In‐situ complex resistivity measurements over the frequency range [Formula: see text] to [Formula: see text] have been made on 26 North American massive sulfide, graphite, magnetite, pyrrhotite, and porphyry copper deposits. The results reveal significant differences between the spectral responses of massive sulfides and graphite and present encouragement for their differentiation in the field. There are also differences between the spectra of magnetite and nickeliferrous pyrrhotite mineralization, which may prove useful in attempting to distinguish between these two common IP sources in nickel sulfide exploration. Lastly, there are differences in the spectra typically arising from the economic mineralization and the barren pyrite halo in porphyry copper systems. It appears that all these differences arise mainly from mineral texture, since laboratory studies of different specific mineral‐electrolyte interfaces show relatively small variations. All of the in‐situ spectra may be described by one or two simple Cole‐Cole relaxation models. Since the frequency dependence of these models is typically only about 0.25, and the frequency dependence of inductive electromagnetic coupling is near 1.0, it is possible to recognize and to remove automatically the effects of inductive coupling from IP spectra. The spectral response of small deposits or of deeply buried deposits varies from that of the homogeneous earth response, but these variations may be readily determined from the same “dilution factor” [Formula: see text] currently used to calculate apparent IP effects.
This paper discusses a new method for the investigation of self potentials (SP) based on induced current sources. The induced current sources are due to divergences of the convection current which is driven, in turn, by a primary flow, either heat or fluid. As a result of using this approach there is a shift in emphasis toward the vector flow field and its interaction with current cross‐coupling structure when compared with the total potential approach of Nourbehecht (1963) which emphasized the primary flow potential and the voltage cross‐coupling. This shift in emphasis is advantageous because it is analogous to the actual physical processes. For example, fluid flow in the ground gives rise to drag (convection) currents, and the interaction of the convection currents with the electrical structure gives rise to the electrical potentials (SP). This simple physical picture should aid in developing a better intuitive understanding of the generation of SP effects. The convective current approach is easily adapted to numerical modeling techniques, as illustrated by its implementation using a two‐dimensional (2-D) transmission surface algorithm. When the primary flow is driven by the gradient of a potential, joint modeling of the primary flow and the resultant SP is possible with this algorithm. Examples of the SP effects generated by point sources of the primary flow in the presence of simple geometrical structures show the diversity of the possible responses. The various responses can be understood in terms of the distributions of the induced current sources caused by the primary flow. The results from field studies at Red Hill Hot Springs, Utah, are used in an example of the joint modeling of thermal and SP data.
The finite‐element method can be used to solve the differential equations which describe electrical and electromagnetic (EM) field behavior. The equations are, respectively, Poisson's equation and the vector, damped wave equation. The finite‐element equations are derived, in both cases, using the minimum theorem. While both tetrahedral and hexahedral elements may be used for the modeling of the resistivity problem, only hexahedral elements give satisfactory results for the EM problem. A disadvantage of the relatively simple mesh design used in the approach described here is the presence of long thin elements. Such elements have very poor interpolating properties, and they adversely affect the rate of convergence of the overrelaxation technique used in solving the resulting system of linear equations. For the modeling of resistivity data over an earth with one plane of symmetry, the system of equations typically has about 9000 unknowns. About 50,000 unknowns are needed to give a satisfactory solution to an EM problem where the earth has one plane of symmetry. The advantage of solving these problems with a technique such as the finite‐element method is that earths with an almost arbitrary distribution of conductivity can be modeled. On the other hand, an integral‐equation method can be far more cost effective for small inhomogeneities. The results from the resistivity algorithm show the adverse effect of an irregular, conducting, and polarizable overburden on dipole‐dipole, induced polarization surveys. Modeling of a horizontal loop EM survey illustrates the importance of assessing the host rock conductivity before attempting to interpret inhomogeneity responses.
Rocks which contain clay minerals often display electrical properties which cannot be predicted by bulk electrical properties of the constituents (Cohen, 1981). Interactions between clay minerals and groundwater can produce polarization phenomena, decreases in resistivity, and supposed nonlinear behavior.
Data from the lunar‐orbiting Apollo 17 radar sounding experiment (60‐m wavelength) have been examined in both digital and holographic formats. Surface backscatter (clutter) which masks possible radar returns originating from subsurface changes in lunar electrical properties was reduced by simultaneously comparing radar data from two orbits. Radar returns that correlate from orbit to orbit form two distinct alignments in Mare Serenitatis and one in Mare Crisium. It is proposed that these alignments represent subsurface reflecting horizons. The hypothesis is tested by showing that (1) most of the radar returns fall outside the ambiguity region of the correlation technique, (2) the results are consistent between optically and digitally processed data, (3) the signal levels of the proposed subsurface features are well above the noise floor, (4) the inferred loss tangents appear to be consistent with returned sample measurements, and (5) the discontinuous nature of the reflections most likely arises from interference effects. It is concluded that there are two subsurface radar reflectors with mean apparent depths of 0.9 km and 1.6 km below the surface in Mare Serenitatis and one reflector at a mean depth of 1.4 km below the surface in Mare Crisium. These reflectors represent basin‐wide subsurface interfaces.
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