Ben K. Sternberg

Correction for the static shift in magnetotellurics using transient electromagnetic soundings

Shallow inhomogeneities can lead to severe problems in the interpretation of magnetotelluric (MT) data by shifting the MT apparent resistivity sounding curve by a scale factor, which is independent of frequency on the standard log-apparent-resistivity versus log-frequency display. The amount of parallel shift, commonly referred to as the MT static shift, can not be determined directly from conventionally recorded MT data at a single site. One method for measuring the static shift is a controlled-source measurement of the magnetic field. Unlike the electric field, the magnetic field is relatively unaffected by surface inhomogeneities. The controlled-source sounding (which may be a relatively shallow sounding made with lightweight equipment) can be combined with a deep MT sounding to obtain a complete, undistorted model of the earth. Inversions of the static shift-corrected MT data provide a much closer match to well-log resistivities than do inversions of the uncorrected data.The particular controlled-source magnetic-field sounding which we used was a central-induction Transient ElectroMagnetic (TEM) sounding. Correction for the static shift in the MT data was made by jointly inverting the MT data and the TEM data. A parameter which allowed vertical shifts in the MT apparent resistivity curves was included in the computer inversion to account for static shifts. A simple graphical comparison between the MT apparent resistivities and the TEM apparent resistivities produced essentially the same estimate of the static shift (within 0.1 decade) as the joint computer inversion.Central-induction TEM measurements were made adjacent to over 100 MT sites in central Oregon. The complete data base of over 100 sites showed an average static shift between 0 and 0.2 decade. However, in the rougher topography and more complex structure of the Cascade Mountain Range, the majority of the sites had static shifts of the order of 0.3 to 0.4 decade. The static shifts in this area are probably due to a combination of topography and surficial inhomogeneities. The TEM apparent resistivity (which is used to estimate the unshifted MT apparent resistivity) does not necessarily agree with either the transverse electric (TE) or the transverse magnetic (TM) MT polarization. TEM apparent resistivity may occur between the two, or may agree with one of the two polarizations, or may lie outside the MT polarizations.

Location of subsurface targets in geophysical data using neural networks

Neural networks were used to estimate the offset, depth, and conductivity‐area product of a conductive target given an electromagnetic ellipticity image of the target. Five different neural network paradigms and five different representations of the ellipticity image were compared. The networks were trained with synthetic images of the target and tested on field data and more synthetic data. The extrapolation capabilities of the networks were also tested with synthetic data lying outside the spatial limits of the training set. The data representations consisted of the whole image, the subsampled image, the peak and adjacent troughs, the peak, and components from a two‐dimensional (2-D) fast Fourier transform. The paradigms tested were standard back propagation, directed random search, functional link, extended delta bar delta, and the hybrid combination of self‐organizing map and back propagation. For input patterns with less than 100 elements, the directed random search and functional link networks gave ...

Archaeology studies in southern Arizona using ground penetrating radar

Abstract Conventional ground penetrating radar (GPR) surveys typically have a maximum depth of penetration of 1 2 to 1 m in the basin-fill sediments of the southwestern United States. Although this depth of penetration is too limited for many engineering and environmental surveys, it is often suitable for archaeological investigations in this region. We have found a center frequency of 500 MHz to be optimum. Radar signals having a center frequency of about 80 MHz produce records with much lower resolution and only slightly greater maximum depth of penetration. Successful GPR surveys have imaged buried plaster and adobe walls, roasting pits, canals, trash pits, plastered floors, and artifacts such as pot sherds and knives. We have found that GPR is a valuable tool for archaeological studies in this area. GPR can provide some of the detailed survey information that has been provided in the past by extensive excavation, but without the high cost of excavation, without the dangers of vandalism when artifacts are exposed, and without disturbing sensitive areas.

Neural network pattern recognition of subsurface EM images

Abstract Neural networks are computer simulations of the brains neural functions; as such they perform well on the same types of problems on which humans perform well, namely pattern recognition. Neural networks have shown the capability to learn human speech, read handwritten signatures and recpgnize human faces. Applied to geophysical data, neural networks offer the ability to estimate model parameters in near realtime. A backpropagation neural network was trained to estimate the spatial location (offset and depth) of a target given an image of the electromegnetic ellipticity. Three components of the magnetic field were measured from which the ellipticity was calculated. Theoretical ellipticity images were used for training the neural network; field data were used to test it. The input data representation was important in obtaining results with 10% error or less from the neural network; generally, smaller input vectors yielded more accurate results. Five different representations were examined: the whole image, the subsampled image, trough-peak-trough, peak amplitude and frequency domain. The frequency-domain representation estimated the target locations in the field data with the least error, 0.4% for the offset and 1.5% for the depth. The network was examined for its ability to generalize, to extrapolate beyond the spatial limits of the training set and to ignore discrepancies between synthetic and field data. The generalization from synthetic training data to synthetic test data had errors near 5% for most offset estimates and near 2% for most depth estimates. We considered extrapolation errors satisfactory (10%) up to 1.5 model spacings beyond the limits of the training set.

Electrical parameters of soils in the frequency range from 1 kHz to 1 GHz, using lumped-circuit methods

For studying electrical properties of soil samples with various compositions and water contents, we applied a lumped-circuit approach. The extension of this method up to 1GHz was made possible by using a coaxial sample holder. The complex electrical parameters of soils, such as the relative permittivity , conductivity , and resistivity , were obtained by measuring the magnitude Z and phase ϕ of the sample impedance . The experimental setup is described in our previous paper [Levitskaya and Sternberg, 2000]. The relative real permittivity e′ and imaginary permittivity (dielectric losses) e″ for high-loss soils from Arizona decrease with frequency and increase with water content. Regression equations, derived for the relative permittivity e′ versus water content at a given frequency, can be used to determine the water content in soil from e′ data. The third-degree polynomial equations, which relate the relative permittivity to the volumetric soil moisture content, are different for various frequencies. The complex electrical resistivity components ρ′ and ρ″ reveal a time-dependent polarization process at frequencies above 1 MHz, which shifts to higher frequencies with increasing water content. The propagation parameters, such as attenuation constant α, phase velocity Vp, and penetration depth P, which we calculated from the electrical parameters, also depend on soil wetness. Our comparison of the electrical and propagation parameters for different soils shows that the high-loss soil samples from Avra Valley, Arizona, have higher values e′ and e″, higher attenuation constant α, and lower penetration depth P than the low-loss soils from Brookhaven, New York. For example, at 500 MHz, a high-loss soil (Avra Valley) with volumetric moisture content of ∼10%, exhibits an attenuation of 43 dB/m, whereas for a low-loss soil (Brookhaven) with the same wetness the attenuation constant is only 4 dB/m. We also note that very dry, clean sand in a sheltered “sand box,” which is a favorite medium for testing ground-penetrating radar (GPR), is usually not representative of natural conditions. Therefore, GPR data from such “sand box” experiments must be used with considerable caution because they yield unrealistically large penetration depths and unnatural target responses.

A review of some experience wih the induced-polarization/resistivity method for hydrocarbon surveys; successes and limitations

Our experience with the induced polarization (IP) and resistivity method for hydrocarbon exploration has shown both successful surveys and limitations of the method. Four examples demonstrate a close correlation between shallow IP and resistivity anomalies and deeper hydrocarbon production. In each of these examples, anomalies occurred over the producing fields which have significantly greater amplitudes than the variations in the surrounding background response. Another important result of our research is the development of a geological/geochemical model for the formation of IP and resistivity anomalies over hydrocarbon reservoirs. The two main requirements for formation of IP and resistivity anomalies, according to this model, are: (1) absence of any thick impermeable seals, such as evaporites, above the reservoir and (2) presence of porous, iron‐rich, near‐surface host rocks, such as clastic rock sequences. The IP and resistivity method can be more successfully applied by selecting those areas for surv...

Polarization processes in rocks: 1. Complex Dielectric Permittivity method

This is the first part of a review of research performed in the former USSR. Experimental data were used from several regions of the USSR, including Russia, Ukraine, and Georgia. Many of the publications are available in U.S. libraries. Some of them are translated into English. This part contains results from applying the Complex Dielectric Permittivity method (Dielectric Spectroscopy) for studying the electrical response of rocks in alternating fields with frequencies from 100 Hz to 100 MHz. Data on dielectric properties of sedimentary rocks of different lithology and with various porosities, salinities of saturating solution, and hydrocarbon content are reviewed here. Measurement methods, including means for avoiding or reducing the electrode polarization, are also considered. It is shown that wet rocks exhibit a Maxwell-Wagner polarization process at frequencies 105–107 Hz, caused by charge accumulation on the pore boundaries.

Laboratory measurement of material electrical properties: Extending the application of lumped‐circuit equivalent models to 1 GHz

For measurements of material electrical properties in a frequency range from 1 kHz to 1 GHz, we used a laboratory method based on the concept of lumped R, L, and C circuit elements. While this method has typically been used at frequencies of less than 100 MHz, we have extended its application up to 1 GHz. The complex electrical parameters of a material, such as resistivity, conductivity, and dielectric permittivity were obtained by measuring magnitude Z and phase ϕ of the sample impedance Z. We relate the material electrical parameters to either series or parallel lumped-circuit equivalent models. Depending on the frequency range, two different designs of the sample holder can be used: (1) a parallel-plate capacitor with disk electrodes, for low frequencies (from 1 kHz to 100 MHz), and (2) a coaxial capacitor, for a broad band up to higher frequencies (from 1 kHz to 1 GHz). Measured values of the sample impedance usually include errors due to effects from the sample holder and its connections to the instrument. These effects, caused by the inductance, resistance, and stray capacitance of the measuring system, are taken into account. Our measurements of several standard materials, including air, Teflon, octanol, butanol, and methanol, showed that the relative standard deviation from the mean for the dielectric permittivity (in the range where it is frequency independent) is typically less than 1%. The difference between our mean values and previously published values for these standard materials is also less than 1%.

Recommendations for IP Research

The John S. Sumner Memorial International Workshop on Induced Polarization in Mining and the Environment was held 17–19 October 1994 in Tucson, Arizona. The event, dedicated to the memory of an IP pioneer, attracted 175 people from 18 countries. An objective was to get recommendations for geologic, geochemical, and geophysical research from IP users and practitioners for IP applications to mining and environmental problems. Conventional IP, as well as IP effects in EM and GPR, were considered. These measurements include complex conductivity, complex dielectric permittivity, and complex magnetic permeability in the frequency range 10−3–109 Hz in multidimensional earths.

The variability of naturally occurring magnetic field levels: 10 Hz to 8 kHz

The variability of naturally occurring magnetic fields in the frequency range from 10 Hz to 8 kHz over a period of one year was studied. Contour plots for the Hx , Hy , and Hz components and for frequencies of 10, 100, 1000, 2000, and 8000 Hz were produced. Average, minimum, maximum, and the standard deviations of these fields were also calculated for 12 distinctive time intervals. In the 1– to 8–kHz frequency range, the noise levels are typically higher at night. In the 10- to 100-Hz frequency range, the noise levels are typically higher during the day. During mid- to late-summer, there is frequent thunderstorm activity, known in the southwest United States as the monsoon season. The magnetic field levels are often very high during this time period. These variability ranges can be used to estimate the lowest levels of noise that may be encountered during field surveys, which iswhat the authors are looking for when running controlled-source electrical method surveys. These variability ranges can also be u...