3-D imaging of subsurface magnetic permeability/susceptibility with portable frequency domain electromagnetic sensors for near surface exploration

2019 ◽  
Vol 219 (3) ◽  
pp. 1773-1785 ◽  
Author(s):  
Julien Guillemoteau ◽  
François-Xavier Simon ◽  
Guillaume Hulin ◽  
Bertrand Dousteyssier ◽  
Marion Dacko ◽  
...  
Keyword(s):  
Magnetic Permeability ◽  
Field Data ◽  
Large Data ◽  
Archaeological Site ◽  
Phase Response ◽  
Magnetic Method ◽  
Data Sets ◽  
Near Surface ◽  
Inversion Procedure ◽  
Multichannel Deconvolution

SUMMARY The in-phase response collected by portable loop–loop electromagnetic induction (EMI) sensors operating at low and moderate induction numbers (≤1) is typically used for sensing the magnetic permeability (or susceptibility) of the subsurface. This is due to the fact that the in-phase response contains a small induction fraction and a preponderant induced magnetization fraction. The magnetization fraction follows the magneto-static equations similarly to the magnetic method but with an active magnetic source. The use of an active source offers the possibility to collect data with several loop–loop configurations, which illuminate the subsurface with different sensitivity patterns. Such multiconfiguration soundings thereby allows the imaging of subsurface magnetic permeability/susceptibility variations through an inversion procedure. This method is not affected by the remnant magnetization and theoretically overcomes the classical depth ambiguity generally encountered with passive geomagnetic data. To invert multiconfiguration in-phase data sets, we propose a novel methodology based on a full-grid 3-D multichannel deconvolution (MCD) procedure. This method allows us to invert large data sets (e.g. consisting of more than a hundred thousand of data points) for a dense voxel-based 3-D model of magnetic susceptibility subject to smoothness constraints. In this study, we first present and discuss synthetic examples of our imaging procedure, which aim at simulating realistic conditions. Finally, we demonstrate the applicability of our method to field data collected across an archaeological site in Auvergne (France) to image the foundations of a Gallo-Roman villa built with basalt rock material. Our synthetic and field data examples demonstrate the potential of the proposed inversion procedure offering new and complementary ways to interpret data sets collected with modern EMI instruments.

scholarly journals Application of magnetic method for mapping buried structures around archaeological site of Masjid Tuha Indrapuri

2020 ◽  
Vol 20 (2) ◽  
pp. 31-35
Author(s):  
CUT INTAN KEUMALA ◽  
TOMI AFRIZAL ◽  
MUHAMMAD SYUKRI SURBAKTI ◽  
NAZLI ISMAIL
Keyword(s):  
Magnetic Field ◽  
Field Data ◽  
Archaeological Site ◽  
Magnetic Method ◽  
Buried Structures ◽  
Entire Area ◽  
Magnetic Field Data ◽  
Total Magnetic Field ◽  
Aceh Province ◽  
Reduction To The Pole

Magnetic gradiometer survey has been conducted on the yard of the archaeological site of Masjid Tuha Indrapuri, Aceh Besar Regency, Aceh Province. The site is one of the oldest mosques erected during the Aceh Sultanate period. Magnetic method was applied for mapping archaeological structures buried beneath the surface. Total magnetic field data were measured using Proton Precession Magnetometer with grid spacing of 2 meters between stations covering the entire area of the site. Diurnal and international geomagnetic reference field data were corrected to the measured data in order to calculate total magnetic field anomalies that were influenced by the buried magnetic objects. The total magnetic field anomalies distribution shows two elongated structures with U-shaped patterns surrounding the mosque. The patterns are also revealed in reduction to the pole and derivative vertical filters of the total field anomaly data. The anomaly patterns are considered a response from the rest of the buried fences that were built around the mosque in the past.

Cooperative constrained inversion of multiple electromagnetic data sets

Geophysics ◽  
2014 ◽  
Vol 79 (4) ◽  
pp. B173-B185 ◽  
Author(s):  
Michael S. McMillan ◽  
Douglas W. Oldenburg
Keyword(s):  
Field Data ◽  
Gold Mineralization ◽  
Upper And Lower Bounds ◽  
Data Sets ◽  
Bound Constraints ◽  
Data Set ◽  
Near Surface ◽  
Resistivity Measurements ◽  
Inversion Model ◽  
Resistivity Model

We evaluated a method for cooperatively inverting multiple electromagnetic (EM) data sets with bound constraints to produce a consistent 3D resistivity model with improved resolution. Field data from the Antonio gold deposit in Peru and synthetic data were used to demonstrate this technique. We first separately inverted field airborne time-domain EM (AEM), controlled-source audio-frequency magnetotellurics (CSAMT), and direct current resistivity measurements. Each individual inversion recovered a resistor related to gold-hosted silica alteration within a relatively conductive background. The outline of the resistor in each inversion was in reasonable agreement with the mapped extent of known near-surface silica alteration. Variations between resistor recoveries in each 3D inversion model motivated a subsequent cooperative method, in which AEM data were inverted sequentially with a combined CSAMT and DC data set. This cooperative approach was first applied to a synthetic inversion over an Antonio-like simulated resistivity model, and the inversion result was both qualitatively and quantitatively closer to the true synthetic model compared to individual inversions. Using the same cooperative method, field data were inverted to produce a model that defined the target resistor while agreeing with all data sets. To test the benefit of borehole constraints, synthetic boreholes were added to the inversion as upper and lower bounds at locations of existing boreholes. The ensuing cooperative constrained synthetic inversion model had the closest match to the true simulated resistivity distribution. Bound constraints from field boreholes were then calculated by a regression relationship among the total sulfur content, alteration type, and resistivity measurements from rock samples and incorporated into the inversion. The resulting cooperative constrained field inversion model clearly imaged the resistive silica zone, extended the area of interpreted alteration, and also highlighted conductive zones within the resistive region potentially linked to sulfide and gold mineralization.

scholarly journals Imaging near-surface heterogeneities by natural migration of backscattered surface waves: Field data test

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. S197-S205 ◽  
Author(s):  
Zhaolun Liu ◽  
Abdullah AlTheyab ◽  
Sherif M. Hanafy ◽  
Gerard Schuster
Keyword(s):  
Surface Waves ◽  
Field Data ◽  
Synthetic Data ◽  
Data Sets ◽  
Data Set ◽  
Near Surface ◽  
3D Data ◽  
Strong Surface ◽  
Migration Method ◽  
Recorded Data

We have developed a methodology for detecting the presence of near-surface heterogeneities by naturally migrating backscattered surface waves in controlled-source data. The near-surface heterogeneities must be located within a depth of approximately one-third the dominant wavelength [Formula: see text] of the strong surface-wave arrivals. This natural migration method does not require knowledge of the near-surface phase-velocity distribution because it uses the recorded data to approximate the Green’s functions for migration. Prior to migration, the backscattered data are separated from the original records, and the band-passed filtered data are migrated to give an estimate of the migration image at a depth of approximately one-third [Formula: see text]. Each band-passed data set gives a migration image at a different depth. Results with synthetic data and field data recorded over known faults validate the effectiveness of this method. Migrating the surface waves in recorded 2D and 3D data sets accurately reveals the locations of known faults. The limitation of this method is that it requires a dense array of receivers with a geophone interval less than approximately one-half [Formula: see text].

Voyager: An Interactive Software for Visualizing Large, Geospatial Data Sets

2010 ◽  
Vol 44 (4) ◽  
pp. 8-19 ◽  
Author(s):  
John C. Anderson ◽  
Jose M. Andres ◽  
McKay Davis ◽  
Kayo Fujiwara ◽  
Tie Fang ◽  
...  
Keyword(s):  
Flow Field ◽  
Field Data ◽  
Large Data ◽  
Level Of Detail ◽  
Geospatial Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Interactive Software ◽  
Operational Capabilities ◽  
Global Coverage

AbstractThis paper describes the development and main operational capabilities of Voyager, a PC-based, geospatially-enabled piece of software that can fuse and visualize large, multi-variable data sets that change in space (XYZ) and time (T). The new software has the ability to simultaneously visualize imagery, bathymetry/terrain, true volumetric (voxel), and flow field data in a fully interactive geo-referenced mode. In addition to providing global coverage, a key feature of this software is the capability to interactively visualize large data sets while operating on a desktop PC. This is achieved by using tiling and level-of-detail (LOD) technology for terrain, imagery, and volumetric data, as well as compression techniques and the multi-threading capabilities of modern PCs.

scholarly journals Near-surface diffractor detection at archaeological sites based on an interferometric workflow

Geophysics ◽  
2021 ◽  
pp. 1-75
Author(s):  
Jianhuan Liu ◽  
Deyan Draganov ◽  
Ranajit Ghose ◽  
Quentin Bourgeois
Keyword(s):  
Field Data ◽  
Waveform Inversion ◽  
Synthetic Data ◽  
Spatial Summation ◽  
Archaeological Site ◽  
Archaeological Sites ◽  
Magnetic Survey ◽  
Seismic Reflection Data ◽  
Near Surface ◽  
Reflection Data

Detecting small-size objects is a primary challenge at archaeological sites due to the high degree of heterogeneity present in the near surface. Although high-resolution reflection seismic imaging often delivers the target resolution of the subsurface in different near-surface settings, the standard processing for obtaining an image of the subsurface is not suitable to map local diffractors. This happens because shallow seismic-reflection data are often dominated by strong surface waves which might cover weaker diffractions, and because traditional common-midpoint moveout corrections are only optimal for reflection events. Here, we propose an approach for imaging subsurface objects using masked diffractions. These masked diffractions are firstly revealed by a combination of seismic interferometry and nonstationary adaptive subtraction, and then further enhanced through crosscoherence-based super-virtual interferometry. A diffraction image is then computed by a spatial summation of the revealed diffractions. We use phase-weighted stack to enhance the coherent summation of weak diffraction signals. Using synthetic data, we show that our scheme is robust in locating diffractors from data dominated by strong Love waves. We test our method on field data acquired at an archaeological site. The resulting distribution of shallow diffractors agrees with the location of anomalous objects identified in the Vs model obtained by elastic SH/Love full-waveform inversion using the same field data. The anomalous objects correspond to the position of a suspected burial, also detected in an independent magnetic survey and corings.

Fusion of gravity gradient and magnetic field data for discrimination of anomalies using deformation analysis

Geophysics ◽  
2012 ◽  
Vol 77 (3) ◽  
pp. F13-F20 ◽  
Author(s):  
Kamil Erkan ◽  
Christopher Jekeli ◽  
C. K. Shum
Keyword(s):  
Magnetic Field ◽  
Field Data ◽  
Gravity Gradient ◽  
Deformation Analysis ◽  
Data Sets ◽  
Low Density ◽  
Gravity Gradiometry ◽  
Near Surface ◽  
Magnetic Field Data ◽  
Efficient Integration

Gravity gradiometry and magnetometry methods are powerful noninvasive techniques for near-surface detection problems. Efficient integration of data from these techniques decreases the degree of nonuniqueness in geophysical interpretations. Deformation analysis is a powerful tool for comparison of two fields, which aids in this respect. We propose a fully quantitative approach, which uses the generalized theory of deformation for the geometric comparison of gravimetric and magnetic fields. The resulting deformation maps delineate regions where Poisson’s relation is violated between the two data sets and thus discriminate between air-filled cavities and other similar low-density/susceptibility geophysical sources. We present a practical corresponding algorithm that is robust in the sense that no prior knowledge of the physical properties of the subsurface is needed.

Inversion of potential‐field data by iterative forward modeling in the wavenumber domain

Geophysics ◽  
1992 ◽  
Vol 57 (1) ◽  
pp. 126-130 ◽  
Author(s):  
Jianghai Xia ◽  
Donald R. Sprowl
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Single Layer ◽  
Large Data ◽  
Uniform Density ◽  
Data Sets ◽  
Potential Field Data ◽  
Wavenumber Domain ◽  
Solution Convergence ◽  
Forward Calculation

Direct inversion of potential‐field data is hindered by the nonuniqueness of the general solution. Convergence to a single solution can only be obtained when external constraints are placed on the subsurface geometry. Two such constrained geometries are dealt with here: a single, nonplanar interface between two layers, each of uniform density or magnetization, and the distribution of the density or magnetization contrast within a single layer. Both of these simple geometries have geologic application. Inversion is accomplished by iterative improvement in an initial subsurface model in the wavenumber domain. The inversion process is stable and is efficient for usage on large data sets. Forward calculation of anomalies is by Parker’s (1973) algorithm (Blakely, 1981).

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

1992 ◽  
Vol 50 (2) ◽  
pp. 1488-1489
Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck
Keyword(s):  
Cluster Analysis ◽  
Chemical Reactivity ◽  
Large Data ◽  
Large Data Sets ◽  
Particle Analysis ◽  
Data Sets ◽  
Halogen Chemistry ◽  
Complete Study ◽  
Components Analysis ◽  
Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

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