1D single-site and laterally constrained inversion of multifrequency and multicomponent ground-based electromagnetic induction data — Application to the investigation of a near-surface clayey overburden

Geophysics ◽  
2012 ◽  
Vol 77 (4) ◽  
pp. WB19-WB35 ◽  
Author(s):  
Cyril Schamper ◽  
Fayçal Rejiba ◽  
Roger Guérin
Keyword(s):  
Magnetic Field ◽  
Electromagnetic Induction ◽  
Search Algorithm ◽  
Random Search ◽  
A Priori ◽  
Synthetic Data ◽  
Conductive Layer ◽  
Near Surface ◽  
Priori Information ◽  
Definition Of

Electromagnetic induction (EMI) methods are widely used to determine the distribution of the electrical conductivity and are well adapted to the delimitation of aquifers and clayey layers because the electromagnetic field is strongly perturbed by conductive media. The multicomponent EMI device that was used allowed the three components of the secondary magnetic field (the radial [Formula: see text], the tangential [Formula: see text], and the vertical [Formula: see text]) to be measured at 10 frequencies ranging from 110 to 56 kHz in one single sounding with offsets ranging from 20 to 400 m. In a continuing endeavor to improve the reliability with which the thickness and conductivity are inverted, we focused our research on the use of components other than the vertical magnetic field Hz. Because a separate sensitivity analysis of [Formula: see text] and [Formula: see text] suggests that [Formula: see text] is more sensitive to variations in the thickness of a near-surface conductive layer, we developed an inversion tool able to make single-sounding and laterally constrained 1D interpretation of both components jointly, associated with an adapted random search algorithm for single-sounding processing for which almost no a priori information is available. Considering the complementarity of [Formula: see text] and [Formula: see text] components, inversion tests of clean and noisy synthetic data showed an improvement in the definition of the thickness of a near-surface conductive layer. This inversion code was applied to the karst site of the basin of Fontaine-Sous-Préaux, near Rouen (northwest of France). Comparison with an electrical resistivity tomography tends to confirm the reliability of the interpretation from the EMI data with the developed inversion tool.

scholarly journals Magnetic Target Position Estimation Method Based on Prior Information

2021 ◽  
Vol 2113 (1) ◽  
pp. 012020
Author(s):  
Guangfa Sun
Keyword(s):  
Magnetic Field ◽  
Target Recognition ◽  
A Priori ◽  
Magnetic Sensor ◽  
Test Method ◽  
Target Area ◽  
A Priori Information ◽  
Measurement Results ◽  
Magnetic Target ◽  
Priori Information

Abstract Aiming at the problem of detection and location of magnetic targets in water beach, the acoustic magnetic composite detection method is studied. After the sonar obtains the image of the suspicious object in the target area, the magnetic target recognition and location are realized by using the abnormal magnetic field distribution data near the target area measured by the shipborne magnetic sensor and the multi-sensor information fusion method. A target recognition and location method based on a priori information is proposed to solve the problem that the measurement results of magnetic sensor can not fully reflect the influence of ferromagnetic target on the surrounding magnetic field due to terrain constraints. In order to make up for this lack of information, taking the sonar measurement results as a priori information, the hypothesis test method is adopted to make full use of all the measurement results of different types of sensors to realize the recognition and positioning of magnetic targets.

scholarly journals Two-grid full-waveform Rayleigh-wave inversion via a genetic algorithm — Part 1: Method and synthetic examples

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. R805-R814 ◽  
Author(s):  
Zhen Xing ◽  
Alfredo Mazzotti
Keyword(s):  
Genetic Algorithm ◽  
Rayleigh Wave ◽  
A Priori ◽  
Computing Time ◽  
A Priori Information ◽  
Fine Grid ◽  
Near Surface ◽  
Reference Models ◽  
Full Waveform ◽  
Priori Information

When reliable a priori information is not available, it is difficult to correctly predict near-surface S-wave velocity models from Rayleigh waves through existing techniques, especially in the case of complex geology. To tackle this issue, we have developed a new method: two-grid genetic-algorithm Rayleigh-wave full-waveform inversion (FWI). Adopting a two-grid parameterization of the model, the genetic algorithm inverts for unknown velocities and densities at the nodes of a coarse grid, whereas the forward modeling is performed on a fine grid to avoid numerical dispersion. A bilinear interpolation brings the coarse-grid results into the fine-grid models. The coarse inversion grid allows for a significant reduction in the computing time required by the genetic algorithm to converge. With a coarser grid, there are fewer unknowns and less required computing time, at the expense of the model resolution. To further increase efficiency, our inversion code can perform the optimization using an offset-marching strategy and/or a frequency-marching strategy that can make use of different kinds of objective functions and allows for parallel computing. We illustrate the effect of our inversion method using three synthetic examples with rather complex near-surface models. Although no a priori information was used in all three tests, the long-wavelength structures of the reference models were fairly predicted, and satisfactory matches between “observed” and predicted data were achieved. The fair predictions of the reference models suggest that the final models estimated by our genetic-algorithm FWI, which we call macromodels, would be suitable inputs to gradient-based Rayleigh-wave FWI for further refinement. We also explored other issues related to the practical use of the method in different work and explored applications of the method to field data.

Generalized positivity constraint on magnetic equivalent layers

Geophysics ◽  
2020 ◽  
Vol 85 (6) ◽  
pp. J99-J110
Author(s):  
André L. A. Reis ◽  
Vanderlei C. Oliveira Jr. ◽  
Valéria C. F. Barbosa
Keyword(s):  
Inverse Problem ◽  
Magnetic Moment ◽  
A Priori ◽  
Synthetic Data ◽  
Total Field ◽  
Magnetization Direction ◽  
Moment Distribution ◽  
Equivalent Sources ◽  
Priori Information ◽  
Equivalent Layer

It is known from the potential theory that a continuous and planar layer of dipoles can exactly reproduce the total-field anomaly produced by arbitrary 3D sources. We have proven the existence of an equivalent layer having an all-positive magnetic-moment distribution for the case in which the magnetization direction of this layer is the same as that of the true sources, regardless of whether the magnetization of the true sources is purely induced or not. By using this generalized positivity constraint, we have developed a new iterative method for estimating the total magnetization direction of 3D magnetic sources based on the equivalent-layer technique. Our method does not impose a priori information about the shape or the depth of the sources, does not require regularly spaced data, and presumes that the sources have a uniform magnetization direction. At each iteration, our method performs two steps. The first step solves a constrained linear inverse problem to estimate a positive magnetic-moment distribution over a discrete equivalent layer of dipoles. We consider that the equivalent sources are located on a plane and have a uniform and fixed magnetization direction. In the second step, we use the estimated magnetic-moment distribution and solve a nonlinear inverse problem for estimating a new magnetization direction for the dipoles. The algorithm stops when the equivalent layer yields a total-field anomaly that fits the observed data. Tests with synthetic data simulating different geologic scenarios show that the final estimated magnetization direction is close to the true one. We apply our method to field data from the Goiás alkaline province, over the Montes Claros complex, in the center of Brazil. The results suggest the presence of intrusions with remarkable remanent magnetization, in agreement with the current literature for this region.

Isostatic constraint for 2D nonlinear gravity inversion on rifted margins

Geophysics ◽  
2019 ◽  
Vol 85 (1) ◽  
pp. G17-G34
Author(s):  
B. Marcela S. Bastos ◽  
Vanderlei C. Oliveira Jr.
Keyword(s):  
Gravity Data ◽  
A Priori ◽  
Petroleum Industry ◽  
Synthetic Data ◽  
Gravity Inversion ◽  
Rifted Margins ◽  
Isostatic Equilibrium ◽  
Priori Information ◽  
Pelotas Basin ◽  
Nonlinear Gravity

We have developed a nonlinear gravity inversion for simultaneously estimating the basement and Moho geometries, as well as the depth of the reference Moho along a profile crossing a passive rifted margin. To obtain stable solutions, we impose smoothness on basement and Moho, force them to be close to previously estimated depths along the profile and also impose local isostatic equilibrium. Different from previous methods, we evaluate the information of local isostatic equilibrium by imposing smoothness on the lithostatic stress exerted at depth. Our method delimits regions that deviate and those that can be considered in local isostatic equilibrium by varying the weight of the isostatic constraint along the profile. It also allows controlling the degree of equilibrium along the profile, so that the interpreter can obtain a set of candidate models that fit the observed data and exhibit different degrees of isostatic equilibrium. Our method also differs from earlier studies because it attempts to use isostasy for exploring (but not necessarily reducing) the inherent ambiguity of gravity methods. Tests with synthetic data illustrate the effect of our isostatic constraint on the estimated basement and Moho reliefs, especially at regions with pronounced crustal thinning, which are typical of passive volcanic margins. Results obtained by inverting satellite data over the Pelotas Basin, a passive volcanic margin in southern Brazil, agree with previous interpretations obtained independently by combining gravity, magnetic, and seismic data available to the petroleum industry. These results indicate that combined with a priori information, simple isostatic assumptions can be very useful for interpreting gravity data on passive rifted margins.

Toward subsurface magnetic permeability imaging with electromagnetic induction sensors: Sensitivity computation and reconstruction of measured data

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. E335-E345 ◽  
Author(s):  
Tim Klose ◽  
Julien Guillemoteau ◽  
François-Xavier Simon ◽  
Jens Tronicke
Keyword(s):  
Electrical Conductivity ◽  
Magnetic Permeability ◽  
Electromagnetic Induction ◽  
Synthetic Data ◽  
Forward Modeling ◽  
Measured Data ◽  
Modeling Method ◽  
Near Surface ◽  
Sensitivity Functions ◽  
Modeling Approach

In near-surface geophysics, small portable loop-loop electromagnetic induction (EMI) sensors using harmonic sources with a constant and rather small frequency are increasingly used to investigate the electrical properties of the subsurface. For such sensors, the influence of electrical conductivity and magnetic permeability on the EMI response is well-understood. Typically, data analysis focuses on reconstructing an electrical conductivity model by inverting the out-of-phase response. However, in a variety of near-surface applications, magnetic permeability (or susceptibility) models derived from the in-phase (IP) response may provide important additional information. In view of developing a fast 3D inversion procedure of the IP response for a dense grid of measurement points, we first analyze the 3D sensitivity functions associated with a homogeneous permeable half-space. Then, we compare synthetic data computed using a linear forward-modeling method based on these sensitivity functions with synthetic data computed using full nonlinear forward-modeling methods. The results indicate the correctness and applicability of our linear forward-modeling approach. Furthermore, we determine the advantages of converting IP data into apparent permeability, which, for example, allows us to extend the applicability of the linear forward-modeling method to high-magnetic environments. Finally, we compute synthetic data with the linear theory for a model consisting of a controlled magnetic target and compare the results with field data collected with a four-configuration loop-loop EMI sensor. With this field-scale experiment, we determine that our linear forward-modeling approach can reproduce measured data with sufficiently small error, and, thus, it represents the basis for developing efficient inversion approaches.

3D controlled-source electromagnetic imaging of gas hydrates: Insights from the Pelotas Basin offshore Brazil

2019 ◽  
Vol 7 (4) ◽  
pp. SH111-SH131 ◽  
Author(s):  
Raghava Tharimela ◽  
Adolpho Augustin ◽  
Marcelo Ketzer ◽  
Jose Cupertino ◽  
Dennis Miller ◽  
...  
Keyword(s):  
Gas Hydrates ◽  
Gas Hydrate ◽  
A Priori ◽  
Inversion Algorithm ◽  
Near Surface ◽  
Bottom Simulating Reflector ◽  
Controlled Source ◽  
Lateral Extent ◽  
Priori Information ◽  
Pelotas Basin

Mapping of natural gas hydrate systems has been performed successfully in the past using the controlled-source electromagnetic (CSEM) method. This method relies on differentiating resistive highly saturated free gas or hydrate-bearing host sediment from a less resistive low-saturated gas or brine-bearing host sediments. Knowledge of the lateral extent and resistivity variations (and hence the saturation variations) within sediments that host hydrates is crucial to be able to accurately quantify the presence of saturated gas hydrates. A 3D CSEM survey (PUCRS14) was acquired in 2014 in the Pelotas Basin offshore Brazil, with hydrate resistivity mapping as the main objective. The survey was acquired within the context of the CONEGAS research project, which investigated the origin and distribution of gas hydrate deposits in the Pelotas Basin. We have inverted the acquired data using a proprietary 3D CSEM anisotropic inversion algorithm. Inversion was purely CSEM data driven, and we did not include any a priori information in the process. Prior to CSEM, interpretation of near-surface geophysical data including 2D seismic, sub-bottom profiler, and multibeam bathymetry data indicated possible presence of gas hydrates within features identified such as faults, chimneys, and seeps leading to pockmarks, along the bottom simulating reflector and within the gas hydrate stability zone. Upon integration of the same with CSEM-derived resistivity volume, the interpretation revealed excellent spatial correlation with many of these features. The interpretation further revealed new features with possible hydrate presence, which were previously overlooked due to a lack of a clear seismic and/or multibeam backscatter signature. In addition, features that were previously mapped as gas hydrate bearing had to be reinterpreted as residual or low-saturated gas/hydrate features, due to the lack of significant resistivity response associated with them. Furthermore, we used the inverted resistivity volume to derive the saturation volume of the subsurface using Archie’s equation.

Curve Fitting and Deconvolution of Instrumental Broadening: A Simulated Annealing Approach

1995 ◽  
Vol 49 (3) ◽  
pp. 273-278 ◽  
Author(s):  
A. Ferry ◽  
P. Jacobsson
Keyword(s):  
Simulated Annealing ◽  
Curve Fitting ◽  
Simulated Annealing Algorithm ◽  
A Priori ◽  
Global Optimum ◽  
Fitting Procedure ◽  
Instrumental Resolution ◽  
Priori Information ◽  
Definition Of ◽  
The Cost

A curve-fitting procedure based on the simulated annealing algorithm has been developed for the analysis of spectral Raman data. By the inclusion of a priori information about the instrumental broadening in the definition of the cost function that is minimized, effects of the finite instrumental resolution are eliminated from the resulting fit. The ability of the method to reproduce original band shapes is tested on synthesized spectra and FT-Raman spectra of diamond recorded at different resolutions with different apodization functions. The procedure yields the global optimum of the fitted parameters and is easily implemented on a personal computer.

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