Comparing gravity and gravity gradient surveys

Barnes, Gary J.; Lumley, J.; Houghton, Phill; Gleave, R.

doi:10.1111/j.1365-2478.2010.00900.x

Cited by 16 publications

(4 citation statements)

References 9 publications

(7 reference statements)

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“…The advantage of using a calculated signal rather than the actual survey measurements is that the answer is known, so the performance of the processing algorithms can be analyzed more easily. When summing the three inline components of differential curvature from the FTG to form what is known as the inline sum, the signal cancels, leaving a residual that is a measure of the overall noise in the system (Barnes et al, 2011). If we divide this quantity by H3, it can be used as a suitable representation of the noise in each of the six FTG output channels.…”

Section: I40mentioning

confidence: 98%

Processing gravity gradient data

Barnes

Lumley

2011

GEOPHYSICS

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As the demand for high-resolution gravity gradient data increases and surveys are undertaken over larger areas, new challenges for data processing have emerged. In the case of full-tensor gradiometry, the processor is faced with multiple derivative measurements of the gravity field with useful signal content down to a few hundred meters' wavelength. Ideally, all measurement data should be processed together in a joint scheme to exploit the fact that all components derive from a common source. We have investigated two methods used in commercial practice to process airborne full-tensor gravity gradient data; the methods result in enhanced, noise-reduced estimates of the tensor. The first is based around Fourier operators that perform integration and differentiation in the spatial frequency domain. By transforming the tensor measurements to a common component, the data can be combined in a way that reduces noise. The second method is based on the equivalentsource technique, where all measurements are inverted into a single density distribution. This technique incorporates a model that accommodates low-order drift in the measurements, thereby making the inversion less susceptible to correlated time-domain noise. A leveling stage is therefore not required in processing. In our work, using data generated from a geologic model along with noise and survey patterns taken from a real survey, we have analyzed the difference between the processed data and the known signal to show that, when considering the G zz component, the modified equivalent-source processing method can reduce the noise level by a factor of 2.4. The technique has proven useful for processing data from airborne gradiometer surveys over mountainous terrain where the flight lines tend to be flown at vastly differing heights.

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Section: I40mentioning

confidence: 98%

Processing gravity gradient data

Barnes

Lumley

2011

GEOPHYSICS

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“…Notably, Wang (2000) verified the feasibility of using vertical gravity gradient anomalies (VGGs) to predict bathymetry and provided a theoretical basis for future work. Many scholars subsequently argued that VGGs contain more short‐wavelength information about seafloor topography (Barnes et al., 2011; Wan et al., 2019). Therefore, GAs and VGGs should be combined to jointly construct seafloor topography model.…”

Section: Introductionmentioning

confidence: 99%

Bathymetric Prediction Using Multisource Gravity Data Derived From a Parallel Linked BP Neural Network

Heyuan

Feng

et al. 2022

JGR Solid Earth

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Seafloor topography, or bathymetry, has significant economic, military, and scientific research value. High-precision seafloor topographic data are helpful for studying the geographic features of the seafloor and the structure of the Earth's crust. Moreover, the seafloor requires detailed mapping to ensure fairway safety and aid in submarine navigation.Echo sounding techniques have been classically used for mapping the seafloor. From single-beam sounding in the 1950s to multibeam sounding in the 1980s , echo sounding techniques have advanced from point measurement to surface measurement. However, it is still a difficult task to obtain global seafloor topography with traditional methods within a short period (Carron et al., 2001;Sandwell & Smith, 2001). Fortunately, with the improvements in the accuracy and density of satellite altimetry data, using gravity anomalies (GAs) obtained from altimetry data to predict depth has become a feasible approach. Dixon et al. (1983) were among the first researchers to use SEASAT altimetry data to predict depth. Sandwell (1994, 1997) used GAs to construct a global seafloor model and exceedingly filled in the blank areas in nautical charts. Since then, a growing number of global seafloor models have been released, such as ETOPO1 (

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“…Last but not least, current methods have used altimetry‐derived gravity anomalies to infer seafloor topography. Gravity gradients are more sensitive to the short‐wavelength topography than gravity anomalies (Barnes et al, ), so the corresponding performance in topography prediction is expected to be better.…”

Section: Introductionmentioning

confidence: 99%

Seafloor Topography Estimation From Gravity Gradients Using Simulated Annealing

2018

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Inferring seafloor topography from gravity anomaly currently is the dominant method to obtain a global view of the oceans. Standard techniques rely on an approximate, linear relationship between topography and gravity, which is valid only if the local topography is smooth compared with the regional topography, so the estimation accuracy in the very rugged areas is low. Current methods can be improved by removing the linear approximation and estimating the topography through simulated annealing and by using gravity gradiometry that is more sensitive to topography at short wavelengths than the gravity anomaly. Simulated annealing is a global optimization technique that can process nonlinear inverse problems. It is developed to estimate the seafloor depths by minimizing the difference between the observed and forward‐computed vertical gravity gradients. The method is tested on altimetry‐derived gravity gradients in a 2∘×2∘ area of rugged seafloor topography in the West Pacific Ocean and results in estimates with a root‐mean‐square error of ±236 m. Compared to estimates from an existing model obtained by standard techniques this represents an accuracy improvement of 22%.

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Comparing gravity and gravity gradient surveys

Cited by 16 publications

References 9 publications

Processing gravity gradient data

Processing gravity gradient data

Bathymetric Prediction Using Multisource Gravity Data Derived From a Parallel Linked BP Neural Network

Seafloor Topography Estimation From Gravity Gradients Using Simulated Annealing

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