scholarly journals 3D Gravity Inversion on Unstructured Grids

2021 ◽  
Vol 11 (2) ◽  
pp. 722
Author(s):  
Siyuan Sun ◽  
Changchun Yin ◽  
Xiuhe Gao
Keyword(s):  
Objective Function ◽  
Unstructured Grids ◽  
Gravity Data ◽  
Depth Resolution ◽  
Continuous Model ◽  
Gravity Inversion ◽  
Inversion Method ◽  
Model Parameters ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

Compared with structured grids, unstructured grids are more flexible to model arbitrarily shaped structures. However, based on unstructured grids, gravity inversion results would be discontinuous and hollow because of cell volume and depth variations. To solve this problem, we first analyzed the gradient of objective function in gradient-based inversion methods, and a new gradient scheme of objective function is developed, which is a derivative with respect to weighted model parameters. The new gradient scheme can more effectively solve the problem with lacking depth resolution than the traditional inversions, and the improvement is not affected by the regularization parameters. Besides, an improved fuzzy c-means clustering combined with spatial constraints is developed to measure property distribution of inverted models in both spatial domain and parameter domain simultaneously. The new inversion method can yield a more internal continuous model, as it encourages cells and their adjacent cells to tend to the same property value. At last, the smooth constraint inversion, the focusing inversion, and the improved fuzzy c-means clustering inversion on unstructured grids are tested on synthetic and measured gravity data to compare and demonstrate the algorithms proposed in this paper.

3D gravity inversion of basement relief for a rift basin based on combined multinorm and normalized vertical derivative of the total horizontal derivative techniques

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. G107-G118 ◽  
Author(s):  
Xuliang Feng ◽  
Wanyin Wang ◽  
Bingqiang Yuan
Keyword(s):  
Objective Function ◽  
Gravity Data ◽  
Gravity Inversion ◽  
Inversion Method ◽  
Rift Basin ◽  
Constraint Function ◽  
Vertical Derivative ◽  
Model Constraint ◽  
3D Gravity ◽  
Total Horizontal Derivative

The basement of a rift sedimentary basin, often possessing smooth and nonsmooth shapes, is not easily recovered from gravity data by current inversion methods. We have developed a new 3D gravity inversion method to estimate the basement relief of a rift basin. In the inversion process, we have established the objective function by combining the gravity data misfit function, the known depth constraint function, and the model constraint function composed of the [Formula: see text]-norm and [Formula: see text]-norm, respectively. An edge recognition technology based on the normalized vertical derivative of the total horizontal derivative for gravity data is adopted to recognize the discontinuous and continuous parts of the basin and combine the two inputs to form the final model constraint function. The inversion is conducted by minimizing the objective function by the nonlinear conjugate gradient algorithm. We have developed two applications using synthetic gravity anomalies produced from two synthetic rift basins, one with a single graben and one with six differently sized grabens. The test results indicate that the inversion method is a feasible technique to delineate the basement relief of a rift basin. The inversion method is also tested on field data from the Xi’an depression in the middle of the Weihe Basin, Shaanxi Province, China, and the result illustrates its effectiveness.

Architecture and dynamics of the magmatic system feeding the 2018 offshore Mayotte eruption from satellite gravity data

2021 ◽  
Author(s):  
Hélène Le Mével ◽  
Craig A. Miller ◽  
Yan Zhan
Keyword(s):  
Gravity Data ◽  
System Structure ◽  
Constant Thickness ◽  
Gravity Inversion ◽  
Model Parameters ◽  
Low Density ◽  
Submarine Eruption ◽  
Magmatic System ◽  
Magma Flow ◽  
Marine Gravity

<p>In May 2018, a submarine eruption started offshore Mayotte (Comoros archipelago, Indian Ocean), and was first detected as a series of earthquake swarms. Since then, at least 6.4 km<sup>3</sup> of lava has erupted from a newly mapped volcanic edifice (MAYOBS campaigns), about 50 km east of Mayotte island. Since the onset of the eruption, GNSS stations on the island have recorded subsidence (up to 17 cm) and eastward displacement (up to 23 cm). We combine marine gravity data derived from satellite altimetry with finite element models to examine the magmatic system structure and its dynamics. First, we calculate the Mantle Bouguer Anomaly (MBA) by taking into account the gravitational effect of the bathymetry and the Moho interfaces, assuming a crust of constant thickness of 17.5 km and correction densities of 2.8 g/cm<sup>3</sup> and 3.3 g/cm<sup>3</sup> for the crust and mantle, respectively. We then invert the MBA to determine the anomalous density structures within the lithosphere, using the mixed Lp-norm inversion and Gauss-Newton optimization implemented in the SimPEG framework. The gravity inversion reveals two zones of low density, east of Mayotte island. The first is located NE of Petite Terre island between ~15 and 35 km depth, and the second is located further east, south of La Jumelle seamounts and extends from ~25 to 35 km depth. We interpret these low density regions as regions of partial melt stored in the lithosphere and estimate the volume of stored magma. Finally, we use the newly imaged low density bodies to constrain the magma reservoir geometry and simulate magma flow from this reservoir to the eruptive vent in a 3D, time-dependent, numerical model. The model parameters are adjusted by minimizing the misfit between the modeled surface displacement and that measured at the 6 GPS sites, between May 2018 and 2020. The deformation modeling reveals the temporal evolution of the magma flux during the eruption, and the resulting stress distribution in the crust explains the patterns of recorded seismicity. Together with the existing seismic and geodetic studies, the gravity data analysis and FEM models bring new constraints on the architecture of the magma plumbing system and the magmatic processes behind the largest submarine eruption ever documented.</p>

Three‐dimensional gravity inversion using simulated annealing: Constraints on the diapiric roots of allochthonous salt structures

Geophysics ◽  
2001 ◽  
Vol 66 (5) ◽  
pp. 1438-1449 ◽  
Author(s):  
Seiichi Nagihara ◽  
Stuart A. Hall
Keyword(s):  
Simulated Annealing ◽  
Gulf Of Mexico ◽  
Gravity Data ◽  
A Priori ◽  
Gravity Inversion ◽  
Inversion Method ◽  
A Priori Information ◽  
Final Density ◽  
Smoothing Effects ◽  
Priori Information

In the northern continental slope of the Gulf of Mexico, large oil and gas reservoirs are often found beneath sheetlike, allochthonous salt structures that are laterally extensive. Some of these salt structures retain their diapiric feeders or roots beneath them. These hidden roots are difficult to image seismically. In this study, we develop a method to locate and constrain the geometry of such roots through 3‐D inverse modeling of the gravity anomalies observed over the salt structures. This inversion method utilizes a priori information such as the upper surface topography of the salt, which can be delineated by a limited coverage of 2‐D seismic data; the sediment compaction curve in the region; and the continuity of the salt body. The inversion computation is based on the simulated annealing (SA) global optimization algorithm. The SA‐based gravity inversion has some advantages over the approach based on damped least‐squares inversion. It is computationally efficient, can solve underdetermined inverse problems, can more easily implement complex a priori information, and does not introduce smoothing effects in the final density structure model. We test this inversion method using synthetic gravity data for a type of salt geometry that is common among the allochthonous salt structures in the Gulf of Mexico and show that it is highly effective in constraining the diapiric root. We also show that carrying out multiple inversion runs helps reduce the uncertainty in the final density model.

scholarly journals 3D Gravity Inversion with Correlation Image in Space Domain and Application to the Northern Sinai Peninsula

2019 ◽  
Vol 1 (2) ◽  
Author(s):  
Xu Zhang ◽  
Peng Yu ◽  
Jian Wang
Keyword(s):  
Density Distribution ◽  
Gravity Data ◽  
Descent Method ◽  
Steepest Descent Method ◽  
Gravity Inversion ◽  
Inversion Method ◽  
Sinai Peninsula ◽  
Space Domain ◽  
Correlation Image ◽  
Starting Model

We present a 3D inversion method to recover density distribution from gravity data in space domain. Our method firstly employs 3D correlation image of the vertical gradient of gravity data as a starting model to generate a higher resolution image for inversion. The 3D density distribution is then obtained by inverting the correlation image of gravity data to fit the observed data based on classical inversion method of the steepest descent method. We also perform the effective equivalent storage and subdomain techniques in the starting model calculation, the forward modeling and the inversion procedures, which allow fast computation in space domain with reducing memory consumption but maintaining accuracy. The efficiency and stability of our method is demonstrated on two sets of synthetic data and one set of the Northern Sinai Peninsula gravity data. The inverted 3D density distributions show that high density bodies beneath Risan Aniza and low density bodies exist to the southeast of Risan Aniza at depths between 1~10 and 20 km, which may be originated from hot anomalies in the lower crust. The results show that our inversion method is useful for 3D quantitative interpretation.

Gravity data as a tool for detecting faults: In-depth enhancement of subtle Almada’s basement faults, Brazil

Geophysics ◽  
2007 ◽  
Vol 72 (3) ◽  
pp. B59-B68 ◽  
Author(s):  
Valeria C. Barbosa ◽  
Paulo T. Menezes ◽  
João B. Silva
Keyword(s):  
Gravity Data ◽  
Synthetic Data ◽  
Normal Fault ◽  
Northeastern Brazil ◽  
Normal Faults ◽  
Gravity Inversion ◽  
Inversion Method ◽  
Basement Faults ◽  
Depth Enhancement ◽  
Fault Displacement

We demonstrate the potential of gravity data to detect and to locate in-depth subtle normal faults in the basement relief of a sedimentary basin. This demonstration is accomplished by inverting the gravity data with the constraint that the estimated basement relief presents local abrupt faults and is smooth elsewhere. We inverted the gravity data from the onshore Almada Basin in northeastern Brazil, and we mapped several normal faults whose locations and plane geometries were already known from seismic imaging. The inversion method delineated well both the discontinuities with small or large slips and a sequence of step faults. Using synthetic data, we performed a systematic search of normal fault slips versus fault displacement depths to map the fault-detectable region in this space. This mapping helps to assess the ability of gravity inversion to detect normal faults. Mapping shows that normal faults with small [Formula: see text], medium (about [Formula: see text]), and large (about [Formula: see text]) vertical slips can be detected if the maximum midpoint depths of the fault planes are smaller than 1.8, 3.8, and [Formula: see text], respectively.

scholarly journals Evaluation of induced polarization measurements using a new inversion method

2021 ◽  
Author(s):  
Tamás Fancsik ◽  
Endre Turai ◽  
Norbert Péter Szabó ◽  
Judit Somogyiné Molnár ◽  
Tünde Edit Dobróka ◽  
...  
Keyword(s):  
Objective Function ◽  
Series Expansion ◽  
Least Squares Method ◽  
Delta Function ◽  
Induced Polarization ◽  
General Definition ◽  
Inversion Method ◽  
Model Parameters ◽  
Ore Minerals ◽  
Inversion Procedure

AbstractIn this paper, a new inversion method is proposed to process laboratory-measured induced polarization (IP) data. In the new procedure, the concept of the series expansion-based inversion is combined with a more general definition of the objective function. The time constant spectrum of the IP effect is assumed a line spectrum approximated by a series of Dirac’s delta function resulting in a square-integrable forward problem formula. This gives the applicability of the generalized objective function. The expansion coefficients as unknowns represent the model parameters of the inversion procedure. We use the new inversion procedure on an apparent polarizability dataset measured on a rock sample originated from the Recsk ore complex, northeast Hungary. The inversion results was compared to those of three additional laboratory datasets, which were measured on samples rich in ore minerals collected from the same area. The results are compared to those given by the traditional series expansion-based least squares method. It is shown that the newly proposed method gives more accurate and stable parameter estimation.

scholarly journals A Bayesian 3-D linear gravity inversion for complex density distributions: application to the Puysegur subduction system

2020 ◽  
Vol 223 (3) ◽  
pp. 1899-1918
Author(s):  
Erin Hightower ◽  
Michael Gurnis ◽  
Harm Van Avendonk
Keyword(s):  
Subduction Zones ◽  
Structural Changes ◽  
Seismic Velocity ◽  
Equality Constraints ◽  
Moho Depth ◽  
Gravity Inversion ◽  
Inversion Method ◽  
Model Parameters ◽  
Density Distributions ◽  
Complex Density

SUMMARY We have developed a linear 3-D gravity inversion method capable of modelling complex geological regions such as subduction margins. Our procedure inverts satellite gravity to determine the best-fitting differential densities of spatially discretized subsurface prisms in a least-squares sense. We use a Bayesian approach to incorporate both data error and prior constraints based on seismic reflection and refraction data. Based on these data, Gaussian priors are applied to the appropriate model parameters as absolute equality constraints. To stabilize the inversion and provide relative equality constraints on the parameters, we utilize a combination of first and second order Tikhonov regularization, which enforces smoothness in the horizontal direction between seismically constrained regions, while allowing for sharper contacts in the vertical. We apply this method to the nascent Puysegur Trench, south of New Zealand, where oceanic lithosphere of the Australian Plate has underthrust Puysegur Ridge and Solander Basin on the Pacific Plate since the Miocene. These models provide insight into the density contrasts, Moho depth, and crustal thickness in the region. The final model has a mean standard deviation on the model parameters of about 17 kg m–3, and a mean absolute error on the predicted gravity of about 3.9 mGal, demonstrating the success of this method for even complex density distributions like those present at subduction zones. The posterior density distribution versus seismic velocity is diagnostic of compositional and structural changes and shows a thin sliver of oceanic crust emplaced between the nascent thrust and the strike slip Puysegur Fault. However, the northern end of the Puysegur Ridge, at the Snares Zone, is predominantly buoyant continental crust, despite its subsidence with respect to the rest of the ridge. These features highlight the mechanical changes unfolding during subduction initiation.

FUZZY CLUSTERING WITH WEIGHTING OF DATA VARIABLES

2000 ◽  
Vol 08 (06) ◽  
pp. 735-746 ◽  
Author(s):  
ANNETTE KELLER ◽  
FRANK KLAWONN
Keyword(s):  
Objective Function ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Technique ◽  
Fuzzy C Means ◽  
Or Groups ◽  
Fuzzy C Means Clustering ◽  
Influence Parameter ◽  
Individual Variables ◽  
Single Data

We introduce an objective function-based fuzzy clustering technique that assigns one influence parameter to each single data variable for each cluster. Our method is not only suited to detect structures or groups of data that are not uniformly distributed over the structure's single domains, but gives also information about the influence of individual variables on the detected groups. In addition, our approach can be seen as a generalization of the well-known fuzzy c-means clustering algorithm.

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