Depth of investigation for small broadband electromagnetic sensors

Geophysics ◽  
2005 ◽  
Vol 70 (6) ◽  
pp. G135-G142 ◽  
Author(s):  
Haoping Huang
Keyword(s):  
Detection Threshold ◽  
Data Interpretation ◽  
Skin Depth ◽  
High Threshold ◽  
Electromagnetic Sensors ◽  
Quantitative Understanding ◽  
Processing Techniques ◽  
Simple Equations ◽  
Depth Of Investigation ◽  
Conductivity Contrast

The depth of investigation in electromagnetic (EM) soundings is a maximum depth at which a given target in a given host can be detected by a given sensor. It is of primary interest in EM exploration, particularly for small EM sensors having negligible separation between the transmitter and receiver coils. The depth of investigation is related to many factors, such as sensor sensitivity, precision, operating frequencies, ambient noise level, target and host properties, and the techniques used in data processing and interpretation. Quantitative understanding of the relationships between the depth of investigation and these factors will help users meet their geologic objectives, avoid unnecessary survey expenses, and display meaningful geologic features. Simple equations to estimate the depth of investigation for handheld EM sensors have been derived from analyzing the EM response based on layered half-space models. The results show that the depth of investigation is approximately proportional to the square root of the skin depth in the host for a given detection threshold and conductivity contrast between the target and host. For a given skin depth, the depth of investigation increases with the target conductivity and conductivity contrast and decreases with the detection threshold. Choosing a threshold mainly depends on the S/N ratio of the EM data if the sensor setup, data acquisition methods, and processing techniques are well established. A high threshold such as 20% or 30% is recommended for resistive targets or in areas where environmental noise is high or where terrain conductivity is low (<50 mS/m). In contrast, a threshold as low as 5% or 10% can be used for conductive targets in quiet areas. Field examples are presented to illustrate how to use the depth of investigation in data interpretation and presentation.

A global measure for depth of investigation

Geophysics ◽  
2012 ◽  
Vol 77 (4) ◽  
pp. WB171-WB177 ◽  
Author(s):  
Anders Vest Christiansen ◽  
Esben Auken
Keyword(s):  
Absolute Threshold ◽  
Threshold Value ◽  
Skin Depth ◽  
System Response ◽  
Sensitivity Matrix ◽  
Actual System ◽  
Data Noise ◽  
Geophysical Model ◽  
Homogeneous Half Space ◽  
Depth Of Investigation

We tested a new robust concept for the calculation of depth of investigation (DOI) that is valid for any 1D electromagnetic (EM) geophysical model. A good estimate of DOI is crucial when building geologic and hydrological models from EM data sets because the validity of the models varies strongly with data noise and the resistivity of the layers themselves. For diffusive methods, such as ground-based and airborne electromagnetic, it is not possible to define an unambiguous depth below which there is no information on the resistivity structure and a measure of DOI is therefore to what depth the model can be considered reliable. The method we presented is based on the actual model output from the inversion process and we used the actual system response, contrary to assuming, e.g., planar waves over a homogeneous half-space, the widely used skin depth calculation. Equally important, the data noise and the number of data points are integrated into the calculation. Our methodology is based on a recalculated sensitivity (Jacobian) matrix of the final model and thus it can be used on any model type for which a sensitivity matrix can be calculated. Unlike other sensitivity matrix methods, we defined a global and absolute threshold value contrary to defining a relative (such as 5%), sensitivity limit. The threshold value will apply to all 1D inverted data and will thus produce comparable numbers of DOI.

Contrast enhancement of data measured with area detectors: a way to generalize the use of neutron diffraction for thin-film studies

2013 ◽  
Vol 46 (3) ◽  
pp. 726-735 ◽  
Author(s):  
Alexandre M. Bataille ◽  
Vincent Auvray ◽  
Christophe Gatel ◽  
Arsen Gukasov
Keyword(s):  
Contrast Enhancement ◽  
Detection Threshold ◽  
Precise Determination ◽  
Scattering Data ◽  
Core Element ◽  
Denoising Method ◽  
Elastic Neutron ◽  
Processing Techniques ◽  
Cr Film ◽  
Position Sensitive Detectors

A denoising method is reported for the treatment of neutron scattering data obtained with position-sensitive detectors, which enhances the information obtained from weak and very weak Bragg peaks. The core element of the method is the application of a Laplacian of Gaussian filter calculated using the parameters of the resolution of the instrument. This adaptation of well established image-processing techniques offers a very efficient way to denoise the data, as shown through the application of the reported method to a study of the magnetic Bragg peaks of a 300 nm-thick epitaxial Cr film. The procedure enhances the contrast by a factor of more than 35 and thus allows precise determination of the position of the integration mask. The large contrast enhancement also lowers the detection threshold of standard elastic neutron diffractometers down to the level usually available solely on optimized triple-axis spectrometers.

Doubling the effective skin depth with a local source

Geophysics ◽  
1999 ◽  
Vol 64 (3) ◽  
pp. 732-738 ◽  
Author(s):  
James E. Reid ◽  
James C. Macnae
Keyword(s):  
Plane Wave ◽  
Half Space ◽  
Survey Design ◽  
Data Interpretation ◽  
Skin Depth ◽  
Local Source ◽  
Electromagnetic Sounding ◽  
Homogeneous Half Space ◽  
Electric And Magnetic Fields ◽  
Thick Overburden

The depth at which the amplitude of the frequency‐domain electromagnetic fields due to dipole and square loop sources over a homogeneous half‐space fall to 1/e of their value at the surface is compared to the conventional plane‐wave skin depth. The skin depth due to a local source depends on the transmitter frequency, half‐space conductivity, transmitter altitude, and transmitter‐receiver offset, and may range from a fraction of to more than twice the plane‐wave skin depth. Unlike the plane‐wave skin depth, the “local‐source skin depth” is different for electric and magnetic fields, and may be nonunique for some transmitter geometries and field components. For all transmitter geometries, however, the local‐source skin depth approaches the plane‐wave skin depth as the transmitter altitude and/or receiver offset increase. The concept of the local‐source skin depth has direct application to survey design and data interpretation. A theoretical example demonstrates that it is possible to predict, for a given survey geometry and frequency range, whether or not an electromagnetic sounding can detect a conductive basement below a thick overburden layer.

scholarly journals Integrated Approach to Investigating Historic Cemeteries

2020 ◽  
Vol 12 (17) ◽  
pp. 2690
Author(s):  
Christine Downs ◽  
Jaime Rogers ◽  
Lori Collins ◽  
Travis Doering
Keyword(s):  
Air Force ◽  
Laser Scanning ◽  
Integrated Approach ◽  
Data Interpretation ◽  
Grave Markers ◽  
Value Decomposition ◽  
Ground Penetrating ◽  
Traditional Processing ◽  
Processing Techniques ◽  
Historic Cemetery

Ground-penetrating radar (GPR) and terrestrial laser scanning (TLS) surveys were conducted at a historic cemetery at Cape Canaveral Air Force Station, Florida, U.S., in order to confirm the presence of burials corresponding to grave markers and detect potential unmarked burials. Noise in the GPR data from surface features and subtle terrain differences must be addressed to determine the extent of anomalies of interest. We use singular value decomposition (SVD) to isolate and remove energy from GPR data. SVD allows one to remove unwanted signals that traditional processing techniques cannot. With SVD filtering, we resolve an anomaly adjacent to confirmed burials otherwise overprinted by unwanted signal. The migration of SVD-filtered data produces more distinct, spatially constrained point reflectors. Ground elevation is derived from georeferenced TLS data and compared to that from airborne laser scanning (ALS) to highlight subtle terrain that can assist data interpretation. TLS elevations show a subtle modern mound over the burial plot where ALS elevations show a depression. The targets of interest are approximately 20–30 cm higher in elevation if a topographic correction is performed using TLS versus ALS. In archaeological applications, a notable change is often recorded at the sub-meter scale. The combined approach presented here better resolves geophysical response of buried features and their positions in the ground relative to each other.

scholarly journals GPR Data Interpretation Approaches in Archaeological Prospection

2021 ◽  
Vol 11 (16) ◽  
pp. 7531
Author(s):  
Merope Manataki ◽  
Antonis Vafidis ◽  
Apostolos Sarris
Keyword(s):  
Neural Networks ◽  
Data Interpretation ◽  
Good Insight ◽  
Archaeological Prospection ◽  
Automatic Interpretation ◽  
Attribute Analysis ◽  
Interpretation Process ◽  
Integrated Interpretation ◽  
Processing Techniques ◽  
Insight Into

This article focuses on the possible drawbacks and pitfalls in the GPR data interpretation process commonly followed by most GPR practitioners in archaeological prospection. Standard processing techniques aim to remove some noise, enhance reflections of the subsurface. Next, one has to calculate the instantaneous envelope and produce C-scans which are 2D amplitude maps showing high reflectivity surfaces. These amplitude maps are mainly used for data interpretation and provide a good insight into the subsurface but cannot fully describe it. The main limitations are discussed while studies aiming to overcome them are reviewed. These studies involve integrated interpretation approaches using both B-scans and C-scans, attribute analysis, fusion approaches, and recent attempts to automatically interpret C-scans using Deep Learning (DL) algorithms. To contribute to the automatic interpretation of GPR data using DL, an application of Convolutional Neural Networks (CNNs) to classify GPR data is also presented and discussed.

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