WO2017135183A1 - Method for estimating surface density value in gravity gradiometry data - Google Patents
Method for estimating surface density value in gravity gradiometry data Download PDFInfo
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- WO2017135183A1 WO2017135183A1 PCT/JP2017/003103 JP2017003103W WO2017135183A1 WO 2017135183 A1 WO2017135183 A1 WO 2017135183A1 JP 2017003103 W JP2017003103 W JP 2017003103W WO 2017135183 A1 WO2017135183 A1 WO 2017135183A1
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- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V7/00—Measuring gravitational fields or waves; Gravimetric prospecting or detecting
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- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V7/00—Measuring gravitational fields or waves; Gravimetric prospecting or detecting
- G01V7/16—Measuring gravitational fields or waves; Gravimetric prospecting or detecting specially adapted for use on moving platforms, e.g. ship, aircraft
Definitions
- This invention relates to a method of analyzing gravity deviation data and estimating the density distribution of underground formations.
- the geophysical exploration method has remarkably developed, and the air gravity deviation method exploration data using a helicopter has been acquired.
- a wide range of measurements can be made at fine intervals in a short time, and the spatial resolution is greatly improved compared to conventional gravity and gravity deviation measurements on the ground. It is very useful for exploration of metal intrusions on flat terrain.
- gravity exploration data reflects the difference in underground rock density and is used to estimate deep bedrock depth and fracture zone distribution. Expectations are great.
- the gravity deviation value to be measured is sensitive to steep and uneven terrain, and the surface layer density and layer thickness also change greatly, so it is necessary for the gravity deviation value to be measured
- the terrain correction value also changes greatly.
- the topographic correction value calculation based on the fixed topographic correction density value applied in the conventional gravity exploration analysis is employed in the air gravity deviation method exploration data analysis.
- the gravity deviation value to be measured is affected by the influence of deep underground formation density change, the influence of surface layer thickness and density, and other measurement altitudes, tides, fluctuating atmospheric pressure and noise. Therefore, in the air gravity deviation method exploration for the purpose of interpretation of deep underground geology, it is desirable to correct data items as much as possible except for the effects caused by the underground density change in the deep underground.
- the terrain correction value calculated from a fixed terrain correction density value is adopted as in the case of normal gravity exploration.
- the gravity deviation value is proportional to the product of the cube of the reciprocal of the distance between the measurement point and the mass anomaly point and the mass anomaly value. Therefore, the influence of topography and surface density is large, and flat terrain is not a problem, but it is a problem in a steep mountain area where there is a large elevation change with a geothermal zone.
- the method for estimating the surface density value of gravity deviation data grids the measured horizontal gravity gradient data, obtains the horizontal gravity gradient value at each grid point, creates horizontal gravity gradient grid data, and adds the addition coefficient at each grid point.
- the grid data origin is overlaid, and the value obtained by multiplying the horizontal gravity gradient value by the addition coefficient at all grid points that overlap is calculated, and these values are added together to calculate the horizontal gravity gradient overlap value.
- gradient polymerization data is created, and the surface layer density value is estimated from the linear correlation analysis between the horizontal gravity gradient polymerization data and the terrain elevation data, and the combined horizontal gravity gradient polymerization data resulting from the calculated surface mass.
- gravity deviation data can be appropriately processed and the surface layer density distribution can be calculated, and the gravity deviation data can be calculated from the surface layer density distribution due to the influence (gravity deviation component) caused by the surface layer and the deep part excluding the surface layer. It can be separated into gravity deviation components. This is not a terrain correction by surface correction density 2.3 g / cm 3 like constant density being normally performed, and calculates the surface density directly from the correlation analysis between the topographical data and measuring gravity deviation data.
- the gravity gradient value ((Gxz) 2 + (Gyz) 2 ) 0.5 ) calculated from the gravity deviation component from the deep part excluding the influence caused by the surface layer is a good index for estimating the deep fracture zone. Gravity deviation data is measured at fine intervals and the spatial resolution is remarkably large, and each component value, Gxz and Gyz in the gravity gradient calculation can be calculated directly from the measured values, resulting in much more detailed results than the conventional method. It is done.
- a two-layer model consisting of a base layer and a low-density layer such as a weathered layer covering it can be created from the surface layer density distribution, and a layer distribution having a density larger than the assumed base layer density can be grasped.
- the thick low-density layer in the surface layer corresponds to underground water, weathered layer with a high water content, hydrothermal alteration zone, soft layer or slope fracture zone in sloping land, and the like.
- the high density layer suggests the existence of intrusive rocks that have penetrated to near the ground surface.
- geothermal exploration contributes to estimation of underground water areas, fracture zones, alteration zone distributions, and intrusive bodies. It is also useful for estimating the distribution of soft layers including moisture on slopes and is useful for disaster prevention.
- FIG. 4 is a diagram illustrating an example of synthetic horizontal gravity gradient polymerization (Guv) grid data applied to actual DEM data. It is a figure which shows the example of the surface layer density distribution figure calculated by this invention.
- the gravity deviation value is the tensor amount G of 9 components, but it is expressed by 5 different components (Gxx, Gyy, Gxy, Gxz, and Gyz) because it satisfies the symmetric tensor and Laplace conditions.
- two gravity deviation values Gxy for measuring the gravity deviation in the air and Guv obtained by rotating this by 45 degrees counterclockwise, that is, (Gxx ⁇ Gyy) / 2 are referred to as horizontal gravity gradient values.
- the vertical gravity gradient value Gzz and its vertical integration, that is, the so-called gravity value Gz can be calculated. From this Gz, it is possible to interpret geological changes in the density of underground formations by conventional methods.
- FIG. 1 shows a horizontal gravity gradient unit mass response value.
- x northward
- y eastward
- z downward positive
- r (x 2 + y 2 + z 2 ) 0.5 .
- Gxy features zero at the point immediately above the mass anomaly, positive at points far from the northeast and southwest when viewed from the point just above, and negative anomalies at points far from the northwest and southeast.
- the measured value is symmetric at the top of the mass anomaly point.
- the absolute value of the maximum or minimum measured value is inversely proportional to the cube of the distance between the point where these values are measured and the mass anomaly point at a certain depth in the basement.
- the horizontal gravity gradient value from a shallow depth observes a Gxy distribution consisting of many wavelength components with a large offset with a short offset, while the influence of mass anomalies at a deep depth is a wavelength with a small offset with a long offset.
- the Gxy distribution mainly consisting of long components is observed.
- Gxy from a depth at a certain mass anomaly point can be separated and specified from Gxy from other depths.
- Fig. 2 shows the analysis flow of this discovery.
- the horizontal gravity gradient data and terrain elevation data are gridded at equal intervals (hereinafter referred to as horizontal gravity gradient grid data and terrain elevation grid data, respectively). It is desirable that the grid interval of both data is the same.
- Fig. 3 set the height for processing.
- horizontal gravity gradient data measurement flight altitude points are leveled to create a smooth flight altitude leveling surface.
- Topographic elevation grid data is set on the upper surface, and the lower surface is set equidistantly below the flight altitude leveling surface.
- the surface layer is between the ground elevation (surface upper surface) and the surface lower surface, and the difference is the layer thickness.
- the flight altitude leveling plane is set flat for easy data processing.
- the lower surface of the surface layer is also flattened, and the unevenness on the upper surface of the surface layer is conspicuous due to the thickness of the surface layer. It is desirable that the average value of the upper surface of the surface layer is around 0 m of the treatment reference surface.
- This addition coefficient setting range includes grid coordinate points that are the maximum and minimum values of the horizontal gravity gradient unit mass response value.
- the addition coefficient at each grid coordinate point is set to a positive value, and when the horizontal gravity gradient unit mass response value is a negative value, a negative value is set and the origin is set as the center.
- the addition coefficient at each grid coordinate point to be set is desirably a numerical value setting in accordance with the sign of the horizontal gravity gradient unit mass response value, and the horizontal gravity gradient unit mass response value may be used as it is.
- the origin of the addition coefficient grid data is overlaid on one grid point of the horizontal gravity gradient value grid data, and the value obtained by multiplying the horizontal gravity gradient value and the addition coefficient at all the overlapping grid points is obtained.
- Add and calculate the horizontal gravity gradient polymerization value This calculation is called horizontal gravity gradient superposition or Horizontal Gravity Gradient Stack, abbreviated HGGS, and the value is defined as the horizontal gravity gradient superposition value or HGGS value.
- This HGGS value emphasizes the influence of mass anomalies near the unit mass anomaly point, which is the premise for calculating the horizontal gravity gradient unit mass response value, and has the effect of attenuating the influence of mass anomalies distributed deeper and horizontally. .
- FIG. 4 shows the procedure for calculating the above measured horizontal gravity gradient polymerization value.
- the maximum value is obtained from grids (0, -2) and (0,2), and the minimum value is obtained from grids (-2,0) and (2,0). Therefore, the addition coefficient setting range is set to 5x5, coefficient 1 is set for grids (0, -2) and (0,2), coefficient -1 is set for grids (-2,0) and (2,0),
- the grid creates addition coefficient grid data with coefficient 0.
- the surface layer volume in each grid is determined from the surface layer thickness and grid area, and the surface layer mass in each grid is determined assuming a certain surface layer density, for example, 1 g / cm 3.
- the horizontal gravity gradient value resulting from the surface layer mass (hereinafter referred to as the combined horizontal gravity gradient value) is the sum of the horizontal gravity gradient values derived from the surface layer mass in each grid.
- the synthetic horizontal gravity gradient data is set to a certain depth in the surface layer, for example, the processing reference surface is set as the density condensation surface, the surface mass in each grid is condensed on the density condensation surface, and the horizontal gravity gradient unit mass response grid is set. It can be calculated by convolution calculation of data and surface mass grid data. Other calculation methods include a calculation method based on a vertical prism assembly.
- the synthetic surface horizontal gravity gradient polymerization value by the surface mass at each grid point is calculated, and synthetic horizontal gravity gradient polymerization grid data or HGGS (composite) grid data is created.
- This synthetic horizontal gravity gradient polymerization grid data or HGGS (synthetic) grid data is, for example, horizontal gravity gradient polymerization theoretical grid data created with a surface mass assuming a density of 1 g / cm 3.
- the measured horizontal gravity gradient polymerization grid data or HGGS (measurement) grid data is the measured horizontal gravity gradient polymerization value, and in addition to the horizontal gravity gradient polymerization value created by the surface layer mass, the influence from the surface depth mass and the measurement time It is a numerical value with added noise.
- the change in surface layer density is gradual, if the linear correlation is examined in a narrow range between HGGS (measurement) grid data and HGGS (composite) grid data, the slope of linearity will be the average surface layer density in the examined range.
- the surface density distribution in the study area can be calculated by sequentially expanding the linear correlation in this narrow area to the entire study area. This linear correlation processing is called Moving Window Correlation.
- HGGS treatment has the effect of highlighting the effect from the surface layer mass and attenuating the effect from the surface layer mass. Therefore, when the linear correlation is calculated using, for example, the horizontal gravity gradient value without performing the HGGS processing, the influence on the horizontal gravity gradient value from the deep depth below the surface layer is large and it is difficult to calculate the surface layer density.
- JOGGEC Japan Oil, Gas and Metals National Corporation
- CGG CGG Aviation
- FALCON AGG HeliFALCON aerial gravity deviation method
- the level of flight altitude was obtained by suppressing the flight altitude slope value from being less than 7.5 degrees from the flight altitude.
- the treatment reference plane was set to 150m below the leveling flight altitude and the bottom surface was set to 225m. Grid spacing is 25m.
- FIG. 5 is a measured horizontal gravity gradient polymerization distribution diagram (HGGS-Guv measurement)
- FIG. 6 is a synthetic horizontal gravity gradient polymerization distribution diagram (HGGS-Guv synthesis) calculated with a surface layer density of 0.3745 g / cm 3 .
- the addition coefficient setting range is grid 11x11, the grid points with coefficient 1 are (-5,0) and (5,0), the grid points with coefficient -1 are (-5,0) and (5,0) and The other grid points have a coefficient of 0.
- the correlation between both figures is good.
- FIG. 7 is a surface density diagram obtained from linear correlation analysis.
- the examination range is 7x7 grid (150mx150m) and 9x9 grid (200mx200m).
- a surface density map is created using only values that have a correlation coefficient of 0.7 or more and a calculated density in the range of 1.0 to 3.4 g / cm 3 . It can be seen that the change in surface layer density is large and that a constant density value is not appropriate.
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Abstract
[Problem] To estimate a surface density distribution that allows application of terrain correction values having high spatial resolution, in which surface terrain, layer thickness, and density are taken into consideration, in analyzing airborne gravity gradiometry survey data. [Solution] Measured horizontal gravity gradient data are arranged in the form of a grid, horizontal gravity gradient values at individual grid points are obtained to create horizontal gravity gradient grid data, the origin of addition coefficient grid data is set so as to overlap each of the grid points, values of products of the horizontal gravity gradient values and the addition coefficients are obtained at all the overlapping grid points, horizontal gravity gradient weighted values are calculated by adding all these values together to create horizontal gravity gradient weighted data, and surface density values are estimated through linear correlation analysis between the horizontal gravity gradient weighted data and synthetic horizontal gravity gradient weighted data attributable to surface mass calculated from terrain elevation data.
Description
この発明は、重力偏差データを解析し、地下の地層の密度分布を推定する方法に関する。
This invention relates to a method of analyzing gravity deviation data and estimating the density distribution of underground formations.
近年物理探査法は著しく発達し、ヘリコプターを用いた空中重力偏差法探査データが取得されている。短時間に細かい間隔にて広範囲な測定ができ、空間的分解能が従来の地上での重力・重力偏差測定に比べ大幅に改善している。平坦地形に於ける金属貫入鉱床探査には非常に有益である。
In recent years, the geophysical exploration method has remarkably developed, and the air gravity deviation method exploration data using a helicopter has been acquired. A wide range of measurements can be made at fine intervals in a short time, and the spatial resolution is greatly improved compared to conventional gravity and gravity deviation measurements on the ground. It is very useful for exploration of metal intrusions on flat terrain.
地熱資源探査に関しては、重力探査データが地下の岩石密度差を反映することから深部基盤岩深度や断裂帯分布推定に利用され、空間的高分解能なデータを速く取得できる空中重力偏差法探査への期待は大きい。
Regarding geothermal resource exploration, gravity exploration data reflects the difference in underground rock density and is used to estimate deep bedrock depth and fracture zone distribution. Expectations are great.
山岳地帯に於ける地熱資源探査への適用には、測定する重力偏差値が急峻な凸凹した地形に敏感で、また表層の密度、層厚も大きく変化し、従って、測定する重力偏差値に必要な地形補正値も大きく変化する。しかし現状、空中重力偏差法探査データ解析では、従来の重力探査解析にて適用の一定な地形補正密度値による地形補正値計算が採用されている。
For application to exploration of geothermal resources in mountainous areas, the gravity deviation value to be measured is sensitive to steep and uneven terrain, and the surface layer density and layer thickness also change greatly, so it is necessary for the gravity deviation value to be measured The terrain correction value also changes greatly. However, in the present situation, the topographic correction value calculation based on the fixed topographic correction density value applied in the conventional gravity exploration analysis is employed in the air gravity deviation method exploration data analysis.
測定する重力偏差値は地下深い地層密度変化に起因する影響、表層の層厚や密度からの影響、及びその他測定高度、潮汐、変動大気圧や雑音からの影響を受けている。従って、地下深部の地質解釈を目的とした空中重力偏差法探査では地下深部の地層密度変化に起因する影響以外の項目をできるだけデータ処理にて補正することが望ましい。
The gravity deviation value to be measured is affected by the influence of deep underground formation density change, the influence of surface layer thickness and density, and other measurement altitudes, tides, fluctuating atmospheric pressure and noise. Therefore, in the air gravity deviation method exploration for the purpose of interpretation of deep underground geology, it is desirable to correct data items as much as possible except for the effects caused by the underground density change in the deep underground.
現状ヘリコプターを用いた空中重力偏差法探査においても通常の重力探査と同様に一定な地形補正密度値から算出の地形補正値を採用している。重力偏差値は測定点と質量異常点との距離の逆数の3乗と質量異常値の積に比例する。従って、地形や表層密度の影響が大きく、平坦な地形では余り問題にならないが、地熱地帯のある標高変化が大きい急峻な山岳地域では問題である。
In the current gravitational deviation method exploration using a helicopter, the terrain correction value calculated from a fixed terrain correction density value is adopted as in the case of normal gravity exploration. The gravity deviation value is proportional to the product of the cube of the reciprocal of the distance between the measurement point and the mass anomaly point and the mass anomaly value. Therefore, the influence of topography and surface density is large, and flat terrain is not a problem, but it is a problem in a steep mountain area where there is a large elevation change with a geothermal zone.
このため平面的に高分解能なデータ取得をして地下深い地層密度変化の掌握を目指しているにもかかわらず、表層地形、層厚や密度を加味した空間的分解能の高い地形補正値を適用しておらず、補正後の重力偏差異常値やこれから算出される重力異常値も表層の影響が残り、細かい地下深い地層密度変化を十分反映できない点など問題がある。そこで本発明はこのような課題を解決するために考えられたものである。
For this reason, despite the aim of acquiring high-resolution data in a plane and grasping deep underground formation density changes, we applied topographic correction values with high spatial resolution that take into account surface topography, layer thickness and density. However, the gravity anomaly value after correction and the gravity anomaly value calculated from this are still affected by the surface layer, and there is a problem that it cannot sufficiently reflect fine changes in the deep underground depth. The present invention has been conceived to solve such problems.
本発明の重力偏差データの表層密度値推定方法は、測定した水平重力勾配データをグリッド化し、各グリッド点における水平重力勾配値を求め、水平重力勾配グリッドデータを作成し、各グリッド点において加算係数グリッドデータの原点を重ね合わせ、重なり合う総てのグリッド点において水平重力勾配値と加算係数を掛け合わせた数値を求め、これらの数値を総て加算して水平重力勾配重合値を算出し、水平重力勾配重合データを作成し、その水平重力勾配重合データと地形標高データから計算の表層質量に起因する合成水平重力勾配重合データとの線形相関性解析から表層密度値を推定する方法である。
The method for estimating the surface density value of gravity deviation data according to the present invention grids the measured horizontal gravity gradient data, obtains the horizontal gravity gradient value at each grid point, creates horizontal gravity gradient grid data, and adds the addition coefficient at each grid point. The grid data origin is overlaid, and the value obtained by multiplying the horizontal gravity gradient value by the addition coefficient at all grid points that overlap is calculated, and these values are added together to calculate the horizontal gravity gradient overlap value. In this method, gradient polymerization data is created, and the surface layer density value is estimated from the linear correlation analysis between the horizontal gravity gradient polymerization data and the terrain elevation data, and the combined horizontal gravity gradient polymerization data resulting from the calculated surface mass.
本発明によると、重力偏差データを適切に処理し、表層密度分布を算出でき、その表層密度分布から重力偏差データを、表層に起因する影響（重力偏差成分）と表層を除いた深部に起因する重力偏差成分とに分離できる。これは通常行われている表層補正密度2.3g/cm3等一定密度による地形補正ではなく、地形データと測定重力偏差データとの相関性解析から直接表層密度を算出するものである。
According to the present invention, gravity deviation data can be appropriately processed and the surface layer density distribution can be calculated, and the gravity deviation data can be calculated from the surface layer density distribution due to the influence (gravity deviation component) caused by the surface layer and the deep part excluding the surface layer. It can be separated into gravity deviation components. This is not a terrain correction by surface correction density 2.3 g / cm 3 like constant density being normally performed, and calculates the surface density directly from the correlation analysis between the topographical data and measuring gravity deviation data.
表層に起因する影響を取り除いた深部からの重力偏差成分から算出の重力勾配値((Gxz)2＋(Gyz)2)0.5)は深部断裂帯推定の良い指標である。重力偏差データが細かい間隔で計測され空間的分解能が格段に大きいこと、並びに重力勾配値計算の各成分値、Gxz及びGyzを直接測定値から算出できることで従来法に比べ格段に詳細な結果が得られる。
The gravity gradient value ((Gxz) 2 + (Gyz) 2 ) 0.5 ) calculated from the gravity deviation component from the deep part excluding the influence caused by the surface layer is a good index for estimating the deep fracture zone. Gravity deviation data is measured at fine intervals and the spatial resolution is remarkably large, and each component value, Gxz and Gyz in the gravity gradient calculation can be calculated directly from the measured values, resulting in much more detailed results than the conventional method. It is done.
表層密度分布から基層とそれを覆う風化層等低密度層からなる2層モデルを作成でき、また想定基層密度より大きな密度を持つ層分布も掌握できる。表層における厚い低密度層は伏流水、水含有率の多い風化層、熱水変質帯や傾斜地における軟弱層或いは断裂破砕帯等に対応する。一方、高密度層は地表近くまで貫入した貫入岩体の存在が示唆される。
A two-layer model consisting of a base layer and a low-density layer such as a weathered layer covering it can be created from the surface layer density distribution, and a layer distribution having a density larger than the assumed base layer density can be grasped. The thick low-density layer in the surface layer corresponds to underground water, weathered layer with a high water content, hydrothermal alteration zone, soft layer or slope fracture zone in sloping land, and the like. On the other hand, the high density layer suggests the existence of intrusive rocks that have penetrated to near the ground surface.
以上から地熱探査では伏流水域、断裂帯域、変質帯分布、貫入岩体の推定に資する。また傾斜地における水分を含む軟弱層分布推定にも役立ち防災上にも有益である。
From the above, geothermal exploration contributes to estimation of underground water areas, fracture zones, alteration zone distributions, and intrusive bodies. It is also useful for estimating the distribution of soft layers including moisture on slopes and is useful for disaster prevention.
重力偏差値は９成分のテンソル量Ｇであるが、対称テンソルかつラプラスの条件を満たすことから５つの異なった成分（Gxx、Gyy、Gxy、Gxz及びGyz）で表される。
The gravity deviation value is the tensor amount G of 9 components, but it is expressed by 5 different components (Gxx, Gyy, Gxy, Gxz, and Gyz) because it satisfies the symmetric tensor and Laplace conditions.
本発明では空中重力偏差測定の２つの重力偏差値Gxyとこれを反時計まわりに４５度回転したGuv、即ち（Gxx－Gyy）/２を水平重力勾配値と称している。この水平重力勾配値GxyとGuvから垂直重力勾配値Gzz及びその垂直方向の積分、所謂重力値Gzが計算できる。このGzからは従来の方法にて地下地層の密度変化を地質解釈できる。
In the present invention, two gravity deviation values Gxy for measuring the gravity deviation in the air and Guv obtained by rotating this by 45 degrees counterclockwise, that is, (Gxx−Gyy) / 2 are referred to as horizontal gravity gradient values. From the horizontal gravity gradient values Gxy and Guv, the vertical gravity gradient value Gzz and its vertical integration, that is, the so-called gravity value Gz can be calculated. From this Gz, it is possible to interpret geological changes in the density of underground formations by conventional methods.
図１に水平重力勾配単位質量応答値を示す。万有引力定数G、南北方向x、東西方向y及び上下方向zとする座標系にて、y=3Gxy/r5、Guv=1.5G(x2-y2)/r5の計算式にて計算したものである。ここでxは北向き、yは東向き及びzは下向きが正値、r=(x2+y2+z2)0.5である。
FIG. 1 shows a horizontal gravity gradient unit mass response value. Gravitational constant G, the north-south direction x, in the coordinate system of the east-west direction y and the vertical direction z, was calculated by y = 3Gxy / r 5, Guv = 1.5G (x 2 -y 2) / r 5 of formula Is. Here, x is northward, y is eastward, and z is downward positive, r = (x 2 + y 2 + z 2 ) 0.5 .
例えばGxyの特徴は質量異常点直上ではゼロ、その直上点から見て北東と南西に離れた点では正、北西と南東に離れた点では負の異常値となる。測定値は質量異常点頂上点にて対称である。最大及び最少測定値を測定する点はそれぞれ北東と南西、北西と南東に２点ずつ、計４点あり質量異常点頂上よりの距離（オフセット）はその質量異常点深度の１/√１．５(＝約０．８１７)である。
For example, Gxy features zero at the point immediately above the mass anomaly, positive at points far from the northeast and southwest when viewed from the point just above, and negative anomalies at points far from the northwest and southeast. The measured value is symmetric at the top of the mass anomaly point. There are four points for measuring the maximum and minimum measurement values, northeast and southwest, two points northwest and southeast respectively. The distance (offset) from the top of the mass anomaly is 1 / √1.5 of the mass anomaly depth. (= About 0.817).
最大或いは最少測定値の絶対値はこれらを測定する点と地下のある深度にある質量異常点との距離の３乗に反比例する。即ち浅い深度からの水平重力勾配値は大きな測定値を短いオフセットで多くの波長成分からなるGxy分布を観測するのに対し、深い深度にある質量異常点の影響は小さな測定値を長いオフセットで波長の長い成分を主としたGxy分布を観測することとなる。
The absolute value of the maximum or minimum measured value is inversely proportional to the cube of the distance between the point where these values are measured and the mass anomaly point at a certain depth in the basement. In other words, the horizontal gravity gradient value from a shallow depth observes a Gxy distribution consisting of many wavelength components with a large offset with a short offset, while the influence of mass anomalies at a deep depth is a wavelength with a small offset with a long offset. The Gxy distribution mainly consisting of long components is observed.
以上の特徴があるため、ある質量異常点にある深度からのGxyを他の深度からのGxyから分離・特定化できることになる。
Because of the above characteristics, Gxy from a depth at a certain mass anomaly point can be separated and specified from Gxy from other depths.
本発明では、次のデータを出発データとする。
１）測定した水平重力勾配値データ（以下水平重力勾配データと言う）。
２）測定した地形標高データやＤＥＭデータ（以下地形標高データと言う）。図２に本発見の解析フローを示す。 In the present invention, the following data is set as starting data.
1) Measured horizontal gravity gradient value data (hereinafter referred to as horizontal gravity gradient data).
2) Measured terrain elevation data and DEM data (hereinafter referred to as terrain elevation data). Fig. 2 shows the analysis flow of this discovery.
１）測定した水平重力勾配値データ（以下水平重力勾配データと言う）。
２）測定した地形標高データやＤＥＭデータ（以下地形標高データと言う）。図２に本発見の解析フローを示す。 In the present invention, the following data is set as starting data.
1) Measured horizontal gravity gradient value data (hereinafter referred to as horizontal gravity gradient data).
2) Measured terrain elevation data and DEM data (hereinafter referred to as terrain elevation data). Fig. 2 shows the analysis flow of this discovery.
水平重力勾配データ及び地形標高データについて等間隔のグリッド化を行う（以下それぞれ水平重力勾配グリッドデータ及び地形標高グリッドデータと言う）。両データのグリッド間隔は同一であることが望ましい。
The horizontal gravity gradient data and terrain elevation data are gridded at equal intervals (hereinafter referred to as horizontal gravity gradient grid data and terrain elevation grid data, respectively). It is desirable that the grid interval of both data is the same.
図３に示すように高さについて処理上の設定を行う。左図に示すように水平重力勾配データ測定飛行高度点を平準化し滑らかな飛行高度平準化面を作成する。水平重力勾配データ測定飛行高度点が飛行高度平準化面から大きくかい離するグリッド点では、そのグリッド点における水平重力勾配値につき高度補正を行う。地形標高グリッドデータを表層上面に、飛行高度平準化面下に等距離に表層下面を設定する。地表標高（表層上面）と表層下面の間が表層で、その差が層厚となる。
As shown in Fig. 3, set the height for processing. As shown in the figure on the left, horizontal gravity gradient data measurement flight altitude points are leveled to create a smooth flight altitude leveling surface. At the grid point where the horizontal altitude gradient data measurement flight altitude point is far away from the flight altitude leveling plane, altitude correction is performed for the horizontal gravity gradient value at that grid point. Topographic elevation grid data is set on the upper surface, and the lower surface is set equidistantly below the flight altitude leveling surface. The surface layer is between the ground elevation (surface upper surface) and the surface lower surface, and the difference is the layer thickness.
図３右図に示すようにデータ処理の簡便化のため飛行高度平準化面を平らに設定する。この結果、表層下面も平らになり、表層上面の凹凸が表層層厚の厚薄によることが顕著になる。表層上面の平均値が処理基準面の０ｍ前後であることが望ましい。
As shown in the right figure of Fig. 3, the flight altitude leveling plane is set flat for easy data processing. As a result, the lower surface of the surface layer is also flattened, and the unevenness on the upper surface of the surface layer is conspicuous due to the thickness of the surface layer. It is desirable that the average value of the upper surface of the surface layer is around 0 m of the treatment reference surface.
処理基準面上においてある一点を設定し、この点に単位質量異常点を設ける。この点の直上、水平重力勾配値グリッドデータの測定面と交差する点を原点とするグリッド座標を、水平重力勾配値グリッドデータのグリッド間隔と同間隔にて作成する。単位質量を例えば１g/cm3に想定し、この単位質量異常点が作り出す水平重力勾配値を各グリッド座標点にて計算し、各グリッド座標点における水平重力勾配単位質量応答値とする。
設定 Set a certain point on the processing reference plane, and provide a unit mass abnormality point at this point. Immediately above this point, grid coordinates with the origin intersecting with the measurement surface of the horizontal gravity gradient value grid data are created at the same interval as the grid interval of the horizontal gravity gradient value grid data. Assuming that the unit mass is 1 g / cm 3, for example, the horizontal gravity gradient value created by this unit mass abnormality point is calculated at each grid coordinate point, and is set as the horizontal gravity gradient unit mass response value at each grid coordinate point.
原点から一定の範囲内に加算係数設定範囲を設定する。この加算係数設定範囲には、水平重力勾配単位質量応答値の最大値及び最小値となるグリッド座標点が含まれる。
∙ Set the addition coefficient setting range within a certain range from the origin. This addition coefficient setting range includes grid coordinate points that are the maximum and minimum values of the horizontal gravity gradient unit mass response value.
各グリッド座標点における加算係数は水平重力勾配単位質量応答値が正値の場合、正値を設定し、水平重力勾配単位質量応答値が負値の場合、負値を設定し原点を中心とした加算係数グリッドデータを作成する。設定する各グリッド座標点における加算係数は、水平重力勾配単位質量応答値の数値の正負、多寡に準じた数値設定が望ましく、水平重力勾配単位質量応答値をそのまま使用しても良い。
When the horizontal gravity gradient unit mass response value is a positive value, the addition coefficient at each grid coordinate point is set to a positive value, and when the horizontal gravity gradient unit mass response value is a negative value, a negative value is set and the origin is set as the center. Create addition coefficient grid data. The addition coefficient at each grid coordinate point to be set is desirably a numerical value setting in accordance with the sign of the horizontal gravity gradient unit mass response value, and the horizontal gravity gradient unit mass response value may be used as it is.
水平重力勾配値グリッドデータの一つのグリッド点に加算係数グリッドデータの原点を重ね合わせ、重なり合う総てのグリッド点において水平重力勾配値と加算係数を掛け合わせた数値を求め、これらの数値を総て加算して水平重力勾配重合値を計算する。この計算を水平重力勾配重合或いはHorizontal Gravity Gradient Stack、略称HGGSと呼び、その値を水平重力勾配重合値或いはHGGS値とする。
The origin of the addition coefficient grid data is overlaid on one grid point of the horizontal gravity gradient value grid data, and the value obtained by multiplying the horizontal gravity gradient value and the addition coefficient at all the overlapping grid points is obtained. Add and calculate the horizontal gravity gradient polymerization value. This calculation is called horizontal gravity gradient superposition or Horizontal Gravity Gradient Stack, abbreviated HGGS, and the value is defined as the horizontal gravity gradient superposition value or HGGS value.
このHGGS値は水平重力勾配単位質量応答値算出の前提である単位質量異常点近傍における質量異常の影響を強調し、それより深い深度や水平方向に分布する質量異常の影響を減衰する効果がある。
This HGGS value emphasizes the influence of mass anomalies near the unit mass anomaly point, which is the premise for calculating the horizontal gravity gradient unit mass response value, and has the effect of attenuating the influence of mass anomalies distributed deeper and horizontally. .
次に、水平重力勾配重合計算を隣りのグリッド点にて行い、順次検討範囲全体のグリッド点にて計算し、測定水平重力勾配重合グリッドデータ或いはHGGS(測定)グリッドデータを作成する。
Next, horizontal gravity gradient superposition calculation is performed at adjacent grid points, and calculation is sequentially performed at the grid points of the entire examination range, and measured horizontal gravity gradient superposition grid data or HGGS (measurement) grid data is created.
図４に以上の測定水平重力勾配重合値計算の手順を示す。グリッド間隔50m、質量異常深度125mとするGuv=1.5(x2-y2)/r5を計算し、最大値で割った相対的な水平重力勾配単位質量応答値が示してある。最大値はグリッド(0,-2)と(0,2)、最小値はグリッド(-2,0)と(2,0)にて得られる。そこで加算係数設定範囲を5x5とし、グリッド(0,-2)と(0,2)には係数1を、グリッド(-2,0)と(2,0)には係数-1を、その他のグリッドは係数0とする加算係数グリッドデータを作成する。
FIG. 4 shows the procedure for calculating the above measured horizontal gravity gradient polymerization value. Guv = 1.5 (x 2 -y 2 ) / r 5 is calculated for a grid spacing of 50 m and a mass anomaly depth of 125 m, and the relative horizontal gravity gradient unit mass response value divided by the maximum value is shown. The maximum value is obtained from grids (0, -2) and (0,2), and the minimum value is obtained from grids (-2,0) and (2,0). Therefore, the addition coefficient setting range is set to 5x5, coefficient 1 is set for grids (0, -2) and (0,2), coefficient -1 is set for grids (-2,0) and (2,0), The grid creates addition coefficient grid data with coefficient 0.
測定水平重力勾配グリッドデータにて例えばグリッド点(2,2)では、このグリッド点に加算係数グリッドデータの原点(0,0)を重ね合わせると、係数1となる測定水平重力勾配グリッドデータのグリッド点は(2,0)と(2,4)、係数-1となるグリッド点は(0,2)と(4,2)であるから、重なり合うグリッド点の測定水平重力勾配値と加算係数を掛けて加算し、測定水平重力勾配重合値は、10x1+0x1+33x(-1)+0x(-1)=-23となる。これらの計算をグリッド点全体で行う。
In the measured horizontal gravity gradient grid data, for example, at the grid point (2, 2), the grid of the measured horizontal gravity gradient grid data becomes the coefficient 1 when the origin (0, 0) of the addition coefficient grid data is superimposed on this grid point. Since the points are (2,0) and (2,4) and the grid points with a coefficient of -1 are (0,2) and (4,2), the measured horizontal gravity gradient value and the addition coefficient of the overlapping grid points are Multiplying and adding, the measured horizontal gravity gradient superposition value is 10x1 + 0x1 + 33x (-1) + 0x (-1) =-23. These calculations are performed on the entire grid point.
表層層厚とグリッド面積から各グリッドにおける表層体積が求められ、更にある一定の表層密度、例えば１g/cm3を想定すると各グリッドにおける表層質量が求められる。表層質量に起因する水平重力勾配値（以下合成水平重力勾配値と言う）は各グリッドにおける表層質量にて導き出された水平重力勾配値の総和である。
The surface layer volume in each grid is determined from the surface layer thickness and grid area, and the surface layer mass in each grid is determined assuming a certain surface layer density, for example, 1 g / cm 3. The horizontal gravity gradient value resulting from the surface layer mass (hereinafter referred to as the combined horizontal gravity gradient value) is the sum of the horizontal gravity gradient values derived from the surface layer mass in each grid.
実際の計算では、合成水平重力勾配データは表層内のある深度、例えば処理基準面を密度凝縮面と設定し、密度凝縮面に各グリッドにおける表層質量を凝縮設定し、水平重力勾配単位質量応答グリッドデータと表層質量グリッドデータとの畳み込み計算により計算できる。その他の計算方法としては鉛直角柱集合体をベースに計算する方法等がある。
In the actual calculation, the synthetic horizontal gravity gradient data is set to a certain depth in the surface layer, for example, the processing reference surface is set as the density condensation surface, the surface mass in each grid is condensed on the density condensation surface, and the horizontal gravity gradient unit mass response grid is set. It can be calculated by convolution calculation of data and surface mass grid data. Other calculation methods include a calculation method based on a vertical prism assembly.
更に各グリッド点における表層質量による合成表層水平重力勾配重合値を計算し、合成水平重力勾配重合グリッドデータ或いはHGGS(合成)グリッドデータを作成する。
Furthermore, the synthetic surface horizontal gravity gradient polymerization value by the surface mass at each grid point is calculated, and synthetic horizontal gravity gradient polymerization grid data or HGGS (composite) grid data is created.
この合成水平重力勾配重合グリッドデータ或いはHGGS(合成)グリッドデータは例えば密度１g/cm3を前提とした表層質量にて作られる水平重力勾配重合理論値のグリッドデータである。
This synthetic horizontal gravity gradient polymerization grid data or HGGS (synthetic) grid data is, for example, horizontal gravity gradient polymerization theoretical grid data created with a surface mass assuming a density of 1 g / cm 3.
一方、測定水平重力勾配重合グリッドデータ或いはHGGS(測定)グリッドデータは、測定した水平重力勾配重合値であり、表層質量にて作られる水平重力勾配重合値に加え表層深部質量からの影響や測定時のノイズが加わった数値となっている。
On the other hand, the measured horizontal gravity gradient polymerization grid data or HGGS (measurement) grid data is the measured horizontal gravity gradient polymerization value, and in addition to the horizontal gravity gradient polymerization value created by the surface layer mass, the influence from the surface depth mass and the measurement time It is a numerical value with added noise.
表層密度の変化が緩やかであるとするとHGGS(測定)グリッドデータとHGGS(合成)グリッドデータをある狭い範囲において線形相関性を検討すれば線形性の傾きは検討する範囲における平均表層密度となる。
Suppose that the change in surface layer density is gradual, if the linear correlation is examined in a narrow range between HGGS (measurement) grid data and HGGS (composite) grid data, the slope of linearity will be the average surface layer density in the examined range.
この狭い範囲での線形相関性を順次検討範囲全体に拡大することで検討範囲における表層密度分布を計算できる。この線形相関性処理はMoving Window Correlationと言う。
The surface density distribution in the study area can be calculated by sequentially expanding the linear correlation in this narrow area to the entire study area. This linear correlation processing is called Moving Window Correlation.
HGGS処理は表層質量からの影響を際立たせ、表層深部質量からの影響を減衰させる効果がある。従ってHGGS処理をせず、例えば水平重力勾配値にて線形相関性を計算した場合、表層下の深い深度からの水平重力勾配値への影響が大きく表層密度算出は困難である。
HGGS treatment has the effect of highlighting the effect from the surface layer mass and attenuating the effect from the surface layer mass. Therefore, when the linear correlation is calculated using, for example, the horizontal gravity gradient value without performing the HGGS processing, the influence on the horizontal gravity gradient value from the deep depth below the surface layer is large and it is difficult to calculate the surface layer density.
波数領域でも処理でき、加算係数グリッドデータの２次元フーリエ変換値と合成水平重力勾配値グリッドデータの２次元フーリエ変換値を掛け合わせ、その掛け合わせた数値をフーリエ逆変換することで合成水平重力勾配重合グリッドデータ或いはHGGS(合成)グリッドデータを求めることができる。同様に測定水平重力勾配値グリッドデータからも２次元フーリエ変換、逆変換にて測定水平重力勾配重合グリッドデータ或いはHGGS(測定)グリッドデータを作成できる。
It can also be processed in the wave number domain, and the two-dimensional Fourier transform value of the addition coefficient grid data and the two-dimensional Fourier transform value of the combined horizontal gravity gradient value grid data are multiplied, and the resultant numerical value is subjected to inverse Fourier transform to generate the combined horizontal gravity gradient. Superposition grid data or HGGS (composite) grid data can be obtained. Similarly, measurement horizontal gravity gradient superposition grid data or HGGS (measurement) grid data can be created from the measurement horizontal gravity gradient value grid data by two-dimensional Fourier transform and inverse transformation.
実例として、独立行政法人石油天然ガス・金属鉱物資源機構(JOGMEC)が平成24年度地熱資源ポテンシャル調査としてCGG Aviation社(CGG)ヘリコプター搭載システムHeliFALCON空中重力偏差法探査装置(FALCON AGG)にて測定の霧島地区のデータ(東西16km、南部区12kmの範囲にて測線間隔東西250m、南北2.5km)を処理した。
As an example, the Japan Oil, Gas and Metals National Corporation (JOGMEC) conducted a geothermal resource potential survey in 2012 using a CGG Aviation (CGG) helicopter-mounted system HeliFALCON aerial gravity deviation method (FALCON AGG). Kirishima area data (16km from east to west, 12km from south ward to 250km from east to west, 2.5km from north to south) was processed.
飛行高度から飛行高度傾斜値が7.5度未満になるよう抑制して平準化飛行高度面を求めた。処理基準面を平準化飛行高度面下150m、表層下面を225mに設定した。グリッド間隔は25mである。
The level of flight altitude was obtained by suppressing the flight altitude slope value from being less than 7.5 degrees from the flight altitude. The treatment reference plane was set to 150m below the leveling flight altitude and the bottom surface was set to 225m. Grid spacing is 25m.
図５は測定水平重力勾配重合分布図(HGGS-Guv測定)であり、図６は表層密度を0.3745g/cm3として計算の合成水平重力勾配重合分布図(HGGS-Guv合成)である。加算係数設定範囲はグリッド11x11であり、係数1のグリッド点は(-5,0)と(5,0)、係数-1となるグリッド点は(-5,0)と(5,0)およびその他のグリッド点では係数0である。両図の相関性は良好である。
FIG. 5 is a measured horizontal gravity gradient polymerization distribution diagram (HGGS-Guv measurement), and FIG. 6 is a synthetic horizontal gravity gradient polymerization distribution diagram (HGGS-Guv synthesis) calculated with a surface layer density of 0.3745 g / cm 3 . The addition coefficient setting range is grid 11x11, the grid points with coefficient 1 are (-5,0) and (5,0), the grid points with coefficient -1 are (-5,0) and (5,0) and The other grid points have a coefficient of 0. The correlation between both figures is good.
図７は線形相関性解析から求めた表層密度図である。Moving Window Correlation法において検討範囲は7x7グリッド(150mx150m)と9x9グリッド(200mx200m)である。GuvのみならずGxyデータにおいても解析している。相関係数が0.7以上、かつ算出密度が1.0～3.4g/cm3の範囲に入る数値のみを採用し表層密度図を作成している。表層密度の変化が大きく、一定な密度値は適正ではないことが判る。
FIG. 7 is a surface density diagram obtained from linear correlation analysis. In the Moving Window Correlation method, the examination range is 7x7 grid (150mx150m) and 9x9 grid (200mx200m). We analyze not only Guv but also Gxy data. A surface density map is created using only values that have a correlation coefficient of 0.7 or more and a calculated density in the range of 1.0 to 3.4 g / cm 3 . It can be seen that the change in surface layer density is large and that a constant density value is not appropriate.
Claims (2)
- 測定した水平重力勾配データをグリッド化し水平重力勾配グリッドデータを作成し、該水平重力勾配グリッドデータにおけるあるグリッド点に加算係数グリッドデータの原点を重ね合わせ、重なり合う総てのグリッド点において水平重力勾配値と加算係数を掛け合わせた数値を求め、これらの数値の総和を該グリッド点における水平重力勾配重合値とする計算を、該水平重力勾配グリッドデータの各グリッド点にて計算し、測定水平重力勾配重合グリッドデータを作成し、該測定水平重力勾配重合グリッドデータと、表層の層厚と単位質量により計算される合成水平重力勾配重合グリッドデータとの相関性から表層密度を推定することを特徴とする重力偏差データの表層密度値推定方法。 The measured horizontal gravity gradient data is gridded to create horizontal gravity gradient grid data, the origin of the addition coefficient grid data is overlaid on a grid point in the horizontal gravity gradient grid data, and the horizontal gravity gradient value at all overlapping grid points A calculation is made at each grid point of the horizontal gravity gradient grid data by calculating a value obtained by multiplying the addition coefficient and the sum of these numerical values, and calculating the horizontal gravity gradient at the grid points. Superposition grid data is created, and the surface layer density is estimated from the correlation between the measured horizontal gravity gradient superposition grid data and the synthetic horizontal gravity gradient superposition grid data calculated from the surface layer thickness and unit mass. A method for estimating the surface density value of gravity deviation data.
- 加算係数グリッドデータは、水平重力勾配データ測定面鉛直下方における表層内に設定の単位質量異常を持った物体が水平重力勾配データ測定面上に作る水平重力勾配単位質量応答値の最大値及び最小値となるグリッド点を含み、且つ各グリッド点において該水平重力勾配単位質量応答値が正値の場合、加算係数が正値、該水平重力勾配単位質量応答値が負値の場合、加算係数が負値に設定することを特徴とする請求項１に記載の重力偏差データの表層密度値推定方法。 The addition coefficient grid data is the maximum and minimum horizontal gravity gradient unit mass response values created on the horizontal gravity gradient data measurement surface by an object with unit mass abnormality set in the surface layer vertically below the horizontal gravity gradient data measurement surface. If the horizontal gravity gradient unit mass response value is positive at each grid point, the addition coefficient is positive, and if the horizontal gravity gradient unit mass response value is negative, the addition coefficient is negative. 2. The method for estimating a surface density value of gravity deviation data according to claim 1, wherein the value is set to a value.
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|---|---|---|---|---|
| CN109283591A (en) * | 2018-10-22 | 2019-01-29 | 中国人民解放军61540部队 | Using ground point as the airborne gravity data downward continuation method and system of control |
| CN109521488A (en) * | 2018-11-30 | 2019-03-26 | 国家测绘地理信息局卫星测绘应用中心 | ARMA optimal filter model building method for Satellite gravity field data |
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| CN108873091B (en) * | 2018-06-29 | 2019-10-25 | 中国人民解放军61540部队 | The full tensor of Satellite gravity field restores the determination method and system of earth gravitational field |
| CN110440753B (en) * | 2019-08-13 | 2021-04-09 | 中国地质调查局西安地质调查中心 | High-precision DEM aviation gravity remote zone terrain correction method considering earth curvature |
| CN111650659A (en) * | 2020-06-17 | 2020-09-11 | 宁夏大学 | Vertical line deviation estimation method and system based on aerial gravity anomaly |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109283591A (en) * | 2018-10-22 | 2019-01-29 | 中国人民解放军61540部队 | Using ground point as the airborne gravity data downward continuation method and system of control |
| CN109283591B (en) * | 2018-10-22 | 2020-03-13 | 中国人民解放军61540部队 | Method and system for extending aviation gravity data downwards by taking ground point as control |
| CN109521488A (en) * | 2018-11-30 | 2019-03-26 | 国家测绘地理信息局卫星测绘应用中心 | ARMA optimal filter model building method for Satellite gravity field data |
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| JP2017138124A (en) | 2017-08-10 |
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