following steps to update its diffused path distance set path(i):

[0042] (c1) Calculate the shortest path p s between vertices i and through the mapping distance r in the graph G1 = G i ∪ {r ii}, and the number of layers t ii passed by this path in the extended positioning map of the deep iron-rich ore body attribute characteristicsDenoted as the extended representation of this path, let path(i) = path(i) ∪ {p ii};

[0043] (c2) For in G2 = G s ∪{r i , r j}, calculate the shortest path p to through the mapping distance r i ; if p ij satisfies: ① the extended representation value t ij < t ij , ② t ii = min{t ij , t 1j , t 2j … t nj}, let p ij be the diffuse scattering path distance of R(i), let path(i) = path(i) ∪ {p ij}, where, {t 1j , t 2j … t nj} is the set of extended representations of the paths using r j ;

[0044] (c3) Sort all the paths of path(i) calculated in ascending order of the extended representation to form the diffuse scattering path distance set

[0045] In step five, according to the diffuse scattering path distance set path(i), r i ∈ R, calculate the extended representation value t i of the deep iron-rich orebody attribute feature localization index that characterizes the mapping distance r c (i), including:

[0046] (A) Use Done(i) to represent whether the extended representation value of the mapping distance r i is determined, initialize this index, that is

[0047] (B) Let R1 = {r i | Done(i) = 0}, if For Go to step (C); if Go to step (D);

[0048] (C) For If Compare and t c(i);

[0049] If then reorder the paths in path(j);

[0050] If then modify

[0051] (D) For the mapping r where the shortest paths in the set of diffuse scattering path distances are the same i , r j , that is, for compare the extended characterization values of the second shortest path and

[0052] If then

[0053] If then

[0054] In step six, according to the extended characterization values of the mapping distances, calculate the set S of key deep iron-rich orebody attribute feature positioning paths, including:

[0055] (i) Sort all the mapping distances in R in ascending order of the extended characterization values, that is: where t c (n i ) is the extended characterization value of the mapping distance ;

[0056] (ii) Take the first K mapping distances in R, and the expression is: {r1, r2…r K |t c (1) ≤ t c (2) … ≤ t c (K)};

[0057] (iii) Sort the mapping distances r1, r2…r K in ascending order of the values of the size interval areas where they are located in the deep iron-rich orebody attribute feature extended positioning map to obtain the key mapping distance sequence, and the expression is:

[0058] (iv) Convert the key mapping distance sequence into the set of key deep iron-rich orebody attribute feature positioning paths: convert into (v li , v lj , t l ), indicating the size interval area t lMulti-mode profile measurement v li v li To multi-mode profile measurement v lj The positioning path, where t l Represents the value of the dimensional interval area where the positioning path is located; that is, S = {(v 1i , v 1j , t1), (v 2i , v 2j , t2)…(v Ki , v Kj , t K ) | t1 ≤ t2… ≤ t K};

[0059] Through the above positioning path set of the key deep iron-rich orebody attribute characteristics, obtain any one of the attribute characteristics of the positioning information of the high-magnetic, high-density, low-resistance, and high-polarization deep iron-rich orebody.

[0060] Another object of the present invention is to provide an iron-rich ore exploration system with mutual constraints of multiple exploration means. The system implements the iron-rich ore exploration method with mutual constraints of the multiple exploration means. The system includes:

[0061] An information integration and anomaly pre-selection module for information integration and anomaly pre-selection. By collecting geological minerals, geophysical and geochemical exploration, drilling, and scientific research data, analyze the hidden deep geological unit information of the iron ore deposit in the target area, and conduct anomaly pre-selection of the hidden deep geological unit information of the iron ore deposit in the target area;

[0062] An airborne geophysical anomaly selection area module. Based on the information integration and anomaly pre-selection results, determine the key areas of geophysical anomalies through area airborne gravity measurement and area airborne magnetic measurement, and complete the selection of the airborne geophysical anomaly area;

[0063] A ground geophysical anomaly selection section module. Based on the determined key areas of geophysical anomalies, use area ground magnetic measurement and area ground gravity measurement to determine the iron ore prospecting target area and complete the selection of the ground geophysical anomaly section;

[0064] A geophysical profile anomaly selection point module. Based on the determined iron ore prospecting target area, conduct large-scale gravity profile measurement, large-scale magnetic profile measurement, large-scale electrical profile measurement, and two-dimensional seismic profile measurement to locate the favorable positions where the iron ore body occurs and complete the selection of geophysical profile anomaly points;

[0065] A verification and evaluation module. Based on the favorable positions where the iron ore body occurs, conduct drilling and well logging anomaly verification to complete the verification at the borehole logging points, and conduct comprehensive evaluation of iron ore resources based on the results of drilling and well logging anomaly verification; among them, the constraints of drilling are: accurately detect deep geological units and clarify deep alteration characteristics; the constraints of well logging are: detect abnormal parts deep in the borehole and beside the borehole.

[0066] Combining all the above technical solutions, the beneficial effects of the present invention are as follows: The present invention uses the results of aeromagnetic measurement and aerogravity measurement for integrated comparative analysis, analyzes the gravity and magnetic homology, and at the same time compares the regional geology, geophysical exploration, mineral resources and other result data to select areas for exploration of skarn-type rich iron ore, thereby improving the success rate of area selection for exploration work. By analyzing the physical properties of skarn-type rich iron ore and summarizing the exploration work experience of this type of iron ore in recent years, multiple exploration methods for skarn-type rich iron ore are optimized, and their advantages and disadvantages are compared; the optimized combination of multiple exploration methods in the exploration work of rich iron ore in a certain area is refined.

[0067] Conduct a comparative analysis of the measurement results of multiple exploration methods for rich iron ore, and conduct mutual constraint analysis on the occurrence positions of suspected rich iron ore bodies indicated by various methods to achieve precise positioning of the occurrence position of the real iron ore body under (ultra)-deep overburden areas. Combining the information technology achievements of geology, geophysical exploration (techniques such as gravity, magnetism, electro-magnetism, seismic and logging), drilling, comprehensive research, etc., a prospecting technology system for rich iron ore with mutual constraints of multiple exploration methods in (ultra)-deep overburden areas is proposed, in order to improve the exploration success rate of skarn-type rich iron ore. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing the embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure;

[0069] Figure 1 is a flowchart of a rich iron ore exploration method with mutual constraints of multiple exploration methods provided by an embodiment of the present invention;

[0070] Figure 2 is a flowchart of constructing a prospecting technology system for rich iron ore with mutual constraints of multiple exploration methods in (ultra)-deep overburden areas provided by an embodiment of the present invention;

[0071] Figure 3 is a schematic diagram of the principle of a prospecting technology system for skarn-type rich iron ore in deep overburden areas with mutual constraints of multiple exploration methods provided by an embodiment of the present invention;

[0072] Figure 4 is a distribution map of iron ore bodies in a certain city provided by an embodiment of the present invention;

[0073] Figure 5 is a schematic diagram of a rich iron ore exploration system with mutual constraints of multiple exploration methods provided by an embodiment of the present invention;

[0074] In the figure: 1. Information integration and anomaly preliminary selection module; 2. Aerogeophysical anomaly area selection module; 3. Ground geophysical anomaly section selection module; 4. Geophysical profile anomaly point selection module; 5. Verification and evaluation module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0075] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following provides a detailed description of the specific embodiments of the present invention in conjunction with the accompanying drawings. Many specific details are set forth in the following description to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0076] Example 1, as Figure 1 shown, the method for exploring rich iron ore with mutual constraints of multiple exploration means provided by the embodiment of the present invention includes:

[0077] S1. Information integration and preliminary anomaly selection. By collecting geological minerals, geophysical and geochemical exploration, drilling, and scientific research data, analyze the information of deep geological units hidden in the iron ore deposit in the target area, and conduct preliminary anomaly selection for the information of deep geological units hidden in the iron ore deposit in the target area;

[0078] S2. Based on the results of information integration and preliminary anomaly selection, determine the key areas of geophysical anomalies through area airborne gravity measurement and area airborne magnetic measurement, and complete the selection of areas for airborne geophysical anomalies;

[0079] S3. Based on the determined key areas of geophysical anomalies, use area ground magnetic measurement and area ground gravity measurement to determine the iron ore prospecting target areas and complete the selection of sections for ground geophysical anomalies;

[0080] S4. Based on the determined iron ore prospecting target areas, conduct large-scale gravity profile measurement, large-scale magnetic profile measurement, large-scale electrical profile measurement, and two-dimensional seismic profile measurement to locate the favorable positions where iron ore bodies occur, and complete the selection of points for anomalies in geophysical profiles;

[0081] S5. Based on the located favorable positions where iron ore bodies occur, conduct drilling and logging anomaly verification to complete the verification at the borehole logging points, and conduct comprehensive evaluation of iron ore resources based on the results of drilling and logging anomaly verification; among them, the constraint conditions for drilling are: accurately detect deep geological units and clarify deep alteration characteristics; the constraint conditions for logging are: detect abnormal parts in the deep part of the borehole and beside the borehole.

[0082] Exemplarily, in step S3, further including completing the selection of sections for ground geophysical anomalies: after area airborne gravity measurement and area airborne magnetic measurement provided by step S2, conduct bedrock geological mapping, and then extract ore-controlling elements to determine the iron ore prospecting target areas;

[0083] The constraint conditions for conducting bedrock geological mapping include: determining the distribution ranges of late intermediate-basic intrusive rocks, Ordovician carbonate rock strata, and Carboniferous-Permian strata in a certain mountain.

[0084] In step S2, the constraints for areal airborne gravity survey, step S3, large-scale gravity profile survey, and step S4, large-scale gravity profile survey, all include: delineating the high-value areas of gravity anomalies and the high-value areas of residual gravity anomalies;

[0085] In step S2, the constraints for areal airborne magnetic survey, step S3, areal ground magnetic survey, and step S4, large-scale magnetic profile survey, all include: delineating the high magnetic anomaly areas and the high-value areas of reduced pole anomalies, the high-value areas and conversion parts of the first and second vertical derivatives of reduced pole.

[0086] The constraints for the large-scale electrical profile survey in step S4 include delineating the conversion parts of deep high and low anomaly groups;

[0087] The constraints for two-dimensional seismic profile survey include delineating the contact zone between Ordovician strata and intermediate-basic intrusive rock bodies;

[0088] The constraints for the drilling in step S5 include accurately detecting deep geological units and clarifying deep alteration characteristics;

[0089] The constraints for well logging include detecting abnormal parts deep in the borehole and beside the borehole.

[0090] Furthermore, in step S4, based on the determined iron ore prospecting target area, large-scale gravity profile survey, large-scale magnetic profile survey, large-scale electrical profile survey, and two-dimensional seismic profile survey are carried out. The favorable positions for the occurrence of iron ore bodies include: by fusing the information of large-scale gravity profile survey, large-scale magnetic profile survey, large-scale electrical profile survey, and two-dimensional seismic profile survey, obtaining the positioning information of deep high-magnetic, high-density, low-resistance, and high-polarization iron ore bodies.

[0091] Furthermore, the method for obtaining the positioning information of deep high-magnetic, high-density, low-resistance, and high-polarization iron ore bodies by fusing the information of large-scale gravity profile survey, large-scale magnetic profile survey, large-scale electrical profile survey, and two-dimensional seismic profile survey includes:

[0092] Step 1, obtaining the fusion network topology of multi-modal profile surveys and the information of gravity profile, magnetic profile, electrical profile, and two-dimensional seismic profile surveys;

[0093] Step 2, constructing the extended positioning map G T (V,A) to model the fusion network of multi-modal profile surveys; where V represents one of the high-magnetic, high-density, low-resistance, and high-polarization characteristics in the attributes of deep iron ore bodies, and A represents one of the high-magnetic, high-density, low-resistance, and high-polarization characteristics in the set of virtual mapping distances of the extended positioning map of the attributes of deep iron ore bodies;

[0094] Step 3: Calculate the virtual source vertex in the extended positioning map of the deep iron-rich ore body attributes to the virtual destination vertex of the minimum mapping distance segmentation set R;

[0095] Step 4: Calculate the diffuse scattering path distance set for each mapping distance in the minimum mapping distance segmentation set R;

[0096] Step 5: According to the diffuse scattering path distance set path(i), r i ∈R, calculate the extended characterization value t i of the deep iron-rich ore body attribute feature positioning index that characterizes the critical degree of the mapping distance r c (i), the smaller t c (i), the more critical the mapping distance r i ;

[0097] Step 6: Calculate the critical deep iron-rich ore body attribute feature positioning path set S according to the extended characterization value of the mapping distance.

[0098] Exemplarily, the specific steps for Step 1 to obtain the multi-mode profile measurement fusion network topology and the measurement information of the gravity profile, magnetic profile, electrical profile, and two-dimensional seismic profile include:

[0099] (1) Initialize the multi-mode profile measurement fusion network multi-mode profile measurement V1 = {v1, v2…v n} and the source nodes v 1s , v 2s , v 3s , v 4s of the gravity profile, magnetic profile, electrical profile, and two-dimensional seismic profile measurements; Initialize the destination nodes v 1d , v 2d , v 3d , v 4d of the gravity profile, magnetic profile, electrical profile, and two-dimensional seismic profile measurements and the number of diffuse scattering paths K;

[0100] (2) Divide the measurement period [0, total of the multi-mode profile measurement fusion network into T equal-area size interval zones, and the area of each size interval is τ = t total / T;

[0101] (3) Calculate the superposition of the nodes of different profile measurement methods in each size interval zone according to the measurement trajectory of the multi-mode profile measurement.

[0102] In Step 2, construct the extended positioning map G T (V, A) of the deep iron-rich ore body attribute features to model the multi-mode profile measurement fusion network, including:

[0103] (1) Initialize a blank directed graph of T layers, where the k-th layer represents the k-th dimensional interval area of the multi-modal profile measurement fusion network;

[0104] (2) Traverse all multi-modal profile measurements in the multi-modal profile measurement set V1. For each observed multi-modal profile measurement v n ∈V1, add a vertex to the extended localization map of the deep iron-rich ore body attribute characteristics where the vertex is located in the k-th layer of the extended localization map of the deep iron-rich ore body attribute characteristics, representing the multi-modal profile measurement v n in the k-th dimensional interval area. Denote the set of multi-modal profile measurement vertices in the extended localization map of the deep iron-rich ore body attribute characteristics as

[0105] (3) Add mapping distances: For any pair of multi-modal profile measurement nodes v i and v j , if two multi-modal profile measurements overlap in the k-th dimensional interval area, then add a mapping distance to the extended localization map of the deep iron-rich ore body attribute characteristics. Denote the set of mapping distances in the extended localization map of the deep iron-rich ore body attribute characteristics as If a certain profile measurement method v i in the multi-modal profile measurement and another profile measurement method v j are superimposed in the dimensional interval area k, 1 ≤ k ≤ T;

[0106] (4) Add stored mapping distances: For the multi-modal profile measurement v n ∈V1, add a stored mapping distance to the extended localization map of the deep iron-rich ore body attribute characteristics. Denote the set of stored mapping distances in the extended localization map of the deep iron-rich ore body attribute characteristics as

[0107] (5) Add virtual source vertices and virtual destination vertices as the source and destination vertices on the extended localization map of the deep iron-rich ore body attribute characteristics. Add virtual mapping distances and to connect the virtual source and destination vertices to the source and destination nodes in the network. Denote the set of virtual mapping distances in the extended localization map of the deep iron-rich ore body attribute characteristics as

[0108] (6) Let the extended localization map of the deep iron-rich ore body attribute characteristics be G T (V, A), where A = A t ∪A s ∪Avir , Among them, serves as the source vertex, serves as the destination vertex.

[0109] Another exemplary one is that in Step 3, calculate the minimum mapping distance segmentation set R from the virtual source vertex to the virtual destination vertex in the deep iron-rich ore body attribute feature expansion positioning map, including:

[0110] 1) Set the mapping distance capacity of all in the deep iron-rich ore body attribute feature expansion positioning map to 1, and for let c(u, v) = 1;

[0111] 2) Set the stored mapping distance and virtual mapping distance capacity in the deep iron-rich ore body attribute feature expansion positioning map to infinity, and for let c(u, v) = ∞;

[0112] 3) Use the Ford–Fulkerson algorithm to calculate the minimum cut set between the virtual source vertex and the virtual destination vertex in the deep iron-rich ore body attribute feature expansion positioning map, and name this set the minimum mapping distance segmentation set R.

[0113] Another exemplary one is that in Step 4, calculate the diffuse scattering path distance set of each mapping distance in the minimum mapping distance segmentation set R, including:

[0114] (a) Initialize the diffuse scattering path distance set of each mapping distance in the minimum mapping distance segmentation set R, that is, for let where r i is the i-th mapping distance in the minimum mapping distance segmentation set R, and path(i) is the diffuse scattering path distance set of r i ;

[0115] (b) Calculate the residual graph G s of the deep iron-rich ore body attribute feature expansion positioning map after excluding the minimum mapping distance segmentation set, and let G s = G T / R;

[0116] (c) For execute the following steps to update its diffuse scattering path distance set path(i):

[0117] (c1) Calculate the vertex s to i in the graph G1 = G ∪ {r } through the mapping distance ri The shortest path p ii , and record the number of layers t passed by this path in the deep rich iron ore body attribute characteristic expansion positioning map ii as the expansion representation of this path, and let path(i) = path(i) ∪ {p ii};

[0118] (c2) For calculate the shortest path p s between vertices in G2 = G i ∪{r j , r to through the mapped distance r i ; If p ij satisfies: ① The expansion representation value t ij < t ij , ② t ii = min{t ij , t 1j … t 2j … t nj}, let p ij be the diffuse scattering path distance of R(i), and let path(i) = path(i) ∪ {p ij}, where, {t 1j , t 2j … t nj} is the set of expansion representations of the paths using r j ;

[0119] (c3) Sort all the paths of path(i) calculated according to the expansion representation from small to large to form the diffuse scattering path distance set

[0120] Another exemplary one is that in step five, according to the diffuse scattering path distance set path(i), r i ∈R, calculate the expansion representation value t i of the deep rich iron ore body attribute characteristic positioning index that characterizes the key degree of the mapped distance r c (i), including:

[0121] (A) Use Done(i) to represent whether the expansion representation value of the mapped distance r i is determined, and initialize this index, that is

[0122] (B) Let R1 = {r i | Done(i) = 0}, if For go to step (C); if go to step (D);

[0123] (C) For If Compare with t c (i);

[0124] If Then Re - order the paths in path(j);

[0125] If Modify to

[0126] (D) For the mapping r where the shortest paths in the set of diffuse - scattering path distances are the same i , r j , that is, for Compare the extended characterization value of the second - shortest path and

[0127] If Then

[0128] If Then

[0129] Another exemplary one is that in step six, according to the extended characterization value of the mapping distance, calculate the set S of key deep - seated rich iron ore body attribute feature positioning paths, including:

[0130] (i) Sort all the mapping distances in R in ascending order of the extended characterization value, that is: R = where t c (n i ) is the extended characterization value of the mapping distance ;

[0131] (ii) Take the first K mapping distances in R, and the expression is: {r1, r2…r K |t c (1) ≤ t c (2) … ≤ t c (K)};

[0132] (iii) Sort the mapping distances r1, r2…r K in ascending order of the value of the size interval area where they are located in the extended positioning map of the deep - seated rich iron ore body attribute features to obtain the key mapping distance sequence, and the expression is:

[0133] (iv) Convert the key mapping distance sequence into the set of key deep - seated rich iron ore body attribute feature positioning paths: Convert Converted to (v li , v lj , t l ), indicating the dimension interval t l in the multi-modal profile measurement v li v li to the multi-modal profile measurement v lj of the positioning path, where t l represents the value of the dimension interval where the positioning path is located; that is, S = {(v 1i , v 1j , t1), (v 2i , v 2j , t2) … (v Ki , v Kj , t K ) | t1 ≤ t2 … ≤ t K};

[0134] Through the above set of positioning paths of the key deep iron-rich ore body attribute characteristics, any one of the attribute characteristics of the positioning information of the high-magnetic, high-density, low-resistance, and high-polarization deep iron-rich ore body is obtained.

[0135] As can be seen from the above embodiments, the present invention embeds the local relationship between nodes in the network into the node vector representation in a fast iterative manner;

[0136] Uses the node information with partial profile measurement marks to enhance the network structure, improves the network structure quality as much as possible, and improves the model classification performance;

[0137] The present invention makes full use of the node attribute information and the partial node profile measurement mark information to enhance the network structure, and is committed to designing a model with higher classification performance.

[0138] The present invention uses the deep iron-rich ore body attribute characteristics to expand the positioning graph to represent the multi-modal profile measurement fusion network, eliminating the disadvantage that the time-varying graph cannot model the cross-snapshot gravity profile, magnetic profile, electrical profile, and two-dimensional seismic profile measurement. The present invention uses the deep iron-rich ore body attribute characteristics to expand the positioning graph to represent the multi-modal profile measurement fusion network, and converts the set of key deep iron-rich ore body attribute characteristic positioning paths composed of K links between the source node and the destination node in the multi-modal profile measurement fusion network into solving the key mapping distance set between the vertex corresponding to the source node and the vertex corresponding to the destination node on the deep iron-rich ore body attribute characteristic expanded positioning graph G T (V, A) to solve the vertex corresponding to the source node to the vertex corresponding to the destination node of the key mapping distance set.

[0139] Based on the characteristics that the minimum mapping distance segmentation set can represent all paths between source vertices and destination vertices and contains the smallest number of mapping distances, the present invention can narrow the search space and effectively reduce the calculation size by detecting the key deep iron ore body attribute features in the minimum mapping distance segmentation set to locate the path set. The algorithm proposed by the present invention can not only give the key deep iron ore body attribute feature location path set when a given number of key links is provided, but also obtain the deep iron ore body attribute feature location information.

[0140] Example 2, as another implementation manner of the present invention, as Figure 2 shown, the construction of the iron ore prospecting technical system with mutual constraints of multiple exploration means in the (ultra) deep coverage area includes:

[0141] A. Define the exploration idea of skarn-type iron ore in the deep coverage area.

[0142] Fully analyze the previous exploration result data. In view of the large burial depth of the iron ore deposit in the target area, it is necessary to fully analyze the hidden deep geological unit information, and initially establish the exploration idea of "selecting areas on the surface → selecting sections within the area → selecting points within the section → evaluating at the points".

[0143] Exemplarily, the area selection on the surface is the selection of areas based on airborne geophysical anomalies, including: area airborne gravity measurement, area airborne magnetic method measurement, and primary selection of integrated information anomalies;

[0144] The section selection within the area is the selection of sections based on ground geophysical anomalies, including: determining the key iron ore prospecting sections through area ground magnetic method measurement, area ground gravity measurement, and research on ore-controlling factors / mineralization laws;

[0145] The point selection within the section is the selection of points based on geophysical profile anomalies, including locating deep iron ore bodies, and electrical method profiles, seismic profiles, gravity profiles, and magnetic profiles can be used;

[0146] The evaluation at the points is to verify the anomalies through borehole logging, including: drilling construction, geophysical well logging, and deep resource evaluation.

[0147] B. Sort out the metallogenic conditions and ore-hosting positions of this type of iron ore.

[0148] Regional iron ore exploration work and research on mineralization laws show that the skarn-type iron ore bodies in the working area are distributed in the ore storage spaces such as the contact zone between the late Yanshanian intermediate-basic intrusive rock mass and the Ordovician carbonate rock formation and the tectonic system connected therewith.

[0149] C. Optimize the geological and geophysical exploration means for this type of iron ore.

[0150] In view of the physical properties of this type of iron ore, such as “high magnetism, high density, low resistance and high polarization”, the preferred exploration methods include “special geological survey, (aerial and ground) magnetic survey, (aerial and ground) gravity survey, electrical survey, seismic survey, drilling construction and geophysical well logging” (Table 1).

[0151] Table 1 Regional skarn-type iron-rich ore prospecting techniques and constraints

[0152]

[0153]

[0154] D. Establish the exploration process and precautions for this type of rich iron ore.

[0155] In the exploration process of skarn-type rich iron ore in deep coverage areas, the exploration process of rich iron ore is carried out with the following features: "comprehensive research and full guidance - information integration anomaly preliminary selection - aerogeophysical anomaly selection - ground geophysical anomaly selection - geophysical profile anomaly selection - borehole logging verification of anomaly", so as to build a rich iron ore prospecting technology system with mutual constraints of multiple exploration methods in (ultra) deep coverage areas. At the same time, during the exploration process, it is necessary to continuously compare and analyze and comprehensively study the technical advantages of various prospecting methods to obtain the best prospecting effect.

[0156] Advantages of the method of the present invention: The iron-rich ore prospecting technology system with mutually constrained multiple exploration means avoids the one-sidedness of measurement of a single or a few methods to a great extent, and focuses on giving full play to the unique advantages of various exploration methods (such as the joint extraction of deep mine-induced anomaly geophysical exploration methods, etc.). Multiple exploration methods constrain each other and verify each other, and ultimately accurately locate the deep iron ore body, thereby achieving the optimization of skarn-type iron-rich ore areas and improving the success rate of exploration.

[0157] It can be seen from the above embodiments that the present invention has achieved major breakthroughs in prospecting and progress in exploration in the iron-rich ore integrated exploration area in the northwest of a certain province by utilizing the prospecting technology system; among them, 5 prospecting target areas have been delineated, 3 iron ore producing areas such as a certain village have been newly discovered, thick and large iron ore bodies have been encountered, and the average grades of total iron are approximately 52% and 60%, respectively, which are rare high-grade iron-rich ores in recent years. The predicted iron ore resources are approximately 153 million tons, and the prospective resources in the entire area are 384 million tons, which has achieved a major breakthrough in the prospecting of iron-rich ore and provided a typical demonstration for deep prospecting of iron ore in a certain province and the eastern region.

[0158] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0159] For the information interaction, execution process, etc. between the above-mentioned devices / units, since they are based on the same concept as the method embodiments of the present invention, for their specific functions and the technical effects brought about, reference may be specifically made to the method embodiment part, and details will not be elaborated here.

[0160] Those skilled in the art can clearly understand that for the convenience and conciseness of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present invention. The specific working process of the units and modules in the above system can refer to the corresponding process in the foregoing method embodiments.

[0161] The embodiment of the present invention also provides a computer device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor. When the processor executes the computer program, the steps in any of the foregoing method embodiments are implemented.

[0162] Example 3. In the process of exploring skarn-type rich iron ore in a super-deep coverage area of a certain city, the present invention successfully applied various technical means such as "geology - magnetic method - gravity method - electrical method - seismic method - drilling - logging", and established a prospecting technology system for skarn-type rich iron ore in deep coverage areas with mutual constraints of multiple exploration means ( Figure 3 ). On the basis of combining relevant exploration experience of regional minerals, the effectiveness of relevant iron ore exploration technical means was analyzed, and technical means such as comprehensive research, areal magnetic survey, areal gravity survey, large-scale comprehensive geophysical prospecting profile survey (magnetic method, gravity method, electrical method, seismic method, etc.), drilling, and logging were optimized. Through the application and demonstration of the whole-process method, a set of efficient and reliable exploration and prediction system for skarn-type rich iron ore under super-deep coverage areas was formed, providing technical support for iron ore exploration in similar areas.

[0163] Example 4. Using this prospecting technology system in the super-deep coverage area of a certain city, the present invention revealed deep iron ore bodies, delineated prospecting target areas, and newly discovered 3 ore-producing areas, providing a demonstration for deep prospecting.

[0164] (1) Under the guidance of the prospecting technology system for skarn-type high-grade iron ore in ultra-deep coverage areas, the deep iron ore bodies were accurately positioned and drilling verification was carried out. Three ore-producing areas such as a certain village were submitted, achieving a major breakthrough in deep prospecting. Among them, in the PZK1 borehole constructed in a certain village within the prospecting target area of a certain urban area, a total of 5 iron ore bodies with large thickness and high grade were discovered. The ore bodies are buried at a depth of 1,444.44 - 1,542.19 meters, occurring in layers, lenticular layers, and lenses. The cumulative thickness reaches 56.94 meters, and the average total iron grade is 51.82%, achieving a major breakthrough in prospecting. For example, Figure 4 Distribution map of iron ore bodies in a certain city.

[0165] In the DZK1 borehole constructed in a certain village, 2 iron ore bodies were exposed. The ore bodies are buried at a depth of 828.13 - 853.58 meters, occurring in layers and lenticular layers. The cumulative thickness reaches 17.53 meters, and the average total iron grade is 59.75%, opening up space for deep prospecting.

[0166] (2) During the implementation of this prospecting technology system, a total of 3 newly discovered ore-producing areas were submitted, achieving a major breakthrough in deep prospecting, providing a prospecting demonstration for the deep exploration of high-grade iron ore in similar areas. The exploration results obtained by applying this method system provide a relatively sufficient guarantee of high-grade iron ore resources for alleviating the future shortage of iron ore and provide an exploration example for the new round of strategic action for prospecting breakthroughs. At the same time, it has made an important contribution to building a "new development pattern with the domestic big cycle as the main body and the domestic and international dual cycles reinforcing each other".

[0167] Example 5, as Figure 5 shown, the high-grade iron ore exploration system with mutual constraints of multiple exploration means provided by the embodiments of the present invention includes:

[0168] Information integration and anomaly pre-selection module 1, used for information integration and anomaly pre-selection. By collecting geological minerals, geophysical and geochemical exploration, drilling, and scientific research data, analyzing the information of deep geological units hidden in the iron ore deposits in the target area, and performing anomaly pre-selection on the information of deep geological units hidden in the iron ore deposits in the target area;

[0169] Aerial geophysical anomaly selection area module 2, based on the results of information integration and anomaly pre-selection, determines the key areas of geophysical anomalies through area aerial gravity measurement and area aerial magnetic measurement, and completes the selection of areas for aerial geophysical anomalies;

[0170] Ground geophysical anomaly selection section module 3, based on the determined key areas of geophysical anomalies, uses area ground magnetic measurement and area ground gravity measurement to determine the iron ore prospecting target area and complete the selection of sections for ground geophysical anomalies;

[0171] Geophysical profile anomaly selection point module 4, based on the determined iron ore prospecting target area, conducts large-scale gravity profile measurement, large-scale magnetic profile measurement, large-scale electrical profile measurement, and two-dimensional seismic profile measurement to locate the favorable positions where iron ore bodies occur and complete the selection of points for geophysical profile anomalies;

[0172] The verification and evaluation module 5 conducts drilling and logging anomaly verification based on the favorable occurrence positions of the iron ore bodies determined by positioning, completes the verification at the borehole logging points, and conducts a comprehensive evaluation of iron ore resources based on the results of drilling and logging anomaly verification. Among them, the constraints for drilling are: accurately detecting deep geological units and clarifying deep alteration characteristics; the constraints for logging are: detecting abnormal positions in the deep part of the borehole and beside the borehole.

[0173] As mentioned above, the above are only the relatively optimal specific implementation manners of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be covered by the protection scope of the present invention.

Claims

1. A method for exploring rich iron ore with multiple exploration means constrained by each other, characterized in that: The method comprises the following steps: S1, information integration and anomaly preliminary selection, by collecting geological mineral, geophysical and geochemical exploration, drilling, and scientific research data, analyzing the deep geological unit information hidden in the iron ore deposits in the target area, and conducting preliminary selection of deep geological unit information anomalies hidden in the iron ore deposits in the target area; S2, based on information integration and anomaly preliminary selection results, determine the key areas of geophysical anomalies through area-based aerogravimetry and area-based aeromagnetic measurement, and complete the aerogeophysical anomaly selection; S3, based on the determined geophysical anomaly key areas, use area ground magnetic measurement and area ground gravity measurement to determine the iron ore prospecting target area and complete the ground geophysical anomaly selection; S4, based on the determined iron ore prospecting target area, conduct large-scale gravity profile measurement, large-scale magnetic profile measurement, large-scale electrical profile measurement, and two-dimensional seismic profile measurement to locate favorable locations for iron ore deposits and complete the selection of abnormal points for geophysical prospecting profiles; S5, based on the favorable locations of the iron ore bodies located, conduct drilling and logging anomaly verification, complete the verification on the borehole logging points, and conduct a comprehensive evaluation of the iron ore resources based on the drilling and logging anomaly verification results; among which, the constraints of drilling are: accurately detect deep geological units and clarify deep alteration characteristics; the constraints of logging are: detect abnormal locations deep in the borehole and beside the borehole.

2. The method for exploring rich iron ore with mutually constrained multiple exploration means according to claim 1, characterized in that: In step S3, the ground geophysical anomaly selection is completed, including: After providing areal aerial gravity survey and areal aerial magnetic survey, bedrock geological mapping is carried out, and then the ore-controlling elements are extracted to determine the iron ore prospecting target area.

3. The method for exploring rich iron ore with mutually constrained multiple exploration means according to claim 1, characterized in that: In step S4, the constraints of large-scale gravity profile measurement are: delineating the gravity anomaly high value areas and the remaining gravity anomaly high value areas; The constraints of large-scale magnetic profile measurement are: delineating high magnetic anomalies and high-value areas of polarization anomalies, high values ​​of the first-order and second-order derivatives of the vertical polarization, and the transition points; The constraints of large-scale electrical profile measurement are: delineating the abnormal transition position of deep high and low groups; the constraints of 2D seismic profile measurement include delineating the contact zone between Ordovician strata and intermediate-basic intrusive rock masses; Complete the selection of geophysical profile anomaly points, including: obtaining the positioning information of high-magnetism, high-density, low-resistance, and high-polarization deep iron-rich ore bodies by integrating large-scale gravity profile measurement, large-scale magnetic profile measurement, large-scale electrical profile measurement, and two-dimensional seismic profile measurement information.

4. The method for exploring rich iron ore with mutually constrained multiple exploration means according to claim 3, characterized in that: Obtain positioning information of deep iron-rich ore bodies with high magnetic properties, high density, low resistivity and high polarization, including: Step 1: Obtain multi-mode profile measurement fusion network topology and gravity profile, magnetic profile, electrical profile and two-dimensional seismic profile measurement information; Step 2: Construct an extended location map G of deep iron-rich ore body attributes T (V, A) Modeling of multi-mode profile measurement fusion network; wherein V represents one of the high magnetism, high density, low resistance, and high polarization characteristics in the deep iron-rich ore body attribute characteristics, and A represents one of the high magnetism, high density, low resistance, and high polarization characteristics in the virtual mapping distance set of the deep iron-rich ore body attribute characteristic extended positioning map; Step 3: Calculate the virtual source vertex in the extended location map of deep iron-rich ore body attributes To the virtual destination vertex The minimum mapping distance partition set R; Step 4, calculating the diffuse scattering path distance set of each mapping distance in the minimum mapping distance segmentation set R; Step 5: According to the diffuse scattering path distance set path(i), r i ∈R, calculate the representation mapping distance r i Extended characterization value of key deep iron-rich ore body attribute characteristic positioning index t c (i), t c (i) The smaller the mapping distance r i The more critical; Step six, based on the extended characterization value of the mapping distance, calculate the key deep iron-rich ore body attribute feature positioning path set S.

5. The method for exploring rich iron ore with mutually constrained multiple exploration means according to claim 4, characterized in that: In step 1, the multi-mode profile measurement fusion network topology and gravity profile, magnetic profile, electrical profile and two-dimensional seismic profile measurement information are obtained, including: (1) Initialize the multi-mode profile measurement fusion network multi-mode profile measurement V1 = {v1, v2…v n }, gravity profile, magnetic profile, electrical profile and 2D seismic profile measurement source node v 1s ,v 2s ,v 3s ,v 4s ; Initialize gravity profile, magnetic profile, electrical profile and 2D seismic profile measurement destination node v 1d ,v 2d ,v 3d ,v 4d The number of diffuse scattering paths K; (2) The measurement period [0,t total ] is divided into T equal-area size intervals, and the area of ​​each size interval is τ = t total / T; (3) Based on the measurement trajectory of multi-mode profile measurement, the superposition of nodes of different profile measurement methods in each size interval area is calculated.

6. The method for exploring rich iron ore with mutually constrained multiple exploration means according to claim 4, characterized in that: In step 2, construct the extended location map G of deep iron-rich ore body attribute characteristics T (V,A) Modeling of multi-mode profile measurement fusion network, including: (1) Initialize a blank T-layer directed graph, where the k-th layer represents the k-th size interval of the multi-mode profile measurement fusion network; (2) Traverse all multi-mode profile measurements in the multi-mode profile measurement set V1, and observe each multi-mode profile measurement v n ∈V1, add vertices to the extended location graph of deep iron-rich ore body attributes Among them, the vertex The kth layer of the extended localization map of deep iron-rich ore body attributes, indicating the multi-mode profile measurement v in the kth size interval area n , the multi-mode profile measurement vertex set in the extended location map of deep iron-rich ore body attributes is recorded as (3) Add mapping distance: For any one-to-many profile measurement node v i and v j If two multi-mode profile measurements are superimposed on each other in the kth size interval, the mapping distance is added to the extended location map of the deep iron-rich ore body attribute characteristics. k=1,2…T, and the mapping distance set of the extended location map of deep iron-rich ore body attributes is recorded as A profile measurement method v in multi-mode profile measurement i and another profile measurement method v j In the size interval k, they are superimposed, 1≤k≤T; (4) Add storage mapping distance: for multi-mode profile measurement v n ∈V1, add storage mapping distance in the extended location map of deep iron-rich ore body attributes k=1,2…T-1; the storage mapping distance set of the extended location map of deep iron-rich ore body attributes is recorded as (5) Add virtual source vertex Virtual destination vertex As the source and destination vertices on the extended location map of deep iron-rich ore body attributes, add virtual mapping distance k∈[1,T] and k∈[1,T], so that the virtual source and destination vertices are connected to the source and destination nodes in the network, and the virtual mapping distance set of the extended location map of deep iron-rich ore body attributes is recorded as (6) Let the extended location map of deep iron-rich ore body properties be G T (V,A), where A=A t ∪A s ∪A vir , in, As the source vertex, As the destination vertex.

7. The method for exploring rich iron ore with mutually constrained multiple exploration means according to claim 4, characterized in that: In step 3, the virtual source vertex is calculated in the extended localization map of deep iron-rich ore body attributes. To the virtual destination vertex The minimum mapping distance partition set R includes: 1) Set all mapping distance capacities in the deep iron-rich ore body attribute feature extension location map to 1. Let c(u,v)=1; 2) Set the storage mapping distance and virtual mapping distance capacity in the deep iron-rich ore body attribute feature expansion positioning map to infinity. Let c(u,v)=∞; 3) Using Ford–Fulkerson algorithm to calculate the virtual source vertex of the extended location map of deep iron-rich ore body attributes To the virtual destination vertex The minimum cut set between , and this set is named the minimum mapping distance partition set R.

8. The method for exploring rich iron ore with mutually constrained multiple exploration means according to claim 4, characterized in that: In step 4, the diffuse scattering path distance set of each mapping distance in the minimum mapping distance segmentation set R is calculated, including: (a) Initialize the distance set of each mapping distance diffuse scattering path in the minimum mapping distance segmentation set R, that is, for make Among them, r i is the i-th mapping distance in the minimum mapping distance partition set R, path(i) is r i The set of diffuse scattering path distances; (b) Calculate the residual map G after the extended location map of deep iron-rich ore body attributes excludes the minimum mapping distance segmentation set s , let G s =G T / R; (c) Yes Perform the following steps to update its diffuse scattering path distance set path(i): (c1) In the graph G1 = G s ∪{r i }Calculate the vertex arrive The mapping distance r i The shortest path p ii , and the number of layers t that the path passes through in the deep iron-rich ore body attribute feature expansion location map ii Denote as the extended representation of the path, let path(i) = path(i)∪{p ii }; (c2) Yes When G2=G s ∪{r i ,r j }Calculate the vertex arrive The mapping distance r i The shortest path p ij If p ij Satisfies: ① Extended characterization value t ij <t ij , ②t ij =min{t 1j ,t 2j …t nj }, let p ij is the diffuse scattering path distance of R(i), let path(i)=path(i)∪{p ij ), where {t 1j ,t 2j …t nj } is to use r j An extended set of representations of paths; (c3) All the calculated paths of path (i) are sorted from small to large according to the extended representation to form a diffuse scattering path distance set 9. The method for exploring rich iron ore with mutually constrained multiple exploration means according to claim 4, characterized in that: In step 5, according to the diffuse scattering path distance set path(i), r i ∈R, calculate the representation mapping distance r i Extended characterization value of key deep iron-rich ore body attribute characteristic positioning index t c (i) including: (A) Use Done(i) to represent the mapping distance r i Is the extended representation value of determined? Initialize the indicator, that is, Done(i) = 0; (B) Let R1 = {r i |Done(i)=0}, if for Done(i)=1, go to step (C); if Go to step (D); (C) For like Compare and t c (i); like but Reorder the paths in path(j); like Will Modified to (D) For diffuse scattering, the shortest path in the distance set is the same mapping r i ,r j , that is, for Compare the extended representation value of the second shortest path and like but like but In step six, according to the extended characterization value of the mapping distance, the key deep iron-rich ore body attribute feature positioning path set S is calculated, including: (i) Sort all the mapping distances in R from small to large according to the extended representation value, that is: Among them, tc(n i ) is the mapping distance The extended representation value of (ii) Take the first K mapping distances in R, expressed as: {r1,r2…r K |t c (1)≤t c (2)…≤t c (K)}; (iii) Map the distances r1, r2…r K According to the size interval values ​​in the extended location map of deep iron-rich ore body attributes, the key mapping distance sequence is sorted from small to large, and the expression is: (iv) Convert the key mapping distance sequence into a set of key deep iron-rich ore body attribute feature positioning paths: Convert to (v li ,v lj ,t l ), indicating the size interval t l Multi-mode profile measurement li v li To multi-mode profile measurement v lj The positioning path, where t l Indicates the size interval value of the positioning path; that is, S = {(v 1i ,v 1j ,t1),(v 2i ,v 2j ,t2)…(v Ki ,v Kj ,t K )|t1≤t2…≤t K }; Through the above-mentioned key deep iron-rich ore body attribute characteristic positioning path set, any attribute characteristic of the positioning information of the high-magnetism, high-density, low-resistance and high-polarization deep iron-rich ore body can be obtained.

10. A rich iron ore exploration system with multiple exploration methods constrained by each other, characterized in that: The system implements the rich iron ore exploration method with mutually constrained multi-exploration means as described in any one of claims 1 to 9, and the system comprises: The information integration and anomaly preliminary selection module (1) is used for information integration and anomaly preliminary selection. By collecting geological mineral resources, geophysical and geochemical exploration, drilling, and scientific research data, the deep geological unit information hidden in the iron ore deposit in the target area is analyzed, and the deep geological unit information anomaly preliminary selection is performed on the iron ore deposit in the target area; The aerogeophysical anomaly selection module (2) determines the key areas of geophysical anomalies based on information integration and anomaly preliminary selection results through area-based aerogravimetry and area-based aeromagnetic measurement, and completes the aerogeophysical anomaly selection; The ground geophysical anomaly selection module (3) determines the iron ore prospecting target area based on the determined geophysical anomaly key areas by using area ground magnetic measurement and area ground gravity measurement, and completes the ground geophysical anomaly selection; The geophysical profile anomaly point selection module (4) performs large-scale gravity profile measurement, large-scale magnetic profile measurement, large-scale electrical profile measurement, and two-dimensional seismic profile measurement based on the determined iron ore prospecting target area to locate favorable locations for iron ore deposits and complete geophysical profile anomaly point selection; The verification and evaluation module (5) conducts drilling and logging anomaly verification based on the favorable locations of the iron ore bodies located, completes the verification on the borehole logging points, and conducts a comprehensive evaluation of the iron ore resources based on the drilling and logging anomaly verification results; wherein, the constraints of drilling are: accurately detecting deep geological units and clarifying deep alteration characteristics; the constraints of logging are: detecting abnormal locations deep in the borehole and beside the borehole.

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