Coal seam roof aquifer water abundance detection method and device based on transient electromagnetic method

Abstract

The present application belongs to coal seam detection technical field, disclose a coal seam roof aquifer water abundance detection method and device based on transient electromagnetic method. Through the electromagnetic induction equipment arranged in different parts of coal seam roof, the transmitted electromagnetic wave obtains the coal seam roof aquifer water abundance preprocessing data set, constructs the coal seam roof different parts aquifer water abundance architecture image set; Construct the coal seam roof different parts aquifer water abundance initialization resistivity data set; The aquifer water abundance fault inversion method is used to obtain the aquifer water abundance fault image set; Based on the aquifer water abundance fault image set, the water refraction characteristic is combined, and the coal seam roof aquifer water abundance image of different regions is obtained. The transient electromagnetic method has the advantages of low cost, high efficiency, low resistance sensitivity, small volume effect and the like, and the transient electromagnetic method is used for detecting the roof and floor water abundance evaluation, and qualitative interpretation is carried out.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of coal seam exploration, and particularly relates to a coal seam roof aquifer water abundance detection method and device based on transient electromagnetic method. BACKGROUND

[0002] Coal mine coal seam roof water disaster is one of the mine water disasters, which seriously threatens the safety of coal mining. The outburst of mine roof water not only deteriorates the production environment of the working face, but also brings insecurity to mine production. Since the water abundance of the mine roof sandstone cannot be checked, the prediction of the outburst amount of the roof water is improper, which either excessively increases the safety factor and greatly increases the cost of the prevention and control engineering, or the safety factor is too small, and the mine (mining area) drainage capacity is insufficient, causing major accidents such as mine flooding, mining area flooding and even personnel casualties.

[0003] At present, for the quantitative evaluation of the coal seam roof water outburst in the hydrogeological exploration work, the method of combining drilling and pumping test is generally adopted, that is, drilling holes are first constructed in the exploration area, and then pumping test is carried out, and the coal seam roof water outburst amount is obtained according to the pumping test. The drilling holes constructed in this way are blind and have the problems of large quantity, high cost, long construction period, and the identification of the aquifer is directly related to the number of drilling holes. The estimation of the coal seam roof water content at the non-drilling position is insufficient, and cannot meet the requirements of large-scale coal mine production.

[0004] In the past coal mine hydrogeological survey, the resistivity method, the permeable electric field method, the well charging method, the well diffusion method, the induced polarization method, the audio frequency earth electric field method, the seismic refraction method, the very low frequency method, the secondary time difference method, and the frequency domain induced polarization method have played an important role. In recent years, the transient electromagnetic method has been widely used in the field of coalfield hydrogeological exploration due to its low cost, high efficiency, sensitivity to low resistance, small volume effect and other advantages. In the current coal mine hydrogeological exploration research, the transient electromagnetic method is mainly used for the evaluation of water abundance of coal mine goaf, roof, and new strata, and is mainly used for qualitative interpretation. That is, according to the size of the apparent resistivity value obtained by measurement, the spatial position of the water-rich goaf is qualitatively or semi-quantitatively circled, which provides an important basis for coal mine production, resident safety and engineering construction.

[0005] Through the above analysis, the problems and defects of the prior art are that the prior art cannot effectively detect the water abundance of the coal seam roof aquifer, and the detection data accuracy is poor. The water disaster cannot be effectively prevented, and the safety of coal mine production cannot be ensured. SUMMARY

[0006] In order to overcome the problems in the related art, the present application discloses a coal seam roof aquifer water abundance detection method and device based on transient electromagnetic method.

[0007] The technical scheme is as follows: a coal seam roof aquifer water abundance detection method based on transient electromagnetic method, comprising the following steps:

[0008] S1, transmitting electromagnetic waves through electromagnetic induction equipment arranged at different positions of the coal seam roof to obtain coal seam roof aquifer water abundance preprocessing data set, and constructing coal seam roof aquifer water abundance architecture image set based on the preprocessing data set;

[0009] S2, receiving diffracted waves reflected by the electromagnetic waves transmitted by the electromagnetic induction equipment arranged at different positions of the coal seam roof, and constructing coal seam roof aquifer water abundance initialization resistivity data set;

[0010] S3, based on the constructed coal seam roof aquifer water abundance initialization resistivity data set and the coal seam roof aquifer water abundance architecture image set, obtaining aquifer water abundance fault image set by using aquifer water abundance fault inversion method;

[0011] S4, obtaining different regional coal seam roof aquifer water abundance image based on the aquifer water abundance fault image set combined with water refraction characteristics.

[0012] In step S1, transmitting electromagnetic waves to obtain coal seam roof aquifer water abundance preprocessing data set, comprising the following steps:

[0013] S201, setting multiple depth measuring points in the prediction area, obtaining the distance h of the electromagnetic induction equipment from the coal seam roof and the minimum transmission frequency f required for transmission min ;

[0014] S202, determining the minimum and maximum electromagnetic wave transmission and reception distances;

[0015] S203, constructing coal seam roof aquifer water abundance preprocessing data set to obtain coal seam roof distance set from electromagnetic induction equipment.

[0016] In the S202, determining the minimum and maximum electromagnetic wave transmission and reception distances comprises:

[0017]

[0018] In the formula, r min is the minimum transmission and reception distance of electromagnetic waves, a is the transmission and reception coefficient, π is the circular constant, μ is the magnetic permeability, δ is the detection depth, f is the transmission frequency, r is the transmission and reception radius, h is the distance of the electromagnetic induction equipment from the coal seam roof, f min is the minimum transmission frequency required for transmission;

[0019] The determination of the maximum transmitting-receiving distance is related to the minimum electromagnetic intensity that can be detected under a given noise condition, and the specific expression of the maximum transmitting-receiving distance is:

[0020]

[0021] In the formula, r max is the maximum transmitting-receiving distance, I is the size of the transmitting current, dl is the transmitting distance, |E x | min is the minimum electromagnetic intensity that can be detected under a given noise condition of the instrument.

[0022] In step S3, the objective function expression of the water-bearing layer water enrichment fault inversion method is:

[0023]

[0024] In the formula, is the objective function of the water-bearing layer water enrichment fault inversion method, W γ is the uncertainty matrix in the prior coal seam roof physical relationship, γ ρv (m) is the resistivity-fault function estimated from the inversion model, γ ρv obs is the prior resistivity fault function obtained from the prior coal seam roof physical information.

[0025] In one embodiment, the algorithm flow of the water-bearing layer water enrichment fault inversion method includes the following steps:

[0026] (1) The resistivity-fault function fitting term representing the resistivity-fault relationship is added to the objective function, and the gradient of the resistivity-fault function fitting term is added when calculating the gradient of the objective function;

[0027] (2) In the inversion process, the input of clustering is changed from resistivity to resistivity and fault, so that the update of the model is affected by resistivity and fault at the same time;

[0028] (3) The membership is spatially constrained during the clustering process, so that the model update is affected by the resistivity-fault relationship and spatial structure constraints.

[0029] In one embodiment, the expression of the resistivity-fault function is:

[0030]

[0031] In the formula, i is the group number of different data groups divided according to different lag distances h; k and j represent the serial numbers of samples in each data group, k≠j; γ ρv (h) represents the resistivity-fault relationship value, ρ represents resistivity, v represents fault, N(h) represents the total number of data groups of lag distance, ρ i,kresistivity of the i-th data set, k-th sample, v i,j resistivity of the i-th data set, j-th sample, v i,k fault of the i-th data set, k-th sample, v i,j fault of the i-th data set, j-th sample;

[0032] The resistivity differential of the resistivity-fault function with respect to each sample number j is:

[0033]

[0034] where θγ ρv (h) j The resistivity differential of the resistivity-fault function with respect to each sample number j is: j The resistivity differential of the resistivity-fault function with respect to each sample number j is:

[0035] If the resistivity of the j-th sample is not in the i-th data pair, then θρ j = 0 and the i-th data pair has no effect on θγ ρv (h) j The resistivity differential is rewritten as:

[0036]

[0037] where N(h) j is the number of data pairs containing the resistivity model of the j-th sample; θρ j is the resistivity differential of the j-th sample, γ ρv (h) represents the resistivity-fault relationship value, ρ represents the resistivity, v represents the fault, N(h) represents the total number of data sets, ρ i,k resistivity of the i-th data set, k-th sample, v i,j resistivity of the i-th data set, j-th sample, v i,k fault of the i-th data set, k-th sample, v i,j fault of the i-th data set, j-th sample;

[0038] The resistivity differential is rewritten as:

[0039]

[0040] where N(h) represents the total number of data sets, θγ ρv (h) j The resistivity differential of the resistivity-fault function with respect to each sample number j is:

[0041] The first term on the right side is expanded and combined to obtain:

[0042]

[0043] The partial derivative of the resistivity-fault function with respect to the resistivity of the sequence number j is:

[0044]

[0045] The value of the hth row and jth column of the resistivity-fault function is obtained.

[0046] In one embodiment, after obtaining the forward formula of the resistivity-fault function and the resistivity-fault function, the inversion is performed by using an iterative solution method.

[0047] In step S4, the objective function of the water abundance image of the coal seam roof aquifer in different regions is:

[0048]

[0049] In the formula, Φ(m) is the total objective function, Φ d (m) is the geomagnetic data fitting term, λ is the Lagrange operator, Φ m (m) is the model matching term, a is a parameter for controlling the closeness of the resistivity-fault function obtained by the expected inversion to the prior resistivity-fault function, Φ r (m) is the resistivity-fault function fitting term, d is the measured MT data, f(m) is the forward MT data, T is the exponential, C d is the data error covariance, m is the model parameter, m0 is the initial model, C m is the model covariance, m is the model parameter, γ ρv (m) is the resistivity-fault function estimated from the inversion model, γ ρv obs is the prior resistivity-fault function obtained from the prior coal seam roof physical information. The model covariance of the resistivity fault function inversion is represented.

[0050] In one embodiment, by adding the resistivity-fault function fitting term, the geomagnetic inversion result fitting the observed geomagnetic data satisfies the prior resistivity-fault relationship at the same time.

[0051] Another purpose of the present application is to provide a coal seam roof aquifer water abundance detection device based on transient electromagnetic method, and the coal seam roof aquifer water abundance detection method is implemented, and the device comprises:

[0052] The aquifer water abundance architecture image set acquisition module is used for transmitting electromagnetic waves by the electromagnetic induction equipment arranged at different parts of the coal seam roof to obtain the coal seam roof aquifer water abundance preprocessing data set, and constructing the coal seam roof aquifer water abundance architecture image set based on the preprocessing data set.

[0053] The water-bearing layer water enrichment initialization resistivity dataset construction module is used for receiving diffracted waves of electromagnetic wave reflection emitted by electromagnetic induction devices arranged at different positions of the coal seam roof, and constructing a water-bearing layer water enrichment initialization resistivity dataset of different positions of the coal seam roof.

[0054] The water-bearing layer water enrichment fault image set construction module is used for obtaining a water-bearing layer water enrichment fault image set by using a water-bearing layer water enrichment fault inversion method based on the constructed water-bearing layer water enrichment initialization resistivity dataset of different positions of the coal seam roof and the water-bearing layer water enrichment architecture image set of different positions of the coal seam roof.

[0055] The different region coal seam roof water-bearing layer water enrichment image acquisition module is used for obtaining a water-bearing layer water enrichment image of different regions of the coal seam roof based on the water-bearing layer water enrichment fault image set and water refraction characteristics.

[0056] In combination with all the technical solutions described above, the instant transient electromagnetic method has the advantages of low cost, high efficiency, sensitivity to low resistance, small volume effect and the like, and the instant transient electromagnetic method is used for detecting and evaluating the water enrichment of the roof and floor, and qualitative interpretation is performed. According to the size of the apparent resistivity value obtained by measurement, the spatial position of the water-enriched goaf is qualitatively or semi-quantitatively circled, which provides an important basis for coal mine production, resident safety and engineering construction.

[0057] The resistivity-fault function fitting term is added to the conventional inversion target function to represent the relationship between the fault and the resistivity, and modeling inversion is realized. The imaging effect of deep exploration is improved. The method adds the resistivity-fault function fitting term, so that the inversion result can reflect the vertical and horizontal variation law of the physical property parameter, the electromagnetic imaging precision is improved, the complementary characteristics between the resistivity and the fault are utilized, and the physical property distribution and structural characteristics are highlighted. The present application is beneficial to obtaining the geoelectric model of the resistivity in the prediction area, and effectively improving the precision of the CSAMT exploration. BRIEF DESCRIPTION OF DRAWINGS

[0058] The accompanying drawings, which are incorporated into and form a part of the specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure;

[0059] Figure 1 is a flow chart of a coal seam roof water-bearing layer water enrichment detection method based on the instant transient electromagnetic method provided by the embodiments of the present application;

[0060] Figure 2 is a flow chart of emitted electromagnetic wave acquisition of a coal seam roof water-bearing layer water enrichment preprocessing dataset provided by the embodiments of the present application;

[0061] Figure 3This is a schematic diagram of a coal seam roof aquifer water-bearing detection device based on transient electromagnetic method provided in an embodiment of the present invention;

[0062] The diagram shows: 1. Aquifer water-bearing structure image set acquisition module; 2. Aquifer water-bearing initial resistivity dataset construction module; 3. Aquifer water-bearing fault image set construction module; 4. Aquifer water-bearing image acquisition module for coal seam roof in different regions. Detailed Implementation

[0063] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0064] Example 1, such as Figure 1 As shown, the method for detecting the water-bearing capacity of aquifers in the roof of coal seams based on transient electromagnetic methods provided in this embodiment of the invention includes the following steps:

[0065] S1. Electromagnetic waves emitted by electromagnetic induction devices arranged at different parts of the coal seam roof are used to obtain a pre-processed dataset of water-rich aquifers in the coal seam roof. Based on the pre-processed dataset, a set of water-rich architecture images of aquifers at different parts of the coal seam roof is constructed.

[0066] S2 receives the diffracted waves reflected by electromagnetic waves emitted by electromagnetic induction devices arranged at different parts of the coal seam roof, and constructs an initial resistivity dataset of water-bearing properties of aquifers at different parts of the coal seam roof.

[0067] S3, based on the constructed initial resistivity dataset of aquifer water-bearing properties in different parts of the coal seam roof and the image set of aquifer water-bearing architecture in different parts of the coal seam roof, the water-bearing fault image set of the aquifer is obtained by using the aquifer water-bearing fault inversion method.

[0068] S4. Based on the water-bearing fault image set of the aquifer and the water refraction characteristics, water-bearing images of the aquifer roof in different regions are obtained.

[0069] like Figure 2 As shown, in this embodiment of the invention, the electromagnetic wave emitted in step S1 to acquire the pre-processed data set of water-rich aquifer in the coal seam roof includes:

[0070] S201, set up multiple sounding points within the prediction area to obtain the distance h between the electromagnetic induction device and the coal seam roof, as well as the minimum transmission frequency f required for transmission. min ;

[0071] S202, the minimum electromagnetic wave transceiver distance, the maximum transceiver distance is determined according to the following formula:

[0072]

[0073] Or,

[0074]

[0075] In the formula, r min The minimum electromagnetic wave transceiver distance, a is the transceiver coefficient, Pi is the circumference, mu is the magnetic conductivity, delta is the detection depth, F is the transmission frequency, r is the transceiver radius, h is the distance between the electromagnetic induction device and the coal seam roof, f min The minimum transmission frequency required for transmission;

[0076] The determination of the maximum transceiver distance is related to the minimum electromagnetic intensity that can be detected under a given noise condition, and the specific expression of the maximum transceiver distance is:

[0077]

[0078] In the formula, r max The maximum electromagnetic wave transceiver distance, I is the size of the transmission current, dl is the transmission distance, |E x | min Is the minimum electromagnetic intensity that can be detected under the given noise condition of the instrument.

[0079] S203, according to the minimum electromagnetic wave transceiver distance, the maximum transceiver distance, the coal seam roof water-bearing layer water enrichment pretreatment data set is constructed, that is, the distance set of the coal seam roof from the electromagnetic induction device.

[0080] In the embodiment of the application, the objective function of the water enrichment fault inversion method of the water-bearing layer in step S3 comprises:

[0081]

[0082] In the formula, The objective function of the water enrichment fault inversion method of the water-bearing layer, W γ The uncertainty matrix in the prior coal seam roof physical relationship, the uncertainty matrix is a diagonal matrix, and the diagonal element is the reciprocal of the standard deviation of the resistivity-fault function mismatch; Gamma ρv (m) is the resistivity-fault function estimated from the inversion model, gamma ρv Obs is the prior resistivity fault function obtained from the prior coal seam roof physical information.

[0083] In the embodiment of the application, the objective function of the water enrichment image of the coal seam roof water-bearing layer in different regions in step S4 is:

[0084]

[0085] where Φ(m) is the total objective function, Φ d (m) is the MT data fitting term, λ is the Lagrange operator, Φ m (m) is the model matching term, a is the parameter controlling the closeness of the expected inversion resistivity-fault function to the prior resistivity-fault function, Φ r (m) is the resistivity-fault function fitting term, d is the measured MT data, f(m) is the forward MT data, T is the exponential, C d is the data error covariance, m is the model parameter, m0 is the initial model, C m is the model covariance, m is the model parameter, γ ρv (m) is the resistivity-fault function estimated from the inversion model, γ ρv obs is the prior resistivity-fault function from the prior coal roof physical information, represents the model covariance of the resistivity-fault function inversion.

[0086] By adding the resistivity-fault function fitting term, the MT inversion result is made to fit the observed MT data while satisfying the prior resistivity-fault relationship.

[0087] In the embodiment of the present application, the algorithm flow of the aquifer water enrichment fault inversion method in step S3 includes the following three improvements:

[0088] (1) Since the resistivity-fault function fitting term representing the resistivity-fault relationship is added in the objective function, the gradient of the resistivity-fault function fitting term is added when the gradient of the objective function is calculated;

[0089] (2) In the inversion process, the input of clustering is changed from resistivity to resistivity and fault, so that the model update is affected by both resistivity and fault;

[0090] (3) The membership is spatially constrained in the clustering process, so that the model update is affected by the resistivity-fault relationship and the spatial structure constraint.

[0091] In the embodiment of the present application, the resistivity-fault function forward formula includes:

[0092] The resistivity-fault function is rewritten as follows:

[0093]

[0094] where i is the group number of different data groups divided according to different lags h; k and j represent the serial numbers of samples in each data group, k≠j; γ ρv(h) represents the resistivity-fault relationship value, p represents the resistivity, v represents the fault, N(h) represents the total number of data sets of the lag distance, p i,k represents the resistivity of the i-th data set and the k-th sample number, p i,j represents the resistivity of the i-th data set and the j-th sample number, p i,k represents the fault of the i-th data set and the k-th sample number, v i,j represents the fault of the i-th data set and the j-th sample number, v

[0095] The resistivity differential form of the resistivity-fault function with respect to each sample number j is:

[0096]

[0097] wherein θγ ρv (h) j represents the resistivity differential value of the resistivity-fault relationship value with the sample number j, θρ j represents the resistivity differential value of the resistivity-fault relationship value with the sample number j, γ

[0098] If the resistivity with the sample number j is not in the i-th data pair, then θρ j = 0 and the i-th data pair does not have an effect on θγ ρv (h) j If the resistivity with the sample number j is not in the i-th data pair, then the resistivity differential form is rewritten as:

[0099]

[0100] wherein N(h) j is the logarithm of the data pair containing the resistivity model with the sample number j; θρ j represents the resistivity differential value of the resistivity-fault relationship value with the sample number j, γ ρv (h) represents the resistivity-fault relationship value, p represents the resistivity, v represents the fault, N(h) represents the total number of data sets of the lag distance, p i,k represents the resistivity of the i-th data set and the k-th sample number, p i,j represents the resistivity of the i-th data set and the j-th sample number, p i,k represents the fault of the i-th data set and the k-th sample number, v i,j represents the fault of the i-th data set and the j-th sample number, v

[0101] If the resistivity with the sample number j is not in the i-th data pair, then the resistivity differential form is rewritten as:

[0102] If the resistivity with the sample number j is not in the i-th data pair, then the resistivity differential form is rewritten as:

[0103]

[0104] wherein N(h) represents the total number of data sets of the lag distance, θγ ρv(h) j represents a resistivity differential value in the resistivity-fault relationship value with the sequence number j;

[0105] The right first term is expanded and combined to obtain:

[0106]

[0107] The partial derivative of the resistivity-fault function with respect to the resistivity with the sequence number j is:

[0108]

[0109] The value of the hth row and jth column of the resistivity-fault function is obtained.

[0110] In the embodiment of the present application, after the forward formula of the resistivity-fault function and the derivative of the resistivity-fault function are obtained, the inversion is performed by using an iterative solution method.

[0111] As shown in Embodiment 2, Figure 3 The coal seam roof aquifer water abundance detection device based on the transient electromagnetic method provided by the embodiment of the present application comprises:

[0112] An aquifer water abundance architecture image set acquisition module 1 acquires coal seam roof aquifer water abundance pretreatment data sets by emitting electromagnetic waves through electromagnetic induction devices arranged at different parts of the coal seam roof, and constructs an aquifer water abundance architecture image set of different parts of the coal seam roof based on the pretreatment data sets.

[0113] An aquifer water abundance initialization resistivity data set construction module 2 receives diffracted waves reflected by electromagnetic waves emitted by electromagnetic induction devices arranged at different parts of the coal seam roof, and constructs an aquifer water abundance initialization resistivity data set of different parts of the coal seam roof.

[0114] An aquifer water abundance fault image set construction module 3 acquires an aquifer water abundance fault image set by using an aquifer water abundance fault inversion method based on the constructed aquifer water abundance initialization resistivity data set of different parts of the coal seam roof and the aquifer water abundance architecture image set of different parts of the coal seam roof.

[0115] A different regional coal seam roof aquifer water abundance image acquisition module 4 obtains different regional coal seam roof aquifer water abundance images based on the aquifer water abundance fault image set in combination with water refraction characteristics.

[0116] In the above embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0117] The information interaction, execution process and the like between the above devices / units are based on the same concept as the method embodiments of the present application, and the specific functions and brought technical effects can be specifically referred to the method embodiments part, which will not be repeated here.

[0118] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is exemplified, and in actual application, the above-mentioned functions can be completed by 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 one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or software. In addition, the specific name of each functional unit and module is only for easy distinction, and does not limit the protection scope of the present application. The specific working process of the unit and module in the system can be referred to the corresponding process in the foregoing method embodiments.

[0119] Based on the technical solutions of the above-mentioned embodiments of the present application, the following application examples can be further proposed.

[0120] According to the embodiments of the present application, the present application further provides a computer device, comprising at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor implements the steps in any of the above method embodiments when executing the computer program.

[0121] The embodiments of the present application further provide a computer readable storage medium, which stores a computer program, wherein the computer program is executable by a processor to implement the steps in the above method embodiments.

[0122] The embodiments of the present application further provide an information data processing terminal, which is used to provide a user input interface to implement the steps in the above method embodiments when executed on an electronic device, and the information data processing terminal is not limited to a mobile phone, a computer or a switch.

[0123] The embodiments of the present application further provide a server, which is used to provide a user input interface to implement the steps in the above method embodiments when executed on an electronic device.

[0124] The embodiments of the present application further provide a computer program product, which, when executed on an electronic device, enables the electronic device to implement the steps in the above method embodiments.

[0125] The integrated unit, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on such understanding, the present application can implement all or part of the processes in the above-mentioned embodiment methods, which can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer-readable medium can at least include any entity or device capable of carrying the computer program code to the photographing device / terminal equipment, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, such as a U disk, mobile hard disk, magnetic disk or optical disk, etc.

[0126] In the above embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0127] To further prove the positive effect of the above-mentioned embodiments, the present application based on the above technical solutions carries out the following experiment: the water-rich coal mine goaf will have a great impact on the safety production of coal mine. The present application proposes a coal seam roof aquifer water-rich property detection device and method based on transient electromagnetic method, determines the distribution of coal seam goaf and water accumulation area in the exploration area, and delineates the position and range of goaf and water accumulation anomaly area; determines the abnormal water-rich condition of geological structure such as fault and collapse column in the exploration area, and delineates the position and range of water-rich anomaly area. Determine the water-rich area and hydraulic connection of each aquifer in the exploration area of Shanxi Formation, and focus on controlling the relative water-rich area and hydraulic connection of the main aquifer of coal roof. Control the relative water-rich area and hydraulic connection of the goaf range in the exploration area. In order to ensure the quality of original data, the whole construction process adopts electromagnetic wave equipment, multiple quality detection points can be arranged, the horizontal resolution is high, the response to low resistance is sensitive, and the water-rich property of coal seam roof aquifer can be effectively detected. The range of small fault drop is calibrated. In order to do a good job in water exploration and drainage, so as to effectively prevent water disasters and ensure the safety production of coal mine.

[0128] The qualified rate of the coal seam roof aquifer water abundance detection measurement based on the transient electromagnetic method can reach 100%, and the excellent level rate is higher than 95%. The relative error of the measuring point is not more than 0.5 meters, the height error is not more than 0.5 meters, the first-order measuring line point position error is not more than 0.2 meters, and the height error is not more than 0.2 meters. Due to the influence of the terrain, the area that cannot be detected is allowed to lose points, and the loss rate is not more than 2%. For the field collected data, the acceptance of each curve is carried out on the same day, the abnormal distortion points are found, the repeated observation and detection are arranged in time, the quality of the original record is ensured, various attribute data are input into the computer in time, the section graph is drawn, the longitudinal and transverse comparison analysis is carried out, and the electromagnetic exploration precision ensures 40*20 meters.

[0129] The transient electromagnetic method of the application has the advantages of low cost, high efficiency, sensitivity to low resistance, small volume effect, etc., and is used for detecting the roof and floor water abundance evaluation and carrying out qualitative interpretation. According to the size of the apparent resistivity value obtained by measurement, the spatial position of the water-rich goaf is qualitatively or semi-quantitatively circled, which provides an important basis for coal production, resident safety and engineering construction.

[0130] The above is only a preferred specific embodiment of the application, but the protection scope of the application is not limited thereto, any modification, equivalent replacement and improvement within the technical range disclosed by the application and within the spirit and principle of the application should be covered within the protection scope of the application.

Claims

1. A method for detecting water abundance of a coal seam roof aquifer based on transient electromagnetic method, characterized in that, The method comprises the following steps: S1, emitting electromagnetic waves through electromagnetic induction devices arranged at different positions of the coal seam roof to obtain a water enrichment pretreatment data set of the aquifer of the coal seam roof, and constructing an aquifer water enrichment architecture image set of the coal seam roof at different positions based on the pretreatment data set; S2, receiving diffracted waves of electromagnetic wave reflections emitted by electromagnetic induction devices arranged at different positions of the coal seam roof, and constructing an aquifer water enrichment initialization resistivity data set of the aquifer of the coal seam roof at different positions; S3, obtaining an aquifer water enrichment fault image set by using an aquifer water enrichment fault inversion method based on the constructed aquifer water enrichment initialization resistivity data set of the aquifer of the coal seam roof at different positions and the aquifer water enrichment architecture image set of the coal seam roof at different positions; S4, obtaining an aquifer water enrichment image of the coal seam roof in different regions based on the aquifer water enrichment fault image set in combination with water refraction characteristics; In step S3, the objective function expression of the aquifer water enrichment fault inversion method is: ； wherein, is the objective function for the water-rich fault inversion method of the aquifer, is the uncertainty matrix in the prior coal roof physical relationship, is the resistivity-fault function estimated from the inversion model, is the prior resistivity fault function from the prior coal roof physical information; The algorithm flow of the aquifer water enrichment fault inversion method comprises the following steps: (1) An electrical resistivity-fault function fitting term representing the relationship between electrical resistivity and faults is added in the objective function, and the gradient of the electrical resistivity-fault function fitting term is added when the gradient of the objective function is calculated; (2) In the inversion process, the input of clustering is changed from electrical resistivity to electrical resistivity and faults, so that the model is updated simultaneously under the influence of electrical resistivity and faults; (3) The spatial constraint is performed on the membership in the clustering process, so that the model update is influenced by the relationship between electrical resistivity and faults and the constraint of the spatial structure.

2. The coal seam roof aquifer water abundance exploration method based on transient electromagnetic method according to claim 1, characterized in that, In step S1, the coal seam roof aquifer water enrichment pretreatment data set is obtained by emitting electromagnetic waves, which comprises the following steps: S201, multiple sounding points are set in the prediction area, and the distance between the electromagnetic induction device and the coal seam roof in the prediction area is obtained and the minimum transmission frequency required for transmission ; S202, determining the minimum and maximum electromagnetic wave transmitting and receiving distances; S203, constructing the coal seam roof aquifer water enrichment pretreatment data set to obtain a distance set of the coal seam roof from the electromagnetic induction device.

3. The coal seam roof aquifer water abundance exploration method based on transient electromagnetic method according to claim 2, characterized in that, In the S202, the expression for determining the minimum and maximum electromagnetic wave transmitting and receiving distances is: ； wherein, is the minimum transmitting-receiving distance of electromagnetic wave, is the transmitting-receiving coefficient, is the circular constant, is the magnetic permeability, is the detection depth, is the transmitting frequency, is the transmitting-receiving radius, is the distance between the electromagnetic induction device and the coal seam roof, is the minimum transmitting frequency required for transmission; The determination of the maximum transmitting and receiving distance is related to the minimum electromagnetic intensity that can be detected under a given noise condition, and the specific expression of the maximum transmitting and receiving distance is: ； wherein is the maximum electromagnetic wave range, is the magnitude of the transmitted current, is the transmission distance, is the minimum electromagnetic intensity that can be detected by the instrument under given noise conditions.

4. The coal seam roof aquifer water abundance exploration method based on transient electromagnetic method according to claim 1, characterized in that, The expression of the electrical resistivity-fault function is: ； wherein, represents the resistivity-fault relation value, is the group number of different data groups divided according to different lag distances is the group number of different data groups divided according to different lag distances and represents the serial number of the sample in each data group, ; represents the resistivity, represents the fault, represents the total number of data groups of lag distance, represents the resistivity of the first sample serial number of the first data group, represents the resistivity of the first sample serial number of the first data group, represents the fault of the first sample serial number of the first data group, represents the fault of the first sample serial number of the first data group; The resistivity - fault function is given for each sequence number n as the differential form of the resistivity ； In the formula, represents a resistivity differential value in the resistivity-fault relationship value with the serial number represents a resistivity differential value in the resistivity-fault relationship value with the serial number represents a resistivity differential value in the resistivity-fault relationship value with the serial number​ If the number of the resistivity is not in the first data pair, then and the first data pair has no effect on the resistivity differential form is rewritten as: ； In the formula, is a logarithm of a data pair of a resistivity model including a sequence number is a sequence number of a resistivity deviation value represents a resistivity-fault relationship value;​​ Then the differential form of the electrical resistivity is rewritten as: ； In the formula, represents the number of total data sets of the hysteresis distance, represents the resistivity differential value of the resistivity-fault relationship value with the serial number of 1. The first term on the right side is expanded and combined to obtain: ； The partial derivative of the resistivity- fault function with respect to the resistivity of the sequence number is: ； The values of the resistivity-fault function are obtained for the row and column positions.

5. The coal seam roof aquifer water abundance exploration method based on transient electromagnetic method according to claim 4, characterized in that, After the forward formula of the electrical resistivity-fault function and the derivative of the electrical resistivity-fault function are obtained, the inversion is performed by using an iterative solution.

6. The coal seam roof aquifer water abundance exploration method based on transient electromagnetic method according to claim 1, characterized in that, In step S4, the objective function for obtaining the aquifer water enrichment image of the coal seam roof in different regions is: ； wherein, is the total objective function, is the MT data fitting term, is the Lagrangian operator, is the model matching term, is the parameter to control the closeness between the estimated resistivity- fault function and the prior resistivity-fault function, is the resistivity-fault function fitting term, is the measured MT data, is the forward MT data, is the index, is the covariance of data error, is the model parameter, is the initial model, is the model covariance, is the model parameter, is the estimated resistivity-fault function from the inversion model, is the prior resistivity-fault function from the prior coal roof physical information, denotes the model covariance of the resistivity-fault function inversion.

7. The coal seam roof aquifer water abundance exploration method based on transient electromagnetic method according to claim 6, characterized in that, By adding the electrical resistivity-fault function fitting term, the magnetotelluric inversion result fits the observed magnetotelluric data while satisfying the prior electrical resistivity-fault relationship.

8. A device for detecting water abundance of a coal seam roof aquifer based on transient electromagnetic method, characterized in that, The device comprises: An aquifer water enrichment architecture image set acquisition module (1) for emitting electromagnetic waves through electromagnetic induction devices arranged at different positions of the coal seam roof to obtain a coal seam roof aquifer water enrichment pretreatment data set, and constructing an aquifer water enrichment architecture image set of the coal seam roof at different positions based on the pretreatment data set; The water-enriched aquifer water-enriched initialization resistivity dataset construction module (2) is configured to receive diffracted waves reflected by electromagnetic waves emitted by electromagnetic induction devices arranged at different positions of the coal seam roof, and construct a water-enriched aquifer water-enriched initialization resistivity dataset of different positions of the coal seam roof; The water-enriched aquifer fault image set construction module (3) is configured to obtain a water-enriched aquifer fault image set by using a water-enriched aquifer fault inversion method based on the constructed water-enriched aquifer water-enriched initialization resistivity dataset of different positions of the coal seam roof and the water-enriched aquifer architecture image set of different positions of the coal seam roof; The different-area coal seam roof water-enriched aquifer image obtaining module (4) is configured to obtain different-area coal seam roof water-enriched aquifer images based on the water-enriched aquifer fault image set and water refraction characteristics.

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