JP4195167B2 - Ground structure estimation method and system - Google Patents

Description

[0001]
BACKGROUND OF THE INVENTION
The present invention relates to a method and system for estimating the underground structure of a ground, and more particularly, to a method and system that can accurately and efficiently estimate the underground structure of the ground from measured data.
[0002]
[Prior art]
In general, ground survey by vibration measurement is performed by arranging a plurality of vibration sensors on the ground surface and measuring surface waves such as Rayleigh waves transmitted to the ground surface. Then, after obtaining the dispersion characteristics of the phase velocity by the temporal change of the waves recorded by each vibration sensor or the frequency correlation between the vibration sensors, the underground structure of the ground is estimated by using the inverse analysis.
[0003]
[Problems to be solved by the invention]
In the estimation method described in the prior art, the dispersion characteristic obtained by the microtremor exploration method is very important because the value and shape largely reflect the ground structure. As described in pp 26-41, there are many know-hows, such as the need to select an optimal array size in the frequency range of dispersion characteristics, and research has been conducted. However, unsteady vibrations mixed during microtremor exploration observations often contain errors in the dispersion characteristics after analysis, and as a result, errors also occur in the estimation of the final underground structure.
The present invention has been made in view of such problems, and the object of the present invention is to accurately derive the dispersion characteristic of the phase velocity from the measurement data obtained by microtremor observation, and to accurately determine the underground structure of the ground. Furthermore, it is providing the estimation method and estimation system of the ground structure which can be estimated in a short time.
[0004]
[Means for Solving the Problems]
In order to achieve the above object, a ground structure estimation method according to the present invention is a first step of measuring a ground tremor by installing a sensor array having a plurality of size arrays on a ground surface and comprising a plurality of size arrays. When, a second step of calculating a plurality of dispersion characteristics of phase velocity from fine movement data of a plurality of sizes array of the sensor array, of the sensor array, is calculated from the fine movement data of a plurality of sizes arrays of the same size A third step of averaging dispersion characteristics; a fourth step of integrating the dispersion characteristics obtained in the second step and the averaged dispersion characteristics obtained in the third step; The subsurface structure of the ground is estimated using the dispersion characteristics integrated in the above steps.
[0005]
Further, another method of estimating the ground structure according to the present invention includes a first step of measuring a ground tremor by installing a sensor array having a plurality of size arrays on a ground surface and including a plurality of size arrays , a second step of calculating a plurality of spatial autocorrelation coefficients than the spatial autocorrelation method using fine movement data of a plurality of sizes array of the sensor array, of the sensor array, the fine motion data of a plurality of the same size size array third step and said second average spatial autocorrelation coefficients obtained in the third step the obtained spatial autocorrelation coefficient in the step of averaging the spatial autocorrelation coefficients calculated from The dispersion characteristic is derived from the fourth step of integrating the above and the spatial autocorrelation coefficient integrated in the fourth step, and the underground structure of the ground is estimated using this dispersion characteristic.
[0006]
A ground structure estimation system according to the present invention includes a sensor array that includes a plurality of vibration sensors and has a plurality of size arrays to measure fine movement, and a data recording that records a plurality of fine movement data measured by the plurality of size arrays. An analysis device for analyzing fine movement data from the data recording device, the analysis device calculates a plurality of dispersion characteristics by frequency analysis of the sensor array for each size array , and a plurality of the same size a calculating means for averaging the dispersion characteristics of size array, dispersion characteristics calculated from fine movement data measured by a plurality of sizes arrays, and the integration means for integrating the averaged dispersion characteristics, integrated dispersion characteristics It is characterized by estimating the underground structure from the ground.
[0007]
Furthermore, another ground estimation system according to the present invention includes a sensor array configured by a plurality of vibration sensors and having a plurality of size arrays to measure fine movements, and a plurality of fine movement data measured by the plurality of size arrays. A data recording device for analyzing the microtremor data from the data recording device, and the analysis device calculates a plurality of spatial autocorrelation coefficients by frequency analysis of the sensor array for each size array Calculating means for averaging the spatial autocorrelation coefficients of a plurality of size arrays of the same size, a spatial autocorrelation coefficient calculated from microtremor data measured by the plurality of size arrays , and an averaged spatial autocorrelation And a means for integrating the numbers , deriving dispersion characteristics from the integrated spatial autocorrelation coefficient, and estimating the underground structure of the ground.
[0008]
The ground structure estimation method and estimation system according to the present invention configured as described above measure the ground microtremor and obtain it from the dispersion characteristics of the phase velocity or the spatial autocorrelation coefficient calculated by a plurality of sensor arrays of the same size. By averaging the dispersion characteristics, it is possible to effectively remove the indistinguishable noise scattered on the ground surface part, and the accurate underground structure of the ground can be obtained from multiple measurement data without performing another microtremor observation. Can be estimated.
[0009]
DETAILED DESCRIPTION OF THE INVENTION
Hereinafter, an embodiment of a ground structure estimation system according to the present invention will be described in detail with reference to the drawings. FIG. 1 is a block diagram showing an embodiment of an estimation system according to the present invention, and FIG. 2 is a layout diagram of the sensor array of FIG. 1 and 2, a system for estimating the underground structure of the ground by microtremor survey will be described. A plurality of vibration sensors are installed on the surface of the survey destination. As shown in FIG. 2, the vibration sensors 1 to 6 are provided with vibration sensors 1, 2, and 3 on a large equilateral triangle, and the vibration sensors 4, 5, and 6 are located at substantially intermediate positions on the three sides of the large equilateral triangle. The sensor array 7 is installed. With this configuration, the sensor array 7 includes one large triangular size array composed of vibration sensors 1, 2, and 3 and four small same-sized triangular size arrays, that is, vibration sensors 1, 4, 6. Size array composed of vibration sensors 2, 4, 5, size array composed of vibration sensors 4, 5, 6, and size array composed of vibration sensors 3, 5, 6 Consists of
[0010]
The size of the sensor array 7 is determined by the depth of the target ground or the desired ground structure. The deeper the target depth, the larger the sensor array size. In order to estimate the underground structure up to the target depth Large and small array sizes are required. In addition, the size of the sensor array is defined by the interval between vibration sensors constituting the sensor array. The vibration sensors 1 to 6 detect and measure microtremors at all times. The microtremors are not only natural vibrations caused by natural phenomena such as ocean vibrations, wind and crust fluctuations, but also traffic and factories. This is an artificial vibration caused by human activity, and the frequency band is about 1 to 30 Hz.
[0011]
Microtremor data at a plurality of locations measured by the vibration sensors 1 to 6 are input to the data recording device 8 and then input to the analysis device 10. An input device 11 for inputting data or the like is connected to the analysis device 10, and an output device 12 for outputting an analysis result from the analysis device 10 is connected. As the output device 12, a printer, a display device, or the like is used. The estimation system includes a sensor array 7 including a plurality of vibration sensors 1 to 6, an analysis device 10 that analyzes the output, an input device 11, and an output device 12.
[0012]
The analysis device 10 calculates dispersion characteristics of a plurality of size arrays of Rayleigh waves from fine movement data obtained by fine movement observation from the vibration sensors 1 to 6 constituting the sensor array 7 having a plurality of size arrays , and a plurality of the same size. The calculation means 16 for averaging the dispersion characteristics of the size array and the integration means 17 for integrating the plurality of dispersion characteristics and the averaged dispersion characteristics are provided, and the underground structure of the ground is estimated from the integrated dispersion characteristics. For example, the integration unit 17 continuously performs the characteristics of the low frequency band of the small size array and the characteristics of the high frequency band of the large size array,
A wide frequency band characteristic can be obtained accurately in a short time. In order to estimate the underground structure of the ground, inverse analysis is performed by a method such as a genetic algorithm method or a least square method.
[0013]
The operation of the ground structure estimation system of the present embodiment configured as described above will be described below with reference to the flowcharts of FIGS. As a first step, the ground tremor is measured by the plurality of vibration sensors 1 to 6 of the sensor array 7. The fine movement data of the fine movement observation obtained by the measurement is recorded by the data recording device 8 and input to the analysis device 10. As shown in FIG. 2, for each size array of the sensor array 7, i.e. the size array of vibration sensors 1,4,6 constituting the small triangle same size, made of the same vibration sensor 2, 4, 5 size array, the size array of vibration sensors 4,5,6, about the size array consisting vibration sensors 3, 5, 6, also the size array of the vibration sensor 1, 2 and 3 constituting a large triangle, The fine movement data is classified in step S1. As described above, it is advantageous to use a sensor array that is a combination of large and small size arrays in order to shorten the measurement time.
[0014]
As a second step, in the analyzing apparatus 10, the fine motion data classified in step S1 is frequency-converted for each size array. Then, in step S2, fine movement data of each size array is selected, and in step S3, an analysis method such as frequency-wavenumber spectrum method (FK method) or spatial autocorrelation method (SPAC method) is used. The relationship between frequency and phase velocity, that is, dispersion characteristics can be obtained. In particular, when the dispersion characteristic is calculated by the spatial autocorrelation method, the frequency and the spatial autocorrelation coefficient (ρ characteristic) can be derived and calculated during the calculation.
[0015]
As a third step, the dispersion characteristic for each frequency calculated from a plurality of size arrays or the ρ characteristic in the middle of calculation up to the dispersion characteristic is obtained. If there are a plurality of size arrays of the same size, in step S4 The calculation means 16 averages. Thus, accurate dispersion characteristics or ρ characteristics can be derived. When the ρ characteristics are averaged, this is used to derive the dispersion characteristics. In particular, the characteristics obtained with a small-sized array that reflects the vicinity of the surface layer contain a lot of noise due to some factors scattered on the ground surface, so the effect of noise can be reduced by averaging and accurate dispersion characteristics Can be obtained. In this way, the fine movement data of the four small- sized arrays of the same size are averaged, and the ρ characteristic diagram and the dispersion characteristic diagram shown in FIGS. 4C and 4D are obtained. On the other hand, the ρ characteristic diagram and the dispersion characteristic diagram shown in FIGS. 4A and 4B that are not averaged are obtained from a large size array.
[0016]
As a fourth step, for various sizes array of large and small, the dispersion characteristics obtained from the individual size array, integrated by integrating means 17 at step S5, integrates 4 (b) and (d), One continuous dispersion characteristic diagram as shown in FIG. 5 can be obtained. From the dispersion characteristic thus obtained, by reverse analyzed by techniques such genetic algorithm method or a least square method at step S 6, i.e., as at least one parameter of the S-wave velocity and thickness of definitive to a certain depth, By performing reverse analysis (inversion method), the underground structure of the ground at the survey point can be estimated. The underground structure of the S wave velocity of the ground obtained in this way can reduce the influence of noise on the ground surface portion, so that it is extremely accurate and can efficiently design the underground portion during construction.
[0017]
Next, another embodiment of the present invention will be described in detail with reference to FIG. FIG. 6 is a layout view showing another embodiment of the sensor array of the estimation system according to the present invention. In FIG. 6, the sensor array 20 has shown the example comprised from the vibration sensor 21-26 installed in each vertex of a regular hexagon, and the vibration sensor 27 installed in the center. In this example, the same size array of small triangles (21-22-27, 22-23-27, 23-24-27, 24-25-27, 25-26-27, 26-21-27) and Vibration data from the same size array of large triangles (21-23-25, 22-24-26) is obtained.
[0018]
In this embodiment, the dispersion characteristics obtained from the fine movement data size array in the size of the six small triangles in the third step average, determined from the fine movement data of the two large-size array in the size of the triangle dispersion The characteristics are averaged and these averaged dispersion characteristics are combined in a fourth step. FIG. 7A shows the ρ characteristics measured using six small triangular size arrays. FIG. 7B shows an average of these ρ characteristics. Further, when the dispersion characteristic of the phase velocity is calculated from each ρ characteristic of FIG. 7A, FIG. 7C is obtained. Then, as shown in FIG. 7 (d), the dispersion characteristic B1 (shown by a solid line) calculated from the averaged ρ characteristic of FIG. 7 (b) and the individual dispersion characteristic B2 (shown by a broken line) of FIG. 7 (c). ) Are approximately the same, and it has been found that the accuracy of the dispersion characteristic is high in both methods. In other words, in this way, in the low frequency band, it is easily affected by the noise of the ground surface, so by calculating the dispersion characteristics by averaging the fine movement data from multiple size arrays of the same size , more accurate dispersion characteristics It turns out that can be obtained.
[0019]
Similarly, a ρ characteristic is calculated from fine movement data of two large triangles of the same size, and a dispersion characteristic (not shown) obtained by averaging the ρ characteristics is also highly accurate. By integrating the dispersion characteristic with high accuracy (not shown) and the dispersion characteristic B1 of FIG. 7D, a dispersion characteristic with high precision can be obtained in a wide frequency band. And the underground structure of the ground can be accurately estimated by inverse analysis from the integrated dispersion characteristics with high accuracy. As a result, the obtained underground structure of the ground can reduce the influence of noise on the ground surface, so that it becomes highly accurate data and can efficiently design the underground part at the time of building design.
In the above-described embodiment, an example in which equilateral triangles are arranged as a size array constituting the sensor array has been described. However, it is a matter of course that the sensor array may be arranged in a regular polygonal shape or a circular shape. Moreover, although the example which calculates a dispersion | distribution characteristic from a spatial autocorrelation coefficient was shown, it is needless to say that a dispersion | distribution characteristic may be calculated | required by the frequency-wave number method.
[0020]
【The invention's effect】
As can be understood from the above description, the ground structure estimation method and estimation system of the present invention can accurately detect phase velocity in a short time from a plurality of microtremor data without performing microtremor observation again. Dispersion characteristics can be derived, and as a result, more accurate underground structure of the ground can be estimated.
[Brief description of the drawings]
FIG. 1 is a block diagram showing an embodiment of a ground structure estimation system according to the present invention.
FIG. 2 is a layout diagram of a sensor array of the estimation system of FIG. 1;
FIG. 3 is a flowchart of the estimation system of FIG. 1;
4A is a ρ characteristic diagram of a small size sensor array, FIG. 4B is a dispersion characteristic diagram of FIG. 4A, FIG. 4C is a ρ characteristic diagram of a large size sensor array, and FIG. ) Dispersion characteristic diagram.
5 is a dispersion characteristic diagram obtained by integrating (b) and (d) of FIG.
FIG. 6 is a layout view showing another embodiment of a sensor array.
7A is a plurality of ρ characteristic diagrams of the sensor array of FIG. 6, FIG. 7B is a ρ characteristic diagram obtained by averaging (a), and FIG. 7C is a plurality of dispersion characteristic diagrams obtained from FIG. (D) is a dispersion characteristic diagram showing the dispersion characteristic obtained from (b) and the dispersion characteristic obtained from (c).
[Explanation of symbols]
1 to 6 Vibration sensor 7 Sensor array 8 Data recording device 10 Analysis device 16 Calculation means 17 Integration means 20 Sensor array 21 to 27 Vibration sensor

Claims (4)

A first step of measuring a ground microtremor by installing a sensor array composed of a plurality of vibration sensors and having a plurality of size arrays on the ground surface, and a plurality of phase velocities from fine motion data of the plurality of size arrays of the sensor array obtained in the second steps, of the sensor array, a third step of averaging the dispersion characteristic is calculated from the fine movement data of a plurality of sizes arrays of the same size, the second step of calculating the dispersion characteristics of A fourth step of integrating the obtained dispersion characteristic and the averaged dispersion characteristic obtained in the third step, and estimating the underground structure of the ground using the dispersion characteristic integrated in the fourth step Ground structure estimation method. A first step of measuring a ground microtremor by installing a sensor array composed of a plurality of vibration sensors and having a plurality of size arrays on the ground surface, and using the microtremor data of the plurality of size arrays of the sensor array A second step of calculating a plurality of spatial autocorrelation coefficients by a correlation method; and a third step of averaging the spatial autocorrelation coefficients calculated from fine movement data of a plurality of size arrays of the same size among the sensor arrays. A step, a fourth step of integrating the spatial autocorrelation coefficient obtained in the second step and the averaged spatial autocorrelation coefficient obtained in the third step, and the fourth step A ground structure estimation method that derives the dispersion characteristics from the integrated spatial autocorrelation coefficient and estimates the underground structure of the ground using the dispersion characteristics. A sensor array composed of a plurality of vibration sensors and having a plurality of size arrays to measure fine movement, a data recording apparatus for recording a plurality of fine movement data measured by the plurality of size arrays, and fine movement data from the data recording apparatus a estimation system of the ground structure Ru and an analyzer for analyzing,
The analyzer is configured to calculate a plurality of dispersion characteristic than a frequency analysis of the sensor array by size array, and calculating means for averaging the dispersion characteristics of a plurality of sizes arrays of the same size, measured at multiple sizes arrays A ground structure estimation system comprising: a dispersion characteristic calculated from fine movement data ; and an integration means for integrating the averaged dispersion characteristic , and estimating the underground structure of the ground from the integrated dispersion characteristic. A sensor array composed of a plurality of vibration sensors and having a plurality of size arrays to measure fine movement, a data recording apparatus for recording a plurality of fine movement data measured by the plurality of size arrays, and fine movement data from the data recording apparatus a estimation system of the ground structure Ru and a analyzer for analyzing,
The analysis device calculates a plurality of spatial autocorrelation coefficients by frequency analysis of the sensor array for each size array , and also calculates a calculation means for averaging the spatial autocorrelation coefficients of a plurality of size arrays of the same size, It has an integration means that integrates the spatial autocorrelation coefficient calculated from microtremor data measured by multiple size arrays and the averaged spatial autocorrelation coefficient , and the dispersion characteristics are obtained from the integrated spatial autocorrelation coefficient. A ground structure estimation system that guides and estimates the underground structure of the ground.

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