Gravity gradiometer survey errors

Geophysics ◽  
1988 ◽  
Vol 53 (10) ◽  
pp. 1355-1361 ◽  
Author(s):  
Steven J. Brzezowski ◽  
Warren G. Heller
Keyword(s):  
Total Error ◽  
Estimation Algorithm ◽  
Error Variance ◽  
Downward Continuation ◽  
Gravity Gradiometer ◽  
Limited Data ◽  
Gravity Disturbance ◽  
System Noise ◽  
Disturbance Vector ◽  
Moving Base

Gradiometer system noise, sampling effects, downward continuation, and limited data extent are the important contributors to moving‐base gravity gradiometer survey error. We apply a two‐dimensional frequency‐domain approach in simulations of several sets of airborne survey conditions to assess the significance of the first two sources. A special error allocation technique is used to account for the downward continuation and limited extent effects. These two sources cannot be modeled adequately as measurement noise in a linear error estimation algorithm. For a typical characterization of the Earth’s gravity field, our modeling indicates that limited data extent generally contributes about one‐half of the total error variance associated with recovery of the gravity disturbance vector at the Earth’s surface; gradiometer system noise typically contributes about one‐third. However, sampling effects are also very important (and are controlled through the survey track spacing). A 5 km track spacing provides a reasonable tradeoff between survey cost and errors due to track spacing. Furthermore, our results indicate that a moving‐base gravity gradiometer system can recover each component of the gravity disturbance vector with an rms accuracy better than 1.0 mGal.

scholarly journals Sensitivity of Algorithms for Estimating the Gravity Disturbance Vector to Its Model Uncertainty

2021 ◽  
Author(s):  
O. A. Stepanov ◽  
D. A. Koshaev ◽  
O. M. Yashnikova ◽  
A. V Motorin ◽  
L. P. Staroseltsev
Keyword(s):  
Model Uncertainty ◽  
Model Parameters ◽  
Gravity Disturbance ◽  
Disturbance Vector

AbstractThe work considers the results of filtering and smoothing of the gravity disturbance vector horizontal components and focuses on the sensitivity of these results to the model parameters in the case when the inertial-geodesic method is applied in the framework of a marine survey on a sea vessel.

scholarly journals Mathematic Model and Error Analysis of Moving-base Rotating Accelerometer Gravity Gradiometer

2021 ◽  
pp. 1-1
Author(s):  
Mingbiao Yu ◽  
Tijing Cai ◽  
Liangcheng Tu ◽  
Chenyuan Hu ◽  
Ji Fan ◽  
...  
Keyword(s):  
Error Analysis ◽  
Mathematic Model ◽  
Gravity Gradiometer ◽  
Moving Base

Coupled Air–Mixed Layer Temperature Predictability for Climate Reconstruction

2012 ◽  
Vol 25 (2) ◽  
pp. 459-472 ◽  
Author(s):  
Angeline G. Pendergrass ◽  
Gregory J. Hakim ◽  
David S. Battisti ◽  
Gerard Roe
Keyword(s):  
Stochastic Model ◽  
Coupling Strength ◽  
Forecast Error ◽  
Total Error ◽  
Thermal Inertia ◽  
Error Variance ◽  
Climate System ◽  
Frequency Noise ◽  
Forecast Error Variance ◽  
Slab Ocean

Abstract A central issue for understanding past climates involves the use of sparse time-integrated data to recover the physical properties of the coupled climate system. This issue is explored in a simple model of the midlatitude climate system that has attributes consistent with the observed climate. A quasigeostrophic (QG) model thermally coupled to a slab ocean is used to approximate midlatitude coupled variability, and a variant of the ensemble Kalman filter is used to assimilate time-averaged observations. The dependence of reconstruction skill on coupling and thermal inertia is explored. Results from this model are compared with those for an even simpler two-variable linear stochastic model of midlatitude air–sea interaction, for which the assimilation problem can be solved semianalytically. Results for the QG model show that skill decreases as the length of time over which observations are averaged increases in both the atmosphere and ocean when normalized against the time-averaged climatological variance. Skill in the ocean increases with slab depth, as expected from thermal inertia arguments, but skill in the atmosphere decreases. An explanation of this counterintuitive result derives from an analytical expression for the forecast error covariance in the two-variable stochastic model, which shows that the ratio of noise to total error increases with slab ocean depth. Essentially, noise becomes trapped in the atmosphere by a thermally stiffer ocean, which dominates the decrease in initial condition error owing to improved skill in the ocean. Increasing coupling strength in the QG model yields higher skill in the atmosphere and lower skill in the ocean, as the atmosphere accesses the longer ocean memory and the ocean accesses more atmospheric high-frequency “noise.” The two-variable stochastic model fails to capture this effect, showing decreasing skill in both the atmosphere and ocean for increased coupling strength, due to an increase in the ratio of noise to the forecast error variance. Implications for the potential for data assimilation to improve climate reconstructions are discussed.

Prototype Moving Base Gravity Gradiometer

1973 ◽  
Author(s):  
Charles B. Ames ◽  
Robert L. Forward ◽  
Philip M. La Hue ◽  
Robert W. Peterson ◽  
David W. Rouse
Keyword(s):  
Gravity Gradiometer ◽  
Moving Base

Moving Base Gravity Gradiometer Survey (GGSS) Program

1988 ◽  
Author(s):  
Louis Pfohl ◽  
Walter Rusnak ◽  
Albert Jircitano ◽  
Andrew Grierson
Keyword(s):  
Gravity Gradiometer ◽  
Moving Base

Using spherical scaling functions in scalar and vector airborne gravimetry

2020 ◽  
Author(s):  
Vadim Vyazmin ◽  
Yuri Bolotin
Keyword(s):  
Gravity Model ◽  
Kalman Filtering ◽  
A Priori ◽  
Systematic Errors ◽  
Airborne Gravimetry ◽  
Survey Area ◽  
Gravity Disturbance ◽  
Airborne Measurements ◽  
Disturbance Vector ◽  
Vector Gravimetry

<p>Airborne gravimetry is capable to provide Earth’s gravity data of high accuracy and spatial resolution for any area of interest, in particular for hard-to-reach areas. An airborne gravimetry measuring system consists of a stable-platform or strapdown gravimeter, and GNSS receivers. In traditional (scalar) airborne gravimetry, the vertical component of the gravity disturbance vector is measured. In actively developing vector gravimetry, all three components of the gravity disturbance vector are measured.</p><p>In this research, we aim at developing new postprocessing algorithms for estimating gravity from airborne data taking into account a priori information about spatial behavior of the gravity field in the survey area. We propose two algorithms for solving the following two problems:</p><p>1) <em>In scalar gravimetry:</em>  Mapping gravity at the flight height using the gravity disturbances estimated along the flight lines (via low-pass or Kalman filtering), taking into account spatial correlation of the gravity field in the survey area and statistical information on the along-line gravity estimate errors.</p><p>2) <em>In vector gravimetry:</em>  Simultaneous determination of three components of the gravity disturbance vector from airborne measurements at the flight path.</p><p>Both developed algorithms use an a priori spatial gravity model based on parameterizing the disturbing potential in the survey area by three-dimensional harmonic spherical scaling functions (SSFs). The algorithm developed for solving Problem 1 provides estimates of the unknown coefficients of the a priori gravity model using a least squares technique. Due to the assumption that the along-line gravity estimate errors at any two lines are not correlated, the algorithm has a recursive (line-by-line) implementation. At the last step of the recursion, regularization is applied due to ill-conditioning of the least squares problem. Numerical results of processing the GT-2A airborne gravimeter data are presented and discussed.</p><p>To solve Problem 2, one need to separate the gravity horizontal component estimates from systematic errors of the inertial navigation system (INS) of a gravimeter (attitude errors, inertial sensor bias). The standard method of gravity estimation based on gravity modelling over time is not capable to provide accurate results, and additional corrections should be applied. The developed algorithm uses a spatial gravity model based on the SSFs. The coefficients of the gravity model and the INS systematic errors are estimated simultaneously from airborne measurements at the flight path via Kalman filtering with regularization at the last time moment. Results of simulation tests show a significant increase in accuracy of gravity vector estimation compared to the standard method.</p><p>This research was supported by RFBR (grant number 19-01-00179).</p>

Posterror Compensation of Moving-Base Rotating Accelerometer Gravity Gradiometer

2021 ◽  
Vol 70 ◽  
pp. 1-10
Author(s):  
Mingbiao Yu ◽  
Tijing Cai ◽  
Liangcheng Tu ◽  
Chenyuan Hu ◽  
Li Yu
Keyword(s):  
Gravity Gradiometer ◽  
Moving Base
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