CN115436998A - Method for exploring water flowing fracture structure in underburden of coal seam floor - Google Patents
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Abstract
The invention relates to exploration of a water flowing fracture structure, in particular to an exploration method of a water flowing fracture structure in an underburden of a coal seam floor, which is characterized in that a microseismic monitoring system is constructed on a coal seam working surface to monitor and collect seismic wave signals in a mine in a coal seam mining process in real time; detecting microseismic events generated by rock mass fracture in the underburden of the coal seam floor; carrying out space positioning on the seismic source according to absolute arrival time data of transverse waves and longitudinal waves in the microseismic event; abnormal point detection is carried out on the space position of the seismic source, and outlier isolated space points are eliminated; performing spatial position clustering on the microseismic events, and performing linear fitting on clustering results to obtain two-dimensional and three-dimensional spatial distribution of a water flowing fracture structure; the technical scheme provided by the invention can effectively overcome the defect that the spatial distribution description of the water flowing fracture structure of the three-dimensional fracture surface can not be clearly and accurately carried out in the prior art.
Description
Technical Field
The invention relates to exploration of a water flowing fracture structure, in particular to an exploration method of a water flowing fracture structure in an underburden of a coal seam floor.
Background
Along with the development of coal mining towards the deep part, the threat generated by water damage of the coal seam floor is larger and larger. The precondition of water inrush of the coal seam floor is that a water guide fracture channel is formed on a water-bearing stratum overlying a water-bearing stratum under disturbance of coal seam mining, a coal seam floor damage zone is communicated, and water inrush is formed. At present, the method mainly utilizes methods such as resistivity method, seismic exploration and the like to explore the space distribution condition of the water body in the underburden of the coal seam floor and preliminarily obtain the precondition of water inrush of the coal seam floor, but the method lacks the knowledge of the formation process of the water flowing fracture structure in the underburden of the coal seam floor.
A great deal of rock mass micro-fracture can occur in the formation process of the water flowing fracture structure in the underburden of the coal seam floor, and weak seismic waves are released. The microseism monitoring technology is to arrange high-sensitivity microseism sensors around a research area, comprehensively monitor microseism activity generated by rock mass change in real time and high-precision manner, and position the time-space characteristics of the microcracks, including information such as position, size, energy, earthquake moment, earthquake source mechanism and the like of an event.
With the continuous development of the micro-seismic monitoring technology, people begin to research the damage layer position, the damage depth and the damage range of the rock mass underlying the coal seam bottom plate under the influence of mining disturbance according to the space-time distribution characteristics of the micro-seismic events generated by rock mass fracture, so that people have primary knowledge, namely the development spatial position, of the water flowing fracture structure, but the research on the three-dimensional fracture surface of the water flowing fracture structure needs to be further perfected.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects in the prior art, the invention provides a method for exploring a water flowing fracture structure in an underburden of a coal seam floor, which can effectively overcome the defect that the spatial distribution description of the water flowing fracture structure with a three-dimensional fracture surface can not be clearly and accurately carried out in the prior art.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
a method for exploring water flowing fracture structures in an underburden of a coal seam floor comprises the following steps:
s1, constructing a micro-seismic monitoring system on a coal seam working face, and monitoring and acquiring seismic wave signals in a mine in a coal seam mining process in real time;
s2, detecting a microseismic event generated by rock mass fracture in the underlying rock stratum of the coal seam floor;
s3, performing space positioning on the seismic source according to absolute arrival time data of transverse waves and longitudinal waves in the microseismic event;
s4, detecting abnormal points of the space position of the seismic source, and eliminating outlier isolated space points;
s5, carrying out spatial position clustering on the microseismic events;
and S6, performing linear fitting on the clustering result to obtain two-dimensional and three-dimensional spatial distribution of the water flowing fracture structure.
Preferably, detecting microseismic events resulting from rock fractures in the underburden at the floor of the coal seam in S2 comprises:
and detecting the microseismic event generated by rock mass fracture in the underlying rock stratum of the coal bed bottom plate by using a long-time window energy ratio method.
Preferably, the detecting the microseismic event generated by rock mass fracture in the underburden of the coal seam floor by using the long-time window energy ratio method comprises the following steps:
the method comprises the following steps of detecting microseismic events generated by rock mass fracture in the underlying rock stratum of the coal seam floor by adopting the following formula:
wherein, STA (i) is the short time window signal energy average value at the sampling time i, and LTA (i) is the long time window signal energy average value at the sampling time i;
ns is the length of the short time window, nl is the length of the long time window, and lambda is a set trigger threshold, and different values of ns, nl and lambda need to be set according to different geological conditions;
CF (j) is a characteristic function for the microseismic signal that characterizes the amplitude, energy or variation thereof of the microseismic data.
Preferably, in S3, the spatial positioning of the seismic source is performed according to the absolute arrival time data of the shear wave and the longitudinal wave in the microseismic event, and includes:
and performing relative spatial positioning on the seismic source by adopting a double-difference-based seismic positioning method by combining absolute arrival time data of transverse waves and longitudinal waves in the microseismic event and relative arrival time data of seismic data in a seismic catalog.
Preferably, the detecting abnormal points in the spatial position of the seismic source in S4, excluding outlier isolated spatial points, includes:
and (3) carrying out outlier detection on the seismic source space position by adopting an LOF algorithm, and excluding outlier isolated space point positions.
Preferably, the detecting abnormal points of the spatial position of the seismic source by using the LOF algorithm to eliminate outlier isolated spatial point locations includes:
s41, calculating a k-proximity distance: calculating k-neighborhood distance k-distance (p) between the kth nearest point and the data point p among the nearest points to the data point p;
s42, acquiring the nearest neighbor point of the data point p: for data point p, the distance from data point p is notThe data point with k-adjacent distance k-distance (p) larger than the data point p is the nearest adjacent point of the data point p, and the number of the data points is marked as N k (p)；
S43, calculating the reachable distance: the reachable distance between the data point p and the data point o is the k-adjacent distance k-distance (o) of the data point o, and the maximum value of the direct distance d (p, o) between the data point p and the data point o, which is expressed by the following formula:
reach_dist k (p，o)＝max{k-distance(o)，d(p，o)}；
s44, calculating local reachable density: the local achievable density of a data point p is the inverse of its average achievable distance from the nearest neighbor, and is represented by the following equation:
s44, calculating a local abnormal factor: the local anomaly factor is defined by local relative density and is represented by the following formula:
s45, if the local abnormal factor of the data point p is close to 1, the local density of the data point p is close to the nearest point; if the local anomaly factor for data point p is less than 1, indicating that data point p is in a relatively dense region; if the local anomaly factor of the data point p is greater than 1, indicating that the data point p is relatively distant from other data points, and possibly being an anomaly point;
s46, sorting all data points based on the size of the local abnormal factor, determining abnormal points after setting a threshold value, and excluding the abnormal points.
Preferably, spatial location clustering of microseismic events in S5 comprises:
and performing spatial position clustering on the microseismic events by adopting a DBSCAN algorithm.
Preferably, the spatial location clustering of microseismic events by using the DBSCAN algorithm includes:
s51, initializing a core object set omega = theta, initializing the cluster number n, initializing an unaccessed sample set gamma = D, and setting a cluster set C = theta;
s52, inputting a sample set D = { x = 1 ,x 2 ,…,x m And calculating the Euclidean distance to obtain a sample x j Epsilon-neighborhood subsample set N ε (x j )；
S53, if the subsample set N ε (x j ) Is not less than N, sample x j Adding a core object set Ω = Ω & { x j }；
S54, if the core object set omega = theta, directly ending, otherwise, entering S55;
s55, randomly selecting a core object o in a core object set omega, and initializing a current core object queue omega cur = o, initializing class index k = n +1, initializing current cluster set C k = o, update unaccessed sample set Γ = Ω -C k ；
S56, if the current core object queue omega cur If θ, then cluster C is currently clustered k After the generation is finished, outputting the cluster C = { C = { C = 1 ,C 2 ,…,C k H, updating the core object set omega = omega-C k And proceeding to S54, otherwise updating the core object set Ω = Ω -C k ；
S57, in the current core object queue omega cur Taking out a core object o', calculating Euclidean distance, and obtaining an epsilon-neighborhood subsample set N of the core object o ε (o') let Δ = N ε (o') # Γ, update the current clustered cluster C k ＝C k And U delta, updating the unvisited sample set gamma = gamma-delta, and updating the current core object queue omega cur ＝Ω cur U (. DELTA.andgate. OMEGA) -o' and proceeds to S56.
Preferably, the step S6 of performing linear fitting on the clustering result to obtain two-dimensional and three-dimensional spatial distribution of the water flowing fracture structure includes:
linear fitting of the clustering results was performed using the following formula:
y＝f(x，b)＝b 1 x 1 +b 2 x 2 +...+b n x n +c；
where y is an independent variable, x is a dependent variable, b is a linear solution, and c is a constant.
(III) advantageous effects
Compared with the prior art, the method for exploring the water flowing fracture structure in the underlayer of the coal bed has the following beneficial effects:
1) The method has the advantages that the microseismic events near the water flowing fracture structure in the underburden of the coal seam floor have obvious aggregation, so that the potential water flowing fracture structure in the underburden of the coal seam floor is accurately explored according to the distance and density condition of the microseismic events based on the spatial position clustering of the microseismic events;
2) The accuracy of the clustering algorithm is influenced by the accidental microseismic events, and the LOF algorithm is adopted in the method for removing the microseismic events which are dispersed in space, so that the accuracy of the clustering algorithm is effectively improved;
3) The method and the device adopt linear fitting to analyze the spatial position clustering result of the microseismic events, and obtain the position distribution of the water flowing fracture structure in the underburden of the coal seam floor in two-dimensional and three-dimensional spaces according to the point position spatial distribution of the clustered microseismic events.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic diagram of the DBSCAN algorithm of the present invention;
FIG. 3 is a seismic source spatial location distribution diagram obtained by positioning after acquiring microseismic data of a coal mine within one month;
FIG. 4 is a distribution diagram of the seismic source spatial locations of FIG. 3 with outlier detection excluded from outlier isolated spatial points;
FIG. 5 is a distribution map of the spatial locations of the microseismic events of FIG. 4 obtained by spatial location clustering of the microseismic events;
FIG. 6 is a top view of the two-dimensional spatial distribution of the water flowing fracture structure obtained by performing linear fitting on the clustering result in FIG. 5;
FIG. 7 is a north view of the two-dimensional spatial distribution of the water flowing fracture structure obtained by performing linear fitting on the clustering result in FIG. 5;
FIG. 8 is an east view of the two-dimensional spatial distribution of the water flowing fracture structure obtained by performing linear fitting on the clustering results in FIG. 5;
fig. 9 is a schematic diagram of three-dimensional spatial distribution of the water flowing fracture structure obtained by performing linear fitting on the clustering result in fig. 5.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
A method for exploring a water flowing fracture structure in an underburden of a coal seam floor is disclosed, and as shown in figure 1, (1) a micro-seismic monitoring system is constructed on a coal seam working face, and seismic wave signals in a mine during coal seam mining are monitored and collected in real time.
(2) Detecting microseismic events caused by rock mass fracture in an underburden of a coal seam floor, comprising:
the method for detecting the microseismic event generated by rock mass fracture in the underlying rock stratum of the coal seam floor by utilizing the long-time window energy ratio method specifically comprises the following steps:
the microseismic event generated by rock mass fracture in the underburden of the coal seam floor is detected by adopting the following formula:
wherein, STA (i) is the short time window signal energy average value at the sampling time i, and LTA (i) is the long time window signal energy average value at the sampling time i;
ns is the length of the short time window, nl is the length of the long time window, and lambda is a set trigger threshold, and different values need to be set for ns, nl and lambda according to different geological conditions;
CF (j) is a characteristic function on the microseismic signal that characterizes the amplitude, energy or variation thereof of the microseismic data.
The principle of the method is that a sliding long-time window LTA is given based on original seismic data, a short-time window STA is selected in the window, the end points or the starting points of the two windows coincide, and the change of the amplitude or the energy of a microseismic signal is reflected by the ratio of the short-time window signal energy average value (STA) to the long-time window signal energy average value (LTA).
The short time window signal energy average (STA) mainly reflects the average of microseismic signals and the long time window signal energy average (LTA) mainly reflects the average of background noise. At the time of arrival of the microseismic signal, the signal in the short time window STA changes faster than the signal in the long time window LTA, i.e. in the presence of the microseismic signal, the energy in the short time window STA increases rapidly and the energy in the long time window LTA increases more slowly. Correspondingly, the ratio of the short-time window signal energy average value (STA) to the long-time window signal energy average value (LTA) is obviously increased, and when the ratio is greater than a certain threshold value, the microseismic event can be judged to occur, so that the purpose of automatic detection is achieved.
(3) According to the absolute arrival time data of transverse waves and longitudinal waves in the microseismic event, the seismic source is spatially positioned (the spatial position information of the underground microseismic event is determined, and the spatial position information comprises three position information of north direction, east direction and depth), and the method comprises the following steps:
and (3) combining absolute arrival time data of transverse waves and longitudinal waves in the microseismic event and relative arrival time data of seismic data in a seismic catalog, and performing spatial positioning on the seismic source by adopting a double-difference-based seismic tomography method.
The double-difference-based seismic tomography method is shown as the following formula:
the method combines absolute arrival time data of transverse waves and longitudinal waves in microseismic events and relative arrival time data of seismic data in a seismic catalog, reduces errors caused by absolute arrival time in a conventional seismic imaging method, and accurately and efficiently inverts rock mass structures inside and outside a source area, and relative positions and absolute positions of seismic events.
(4) Detecting abnormal points of the space position of the seismic source, and eliminating outlier isolated space points, wherein the method comprises the following steps:
the method comprises the following steps of detecting abnormal points of a seismic source space position by adopting an LOF (Local outlier factor) algorithm, and excluding outlier isolated space points, wherein the method specifically comprises the following steps:
s41, calculating a k-proximity distance: calculating k-proximity distance k-distance (p) between the kth nearest point and the data point p among the several points nearest to the data point p;
s42, acquiring the nearest neighbor point of the data point p: for the data point p, the data point which is not more than k-adjacent distance k-distance (p) of the data point p is the nearest adjacent point of the data point p, and the number of the data points is marked as N k (p)；
S43, calculating the reachable distance: the reachable distance between the data point p and the data point o is the k-adjacent distance k-distance (o) of the data point o, and the maximum value of the direct distance d (p, o) between the data point p and the data point o, which is expressed by the following formula:
reach_dist k (p，o)＝max{k-distance(o)，d(p，o)}；
s44, calculating local reachable density: the local reachable density of a data point p is the reciprocal of the average reachable distance from the nearest neighbor, and is expressed by the following formula:
s44, calculating a local abnormal factor: the local anomaly factor is defined by local relative density and is expressed by the following formula:
s45, if the local abnormal factor of the data point p is close to 1, the local density of the data point p is close to the nearest point; if the local anomaly factor for data point p is less than 1, indicating that data point p is in a relatively dense region; if the local anomaly factor of the data point p is greater than 1, indicating that the data point p is relatively distant from other data points, and possibly an anomaly point;
s46, sorting all data points based on the size of the local abnormal factor, determining abnormal points after setting a threshold value, and excluding the abnormal points.
(5) Spatial location clustering of microseismic events, comprising:
the method for clustering the space positions of the microseismic events by adopting the DBSCAN algorithm specifically comprises the following steps:
s51, initializing a core object set omega = theta, initializing the number n of clustering clusters, initializing an unvisited sample set gamma = D, and setting a clustering cluster set C = theta;
s52, inputting a sample set D = { x = 1 ,x 2 ,…,x m And calculating the Euclidean distance to obtain a sample x j Epsilon-neighborhood subsample set N ε (x j )；
S53, if the subsample set N ε (x j ) Is not less than N, sample x j Adding a core object set Ω = Ω & { x j }；
S54, if the core object set omega = theta, directly ending, otherwise, entering S55;
s55, randomly selecting a core object o in a core object set omega, and initializing a current core object queue omega cur = o, initializing class number k = n +1, initializing current cluster set C k = o, update unvisited sample set Γ = Ω -C k ；
S56, if the current core object queue omega cur = theta, then cluster C is currently clustered k After the generation is finished, outputting the cluster C = { C = { C = 1 ,C 2 ,…,C k }, updating the core object set omega = omega-C k And proceeding to S54, otherwise updating the core object set Ω = Ω -C k ；
S57, in the current core object queue omega cur Taking out a core object o', calculating Euclidean distance, and obtaining an epsilon-neighborhood subsample set N of the core object o ε (o') let Δ = N ε (o') # Γ, update the current clustered cluster C k ＝C k And E, updating an unaccessed sample set gamma = gamma-delta and updating a current core object queue omega-delta cur ＝Ω cur U.g., (. DELTA. # Ω) -o' and enters S56.
The DBSCAN algorithm is a density-based machine learning clustering method that can divide area points having a sufficiently high density into clusters and find clusters having an arbitrary shape in data having noise.
The density of the DBSCAN algorithm is defined as follows: for sample set D = { x 1 ,x 2 ,…,x m There are:
1) ε -neighborhood: for arbitrary x j E.g. D, whose e-neighborhood contains the sum x in the sample set D i A set of subsamples N of distances not greater than epsilon (set by itself) ε (x j )＝{x j ∈D|distance(x i ,x j ) ≦ ε }, the number of subsample sets is denoted as | N ε (x j )|；
2) Core object: for arbitrary x j E.g. D, if e-neighborhood corresponds to the subsample set N ε (x j ) Containing at least N (self-setting) samples, i.e.|N ε (x j ) If | is not less than N, then x j Is a core object;
3) The density is up to: if x i At x j E-neighborhood of (a), and x j Is a core object, then x i From x j The density is direct;
4) The density can reach: for sample x i And x j If there is a sample sequence p 1 ,p 2 ,…,p T Satisfy p 1 ＝x i ，p T ＝x j And p is t+1 From p t Density is direct, x i And x j The density can be reached;
5) Density connection: for sample x i And x j If there is a core object x k So that the sample x i And x j Are all x k Density can be reached, then x i And x j The densities are connected.
As shown in fig. 2, N is 5, red dots are core objects, epsilon-neighborhoods of the core objects are indicated by circles, black dots are non-core objects, all the samples with direct density of the core objects are in a hyper-volume sphere with the red core objects as the center, the core objects connected by green snips constitute a sequence of samples with reachable density, and all the samples in the epsilon-neighborhoods of the sequence of samples with reachable density are connected with density.
(6) Carrying out linear fitting on the clustering result to obtain two-dimensional and three-dimensional spatial distribution of the water flowing fracture structure, and the method comprises the following steps:
linear fitting the clustering results using the following formula:
y＝f(x,b)＝b i x 1 +b 2 x 2 +...+b n x n +c；
where y is the independent variable, x is the dependent variable, b is the linear solution, and c is a constant.
Linear fitting is a form of curve fitting, and for two-dimensional data or multidimensional data, an optimal fitting equation is found to describe the relationship between independent variables and dependent variables, and the fitting equation is shown as the above formula.
As shown in fig. 3 to 5, a practical case is given by combining the technical solution of the present application:
FIG. 3 is a seismic source spatial location distribution diagram obtained by positioning after acquiring microseismic data of a coal mine within one month; fig. 4 is a distribution diagram of the seismic source spatial position obtained by excluding outlier isolated spatial point locations after anomaly detection in fig. 3, where data is more compact and outlier isolated spatial point locations are excluded after two fifths of the outlier in fig. 3 is excluded; FIG. 5 is a diagram of the microseismic event spatial location distribution obtained by spatial location clustering of microseismic events of FIG. 4, wherein darker colored points are the clustering results of two clusters and lighter colored points are noise points.
After the clustering result is subjected to linear fitting, two-dimensional and three-dimensional spatial distribution of the water flowing fracture structure can be obtained:
FIG. 6 is a top view of the two-dimensional spatial distribution of the water flowing fracture structure obtained by performing linear fitting on the clustering result in FIG. 5; FIG. 7 is a north view of the two-dimensional spatial distribution of the water flowing fracture structure obtained by performing linear fitting on the clustering result in FIG. 5; FIG. 8 is an east view of the two-dimensional spatial distribution of the water flowing fracture structure obtained by linear fitting of the clustering results in FIG. 5;
fig. 9 is a schematic diagram of three-dimensional spatial distribution of the water flowing fracture structure obtained by performing linear fitting on the clustering result in fig. 5.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (9)
1. A method for exploring a water flowing fracture structure in an underburden of a coal seam floor is characterized by comprising the following steps: the method comprises the following steps:
s1, constructing a micro-seismic monitoring system on a coal seam working face, and monitoring and acquiring seismic wave signals in a mine in a coal seam mining process in real time;
s2, detecting a microseismic event generated by rock mass fracture in the underburden of the coal seam floor;
s3, carrying out space positioning on the seismic source according to absolute arrival time data of transverse waves and longitudinal waves in the microseismic event;
s4, detecting abnormal points of the space position of the seismic source, and eliminating outlier isolated space points;
s5, carrying out spatial position clustering on the microseismic events;
and S6, performing linear fitting on the clustering result to obtain two-dimensional and three-dimensional spatial distribution of the water flowing fracture structure.
2. The method of exploring for water-conducting fracture structures in a coal seam floor underburden according to claim 1, characterized by: in S2, the method for detecting the microseismic event generated by rock mass fracture in the underburden of the coal seam floor comprises the following steps:
and detecting the microseismic event generated by rock mass fracture in the underburden of the coal seam floor by using a long-time window energy ratio method.
3. The method of exploring for water-conducting fracture structures in a coal seam floor underburden according to claim 2, characterized by: the method for detecting the microseismic event generated by rock mass fracture in the underlying rock stratum of the coal seam floor by using the long-time window energy ratio method comprises the following steps:
the microseismic event generated by rock mass fracture in the underburden of the coal seam floor is detected by adopting the following formula:
wherein, STA (i) is the short time window signal energy average value at the sampling time i, LTA (i) is the long time window signal energy average value at the sampling time i;
ns is the length of the short time window, nl is the length of the long time window, and lambda is a set trigger threshold, and different values need to be set for ns, nl and lambda according to different geological conditions;
CF (j) is a characteristic function on the microseismic signal that characterizes the amplitude, energy or variation thereof of the microseismic data.
4. The method of claim 1 for exploring the structure of water-conducting fractures in a coal seam floor underburden, wherein: and S3, performing space positioning on the seismic source according to absolute arrival time data of transverse waves and longitudinal waves in the microseismic event, wherein the space positioning comprises the following steps:
and performing relative spatial positioning on the seismic source by adopting a double-difference-based seismic positioning method by combining absolute arrival time data of transverse waves and longitudinal waves in the microseismic event and relative arrival time data of seismic data in a seismic catalog.
5. The method of exploring for water-conducting fracture structures in a coal seam floor underburden according to claim 1, characterized by: and S4, abnormal point detection is carried out on the space position of the seismic source, and outlier isolated space point positions are eliminated, wherein the method comprises the following steps:
and (3) carrying out outlier detection on the seismic source space position by adopting an LOF algorithm, and excluding outlier isolated space point positions.
6. The method of exploring for water-conducting fracture structures in a coal seam floor underburden according to claim 5, characterized by: the method for detecting the outliers of the seismic source spatial position by adopting the LOF algorithm and eliminating the outlier isolated spatial point comprises the following steps:
s41, calculating a k-proximity distance: calculating k-proximity distance k-distance (p) between the kth nearest point and the data point p among the several points nearest to the data point p;
s42, acquiring the nearest neighbor point of the data point p: for data point p, the distance from data point p is not greater than the k-neighborhood distance k-The data point of distance (p) is the nearest neighbor of the data point p, and the number of the nearest neighbor is denoted as N k (p)；
S43, calculating the reachable distance: the reachable distance between the data point p and the data point o is k-distance (o) of the data point o, and the maximum value of the direct distance d (p, o) between the data point p and the data point o is represented by the following formula:
reach_dist k (p，o)＝max{k-distance(o)，d(p，o)}；
s44, calculating local reachable density: the local reachable density of a data point p is the reciprocal of the average reachable distance from the nearest neighbor, and is expressed by the following formula:
s44, calculating a local abnormal factor: the local anomaly factor is defined by local relative density and is represented by the following formula:
s45, if the local abnormal factor of the data point p is close to 1, the local density of the data point p is close to the nearest point; if the local anomaly factor of the data point p is less than 1, indicating that the data point p is in a relatively dense area; if the local anomaly factor of the data point p is greater than 1, indicating that the data point p is relatively distant from other data points, and possibly being an anomaly point;
s46, sorting all data points based on the size of the local abnormal factor, determining abnormal points after setting a threshold value, and excluding the abnormal points.
7. The method of exploring for water-conducting fracture structures in a coal seam floor underburden according to claim 1, characterized by: and S5, spatial position clustering is carried out on the microseismic events, and the method comprises the following steps:
and (4) clustering the spatial position of the microseismic event by adopting a DBSCAN algorithm.
8. The method of exploring for water-conducting fracture structures in a coal seam floor underburden according to claim 7, wherein: the method for clustering the spatial positions of the microseismic events by adopting the DBSCAN algorithm comprises the following steps:
s51, initializing a core object set omega = theta, initializing the cluster number n, initializing an unaccessed sample set gamma = D, and setting a cluster set C = theta;
s52, inputting a sample set D = { x = 1 ,x 2 ,…,x m And calculating the Euclidean distance to obtain a sample x j Epsilon-neighborhood subsample set N ε (x j )；
S53, if the subsample set N ε (x j ) Is not less than N, sample x j Adding a core object set Ω = Ω & { x j }；
S54, if the core object set omega = theta, directly ending, otherwise, entering S55;
s55, randomly selecting a core object o from the core object set omega, and initializing a current core object queue omega cur = o, initializing class number k = n +1, initializing current cluster set C k = o, update unaccessed sample set Γ = Ω -C k ；
S56, if the current core object queue omega cur = theta, then cluster C is currently clustered k After the generation is finished, outputting the cluster C = { C = { C = 1 ,C 2 ,…,C k }, updating the core object set omega = omega-C k And proceeding to S54, otherwise updating the core object set Ω = Ω -C k ；
S57, in the current core object queue omega cur Taking out a core object o', calculating Euclidean distance, and obtaining an epsilon-neighborhood subsample set N of the core object o ε (o') let Δ = N ε (o'), # Γ, update the current cluster set C k ＝C k And E, updating an unaccessed sample set gamma = gamma-delta and updating a current core object queue omega-delta cur ＝Ω cur U (. DELTA.andgate. OMEGA) -o' and proceeds to S56.
9. The method of exploring for water-conducting fracture structures in a coal seam floor underburden according to claim 1, characterized by: and S6, performing linear fitting on the clustering result to obtain two-dimensional and three-dimensional spatial distribution of the water flowing fracture structure, wherein the method comprises the following steps:
linear fitting the clustering results using the following formula:
y＝f(x，b)＝b 1 x 1 +b 2 x 2 +...+b n x n +c；
where y is an independent variable, x is a dependent variable, b is a linear solution, and c is a constant.
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