scholarly journals Tabulated gravity field data, north flank of the Alaska Range

10.14509/57 ◽  
1981 ◽  
Author(s):  
S. W. Hackett
Keyword(s):  
Gravity Field ◽  
Field Data ◽  
Alaska Range

Combining the deep Earth and lithospheric gravity field to study the density structure of the upper mantle

2021 ◽  
Author(s):  
Bart Root ◽  
Javier Fullea ◽  
Jörg Ebbing ◽  
Zdenek Martinec
Keyword(s):  
Gravity Field ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Field Data ◽  
Wavelength Region ◽  
Density Structure ◽  
Long Wavelength ◽  
Global Gravity ◽  
Deep Earth

<p>Global gravity field data obtained by dedicated satellite missions is used to study the density distribution of the lithosphere. Different multi-data joint inversions are using this dataset together with other geophysical data to determine the physical characteristics of the lithosphere. The gravitational signal from the deep Earth is usually removed by high-pass filtering of the model and data, or by appropriately selecting insensitive gravity components in the inversion. However, this will remove any long-wavelength signal inherent to lithosphere. A clear choice on the best-suited approach to remove the sub-lithospheric gravity signal is missing. </p><p>Another alternative is to forward model the gravitational signal of these deep situated mass anomalies and subtract it from the observed data, before the inversion. Global tomography provides shear-wave velocity distribution of the mantle, which can be transformed into density anomalies. There are difficulties in constructing a density model from this data. Tomography relies on regularisation which smoothens the image of the mantle anomalies. Also, the shear-wave anomalies need to be converted to density anomalies, with uncertain conversion factors related to temperature and composition. Understanding the sensitivity of these effects could help determining the interaction of the deep Earth and the lithosphere.</p><p>In our study the density anomalies of the mantle, as well as the effect of CMB undulations, are forward modelled into their gravitational potential field, such that they can be subtracted from gravity observations. The reduction in magnitude of the density anomalies due to the regularisation of the global tomography models is taken into account. The long-wavelength region of the density estimates is less affected by the regularisation and can be used to fix the mean conversion factor to transform shear wave velocity to density. We present different modelling approaches to add the remaining dynamic topography effect in lithosphere models. This results in new solutions of the density structure of the lithosphere that both explain seismic observations and gravimetric measurements. The introduction of these dynamic forces is a step forward in understanding how to properly use global gravity field data in joint inversions of lithosphere models.</p>

THE USE OF POISSON’S RELATION FOR THE EXTRACTION OF PSEUDOTOTAL MAGNETIC FIELD INTENSITY FROM GRAVITY OBSERVATIONS

Geophysics ◽  
1971 ◽  
Vol 36 (3) ◽  
pp. 605-608 ◽  
Author(s):  
Edwin S. Robinson
Keyword(s):  
Magnetic Field ◽  
Gravity Field ◽  
Field Data ◽  
Field Potential ◽  
Geological Structure ◽  
Common Source ◽  
Uniform Density ◽  
Practical Applications ◽  
Uniform Magnetization ◽  
Gravity Fields

Investigation of geological structure by gravimetric and magnetic field surveys requires consideration of relationships between gravity anomaly and magnetic anomaly generating sources. The possibility of using Poisson’s Relation to examine magnetic and gravity fields related to a common source is intriguing. This relation is expressed as follows: [Formula: see text] (1) where A (x, y, z) is the magnetic field potential and U (x, y, z) is the gravity field potential at a point in space due to a source of uniform density ρ and uniform magnetization I in the direction α. This expression has been used to derive magnetic anomalies over idealized forms (Nettleton, 1940) and, by Baranov (1957), to extract pseudogravity fields from magnetic field data. The purpose of this paper is to develop an expression for extracting a pseudomagnetic field from gravity field data and to examine the practical applications of this expression.

Discussion by Leo J. Peters and Thomas A. Elkins

Geophysics ◽  
1953 ◽  
Vol 18 (4) ◽  
pp. 907-909 ◽  
Author(s):  
L. J. Peters ◽  
T. A. Elkins
Keyword(s):  
Gravity Field ◽  
Field Data ◽  
Gravity Data ◽  
Practical Importance ◽  
Gravity Survey ◽  
Second Derivative ◽  
Geologic Structure ◽  
The Real ◽  
Real Effects

We would like to call attention to a point of considerable practical importance which is neglected in this interesting and ingenious paper on the computation of the second derivative. This is the fact that gravity field data inevitably contain errors so that the second derivative values computed by coefficients from this gravity data also will contain errors, which may be of such magnitude as to mask the real effects caused by geologic structure, the finding of which was the purpose of the gravity survey.

Imaging gravity field data

1987 ◽  
Author(s):  
Roman Alvarez ◽  
Alfredo Cortés
Keyword(s):  
Gravity Field ◽  
Field Data

Interactive gravity inversion

Geophysics ◽  
2006 ◽  
Vol 71 (1) ◽  
pp. J1-J9 ◽  
Author(s):  
João B. C. Silva ◽  
Valéria C. F. Barbosa
Keyword(s):  
Gravity Field ◽  
Field Data ◽  
Synthetic Data ◽  
The Other ◽  
Density Contrast ◽  
Gravity Inversion ◽  
Homogeneous Body ◽  
New Approach ◽  
Line Segments ◽  
Gravity Fields

We have developed a new approach for estimating the location and geometry of several density anomalies that give rise to a complex, interfering gravity field. The user interactively defines the assumed outline of the true gravity sources in terms of points and line segments, and the method estimates sources closest to the specified outline to achieve a match between the predicted and observed gravity fields. Each gravity source is assumed to be a homogeneous body with a known density contrast; different density contrasts may be assigned to each source. Tests with synthetic data show that the method can be of use in estimating (1) multiple laterally adjacent and closely situated gravity sources, (2) single gravity sources consisting of several homogeneous compartments with different density contrasts, and (3) two gravity sources with different density contrasts of the same sign, one totally enclosed by the other. The method is also applied to three different sets of field data where the gravity sources belong to the same categories established in the tests with synthetic data. The method produces solutions consistent with the known geologic attributes of the gravity sources, illustrating its potential practicality.

On: “Reduction of Unevenly Spaced Potential Field Data to a Horizontal Plane by Means of Finite Harmonic Series” by Roland G. Henderson and Lindrith Cordell (GEOPHYSICS, October 1971, p. 856–866)

Geophysics ◽  
1972 ◽  
Vol 37 (4) ◽  
pp. 703-703
Author(s):  
Alan T. Herring
Keyword(s):  
Gravity Field ◽  
Potential Field ◽  
Horizontal Plane ◽  
Field Data ◽  
Bouguer Anomaly ◽  
Harmonic Series ◽  
Potential Field Data ◽  
Topographic Relief ◽  
Vertical Gradients

The authors recommend on page 864, that their technique for correcting for anomalous vertical gradients in the gravity field be applied only in “cases where high topographic relief produces large corrections to the station Bouguer anomaly.” The correction applied by the authors amounts to the continuation of the observed gravity field onto a single‐elevation plane from the varying elevations of the observation points. The authors should therefore caution the reader against choosing the datum plane such that the gravity field is continued through sources of interest lying above the datum plane—an easily made mistake in areas of high topographic relief.

scholarly journals A Global Gravity Reconstruction Method for Mercury Employing Deep Convolutional Neural Network

2020 ◽  
Vol 12 (14) ◽  
pp. 2293
Author(s):  
Shuheng Zhao ◽  
Denghong Liu ◽  
Qiangqiang Yuan ◽  
Jie Li
Keyword(s):  
Neural Network ◽  
Spatial Structure ◽  
Gravity Field ◽  
Field Data ◽  
Gravity Data ◽  
Space Environment ◽  
Reconstruction Method ◽  
High Quality ◽  
Low Degree ◽  
High Degree

Mercury, the enigmatic innermost planet in the solar system, is one of the most important targets of space exploration. High-quality gravity field data are significant to refine the physical characterization of Mercury in planetary exploration missions. However, Mercury’s gravity model is limited by relatively low spatial resolution and stripe noises, preventing fine-scale analysis and applications. By analyzing Mercury’s gravity data and topography data in the 2D spatial field, we find they have fairly high spatial structure similarity. Based on this, in this paper, a novel convolution neural network (CNN) approach is proposed to improve the quality of Mercury’s gravity field data. CNN can extract the spatial structure features of gravity data and construct a nonlinear mapping between low- and high-degree data directly. From a low-degree gravity input, the corresponding initial high-degree result can be obtained. Meanwhile, the structure characteristics of the high-resolution digital elevation model (DEM) are extracted and fused to the initial data, to get the final stripe-free result with improved resolution. Given the paucity of Mercury’s data, high-quality lunar datasets are employed as pretraining data after verifying the spatial similarity between gravity and terrain data of the Moon. The HgM007 gravity field obtained by the MErcury Surface, Space ENvironment, GEochemistry and Ranging (MESSENGER) mission at Mercury is selected for experiments to test the ability of the proposed algorithm to remove the stripes caused by quality differences of the highly eccentric orbit data. Experimental results show that our network can directly obtain stripe-free and higher-degree data via inputting low-degree data and implicitly assuming a lunar-like relation between crustal density and porosity. Albeit the CNN-based method cannot be sensitive to subsurface features not present in the initial dataset, it still provides a new perspective for the gravity field refinement.

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