Coupling USArray and satellite gravity data – an integrated conductivity, density and seismic velocity model of the western USA

2020 ◽  
Author(s):  
Bernhard Weise ◽  
Max Moorkamp ◽  
Stewart Fishwick
Keyword(s):  
Seismic Velocity ◽  
Joint Inversion ◽  
Gravity Data ◽  
Velocity Model ◽  
Data Sets ◽  
Satellite Gravity ◽  
Consistent Model ◽  
The Usa ◽  
The Individual ◽  
Tectonic Boundaries

<p>The EarthScope USArray project provides high quality magnetotelluric and seismic observations, which have been used to identify tectonic boundaries of the USA. Combining these data sets together with satellite gravity observations, we investigate how the different data sets can complement each other in order to find a consistent model of the subsurface. Using a cross-gradient constraint, we first invert the magnetotelluric and gravity data sets in order to demonstrate the feasibility of our approach and to identify any difficulties. Once a joint conductivity and density model is found, we perform a full joint inversion of all three data sets. By comparison with models derived from separate inversions of the individual observables we can show how the different data sets interact. Examining the magnitude of the cross-gradient lets us distinguish parts of the model where a good agreement of the recovered structures has been achieved from those where differing patterns are necessary in order to achieve an acceptable data fit. In this presentation we will give an overview of our approach, highlight our strategy and show results from individual and joint inversions.</p>

scholarly journals Comparing global tomography-derived and gravity-based upper mantle density models

2020 ◽  
Vol 221 (3) ◽  
pp. 1542-1554 ◽  
Author(s):  
B C Root
Keyword(s):  
Seismic Tomography ◽  
Upper Mantle ◽  
Seismic Data ◽  
Gravity Data ◽  
Heterogeneous Data ◽  
Clear Correlation ◽  
Data Sets ◽  
Satellite Gravity ◽  
Similar Density ◽  
Similar Peak

SUMMARY Current seismic tomography models show a complex environment underneath the crust, corroborated by high-precision satellite gravity observations. Both data sets are used to independently explore the density structure of the upper mantle. However, combining these two data sets proves to be challenging. The gravity-data has an inherent insensitivity in the radial direction and seismic tomography has a heterogeneous data acquisition, resulting in smoothed tomography models with de-correlation between different models for the mid-to-small wavelength features. Therefore, this study aims to assess and quantify the effect of regularization on a seismic tomography model by exploiting the high lateral sensitivity of gravity data. Seismic tomography models, SL2013sv, SAVANI, SMEAN2 and S40RTS are compared to a gravity-based density model of the upper mantle. In order to obtain similar density solutions compared to the seismic-derived models, the gravity-based model needs to be smoothed with a Gaussian filter. Different smoothening characteristics are observed for the variety of seismic tomography models, relating to the regularization approach in the inversions. Various S40RTS models with similar seismic data but different regularization settings show that the smoothening effect is stronger with increasing regularization. The type of regularization has a dominant effect on the final tomography solution. To reduce the effect of regularization on the tomography models, an enhancement procedure is proposed. This enhancement should be performed within the spectral domain of the actual resolution of the seismic tomography model. The enhanced seismic tomography models show improved spatial correlation with each other and with the gravity-based model. The variation of the density anomalies have similar peak-to-peak magnitudes and clear correlation to geological structures. The resolvement of the spectral misalignment between tomographic models and gravity-based solutions is the first step in the improvement of multidata inversion studies of the upper mantle and benefit from the advantages in both data sets.

The experimental geoid 2020 – the first joint geoid model for the North American and Pacific Geopotential Datum 

2021 ◽  
Author(s):  
Yan Ming Wang ◽  
Xiaopeng Li ◽  
Kevin Ahlgren ◽  
Jordan Krcmaric ◽  
Ryan Hardy ◽  
...  
Keyword(s):  
North American ◽  
Gravity Data ◽  
The United States ◽  
Geodetic Survey ◽  
Airborne Gravity ◽  
Data Sets ◽  
National Geodetic Survey ◽  
Geoid Model ◽  
Satellite Gravity ◽  
The North

<p>For the upcoming North American-Pacific Geopotential Datum of 2022, the National Geodetic Survey (NGS), the Canadian Geodetic Survey (CGS) and the National Institute of Statistics and Geography of Mexico (INEGI) computed the first joint experimental gravimetric geoid model (xGEOID) on 1’x1’ grids that covers a region bordered by latitude 0 to 85 degree, longitude 180 to 350 degree east. xGEOID20 models are computed using terrestrial gravity data, the latest satellite gravity model GOCO06S, altimetric gravity data DTU15, and an additional nine airborne gravity blocks of the GRAV-D project, for a total of 63 blocks. In addition, a digital elevation model in a 3” grid was produced by combining MERIT, TanDEM-X, and USGS-NED and used for the topographic/gravimetric reductions. The geoid models computed from the height anomalies (NGS) and from the Helmert-Stokes scheme (CGS) were combined using two different weighting schemes, then evaluated against the independent GPS/leveling data sets. The models perform in a very similar way, and the geoid comparisons with the most accurate Geoid Slope Validation Surveys (GSVS) from 2011, 2014 and 2017 indicate that the relative geoid accuracy could be around 1-2 cm baseline lengths up to 300 km for these GSVS lines in the United States. The xGEOID20 A/B models were selected from the combined models based on the validation results. The geoid accuracies were also estimated using the forward modeling.</p>

Joint inversion of seismic traveltime and frequency-domain airborne electromagnetic data for hydrocarbon exploration

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. U9-U22 ◽  
Author(s):  
Jide Nosakare Ogunbo ◽  
Guy Marquis ◽  
Jie Zhang ◽  
Weizhong Wang
Keyword(s):  
Frequency Domain ◽  
Joint Inversion ◽  
Sedimentary Environment ◽  
Computational Cost ◽  
Velocity Model ◽  
Two Dimensions ◽  
Hydrocarbon Exploration ◽  
Three Dimensions ◽  
Data Sets ◽  
The One

Geophysical joint inversion requires the setting of a few parameters for optimum performance of the process. However, there are yet no known detailed procedures for selecting the various parameters for performing the joint inversion. Previous works on the joint inversion of electromagnetic (EM) and seismic data have reported parameter applications for data sets acquired from the same dimensional geometry (either in two dimensions or three dimensions) and few on variant geometry. But none has discussed the parameter selections for the joint inversion of methods from variant geometry (for example, a 2D seismic travel and pseudo-2D frequency-domain EM data). With the advantage of affordable computational cost and the sufficient approximation of a 1D EM model in a horizontally layered sedimentary environment, we are able to set optimum joint inversion parameters to perform structurally constrained joint 2D seismic traveltime and pseudo-2D EM data for hydrocarbon exploration. From the synthetic experiments, even in the presence of noise, we are able to prescribe the rules for optimum parameter setting for the joint inversion, including the choice of initial model and the cross-gradient weighting. We apply these rules on field data to reconstruct a more reliable subsurface velocity model than the one obtained by the traveltime inversions alone. We expect that this approach will be useful for performing joint inversion of the seismic traveltime and frequency-domain EM data for the production of hydrocarbon.

Decoupling strategy for large-scale multiphysics joint inversion

2020 ◽  
Author(s):  
Dmitry Molodtsov ◽  
Duygu Kiyan ◽  
Christopher Bean
Keyword(s):  
Large Scale ◽  
Joint Inversion ◽  
Gravity Data ◽  
Geophysical Methods ◽  
Quadratic Model ◽  
Reference Models ◽  
The Individual ◽  
Norm Penalty ◽  
Regional Problems ◽  
Cooperative Inversion

<p>We present a generalized 3-D multiphysics joint inversion scheme with a focus on large-scale regional problems. One of the key features of this scheme is the formulation of the structure coupling as a sparsity-promoting joint regularization. This approach makes it possible to simplify the structure of the objective function and to keep the number of hyperparameters relatively low, so that the inversion framework complexity scales well with respect to the number of geophysical methods and possible reference models used. To further simplify adding geophysical solvers to the framework and to optimize the discretization, we propose an alternating minimization scheme that decouples the inversion and the joint regularization steps. Decoupling is achieved by introducing an auxiliary multi-parameter model. This allows the individual subproblems to make use of problem-tailored grids and specialized optimization algorithms. As we will see, this is in particular important for the regularization subproblem. In contrast to straightforward 'cooperative inversion' formulation, decoupled inversion steps appear to be regularized by a standard quadratic model-norm penalty, and as a result existing separate inversion codes can be used with minimal, if any, modifications. The developed scheme is applied to magnetotelluric, seismic and gravity data and tested on synthetic model examples.</p>

Unraveling the upper mantle heterogeneity from integrated multi-observable inversions

2020 ◽  
Author(s):  
Javier Fullea ◽  
Sergei Lebedev ◽  
Zdenek Martinec ◽  
Nicolas Celli
Keyword(s):  
Seismic Tomography ◽  
Upper Mantle ◽  
Plate Tectonics ◽  
Seismic Velocity ◽  
Gravity Data ◽  
Density Field ◽  
Seismic Velocities ◽  
Satellite Gravity ◽  
The Earth ◽  
Multiple Data Sets

<p>The lateral and vertical thermochemical heterogeneity in the mantle is a long standing question in geodynamics. The forces that control mantle flow and therefore Plate Tectonics arise from the density and viscosity lateral and vertical variations. A common approach to estimate the density field for geodynamical purposes is to simply convert seismic tomography anomalies sometimes assuming constraints from mineral physics. Such converted density field does not match in general with the observed gravity field, typically predicting anomalies the amplitudes of which are too large. Knowledge on the lateral variations in lithospheric density is essential to understand the dynamic/residual isostatic components of the Earth’s topography linking deep and surface processes. The cooling of oceanic lithosphere, the bathymetry of mid oceanic ridges, the buoyancy and stability of continental cratons or the thermochemical structure of mantle plumes are all features central to Plate Tectonics that are dramatically related to mantle temperature and composition.</p><p><br>Conventional methods of seismic tomography, topography and gravity data analysis constrain distributions of seismic velocity and density at depth, all depending on temperature and composition of the rocks within the Earth. However, modelling and interpretation of multiple data sets provide a multifaceted image of the true thermochemical structure of the Earth that needs to be appropriately and consistently integrated. A simple combination of gravity, petrological and seismic models alone is insufficient due to the non-uniqueness and different sensitivities of these models, and the internal consistency relationships that must connect all the intermediate parameters describing the Earth involved. In fact, global Earth models based on different observables often lead to rather different, even contradictory images of the Earth.</p><p><br> Here we present a new global thermochemical model of the lithosphere-upper mantle (WINTERC-grav) constrained by state-of-the-art global waveform tomography, satellite gravity (geoid and gravity anomalies and gradiometric measurements from ESA's GOCE mission), surface elevation and heat flow data. WINTERC-grav is based upon an integrated geophysical-petrological approach where all relevant rock physical properties modelled (seismic velocities and density) are computed within a thermodynamically self-consistent framework allowing for a direct parameterization of the temperature and composition variables.</p>

scholarly journals Bayesian joint inversion of controlled source electromagnetic and magnetotelluric data to image freshwater aquifer offshore New Jersey

2019 ◽  
Vol 218 (3) ◽  
pp. 1822-1837 ◽  
Author(s):  
Daniel Blatter ◽  
Kerry Key ◽  
Anandaroop Ray ◽  
Chloe Gustafson ◽  
Rob Evans
Keyword(s):  
Parameter Uncertainty ◽  
Joint Inversion ◽  
Data Sets ◽  
Magnetotelluric Data ◽  
Model Parameter ◽  
Controlled Source ◽  
Low Salinity ◽  
Electromagnetic Data ◽  
Model Parameter Uncertainty ◽  
The Individual

SUMMARY Joint inversion of multiple electromagnetic data sets, such as controlled source electromagnetic and magnetotelluric data, has the potential to significantly reduce uncertainty in the inverted electrical resistivity when the two data sets contain complementary information about the subsurface. However, evaluating quantitatively the model uncertainty reduction is made difficult by the fact that conventional inversion methods—using gradients and model regularization—typically produce just one model, with no associated estimate of model parameter uncertainty. Bayesian inverse methods can provide quantitative estimates of inverted model parameter uncertainty by generating an ensemble of models, sampled proportional to data fit. The resulting posterior distribution represents a combination of a priori assumptions about the model parameters and information contained in field data. Bayesian inversion is therefore able to quantify the impact of jointly inverting multiple data sets by using the statistical information contained in the posterior distribution. We illustrate, for synthetic data generated from a simple 1-D model, the shape of parameter space compatible with controlled source electromagnetic and magnetotelluric data, separately and jointly. We also demonstrate that when data sets contain complementary information about the model, the region of parameter space compatible with the joint data set is less than or equal to the intersection of the regions compatible with the individual data sets. We adapt a trans-dimensional Markov chain Monte Carlo algorithm for jointly inverting multiple electromagnetic data sets for 1-D earth models and apply it to surface-towed controlled source electromagnetic and magnetotelluric data collected offshore New Jersey, USA, to evaluate the extent of a low salinity aquifer within the continental shelf. Our inversion results identify a region of high resistivity of varying depth and thickness in the upper 500 m of the continental shelf, corroborating results from a previous study that used regularized, gradient-based inversion methods. We evaluate the joint model parameter uncertainty in comparison to the uncertainty obtained from the individual data sets and demonstrate quantitatively that joint inversion offers reduced uncertainty. In addition, we show how the Bayesian model ensemble can subsequently be used to derive uncertainty estimates of pore water salinity within the low salinity aquifer.

Integration of seismic and gravity data — A case study from the western Gulf of Mexico

2015 ◽  
Vol 3 (4) ◽  
pp. SAC99-SAC106 ◽  
Author(s):  
Irina Filina ◽  
Nicholas Delebo ◽  
Gopal Mohapatra ◽  
Clayton Coble ◽  
Gary Harris ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
Gravity Field ◽  
Gravity Model ◽  
Seismic Velocity ◽  
Gravity Data ◽  
Public Domain ◽  
Gravity Modeling ◽  
Data Sets ◽  
Gardner Equation ◽  
Well Data

A 3D gravity model was developed in the western Gulf of Mexico in the East Breaks and Alaminos Canyon protraction areas. This model integrated 3D seismic, gravity, and well data; it was constructed in support of a proprietary seismic reprocessing project and was updated iteratively with seismic. The gravity model was built from seismic horizons of the bathymetry, salt layers, and the acoustic basement; however, the latter was only possible to map in seismic data during the latest iterations. In addition, a deep layer representing the Moho boundary was derived from gravity and constrained by public-domain refraction data. A 3D density distribution was derived from the seismic velocity volume using a modified Gardner equation. The modification comprised imposing a depth dependency on the Gardner coefficient, which is constant in the classic Gardner equation. The modified coefficient was derived from well data in the study area and public-domain velocity-density data sets. The forward-calculated gravity response of the composed density model was then compared with the observed gravity field, and the mismatch was analyzed jointly by a seismic interpreter and a gravity modeler. Adjustments were then made to the gravity model to ensure that the resultant salt model was geologically reasonable and supported by gravity, seismic, and well data sets. The output of the gravity modeling was subsequently applied to the next phase of seismic processing. Overall, this integration resulted in a more robust salt model, which has led to significant improvements in subsalt seismic imaging. The analysis of the regional trend in the observed gravity field suggested that a stretched continental crust underlay our seismic reprocessing area, with an oceanic-continental transition zone located to the southeast of our reprocessing region.

Reconstruction of 1-D conductivity from dual‐loop EM data

Geophysics ◽  
2000 ◽  
Vol 65 (2) ◽  
pp. 492-501 ◽  
Author(s):  
Zhiyi Zhang ◽  
Partha S. Routh ◽  
Douglas W. Oldenburg ◽  
David L. Alumbaugh ◽  
Gregory A. Newman
Keyword(s):  
Inverse Problem ◽  
Joint Inversion ◽  
Synthetic Data ◽  
Data Sets ◽  
Inversion Algorithm ◽  
Geological Structures ◽  
Electromagnetic Data ◽  
Independent Information ◽  
The Individual ◽  
Data Constraints

Inversions of electromagnetic data from different coil configurations provide independent information about geological structures. We develop a 1-D inversion algorithm that can invert data from the horizontal coplanar (HC), vertical coplanar, coaxial (CA), and perpendicular coil configurations separately or jointly. The inverse problem is solved by minimizing a model objective function subject to data constraints. Tests using synthetic data from 1-D models indicate that if data are collected at a sufficient number of frequencies, then the recovered models from individual inversions of different coil systems can be quite similar. However, if only a limited number of frequencies are available, then joint inversion of data from different coils produces a better model than the individual inversions. Tests on 3-D synthetic data sets indicate that 1-D inversions can be used as a fast and approximate tool to locate anomalies in the subsurface. Also for the test example presented here, the joint inversion of HC and CA data over a 3-D conductivity provided a better model than that produced by the individual inversion of the data sets.

Basin architecture and density structure beneath the Strait of Georgia, British Columbia

2003 ◽  
Vol 40 (7) ◽  
pp. 965-981 ◽  
Author(s):  
C Lowe ◽  
S A Dehler ◽  
B C Zelt
Keyword(s):  
Seismic Velocity ◽  
Gravity Data ◽  
Velocity Model ◽  
Seismic Structure ◽  
Strait Of Georgia ◽  
3 Dimensional ◽  
The North ◽  
Georgia Basin ◽  
Geologic Interpretation ◽  
Velocity Information

Georgia Basin is located within one of the most seismically active and populated areas on Canada's west coast. Over the last decade, geological investigations have resolved important details concerning the basin's shallow structure and composition. Yet, until recently, relatively little was known about deeper portions of the basin. In this study, new seismic velocity information is employed to develop a 3-dimensional density model of the basin. Comparison of the calculated gravity response of this model with the observed gravity field validates the velocity model at large scales. At smaller scales, several differences between model and observed gravity fields are recognized. Analysis of these differences and correlation with independent geoscience data provide new insights into the structure and composition of the basin-fill and underlying basement. Specifically, four regions with thick accumulations of unconsolidated Pleistocene and younger sediments, which were not resolved in the velocity model, are identified. Their delineation is particularly important for studies of seismic ground-motion amplification and offshore aggregate assessment. An inconsistency between the published geology and the seismic structure beneath Texada and Lasqueti Islands in the central Strait of Georgia is investigated; however, the available gravity data cannot preferentially validate either the geologic interpretation or the seismic model in this region. We interpret a northwest-trending and relatively linear gradient extending from Savory Island in the north to Boundary Bay in the south as the eastern margin of Wrangellia beneath the basin. Finally, we compare Georgia Basin with the Everett and Seattle basins in the southern Cascadia fore arc. This comparison indicates that while a single mechanism may be controlling present-day basin tectonics and deformation within the fore arc this was not the case for most of the Mesozoic and Tertiary time periods.

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