CN113902162A - Geological activity prediction system based on particle swarm optimization algorithm - Google Patents
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Abstract
The invention relates to the technical field of geological activity prediction, in particular to a geological activity prediction system based on a particle swarm optimization algorithm, which comprises the following operation steps: drilling an observation well, and lowering an underwater monitoring device to adapt to the depth of an aquifer; starting an underwater monitoring device, measuring data such as water pressure, flow rate and the like near the monitoring device in real time, and sending the data to a data processing center through a wireless data transmission module; the data processing center processes the received data, obtains the depth of the monitoring device and draws a time depth-reduction (t-s) curve; the parameter solving functional module adopts a particle swarm optimization algorithm to distribute the time-drawdown curve and the standard Tech well function curve, and solves the hydrogeological parameters; the prediction function module compares the real-time value and the typical value of the hydrogeological parameter, and when larger parameter fluctuation occurs, the prediction judgment of the impending geological activity is made.
Description
Technical Field
The invention relates to the technical field of geological activity prediction, in particular to a geological activity prediction system based on a particle swarm optimization algorithm.
Background
Most geological activities are accompanied by violent movement of the water under the ground and cause great changes in geological conditions, directly or indirectly leading to the occurrence of various geological disasters. Such as earthquakes, volcanoes, landslides, debris flows, ground cracks, water spills from tunnels, and flooding and desertification of the ground caused by ground subsidence, deforestation, and the like.
With the rapid development of national economy and the continuous improvement of the development and utilization degree of the national soil, naturally and artificially induced geological disasters have greater and greater influence on human beings and become a hidden danger in economic development and engineering construction. According to statistics, the desertification and soil and water loss areas of Chinese land have the tendency of expanding in nearly 50 years; landslide, collapse and torrential flow damage to large-scale engineering and transportation and endanger the lives and properties of people occur; the unregulated exploitation of groundwater results in ground subsidence in many cities.
At present, the research of geological disasters draws attention from the geological community and related departments, and is listed as an important content of various engineering plans. The geological disasters have the characteristics of multiple types, large harm, wide related range and the like, and the prevention and treatment work mainly takes prevention and treatment, and the prevention and treatment are combined and comprehensively treated. The prevention of geological disasters is based on accurate prediction, and the analysis of the variation trend of hydrogeological parameters is a feasible and accurate geological activity prediction method.
Disclosure of Invention
The invention aims to provide a geological activity prediction system based on a particle swarm optimization algorithm, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a geological activity prediction system based on a particle swarm optimization algorithm operates and comprises the following contents:
the method comprises the following steps: drilling an observation well, and lowering an underwater monitoring device to adapt to the depth of an aquifer;
step two: starting an underwater monitoring device, measuring data such as water pressure, flow rate and the like near the monitoring device in real time, and sending the data to a data processing center through a wireless data transmission module;
step three: the data processing center processes the received data, obtains the depth of the monitoring device and draws a time depth-reduction (t-s) curve;
step four: the parameter solving functional module adopts a particle swarm optimization algorithm to distribute the time-drawdown curve and the standard Tech well function curve, and solves the hydrogeological parameters;
step five: the prediction function module compares the real-time value and the typical value of the hydrogeological parameter, and when larger parameter fluctuation occurs, the prediction judgment of the impending geological activity is made.
Preferably, the underwater monitoring device comprises a pressure gauge and a flow meter, and can measure the water pressure p and the flow rate v around the monitoring device.
Preferably, in the second step, the time interval for measuring the water flow parameter may be set manually, and the shorter the interval is, the higher the prediction accuracy is.
Preferably, in the third step, the depth of the underwater monitoring device is represented by a static water head h _ static, and the formula is as follows: h _ static is p/rho g-v ^2/2 g;
p is water pressure; v is the flow rate; ρ is the liquid density; g is a gravity coefficient; h _ quiet is the static head height.
Preferably, in step three, the difference between the hydrostatic head at different time t and the initial time t0, Δ h, as the water level drop s at that time is used to plot a t-s curve, where the formula is: s ═ h _ still-h _ still'
In the formula: s is the water level lowering at the moment t; h _ quiet is the still head at time t 0; h _ quiet' is the still head at time t.
Preferably, the content of the particle swarm optimization algorithm in the third step is as follows: and (3) establishing a target function fv which is minF (a, b) and is the deviation square sum of the depth reduction time curve and the standard Tess well function wiring, solving the minimum value of the target function by utilizing a particle swarm algorithm, and simulating artificial wiring to calculate hydrogeological parameters by applying a least square method.
An objective function:
in the formula: a is the moving distance of the monitoring point (ti, si) along the abscissa axis (a is more than or equal to 0); b is the moving distance of the monitoring point (ti, si) along the ordinate axis (b is more than or equal to 0); ti is the time of the ith monitoring; si-depth of drop monitored the ith time.
Preferably, in step three, the hydrogeological parameters include, but are not limited to, water conductivity T, water storage S and pressure conductivity α, and are calculated by the following formula:
T＝Q/4π[s][W(u)]
S＝4T[t]/(r^2[1/u])
α＝T/S
in the formula: the [1/u ], [ W (u)) ], [ t ], [ s ] are respectively the horizontal and vertical coordinate values of the W (u)) -1/u and s-t coordinate systems corresponding to the matching points; and r is the radius of the observation well.
Preferably, in the fifth step, a curve is formed by the ratio of the measured value of the water conductivity coefficient T, the measured value of the water storage coefficient S, the measured value of the pressure conductivity coefficient alpha and the typical value, and when the numerical fluctuation of any curve is out of the safe fluctuation range (0.7-1.3), the prediction of the impending large geological activity is made.
Preferably, in step five, the safety fluctuation range of the graph can be adjusted, and the smaller the range, the easier the prediction of the geological activity with a lighter degree is.
Compared with the prior art, the invention has the beneficial effects that:
1. the novel water level depth reduction determination method is provided, and is simultaneously suitable for different hydrogeological conditions such as a confined aquifer and a diving aquifer;
2. the method for wiring and solving the hydrogeological parameters based on the particle swarm optimization algorithm can accurately and rapidly observe the change condition of the geological parameters;
3. the new idea of predicting the geological activity by observing the change of the hydrogeological parameters is provided, and the geological activity can be predicted more intuitively, specifically, accurately and rapidly.
Drawings
FIG. 1 is a schematic view of a time curve for a depth reduction according to the present invention;
FIG. 2 is a schematic diagram of a deep time wiring method according to the present invention;
FIG. 3 is a schematic diagram of a geological parameter fluctuation map of the present invention;
FIG. 4 is a block diagram of a prediction system according to the present invention.
Detailed Description
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, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1 to 4, the present invention provides a technical solution: a system for predicting geological activity based on particle swarm optimization, the operation of the system comprising: drilling an observation well, and lowering an underwater monitoring device 11 to adapt to the depth of an aquifer, wherein the underwater monitoring device 11 comprises a pressure gauge 12 and a flow meter 13, can measure the water pressure p and the flow velocity v around the monitoring device, can determine the hydrostatic head h _ quiet by measuring the water pressure p and the flow velocity v, and is used for determining the height of the groundwater level and the change condition of the groundwater level;
starting the underwater monitoring device 11, measuring data such as water pressure, flow rate and the like near the monitoring device in real time, and sending the data to the data processing center 21 through the wireless data transmission module 14; the data processing center 21 processes the received data, and the depth of the underwater monitoring device 11 is represented by a static head h _ static, and the formula is as follows: h _ static is p/rho g-v ^2/2 g; p is water pressure; v is the flow rate; ρ is the liquid density; g is a gravity coefficient; h _ quiet is the height of the hydrostatic head, and in the third step, the difference between the hydrostatic head at different time t and the initial time t0, Δ h, is taken as the water level drop s at that time to draw a t-s curve, where the formula is: s-h _ quiet, wherein: s is the water level lowering at the moment t; h _ quiet is the still head at time t 0; h _ static is the depth of the hydrostatic head at the time t, and a time depth reduction (t-s) curve is drawn, wherein the underwater monitoring device 11 comprises a wireless data transmission module 14 which can transmit the measured water pressure and flow rate to a data processing center 21 in real time, the data processing center 21 comprises a wireless data receiving module 22, a data processing module 23, a parameter obtaining module 31 and a prediction function module 41, the wireless data receiving module 22 is used for receiving the data sent by the monitoring device 11 and transmitting the data to the data processing module 23, the data processing module 23 processes the measured real-time data by adopting the formula h _ static ═ p/ρ g-v ^2/2g to determine the hydrostatic head, namely the height of the underwater monitoring device 11 from the free water surface, the data processing module 23 further determines the water depth reduction at each time after calculating the hydrostatic heads at different times, drawing a time depth reduction curve;
the parameter solving functional module (31) adopts a particle swarm optimization algorithm to distribute a time depth reduction curve and a standard Tess well function curve, a least square method is applied to establish a dispersion square sum minimum target function fv of the depth reduction time curve and the standard Tess well function curve as minF (a, b), the particle swarm optimization algorithm is used for solving the minimum value of the target function, and the artificial distribution is simulated to calculate hydrogeological parameters and the target function:
T＝Q/4π[s][W(u)]
S＝4T[t]/(r^2[1/u])
α＝T/S
in the formula: the [1/u ], [ W (u)) ], [ t ], [ s ] are respectively the horizontal and vertical coordinate values of the W (u)) -1/u and s-t coordinate systems corresponding to the matching points; r is the radius of the observation well, and the hydrogeological parameters are solved;
the prediction function module (41) compares the real-time value and the typical value of the hydrogeological parameter, when larger parameter fluctuation occurs, the prediction judgment of the geological activity which is about to occur is made, the ratio of the measured value of the water guide coefficient T, the water storage coefficient S, the pressure conduction coefficient alpha and the typical value is made into a curve graph, when the numerical fluctuation of any curve is out of the safe fluctuation range (0.7-1.3), the prediction of the larger geological activity which is about to occur is made, wherein the prediction function module (41) makes the ratio of the measured value and the typical value into a geological parameter fluctuation graph, and when the numerical fluctuation is out of 0.7-1.3, the prediction of the geological activity which is about to occur is made.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (9)
1. A geological activity prediction system based on particle swarm optimization algorithm is characterized in that: the operation of the system includes the following:
the method comprises the following steps: drilling an observation well, and lowering an underwater monitoring device (11) to adapt to the depth of an aquifer;
step two: starting an underwater monitoring device (11), measuring data such as water pressure, flow rate and the like near the monitoring device in real time, and sending the data to a data processing center (21) through a wireless data transmission module (14);
step three: the data processing center (21) processes the received data, obtains the depth of the monitoring device and draws a time-depth-reduction (t-s) curve;
step four: the parameter solving functional module (31) adopts a particle swarm optimization algorithm to distribute the time-drawdown curve and the standard Tess well function curve, and solves the hydrogeological parameters;
step five: the prediction function (41) makes a prediction decision of imminent geological activity when large parameter fluctuations occur by comparing the real-time values and typical values of the hydrogeological parameters.
2. The system for predicting geological activity based on particle swarm optimization algorithm according to claim 1, wherein: the underwater monitoring device (11) comprises a pressure gauge (12) and a flow meter (13), and can measure the water pressure p and the flow rate v around the monitoring device.
3. The system for predicting geological activity based on particle swarm optimization algorithm according to claim 1, wherein: in the second step, the time interval for measuring the water flow parameters can be set manually, and the shorter the interval is, the higher the prediction precision is.
4. The system for predicting geological activity based on particle swarm optimization algorithm according to claim 1, wherein: in the third step, the depth of the underwater monitoring device (11) is represented by a static water head h _ static, and the formula is as follows: h _ static is p/rho g-v ^2/2 g; p is water pressure; v is the flow rate; ρ is the liquid density; g is a gravity coefficient; h _ quiet is the static head height.
5. The system for predicting geological activity based on particle swarm optimization algorithm according to claim 1, wherein: in the third step, the differences of the hydrostatic heads at different time t and initial time t0 (Δ h) are taken as the water level drop s at the time to draw a t-s curve, wherein the formula is as follows: s ═ h _ still-h _ still'
In the formula: s is the water level lowering at the moment t; h _ quiet is the still head at time t 0; h _ quiet' is the still head at time t.
6. The system for predicting geological activity based on particle swarm optimization algorithm according to claim 1, wherein: the particle swarm optimization algorithm in the third step comprises the following steps: and (3) establishing a target function fv which is minF (a, b) and is the deviation square sum of the depth reduction time curve and the standard Tess well function wiring, solving the minimum value of the target function by utilizing a particle swarm algorithm, and simulating artificial wiring to calculate hydrogeological parameters by applying a least square method.
An objective function:
in the formula: a is the moving distance of the monitoring point (ti, si) along the abscissa axis (a is more than or equal to 0); b is the moving distance of the monitoring point (ti, si) along the ordinate axis (b is more than or equal to 0); ti is the time of the ith monitoring; si-depth of drop monitored the ith time.
7. The system for predicting geological activity based on particle swarm optimization algorithm according to claim 1, wherein: in the third step, the hydrogeological parameters include, but are not limited to, a water conductivity coefficient T, a water storage coefficient S and a pressure conductivity coefficient α, and the calculation formula is as follows:
T＝Q/4π[s][W(u)]
S＝4T[t]/(r^2[1/u])
α＝T/S
in the formula: the [1/u ], [ W (u)) ], [ t ], [ s ] are respectively the horizontal and vertical coordinate values of the W (u)) -1/u and s-t coordinate systems corresponding to the matching points; and r is the radius of the observation well.
8. The system for predicting geological activity based on particle swarm optimization algorithm according to claim 1, wherein: and in the fifth step, a curve diagram is prepared by the ratio of the measured value of the water guide coefficient T, the water storage coefficient S and the pressure conduction coefficient alpha to the typical value, and when the numerical fluctuation of any curve is out of the safe fluctuation range (0.7-1.3), the prediction of the impending large geological activity is made.
9. The system for predicting geological activity based on particle swarm optimization algorithm according to claim 1, wherein: in the fifth step, the safety fluctuation range of the graph can be adjusted, and the smaller the range is, the easier the geological activity with a lighter degree can be predicted.
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