CN117471532A - Ground stress inversion method of deep rock mass based on blasting seismic wave velocity - Google Patents
Ground stress inversion method of deep rock mass based on blasting seismic wave velocity Download PDFInfo
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- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
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Abstract
According to the method for inverting the ground stress of the deep rock mass based on the explosion seismic wave velocity, the explosion seismic wave velocity of the deep rock mass is obtained by measuring the deep rock mass according to the field monitoring quantity, parameter inversion is carried out by referring to a network with a structure of pix2pix, the network takes the wave velocity propagation data of a seismic wave field as input, the corresponding actual value of a seismic wave velocity model is trained, thus an inversion model is built, certain initial parameters of the rock mass are calculated reversely, the purpose is to build a proper model, the prediction result is consistent with the result of a field test, the mechanical characteristics of the deep rock mass can be accurately reflected or predicted, a data set model conforming to the actual geological situation is set aiming at the explosion seismic wave velocity inversion problem of the deep rock mass, and the network pre-training of the supervision learning is successfully carried out on the tunnel seismic wave velocity inversion network by training, so that the network parameter initialization is completed, and the effectiveness of the parameter inversion network is proved.
Description
Technical Field
The invention relates to the field of underground tunnel and underground engineering rock mass ground stress inversion, in particular to a deep rock mass ground stress inversion method based on blasting seismic wave velocity.
Background
In recent years, due to the increasing exhaustion of shallow resources, development is started towards the exploitation of deep resources at home and abroad. The damage of the deep rock mass is the result of the combined action of high ground stress and explosion impact load, and particularly in the field of researching the seismic wave velocity generated after the ground stress is blasted to the deep rock mass, the influence caused by the ground stress state must be considered in the design and construction of the deep tunnel and underground engineering. The information of the ground stress state of the rock mass can be obtained through ground stress inversion, so that the optimization of engineering design and construction schemes is facilitated, and the engineering quality and safety are improved. Therefore, the ground stress factors influencing the blasting seismic waves are fully considered, and the method has great practical engineering significance on the influence of related blasting engineering.
At present, few ground stress inversion methods based on the seismic wave velocity are considered, and the existing seismic inversion methods need a large amount of tag data, namely a real wave velocity model is trained, and under the condition, the wave velocity model is difficult to acquire under the actual condition and needs to rely on more accurate wave velocity priori data; secondly, the data of the aspects such as the working condition of the field project, the drilling information, the excavation disclosure and the like cannot be effectively utilized, and the data cannot be completely matched with the actual detection condition, so that the obtained rock mass ground stress state information is inaccurate.
Therefore, in order to solve the above technical problems, it is necessary to provide a method for inversion of the ground stress of a deep rock based on the velocity of the blasted seismic wave.
Disclosure of Invention
The invention aims to:
the invention provides a ground stress inversion method of a deep rock mass based on explosion seismic wave velocity, which aims to solve the problem that the existing seismic inversion method requires a large number of real wave velocity models for training, but the wave velocity models are difficult to acquire under actual conditions; and secondly, knowledge in the aspects of field project working conditions, drilling information, excavation disclosure and the like cannot be effectively utilized, and the knowledge cannot be completely matched with actual detection conditions, so that the technical problem of inaccurate rock mass ground stress state information is caused.
The invention provides a ground stress inversion method of a deep rock mass based on explosion seismic wave velocity, which comprises the following specific steps:
step 1: the method comprises the steps of collecting seismic wave velocity information through arranging a wave detector, encoding geological environment information and combining drilling priori information, so that the arrangement and collection of tunnel seismic wave velocity data are established, and a seismic wave velocity database is provided for subsequent inversion;
step 2: performing supervised pre-training through a seismic wave velocity inversion database, establishing a tunnel wave velocity model inversion method and a pix2pix depth neural network model, performing overall-process specific blasting damage simulation by using a PD-FEM numerical calculation method and a PD-FEM numerical calculation system, establishing a surrounding rock damage constitutive model under the condition of high strain rate, and simulating deep surrounding rock blasting unloading damage effect;
Step 3: the rock mass model data are brought into a longitudinal wave instantaneous energy density model and a longitudinal wave average energy density model through deep surrounding rock blasting unloading damage effect simulation, so that a rock mass stress strain state is obtained, PD program operation conditions are provided for stress strain change under a blasting loading state, and further an inversion optimization mode of synchronous tunnel excavation is established, and an inversion database is updated;
step 4: dividing a concrete damaged part by updating an inversion optimization mode of synchronous tunnel excavation of an inversion database, correlating a mechanical model with stress waves, and pre-training geological environment vectors by using a deep neural network model by controlling the blasting stress wave velocity to obtain a tunnel inversion deep neural network model after parameter optimization so as to realize the ground stress inversion of a deep buried tunnel rock based on the blasting seismic wave velocity.
The invention has the beneficial effects that:
according to the method for inverting the ground stress of the deep rock mass based on the explosion seismic wave velocity, the explosion seismic wave velocity of the deep rock mass is obtained by measuring the deep rock mass according to the field monitoring quantity, parameter inversion is carried out by referring to a network with a structure of pix2pix, the network takes the wave velocity propagation data of the seismic wave field as input, the corresponding true value of the seismic wave velocity model is trained, thus an inversion model is built, certain initial parameters of the rock mass are calculated reversely, the purpose is to build a proper model, the prediction result is consistent with the result of the field test, the mechanical characteristics of the deep rock mass can be accurately reflected or predicted, a data set model conforming to the actual geological situation is set aiming at the explosion seismic wave velocity inversion problem of the deep rock mass, the network pre-training of the supervision learning is successfully carried out on the tunnel seismic wave velocity inversion network by training, the network parameter initialization is further completed, and the validity of the parameter inversion network is verified, so that the ground stress inversion of the deep rock mass based on the explosion seismic wave velocity is realized.
In addition, the ground stress inversion method also utilizes a blasting experiment to acquire seismic wave velocity and waveform data, directly inverts the ground stress distribution condition in the deep rock mass, and avoids the problems that the ground stress inversion method in the prior art needs to estimate the underground medium parameters and the influence of a seismic wave source. Meanwhile, the method is suitable for carrying out ground stress inversion in deeper rock mass, and can provide valuable information and support for geotechnical engineering and earthquake disaster prediction.
Drawings
FIG. 1 is a general flow chart of the method for inversion of the ground stress of a deep rock mass based on the velocity of a blasted seismic wave of the present invention;
FIG. 2 is a schematic diagram of the field observation and system arrangement of the deep rock blasting of the present invention;
FIG. 3 is a graph showing bond breakage damage determination in PD region according to the present invention;
FIG. 4 is a two-dimensional model of an inverted rock mass using near field dynamics in accordance with the present invention;
fig. 5 is a flowchart of a PD-FEM procedure according to an embodiment of the present invention.
Detailed Description
The invention is described in more detail below with reference to the drawings accompanying the specification.
The embodiment provides a ground stress inversion method of a deep rock mass based on explosion seismic wave velocity, which is used for measuring the explosion seismic wave velocity of the deep rock mass according to the field monitoring quantity.
The network takes the wave velocity propagation data of the seismic wave field as input to train the corresponding real value of the seismic wave velocity model by referring to a network with a structure of pix2pix, thereby establishing an inversion model and back-calculating certain initial parameters of the rock mass.
Aiming at the inversion problem of the deep rock burst seismic wave velocity, a dataset model conforming to geological significance is set, and network parameter initialization is successfully completed by carrying out preliminary supervised learning network pre-training on the tunnel seismic wave velocity inversion network through training, verification and testing on the parameter inversion network, so that inversion is realized.
The input stress wave data can be converted into an output seismic wave velocity model by performing parameter inversion through a pix2pix network. In stress wave inversion, pix2pix is used to convert the input seismic data into a deep rock burst seismic wave velocity model.
Specifically, pix2pix is used to convert the seismic data into a deep rock burst seismic wave velocity model.
And pre-training the pix2pix tunnel inversion deep neural network model in a supervision form containing a wave velocity model label by utilizing a pre-constructed tunnel inversion database, and preliminarily determining network model parameters.
The construction process of the pix2pix tunnel inversion database comprises the following steps: and constructing a pre-tunnel square wave velocity model by using the existing geological exploration report, obtaining corresponding seismic observation data through numerical simulation, and obtaining noisy seismic data and a wave velocity model which accord with field characteristics by combining field tunnel air mining noise signals to form a tunnel inversion database.
Seismic wave data is used as input data, deep rock blasting seismic waves are used as output data, and a generator model is trained by using pix2pix, so that the generator model can convert the input data into the output data. In the training process, a real deep rock burst seismic wave velocity model can be used as a target output, so that a generator can gradually learn how to convert seismic wave data into the deep rock burst seismic wave velocity model.
In stress wave inversion, the method can be used for converting seismic wave data into a deep rock burst seismic wave velocity model, so that the properties of the underground medium can be predicted more accurately.
The training set adopts successive blasting through a fixed seismic source according to a field geological wave velocity model of the rock mass, a wave detector is fixed on each face, field seismic wave velocity signals are collected in an observation mode, and the collected field seismic wave velocity signals are added into a rock mass blasting record to serve as data of a deep rock mass inversion database.
The obtained deep rock inversion database is used for setting a blasting deep rock model which accords with geological significance in combination with actual engineering, the size, the position and the distribution form of the deep rock model are rebuilt through training and testing of a data-model, and the tunnel inversion deep neural network model is subjected to pre-training in a supervision mode comprising wave velocity model labels, so that network model parameters are primarily determined.
The geological environment, noise information and observation system layout mode of the blasting deep rock mass are used as additional two channels and seismic observation data to be input into a tunnel inversion depth neural network model together, so that a tunnel engineering geological environment vector, an environment noise matrix and an observation mode matrix are formed; generating a drilling wave velocity matrix containing drilling position and wave velocity information under the condition of drilling prior information;
and the construction environment matrix and the observation mode matrix are used as two channels to be input into a tunnel pix2pix inversion depth neural network model together with the seismic observation data, the tunnel engineering geological environment vector is the other input of the tunnel inversion depth neural network model, the predicted wave velocity model output by the tunnel inversion depth neural network model is used for carrying out parameter inversion of measured data by adopting a parameter inversion network, and the result shows that the parameter inversion network is suitable for real data and has better inversion performance so as to optimize and update network parameters.
In conclusion, the property of the underground medium can be predicted by carrying out seismic wave velocity inversion through a pix2pix network, and the accurate inversion of the seismic wave velocity in front of the tunnel is realized by combining geological environment and observation information. By training a generator model, seismic wave data is taken as input, and a corresponding deep rock blasting seismic wave velocity model is generated as output. When the tunnel inversion database is constructed, a pre-tunnel square wave velocity model can be constructed by utilizing a field geological exploration report, and corresponding seismic observation data can be obtained through numerical simulation. Meanwhile, by combining the field tunnel air mining noise signals, noise-containing seismic data and a wave velocity model can be obtained. These data constitute a tunnel inversion database for training and validating pix2pix network models. In the training process, a real deep rock blasting seismic wave velocity model is used as a target output, so that a generator model gradually learns how to convert seismic wave data into the deep rock blasting seismic wave velocity model. And the inversion of the tunnel seismic wave velocity is realized by optimizing network parameters. And geological environment, noise information and an observation system layout mode are taken as additional channels to be input into a network model together with seismic observation data, so that more constraint and context information are provided, and inversion accuracy is improved.
As shown in fig. 1 and 2, a method of a ground stress inversion system of a deep rock based on a blasted seismic wave velocity includes the steps of:
step 1: the method has the advantages that the wave detector is arranged to collect the seismic wave velocity information, the geological environment information is encoded and the prior drilling information is combined, so that the arrangement and collection of the tunnel seismic wave velocity data are established, and a seismic wave velocity database is provided for subsequent inversion.
In deep rock mass construction, seismic wave signals carrying front bad geological body information are collected by arranging seismic sources and detectors on side walls and tunnel faces, so that detection and identification of a tunnel front bad geological structure are realized. In order to select the installation point positions of the wave detectors, five wave detectors are respectively installed on the left side and the right side of the side wall, every two wave detectors are separated by 10 meters, and the wave detectors which are 30 meters away from the face are nearest wave detectors. Meanwhile, a large number of front-tunnel square wave velocity models are generated, corresponding seismic observation data are obtained through numerical simulation, and the data are used as label data of a tunnel inversion depth neural network model. In addition, the geological environment vector of tunnel engineering, including stratum lithology, tunnel burial depth, geological structure, surrounding rock grade and other information, is encoded, and the environment noise matrix and the observation mode matrix are encoded. In the presence of borehole prior information, a borehole wave velocity matrix is generated that includes borehole position and wave velocity information. Through the work, the method can realize accurate detection and identification of the bad geological structure in front of the tunnel, optimize the seismic wave energy propagation effect and improve the construction safety and efficiency.
And selecting mounting points of the wave detector according to the actual conditions of the blasting site, and collecting seismic wave data through the wave detector. The wave detectors are distributed on the left side and the right side of the face, the distance between every two wave detectors is 10 meters, the wave detectors which are 30 meters away from the face are nearest wave detectors, and the whole implementation equipment and the blasting sequence are arranged and shown.
And automatically designing a large number of front tunnel wave velocity models based on the field geological exploration report, obtaining corresponding seismic observation data through numerical simulation, obtaining noise signals collected on the field, and finally obtaining noisy seismic data and wave velocity models which accord with field characteristics to form a tunnel pix2pix inversion database, wherein the wave velocity models are used as label data for pre-training of a tunnel inversion deep neural network model.
Coding stratum lithology, tunnel burial depth, geological structure and surrounding rock grade of the current construction standard section of the tunnel to form a tunnel engineering geological environment vector; acquiring air sampling noise in the current construction environment, collecting seismic data to estimate signal to noise ratio, and coding noise information with different sizes to form an environmental noise matrix; coding layout form information of different side wall observation systems to form an observation mode matrix; in the presence of borehole prior information, a borehole wave velocity matrix is generated that includes borehole position and wave velocity information.
In deep rock mass construction, seismic wave signals carrying forward bad geological body information are collected by utilizing seismic sources and detectors arranged on side walls and tunnel faces, and the arrangement mode of the seismic sources and the detectors of the rock mass is determined by detecting and identifying the forward bad geological structure of a tunnel through data processing. The seismic source is also arranged on the tunnel following construction face, and the seismic wave energy propagating to the front of the tunnel is enhanced to obtain a better detection effect.
Collecting seismic wave data through a wave detector in the rock mass, comprising the following steps:
(1) In the blasting stress wave propagation process, the deformation generated by the rock mass medium is related to the amplitude of the stress wave, and the rock mass medium is taken as an elastic medium in the blasting seismic wave action area;
(2) In the process of rock mass medium propagation, the propagation speed, vibration amplitude and waveform frequency of the blasting seismic wave are affected by rock mechanical properties, and information related to the rock physical mechanical properties is carried in the propagation process;
(3) Rock mass characteristics including the modulus of elasticity, poisson's ratio and integrity of the rock mass are further obtained by analysis of the measured blast seismic wave signals.
Step 2: and performing supervised pre-training through a seismic wave velocity inversion database, establishing a tunnel wave velocity model inversion method and a pix2pix depth neural network model, performing overall-process specific blasting damage simulation by using a PD-FEM numerical calculation method and a PD-FEM numerical calculation system, establishing a surrounding rock damage constitutive model under the condition of high strain rate, and simulating the deep surrounding rock blasting unloading damage effect.
PD is near field dynamics, FEM is finite element, PD-FEM is numerical calculation method combining near field dynamics and finite element, and network parameters are optimized by performing supervised pre-training on a seismic wave velocity inversion database. The inversion method of the tunnel wave velocity model is established, and the pix2pix inversion depth neural network model is used. The model uses the earthquake observation data, the actual working condition information and the geological environment vector as input, and the accuracy of the model is improved through the additional channels of the construction environment matrix and the observation mode matrix. In addition, the PD-FEM numerical calculation method and system are used for carrying out overall process simulation, a rock mass model is divided into a near field dynamics area, a finite element area and a coupling area, and transmission of force and displacement and rock mass fracture simulation are realized. And a surrounding rock damage constitutive model under the condition of high strain rate is also established, and finite element simulation is carried out through PD-FEM to simulate the deep surrounding rock blasting unloading damage effect.
And (3) constructing a tunnel pix2pix inversion deep neural network model according to the seismic wave data obtained in the step (1), wherein the input of the network is the seismic observation data and the actual working condition information in the step (1), and the input is a predicted wave velocity model.
And (3) constructing a tunnel pix2pix inversion depth neural network model according to the seismic wave data obtained in the step (1), wherein the pix2pix inversion depth neural network model is operated through a loss function of the CGAN, and is optimized through an optimization function of wave velocity data. Wherein the loss function includes the loss function L of CGAN CGAN (G, D) and a loss function L1 representing the difference of the images. From the perspective of the loss function, D is a CGAN discriminator, the role of D remains unchanged, G is a generator of CGAN, and G acts to clarify wave velocity data. Wherein, the loss function of CGAN is:
L cGAN (G,D)＝E x,y [logD(x,y)]+E x,z [log(1-D(x,G(x,z)))]
L L1 (G)＝E x，y，z [||y-G(x，y)|| 1 ]
the optimization function of the wave velocity data is:
wherein: lambda is balance discriminator and L L1 Influence of distance on generator training. Wherein: x is wave speed data input by a generator; y is the label wave speed data corresponding to x. In the network structure, x is input wave velocity data of G, G (x) is generated wave velocity data corresponding to x, y is tag wave velocity data corresponding to x, and G is inputThe wave speed data is converted into corresponding output wave speed data, G is a U-shaped network, and the depth of the network is adjusted through the resolution of x and y. And (3) splicing x, G (x) and y, inputting the spliced x, G (x) and y into D, judging the spliced x and G (x) as false by D, and judging the spliced x and y as true by D.
Embedding a tunnel engineering geological environment vector serving as the other input of the network into the network structure through a full-connection structure, and jointly inputting a construction environment matrix and an observation mode matrix serving as two additional channels and input seismic observation data into the network; for the drilling information, calculating a loss function by using the output of the network and the drilling wave velocity matrix for updating network parameters; pre-training the tunnel inversion depth neural network model in a supervision form containing a wave velocity model label through a tunnel inversion database, and preliminarily determining network parameters;
the PD-FEM numerical value calculation method and system for whole process simulation divide a rock mass calculation model into a near field dynamics area, a finite element area and a coupling area according to whether damage occurs or not; and establishing a corresponding size model according to actual engineering, dividing the model into three areas, wherein the area where the rock mass is likely to crack is a near-field dynamic area, the area far away from the crack is a finite element area, and the joint part of the two areas is a coupling area. Transmitting force and displacement in the coupling region, converting the gravity and structural stress of the overlying strata into a finite element region force boundary condition, converting constraint into a displacement boundary condition, calculating the node displacement of the finite element region, and taking the node displacement as the boundary condition of the near field dynamics region; and under the boundary condition, the displacement and damage of the object particles are calculated iteratively, the deformation characteristics of the rock mass are solved, and the engineering scale simulation of the rock mass fracture is realized.
And (3) carrying out PD-FEM numerical calculation on the whole process, and dividing crack damage states of each part of the rock mass model into different areas, so that the FEM part models a nondestructive part, the PD part models a failure area, and the PD part is directly coupled to finish the simulation of the expansion and damage of the deep rock mass crack.
The idea is to adopt a method of directly coupling two parts, and the steps are as follows:
firstly, initializing rock mass model and parameters, and determiningTotal number of particles n p And a total time step number n t . Secondly, the keys among the particles are determined, and the total number of keys around the ith particle is n b (i) A. The invention relates to a method for producing a fibre-reinforced plastic composite And when initial conditions are applied and whether dynamic relaxation is added or not is judged, if the dynamic relaxation is not added, the time step delta t is equal to 1, the t-th iteration is started, and boundary conditions are applied, so that the total PD force F (i) of i particles in the near field range is calculated.
Using the displacement field and the velocity field (i < n) known in time step i; calculating the displacement of the configuration point in the overlapping area by utilizing the market point displacement in the overlapping area; calculating a force density at a deployment point within the overlap region; applying a force density in the form of a physical force to the finite element of the overlap region; and (3) integrating to obtain the displacement of the (n+1) th time step, wherein the force generated by the interaction between the embedded near field dynamics material point and the material point outside the adapter unit is called the coupling force, and the coupling force is distributed to the nodes of the adapter unit at the interface through the shape function and is used as a part of the internal force received by the nodes.
The domain is discretized into a number of mass points, each mass point being associated with its volume, and the union of all volumes forms the entire volume of the object. Each material point x after discretization i Is the constitutive equation of (2):
wherein u is i The displacement is in the direction of the moment i; u (u) j The displacement is in the j direction at the moment t; x is x i Is a substance node x; x is x j For x neighborhood H xj Any node in the network; v'. j To represent the volume of the object point in the j direction; b j Is the physical strength in the j direction.
In the key displacement after rock burst, the external force applied by the object points can be written as follows:
wherein F is the external force applied to the particle; c is the micro modulus; v'. j The volume of the object point in the j direction; v (V) i The volume of the object point in the i direction; η is the relative displacement; ζ is the key vector.
The relative displacement and relative position of the object points are expressed as:
wherein u is i Is the displacement of the material point i direction; u (u) j Is the displacement of the material point j direction; l (L) ij Is the dot bond length of the substance.
Since the forces of the points in the bond PD theory are equal and opposite, the stiffness matrix between the material points is:
where k is the stiffness between the points of matter; b is physical strength of the substance point.
To achieve direct projection coupling, the discrete PD equations of motion can be rewritten as:
wherein U is the displacement vector of the PD particle; vector F is the sum of the internal and external forces; subscript p denotes a variable related to the PD region; single and double underlines represent variables located outside and inside the overlap region, respectively; parameter c n Representing the damping coefficient of the nth time step. The coefficients of the virtual diagonal density matrix D can be determined by Greschgorin's theorem.
And (3) directly assembling a finite element equation without constructing a global stiffness matrix to realize the coupling of the FEM and the PD, wherein the FEM model is as follows:
wherein U is the displacement vector of the PD particle;vector F is the sum of the internal and external forces; subscript p denotes a variable related to the PD region; single and double underlines represent variables located outside and inside the overlap region, respectively; parameter c n A damping coefficient representing the nth time step; the coefficients of the virtual diagonal density matrix M are determined by Greschgorin's theorem.
The dynamic damage constitutive model of rock material under high strain rate conditions for surrounding rock damage comprises: loading a static state model (linear elastic stage), a blasting loading state model (plastic stage), a blasting unloading state model (plastic stage), a yield function model, a damage evolution model, a volume strain model and an equivalent stress model, wherein the method comprises the following steps of:
(1) Loading a static state model (line elastic phase):
wherein K is bulk modulus, p c Is the hydrostatic pressure, N, of rock during uniaxial compression; mu (mu) c Is the volumetric strain.
(2) Blast loading state model (plastic phase):
Blast unload state model (plastic phase):
wherein p is max N, the maximum hydrostatic pressure reached before unloading; mu (mu) max For volume strain, K 1 Bulk modulus at the plastic stage.
(3) Yield function model:
wherein A is a cohesion coefficient; d is a damage variable; b is a pressure strengthening coefficient; c is a strain rate sensitivity coefficient; f is the yield coefficient; sigma (sigma) eq Critical stress for model crack propagation, MPa; k (K) s Is a model fracture strength factor; p (P) * Gas pressure in the model cracks, N; r is the radius of the blast hole, cm; epsilon * Is equivalent plastic strain.
The equation consists of the equivalent stress defined by the difference between the dimensionless equivalent stress and the compressive and tensile strengths of the rock material.
(4) Injury evolution model:
wherein D is a damage variable,delta mu, the equivalent plastic strain increment p For plastic volume strain increment, +.>Is the equivalent plastic strain at material failure, +.>Is a volume plastic strain.
(5) Satisfy the volume strain, wherein V and V 0 Post-deformation and pre-deformation volumes, ε, respectively x 、ε y 、ε z Is a strain component. The volumetric strain model is:
wherein ε x Is a model x-direction strain component; epsilon y The strain component in the y direction of the model; epsilon z Is the model z-direction strain component.
(6) The equivalent stress model is:
Wherein sigma x Is a stress component in the x direction of the model; sigma (sigma) y The stress component in the y direction of the model; sigma (sigma) z Is the model z-direction stress component.
After the step 2 is completed, a surrounding rock damage constitutive model under the condition of high strain rate is established, a constitutive subroutine of the material is written by adopting Fortran language, finite element simulation of the material constitutive is realized through PD-FEM, and finally finite element simulation is carried out on the deep surrounding rock blasting unloading damage effect based on the newly established constitutive model.
Through the step 2, automatic wave velocity model updating and rock stress and strain state calculation carried out along with tunnel excavation are realized, and more accurate simulation and optimization design basis is provided for tunnel engineering.
Step 1 and step 2 provide PD program operating conditions for step 3.
Step 3: and (3) carrying rock mass model data into a longitudinal wave instantaneous energy density model and a longitudinal wave average energy density model through deep surrounding rock blasting unloading damage effect simulation, so as to obtain a rock mass stress strain state, providing PD program operation conditions for stress strain change under a blasting loading state, helping to establish an inversion optimization mode of synchronous tunnel excavation, and updating an inversion database.
After the simulation of the deep surrounding rock blasting unloading damage effect is completed, a network updating synchronous tunnel excavation inversion optimization mode is established. In the tunnel excavation process, a tunnel wave velocity model is automatically designed again by using a new excavation disclosure result, and corresponding observation data is generated. Therefore, the method can replace a wave velocity model designed before in the tunnel pix2pix inversion database to update the label data in the tunnel inversion database. In addition, the established rock mass model data is taken into an energy density formula. First, in a constant force state, a stress strain state of the rock mass is obtained. Then, in the blasted state, the stress strain of the rock mass changes with time.
Establishing an inversion optimization mode of network updating synchronous tunnel excavation, namely automatically redesigning a tunnel wave velocity model by utilizing a new excavation exposure result along with tunnel excavation, and generating corresponding observation data to replace a wave velocity model designed before in a tunnel pix2pix inversion database so as to update label data in the tunnel inversion database;
the stress wave forms a hoop tensile stress and a tangential stress in the rock, and under the action of the hoop tensile stress and the tangential stress, the crack formed after the rock is blasted is combined, and the stress wave propagates the hoop stress. When the dynamic tensile strength of the rock is larger than that of the rock, cracks in the rock are activated, after the rock forms cracks, the number of activated cracks is exponentially distributed under the action of stress waves, and under the action of the stress waves, the relation between the number of cracks of the rock and the volume strain of the cracked rock is as follows:
wherein n is the number of cracks generated; epsilon v Is the volumetric strain; a and m are coefficients related to the material.
Radial cracks do not develop uniformly, and dominant cracks will protrude from the crack population. Under the action of stress wave, the crack development adopts a planar crack model. When the rock is blasted, the energy release rate is obtained, and under the action of stress waves, the energy release rate during the rock blasting is expressed as follows:
Wherein K is 1 Is a stress intensity factor; e (E) m The dynamic elastic modulus of the rock is GPa; g I Is the energy release rate; mu is the Poisson's ratio of the rock mass.
Crack propagation delta α When the crack tip stress intensity factors are equal, the crack expansion delta is obtained α In-process shear stress sigma θ The work is as follows:
wherein n is the number of radial cracks; r is the distance between rock mass points and the explosive core, cm; Δa is the variation in r, cm. During blasting, as the distance between the rock mass point and the blasting center increases, the fracture characteristics of the rock mass change.
In the plane, due to the strength sigma i Plane longitudinal wave of (c) p Velocity propagates in any direction in the plane, and therefore longitudinal wave instantaneous energy density e p The model is as follows:
wherein e p kJ/m for instantaneous energy density 3 ；σ i The intensity of the plane longitudinal wave is km/s; c p The wave speed is plane longitudinal wave speed, km/s; ρ is the density of the rock mass, g/cm.
Longitudinal wave average energy densityThe model is as follows:
wherein,to mean energy density of longitudinal wave, kJ/m 3 The method comprises the steps of carrying out a first treatment on the surface of the ρ is the density of the rock mass, g/cm; c p The wave speed is plane longitudinal wave speed, km/s; t is the propagation time of the longitudinal wave; sigma (sigma) i The intensity of the plane longitudinal wave is km/s; x is x i The plane longitudinal wave propagates the displacement.
Step 4: the inversion optimization mode of synchronous tunnel excavation of the inversion database is updated, a specific damage area is divided through PD-FEM numerical calculation, a mechanical model is associated with stress waves, a geological environment vector is pre-trained by controlling the blasting stress wave velocity and utilizing a deep neural network model, a tunnel inversion deep neural network model after parameter optimization is obtained, and further the ground stress inversion of the deep buried tunnel rock based on the blasting seismic wave velocity is achieved.
The method comprises the steps of updating an inversion optimization mode of network synchronous tunnel excavation, firstly, calculating a static stress field under initial stress by using a dynamic relaxation algorithm, outputting displacement data, initializing a model by using the displacement data, performing dynamic calculation, dividing a damaged area into a PD (near field dynamics) subarea and a FEM (finite element) subarea, considering key force interaction and damage value updating, associating a mechanical model with stress waves, and controlling a blasting influence range by controlling blasting stress wave velocity. And simultaneously, taking tunnel engineering geological environment vectors as network input, and performing pre-training through a deep neural network model. And (3) along with the progress of tunnel excavation, redesigning a tunnel wave speed model by utilizing a new excavation disclosure result, generating corresponding observation data, and updating a tunnel inversion database. And finally, optimizing the seismic wave velocity inversion of the region to be constructed in front of the tunnel by using the depth neural network model after parameter optimization.
As shown in fig. 3, a numerical calculation program is written, and the algorithm flow of the programming is as follows:
firstly, calculating a static stress field of a model under the action of initial stress by using a dynamic relaxation algorithm, then outputting displacement data of each particle of the model under the static stress field, initializing the model by using the particle displacement data obtained by solving under the static stress field, and then carrying out dynamic calculation.
As shown in fig. 4, a specific damage area is divided by a PD-FEM numerical calculation method, a study object is divided into a PD sub-area and a FEM sub-area, the study object is divided into the PD sub-area and the FEM sub-area in a non-local action area, near field dynamics modeling is adopted in the non-local action area, four-node modeling is adopted in other areas, and no overlapping area is set. Near field dynamics modeling is adopted, and other areas are modeled by four nodes and other parameters. An overlap region is provided between the two sub-regions. The interaction between two substance points in the bond PD theory only considers the bond force between the two substance points. The model PD theory considers the influence of other material particles in the neighborhood on the bond force when analyzing the interaction between two material particles.
Specifically, the key adopts four nodes to realize mixed modeling:
the cell stiffness can be taken as:
wherein, xi' is the relative distance between the finite element node and the object point; k (k) p Is the overall unit stiffness; k is the cell stiffness; l is the dot bond length of the substance; m is mass of a substance point.
The force vector states of the PD are collectively expressed as:
T[X,T]<X'-X>＝t<ξ>M(Y)
ξ＝x'-x，η＝u'-u
the PD theory is a non-local continuous medium mechanics theory which expresses interaction force between substance points x and x' in a neighborhood radius delta in an integral mode.
The kinetic equation for object point x is expressed as:
wherein ρ is the density of node x; u is the displacement at time t; b (x, t) is physical strength; x' is any node in the x neighborhood Hx; ζ is a key vector; η is the relative displacement; t [ x, T ] and T [ x ', T ] are the node x and x' force vector density states, respectively.
As shown in fig. 5, the interaction between two material points in the PD theory only considers the bond force between the two material points, and the PD theory considers the influence of other material points in the neighborhood on the bond force when analyzing the blasting interaction between the two material points. If the bond is judged to be not broken, the point-to-point force between the particles is calculated, and the damage value is updated. And judging whether power relaxation is added again, if any fracture occurs, updating particle displacement by using a power relaxation algorithm, and thus completing the algorithm of PD serving as a non-local mechanical model.
The correlation of the mechanical model to the stress wave is linked, the influence of the stress wave energy on crack expansion is clarified through the analysis of single parameters of the stress wave, the blasting influence range is larger through prolonging the acting time in blasting, and the development condition of the stress wave is controlled through selecting a blasting method with proper type and proportion, so that the blasting stress wave speed is controllable.
Along with the extension of the data acquisition depth to the deep part, the field noise data and the seismic observation data detected by the explosion vibration meter are utilized to expand the tunnel pix2pix inversion database, and the special explosion vibration testing instrument is adopted to test the vibration caused by explosion, so as to judge the monitoring activities of indexes such as explosion vibration peak value, main vibration frequency, vibration duration time and the like. By means of blasting vibration monitoring, the attenuation rule of blasting earthquake waves, the relation between earthquake wave parameters and blasting modes can be measured, problems in blasting design are optimized in a targeted mode, a tunnel inversion database is converted from an initial full database with wave velocity model labels to a semi-supervised database with partial non-labels, and finally the tunnel inversion database gradually approaches to the non-supervised database. The expanded inversion database is used for carrying out additional training on the tunnel inversion neural network in a seismic wave forward physical driving mode to adjust network parameters, and the training process can be gradually unsupervised along with the progress of detection and the expansion of the database.
And simulating the cracking process of the rock containing the prefabricated cracks under the action of blast hole explosion impact load based on a rock cracking process analysis program of a finite element method. Different explosive stress waveforms are generated through different types of explosives with different proportions and different charging schemes. For the simulation of the explosion stress wave, a semi-theoretical and semi-empirical simplification method is adopted to simplify the explosion stress time course curve, and the explosion stress time course curve is compared with a typical simplification model.
Specifically, in the calculation process, the solid part finite element region only elastically deforms, and no damage or fracture occurs, so that the permeability of the fluid grid node corresponding to the finite element region is not changed, namely the permeability of the rock matrix. Solid domains, transition domains, and matter particles in the fracture domain in the solid portion near field dynamics region all need to be considered.
After the calculation is finished, displacement and damage data of finite element nodes and object points in the model can be obtained, and a deformation and damage diagram in the blasting process of the model can be drawn according to the data, so that the law and state of deep rock blasting seismic waves can be obtained. And constructing a tunnel inversion depth neural network model, wherein the input of the network is seismic observation data and actual working condition information, and the output is a predicted wave velocity model.
Embedding a tunnel engineering geological environment vector serving as the other input of the network into the network structure through a full-connection structure, and jointly inputting a construction environment matrix and an observation mode matrix serving as two additional channels and input seismic observation data into the network; for the drilling information, calculating a loss function by using the output of the network and the drilling wave velocity matrix for updating network parameters; and pre-training the tunnel inversion deep neural network model in a supervised mode containing wave velocity model labels through a tunnel pix2pix inversion database, and preliminarily determining network parameters.
And establishing an inversion optimization mode of network updating synchronous tunnel excavation, namely automatically redesigning a tunnel wave velocity model by utilizing a new excavation exposure result along with tunnel excavation, and generating corresponding observation data to replace a wave velocity model designed before in a tunnel inversion database so as to update label data in the tunnel inversion database.
The method can realize the following-digging following detection, and expands the tunnel inversion database by utilizing newly detected field noise data and seismic observation data, so that the tunnel inversion database is converted from the original all databases with wave velocity model labels into the partially non-labeled semi-supervised database, and finally gradually approaches to the non-supervised database. And carrying out additional training on the tunnel pix2pix inversion neural network by using the expanded inversion database in a seismic wave forward physical driving mode for several rounds to adjust network parameters, wherein the training process can be gradually unsupervised along with the detection progress and the expansion of the database.
Backing up the basic network parameters of the current excavation stage obtained after the optimization: and performing independent iterative optimization on the observation data of the current position of the tunnel excavation to determine the front speed condition reflected by the detection data of the current position of the tunnel construction, and performing iterative updating on network parameters by adopting the single data to generate the speed distribution of the detection area in front of the tunnel of the current position. The basic network parameters are restored to be backup before the next excavation and detection, and the tunnel inversion depth neural network model with optimized parameters is utilized to realize the seismic wave velocity inversion task of the area to be constructed in front of the tunnel, so that the data in the aspects of field project working conditions, drilling information, excavation disclosure and the like can be effectively utilized, and the data are matched with the actual detection conditions to obtain accurate rock mass ground stress state information, thereby realizing the ground stress inversion of deep rock mass based on the blasting seismic wave velocity.
The ground stress inversion method of the deep rock mass based on the explosion seismic wave velocity is integrated into a computer program system which can integrate the experiment and inversion method, and is realized in a software mode.
According to the ground stress inversion method of the deep rock mass based on the blasting seismic wave velocity, the whole simulation process is divided into three parts by controlling the whole simulation blasting experiment process, and then the simulation process is respectively carried out by providing blasting, in the process, the strain stress change of the deep rock mass is recorded, data are provided for subsequent finite elements, and conditions are provided for subsequent pix2pix program inversion, so that the whole experiment inversion process is completed. By summarizing this process, a computer program system is integrated that integrates the experimental and inversion methods.
In particular, the system presents a computer readable storage medium storing computer instructions that, when executed by a processor, perform steps in a PD-FEM numerical calculation method for deep rock mass breaking whole process simulation. The use mode and the storage mode of the invention are realized in a software form and can be stored in a readable storage medium in a computer, so that the technical scheme of the invention is essentially or partly contributing to the prior art or the technical scheme is embodied in the form of a software product.
Claims (9)
1. The method for inversion of the ground stress of the deep rock mass based on the explosion seismic wave velocity is characterized by comprising the following specific steps:
step 1: the method comprises the steps of collecting seismic wave velocity information through arranging a wave detector, encoding geological environment information and combining drilling priori information, and establishing arrangement and collection of tunnel seismic wave velocity data to obtain an inversion providing seismic wave velocity database;
step 2: performing supervised pre-training through a seismic wave velocity inversion database, establishing a tunnel wave velocity model inversion method and a pix2pix depth neural network model, performing overall-process specific blasting damage simulation by using a PD-FEM numerical calculation method and a PD-FEM numerical calculation system, establishing a surrounding rock damage constitutive model under a high strain rate condition, and simulating blasting unloading damage effects of deep surrounding rock;
step 3: the rock mass model data are brought into a longitudinal wave instantaneous energy density model and a longitudinal wave average energy density model through deep surrounding rock blasting unloading damage effect simulation, so that a rock mass stress strain state is obtained, PD program operation conditions are provided for stress strain change under a blasting loading state, and further an inversion optimization mode of synchronous tunnel excavation is established, and an inversion database is updated;
Step 4: the inversion optimization mode of synchronous tunnel excavation of the inversion database is updated, a specific damage area is divided through PD-FEM numerical calculation, a mechanical model is associated with stress waves, a geological environment vector is pre-trained by controlling the blasting stress wave velocity and utilizing a deep neural network model, a tunnel inversion deep neural network model after parameter optimization is obtained, and further the ground stress inversion of the deep buried tunnel rock based on the blasting seismic wave velocity is achieved.
2. The method of inversion of the ground stress of a deep rock mass based on the velocity of the blasts seismic waves of claim 1, wherein in step 1, collecting the seismic wave velocity information by arranging a wave detector comprises:
(1) In the blasting stress wave propagation process, the deformation generated by the rock mass medium is related to the amplitude of the stress wave, and the rock mass medium is taken as an elastic medium in the blasting seismic wave action area;
(2) In the process of rock mass medium propagation, the propagation speed, vibration amplitude and waveform frequency of the blasting seismic wave are affected by rock mechanical properties, and information related to the rock physical mechanical properties is carried in the propagation process;
(3) Rock mass characteristics including the modulus of elasticity, poisson's ratio and integrity of the rock mass are further obtained by analysis of the measured blast seismic wave signals.
3. The method for inversion of the ground stress of a deep rock mass based on the velocity of a blasted earthquake wave according to claim 1, wherein in step 2, pix2pix inversion depth neural network model is operated by the loss function of CGAN and optimized by the optimization function of wave velocity data;
the loss function includes the loss function L of CGAN CGAN (G, D) and a loss function L1 representing the image difference, D being a CGAN discriminator, G being a generator of CGAN;
wherein, the loss function of CGAN is:
L cGAN (G，D)＝E x，y [log D(x，y)]+E x，z [log(1-D(x，G(x，z)))]
L L1 (G)＝E x，y，z [||y-G(x，z)|| 1 ]
the optimization function of the wave velocity data is:
wherein: lambda is balance discriminator and L L1 Influence of distance on generator training; x is wave speed data input by a generator; y is the label wave speed data corresponding to x; in the network structure, x is taken as input wave speed data of G, G (x) is generated wave speed data corresponding to x, y is label wave speed data corresponding to x, G converts the input wave speed data into output wave speed data corresponding to the input wave speed data, G is a U-shaped network, and the depth of the network is adjusted through the resolution of x and y.
4. The method of inversion of the ground stress of a deep rock mass based on the velocity of the blasted seismic waves according to claim 1, wherein in step 2, the surrounding rock damage constitutive model under the condition of high strain rate comprises: loading a static state model, a blasting loading state model, a blasting unloading state model, a yield function model, a damage evolution model, a volume strain model and an equivalent stress model.
5. The method for inversion of the ground stress of a deep rock mass based on the velocity of a blasted seismic wave of claim 4, wherein the loading of the static state model is:
wherein K is bulk modulus, p c Is the hydrostatic pressure, N, of rock during uniaxial compression; mu (mu) c Is the volume strain;
blasting loading state model:
blasting unloading state model:
wherein p is max N, the maximum hydrostatic pressure reached before unloading; mu (mu) max Is the volume strain; k (K) 1 Bulk modulus in the plastic phase;
yield function model:
wherein A is a cohesion coefficient; d is injuryA variable; b is a pressure strengthening coefficient; c is a strain rate sensitivity coefficient; f is the yield coefficient; sigma (sigma) eq Critical stress for model crack propagation, MPa; k (K) s Is a model fracture strength factor; p (P) * Gas pressure in the model cracks, N; r is the radius of the blast hole, cm; epsilon * Is equivalent plastic strain;
injury evolution model:
wherein D is a damage variable;is equivalent plastic strain increment; Δμ p Is the plastic volume strain increment; />Is the equivalent plastic strain upon material failure; />Is volume plastic strain;
satisfy the volume strain, wherein V and V 0 Post-deformation and pre-deformation volumes, ε, respectively x 、ε y 、ε z Is a strain component; the volumetric strain model is:
Wherein ε x Is a model x-direction strain component; epsilon y The strain component in the y direction of the model; epsilon z Is a model z-direction strain component;
the equivalent stress model is:
wherein sigma x Is a stress component in the x direction of the model; sigma (sigma) y The stress component in the y direction of the model; sigma (sigma) z Is the model z-direction stress component.
6. The method of inversion of the ground stress of a deep rock mass based on the velocity of the blasts seismic waves of claim 1, wherein in step 3, the longitudinal wave instantaneous energy density model is:
wherein e p kJ/m for instantaneous energy density 3 ；σ i The intensity of the plane longitudinal wave is km/s; c p The wave speed is plane longitudinal wave speed, km/s; ρ is the density of the rock mass, g/cm;
longitudinal wave average energy densityThe model is as follows:
wherein,to mean energy density of longitudinal wave, kJ/m 3 The method comprises the steps of carrying out a first treatment on the surface of the ρ is the density of the rock mass, g/cm; c p The wave speed is plane longitudinal wave speed, km/s; t is the propagation time of the longitudinal wave; sigma (sigma) i The intensity of the plane longitudinal wave is km/s; x is x i The plane longitudinal wave propagates the displacement.
7. The method for inversion of the ground stress of a deep rock mass based on the velocity of the blasted seismic waves according to claim 1, characterized in that in step 4, the association of the mechanical model with the stress waves is specifically:
the key adopts four nodes to realize mixed modeling:
the cell stiffness can be taken as:
Wherein, xi' is the relative distance between the finite element node and the object point; k (k) p Is the overall unit stiffness; k is the cell stiffness; l is the dot bond length of the substance; m is mass of a substance point;
the force vector states of the PD are collectively expressed as:
T[X,T]<X'-X>＝t<ξ>M(Y)
ξ＝x'-x，η＝u'-u
the kinetic equation for object point x is expressed as:
wherein ρ is the density of node x; u is the displacement at time t; b (x, t) is physical strength; x' is any node in the x neighborhood Hx; ζ is a key vector; η is the relative displacement; t [ x, T ] and T [ x ', T ] are the node x and x' force vector density states, respectively.
8. A computer device, characterized by: the computer device comprises a memory storing a computer program and at least one processor for executing the computer program to implement the method of earth stress inversion of a deep rock mass based on blasts seismic wave velocity as claimed in any one of claims 1 to 7.
9. A computer-readable storage medium, characterized by: the computer readable storage medium stores therein a computer program containing a method for inversion of the ground stress of a deep rock mass based on the velocity of a blasted earthquake wave, which when executed by a processor, implements the method for inversion of the ground stress of a deep rock mass based on the velocity of a blasted earthquake wave as set forth in any one of claims 1 to 7.
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| CN117741734B (en) * | 2024-02-20 | 2024-05-07 | 四川公路桥梁建设集团有限公司 | Stress measurement method of tunnel surrounding rock and application of stress measurement method in rock burst prevention and control |
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| CN117741734A (en) * | 2024-02-20 | 2024-03-22 | 四川公路桥梁建设集团有限公司 | Stress measurement method of tunnel surrounding rock and application of stress measurement method in rock burst prevention and control |
| CN117741734B (en) * | 2024-02-20 | 2024-05-07 | 四川公路桥梁建设集团有限公司 | Stress measurement method of tunnel surrounding rock and application of stress measurement method in rock burst prevention and control |
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