**SATELLITE MAGNETIC ANOMALIES FOR LITHOSPHERIC EXPLORATION**

*Ralph R.B. von Frese and Hyung Rae Kim*

*Dept. of Geological Sciences, The Ohio State University*

*Columbus, OH 43210 USA*

**[1. Introduction]{.underline}**

Significant new and unique constraints for understanding regional
petrologic variations of the crust and upper mantle, and crustal
thickness and thermal perturbations as generalized in **Figure 1** are
available from magnetic anomalies mapped in low-Earth orbit by NASA's
POGO and Magsat, US-Argentina-Chile's SAC-C, Denmark's Ørsted, and
Germany's CHAMP satellite missions. Satellite magnetic anomalies are
commonly presented and analyzed as predictions from global spherical
harmonic models. However, these predictions commonly do not resolve
local anomaly attributes to the detail that is available in the local
data coverage. Further local corruptions of the predictions result from
coefficient errors due to using global data with gaps and uneven
coverage, and variable data measurement and processing errors.

![](media/image1.png){width="3.3in" height="1.2729166666666667in"}

Figure 1. Lithospheric sources of satellite magnetic anomalies.

For example, spherical harmonic magnetic models are typically based on
orbits that have been screened for external field activity in terms of
planetary indices (e.g., K~p~ or A~e~) derived at ground-based
observatories. However, over a local region the indices commonly show
little or no correlation to local disturbances as revealed by the data
variances in the orbital segments covering the region (e.g., *Alsdorf et
al.,* 1994; *Kim,* 2002). Figure 2 shows an example of this poor
correlation for a selected Antarctic set of Ørsted data orbits.

The horizontal line in each insert is for K~p~ = 2+ that is typically
the maximum value allowed in selecting orbits for global anomaly maps
(e.g., *Langel & Hinze,* 1999). However, as shown in **Figure 2.A**, the
use of this planetary index criterion would reject roughly 335 orbits
(35%) from the 952 ascending orbits across the Antarctic that are
acceptable based on local data variance estimates. Similarly, **Figure
2.B** shows that roughly 307 (32%) of the 953 acceptable descending
orbits would be rejected. Hence, of the 1905 Antarctic orbits deemed
acceptable by local variance estimates, 642 or 34% of them would have
been wasted if the orbits were selected only for K~p~ values equal to or
less than 2+. Furthermore, as shown in **Figures 2.C** and **2.D**, this
criterion would have passed 26% and 28% of the rejected ascending and
descending passes, respectively. Antarctic uses of anomaly
representations based on K~p~ are clearly limited.

![](media/image2.jpeg){width="3.423611111111111in"
height="2.7472222222222222in"}

Figure 2. Antarctic Ørsted orbital data variances vs. K*~p~* for
accepted A) ascending and B) descending passes and rejected C) ascending
and D) descending orbits. Values are plotted as follows: 0 = (K*~p~* =
0), 3 = (K*~p~* = 0+), 7 = (K*~p~* = 1-), 10 = (K*~p~* = 1), 13 =
(K*~p~* = 1+), 17 = (K*~p~* = 2-), 20 = (K*~p~* = 2), 23 = (K*~p~* =
2+), etc.

> **[2. Comprehensive Extraction of Lithospheric
> Anomalies]{.underline}**

To extract lithospheric components from satellite magnetometer
observations with enhanced sensitivity for local anomaly details, we
recommend the procedures outlined by the flow chart in **Figure 3**
(*Alsdorf et al.,* 1994; *Alsdorf & von Frese,* 1994; *von Frese et
al.,* 1999*; Kim,* 2002). These procedures characterize the separation
of the lithospheric, external, and core field components as a
statistical problem that exploits the coherent or static properties of
lithospheric and core field components (*Alsdorf et al.,* 1994). The
approach uses the spectral correlation theory in **Figure 4** (*von
Frese et al.,* 1997) to differentiate these components from the
spatially and temporally dynamic effects of the external fields.

For this approach, we divide the orbital observations into subsets of
ascending and descending orbit passes over the study region for separate
processing. Common lithospheric anomaly components will be registered on
neighboring passes separated by distances that are small compared to
their altitude, while data components that are not correlated between
these passes must involve non-lithospheric effects (*Alsdorf et al.,*
1994). Hence, the correlative and non-correlative data components can be
extracted using the correlation spectrum (**Figure 4**) given by CC(k) =
cos(Δθ~k~), where CC(k) is the correlation coefficient between the k-th
wavenumber components of the two signals and Δθ~k~ is the phase
difference in these components. Here the fast Fourier transform can be
used with absolutely no loss of generality because the correlation
spectrum depends only on the phase differences between co-registered and
orthogonally gridded data.

As an example, **Figure 5** gives a spectral correlation filtering
application for two Magsat ascending (i.e., dusk) orbits separated by an
average distance of about 7 km at roughly 330 km altitude across the
Arctic from northeastern Greenland to southwestern Finland. **Figure
5.A** shows the raw data after removal of a core field model using the
adjustment procedures of *Alsdorf et al.* (1994). The relatively low
correlation coefficient (= 0.479) between the data passes reflects
non-correlative features from auroral external field effects,
measurement errors, and other non-lithospheric sources.

Using the correlation spectrum for the signals in **5.A**, we inversely
transformed all wavenumber components with CC(k) ≥ 0.5 for the estimates
in **Figure 5.B** of their lithospheric components. The enhanced
correlation (= 0.934) obtained by these results can be broadly
interpreted for improvements in the signal-to-noise ratio (e.g., *Foster
& Guinzy,* 1967; *Kim,* 1995) from (N/S) ≈ √\[‌CC‌ ^-1^ -- 1\].
Accordingly, relative to the raw residuals in **Figure 5.A**, the
correlation filtered signals reflect roughly a 75% reduction in the
non-lithospheric noise effects.

![](media/image3.jpeg){width="3.1805555555555554in" height="4.40625in"}

Figure 3. Data reduction scheme for extracting lithospheric anomalies
from satellite magnetometer data.

**Figure 5.C** shows the components of the raw residuals that were
rejected by the correlation filters. Although partly correlated, they
reveal little apparent sensitivity for the regional magnetic effects of
the crustal geology (e.g., ocean-continent margins, ocean ridges,
basins, etc.). They appear to be more readily ascribable to
non-lithospheric effects (e.g., external field effects, core field
reduction errors, etc.) and hence were discarded. Accordingly, **Figure
5.D** gives the least squares estimate of lithospheric anomalies from
the point-by-point averages of the coherent signals in **Figure 5.B**.
Here, we also give the differences between the coherent signals as
root-mean-square-errors (RMSE) to constrain interpretations of the
lithospheric anomaly estimates.

From the correlation filtered ascending and descending passes, we
construct maps at common altitude and spherical coordinates by least
squares collocation (*Goyal et al.,* 1990). To further reduce
non-lithospheric noise, these co-registered anomaly maps are also
correlation filtered for their common components. The correlation
filtered ascending and descending anomaly maps are then combined by
spectrum reconstruction (*Kim et al.,* 1998) to minimize track-line or
corrugation noise due to along-track processing effects.

![](media/image4.png){width="3.3in" height="1.7875in"}

Figure 4. Exponential representation and polar plot of wavevectors at a
common wavenumber k for transforms $\overline{X}$and $\overline{Y}$of
co-registered signals X and Y, respectively. The complex plane of
projection is defined by the imaginary or sine transform (ST) axis and
the real or cosine transform (CT) axis. The correlation coefficient
between the wavevectors given by the cosine of their phase difference
\[cos(∆θ*~k~*)\] is equal to normalized dot product and admittance
between the wavevectors $\overline{X}(k)$and $\overline{Y}(k)$.

**Figure 6** gives an example of the spectrum reconstruction of
ascending (**6.A**) and descending (**6.C**) Magsat anomalies at 400 km
altitude over a spherical grid patch of Southeast Asia. **Figures 6.B**
and **6.D** of the corresponding DC-centered power spectra clearly
reveal track-directional noise effects in spectral quadrants B2 and B4
and D1 and D3 of the ascending and descending anomaly maps,
respectively. Combining the two cleaner quadrants from each spectrum as
shown in **Figure 6.F** yields the Magsat anomaly estimates in **Figure
6.E** that are largely devoid of track-line noise. Subtracting **Figure
6.E** from **Figures 6.A** and **6.B** reveals the track-line noise
effects shown in **Figures 6.G** and **6.H**, respectively, that were
suppressed by this spectrum reconstruction.

Spherical harmonic satellite magnetic anomaly models also do a poor job
of representing the lithospheric components at degree 13 and lower. The
geomagnetic field spectrum shows a transition zone roughly between
degrees 11 and 15 where the slopes for the core field and regional
lithospheric effects are difficult to differentiate (e.g., *Langel &
Estes,* 1982). Hence, regional lithospheric effects can corrupt core
field estimates at degree 13 and lower so that the core field reduction
also removes significant lithospheric signal (*Meyer et al.,* 1985; *von
Frese et al.,* 1999).

![](media/image5.png){width="3.540277777777778in"
height="5.198611111111111in"}

Figure 5. Wavenumber correlation filtering (WCF) of two nearest-neighbor
orbits from the Magsat mission. Additional data attributes listed
include the amplitude range (AR) of (MIN, MAX)-values, amplitude mean
(AM), amplitude standard deviation (ASD), the correlation coefficient
(CC), and the root-mean-square error (RMSE).

The flow chart in **Figure 3** illustrates our approach for separating
degree 13 and lower core field and lithospheric anomaly components.
Basically, we obtain two sets of satellite anomalies with one set
reduced for the core field estimates through degree 13 while the other
set is reduced relative to the degree 11 components that appear to be
largely uncontaminated by lithospheric effects in the geomagnetic field
spectrum (*von Frese et al.,* 1999; 2003). Subtracting the degree 13 and
higher anomaly field from the degree 11 results leaves residual degree
11 through 13 anomalies dominated by core field and regional
lithospheric components that include the magnetic effects of crustal
thickness variations.

![](media/image6.jpeg){width="4.259722222222222in"
height="7.658333333333333in"}

Figure 6. Spectrum reconstruction for minimizing track-line noise in
Magsat magnetic anomalies of Southeast Asia at 400 km altitude.

To estimate the crustal thickness effects in the residual degree 11-13
anomalies, we use crustal

thickness models obtained from spectral correlation analyses at
satellite altitude of free-air gravity anomalies and the gravity effects
of the rock, water, and ice components of the terrain (*von Frese et
al.,* 1999a). The pseudo magnetic anomalies of the crustal thickness
model can be obtained via Poisson's relation for correlative magnetic
and gravity potentials (**Figure 7.A**). Spectral correlation analysis
of these pseudo magnetic effects and the residual degree 11-13 anomalies
reveals the phase coherent residual anomalies that can be interpreted
for degree 13 and lower crustal thickness magnetic anomaly components
(*von Frese et al.,* 1999; 2003).

In general, considerable lithospheric signal can be missing in satellite
anomaly maps where the data are reduced for core field estimates through
only degree 13. Adding the degree 13 anomalies to the degree 11-13
crustal thickness anomalies as suggested near the bottom of the flow
chart in **Figure 3** yields a much more comprehensive set of anomalies
for studying the regional magnetic properties of the lithosphere.
Furthermore, the longer wavelength components of the non-lithospheric
degree 11-13 residuals may provide enhanced core field estimates for
improving models of the core field (*von Frese et al.,* 1999).

> **[3. Lithospheric Anomaly Transformations for Enhanced
> Interpretation]{.underline}**

Lithospheric anomaly interpretation is complicated by the complex
spatial relationships between lithospheric sources and their magnetic
effects at satellite altitudes caused by the inclination, declination,
and intensity variations of the polarizing core field. However, reducing
the satellite magnetic anomalies differentially to the radial pole can
greatly simplify these distortions (*von Frese et al.,* 1981; *von
Frese,* 1998). As shown in **Figure 7.A**, reduced-to-pole magnetic
anomalies have induced anomaly components that are centered over their
lithospheric magnetic sources in the same way that first vertical
derivative gravity anomalies are centered over their sources of
lithospheric mass variations.

Hence at the bottom of the flow chart in **Figure 3**, we suggest that
satellite magnetic anomalies be differentially reduced-to-pole (DRTP) to
facilitate lithospheric interpretation. As shown in **Figure 7.B**,
equivalent point source (EPS) inversion can transform spherical
coordinate magnetic anomalies into effective DRTP-anomaly estimates.
Here, the magnetic anomalies are related by inversion to the volume
magnetic susceptibilities of a distribution of point dipoles such that
the integrated anomaly effects of dipoles match the anomalies with least
squares accuracy (*von Frese et al.,* 1981; 1988). The DRTP anomalies
are obtained by re-computing the magnetic effect of the point dipole
array using a normalizing (i.e., constant) polarization intensity and
90^o^ inclination and 0^o^ declination at all source and observation
points. The EPS inversion procedures in **Figure 7.B** can also be used
to evaluate anomaly continuations, derivatives, correlations, pseudo
effects, and other components to facilitate lithospheric analysis (*von
Frese et al.,* 1981).

![](media/image7.png){width="3.295138888888889in"
height="2.1416666666666666in"}

**Figure 7**. Critical parameters of analyzing spherical coordinate
geopotential anomalies for A) their correlations via Poisson's relation
and by B) equivalent point source (EPS) inversion. Anomaly signals in A)
are for a point source (large dot) subjected to the Earth's magnetic
(*F~e~*) and gravity (*g~e~*) fields

**Figure 8** gives a simple illustration of the lithospheric utility of
DRTP magnetic anomalies. **Figure 8.A** shows the polarization
intensities (shaded), inclinations (thick contours), and declinations
(dot-dash contours) from the core field model GSFC 12/83 (*Langel &
Estes,* 1985) updated to 1980 at 50 km below sea level, while **Figure
8.B** gives the inclinations and declinations at 400 km above sea level.
At the center of each of the 4 red dots in **Figure 8.B** is a point
source at 50 km below sea level with susceptibility = 10^4^ cgs and
density = 10^10^ gm/cm^3^.

![](media/image8.png){width="6.605555555555555in" height="7.9125in"}

Figure 8. Comparison of the magnetic and gravity point source anomalies
over the Americas in transverse Mecator projection between
(130^o^-30^o^)W.

**Figure 8.C** shows the related scalar total magnetic field anomalies
of the 4 dipoles at 400 km altitude. For each anomaly, maximum and
minimum values are listed in nT and the negative values are contoured in
red. The complicated anomaly signatures predominantly reflect the
variable induction effects of the core field over the source and
observation points making it difficult to appreciate the commonality of
the geometric and magnetic properties among the 4 dipoles. However, the
commonality of properties for the regional distribution of lithospheric
point sources is clearly evident in **Figure 8.D** where the scalar
anomalies have been differentially reduced to the pole by EPS inversion
using a normalization intensity of 60,000 nT.

Lithospheric applications of DRTP magnetic anomalies are considerably
complicated when source magnetizations contain significant remanent
components because DRTP anomalies are generally produced assuming only
induction. However, further insights on the magnetization properties of
the lithosphere can be developed when DRTP anomalies are combined with
complementary geophysical observations like the gravity anomalies being
mapped by the CHAMP and GRACE missions. In particular, when the magnetic
anomaly source also involves a corresponding density contrast, it is
possible to estimate the inductive pseudo magnetic anomaly from the
correlative gravity anomaly via Poisson's relation (**Figure 7.A**).
Comparing the observed and pseudo magnetic anomalies can reveal the
presence of remanent components in the lithospheric magnetizations.

**[4. Lithospheric Anomaly Correlations]{.underline}**

The geologic interpretation of satellite magnetic and gravity anomalies
is hindered by the effects of anomaly superposition and the source
ambiguity that is inherent to the analysis of potential fields. A common
approach to minimizing these interpretational limitations is to use
Poisson's relation (**Figure 7**.**A**) to evaluate the correlation
between anomalous gravity and magnetic potentials (*von Frese et al.,*
1982; 1997a).

For example, **Figure 8.E** shows the satellite altitude gravity
anomalies for the 4 dipoles that obviously cannot be directly correlated
with the scalar magnetic anomalies in **Figure 8.C**. However, we can
correlate them with the DRTP anomalies in **Figure 8.D** by computing
their first vertical derivatives by EPS inversion (**Figure 7.B**) and
multiplying by the magnetization-to-(density × G) ratio of the dipoles
according to Poisson's relation (**Figure 7.A**). **Figure 8.F** gives
the resulting EPS-estimated pseudo DRTP anomalies that differ by less
than 1% from the corresponding DRTP anomalies in **Figure 8.D**.

The small difference between the results in **Figures 8.D** and **8.F**
clearly provides strong quantitative support for ascribing common
geometric and magnetic properties to the lithospheric sources of the
satellite altitude magnetic anomalies of **Figure 8.C**. However, had a
dipole been remanently magnetized, a prominent difference between the
observed and pseudo magnetic anomalies would occur that could be readily
analyzed for the intensity and attitude attributes of the magnetic
remanence.

Poisson's relation (**Figure 7.A**) can be readily extended to higher
order derivatives to bring out more details in the correlations between
magnetic and gravity anomalies (*von Frese et al.,* 1981). **Figure 9**
from *von Frese et al.* (1982) illustrates this approach and the
potential synergism of complementary satellite magnetic and gravity
anomalies. Here Bouguer anomalies were evaluated at the Earth's surface
from a 180^o^ × 180^o^ spherical harmonic free-air gravity model due to
*Rapp* (1981) with the second vertical derivatives (SVD) taken at 450 km
altitude by EPS inversion (**Figure 7.B**). The complementary POGO first
vertical derivative (FVD) differentially reduced-to-pole (DRTP) magnetic
anomalies were also evaluated at 450 km altitude by EPS inversion.

The anomaly correlations in **Figure 9** reflect predominantly inverse
associations between the lithospheric magnetization and density
variations of the US (*von Frese et al.,* 1982). For example, the
mid-continent is mostly characterized by three large magnetic maxima
with correlative gravity minima that may reflect a regionally deepened
Moho and thicker crust. The two magnetic maxima on the east are breached
by a magnetic minimum correlated with an elongate N-S gravity maximum.
Spherical Earth modeling has related these correlative anomalies to the
Late Precambrian aulagogen underlying the Mississippi River Embayment
(*von Frese et al.,* 1981a).

To the west, a magnetic minimum separates the third mid-continent
maximum from another large maximum further west overlying the Colorado
Plateau. The correlation of this minimum with an elongate gravity
maximum may reflect thinned crust due to increased heat flow in the
Cenozoic Rio Grande Rift (*von Frese et al.,* 1982). This inverse
correlation of satellite altitude anomalies can be traced northwestwards
to the Yellowstone Hotspot.

![](media/image9.png){width="3.3055555555555554in"
height="1.9861111111111112in"}

Figure 9. Satellite altitude geopotential anomaly field correlations of
North America. Red contours give the second vertical derivative (SVD)
gravity anomalies in 10^-5^ mGals/km^2^ while the black contours give
the first vertical derivative (FVD) DRTP magnetic anomalies in 10^-3^
nT/km.

Satellite altitude anomaly correlations are subject in practice to
numerous complicating geological variations including sources with
appreciable remanence not aligned with the present Earth\'s field,
inhomogeneous magnetization and density properties, or significant
differences in their depths of origin (*von Frese et al.,* 1982; 1997a).
However, the correlations also can reveal regional patterns of intrusive
activity for new insights on tectonic evolution and plate dynamics, as
well as crustal thickness and thermal perturbations that concentrate
crustal stresses producing earthquakes, volcanism, and other neotectonic
effects.

The CHAMP mission provides the first real opportunity to study the
Earth's regional magnetic and gravity anomaly correlations. CHAMP is
capturing the highest resolution global combination of the geopotential
fields ever mapped for the Earth. Furthermore, these geopotential
observations are co-registered in altitude so that the great ambiguities
involved in continuing anomalies between the Earth\'s surface and
satellite altitude are avoided. Hence, CHAMP provides a truly
exceptional opportunity for investigating the lithospheric utility of
regional gravity and magnetic anomaly correlations. However, these
studies will be significantly augmented by improved gravity anomalies
from the GRACE and pending GOCE missions, while critical new boundary
conditions for modeling lithospheric anomaly correlations result from
the higher altitude Ørsted and SAC-C magnetic mapping missions.

**[5. Modeling Lithospheric Anomalies]{.underline}**

The capacity for modeling arbitrary distributions of magnetization and
density in spherical coordinates is essential for fully exploiting
satellite geopotential anomaly observations for lithospheric
information. An effective procedure for the spherical coordinate
modeling of magnetic and gravity anomalies combines the equivalent point
source formulae in **Figure 7.B** with Gauss-Legendre quadrature (GLQ)
integration (*von Frese et al.,* 1981a).

As outlined in the left panel of **Figure 10**, the procedure involves
distributing point sources within the anomalous body, as represented by
the GLQ decomposition of the body\'s surface envelope (*Stroud &
Secrest,* 1966), and computing the integrated effects of the point
sources for comparison with observed anomalies. The unit point source
gravity (Δg) and magnetic (ΔT) anomalies are given at the top of the
equation tree in the left panel of **Figure 10**. These unit point
anomalies may be generalized in terms of the product of a geometric
source function q(**R**) and the effective physical property contrast
(Δx), where **R** is the distance or displacement vector between the
source point coordinates (primed) and observation point coordinates
(unprimed), and Δx is the density (Δm) and susceptibility (Δj) contrast,
respectively, for gravity and magnetic anomaly calculations. The
generalized theoretical anomaly of triple integrals can be evaluated
numerically with least-squares accuracy by the nested triple series
given at the bottom of the equation tree in the left panel of **Figure
10**, where (A~i~, A~j~, A~k~) are the Gauss-Legendre quadrature weights
(*Stroud & Secrest,* 1966), and (Φ'~ka~, Φ'~kb~), (θ'~ja~, θ'~jb~) and
(r'~ia~, r'~ib~) are the lower (a) and upper (b) volume limits of the
body, respectively, in the k-th coordinate of longitude (Φ), the j-th
coordinate of co-latitude (θ), and i-th radial coordinate (r).

This GLQ procedure can also be adapted for regional heat flow modeling
to take advantage of heat flow constraints for modeling satellite
geopotential fields, as well as to investigate the geothermal
implications of the related mass and magnetization models for mapping
Curie isotherm depths and other thermal variations of the lithosphere.
*Simmons* (1967) developed an extension of Poisson\'s relation whereby
the point pole heat flow effect can be related to Δg essentially by the
ratio (Δh/Δm), where Δh is the heat contrast produced per unit time per
unit volume of the point pole. By this extension, regional heat flow
effects (ΔHF) can be modeled as shown at the top of the equation tree in
the left panel of **Figure 10**.

![](media/image10.png){width="3.2895833333333333in"
height="2.2041666666666666in"}

Figure 10. [Left panel]{.underline} outlines gravity, magnetic, and heat
flow anomaly modeling in spherical coordinates for a geologic body with
arbitrary shape and physical properties by Gauss-Legendre quadrature
integration. [Right panel]{.underline} shows an Icelandic crustal model
that accounts for satellite magnetic and gravity observations, and very
limited seismic and heat flow data. The lower crustal region 'A' of
viscous remanent magnetization (VRM) was developed from combined gravity
and heat flow modeling. Magnetization contrasts within this region and
the overlying crust produce magnetic anomaly effects at 400 km altitude
given by the red and blue profiles, respectively, which integrate to
account for much of the Magsat anomaly in the green profile.

The right panel of **Figure 10** gives an example from Iceland
(*Leftwich et al.,* 2001) that demonstrates the utility of heat flow
modeling for interpreting the geopotential anomalies. Here, we modeled
the Moho using the spectral correlation of the terrain gravity effects
and free-air anomalies from terrestrial and satellite marine altimetery
observations. The gravity estimates for the Moho compare very well with
seismic estimates everywhere except in the thick crustal root of Iceland
where we infer partial melting to account for the disparate estimates. A
strong candidate for the source of the prominent positive Magsat anomaly
over Iceland (**Figure 10**) is the lower crustal region of enhanced
viscous magnetization labeled 'A' that may occur within about 150^o^C of
the Curie isotherm.

To test this idea, we modeled the Curie and 450^o^C isotherm depths
using the idealized body calculations from *Simmons* (1967).
Accordingly, we used crude point heat sources for the mantle
undulations, a vertical cylinder source for Iceland\'s thermal plume,
and a vertical sheet source for the Mid-Atlantic Ridge. Scaling the
calculations to the measured heat flow values in the region, we
identified the undulating layer 'A' of the lower crust with the top at
the 450^o^C isotherm and the bottom at the Curie isotherm or Moho,
whichever is shallower, that may serve as the source for the Magsat
anomaly. Magnetizations for this region were then obtained from the
Magsat anomaly by least squares inversion. These magnetization contrasts
range over only a few A/m and are quite consistent with oceanic crustal
magnetizations inferred directly from studies of oceanic rocks (*Hinze
et al.,* 1991) and account for much of the Magsat anomaly as shown in
the right panel of **Figure 10**.

Hence, we may conclude that thermoviscous magnetic remanence in the
lower crust is a viable source for the Magsat anomaly over Iceland. More
generally, VRM in the lower crust may well be a major contributor to
satellite magnetic anomalies of the lithosphere (*von Frese et al.,*
1982). However, these effects are likely to be phase coherent with the
core field and hence may operate mostly to enhance the lower crustal
magnetization.

6.  **[Conclusions]{.underline}**

The Magsat mission operated over a short 6-month period more than 20
years ago and spawned two contrasting perspectives for processing
satellite magnetic data for lithospheric anomalies (e.g., *Langel &
Hinze,* 1999). The whole-Earth or global geophysics perspective
represents and analyzes satellite magnetic data by spherical harmonics
that emphasize the more regional anomaly attributes for lithospheric
analysis. In the present paper, the exploration geophysics perspective
is considered that focuses on resolving and analyzing the local anomaly
details in the track coverage. Accordingly, procedures were presented
for extracting maximum lithospheric anomaly detail from the available
local track coverage. Additional spherical coordinate procedures were
considered to transform, correlate and model satellite magnetic
anomalies for enhanced lithospheric analysis.

In principle, these two approaches should produce complementary results
at both local and global scales. However, for local (i.e., spherical
patch) applications the cost/benefit ratio favors the implementation of
the above described exploration procedures for the fullest lithospheric
exploitation of the satellite magnetic observations.

Relative to Magsat, the magnetic observations from the Ørsted, SAC-C,
and CHAMP missions are greatly improved in measurement accuracy and in
spatial and temporal coverage. The gravity field at complementary
satellite altitudes is also being mapped for the first time by the
CHAMP, GRACE, and pending GOCE missions. These recent developments offer
a truly exceptional opportunity for achieving new insight on the
tectonic processes and compositional, structural, and thermal properties
of the Earth's lithosphere.

**[7. References Cited]{.underline}**

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Foster, M.R., and Guinzy, N.J., 1967, The coefficient of coherence: Its
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Kim, J-H., 1995, Improved Recovery of Gravity Anomalies from Dense
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Kim, J.W., J-H Kim, R.R.B. von Frese, D.R. Roman, and K.C. Jezek, 1998,
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Langel, R.A., Estes, R.H., 1982, A geomagnetic field spectrum, Geophys.
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Langel, R.A., and W.J. Hinze, 1999, The Magnetic Field of the Earth's
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Leftwich, T.E., R.R.B. von Frese, and H. R. Kim, 2001, Icelandic thermal
modeling by satellite geopotential field anomalies, Eos (Trans. Am.
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Meyer, J., Hufer, J.H., Siebert, M., Hahn, A., 1985, On the
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Rapp, R.H., 1981, The earth's gravity field to degree and order 180
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