CN114935319B - Multi-offset-range seismoelectric frequency spectrum ratio acquisition method and method for monitoring diving surface - Google Patents
Multi-offset-range seismoelectric frequency spectrum ratio acquisition method and method for monitoring diving surface Download PDFInfo
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- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B17/00—Measuring arrangements characterised by the use of infrasonic, sonic or ultrasonic vibrations
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- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
- G01V1/30—Analysis
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- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/10—Aspects of acoustic signal generation or detection
- G01V2210/12—Signal generation
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- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/61—Analysis by combining or comparing a seismic data set with other data
- G01V2210/616—Data from specific type of measurement
- G01V2210/6161—Seismic or acoustic, e.g. land or sea measurements
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- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/61—Analysis by combining or comparing a seismic data set with other data
- G01V2210/616—Data from specific type of measurement
- G01V2210/6163—Electromagnetic
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Abstract
The invention discloses a method for acquiring the ratio of multi-offset-distance seismic electric spectrum and a method for monitoring a submarine surface, wherein frequency spectrum ratio imaging of a multi-offset-distance seismic electric field and a seismic ground acceleration field is obtained through numerical simulation calculation of seismic waves excited by a seismic source and a seismic electromagnetic field; the sensitivity of the seismic-electrical frequency spectrum ratio to the depth of the underground diving surface can be improved, and the depth of the diving surface is further monitored by adopting the multi-offset-distance seismic-electrical frequency spectrum ratio. By using the method, the change condition of the depth of the underground water surface can be inferred only by using the seismic electrical signals and the ground acceleration data which are dynamically measured near the earth surface, the depth of the underground water surface is obtained by a seismic electrical spectrum observation mode with adjustable channel number, the workload of the field is greatly reduced, and the efficiency is improved.
Description
Technical Field
The invention belongs to the technical field of hydrology and geophysical, relates to a seismic-electrical spectrum ratio acquisition and stratum submergence monitoring technology, and particularly relates to a multi-offset-distance seismic-electrical spectrum ratio acquisition method and a method for monitoring a submergence.
Background
In the prior seismic electromagnetic calculations of formation pore media and pore fluid parameters, in 2013, warden et al, in document 1 (Warden, s., gamma, s., journal, l., brito, d., sailhac, p., & boxes, c. (2013) Seismoelectric wave propagation nuclear modeling in partial saturation materials. Geographic axial Journal International,194 (3), 1498-1513) described calculations of seismic electromagnetic signals under partial saturation conditions derived from Pride Seismoelectric equations, discussed the effect of the presence of capillary zones above the submergence on interface response; zyserman et al, 2017, describe in document 2 (Zyserman, F.I., monarchisis, L.B., & Journal ux, L. (2017.) dependency of shear wave differential on soil measurements a numerical study in the seismic zone. Geological natural International,208 (2), 918-935.) the effect of different sand material compositions on seismoelectric transfer function calculations approximated under fixed water surface conditions, dzizin et al, 2019, describe in document 3 (Dzian, L., thwarort, M., rabbit, W., & Ritter, O. (2019.) Quantifying interface pressures with pore space measurements.108. The SR method avoids the use of a seismic medium analysis with a complex SR parameter that records the seismic medium ratio and the SR parameters that can be used to calculate the seismic medium parameters. In addition, in 2020 Dzieran et al, document 4 (Dzieran, L., thorwart, M., & Rabbel, W. (2020.) Seismatic monitoring of acquisition using local semiconductor-a seismic student. Geographic Journal International,222 (2), 874-892.) describes a method for analyzing the parameters of the pore medium and pore fluid, respectively, using a natural source excited single seismic spectrum, which has the following problems: (1) Simulation of multi-channel seismoelectric frequency spectrum ratio excited by an active source (earth surface source) is not realized; (2) The seismoelectric ratio values of different offset distances are not analyzed and the depth of the diving surface is obtained aiming at the changed diving surface. Zheng et al, in document 5 (Zheng, x. -z., ren, h., butler, k.e., zhang, h., sun, y. -c., zhang, w., huang, q., & Chen, [ 2021 ]. Seismoelectric and electroseismic modulation in structured pore with a short wave or ground surface source, journal of geographic Research: solid Earth,126 (9), e2021JB 950), proposed the calculation of the seismic field and the seismic wavefield by a generalized back transmission method based on the peak-to-valley average, which can solve the above-mentioned problem (1), but still has the above-mentioned problem (2).
In conclusion, the existing seismoelectric frequency spectrum ratio method does not solve the problem of low calculation efficiency of near-surface seismic electromagnetism excited by a surface active source, and is difficult to be applied to monitoring the depth of an actual field diving surface.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an active source multi-offset seismic-electromagnetic simulation seismoelectric frequency spectrum ratio method, the number of data receiving points of a monitoring diving surface is determined according to the multi-offset seismic-electrical frequency spectrum ratio, the change of the diving surface is monitored according to the change of the seismoelectric frequency spectrum obtained by different offsets (data receiving points), and the numerical value of the depth of the diving surface can be further obtained. The method can be used for dynamic monitoring of the stratum diving surface, complex calculation of a seismic source time function is avoided, and the spatial resolution of data to the depth of the diving surface is improved by using multi-channel data of different offset distances of the near-surface.
In the invention, the seismoelectric spectrum ratio of multiple offset distances is abbreviated as SESR-VO.
The method calculates the coupling coefficient based on the residual charge density, adopts an earthquake electric spectrum ratio simulation method of the active source multi-offset distance, and can improve the sensitivity of the earthquake electric spectrum ratio to the depth of the underground diving surface by the frequency spectrum ratio imaging of the multi-offset distance earthquake electric field and the earthquake ground acceleration field obtained by numerical simulation calculation of earthquake waves excited by an earthquake source and an earthquake electromagnetic field. Aiming at the sensitivity analysis of different offset distance seismoelectric frequency spectrum ratios to the depth of the diving surface, the invention provides an observation mode capable of adjusting the original seismoelectric frequency spectrum ratio, and the depth of the diving surface is monitored by adopting the frequency spectrum ratio of a multi-channel seismic electric field with different offset distances and the acceleration of a single channel. By using the method for simulating the seismic-electrical spectrum ratio of the active source multi-offset distance, provided by the invention, the change condition of the depth of the underground diving surface can be estimated only by using the seismic electrical signals and ground acceleration data which are dynamically measured near the surface, so that technical support is provided for the subsequent inversion of the depth of the diving surface. The underground water level depth is obtained through the seismoelectric frequency spectrum observation mode with the adjustable channel number, the field workload can be greatly reduced, and the efficiency is improved.
The invention provides a multi-offset-distance seismoelectric frequency spectrum ratio obtaining method and a method for monitoring a diving surface, which comprise the following steps:
A. designing a geometric stratigraphic model of the stratum and physical parameters of each layer of medium, and acquiring physical parameters (including effective dielectric constant, complex conductivity and effective residual charge density) and seismic-electric coupling coefficients of the stratum; the method comprises the following steps:
A1. designing a proper geometric lamellar model aiming at the exploration area;
A2. setting physical parameters of each layer of material of the geometric layered model according to geological and well drilling data, and setting possible depths/positions of an underground water surface according to local underground water change conditions (such as seasonal changes);
A3. setting a seismic source time function and an observation system:
set the seismic source time function, assume M (kg) weight falls vertically from h (M) height (acceleration of gravity is g (M/s)) 2 ) Dt(s) strike the origin of the earth surface, the seismic source having a dominant frequency f 0 (Hz) Ricker wavelet at t 0 (s) time of day excitation, seismic source time function F s (t) can be written as:
an observation system is arranged, and the distance from a receiving point (a measuring electrode pair and a detector) to a striking point is an offset distance x i (m), assuming 101 reception points, i =1, 2. (ii) a
A4. Calculating to obtain the water saturation and the seismic electric coupling coefficient above the diving surface;
according to the depth of the diving surface, zwt (m), by using a van Genuchten empirical model and using alpha VG (m -1 ) And n VG Estimating the water saturation, the pore water pressure head at the diving surface is 0m, the water saturation below the diving surface is 1, the pore pressure head at the depth z (m) above the diving surface is assumed to be the negative of the distance from the point to the diving surface, and the water saturation above the diving surface is assumed to be S w The formula of (c) is written as:
the invention adopts the prior empirical formula of relaxation time to calculate the conversion frequency f from viscous laminar flow to inertial laminar flow t ：
In the formula eta w (Pa · s) is the pore fluid viscosity coefficient, [ phi ] (m) 3 /m 3 ) Is medium porosity, S w (-) is the water saturation, ρ w (kg/m 3 ) Is the fluid mass density, k 0 (m 2 ) And τ w (-) is related to the water saturation S w The effective permeability is related to the degree of pore tortuosity.
The effective dielectric constant, complex conductivity and effective residual charge density dependent on water saturation were then calculated according to established methods
Calculating the seismoelectric coupling coefficient L * (ω,S w )：
Wherein f (Hz) is the frequency;
B. substituting the stratum parameters and the seismoelectric coupling coefficient calculated in the step A4 into a Pride seismic electric wave equation system, and calculating by using a generalized back transmission method based on peak-trough averaging to obtain a seismic electric field and a seismic acceleration field of a near-surface frequency domain;
C. and D, obtaining the modulus of the horizontal component of the seismic electric field of the frequency domain and the seismic ground acceleration field obtained in the step B to obtain the seismic electric spectrum ratio of the multiple offset distances, which is provided by the invention:
for horizontal component E of seismic electric field of different frequencies f (Hz) x,i (f) (V/m) and the horizontal component a of the ground and velocity fields x,i (f)(m/s 2 ) Obtaining the ratio after modulus taking to obtain the seismoelectric frequency spectrum ratio SESR-VO (f, x) of multiple offset distances i ) (ii) a For different offset distances x i (m) and frequency f (Hz) to obtain different seismoelectric spectral ratio data, wherein i represents the serial number of the receiving point (for example, the serial numbers of the receiving points from the seismic source from near to far, i =1,2, \ 8230;, 101). The calculation formula of the seismoelectric frequency spectrum ratio of the multiple offset distances is as follows:
because the seismic electric signal is homologous with the earth surface acceleration signal, theoretically, the influence of the source on the seismic electric field and the seismic acceleration field can be assumed to be the same, and when the method is implemented, only one geophone with a near offset and a plurality of electrode pairs with different offsets are required to be arranged, so that the ratio of the multi-channel seismic electric field component to the single-channel seismic ground acceleration component (the ratio of the multi-offset seismic electric spectrum):
equation (6) fixes the offset of the seismic ground acceleration field to x, as compared to equation (5) ,i0 I.e. selecting only a certain receiving point (i) of close offset 0 ) The data of the ground acceleration field of (2) are involved in the calculation, which shows that when the specific implementation is carried out, the seismic electric field is measured by adopting 101 receiving points, wherein only i = i 0 Both the seismic electric field and the ground acceleration field are measured.
The multi-offset-distance seismoelectric frequency spectrum ratio obtained by the method is tested, and the seismoelectric frequency spectrum ratio of the multi-offset distance has high sensitivity to the depth of the diving surface, and can be used for monitoring the formation diving surface.
D. Testing the sensitivity of the seismoelectric frequency spectrum ratio of the multiple offset distances to the depth of a diving surface;
different depths of the diving surface are set, the sensitivity degree of the change of the multi-offset distance seismoelectric frequency spectrum ratio to the depth of the diving surface is tested, and the method comprises the following steps:
and D, changing the depth of the underground diving surface in the step A2, calculating according to the methods of the step A3, the step A4, the step B and the step C, and carrying out the SESR-VO sensitivity test on the depth of the underground diving surface. Aiming at different diving surface depth calculation formulas (5), if the obtained SESR-VO value change is larger, the obtained SESR-VO value change is more sensitive to the diving surface depth, so that the method can be applied to monitoring the diving surface depth change.
When the sensitivity test of the seismoelectric frequency spectrum ratio to the depth of the diving surface is specifically implemented, the depth of the diving surface is set to be 1m, 3m and 5m deep, and the seismoelectric frequency spectrum ratio of the horizontal component of the seismic electric field and the horizontal component of the seismic ground acceleration field with different offset distances is calculated to obtain an imaging graph.
E. And C, carrying out inversion based on the multi-offset-range seismoelectric frequency spectrum ratio obtained in the step C to obtain the position of the submerging surface of the stratum:
the multi-offset-range seismoelectric frequency spectrum ratio data provided by the invention can be inverted by adopting the existing inversion method to obtain the position of the diving surface:
taking a machine learning method of supervision type as an example, setting physical parameters of a layered medium, a seismic source and an observation system based on existing stratum information of a measuring area as step A, regenerating a sample, obtaining seismic electric fields and ground acceleration fields observed on the ground surface under different diving surfaces through steps B-C, and calculating to obtain seismic electric spectrum ratio data of multiple offset distances by using formula (5) or formula (6) through step C. Through training of a large number of samples, the machine-learned neural network can be used to predict the depth of the diving surface at any time. And inverting the depth of the diving surface at the corresponding moment according to the multi-offset seismic-electrical spectrum ratio obtained by measuring and calculating the measurement areas at different moments, thereby realizing the dynamic monitoring of the underground water level surface.
Compared with the prior art, the invention has the beneficial effects that:
the method for monitoring the underwater surface based on the multi-offset-distance seismoelectric frequency spectrum ratio avoids complex calculation of a seismic source time function by adopting the multi-offset-distance seismoelectric frequency spectrum ratio method, improves the spatial resolution of data to the underwater surface depth by utilizing multi-channel data of different offset distances, and also provides a seismoelectric frequency spectrum observation mode capable of adjusting the number of channels to monitor the underground water surface depth, thereby greatly reducing the field workload and improving the efficiency.
Drawings
FIG. 1 is a flow chart of calculating seismoelectric spectral ratios for multiple offset distances.
FIG. 2 is a waveform diagram of a horizontal ground acceleration field and a horizontal seismic electric field of a base model.
FIG. 3 is a graph of seismoelectric frequency spectrum ratio curves as a function of offset in an embodiment of the present invention;
wherein (a) the offset distance is 5m; (b) an offset distance of 10m; (c) Offset distance of 50m
FIG. 4 is a graph of the sensitivity analysis imaging of seismoelectric frequency spectrum ratio to depth of the diving surface in an embodiment of the present invention;
wherein (a) is that the diving surface is 1m deep; (b) the diving surface is 3m deep; and (c) the diving surface is 5m deep.
FIG. 5 is a neural network structure for inverting multi-offset seismoelectric spectral ratio data by a width learning method in accordance with an embodiment of the present invention;
wherein, (a) the seismoelectric frequency spectrum ratio is a 10 log image; (b) a breadth learning neural network structure; (c) a diving face depth and permeability coefficient structure.
FIG. 6 is a scatter-to-contrast plot of seismoelectric frequency spectrum ratio inverted submergence depth in an embodiment of the present invention;
wherein, (a) is the inversion result of the multi-offset seismoelectric frequency spectrum ratio data calculated by using the formula (5); (b) The inversion result of the multi-offset seismoelectric spectrum ratio data calculated by the formula (6) is obtained.
Detailed Description
The invention will be further described by way of examples, without in any way limiting the scope of the invention, with reference to the accompanying drawings.
The invention provides a seismic-electrical spectrum ratio simulation method which adopts multiple offset distances of an active source based on residual charge density calculation coupling coefficient, and can improve the sensitivity of the seismic-electrical spectrum ratio to the depth of a ground water level. In the invention, the seismoelectric spectrum ratio of multiple offset distances is abbreviated as SESR-VO.
In specific implementation, the method provided by the invention comprises the following steps:
A. model design
Fig. 1 shows a flow of an implementation method for generating a multi-offset-range seismoelectric frequency spectrum ratio simulation model according to the present invention, where the implementation method includes the following steps:
A1. the geometry of the model and the physical parameters of the layers of media are designed for known data (including geological survey and well logging data) of the exploration area: the water-based porous solid material comprises a hydraulic conductivity coefficient, porosity, formation factors, density of a porous solid skeleton, density of a porous fluid and gas, a viscosity coefficient, a dielectric constant, conductivity, water saturation, parameters of a water-soil characteristic curve, a solid material, bulk modulus and shear modulus of the porous fluid and the porous gas, bulk modulus of the skeleton and salinity of the porous fluid.
A2. The vertical downward weight is used as a seismic source, the magnitude of the acting force on the ground (striking point) is calculated according to the mass of the weight, the landing time and the striking height of the weight, in this example, a 40kg weight is used, and the acting force is 2.506 multiplied by 10 5 N as a coefficient in the formula (1)
Then, the Ricker wavelet (Rake wavelet) is multiplied by the ground acting force as a time function of a seismic source, and the central frequency of the formula (1) is set to be 20Hz. The time function of the seismic source can be obtained by using the formula (1) by taking the hitting point as the coordinate origin. Receiving points with different offset distances are set, 101 electrode pairs and detectors are uniformly placed from 5 meters to 105 meters horizontally away from a striking point, the interval between two adjacent receiving points is 1 meter, and a sensor is buried at the position close to the ground surface (0.1 meter deep).
A3. According to the time of data recording and the frequency range calculated by numerical values, calculating the effective residual charge density, the effective permeability (hydraulic conductivity coefficient), the effective dielectric constant and the complex conductivity along with the frequency and the water saturation under the model set on the basis of A1 by using the existing method, and then using the seismic-electric coupling coefficient calculated by using the effective residual charge density as the coupling coefficient in the Pride equation set, namely calculating the conversion frequency and the seismic-electric coupling coefficient by using the formulas (3) to (4) and substituting the conversion frequency and the seismic-electric coupling coefficient into the Pride equation set;
B. based on the model setting in the step A and the calculated physical quantities such as the effective residual charge density, the effective dielectric constant, the complex conductivity and the like which change along with the frequency and the water saturation, calculating the seismic electric field and the ground acceleration field of the frequency domain at different receiving point positions by adopting a generalized back transmission method (LAC GRTM) based on the peak-trough average;
C. and D, obtaining the seismic-electric frequency spectrum ratio of the multiple offset distances by taking the modulus of the horizontal components of the seismic electric field of the frequency domain and the ground acceleration field calculated in the step B to obtain the ratio:
for horizontal component E of seismic electric field of different frequency f (Hz) x,i (f) (V/m) and the horizontal component a of the ground and velocity fields x,i (f)(m/s 2 ) Obtaining the ratio after modulus taking, and calculating by using a formula (5) to obtain the seismoelectric spectrum ratio SESR-VO (f, x) i ) For different offset distances x i (m) and frequency f (Hz) have different ratio data.
D. Carrying out sensitivity test of the seismoelectric frequency spectrum ratio to the depth of the diving surface;
the sensitivity test can be used for adjusting an observation system (the layout position of data receiving points), and the layout number of the detectors can be correspondingly reduced aiming at the detection of the diving surface, so that the cost is reduced, the exploration efficiency is improved, and the specific steps comprise:
and (4) changing the depth of the underground diving surface, calculating according to the methods of the steps A3, A4, B and C, and carrying out an SESR-VO sensitivity test.
In the embodiment, the depth of the diving surface is set to be 1, 3m and 5m deep, and the frequency spectrum ratio of the horizontal component of the seismic electric field and the horizontal component of the seismic ground acceleration field with different offset distances is calculated to obtain an imaging graph.
And adjusting the observation mode by increasing or decreasing the number of channels and the position of a receiving point according to the sensitivity of the seismoelectric spectrum ratio SESR-VO to the target diving surface (step A2). The invention proposes a multi-offset seismic electric field horizontal component E that can adopt the formula (6) x,i (f) The horizontal component of the ground acceleration field is obtained from a certain near offset distance
The seismoelectric frequency spectrum ratio is obtained, so that the sensitivity to the water level change is high.
E. Inversion testing, this example uses a width learning method to invert the depth of the submergence.
E1. Generating a sample model with the depth and the permeability coefficient of a random diving surface and seismoelectric frequency spectrum ratio data matrix data for training and testing a width learning network model;
setting physical parameters of the layered medium based on the formation information of the survey area (Table 1), selecting and randomly generating 4000 diving surface depths within the range of 1-5m by using a seismic source and an observation system such as a 5-layer medium model in the example, and calculating the water saturation by using the formula (2)And the permeability coefficient of each sample is randomly generated within a certain range in consideration of uncertainty of the permeability coefficient, the permeability coefficients of the first layer and the second layer are consistent and take values in 3-35cm/h, and the permeability coefficients of the third layer and the fourth layer are 0.02-15cm/h. And (3) taking the 4000 models with the depth and the permeability coefficient of the random diving surface in a certain range as the output of the sample, and then performing the steps B-C to obtain the seismic electric field and the ground acceleration field of the earth surface observation under different diving surfaces. And (4) calculating the seismoelectric frequency spectrum ratio data of the multiple offset distances by using the formula (6). 1 vertical single-force seismic source is excited on the ground surface (40 kg), assuming that the horizontal position of a hitting point is a coordinate origin, an electric dipole is arranged every 1 meter from 5 meters to 105 meters to measure a seismic electric field in the horizontal direction (101 electric fields in total), and a detector is arranged at the same position to measure the horizontal component of a seismic acceleration field. In the formula (6), the frequency points are 36 frequency points from 2Hz to 72 Hz. 4000 sample models can generate 4000 seismoelectric spectrum ratio data matrixes SE of 36 multiplied by 101 s (4000×36×101)。
TABLE 1 Table of physical parameters of the media used in the examples
E2. Randomly selecting a model [ zwt, K ] with the depth of the diving surface and the permeability coefficient information generated in the step E1 s ]3500 combinations of the corresponding seismoelectric frequency spectrum data matrixes are used as training sets to be substituted into the width learning neural network model, and the width learning neural network model is trained;
based on the sensitivity test in step D, different diving surface models generate different seismoelectric frequency spectrum ratio responses. Firstly, 3500 seismoelectric frequency spectrum ratio matrix data SE which are input and participate in training are identified and extracted s (3500 × 36 × 101), completing feature mapping, as shown in fig. 5. This example sets up 19 mapping features (n = 19), with each mapping corresponding to 16 feature nodes, the ith (i =1,2, \8230;, 19) feature F i By mapping functions
Expressed as:
in the formula, W i For an initial randomly generated weight matrix, beta i For randomly generated bias factors, 19 mapping features [ F ] are integrated 1 ,F 2 ,...,F n ]Written as F n (n = 19). Thus, each mapping feature F i Is a matrix of 36 × 101 × 16, F n A 36 × 101 × 16 × 19 matrix. The mapping complexity is determined by the number n of mapping features and the number p of feature nodes of each mapping, and a larger value is preferably adopted when a more complex mapping relation needs to be described. If the embodiment needs to map the diving surface in a shallow layer and has the corresponding seismoelectric frequency spectrum ratio when the soil layer is partially saturated, 16 and 19 are adopted as the number of the characteristic nodes and the number of the mapping characteristics. The present example utilizes existing self-encoder methods to adjust the weight matrix for each mapped feature to better extract the features of the input data.
E3. The mapped feature matrix F generated in step E2 is then used in a breadth-learning neural network model n Performing feature mapping enhancement to obtain an enhancement layer matrix consisting of 200 enhancement nodes;
a process similar to step E2 is performed to randomly generate a weight matrix W j And a deviation factor beta j Using a non-linear activation function xi j (. To) map feature F to be integrated n (n = 19) enhancement layer E extended to a breadth-learning neural network model for inverting the depth of the water table m (m = 200), it is assumed here that the enhancement layer has a total of 200 enhancement nodes, the jth enhancement node matrix E j (j =2, \8230;, m) the calculation formula of the enhanced node is:
E j ＝ξ j (F n W j +β j ) Formula (8)
The embodiment selects a Tan-Sigmoid conversion function (Sigmoid function) as the activation function xi j . Similarly, the inversion error can be tested for training samples by increasing the number of enhanced nodes m, typically the larger m the smaller the inversion error for training samples, butThis trend does not necessarily occur in the untrained test model, and when m is too large, it may increase the inversion error of the test set samples. Generally, as n, p and m start to increase, the inversion error of the training samples decreases rapidly and then gradually stabilizes.
E4. Finally, using the connection weight matrix W m (m = 200) mapping feature layer, enhancement layer and output layer submergence depth and permeability coefficient matrix [ zwt, K [ ] s ]Connecting:
[zwt,K s ]＝[F n |E m ]W m formula (9)
Here [ zwt, K s ]For the training 3500 sets of submarine depth and permeability coefficient 3500 x 5 combined matrix indicated by the present invention, where the first column zwt is the submarine depth, the [ F ] can be solved using the existing ridge regression theory n |E m ]The pseudo-inverse matrix of (A) obtains a connection matrix W m 。
E5. For the test data set (500 sample models), performing submarine surface inversion by using the trained width learning neural network model to obtain the corresponding submarine surface position/depth:
using the 500 models which are generated in the step E1 and do not participate in the training of the steps E2-E4 to calculate the multi-offset seismoelectric frequency spectrum ratio data of the invention, using the data as test data to substitute the formula (7) to complete feature mapping, substituting the test data into the formula (8) to calculate an enhanced node matrix, finally substituting the integrated mapping features and the enhanced node matrix into the formula (9), and using the pseudo-inverse matrix W calculated by the E4 to calculate the formula (9) m Completing the connection of input-output matrix to obtain the depth of the diving surface (as figure 6).
When the sensitivity test of the seismoelectric frequency spectrum ratio to the depth of the diving surface is specifically implemented, firstly, a foundation model of a stratum is designed according to the step A1, the stratum in the model consists of 5 layers of media, the thicknesses of the first layer to the fourth layer of media are respectively 6, 9, 5 and 15m, and the parameters of the materials of each layer are set to comprise hydraulic conductivity, porosity, formation factor, density of a pore solid framework, density of pore fluid and gas, viscosity coefficient, dielectric constant and conductivity, water saturation, parameters of a water-soil characteristic curve, volume modulus and shear modulus of the solid material, the pore fluid and the pore gas, the volume modulus of the framework and the salinity of the pore fluid (Table 1). It is assumed that the shallow two layers of pore media are composed primarily of loamy sand and sand, and the third through fourth layers are composed primarily of clay materials. The initial groundwater level was set at 5m, which indicates that the first layer is a percolation region in a partially saturated state. The physical parameter settings of each layer of pore media of the base model specifically used for the tests are referred to table 1.
The richtz equation is used to calculate the flow in the vadose zone, and the relationship between the water saturation, the effective permeability and the pressure head of each formation (equation (2)) is estimated using a Van Geniuchten (VG) empirical model, which assumes that the pore water pressure is in a saturated state when it reaches atmospheric pressure (the pore water pressure head is zero). The frequency dependence of the relative residual charge density and permeability depending on the frequency was calculated using the Biot conversion frequency (equation (3)).
Setting a vertical downward force of 2.506 x 10 according to step A2 5 N (40 kg), a Ricker wavelet with a main frequency of 20Hz was set as a function of source time. The receiving points are at the position of 0.1m depth, the offset range is 5-105m, the measurement interval is 1m, 101 receiving points are totally arranged, and each receiving point is provided with 1 electric dipole pair and 1 detector. I.e. horizontal offset x i In the range of 5 to 105m, reception point i =1,2, \ 8230;, 101. The hydraulic conductance of the shallow 4 layers (j =1,2,3, 4) in the basic model was 14.59, 29.7, 0.25 and 0.2cm/h, respectively. After determining the relevant parameters depending on the frequency characteristics and the water saturation, solving the control equation by applying a peak averaging method of LAC GRTM to obtain a waveform diagram (figure 2) of the horizontal components of the seismic ground acceleration (figure 2 (a)) and the seismic electric field (figure 2 (b)) in the time domain, converting the waveform diagram into the frequency domain, and calculating the change of the frequency domain ratio with different offsets by using an equation (3) (figure 3).
On the basis of the basic model, the depth of the diving surface is changed to be respectively 1m (shown in (a) of figure 4), 3m (shown in (b) of figure 4) and 5m (shown in (c) of figure 4), and the seismoelectric frequency spectrum of each model is calculated to obtain an imaging contour map of a frequency domain and a space domain. As shown in fig. 4, when the depth of the subsurface changes, a significant SESR-VO change can be generated, which indicates the possibility that the SESR-VO imaging plot of the present invention can be applied to monitor subsurface depth changes. As shown in the seismoelectric spectral ratio imaging results of FIG. 4, the seismoelectric spectral ratio with higher energy is mainly concentrated in the low frequency (less than 10 Hz), near offset region. When the submergence surface is deeper (fig. 4 (c)), the seismoelectric spectral ratio at a relatively longer offset records a larger energy (50 to 100 m) than in fig. 4 (a) - (b), in which case more data of the seismic electrical signals recorded at the medium and far offsets are required. Therefore, based on the basic model, when the target diving surface changes on the shallow ground surface, the seismic-electrical spectrum ratio numerical simulation method with multiple offset distances provided by the invention is adopted, the seismic-electrical spectrum ratio with the near offset distance has high sensitivity, and when the position of the researched diving surface is deeper, data with the farther offset distance is needed. Therefore, according to the invention, when the detection target layer is deeper, more analysis of the seismoelectric frequency spectrum ratio of the relative far offset is needed, and when the target layer is shallower, only a small amount of data recorded by receiving points with the near offset can be used for analysis, thereby realizing the monitoring of the stratum diving surface with high efficiency and low cost.
The numerical value of the multi-offset-distance seismoelectric frequency spectrum ratio obtained by the method can be applied to inversion and monitoring of the diving surface. The inversion example is a 5-layer medium model (table 1), 4000 randomly generated diving surface depths within the range of 1-5m are selected, the water saturation is calculated by the formula (2), the permeability coefficient of each sample is randomly generated within a certain range in consideration of the complexity of the field actual environment, the permeability coefficients of the first layer and the second layer are consistent and are taken in the range of 3-35cm/h, and the permeability coefficients of the third layer and the fourth layer are 0.02-15cm/h. And then, the steps B-C are carried out to obtain the earthquake electric field and the ground acceleration field observed on the ground surface under different diving surfaces, the example respectively tests that the frequency spectrum (formula 5) of the multi-channel earthquake ground acceleration field and the frequency spectrum (formula 6) of the single-channel earthquake ground acceleration field participate in calculation, namely, the step E is carried out to calculate the earthquake electric spectrum ratio data of the multiple offset distances by using the formula (5) or the formula (6). Therefore, the data for the test are calculated by setting an electric dipole every 1 meter starting from 5 meters to 150 meters to measure the horizontal seismic electric field (101 in total) and a geophone to measure the horizontal component (101) of the seismic acceleration field. The frequency points are 36 frequency points from 2Hz to 72 Hz. 3500 sample models can generate 3500 36 x 101 seismoelectric spectral ratio data matrices. Setting the feature mapping number 19, the node number 16 of each group of mapping features and the enhanced node number 200 as shown in fig. 5, extracting 3500 group models from 4000 group models as a training set, inputting the multi-offset seismoelectric spectrum ratio data calculated by the formula (5) or (6) into the feature mapping layer in fig. 5, calculating the mapping features and enhancing the mapping to obtain enhanced features, and calculating the weight matrix between the hidden layer and the output layer through a ridge regression training network (fig. 5). The remaining 500 untrained models were used as test sets, and the test network was inverted to obtain the depth of the water table (fig. 6). In fig. 6, the horizontal axis is the true depth of the diving surface, the vertical axis is the predicted depth of the diving surface, in fig. 6, (a) the ratio of the horizontal seismic electric field frequency spectrum of 101 channels to the corresponding horizontal seismic ground acceleration field of 101 channels calculated by equation (5) is used for inversion, the root mean square error of the inversion is 0.0512m, in fig. 6, (b) the ratio of the horizontal seismic electric field frequency spectrum of 101 channels to the corresponding single-channel horizontal seismic ground acceleration field at 10m calculated by equation (6) is used for inversion, and the root mean square error of the inversion is 0.0471m. The embodiment illustrates that when monitoring shallow diving surfaces, high-precision diving surface depth positions can be obtained by using seismic acceleration field data of a single offset.
The diving surface is monitored by the multi-offset distance seismic-electric frequency spectrum ratio method, so that the complex calculation of a seismic source time function is avoided, the spatial resolution of data to the depth of the diving surface is improved by utilizing multi-channel data with different offset distances, and when the depth (position) of a underground diving surface changes, the obvious change of the frequency spectrum ratio of a near-surface seismic electric field and a ground acceleration field can be reflected. The invention also provides a seismoelectric frequency spectrum observation mode capable of adjusting the number of channels, the depth of the diving surface is estimated by using a width learning inversion method, and the depth of the diving surface can be monitored in real time by using the frequency spectrum ratio of the measured multi-offset seismic electric field and the seismic ground acceleration field under the condition that the environmental parameters of a region are not changed, so that the field workload can be greatly reduced, and the efficiency can be improved.
It is noted that the disclosed embodiments are intended to aid in further understanding of the invention, but those skilled in the art will appreciate that: various alternatives and modifications are possible without departing from the invention and scope of the appended claims. Therefore, the invention should not be limited to the embodiments disclosed, but the scope of the invention is defined by the appended claims.
Claims (5)
1. A multi-offset distance seismic electric spectrum ratio obtaining method includes obtaining frequency spectrum ratio imaging of a multi-offset distance seismic electric field and a seismic ground acceleration field through numerical simulation calculation of seismic waves excited by a seismic source and a seismic electromagnetic field; the method comprises the following steps:
A. designing a geometric stratigraphic model of the stratum and media of each layer, and acquiring stratum parameters and seismic-electric coupling coefficients; formation parameters include effective dielectric constant, complex conductivity, and effective residual charge density; the method comprises the following steps:
A1. designing a geometric lamellar model for the exploration area;
A2. setting physical parameters of each layer of material of the geometric layered model according to geological and well drilling data, and setting possible depths/positions of an underground water surface according to the change condition of local underground water;
A3. setting a seismic source time function and an observation system:
setting a seismic source time function, and setting a seismic source time function F s (t) is represented by formula (1):
the formula (1) represents: the heavy hammer with the weight of M vertically falls from the height h to hit the origin of the earth surface, the elapsed time is dt, and the seismic source takes the main frequency as f 0 The Ricker wavelet at t 0 Time excitation, g gravity acceleration and F seismic source time function s (t)；
Arranging an observation system, wherein the measuring electrode pair and the detector are receiving points; the distance from the receiving point to the striking point is offset distance x i A plurality of receiving points can be arranged;
A4. calculating the water saturation above the submergence:
according to the depth of the diving surface zwt, a van Genuchten empirical model is utilized, and alpha is used VG (m -1 ) And n VG Estimating the water saturation, wherein the pore water pressure head at the diving surface is 0m, and the water saturation below the diving surface is 1; setting the pore pressure water head of a certain point depth z above the diving surface as the negative number of the distance from the point to the diving surface, and calculating by the formula (2) to obtain the water saturation S above the diving surface w ：
Calculating the conversion frequency f from viscous laminar flow to inertial laminar flow by the formula (3) t ：
In the formula eta w Is the pore fluid viscosity coefficient, phi is the medium porosity, S w As the water saturation, p w Is the fluid mass density, k 0 And τ w To the water saturation S w Effective permeability and pore tortuosity of interest;
calculating to obtain effective dielectric constant, complex conductivity and effective residual charge density dependent on water saturation
Calculating by formula (4) to obtain the seismoelectric coupling coefficient L * (ω,S w )：
Wherein f is frequency;
B. substituting the acquired stratum parameters and the seismoelectric coupling coefficient into a Pride seismic electric wave equation set, and calculating to obtain a seismic electric field and a seismic ground acceleration field of a stratum frequency domain; specifically, calculating a seismic electric field and a seismic ground acceleration field of a stratum frequency domain by using a generalized back-transmission method based on peak-trough averaging;
C. obtaining a modulus ratio of the horizontal component of the seismic electric field of the stratum frequency domain and the horizontal component of the ground acceleration field of the seismic electric field to obtain a multi-offset-distance seismoelectric frequency spectrum ratio;
specifically, the seismoelectric frequency spectrum ratio SESR-VO (f, x) of multiple offset distances can be calculated by adopting formula (5) or formula (6) i )：
Wherein f is the seismic electric field frequency; e x,i (f) Is the horizontal component of the seismic electric field; a is x,i (f) Is the horizontal component of the ground velocity field of the seismic electric field; x is the number of i Different offset distances; i represents the serial number of the receiving point;
the setting source influences the seismic electric field and the seismic acceleration field the same, only a detector with a near offset is needed to be arranged, the detector is provided with a plurality of electrode pairs with different offsets, and the ratio of the multi-channel seismic electric field component to the single-channel seismic ground acceleration component, namely the seismic electric spectrum ratio of the multi-offset is obtained through the formula (6):
in the formula (6), the offset distance of the seismic ground acceleration field is x ,i0 I.e. selecting only near offset reception points i 0 The data of the ground acceleration field is used for calculation;
through the steps, the multi-offset distance seismoelectric frequency spectrum ratio can be obtained.
2. A method for monitoring a diving surface based on a multi-offset-range seismic-electrical spectrum ratio comprises the steps of utilizing the multi-offset-range seismic-electrical spectrum ratio calculated by the multi-offset-range seismic-electrical spectrum ratio acquisition method according to claim 1, further carrying out inversion by an inversion method, and acquiring the position of the diving surface of a stratum; the depth change of the underground diving surface can be monitored only by using seismic electric signals and ground acceleration data which are dynamically measured near the surface of the earth;
specifically, the method for obtaining the depth of the diving surface by inversion by adopting a width learning inversion method comprises the following steps:
E1. the sample model with the depth and the permeability coefficient of the random diving surface and the seismoelectric frequency spectrum ratio data matrix data are used for training and testing a width learning network model;
E2. randomly selecting the model [ zwt, K ] with the depth of the diving surface and the permeability coefficient information generated in the step E1 s ]Substituting the seismoelectric frequency spectrum data matrix training set data into the width learning neural network model to train the width learning neural network model; the method comprises the following steps:
identifying and extracting the characteristics of input seismoelectric frequency spectrum ratio matrix training set data, and performing characteristic mapping; the weight matrix of each mapping characteristic can be adjusted to extract the characteristics of the input data;
characteristic F i By mapping functions
Expressed as:
in the formula, W i For an initial randomly generated weight matrix, beta i Is a randomly generated bias factor;
E3. performing feature mapping enhancement on the mapping feature matrix generated in the step E2 in a width learning neural network model to obtain an enhancement layer matrix consisting of enhancement nodes;
E4. connecting the mapping characteristic layer, the enhancement layer and the output layer diving surface depth and permeability coefficient matrix by using a connection weight matrix;
E5. and (4) carrying out the underwater surface inversion on the test data set by using the trained width learning neural network model, so as to obtain the corresponding underwater surface position/depth.
3. The method for monitoring the diving surface based on the multi-offset-distance seismoelectric frequency spectrum ratio as claimed in claim 2, wherein the sensitivity test of the seismoelectric frequency spectrum ratio to the depth of the diving surface is performed, and the adjustment of the layout positions of the data receiving points specifically comprises:
changing the depth of the underground diving surface;
calculating to obtain the frequency spectrum ratio of the horizontal components of the seismic electric field and the horizontal components of the seismic ground acceleration field at different offset distances to obtain an imaging graph;
and adjusting the observation mode by increasing or decreasing the number of channels and the positions of receiving points according to the sensitivity of the seismoelectric frequency spectrum ratio to the target diving surface.
4. The method for monitoring the diving surface based on the multi-offset-distance seismoelectric frequency spectrum ratio as claimed in claim 2, wherein the step E1 further comprises:
setting physical parameters, a seismic source and an observation system of the layered medium based on the formation information of the survey area, and randomly generating the depth of a diving surface within the range of 1-5 m; calculating to obtain the water saturation; randomly generating a permeability coefficient of each layer of each sample; outputting a sample model with the depth and the permeability coefficient of the random submergible surface;
and acquiring seismic electric fields and ground acceleration fields observed on the earth surface under different diving surfaces, and calculating to obtain seismic electric spectrum ratio data of multiple offset distances.
5. The method for monitoring a diving surface based on multi-offset-range seismoelectric-to-electrical-spectrum ratios as claimed in claim 2, wherein the step E3 further comprises:
randomly generating a weight matrix W j And a deviation factor beta j Using non-linear activation functions ξ j (. To) map feature F to be integrated n (ii) a Enhancement layer E extended to a Width learning neural network model for inverting the depth of the subsurface m (ii) a Wherein the jth enhanced node matrix E j The calculation of the enhanced node of (a) is represented as:
E j ＝ξ j (F n W j +β j ) Formula (8).
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