A quantitative appraisal of airborne and ground‐based transient electromagnetic (TEM) measurements in Denmark

Geophysics ◽  
2003 ◽  
Vol 68 (2) ◽  
pp. 523-534 ◽  
Author(s):  
Anders Vest Christiansen ◽  
Niels Bøie Christensen
Keyword(s):  
Field Data ◽  
Synthetic Data ◽  
Quantitative Model ◽  
Field Amplitude ◽  
One Dimensional ◽  
Transient Electromagnetic ◽  
Resolution Capability ◽  
Maximum Penetration ◽  
Quantitative Appraisal ◽  
Distorted Data

The last decade has seen growing use of ground‐based transient electromagnetic (TEM) methods in Denmark for hydrogeological purposes. Due to an intensified mapping campaign, airborne TEM methods were proposed as a possible tool for mapping large areas. The first test flights were flown in June 2000 using the GEOTEM system. Traditional approximate interpretation tools for airborne data are insufficient in hydrogeological investigations where a quantitative model specifying model parameter reliability is needed. We have carried out full nonlinear one‐dimensional inversion on the field amplitude of airborne synthetic and field data and compared the airborne method with the traditional ground‐based PROTEM 47 system that has found extensive use in Denmark. An improved measuring procedure for airborne systems is suggested to facilitate the estimation of noise that is necessary in a quantitative inversion. The analyses of synthetic data demonstrate the differences in resolution capability between ground‐based and airborne data. Ground‐based data typically resolve three‐ or four‐layer models and occasionally up to five layers. Airborne data resolve three layers as a maximum, one or two layers being common. The airborne GEOTEM system detects layers to depths of more than 300 m, bearing only little information about the top 50–70 m. The ground‐based PROTEM 47 system has a maximum penetration of approximately 170 m, with higher resolution capabilities in the top 100 m. Coupling to man‐made conductors is a serious problem for all TEM methods in densely populated areas and results in distorted data. Coupling influences the airborne data from Denmark on two‐thirds of the area covered. These data must be eliminated to avoid misinterpretation.

On the Influence of 3-D structures in the Interpretation of transient electromagnetic sounding data

Geophysics ◽  
1994 ◽  
Vol 59 (6) ◽  
pp. 889-901 ◽  
Author(s):  
Mark Goldman ◽  
Leonty Tabarovsky ◽  
Michael Rabinovich
Keyword(s):  
Field Data ◽  
Synthetic Data ◽  
Real Field ◽  
Good Reliability ◽  
Electromagnetic Sounding ◽  
Transient Electromagnetic ◽  
Long Offset ◽  
Near Zone ◽  
Two Layer Model ◽  
Central Loop

The limitations of a standard 1-D inversion applied to multidimensional (synthetic) data are investigated. Simple correction procedures for interpreting field data distorted by 3-D structures are suggested. Two different transmitter/receiver configurations of the transient electromagnetic (TEM) sounding method are examined: a central loop configuration for the near zone (sometimes called short offset) and a fixed transmitter/moving receiver configuration for the far zone (long offset). The 3-D models are structural depressions and highs in both resistive and conductive basements. The fixed transmitter (grounded dipole) in the long offset TEM configuration is located at a distance significantly greater than both the size and depth of the structure. In all cases, 1-D interpretation of the central loop soundings recovers geoelectric parameters of the section with good reliability, although fictitious layers may appear near vertical boundaries. The 1-D interpretation of long offset soundings does not, in most cases, show the actual structures. The data along various profiles are interpreted in terms of a two‐layer model without the structures. In some cases 1-D interpretation does show the structure, but the errors are far greater than those obtained in the inversion of central loop soundings. In all cases considered, the distortion of central loop soundings caused by 3-D effects is systematic and could, therefore, be corrected by simple procedures. These procedures permit interpretation of real field data that were previously abandoned owing to the strong distortions by lateral inhomogeneities.

scholarly journals One-dimensional constrained inversion study of TEM and application in coal goafs’ detection

2020 ◽  
Vol 12 (1) ◽  
pp. 1533-1540
Author(s):  
Si Yuanlei ◽  
Li Maofei ◽  
Liu Yaoning ◽  
Guo Weihong
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Data Interpretation ◽  
Volume Effect ◽  
Underground Space ◽  
Attenuation Curve ◽  
One Dimensional ◽  
Transient Electromagnetic ◽  
Geological Conditions ◽  
Geological Information

AbstractTransient electromagnetic method (TEM) is often used in urban underground space exploration and field geological resource detection. Inversion is the most important step in data interpretation. Because of the volume effect of the TEM, the inversion results are usually multi-solvable. To reduce the multi-solvability of inversion, the constrained inversion of TEM has been studied using the least squares method. The inversion trials were performed using two three-layer theoretical geological models and one four-layer theoretical geological model. The results show that one-dimensional least squares constrained inversion is faster and more effective than unconstrained inversion. The induced electromotive force attenuation curves of the inversion model indicate that the same attenuation curve may be used for different geological conditions. Therefore, constrained inversion using known geological information can more accurately reflect the underground geological information.

scholarly journals Coupled hydrogeophysical parameter estimation using a sequential Bayesian approach

2010 ◽  
Vol 14 (3) ◽  
pp. 545-556 ◽  
Author(s):  
J. Rings ◽  
J. A. Huisman ◽  
H. Vereecken
Keyword(s):  
Water Content ◽  
Particle Filtering ◽  
Field Data ◽  
Synthetic Data ◽  
Time Domain Reflectometry ◽  
Second Step ◽  
Parameter Estimates ◽  
Posterior Distributions ◽  
Data Set ◽  
Resistivity Tomography

Abstract. Coupled hydrogeophysical methods infer hydrological and petrophysical parameters directly from geophysical measurements. Widespread methods do not explicitly recognize uncertainty in parameter estimates. Therefore, we apply a sequential Bayesian framework that provides updates of state, parameters and their uncertainty whenever measurements become available. We have coupled a hydrological and an electrical resistivity tomography (ERT) forward code in a particle filtering framework. First, we analyze a synthetic data set of lysimeter infiltration monitored with ERT. In a second step, we apply the approach to field data measured during an infiltration event on a full-scale dike model. For the synthetic data, the water content distribution and the hydraulic conductivity are accurately estimated after a few time steps. For the field data, hydraulic parameters are successfully estimated from water content measurements made with spatial time domain reflectometry and ERT, and the development of their posterior distributions is shown.

Using an induction coil sensor to indirectly measure the B-field response in the bandwidth of the transient electromagnetic method

Geophysics ◽  
2000 ◽  
Vol 65 (5) ◽  
pp. 1489-1494 ◽  
Author(s):  
Richard S. Smith ◽  
A. Peter Annan
Keyword(s):  
Magnetic Field ◽  
Field Data ◽  
Indirect Measurement ◽  
Rate Of Change ◽  
Induction Coil ◽  
Voltage Response ◽  
Magnetic Field Data ◽  
Transient Electromagnetic ◽  
Different Characteristics ◽  
The Magnetic Field

The traditional sensor used in transient electromagnetic (EM) systems is an induction coil. This sensor measures a voltage response proportional to the time rate of change of the magnetic field in the EM bandwidth. By simply integrating the digitized output voltage from the induction coil, it is possible to obtain an indirect measurement of the magnetic field in the same bandwidth. The simple integration methodology is validated by showing that there is good agreement between synthetic voltage data integrated to a magnetic field and synthetic magnetic‐field data calculated directly. Further experimental work compares induction‐coil magnetic‐field data collected along a profile with data measured using a SQUID magnetometer. These two electromagnetic profiles look similar, and a comparison of the decay curves at a critical point on the profile shows that the two types of measurements agree within the bounds of experimental error. Comparison of measured voltage and magnetic‐field data show that the two sets of profiles have quite different characteristics. The magnetic‐field data is better for identifying, discriminating, and interpreting good conductors, while suppressing the less conductive targets. An induction coil is therefore a suitable sensor for the indirect collection of EM magnetic‐field data.

A new method for absolute underground positioning based on transient electromagnetics

2019 ◽  
Vol 221 (1) ◽  
pp. 87-96
Author(s):  
S Malecki ◽  
R-U Börner ◽  
K Spitzer
Keyword(s):  
Field Data ◽  
Synthetic Data ◽  
Trust Region ◽  
Trust Region Algorithm ◽  
Location Data ◽  
The Earth ◽  
Time Regime ◽  
Em Method ◽  
Em Field ◽  
Confidence Ellipses

SUMMARY We present a procedure for localizing underground positions using a time-domain inductive electromagnetic (EM) method. The position to be localized is associated with an EM receiver placed inside the Earth. An EM field is generated by one or more transmitters located at known positions at the Earth’s surface. We then invert the EM field data for the receiver positions using a trust-region algorithm. For any given time regime and source–receiver geometry, the propagation of the electromagnetic fields is determined by the electrical conductivity distribution within the Earth. We show that it is sufficient to use a simple 1-D model to recover the receiver positions with reasonable accuracy. Generally, we demonstrate the robustness of the presented approach. Using confidence ellipses and confidence intervals we assess the accuracy of the recovered location data. The proposed method has been extensively tested against synthetic data obtained by numerical experiments. Furthermore, we have successfully carried out a location recovery using field data. The field data were recorded within a borehole in Alberta (Canada) at 101.4 m depth. The recovered location of the borehole receiver differs from the actual location by 0.70 m in the horizontal plane and by 0.82 m in depth.

De-noising of transient electromagnetic data based on the long short-term memory-autoencoder

2020 ◽  
Vol 224 (1) ◽  
pp. 669-681
Author(s):  
Sihong Wu ◽  
Qinghua Huang ◽  
Li Zhao
Keyword(s):  
Field Data ◽  
Short Term Memory ◽  
Noise Removal ◽  
Data Sets ◽  
Late Time ◽  
Short Term ◽  
Term Memory ◽  
Transient Electromagnetic ◽  
Long Short Term Memory

SUMMARY Late-time transient electromagnetic (TEM) data contain deep subsurface information and are important for resolving deeper electrical structures. However, due to their relatively small signal amplitudes, TEM responses later in time are often dominated by ambient noises. Therefore, noise removal is critical to the application of TEM data in imaging electrical structures at depth. De-noising techniques for TEM data have been developed rapidly in recent years. Although strong efforts have been made to improving the quality of the TEM responses, it is still a challenge to effectively extract the signals due to unpredictable and irregular noises. In this study, we develop a new type of neural network architecture by combining the long short-term memory (LSTM) network with the autoencoder structure to suppress noise in TEM signals. The resulting LSTM-autoencoders yield excellent performance on synthetic data sets including horizontal components of the electric field and vertical component of the magnetic field generated by different sources such as dipole, loop and grounded line sources. The relative errors between the de-noised data sets and the corresponding noise-free transients are below 1% for most of the sampling points. Notable improvement in the resistivity structure inversion result is achieved using the TEM data de-noised by the LSTM-autoencoder in comparison with several widely-used neural networks, especially for later-arriving signals that are important for constraining deeper structures. We demonstrate the effectiveness and general applicability of the LSTM-autoencoder by de-noising experiments using synthetic 1-D and 3-D TEM signals as well as field data sets. The field data from a fixed loop survey using multiple receivers are greatly improved after de-noising by the LSTM-autoencoder, resulting in more consistent inversion models with significantly increased exploration depth. The LSTM-autoencoder is capable of enhancing the quality of the TEM signals at later times, which enables us to better resolve deeper electrical structures.

Signal leakage in f-x deconvolution algorithms

Geophysics ◽  
2017 ◽  
Vol 82 (5) ◽  
pp. W31-W45 ◽  
Author(s):  
Necati Gülünay
Keyword(s):  
Field Data ◽  
New Technologies ◽  
Filter Design ◽  
Synthetic Data ◽  
Data Sets ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Backward Design ◽  
Use Of Data ◽  
Domain Prediction

The old technology [Formula: see text]-[Formula: see text] deconvolution stands for [Formula: see text]-[Formula: see text] domain prediction filtering. Early versions of it are known to create signal leakage during their application. There have been recent papers in geophysical publications comparing [Formula: see text]-[Formula: see text] deconvolution results with the new technologies being proposed. These comparisons will be most effective if the best existing [Formula: see text]-[Formula: see text] deconvolution algorithms are used. This paper describes common [Formula: see text]-[Formula: see text] deconvolution algorithms and studies signal leakage occurring during their application on simple models, which will hopefully provide a benchmark for the readers in choosing [Formula: see text]-[Formula: see text] algorithms for comparison. The [Formula: see text]-[Formula: see text] deconvolution algorithms can be classified by their use of data which lead to transient or transient-free matrices and hence windowed or nonwindowed autocorrelations, respectively. They can also be classified by the direction they are predicting: forward design and apply; forward design and apply followed by backward design and apply; forward design and apply followed by application of a conjugated forward filter in the backward direction; and simultaneously forward and backward design and apply, which is known as noncausal filter design. All of the algorithm types mentioned above are tested, and the results of their analysis are provided in this paper on noise free and noisy synthetic data sets: a single dipping event, a single dipping event with a simple amplitude variation with offset, and three dipping events. Finally, the results of applying the selected algorithms on field data are provided.

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