Inversion of magnetotelluric data for 2D structure with sharp resistivity contrasts

Geophysics ◽  
2004 ◽  
Vol 69 (1) ◽  
pp. 78-86 ◽  
Author(s):  
Catherine de Groot‐Hedlin ◽  
Steven Constable
Keyword(s):  
Real Data ◽  
Fixed Number ◽  
Inversion Method ◽  
Magnetotelluric Data ◽  
2D Structure ◽  
Discrete Values ◽  
Modeling Code ◽  
Occam Inversion ◽  
Synthetic Models ◽  
Good Agreement

We have developed a linearized algorithm to invert noisy 2‐D magnetotelluric data for subsurface conductivity structures represented by smooth boundaries defining sharp resistivity contrasts. We solve for both a fixed number of subsurface resistivities and for the boundary locations between adjacent units. The boundary depths are forced to be discrete values defined by the mesh used in the forward modeling code. The algorithm employs a Lagrange multiplier approach in a manner similar to the widely used Occam method. The main difference is that we penalize variations in the boundary depths, rather than in resistivity contrasts between a large number of adjacent blocks. To reduce instabilities resulting from the breakdown of the linear approximation, we allow an option to penalize contrasts in the resistivities of adjacent units. We compare this boundary inversion method to the smooth Occam inversion for two synthetic models, one that includes a conductive wedge between two resistors and another that includes a resistive wedge between two conductors. The two methods give good agreement for the conductive wedge, but the solutions differ for the more poorly resolved resistive wedge, with the boundary inversion method giving a more geologically realistic result. Application of the boundary inversion method to the resistive Gemini subsalt petroleum prospect in the Gulf of Mexico indicates that the shape of this salt feature is accurately imaged by this method, and that the method remains stable when applied to real data.

scholarly journals 3D stochastic inversion of gravity data using cokriging and cosimulation

Geophysics ◽  
2010 ◽  
Vol 75 (1) ◽  
pp. I1-I10 ◽  
Author(s):  
Pejman Shamsipour ◽  
Denis Marcotte ◽  
Michel Chouteau ◽  
Pierre Keating
Keyword(s):  
Gravity Data ◽  
A Priori ◽  
Inversion Method ◽  
Geostatistical Techniques ◽  
Priori Information ◽  
Surface Geology ◽  
Synthetic Models ◽  
Secondary Variable ◽  
Good Agreement

A new application has been developed, based on geostatistical techniques of cokriging and conditional simulation, for the 3D inversion of gravity data including geologic constraints. The necessary gravity, density, and gravity-density covariance matrices are estimated using the observed gravity data. Then the densities are cokriged or simulated using the gravity data as the secondary variable. The model allows noise to be included in the observations. The method is applied to two synthetic models: a short dipping dike and a stochastic distribution of densities. Then some geologic information is added as constraints to the cokriging system. The results show the ability of the method to integrate complex a priori information. The survey data of the Matagami mining camp are considered as a case study. The inversion method based on cokriging is applied to the residual anomaly to map the geology through the estimation of the density distribution in this region. The results of the inversion and simulation methods are in good agreement with the surface geology of the survey region.

3D stochastic gravity inversion using nonstationary covariances

Geophysics ◽  
2013 ◽  
Vol 78 (2) ◽  
pp. G15-G24 ◽  
Author(s):  
Pejman Shamsipour ◽  
Denis Marcotte ◽  
Michel Chouteau ◽  
Martine Rivest ◽  
Abderrezak Bouchedda
Keyword(s):  
Real Data ◽  
Gravity Inversion ◽  
Data Set ◽  
The Real ◽  
Statistical Homogeneity ◽  
Nonstationary Covariance ◽  
Surface Geology ◽  
Synthetic Models ◽  
Stochastic Gravity

The flexibility of geostatistical inversions in geophysics is limited by the use of stationary covariances, which, implicitly and mostly for mathematical convenience, assumes statistical homogeneity of the studied field. For fields showing sharp contrasts due, for example, to faults or folds, an approach based on the use of nonstationary covariances for cokriging inversion was developed. The approach was tested on two synthetic cases and one real data set. Inversion results based on the nonstationary covariance were compared to the results from the stationary covariance for two synthetic models. The nonstationary covariance better recovered the known synthetic models. With the real data set, the nonstationary assumption resulted in a better match with the known surface geology.

scholarly journals Necessity of Terrain Correction in Magnetotelluric Data Recorded from Garhwal Himalayan Region, India

Geosciences ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 482
Author(s):  
Dharmendra Kumar ◽  
Arun Singh ◽  
Mohammad Israil
Keyword(s):  
Real Data ◽  
Terrain Correction ◽  
Himalayan Region ◽  
General Character ◽  
Period Range ◽  
Magnetotelluric Data ◽  
Heterogeneous Models ◽  
Corrected Data ◽  
Terrain Corrections ◽  
And Inversion

The magnetotelluric (MT) method is one of the useful geophysical techniques to investigate deep crustal structures. However, in hilly terrains, e.g., the Garhwal Himalayan region, due to the highly undulating topography, MT responses are distorted. Such responses, if not corrected, may lead to the incorrect interpretation of geoelectric structures. In the present paper, we implemented terrain corrections in MT data recorded from the Garhwal Himalayan Corridor (GHC). We used AP3DMT, a 3D MT data modeling and inversion code written in the MATLAB environment. Terrain corrections in the MT impedance responses for 39 sites along the Roorkee–Gangotri profile in the period range of 0.01 s to 1000 s were first estimated using a synthetic model by recording the topography and locations of MT sites. Based on this study, we established the general character of the terrain and established where terrain corrections were necessary. The distortion introduced by topography was computed for each site using homogenous and heterogeneous models with actual topographic variations. Period-dependent, galvanic and inductive distortions were observed at different sites. We further applied terrain corrections to the real data recorded from the GHC. The corrected data were inverted, and the inverted model was compared with the corresponding inverted model obtained with uncorrected data. The modification in electrical resistivity features in the model obtained from the terrain-corrected response suggests the necessity of terrain correction in MT data recorded from the Himalayan region.

scholarly journals Identification of a nonseparable model under endogeneity using binary proxies for unobserved heterogeneity

10.3982/qe674 ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 527-563 ◽  
Author(s):  
Benjamin Williams
Keyword(s):  
Sample Size ◽  
Instrumental Variable ◽  
Unobserved Heterogeneity ◽  
Real Data ◽  
Fixed Number ◽  
Nonparametric Estimator

In this paper, I study identification of a nonseparable model with endogeneity arising due to unobserved heterogeneity. Identification relies on the availability of binary proxies that can be used to control for the unobserved heterogeneity. I show that the model is identified in the limit as the number of proxies increases. The argument does not require an instrumental variable that is excluded from the outcome equation nor does it require the support of the unobserved heterogeneity to be finite. I then propose a nonparametric estimator that is consistent as the number of proxies increases with the sample size. I also show that, for a fixed number of proxies, nontrivial bounds on objects of interest can be obtained. Finally, I study two real data applications that illustrate computation of the bounds and estimation with a large number of items.

scholarly journals Bayesian inversion of magnetotelluric data considering dimensionality discrepancies

2020 ◽  
Vol 223 (3) ◽  
pp. 1565-1583
Author(s):  
Hoël Seillé ◽  
Gerhard Visser
Keyword(s):  
Learning Algorithm ◽  
Synthetic Data ◽  
Real Data ◽  
Error Model ◽  
Bayesian Inversion ◽  
Magnetotelluric Data ◽  
Data Set ◽  
Likelihood Functions ◽  
Training Images ◽  
Phase Tensor

SUMMARY Bayesian inversion of magnetotelluric (MT) data is a powerful but computationally expensive approach to estimate the subsurface electrical conductivity distribution and associated uncertainty. Approximating the Earth subsurface with 1-D physics considerably speeds-up calculation of the forward problem, making the Bayesian approach tractable, but can lead to biased results when the assumption is violated. We propose a methodology to quantitatively compensate for the bias caused by the 1-D Earth assumption within a 1-D trans-dimensional Markov chain Monte Carlo sampler. Our approach determines site-specific likelihood functions which are calculated using a dimensionality discrepancy error model derived by a machine learning algorithm trained on a set of synthetic 3-D conductivity training images. This is achieved by exploiting known geometrical dimensional properties of the MT phase tensor. A complex synthetic model which mimics a sedimentary basin environment is used to illustrate the ability of our workflow to reliably estimate uncertainty in the inversion results, even in presence of strong 2-D and 3-D effects. Using this dimensionality discrepancy error model we demonstrate that on this synthetic data set the use of our workflow performs better in 80 per cent of the cases compared to the existing practice of using constant errors. Finally, our workflow is benchmarked against real data acquired in Queensland, Australia, and shows its ability to detect the depth to basement accurately.

scholarly journals A new lidar inversion method using a surface reference target applied to the backscattering coefficient and lidar ratio retrievals of a fog-oil plume at short range

2020 ◽  
Vol 13 (4) ◽  
pp. 1921-1935
Author(s):  
Florian Gaudfrin ◽  
Olivier Pujol ◽  
Romain Ceolato ◽  
Guillaume Huss ◽  
Nicolas Riviere
Keyword(s):  
Short Range ◽  
A Priori ◽  
Inversion Method ◽  
Backscattering Coefficient ◽  
Reference Target ◽  
Lidar Measurements ◽  
Lidar Ratio ◽  
Aerosol Plume ◽  
Good Agreement

Abstract. In this paper, a new elastic lidar inversion equation is presented. It is based on the backscattering signal from a surface reference target (SRT) rather than that from a volumetric layer of reference (Rayleigh molecular scatterer) as is usually done. The method presented can be used when the optical properties of such a layer are not available, e.g., in the case of airborne elastic lidar measurements or when the lidar–target line is horizontal Also, a new algorithm is described to retrieve the lidar ratio and the backscattering coefficient of an aerosol plume without any a priori assumptions about the plume. In addition, our algorithm allows a determination of the instrumental constant. This algorithm is theoretically tested, viz. by means of simulated lidar profiles and then using real measurements. Good agreement with available data in the literature has been found.

Identification of the Velocity, Thickness, and Interfacial Roughness of Coating Using Full Time-Domain URCPS: Cross-Correlation-Based Inverse Problem

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Zhiyuan Ma ◽  
Li Lin ◽  
Shijie Jin ◽  
Mingkai Lei
Keyword(s):  
Time Domain ◽  
Measurement Errors ◽  
Cross Correlation ◽  
Inversion Method ◽  
Full Time ◽  
Interfacial Roughness ◽  
Measured Velocity ◽  
Detection Frequency ◽  
Relative Errors ◽  
Good Agreement

Aiming at characterizing interfacial roughness of thin coatings with unknown sound velocity and thickness, we derive a full time-domain ultrasonic reflection coefficient phase spectrum (URCPS) as a function of interfacial roughness based on the phase screen approximation theory. The constructed URCPS is used to determine the velocity, thickness, and interfacial roughness of specimens through the cross-correlation algorithm. The effect of detection frequency on the roughness measurement is investigated through the finite element method. A series of simulations were implemented on Ni-coating specimens with a thickness of 400 μm and interfacial roughness of 1.9–39.8 μm. Simulation results indicated that the measurement errors of interfacial roughness were less than 10% when the roughness satisfies the relationship of Rq = 1.6–10.0%λ. The measured velocity and thicknesses were in good agreement with those imported in simulation models with less than 9.3% error. Ultrasonic experiments were carried out on two Ni-coating specimens through a flat transducer with an optimized frequency of 15 MHz. Compared with the velocities measured by time-of-flight (TOF) method, the relative errors of inversed velocities were all less than 10%. The inversed thicknesses were in good agreement with those observed by optical microscopy with less than 10.9% and 7.6% error. The averaged interfacial roughness determined by the ultrasonic inversion method was 16.9 μm and 30.7 μm, respectively. The relative errors were 5.1% and 2.0% between ultrasonic and confocal laser scanning microscope (CLSM) method, respectively.

Frequency‐domain acoustic‐wave modeling and inversion of crosshole data: Part II—Inversion method, synthetic experiments and real‐data results

Geophysics ◽  
1995 ◽  
Vol 60 (3) ◽  
pp. 796-809 ◽  
Author(s):  
Zhong‐Min Song ◽  
Paul R. Williamson ◽  
R. Gerhard Pratt
Keyword(s):  
Frequency Domain ◽  
Real Data ◽  
Forward Modeling ◽  
Two Dimensions ◽  
Inversion Method ◽  
Inversion Algorithm ◽  
Data Set ◽  
Wave Inversion ◽  
Full Wave ◽  
And Inversion

In full‐wave inversion of seismic data in complex media it is desirable to use finite differences or finite elements for the forward modeling, but such methods are still prohibitively expensive when implemented in 3-D. Full‐wave 2-D inversion schemes are of limited utility even in 2-D media because they do not model 3-D dynamics correctly. Many seismic experiments effectively assume that the geology varies in two dimensions only but generate 3-D (point source) wavefields; that is, they are “two‐and‐one‐half‐dimensional” (2.5-D), and this configuration can be exploited to model 3-D propagation efficiently in such media. We propose a frequency domain full‐wave inversion algorithm which uses a 2.5-D finite difference forward modeling method. The calculated seismogram can be compared directly with real data, which allows the inversion to be iterated. We use a descents‐related method to minimize a least‐squares measure of the wavefield mismatch at the receivers. The acute nonlinearity caused by phase‐wrapping, which corresponds to time‐domain cycle‐skipping, is avoided by the strategy of either starting the inversion using a low frequency component of the data or constructing a starting model using traveltime tomography. The inversion proceeds by stages at successively higher frequencies across the observed bandwidth. The frequency domain is particularly efficient for crosshole configurations and also allows easy incorporation of attenuation, via complex velocities, in both forward modeling and inversion. This also requires the introduction of complex source amplitudes into the inversion as additional unknowns. Synthetic studies show that the iterative scheme enables us to achieve the theoretical maximum resolution for the velocity reconstruction and that strongly attenuative zones can be recovered with reasonable accuracy. Preliminary results from the application of the method to a real data set are also encouraging.

Bayesian inversion of CSEM and magnetotelluric data

Geophysics ◽  
2012 ◽  
Vol 77 (1) ◽  
pp. E33-E42 ◽  
Author(s):  
Arild Buland ◽  
Odd Kolbjørnsen
Keyword(s):  
Prior Knowledge ◽  
Inversion Method ◽  
Earth Model ◽  
Magnetotelluric Data ◽  
Inversion Result ◽  
Electromagnetic Data ◽  
Magnetic Components ◽  
Resistivity Model ◽  
Frequency Components ◽  
Inversion Procedure

We have developed a Bayesian methodology for inversion of controlled source electromagnetic (CSEM) data and magnetotelluric (MT) data. The inversion method provided optimal solutions and also the associated uncertainty for any sets of electric and magnetic components and frequencies from CSEM and MT data. The method is based on a 1D forward modeling method for the electromagnetic (EM) response for a plane-layered anisotropic earth model. The inversion method was also designed to invert common midpoint (CMP)-sorted data along a 2D earth profile assuming locally horizontal models in each CMP position. The inversion procedure simulates from the posterior distribution using a Markov chain Monte Carlo (McMC) approach based on the Metropolis-Hastings algorithm. The method that we use integrates available geologic prior knowledge with the information in the electromagnetic data such that the prior model stabilizes and constrains the inversion according to the described knowledge. The synthetic examples demonstrated that inclusion of more data generally improves the inversion results. Compared to inversion of the inline electric component only, inclusion of broadside and magnetic components and an extended set of frequency components moderately decreased the uncertainty of the inversion. The results were strongly dependent on the prior knowledge imposed by the prior distribution. The prior knowledge about the background resistivity model surrounding the target was highly important for a successful and reliable inversion result.

Traveltime inversion of offset vertical seismic profiles—A feasibility study

Geophysics ◽  
1984 ◽  
Vol 49 (3) ◽  
pp. 250-264 ◽  
Author(s):  
L. R. Lines ◽  
A. Bourgeois ◽  
J. D. Covey
Keyword(s):  
Inverse Method ◽  
Synthetic Data ◽  
Real Data ◽  
Seismic Profile ◽  
Inversion Method ◽  
Earth Model ◽  
Seismic Profiles ◽  
Traveltime Inversion ◽  
Vertical Seismic Profiles ◽  
Inversion Techniques

Traveltimes from an offset vertical seismic profile (VSP) are used to estimate subsurface two‐dimensional dip by applying an iterative least‐squares inverse method. Tests on synthetic data demonstrate that inversion techniques are capable of estimating dips in the vicinity of a wellbore by using the traveltimes of the direct arrivals and the primary reflections. The inversion method involves a “layer stripping” approach in which the dips of the shallow layers are estimated before proceeding to estimate deeper dips. Examples demonstrate that the primary reflections become essential whenever the ratio of source offset to layer depth becomes small. Traveltime inversion also requires careful estimation of layer velocities and proper statics corrections. Aside from these difficulties and the ubiquitous nonuniqueness problem, the VSP traveltime inversion was able to produce a valid earth model for tests on a real data case.

Sign in / Sign up

Export Citation Format

Share Document