The first pan-Alpine surface-gravity database, a modern compilation that crosses frontiers

Abstract. The AlpArray Gravity Research Group (AAGRG), as part of the European AlpArray program, focuses on the compilation of a homogeneous surface-based gravity data set across the Alpine area. In 2017 10 European countries in the Alpine realm agreed to contribute with gravity data for a new compilation of the Alpine gravity field in an area spanning from 2 to 23∘ E and from 41 to 51∘ N. This compilation relies on existing national gravity databases and, for the Ligurian and the Adriatic seas, on shipborne data of the Service Hydrographique et Océanographique de la Marine and of the Bureau Gravimétrique International. Furthermore, for the Ivrea zone in the Western Alps, recently acquired data were added to the database. This first pan-Alpine gravity data map is homogeneous regarding input data sets, applied methods and all corrections, as well as reference frames. Here, the AAGRG presents the data set of the recalculated gravity fields on a 4 km × 4 km grid for public release and a 2 km × 2 km grid for special request. The final products also include calculated values for mass and bathymetry corrections of the measured gravity at each grid point, as well as height. This allows users to use later customized densities for their own calculations of mass corrections. Correction densities used are 2670 kg m−3 for landmasses, 1030 kg m−3 for water masses above the ellipsoid and −1640 kg m−3 for those below the ellipsoid and 1000 kg m−3 for lake water masses. The correction radius was set to the Hayford zone O2 (167 km). The new Bouguer anomaly is station completed (CBA) and compiled according to the most modern criteria and reference frames (both positioning and gravity), including atmospheric corrections. Special emphasis was put on the gravity effect of the numerous lakes in the study area, which can have an effect of up to 5 mGal for gravity stations located at shorelines with steep slopes, e.g., for the rather deep reservoirs in the Alps. The results of an error statistic based on cross validations and/or “interpolation residuals” are provided for the entire database. As an example, the interpolation residuals of the Austrian data set range between about −8 and +8 mGal and the cross-validation residuals between −14 and +10 mGal; standard deviations are well below 1 mGal. The accuracy of the newly compiled gravity database is close to ±5 mGal for most areas. A first interpretation of the new map shows that the resolution of the gravity anomalies is suited for applications ranging from intra-crustal- to crustal-scale modeling to interdisciplinary studies on the regional and continental scales, as well as applications as joint inversion with other data sets. The data are published with the DOI https://doi.org/10.5880/fidgeo.2020.045 (Zahorec et al., 2021) via GFZ Data Services.

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The first pan-Alpine surface-gravity database, a modern compilation that crosses frontiers

10.5194/essd-2020-375 ◽

2021 ◽

Author(s):

Pavol Zahorec ◽

Juraj Papčo ◽

Roman Pašteka ◽

Miroslav Bielik ◽

Sylvain Bonvalot ◽

...

Keyword(s):

Reference Frames ◽

Joint Inversion ◽

Gravity Data ◽

Grid Point ◽

Bouguer Anomaly ◽

Gravity Anomalies ◽

Interdisciplinary Studies ◽

Data Set ◽

Homogeneous Surface ◽

Gravity Database

Abstract. The AlpArray Gravity Research Group (AAGRG), as part of the European AlpArray program, focuses on the compilation of a homogeneous surface-based gravity dataset across the Alpine area. From this data set, Bouguer- and Free Air anomalies are calculated and presented here. In 2016/17 ten European countries in the Alpine realm have agreed to contribute with gravity data for a new compilation of the Alpine gravity field in an area from 2° to 23° East and from 41° to 51° North. This compilation relies on existing national gravity databases and, for the Ligurian and the Adriatic seas, on ship-borne data of the Bureau Gravimétrique International. Furthermore, for the Ivrea zone in the Western Alps, recently acquired data were added to the database. This first pan-Alpine gravity data map is homogeneous regarding input data sets, applied methods and all corrections as well as reference frames. Here, the AAGRG presents the data set of the recalculated gravity fields on a 4 km × 4 km grid for public release, 2 km × 2 km for special request. The final products also include calculated values for mass/bathymetry corrections of the measured gravity at each grid point, as well as height. This allows users to use later customized densities for their own calculations of mass corrections. Correction densities used are 2670 kg m−3 for landmasses, 1030 kg m−3 for water masses above and −1640 kg m−3 below the ellipsoid. The correction radius was set to the Hayford zone O2 (167 km). The new Bouguer anomaly is station completed (CBA) and compiled according to the most modern criteria and reference frames (both positioning and gravity), including atmospheric corrections. Special emphasis was put on the gravity effect of the numerous lakes in the study area, which can have an effect of up to 5 mGal for gravity stations located at shorelines with steep slopes, e.g., for the rather deep reservoirs in the Alps. The results of an error statistic based on cross validations and/or interpolations residuals is provided for the entire database. As an example, the interpolation residuals of the Austrian data set range between about −8 and +8 mGal, the cross-validation residuals between −14 mGal and +10 mGal; standard deviations are well below 1 mGal. The accuracy of the newly compiled gravity database is close to ±5 mGal for most areas. A first interpretation of the new map shows that the resolution of the gravity anomalies is suited for applications ranging from intra-crustal to crustal scale modelling to interdisciplinary studies on the regional and continental scales as well as applications as joint inversion with other datasets. The data will be published with the DOI https://doi.org/10.5880/fidgeo.2020.045 (Zahorec et al., 2020) when the final paper is accepted. In the meantime, the data is accessible via this temporary review link: https://dataservices.gfz-potsdam.de/panmetaworks/review/fdc35a9f6551b01b6152ee1af7b91a5a0c3de5341d067644522c192ad7f25e7f.

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Completed in Spring 2020: AAGRG´s new recompilation of the Alpine gravity field

10.5194/egusphere-egu2020-5024 ◽

2020 ◽

Author(s):

Hans-Jürgen Götze ◽

Keyword(s):

Reference Frames ◽

Joint Inversion ◽

Gravity Data ◽

Bouguer Anomaly ◽

Interdisciplinary Studies ◽

Digital Data ◽

Long Distance ◽

Data Set ◽

Homogeneous Surface ◽

The Alps

The AlpArray gravity research group (AAGRG) focuses on compiling a homogeneous surface-based gravity dataset across the Alpine area, on creating digital data sets for Bouguer-, Free Air- and isostatic anomalies. In 2016/17 all ten countries around the Alps have agreed to contribute with point/gridded gravity data and/or gravity data processing techniques to recompilation of the Alpine gravity in an area from 2&#176; East to 23&#176; East and 50&#176; North to 42&#176; North. For this recompilation, the group was able to rely on existing national data. For the Ivrea zone in the western Alps, newly surveyed data were also integrated into the database.The AAGRG decided to present the data set of the recalculated gravity fields on a 2 km x 2 km and 4 km x 4 km grid for the public. The final products will also include the calculated values for mass corrections of the measured gravity at each grid point. This allows users to use later customized densities for their own calculations of mass corrections between the physical surface and the ellipsoidal reference. The densities used are 2 670 kg/m3 for landmasses, 1 030 kg/m3 for water masses above and&#160; -1 640 kg/m3 below the ellipsoid. The correction radius was set to the Hayford zone O2 (167 km). In the future, the calculation of long-distance effects of topography/bathymetry and its compensating masses (roots) are planned. The new Bouguer anomaly will be station completed (CBA) and compiled according to the most modern criteria and reference frames (both location and gravity). The concept of ellipsoidal heights implicitly includes the geophysical indirect effect. Atmospheric corrections are also considered. Special emphasis was put on the numerous lakes in the study area. They partly have a considerable effect on the gravity of stations that lie at their edges (for example, the rather deep reservoirs in the Alps). In the Ligurian and the Adriatic seas, ship data of the Bureau Gravim&#233;trique International were used. Although not unproblematic, these data got the preference over satellite data.&#160;It is the aim of the work of the AAGRG to release a gravity database that can be used for high-resolution modeling, interdisciplinary studies from local to regional to continental scales, as well as for joint inversion with other datasets.

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A Bayesian approach to modeling 2D gravity data using polygons

Geophysics ◽

10.1190/geo2016-0153.1 ◽

2017 ◽

Vol 82 (1) ◽

pp. G1-G21 ◽

Cited By ~ 3

Author(s):

William J. Titus ◽

Sarah J. Titus ◽

Joshua R. Davis

Keyword(s):

Gravity Data ◽

Synthetic Data ◽

Gravity Inversion ◽

Limiting Factor ◽

Data Sets ◽

Data Set ◽

Local Optima ◽

Occupancy Probability ◽

Compute Model ◽

Parameter Values

We apply a Bayesian Markov chain Monte Carlo formalism to the gravity inversion of a single localized 2D subsurface object. The object is modeled as a polygon described by five parameters: the number of vertices, a density contrast, a shape-limiting factor, and the width and depth of an encompassing container. We first constrain these parameters with an interactive forward model and explicit geologic information. Then, we generate an approximate probability distribution of polygons for a given set of parameter values. From these, we determine statistical distributions such as the variance between the observed and model fields, the area, the center of area, and the occupancy probability (the probability that a spatial point lies within the subsurface object). We introduce replica exchange to mitigate trapping in local optima and to compute model probabilities and their uncertainties. We apply our techniques to synthetic data sets and a natural data set collected across the Rio Grande Gorge Bridge in New Mexico. On the basis of our examples, we find that the occupancy probability is useful in visualizing the results, giving a “hazy” cross section of the object. We also find that the role of the container is important in making predictions about the subsurface object.

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Geostatistical Joint Interpretation of Gravity and Magnetotelluric Data of the US Cordillera

10.5194/egusphere-egu21-45 ◽

2021 ◽

Author(s):

Mohammad Shehata ◽

Hideki Mizunaga

Keyword(s):

Gravity Data ◽

Three Dimensional ◽

Data Sets ◽

Spatial Expansion ◽

Magnetotelluric Data ◽

Data Set ◽

Entire Area ◽

Mean Values ◽

Local Means ◽

The Us

Long-period magnetotelluric and gravity data were acquired to investigate the US cordillera's crustal structure. The magnetotelluric data are being acquired across the continental USA on a quasi-regular grid of &#8764;70 km spacing as an electromagnetic component of the National Science Foundation EarthScope/USArray Program. International Gravimetreique Bureau compiled gravity Data at high spatial resolution. Due to the difference in data coverage density, the geostatistical joint integration was utilized to map the subsurface structures with adequate resolution. First, a three-dimensional inversion of each data set was applied separately.The inversion results of both data sets show a similarity of structure for data structuralizing. The individual result of both data sets is resampled at the same locations using the kriging method by considering each inversion model to estimate the coefficient. Then, the Layer Density Correction (LDC) process's enhanced density distribution was applied to MT data's spatial expansion process. Simple Kriging with varying Local Means (SKLM) was applied to the residual analysis and integration. For this purpose, the varying local means of the resistivity were estimated using the corrected gravity data by the Non-Linear Indicator Transform (NLIT), taking into account the spatial correlation. After that, the spatial expansion analysis of MT data obtained sparsely was attempted using the estimated local mean values and SKLM method at the sections where the MT survey was carried out and for the entire area where density distributions exist. This research presents the integration results and the stand-alone inversion results of three-dimensional gravity and magnetotelluric data.

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A novel hydrographic gridded data set for the northern Antarctic Peninsula

Earth System Science Data ◽

10.5194/essd-13-671-2021 ◽

2021 ◽

Vol 13 (2) ◽

pp. 671-696

Author(s):

Tiago S. Dotto ◽

Mauricio M. Mata ◽

Rodrigo Kerr ◽

Carlos A. E. Garcia

Keyword(s):

Southern Ocean ◽

Antarctic Peninsula ◽

Initial Conditions ◽

Transitional Zone ◽

Water Masses ◽

Easy Access ◽

Data Sets ◽

Gridded Data ◽

Data Set ◽

Early 21St Century

Abstract. The northern Antarctic Peninsula (NAP) is a highly dynamic transitional zone between the subpolar-polar and oceanic-coastal environments, and it is located in an area affected by intense climate change, including intensification and spatial shifts of the westerlies as well as atmospheric and oceanic warming. In the NAP area, the water masses originate mainly from the Bellingshausen and Weddell seas, which create a marked regional dichotomy thermohaline characteristic. Although the NAP area has relatively easy access when compared to other Southern Ocean environments, our understanding of the water masses' distribution and the dynamical processes affecting the variability of the region is still limited. That limitation is closely linked to the sparse data coverage, as is commonly the case in most Southern Ocean environments. This work provides a novel seasonal three-dimensional high-resolution hydrographic gridded data set for the NAP (version 1), namely the NAPv1.0. Hydrographic measurements from 1990 to 2019 comprising data collected by conductivity, temperature, depth (CTD) casts; sensors from the Marine Mammals Exploring the Oceans Pole to Pole (MEOP) consortium; and Argo floats have been optimally interpolated to produce maps of in situ temperature, practical salinity, and dissolved oxygen at ∼ 10 km spatial resolution and 90 depth levels. The water masses and oceanographic features in this regional gridded product are more accurate than other climatologies and state estimate products currently available. The data sets are available in netCDF format at https://doi.org/10.5281/zenodo.4420006 (Dotto et al., 2021). The novel and comprehensive data sets presented here for the NAPv1.0 product are a valuable tool to be used in studies addressing climatological changes in the unique NAP region since they provide accurate initial conditions for ocean models and improve the end of the 20th- and early 21st-century ocean mean-state representation for that area.

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Reservoir-parameter identification using minimum relative entropy-based Bayesian inversion of seismic AVA and marine CSEM data

Geophysics ◽

10.1190/1.2348770 ◽

2006 ◽

Vol 71 (6) ◽

pp. O77-O88 ◽

Cited By ~ 32

Author(s):

Zhangshuan Hou ◽

Yoram Rubin ◽

G. Michael Hoversten ◽

Don Vasco ◽

Jinsong Chen

Keyword(s):

Relative Entropy ◽

Joint Inversion ◽

Parameter Estimates ◽

Data Sets ◽

Data Set ◽

Mean Values ◽

Prior Probabilities ◽

Reservoir Fluid ◽

Minimum Relative Entropy ◽

Reservoir Parameter

A stochastic joint-inversion approach for estimating reservoir-fluid saturations and porosity is proposed. The approach couples seismic amplitude variation with angle (AVA) and marine controlled-source electromagnetic (CSEM) forward models into a Bayesian framework, which allows for integration of complementary information. To obtain minimally subjective prior probabilities required for the Bayesian approach, the principle of minimum relative entropy (MRE) is employed. Instead of single-value estimates provided by deterministic methods, the approach gives a probability distribution for any unknown parameter of interest, such as reservoir-fluid saturations or porosity at various locations. The distribution means, modes, and confidence intervals can be calculated, providing a more complete understanding of the uncertainty in the parameter estimates. The approach is demonstrated using synthetic and field data sets. Results show that joint inversion using seismic and EM data gives better estimates of reservoir parameters than estimates from either geophysical data set used in isolation. Moreover, a more informative prior leads to much narrower predictive intervals of the target parameters, with mean values of the posterior distributions closer to logged values.

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Joint inversion of gravity and gravity gradient data: A systematic evaluation

Geophysics ◽

10.1190/geo2020-0729.1 ◽

2021 ◽

pp. 1-93

Author(s):

Joseph Capriotti ◽

Yaoguo Li

Keyword(s):

Noise Level ◽

Joint Inversion ◽

Low Noise ◽

Systematic Evaluation ◽

Gravity Gradient ◽

Gravity Inversion ◽

Data Sets ◽

Noise Levels ◽

Data Set ◽

Test Range

Gravity and gravity gradiometry measurements are commonly used to map density variations in the subsurface. Gravity measurements can characterize gravitational anomalies at both long and short wavelengths effectively, but the cost of collecting a sufficiently spatially dense survey to characterize the short wavelengths can be prohibitive. Gravity gradient data can be quickly collected with short wavelength information at a low noise level, but have decreasing sensitivity to longer wavelengths. We describe a method to jointly invert gravity and gravity gradient data that takes advantage of the differing frequency contents and noise levels of the two methods to create an improved image of the subsurface. Previous work simply treated the inversion as a multiple component gravity inversion, however this can cause unintended errors in the recovered models because each data set is not guaranteed to be fit within its noise level. Our joint inversion methodology ensures that both the gravity and gravity gradient data sets are fit to within their individual noise levels by incorporating a relative weighting parameter, and we describe how to find that parameter. This method can also be used to create an improved broadband gravity anomaly map that has a reduced noise level at long wavelengths using a joint equivalent source reconstruction. We first build a synthetic model using a Minecraft world editor, that has different wavelength anomalies, and show the improvement with joint inversion. These results are also confirmed using a real world example at the R. J. Smith test range in Kauring, Australia.

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Joint inversion of seismic traveltimes and gravity data on unstructured grids with application to mineral exploration

Geophysics ◽

10.1190/geo2011-0154.1 ◽

2012 ◽

Vol 77 (1) ◽

pp. K1-K15 ◽

Cited By ~ 93

Author(s):

Peter G. Lelièvre ◽

Colin G. Farquharson ◽

Charles A. Hurich

Keyword(s):

Seismic Data ◽

Unstructured Grids ◽

Joint Inversion ◽

Mineral Exploration ◽

Gravity Data ◽

Physical Property ◽

Sulfide Deposit ◽

Earth Model ◽

Data Set ◽

First Arrival

Seismic methods continue to receive interest for use in mineral exploration due to the much higher resolution potential of seismic data compared to the techniques traditionally used, namely, gravity, magnetics, resistivity, and electromagnetics. However, the complicated geology often encountered in hard-rock exploration can make data processing and interpretation difficult. Inverting seismic data jointly with a complementary data set can help overcome these difficulties and facilitate the construction of a common earth model. We considered the joint inversion of seismic first-arrival traveltimes and gravity data to recover causative slowness and density distributions. Our joint inversion algorithm differs from previous work by (1) incorporating a large suite of measures for coupling the two physical property models, (2) slowly increasing the effect of the coupling to help avoid potential convergence issues, and (3) automatically adjusting two Tikhonov tradeoff parameters to achieve a desired fit to both data sets. The coupling measures used are both compositional and structural in nature and allow the inclusion of explicitly known or implicitly assumed empirical relationships, physical property distribution information, and cross-gradient structural coupling. For any particular exploration scenario, the combination of coupling measures used should be guided by the geologic knowledge available. We performed our inversions on unstructured grids comprised of triangular cells in 2D, or tetrahedral cells in 3D, but the joint inversion methods are equally applicable to rectilinear grids. We tested our joint inversion methodology on scenarios based on the Voisey’s Bay massive sulfide deposit in Labrador, Canada. These scenarios present a challenge to the inversion of first-arrival traveltimes and we show how joint inversion with gravity data can improve recovery of the subsurface features.

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Pareto Joint Inversion of 2D magnetometric and gravity data- synthetic study

E3S Web of Conferences ◽

10.1051/e3sconf/201913301009 ◽

2019 ◽

Vol 133 ◽

pp. 01009

Author(s):

Tomasz Danek ◽

Andrzej Leśniak ◽

Katarzyna Miernik ◽

Elżbieta Śledź

Keyword(s):

Pareto Front ◽

Joint Inversion ◽

Gravity Data ◽

Group Analysis ◽

Geophysical Methods ◽

Model Parameters ◽

Data Sets ◽

Inversion Algorithm ◽

Promising Tool ◽

Real Model

Pareto joint inversion for two or more data sets is an attractive and promising tool which eliminates target functions weighing and scaling, providing a set of acceptable solutions composing a Pareto front. In former author’s study MARIA (Modular Approach Robust Inversion Algorithm) was created as a flexible software based on global optimization engine (PSO) to obtain model parameters in process of Pareto joint inversion of two geophysical data sets. 2D magnetotelluric and gravity data were used for preliminary tests, but the software is ready to handle data from more than two geophysical methods. In this contribution, the authors’ magnetometric forward solver was implemented and integrated with MARIA. The gravity and magnetometry forward solver was verified on synthetic models. The tests were performed for different models of a dyke and showed, that even when the starting model is a homogeneous area without anomaly, it is possible to recover the shape of a small detail of the real model. Results showed that the group analysis of models on the Pareto front gives more information than the single best model. The final stage of interpretation is the raster map of Pareto front solutions analysis.

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Bouguer density determination by fractal analysis

Geophysics ◽

10.1190/1.1442909 ◽

1990 ◽

Vol 55 (7) ◽

pp. 932-935 ◽

Cited By ~ 12

Author(s):

Freyr Thorarinsson ◽

Stefan G. Magnusson

Keyword(s):

Fractal Dimension ◽

Fractal Analysis ◽

Gravity Data ◽

Bouguer Anomaly ◽

New Method ◽

Data Sets ◽

Isostatic Compensation ◽

Topographic Features ◽

Density Determination ◽

Density Values

Density values for the Bouguer reduction of two gravity data sets from Iceland are determined using a new method based on minimization of the roughness of the Bouguer anomaly surface. The fractal dimension of the surface is used as a gauge of the roughness. The analysis shows the size of topographic features supported by crust without isostatic compensation to be 25 to 30 km in southwest Iceland and 9 to 10 km inside the active rifting zone. The densities selected for these areas are 2490 and [Formula: see text], respectively.

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