Space-Wise approach for airborne gravity data modelling

Sampietro, Daniele; Capponi, Martina; Mansi, Ahmed Hamdi; Gatti, Andrea; Marchetti, Paolo; Sansò, F.

doi:10.1007/s00190-016-0981-y

Cited by 26 publications

(17 citation statements)

References 28 publications

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“…Usually the very-high frequencies are removed. To give an example: let examine a grid of gravity anomalies from a global gravity field model up to degree 2190; it is well known that the maximum spatial resolution achievable is around 10 km half-wavelength [34]. Now, a DTM used to compute the Bouguer anomalies from such a global model should be chosen in such a way that its gravitational effect has a similar spatial resolution.…”

Section: Data Reductionmentioning

confidence: 99%

Practical Tips for 3D Regional Gravity Inversion

Sampietro¹,

Capponi

2019

Geosciences

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To solve the inverse gravimetric problem, i.e., to estimate the mass density distribution that generates a certain gravitational field, at local or regional scale, several parameters have to be defined such as the dimension of the 3D region to be considered for the inversion, its spatial resolution, the size of its border, etc. Determining the ideal setting for these parameters is in general difficult: theoretical solutions are usually not possible, while empirical ones strongly depend on the specific target of the inversion and on the experience of the user performing the computation. The aim of the present work is to discuss empirical strategies to set these parameters in such a way to avoid distortions and errors within the inversion. In particular, the discussion is focused on the choice of the volume of the model to be inverted, the size of its boundary, its spatial resolution, and the spatial resolution of the a-priori information to be used within the data reduction. The magnitude of the possible effects due to a wrong choice of the above parameters is also discussed by means of numerical examples.

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Section: Data Reductionmentioning

confidence: 99%

Practical Tips for 3D Regional Gravity Inversion

Sampietro¹,

Capponi

2019

Geosciences

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“…The former research has shown that the shortcoming of using a model to correct the error is that the model usually has constant deviation, when it is compared with the truth data in a certain area [27]. However, this shortcoming could be largely eliminated by ground gravimetry and upward extension or by using satellite-only model [28]. Thus, the forward and backward fusion method is divided into four steps, the scheme of which is shown in Figure 8: Use the forward algorithm to get gravity disturbance result,Use the backward algorithm to get gravity disturbance result,Correct the low frequency error by high precision global gravity field model,Fuse the data according to Equations (10) and (11).…”

Section: Applying Backward Navigation Algorithm To Improve Strapdomentioning

confidence: 99%

Improving the Strapdown Airborne Vector Gravimetry by a Backward Inertial Navigation Algorithm

Wang¹,

Cao²,

Cai³

et al. 2018

Sensors

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Strapdown airborne gravimetry is an efficient way to obtain gravity field data. A new method has been developed to improve the accuracy of airborne vector gravimetry. The method introduces a backward strapdown navigation algorithm into the strapdown gravimetry, which is the reverse process of forward algorithm. Compared with the forward algorithm, the backward algorithm has the same performance in the condition of no sensor error, but has different error characteristics in actual conditions. The differences of the two algorithms in the strapdown gravimetry data processing are presented by simulations, which show that the two algorithms have different performance in the horizontal attitude measurement and convergence of integrated navigation filter. On the basis of detailed analysis, the procedures of accuracy improvement method are presented. The result of this method is very promising when applying to an actual flight test carried out by a SGA-WZ02 strapdown gravimeter. After applying the proposed method, the repeatability of two gravity disturbance horizontal components were 1.83 mGal and 1.80 mGal under the resolution of 6 km, which validate the effectiveness of the method. Furthermore, the wavenumber correlation filter is also discussed as an alternative data fusion method.

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“…It is in fact well known that the gravimeter observations are affected from biases and trends. However, we will suppose in the current work that the observations, biases, and trends (for sufficiently long paths, i.e., more than 80 km) can be proficiently repaired by exploiting information coming from satellite observations as shown in [15]. As a consequence, we are entitled to apply also this second approximation in the following.…”

Section: Methodsmentioning

confidence: 99%

“…The final objective of this project is the definition of a procedure to optimally plan the airborne survey, according to the required final accuracy and spatial resolution. Thus, once the signal had been simulated, the processing technique described in [15] is applied, thus recovering the expected filtered gravitational field, together with its predicted accuracy. The whole methodology used to process the simulated signal is summarized here for the sake of completeness; however, the interested reader can refer to the already cited [15].…”

Section: Methodsmentioning

confidence: 99%

A New Tool for Airborne Gravimetry Survey Simulation

Sampietro¹,

Mansi

Capponi

2018

Geosciences

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Airborne gravimetry represents nowadays probably the most efficient technique to collect gravity observations close to the Earth's surface. In the 1990s, thanks to the development of the Global Navigation Satellite Systems (GNSS), which has made accurate navigational data available, this technique started to spread worldwide because of its capability to provide measurements in a fast and cost-effective way. Differently from other techniques such as shipborne gravimetry, it has the advantage to provide gravity measurements also in challenging environments which can be difficult to access otherwise, like mountainous areas, rain forests and polar regions. For such reasons, airborne gravimetry is used for various applications related to the regional gravity field modelling: from the computation of high accurate local geoid for geodetic applications to geophysical ones, specifically related to oil and gas exploration activities or more in general for regional geological studies. Depending on the different kinds of application and the final required accuracy, the definition of the main characteristics of the airborne survey, e.g., the planar distance between consecutive flight tracks, the aircraft velocity, etc., can be a difficult task. In this work, we present a new software package, which would help in properly accomplishing the survey design task. Basically, the developed software solution allows for generating a realistic (from the observation noise point of view) gravimetric signal, and, after that, to predict the accuracy and spatial resolution of the final retrievable gravimetric field, in terms of gravity disturbances, given the flight main characteristics. The proposed procedure is suited for airborne survey planning in order to be able to optimize the design of the survey according to the required final accuracy. With the aim to evaluate the influence of the various survey parameters on the expected accuracy of the airborne survey, different numerical tests have been performed on simulated and real datasets. For instance, it has been shown that if the observation noise is not properly modeled in the data filtering step, the survey results degrade about 25%, while not acquiring control lines during the survey will basically reduce the final accuracy by a factor of two.

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Space-Wise approach for airborne gravity data modelling

Cited by 26 publications

References 28 publications

Practical Tips for 3D Regional Gravity Inversion

Practical Tips for 3D Regional Gravity Inversion

Improving the Strapdown Airborne Vector Gravimetry by a Backward Inertial Navigation Algorithm

A New Tool for Airborne Gravimetry Survey Simulation

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