JP2001249187A - Estimation method of ground structure and estimation system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an estimation method of a ground structure and an estimation system capable of estimating accurately an underground structure of the ground. SOLUTION: In this estimation method of the ground structure, estimation is executed by a first step for measuring a slight movement of the ground by installing sensor arrays composed of plural vibration sensors on the ground surface, a second step for calculating dispersion characteristics of a phase velocity from slight movement data of the sensor arrays, a third step for averaging the dispersion characteristics calculated from the slight movement data of same- sized sensor arrays among the sensor arrays, and a fourth step for integrating the dispersion characteristics obtained in the second step and the averaged dispersion characteristic obtained in the third step among the sensor arrays. This estimation method is performed by using the dispersion characteristic integrated in the fourth step.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

The present invention relates to a method and a system for estimating an underground structure of a ground, and more particularly to a method and a system for accurately and efficiently estimating an underground structure of a ground from measured data.

[0002]

2. Description of the Related Art Generally, a 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 characteristic of the phase velocity by the temporal change of the wave recorded by each vibration sensor or the frequency correlation between the vibration sensors, the underground structure of the ground is estimated by inverse analysis using the obtained characteristic.

[0003]

In the estimation method described in the prior art, the dispersion characteristics obtained by the microtremor exploration method are as follows:
It is very important because the value and shape are greatly reflected in the ground structure, and the Geophysical Exploration Society of Japan, Vol. 49, No. 1, pp. 26
As described in −41, the calculation has a lot of know-how, such as the need to select an optimal array size in the frequency range of dispersion characteristics, and has been studied. However, unsteady vibrations mixed during microtremor observations often include errors in the dispersion characteristics after analysis, and as a result, errors also occurred in the estimation of the final underground structure of the ground. The present invention has been made in view of such a problem, and an object of the present invention is to accurately derive a dispersion characteristic of a phase velocity from measurement data obtained by microtremor observation and to accurately determine an underground structure of the ground. To
An object of the present invention is to provide an estimation method and an estimation system for a ground structure that can be estimated in a short time.

[0004]

In order to achieve the above object,
The method for estimating the ground structure according to the present invention includes a first step of installing a sensor array composed of a plurality of vibration sensors on the ground surface and measuring the fine movement of the ground, and calculating a phase velocity from fine movement data of the sensor array. A second step of calculating dispersion characteristics, and a third step of averaging dispersion characteristics calculated from fine movement data of the same size sensor array among the sensor arrays.
And a fourth step of integrating the dispersion characteristics obtained in the second step with the averaged dispersion characteristics obtained in the third step; and a variance integrated in the fourth step. Estimate the underground structure of the ground using the characteristics.

Further, another method of estimating the ground structure according to the present invention includes a first step of installing a sensor array composed of a plurality of vibration sensors on the ground surface and measuring a fine movement of the ground; A second step of calculating a spatial autocorrelation coefficient by a spatial autocorrelation method using the microtremor data, and averaging a spatial autocorrelation coefficient calculated from microtremor data of a sensor array of the same size among the sensor arrays. A third step of integrating the spatial autocorrelation coefficient obtained in the second step with the spatial autocorrelation coefficient obtained in the third step, and the fourth step The variance characteristic is derived from the spatial autocorrelation coefficient integrated in the above, and the underground structure of the ground is estimated using the variance characteristic.

A ground structure estimation system according to the present invention comprises:
A sensor array constituted by a plurality of vibration sensors and measuring fine movement, a data recording device for recording a plurality of fine movement data by the sensor array, and an analyzing device for analyzing the fine movement data from the data recording device; The equipment is
Calculating means for calculating a plurality of dispersion characteristics from the frequency analysis of the sensor array for each size and averaging the dispersion characteristics of the sensor array of the same size, and integrating means for integrating the plurality of dispersion characteristics; It is characterized by estimating the underground structure of the ground from the dispersion characteristics.

Further, another ground estimating system according to the present invention is a sensor array comprising a plurality of vibration sensors for measuring micromotion, a data recording device for recording a plurality of micromotion data by the sensor array, An analysis device for analyzing microtremor data from the recording device, wherein the analysis device calculates a plurality of spatial autocorrelation coefficients from frequency analysis of the sensor array for each size, and calculates the spatial autocorrelation coefficient of the sensor array of the same size. Calculating means for averaging a correlation coefficient, and integrating means for integrating the plurality of spatial autocorrelation coefficients, deriving dispersion characteristics from the integrated spatial autocorrelation coefficients, and estimating an underground structure of the ground. It is characterized by.

The ground structure estimation method and system according to the present invention configured as described above measure the ground tremor and disperse the phase velocity calculated by a plurality of sensor arrays of the same size or the spatial self-phase relationship. By averaging the dispersion characteristics obtained from the numbers, the indistinguishable noise scattered on the ground surface can be effectively removed, and the accurate ground Underground structure can be estimated.

[0009]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of a ground structure estimation system according to the present invention will be described below in detail with reference to the drawings. FIG. 1 is a block diagram showing an embodiment of the estimation system according to the present invention, and FIG. 2 is a layout diagram of the sensor array of FIG. 1 and 2, a description will be given of a system for estimating the underground structure of the ground by microtremor exploration. Several vibration sensors are installed on the surface of the survey destination. As shown in FIG. 2, the vibration sensors 1 to 6 have the vibration sensors 1, 2, and 3 installed on a large equilateral triangle, and the vibration sensors 4, 5, and 6 are located at approximately intermediate positions of three sides of the large equilateral triangle. It is installed and constitutes the sensor array 7.

The size of the sensor array 7 is determined by the depth of the target ground or the desired ground structure. Since the size of the sensor array increases as the target depth increases, the underground structure up to the target depth is estimated. For this purpose, a large and small array size is required. Further, the size of the sensor array is defined by the interval between the vibration sensors constituting the sensor array. The vibration sensors 1 to 6 always detect and measure microtremors. Microtremors are vibrations of the ocean, vibrations specific to the earth caused by natural phenomena such as wind and crustal movement,
It is an artificial vibration caused by human activities such as traffic and factories,
The frequency band is about 1 to 30 Hz.

The 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 to the analysis device 10. An input device 11 for inputting data and the like is connected to the analysis device 10.
An output device 12 for outputting an analysis result from 0 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 analyzer 10 for analyzing an output of the sensor array 7, an input device 11, and an output device 12.

The analyzing apparatus 10 calculates a dispersion characteristic of a Rayleigh wave from the tremor data obtained by observation of the tremor from the vibration sensors 1 to 6, calculates the dispersion characteristic of a sensor array of the same size, and calculates a plurality of variances. And an integrating means for integrating the characteristics, and estimates the underground structure of the ground from the integrated dispersion characteristics. The integrating means 17 can, for example, make the characteristics of a low frequency band of a small sensor array and the characteristics of a high frequency band of a large sensor array continuous, and can accurately obtain the characteristics of a wide frequency band in a short time. . Estimation of the underground structure of the ground is performed by inverse analysis using a method such as a genetic algorithm method or a least squares method.

The operation of the ground structure estimating system of the present embodiment having the above-described configuration will be described below with reference to the flowcharts of FIGS. As a first step, a plurality of vibration sensors 1 to 6 of the sensor array 7
To measure the ground tremor. The microtremor data of the microtremor 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 sensor array of each size, that is, a sensor array of vibration sensors 1, 4, and 6 forming a small triangle, a sensor array of vibration sensors 2, 4, and 5 similarly, A sensor array composed of sensors 4, 5, 6;
The fine movement data is classified in step S1 with respect to the sensor array including the sensor arrays 5 and 6 and the sensor array including the vibration sensors 1, 2, and 3 forming a large triangle. As described above, it is convenient to use a sensor array in which large and small arrays are combined to reduce the measurement time.

As a second step, in the analyzer 10, the fine movement data classified in step S1 is frequency-converted for each sensor array. Then, step S2
In step S3, fine movement data of each sensor array is selected, and in step S3, an analysis method such as a frequency-wavenumber spectrum method (FK method) or a spatial autocorrelation method (SPAC method) is used.
It is possible to obtain the relationship between the frequency peculiar to the survey ground and the phase velocity, that is, the dispersion characteristics. 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.

As a third step, if there are a plurality of sensor arrays of the same size, the dispersion characteristic for each frequency calculated from the plurality of sensor arrays or the ρ characteristic which is being calculated up to the dispersion characteristic is determined. In S4, the average is calculated by the calculating means 16. Thereby, an accurate dispersion characteristic or ρ characteristic can be derived. When ρ characteristics are averaged,
This is used to derive dispersion characteristics. In particular, the characteristics obtained with a small-sized array reflecting the vicinity of the surface layer include many noise parts due to some factors scattered on the ground surface,
By averaging, the influence of noise can be reduced, and accurate dispersion characteristics can be obtained. By averaging the fine movement data of the four small sensor arrays in this manner, FIG.
The ρ characteristic diagram and the dispersion characteristic diagram shown in (c) and (d) are obtained. On the other hand, figures not averaged from a large sensor array
4A and 4B, the ρ characteristic diagram and the dispersion characteristic diagram are obtained.

As a fourth step, for various sensor arrays of large and small sizes, the dispersion characteristics obtained from the individual sensor arrays are integrated by the integrating means 17 in step S5.
By integrating FIGS. 4B and 4D, it is possible to obtain one continuous dispersion characteristic diagram as shown in FIG. From the dispersion characteristics thus obtained, an inverse analysis is performed in step 6 by a method such as a genetic algorithm method or a least squares method, that is, an inverse analysis is performed using at least one of the S-wave velocity and the layer thickness at a certain depth as parameters. By performing 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 manner can reduce the influence of noise on the surface of the ground, so that the accuracy is extremely high and the underground part can be efficiently designed at the time of construction.

Next, another embodiment of the present invention will be described in detail with reference to FIG. FIG. 6 is a layout diagram showing another embodiment of the sensor array of the estimation system according to the present invention. FIG.
1 shows an example in which the sensor array 20 includes vibration sensors 21 to 26 installed at each vertex of a regular hexagon and a vibration sensor 27 installed at the center. In this example,
Small triangular sensor arrays (21-22-27, 22
-23-27, 23-24-27, 24-25-27,
25-26-27, 26-21-27) and a large triangular sensor array (21-23-25, 22-24-2).
The vibration data from 6) is obtained.

In this embodiment, in the third step, the dispersion characteristics obtained from the fine movement data of the six small triangular sensor arrays are averaged, and the dispersion characteristics obtained from the fine movement data of the two large triangular sensor arrays are averaged. Then, these averaged dispersion characteristics are integrated in a fourth step.
FIG. 7A shows a ρ characteristic measured using six small triangular sensor arrays. FIG. 7B shows an average of the ρ characteristic. Further, when the dispersion characteristic of the phase velocity is calculated from each ρ characteristic of FIG. Then, as shown in FIG. 7D, a dispersion characteristic B1 (shown by a solid line) calculated from the averaged ρ characteristic of FIG. 7B
7C, the dispersion characteristics obtained by averaging the individual dispersion characteristics B2 (shown by broken lines) in FIG. 7C were substantially the same, and it was found that the accuracy of the dispersion characteristics was high in both methods. In other words, in the low-frequency band, the influence of the noise on the surface of the ground is liable to be obtained. Therefore, by calculating the dispersion characteristics by averaging the fine movement data from a plurality of sensor arrays, it is possible to obtain a more accurate dispersion characteristic. It turns out that you can.

Similarly, the ρ characteristics are calculated from the fine movement data of the two large triangular sensor arrays, and the dispersion characteristics (not shown) obtained by averaging the ρ characteristics are also highly accurate. This highly accurate dispersion characteristic (not shown) and FIG.
By integrating the dispersion characteristic B1 of (d), a highly accurate dispersion characteristic can be obtained in a wide frequency band. And
From the integrated and highly accurate dispersion characteristics, the underground structure of the ground can be accurately estimated by inverse analysis. as a result,
The obtained underground structure of the ground can reduce the influence of noise on the surface of the ground, so that it becomes highly accurate data, and the underground part can be efficiently designed at the time of architectural design. In the above-described embodiment, an example is shown in which the sensor array is arranged in a regular triangle, but it is needless to say that the sensor array may be arranged in a regular polygonal shape or a circular shape. Also, an example has been shown in which the dispersion characteristic is calculated from the spatial autocorrelation coefficient. However, it is needless to say that the dispersion characteristic may be obtained by the frequency-wavenumber method.

[0020]

As can be understood from the above description, the method and the system for estimating the ground structure according to the present invention can accurately and shortly obtain a plurality of tremor data from a plurality of tremor data in a single observation without performing a new tremor observation. The dispersion characteristic of the phase velocity can be derived in time, and as a result, a 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 in FIG. 1;

FIG. 4A is a ρ characteristic diagram of a small-sized sensor array,
(B) is a dispersion characteristic diagram of (a), (c) is a ρ characteristic diagram of a large-sized sensor array, and (d) is a dispersion characteristic diagram of (c).

FIG. 5 is a dispersion characteristic diagram integrating (b) and (d) of FIG. 4;

FIG. 6 is a layout diagram showing another embodiment of the 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), FIG. 7C is a plurality of dispersion characteristic diagrams obtained from FIG. (D) is a dispersion characteristic diagram showing the dispersion characteristics obtained from (b) and the dispersion characteristics obtained from (c).

[Explanation of symbols]

 1-6 Vibration sensor 7 Sensor array 8 Data recording device 10 Analysis device 16 Calculation means 17 Integration means 20 Sensor array 21-27 Vibration sensor

Claims (4)

[Claims] 1. A first step of installing a sensor array composed of a plurality of vibration sensors on the ground surface and measuring a fine movement of the ground, and calculating a dispersion characteristic of a phase velocity from the fine movement data of the sensor array. Step 2, a third step of averaging the dispersion characteristics calculated from the fine movement data of the sensor array of the same size in the sensor array, and the dispersion characteristic obtained in the second step and the third step. A fourth step of integrating the averaged dispersion characteristics obtained in the step; and a ground structure estimation method of estimating an underground structure of the ground using the dispersion characteristics integrated in the fourth step. 2. A first step in which a sensor array composed of a plurality of vibration sensors is installed on the ground surface to measure micromotion of the ground, and a spatial autocorrelation method using the micromotion data of the sensor array. A second step of calculating a correlation coefficient, a third step of averaging a spatial autocorrelation coefficient calculated from fine movement data of a sensor array of the same size in the sensor array, and A fourth step of integrating the obtained spatial autocorrelation coefficient and the spatial autocorrelation coefficient obtained in the third step, and calculating a dispersion characteristic from the spatial autocorrelation coefficient integrated in the fourth step. A method of estimating the underground structure of the ground using the dispersion characteristics. 3. A sensor array comprising a plurality of vibration sensors for measuring fine movement, a data recording device for recording a plurality of fine movement data by the sensor array, and an analyzing device for analyzing fine movement data from the data recording device. The analysis device is configured to calculate a plurality of dispersion characteristics from frequency analysis of the sensor array for each size and average the dispersion characteristics of the sensor array of the same size, and integrate the plurality of dispersion characteristics. Means for estimating the underground structure of the ground from the integrated dispersion characteristics. 4. A sensor array comprising a plurality of vibration sensors for measuring fine movement, a data recording device for recording a plurality of fine movement data by the sensor array, and an analyzing device for analyzing fine movement data from the data recording device. Calculating means for calculating a plurality of spatial autocorrelation coefficients from frequency analysis of the sensor array for each size, and averaging the spatial autocorrelation coefficients of the same size sensor array; and An integration means for integrating the spatial autocorrelation coefficients of the above, and estimating an underground structure of the ground by deriving dispersion characteristics from the integrated spatial autocorrelation coefficients.