An integrated geophysical approach for imaging subbasalt sedimentary basins: Case study of Jam River Basin, India

Geophysics ◽  
2007 ◽  
Vol 72 (6) ◽  
pp. B141-B147 ◽  
Author(s):  
V. Chakravarthi ◽  
G. B. K. Shankar ◽  
D. Muralidharan ◽  
T. Harinarayana ◽  
N. Sundararajan
Keyword(s):  
Gravity Field ◽  
River Basin ◽  
Gravity Data ◽  
Sedimentary Basins ◽  
Gravity Inversion ◽  
Data Points ◽  
3D Gravity ◽  
Regional Gravity ◽  
Godavari Basin

An integrated geophysical strategy comprising deep electrical resistivity and gravity data was devised to image subbasalt sedimentary basins. A 3D gravity inversion was used to determine the basement structure of the Permian sediments underlying the Cretaceous formation of the Jam River Basin in India. The thickness of the Cretaceous formation above the Permian sediments estimated from modeling 60 deep-electric-sounding data points agrees well with drilling information. The gravity effect of mass deficit between the Cretaceous and Permian formations was found using 3D forward modeling and subsequently removed from the Bouguer gravity anomaly along with the regional gravity field. The modified residual gravity field was then subjected to3D inversion to map the variations in depth of the basement beneath the Permian sediments. Inversion of gravity data resulted in two basement ridges, running almost east to west, dividing the basin into three independent depressions. It was found that the Katol and Kondhali faults were active even during post-Cretaceous time and were responsible for the development of the subsurface basement ridges in the basin. The inferred 3D basement configuration of the basin clearly brought out the listric nature of these two faults. Further, the extension of the Godavari Basin into the Deccan syneclise and the fact that the source-rock studies show the presence of hydrocarbons in the Sironcha block in the northern part of the Godavari Basin also shed some light on the hydrocarbon potential of the Jam River Basin.

Antarctica crustal model by means of the Bayesian gravity inversion

2020 ◽  
Author(s):  
Martina Capponi ◽  
Daniele Sampietro
Keyword(s):  
Gravity Field ◽  
Gravity Data ◽  
Mass Density ◽  
Geophysical Methods ◽  
Gravity Inversion ◽  
Inversion Algorithm ◽  
Global Gravity ◽  
Uniqueness Of The Solution ◽  
Definition Of ◽  
Regional Gravity

<p>The Antarctica crustal structure is still not completely unveiled due to the presence of thick ice shields all over the continent which prevent direct in situ measurements. In the last decades, various geophysical methods have been used to retrieve information of the upper crust and sediments distribution however there are still regions, especially in central Antarctica, where our knowledge is limited. For these kind of situations, in which the indirect investigation of the subsurface is the only valuable solution, the gravity data are an important source of information. After the recent dedicated satellite missions, like GRACE and GOCE, it has been possible to obtain global gravity field data with spatial resolution and accuracy almost comparable to those of local/regional gravity acquisitions, paving the way to new geophysical applications. The new challenge today is the capability to invert such gravity data on large areas with the aim to obtain a 3D density model of the Earth crust. This is in fact a problem characterized by intrinsic instability and non-uniqueness of the solution that to be solved requires the definition of strong constrains and numerical regularization.</p><p>In this work the authors propose the application of a Bayesian inversion algorithm to the Antarctica continent to infer a model of mass density distribution. The first operation is the definition of an initial geological model in terms of geological horizons and density. These two variables are considered as random variables and, within the iterative procedure based on Markov Chain Monte Carlo methods, they are adjusted in such a way to fit the gravity field on the surface. The test performed show that the method is capable of retrieving an estimated model consistent with the prior information and fitting the gravity observations according to their accuracy.</p>

Towards an updated, enhanced regional gravity field solution for Antarctica

2021 ◽  
Author(s):  
Mirko Scheinert ◽  
Philipp Zingerle ◽  
Theresa Schaller ◽  
Roland Pail ◽  
Martin Willberg
Keyword(s):  
Gravity Anomaly ◽  
Gravity Field ◽  
Gravity Data ◽  
Gravity Anomalies ◽  
Data Set ◽  
Gravity Field Model ◽  
Terrestrial Gravity ◽  
Field Solution ◽  
Gravity Disturbances ◽  
Regional Gravity

<p>In the frame of the IAG Subcommission 2.4f “Gravity and Geoid in Antarctica” (AntGG) a first Antarctic-wide grid of ground-based gravity anomalies was released in 2016 (Scheinert et al. 2016). That data set was provided with a grid space of 10 km and covered about 73% of the Antarctic continent. Since then a considerably amount of new data has been made available, mainly collected by means of airborne gravimetry. Regions which were formerly void of any terrestrial gravity observations and have now been surveyed include especially the polar data gap originating from GOCE satellite gravimetry. Thus, it is timely to come up with an updated and enhanced regional gravity field solution for Antarctica. For this, we aim to improve further aspects in comparison to the AntGG 2016 solution: The grid spacing will be enhanced to 5 km. Instead of providing gravity anomalies only for parts of Antarctica, now the entire continent should be covered. In addition to the gravity anomaly also a regional geoid solution should be provided along with further desirable functionals (e.g. gravity anomaly vs. disturbance, different height levels).</p><p>We will discuss the expanded AntGG data base which now includes terrestrial gravity data from Antarctic surveys conducted over the past 40 years. The methodology applied in the analysis is based on the remove-compute-restore technique. Here we utilize the newly developed combined spherical-harmonic gravity field model SATOP1 (Zingerle et al. 2019) which is based on the global satellite-only model GOCO05s and the high-resolution topographic model EARTH2014. We will demonstrate the feasibility to adequately reduce the original gravity data and, thus, to also cross-validate and evaluate the accuracy of the data especially where different data set overlap. For the compute step the recently developed partition-enhanced least-squares collocation (PE-LSC) has been used (Zingerle et al. 2021, in review; cf. the contribution of Zingerle et al. in the same session). This method allows to treat all data available in Antarctica in one single computation step in an efficient and fast way. Thus, it becomes feasible to iterate the computations within short time once any input data or parameters are changed, and to easily predict the desirable functionals also in regions void of terrestrial measurements as well as at any height level (e.g. gravity anomalies at the surface or gravity disturbances at constant height).</p><p>We will discuss the results and give an outlook on the data products which shall be finally provided to present the new regional gravity field solution for Antarctica. Furthermore, implications for further applications will be discussed e.g. with respect to geophysical modelling of the Earth’s interior (cf. the contribution of Schaller et al. in session G4.3).</p>

3D Gravity Inversion of Northern Sinai Peninsula: A Case Study

2013 ◽  
Vol 171 (7) ◽  
pp. 1557-1569 ◽  
Author(s):  
Mohamed A. Khalil ◽  
Fernando M. Santos
Keyword(s):  
Gravity Inversion ◽  
Sinai Peninsula ◽  
3D Gravity

Two-step approach to 3D gravity inversion: Case study in the State of Utah

2021 ◽  
Author(s):  
Michael S. Zhdanov ◽  
Michael Jorgensen ◽  
Le Wan
Keyword(s):  
The State ◽  
Gravity Inversion ◽  
3D Gravity

Geophysical subsurface modelling based on the updated, enhanced regional gravity field solution in Antarctica

2021 ◽  
Author(s):  
Theresa Schaller ◽  
Mirko Scheinert ◽  
Philipp Zingerle ◽  
Roland Pail ◽  
Martin Willberg
Keyword(s):  
Gravity Field ◽  
Gravity Data ◽  
Average Density ◽  
Weddell Sea ◽  
Moho Depth ◽  
Gravity Inversion ◽  
Satellite Gravity ◽  
German Research Foundation ◽  
Field Solution ◽  
Gravity Disturbances

<p>The gravity field reflects mass inhomogeneities (mostly) inside the Earth. Therefore, gravity inversion and geophysical gravity field modelling are important tools to study the Earth's inner structure and tectonic evolution. In Antarctica, it is extremely challenging to carry out geoscientific studies due to its harsh environment and difficult logistics. Additionally, the continent is covered by an up to 5 km thick ice sheet. However, in the framework of IAG Subcommission 2.4f “Gravity and Geoid in Antarctica” (AntGG) a large database of airborne, shipborne and ground based gravity data has been compiled. Especially airborne data have been acquired during recent years, among others in the area of the polar gap of satellite gravity data. Now, in a joint project funded by the German Research Foundation (DFG) all existing and new gravity data were processed to infer an enhanced gravity field solution for Antarctica (see contribution by Scheinert et al., session G1.5). Processed data e.g. gravity disturbances on the ground or a constant height and other functionals will be provided on a regular grid with 5 km grid spacing. Subsequently, the new Antarctic gravity field solution can now be used for further geophysical and tectonic studies. We use the newly calculated gravity disturbances to study subglacial topography, sediment thickness and Moho depth and to improve respective existing models in Antarctica. For this, we apply 2D Parker-Oldenburg inversion in combination with results from other gravity based studies and further constraining data (e.g. seismic data and ice penetrating radar). We investigate how the higher resolution (5 km) of the new Antarctic gravity field solution facilitates the study of smaller regions in more detail, specifically parts of Wilkes Land, Dronning Maud Land and the Weddell Sea. Additionally, we will infer accuracy estimates for the resulting boundaries in terms of the used inversion parameters (density contrast, average density and filter wavelengths) and their respective gravity signal. Thus, the challenges of gravity field inversion in Antarctica will be discussed in detail and first results of the subsurface modelling will be presented.</p>

3D gravity inversion with optimized mesh based on edge and center anomaly detection

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. G13-G23 ◽  
Author(s):  
Min Yang ◽  
Wanyin Wang ◽  
J. Kim Welford ◽  
Colin G. Farquharson
Keyword(s):  
Edge Detection ◽  
Gravity Data ◽  
Signal Amplitude ◽  
Spatial Extent ◽  
Gravity Inversion ◽  
Unknown Parameters ◽  
Vertical Derivative ◽  
Cpu Time ◽  
3D Gravity ◽  
Total Horizontal Derivative

Gravity inversion is inherently nonunique. Minimum-structure inversion has proved effective at dealing with this nonuniqueness. However, such an inversion approach, which involves a large number of unknown parameters, is computationally expensive. To improve efficiency while retaining the advantages of a minimum-structure-style inversion, we have developed a new method, based on edge detection and center detection of geologic bodies, to help to focus the spatial extent of meshing for gravity inversion. The chosen method of edge detection, normalized vertical derivative of the total horizontal derivative, helps to outline areas to be meshed by approximating the edges of key geophysical bodies. Next, the method of center detection, normalized vertical derivative of the analytic signal amplitude, helps to confirm the center of the areas to be meshed, then a binary mesh flag is generated. In this paper, the binary mesh flag, restricting the spatial extent of meshing, is first undertaken using the two methods, and it is shown to dramatically reduce the number of grid cells from 574,992 for the whole research volume to 170,544 for the localized mesh by the same size of cell, which is decreased by almost 70%. Second, gravity inversion is performed using the spatially restricted mesh. The recovered model constructed using the binary mesh flag is similar to the model obtained using the mesh spanning the whole volume and saves approximately 80% of the CPU time. Finally, a real gravity data example from Olympic Dam in Australia is successfully used to test the validity and practicability of this proposed method. The geologic source bodies are resolved between 250 and 750 m depth. Overall, the combination of edge detection and center detection, and our binary mesh flag, succeed in reducing the number of cells and saving the CPU time and computer storage required for gravity inversion.

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