CN110414675B - Underground shallow seismic source positioning method based on deep learning - Google Patents
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Abstract
The invention relates to an underground shallow seismic source positioning method based on deep learning, which comprises the following steps: the method comprises the steps of distributing a distributed vibration sensor array, generating a learning sample, setting a seismic source bomb position corresponding to a three-dimensional energy field image sample as a training label, constructing a deep learning network frame, training a network and positioning an actual explosion seismic source. The method reduces the intermediate steps of positioning parameter extraction, positioning model modeling, positioning model calculation and the like in the traditional shallow seismic source positioning process, greatly improves the seismic source positioning efficiency, eliminates the positioning blind area, reduces the dependence of seismic source positioning precision on the channel reconstruction precision of the monitored area, and provides a new seismic source positioning method for the underground shallow seismic source positioning.
Description
Technical Field
The invention belongs to the technical field of blasting vibration testing, and particularly relates to an underground shallow seismic source positioning method based on deep learning, which is suitable for fixed point group striking positioning under the condition of unknown geological structures.
Background
The underground shallow seismic source refers to an event which occurs in a space with the depth of the underground seismic source not exceeding 100m, and passive positioning of the underground shallow seismic source is a main way for solving the problems of high-value ammunition underground explosion point positioning, penetration track measurement and the like in the military field; the method is an important means for realizing geological monitoring, engineering blasting, cultural relic anti-theft monitoring, coal mine exploration, earth surface structure composition analysis, geological structure exploration, underground rare mineral exploration, petroleum exploration and excavation and the like in the civil field.
Compared with the seismic source positioning in large areas, large equivalent, large depth and long time such as deep earthquake, coal mine deep mining, oil exploration and the like, the seismic source positioning has the following characteristics: (1) the depth of an underground seismic source is shallow, generally not more than 100m, the shallow geological structure is complex and unknown, and a shallow velocity field model cannot be established by using a deep crustal structure model; (2) the shock wave group is complex to be aliased, the constitutive property of the explosion near-field soil is elastoplasticity, the elastic wave is greatly influenced by ground reflection and refraction, and the seismic phase characteristic is not obvious; (3) underground media are complex, transmission channels are more complex and uneven, and the transmission channels often comprise various types such as floating soil, rocks, sand and stones, dense soil and the like; (4) the positioning precision requirement is high, the three-dimensional positioning error in a 100m scattering area is less than 1m, the requirement of hundred-meter positioning error in large-depth seismic positioning is different, and the method belongs to the problem of small-area high-precision positioning.
At present, in underground space positioning, deep seismic positioning is mainly aimed at deep seismic positioning, and is not directly used in the shallow small area positioning method, but the deep seismic positioning method cannot be directly applied to shallow positioning, and the following problems mainly exist:
(1) the travel time positioning method based on Geiger mainly solves the problem of positioning the initiation point under the conditions of uniform geology and known speed, but for complicated geological conditions such as stratiform and hollow, the speed parameter can only be set as an average value, and the positioning error is large;
(2) based on a polarization angle positioning method, the method can realize quick positioning of the initiation point by adopting a small number of observation nodes, but under the condition of shallow complex geology and particularly when a strong reflection interface exists, the surface incident angle cannot represent the real ray path between a seismic source and a sensor receiving point, so that a positioning false image exists;
(3) a joint iterative inversion positioning (SSH) model of a multi-seismic source and a velocity structure is suitable for the problem of fixed point group striking positioning under the condition of unknown geological structures, wherein the positioning precision of a detonation point is influenced by the modeling precision of a velocity field and the excitation times of the seismic source. Due to the fact that the single initiation point is positioned, shot projection data are limited, velocity matrixes are sparse, and positioning accuracy is still difficult to guarantee although constraint means such as regularization are added.
Disclosure of Invention
The invention provides an underground shallow seismic source positioning method based on deep learning, which aims to solve the technical problems that: the problem that the shallow small-area target cannot be positioned with high precision.
In order to solve the technical problems, the invention provides a deep learning-based underground shallow seismic source positioning method which is characterized by comprising the following steps:
s1, distributing a distributed vibration sensor array: presetting a coordinate origin at monitoring area randomly, arranging n sensors on the earth surface at a certain angle as a rotation interval and a certain length as an increasing radius to form a spiral array, and acquiring coordinate information (x) of each sensorj,yj,zj)(j＝1,2,3,...,n)；
S2, generating a learning sample: performing grid division on a monitoring area, presetting a seismic source bomb in the monitoring area, obtaining a three-dimensional energy field image sequence corresponding to all sampling points after one seismic source bomb explodes, and taking the three-dimensional energy field image sequence as a learning sample;
s3, using the seismic source bomb position corresponding to the three-dimensional energy field image sample as a training label;
s4, constructing a deep learning network framework;
s5, matching the sample generated in the step S2 with the corresponding seismic source position determined in the step S3 to form a data set, training a network, and obtaining a trained deep learning network;
s6, seismic source positioning: and acquiring a vibration signal generated by actual explosion by using the sensor array to obtain a three-dimensional energy field image sequence corresponding to an actual seismic source, randomly extracting samples from the three-dimensional energy field image sequence, and sending the samples into a trained deep learning network to obtain an output result, namely the seismic source coordinate.
Has the advantages that:
(1) the method adopts a deep learning method, takes a monitoring area as a black box, takes a three-dimensional energy field reconstructed by a sensor acquiring signal as input, takes a seismic source position as output, and establishes the identification learning method of the seismic source position in the energy field.
(2) Compared with the prior art, the method has the advantages that the sensor array is distributed in a spiral mode, so that the receiving range of vibration signals is expanded, the seismic phase information redundancy under different explosive center distances is increased, the positioning blind area is eliminated, the focusing degree of an energy field is improved, the data sample size in the axial direction and the shearing direction is expanded, and the data guarantee is provided for seismic source positioning;
(3) the method comprises the steps of dividing grids in a monitoring area, setting each grid as a virtual seismic source, filling the grids by a reverse time migration amplitude superposition method, constructing a three-dimensional energy field image containing seismic source information, and acquiring time sequence information of signals by using a sensor to obtain a three-dimensional energy field image sequence containing the seismic source information.
Drawings
FIG. 1 is a positioning flow chart of the present invention;
FIG. 2 is a sensor stationing diagram;
FIG. 3 is a schematic view of monitoring area meshing;
fig. 4 is a diagram of a deep learning network architecture.
Detailed Description
In order to make the objects, contents and advantages of the present invention clearer, the following detailed description of the embodiments of the present invention is provided.
The invention provides an underground shallow seismic source positioning method based on deep learning, which is characterized by comprising the following steps:
s1, distributing distributed vibration sensor array
Presetting a coordinate origin at monitoring areas randomly, arranging n sensors on the earth surface by taking 10 degrees as rotation intervals and 1m as an increasing radius according to a clockwise direction to form a spiral array, and acquiring coordinate information (x) of each sensor by using high-precision Beidouj,yj,zj)(j＝1,2,3,...,n)；n＝44；
S2, generating a learning sample
S2.1: dividing a monitoring area grid:
as shown in fig. 3, the monitored area is gridded, and the space is divided into N cubic grids with the same size (according to the positioning accuracy requirement), so as to obtain each grid coordinate (h)i,li,ki)(i＝1,2,3,...,N)；
S2.2: acquiring a preset seismic source signal:
respectively presetting 1 seismic source bomb in 4 quadrants of a monitoring area, detonating the seismic source bomb in the first quadrant, and acquiring a vibration signal generated by explosion by using the vibration sensor array;
s2.3, extracting the arrival time of the first arrival wave:
obtaining the first-arrival time information of all sensor signals by a first-arrival time extraction method (such as a long-short time window method);
s2.4, sequentially detonating the seismic source bombs from the second quadrant to the fourth quadrant, and repeating the step S2.3 to obtain corresponding first arrival times after the seismic source bombs in different quadrants are detonated;
s2.5: establishing a monitoring area velocity field model:
after first arrival time information of 4 explosions is obtained, the first arrival time information, corresponding sensor position information and position information of four seismic source bombs are used as input parameters, an underground shallow velocity field is established by adopting a first arrival travel time chromatography, and corrected velocity field information is obtained by a parabolic interpolation shortest path ray tracing method (PTISPR);
s2.6: calculating travel time information of each grid
After obtaining the velocity field information, in each grid (h)i,li,ki) As a virtual source, its propagation to each sensor (x) is calculatedj,yj,zj) Travel time information t of (j ═ 1,2, 3.., n)ij；
S2.7: generating a three-dimensional energy field image under the explosion condition of a seismic source bomb
Taking the ith grid as an example, assuming that the ith grid is a virtual seismic source and the total sampling point number of each sensor is M, the vibration signal acquired by the jth sensor is processed according to the obtained time tijReverse shift is carried out, namely the vibration signal collected by the jth sensor is pushed back to the 0 moment direction from the time axisijI.e. removing tijAdding a corresponding number of 0 to the tail of a sample point before the moment to keep the total number of sampling points as M, repeating the steps, performing reverse time migration on the signals acquired by the n sensors, superposing the deflected sensor array signals by the amplitude corresponding to the sampling point M to obtain the energy sum corresponding to the sampling point M by the ith grid
In the formula: a isj(m)The amplitude of the jth sensor at the sampling point m; n is the number of sensors; t is the length of a time window for acquiring signals;
circularly traversing all grids in the region, and sequentially filling energy to obtain a three-dimensional energy map corresponding to the m-th sampling point;
s2.8: generating a three-dimensional energy field image sequence corresponding to a preset seismic source
At a sampling rate of 1/fsSequentially stepping, repeating S2.7, and sequentially performing amplitude superposition refilling at different sampling points to obtain a three-dimensional energy field image sequence corresponding to all the sampling points after the seismic source bomb explodes;
s2.9: expanding sample volume
Repeating S2.7-2.8 to obtain a three-dimensional energy field image sample sequence corresponding to 4 times of seismic source bomb explosion;
s3, using the position of a seismic source bomb corresponding to the three-dimensional energy field image sample as a training label;
s4, constructing a deep learning network framework
As shown in table 1, the seismic source deep learning network is formed by sequentially connecting a first convolution layer, a first close-coupled module, a second convolution layer, a first pooling layer, a second close-coupled module, a third convolution layer, a second pooling layer, a third close-coupled module, a fourth convolution layer and a third pooling layer.
Wherein the close-connected module consists of 5 convolution layers which are connected in pairs to form 10 connection modes. Except for the first convolutional layer, all other convolutional layers use an implicit regularization processing method with a probability of 20 percent such as: random inactivation (dropout algorithm).
TABLE 1 seismic source deep learning network architecture
S5. training network
S5.1, matching the learning samples generated in the step S2 with the corresponding seismic source positions determined in the step S3 to form a data set (S2, S3), randomly extracting 80% of the data set as training samples, and taking the rest 20% of the data set as verification samples;
s5.2, inputting the training samples into the deep learning network to obtain parameter information of each layer of the deep learning network framework;
s5.3, substituting the parameters into a deep learning network frame, inputting the verification sample into the deep learning network to obtain the information of the predicted seismic source position, and comparing the information with the seismic source position corresponding to the verification sample to obtain the prediction precision of the model;
and S5.4, if the prediction accuracy is not ideal, continuously adjusting the size and the number of each layer of convolution kernel until the expected target is reached to obtain the trained deep learning network.
S6, seismic source positioning
After the iterative training is finished, the actual seismic source positioning is started.
S6.1, acquiring a vibration signal generated by actual explosion by using a sensor array;
and S6.2, on the basis of the monitoring area grid information and the velocity field model obtained in the step 2, repeating the step 2.3 and the step 2.6-the step 2.8 to obtain a three-dimensional energy field image sequence corresponding to the actual seismic source.
S6.3, randomly extracting 50 samples from the data and sending the samples into a trained deep learning network; and (4) obtaining accurate source coordinates by using 50 sets of positioning results which are correspondingly output in a weighted average mode.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, it is possible to make various improvements and modifications without departing from the technical principle of the present invention, and those improvements and modifications should be considered as the protection scope of the present invention.
Claims (9)
1. A deep learning-based underground shallow seismic source positioning method specifically comprises the following steps:
s1, distributing a distributed vibration sensor array: presetting a coordinate origin at monitoring area randomly, arranging n sensors on the earth surface at a certain angle as a rotation interval and a certain length as an increasing radius to form a spiral array, and acquiring coordinate information (x) of each sensorj,yj,zj)(j＝1,2,3,...,n)；
S2, generating a learning sample: performing grid division on a monitoring area, presetting a seismic source bomb in the monitoring area, obtaining a three-dimensional energy field image sequence corresponding to all sampling points after the seismic source bomb explodes, and taking the three-dimensional energy field image sequence as a learning sample; the method specifically comprises the following steps:
s2.1: dividing a monitoring area grid:
carrying out grid division on the monitoring area, dividing the space into N cubic grids with the same size, and obtaining each grid coordinate (h)i,li,ki)(i＝1,2,3,...,N)；
S2.2: acquiring a preset seismic source signal:
respectively presetting 1 seismic source bomb in 4 quadrants of a monitoring area, detonating the seismic source bomb in the first quadrant, and acquiring a vibration signal generated by explosion by using the vibration sensor array;
s2.3: extracting the arrival time information of the first arrival waves of all the sensor signals;
s2.4: sequentially detonating the seismic source bombs from the second quadrant to the fourth quadrant, and repeating the step S2.3 to obtain corresponding arrival times of the seismic source bombs in different quadrants after detonation;
s2.5: establishing a monitoring area velocity field model;
s2.6: calculating the travel time information of each grid:
with each grid (h)i,li,ki) As a virtual source, its propagation to each sensor (x) is calculatedj,yj,zj) Travel time information t of (j ═ 1,2, 3.., n)ij；
S2.7: generating a three-dimensional energy field image under the explosion condition of a seismic source bomb;
s2.8: repeating S2.7 by taking the sampling rate as the step length, sequentially progressing, and sequentially performing amplitude superposition refilling on different sampling points to obtain a three-dimensional energy field image sequence corresponding to all the sampling points after the seismic source bomb explodes;
s2.9: repeating S2.7-2.8 to obtain a three-dimensional energy field image sample sequence corresponding to 4 times of seismic source bomb explosion;
s3, using the seismic source bullet position corresponding to the learning sample as a training label;
s4, constructing a deep learning network framework;
s5, inputting the learning sample generated in the step S2 and the training label determined in the step S3 as a data set into the network constructed in the step S4 to train the network, and obtaining a trained deep learning network;
s6, seismic source positioning: and acquiring a vibration signal generated by actual explosion by using the sensor array to obtain a three-dimensional energy field image sequence corresponding to an actual seismic source, randomly extracting samples from the three-dimensional energy field image sequence, and sending the samples into a trained deep learning network to obtain an output result, namely the seismic source coordinate.
2. The method for deep learning-based seismic source location of the shallow underground layer as claimed in claim 1, wherein in S1, the origin of coordinates is randomly preset in the monitoring area, n sensors are arranged on the ground surface at a rotation interval of 10 ° and a growth radius of 1m in a clockwise direction, and a spiral array is formed.
3. The method as claimed in claim 1, wherein in S2.7, assuming that the ith grid is a virtual seismic source, the total number of sampling points of each sensor is M, and the seismic signal collected by the jth sensor is processed according to the obtained travel time tijReverse shift is performed to remove tijSample points before the moment, and 0 with corresponding quantity is supplemented at the tail part of the sample to keep the total sampling point number as M, and by analogy, the signals collected by n sensors are reversely shifted, the shifted sensor array signals are superposed by the amplitude corresponding to the sampling point M to obtain the energy sum corresponding to the sampling point M of the ith grid
In the formula: a isj(m)The amplitude of the jth sensor at the sampling point m is shown; n is the number of sensors;
and circularly traversing all grids in the region, and sequentially filling energy to obtain a three-dimensional energy map corresponding to the m-th sampling point.
4. The deep learning-based method for locating a seismic source at a shallow underground layer as claimed in claim 3, wherein in step S4, the seismic source deep learning network is formed by sequentially connecting a first convolutional layer, a first close-coupled module, a second convolutional layer, a first pooling layer, a second close-coupled module, a third convolutional layer, a second pooling layer, a third close-coupled module, a fourth convolutional layer and a third pooling layer.
5. The deep learning-based method for locating seismic sources in shallow layers of the earth as claimed in claim 4, wherein the close-coupled modules are composed of 5 convolutional layers, and are connected in pairs to form 10 connection modes.
6. The method for locating the seismic source of the shallow stratum based on the deep learning as claimed in any one of claims 1 to 5, wherein S5 comprises the following steps:
s5.1, matching the learning sample generated in the S2 with the corresponding seismic source position determined in the S3 to form a data set, randomly extracting a part of the data set to serve as a training sample, and using the rest as a verification sample;
s5.2, inputting the training samples into the deep learning network to obtain parameter information of each layer of the deep learning network framework;
and S5.3, substituting the parameters into a deep learning network frame, inputting the verification sample into the deep learning network to obtain the predicted seismic source position information, and comparing the predicted seismic source position information with the real seismic source position corresponding to the verification sample to obtain the prediction precision of the model.
7. The deep learning-based method for locating seismic sources in shallow layers of the earth as claimed in claim 6, wherein if the prediction accuracy of S5.3 is not ideal, the sizes and the number of the convolution kernels in each layer are continuously adjusted until the expected target is reached, so as to obtain the trained deep learning network.
8. The method as claimed in claim 7, wherein in S6, 50 samples are randomly extracted and fed into the trained deep learning network, and the source coordinates are obtained by weighted averaging of the corresponding 50 sets of output positioning results.
9. The deep learning-based underground shallow seismic source positioning method according to any one of claims 1 to 5, wherein in S2.5, an underground shallow velocity field is established by using a first-arrival travel-time chromatography method by taking the acquired first-arrival time information, the sensor position information corresponding to the first-arrival time information and the position information of seismic source bombs as input parameters, and the corrected velocity field information is obtained by using a parabolic interpolation shortest path ray tracing method.
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