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Journal of Petrology | Volume 44 | Number 5 | Pages 833-849 | 2003
© Oxford University Press 2003
Using Quantitative Textural Analysis to Understand the Emplacement of Shallow-Level Rhyolitic Laccoliths—a Case Study from the Halle Volcanic Complex, Germany
1 TECHNISCHE UNIVERSITÄT BERGAKADEMIE FREIBERG, BERNHARD-VON-COTTA-STRASSE 2, 09599 FREIBERG, GERMANY
2 DEPARTMENT OF GEOLOGICAL SCIENCES, UNIVERSITY OF DURHAM, SOUTH ROAD, DURHAM DH1 3LE, UK
Telephone: +49-3731-392429. Fax: +49-3731-393599. E-mail: mock{at}geo.tu-freiberg.de
RECEIVED FEBRUARY 1, 2002; ACCEPTED NOVEMBER 8, 2002
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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In qualitatively homogeneous magmatic bodies, quantitative textural analysis—such as crystal size distribution, modal abundance, and spatial distribution pattern analyses—allows their internal heterogeneity to be measured and interpreted. In this study, these methods are applied to samples from a 300 m drill core through one of the porphyritic rhyolitic laccoliths (Petersberg unit) of the
300 Ma Halle Volcanic Complex, Germany.Qualitatively, the geochemically homogeneous Petersberg unit does not show much textural variation. Quantitatively, however, the crystal size distributions of the three most common phenocryst phases (orthoclase, plagioclase and quartz) suggest continuous crystal growth during magma ascent and emplacement, but different growth histories of the phenocryst phases throughout the genesis of the laccolith. In situ cooling did not affect the phenocryst population. Size distributions of the phenocrysts vary on a centimetre to decimetre scale, but are similar on the scale of the laccolith. The modal abundance of the phenocryst phases is very similar throughout the drill core. Quantification of the spatial distribution of phenocrysts, however, reveals a trend for clustering towards the interior or upper part of the laccolith, which is attributed to flow and shear processes during emplacement and discontinuities in the interior relating to the intrusion of different magma pulses. Circular statistics of the orientation of long axes of crystals reveal a weak alignment of the orthoclase and plagioclase phenocrysts on the sample scale as a result of flow in the magma in spite of little acicularity. In general, laccoliths can be fed by several pulses of magma without major cooling between batches. KEY WORDS: crystal size distribution (CSD); Halle Volcanic Complex (HVC); laccoliths; porphyritic rhyolites; spatial distribution patterns (SDP)
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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Shallow-level intrusions represent high-level storage reservoirs during the final stages of transit of magma to the Earth's surface. Shallow-level silicic intrusions potentially provide important information regarding the magmatic plumbing systems feeding explosive volcanoes (Eichelberger et al., 1986
; Brophy & Dreher, 2000). Studies of phenocryst populations in the products of silicic eruptions commonly suggest a rather complex history of magmatic evolution (e.g. Knesel et al., 1999; Hawkesworth et al., 2000). On the other hand, some systems seem to display a simple crystallization history (e.g. Higgins, 1996a, 1996b). In this study, we use a stratigraphically well-constrained set of samples obtained by drilling of part of the Halle Volcanic Complex (HVC), Germany, to apply quantitative textural analysis to a rhyolitic, highly porphyritic, shallow-level laccolith intrusion. Another objective was to quantify possible in situ growth effects on the phenocryst population. Samples were taken from a solid core drilled through the Petersberg unit of the HVC. Using the methods described below, we demonstrate changes in the size and packing arrangement of the phenocryst populations throughout the unit. We then use these textural data to develop a model for the emplacement of the Petersberg laccolith that previously has been difficult to constrain because of a general lack of exposure. This model has implications for processes occurring during the emplacement of shallow-level silicic laccoliths in general.
Geological setting
The HVC is situated in the Saale Basin in Eastern Germany—one of several late Palaeozoic transtensional volcano-sedimentary basins in the area of the decaying Variscan orogen (Eigenfeld & Schwab, 1974; Lorenz & Nicholls, 1984). The Saale Basin developed in the Saxothuringian zone and the Mid-German Crystalline Rise—structural units of the Variscan Orogen [Fig. 1 and Romer et al. (2001)]. The overall tectonic setting in Central Europe 20 Myr after the culmination of the Variscan orogeny was one of dextral strike-slip (Arthaud & Matte, 1977). Roughly contemporaneous basins associated with Permo-Carboniferous rifting or transtension in Europe within the orogen and its northern foreland can be found in the Oslo region, Norway (Sundvoll et al., 1990, and references therein), the Midland Valley, Scotland (Upton, 1994), the Saar–Nahe region, Western Germany (Stollhofen & Stanistreet, 1994; Stollhofen, 1998) and the Sudetic Mountains, Poland (Awdankiewicz, 1999). In all these basins, subvolcanic intrusive complexes are common.
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In the HVC, volcanic activity commenced with the eruption of a trachybasaltic to trachydacitic suite of lavas and pyroclastics with minor intrusive activity, exposed during coal exploration drilling (Kampe et al., 1965
; Siegert, 1967; Romer et al., 2001). A small rhyolitic laccolith intruded during this period (Schwerz laccolith, 307 ± 3 Ma, 1
error). This mainly extrusive magmatic phase was followed by the emplacement of the main porphyritic rhyolitic laccolith complex of about 200 km3 between 301 and 294 ± 3 Ma (1 errors; Landsberg, Löbejün, Petersberg, Wettin units). The ages of emplacement of the above-mentioned laccoliths have been determined by Breitkreuz & Kennedy (1999), using the 206Pb/238U SHRIMP method on zircons. The presence of aphanitic SiO2-rich lavas overlying erosional debris from the intrusions suggests that volcanism continued after the emplacement and partial exhumation of the laccoliths (Rüffer et al., 1998).
Of the laccolith units, the Löbejün, Landsberg and Schwerz units have large feldspar phenocrysts (up to 30 mm on the long axis); the Landsberg and Schwerz units also contain a variant with smaller phenocrysts. The Wettin and Petersberg units both have smaller feldspar phenocrysts (10 mm) with the Wettin unit containing small schlieren-like domains with larger phenocrysts. Within the Petersberg unit, the focus of this study, the apparent textural variation of the phenocrysts is very small.
After much debate about the extrusive vs intrusive mode of formation of the porphyritic rhyolite units, recent studies have reached the conclusion that all the units, which are depicted in Fig. 1c, are intrusive (Kunert, 1995; Breitkreuz et al., 1998; Knoth et al., 1998; Mock et al., 1999, and references therein), the evidence for this being their large thicknesses (at least several hundred metres), sedimentary rocks tilted during laccolith emplacement between the units, and the architecture of the internal flow structures [Fig. 2 and Mock et al. (1999)]. Thin to absent contact metamorphic aureoles and chilled margins advocate a very shallow emplacement of these laccoliths. In some laccolith units, e.g. the Petersberg unit, flow structures are recognized in the field by layers of partly stretched vesicles, bands of weakly aligned phenocrysts (mostly feldspars), or bands of higher phenocryst content.
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The drill samples used in this study were obtained during coal exploration in the 1960 s, which intersected the margin of the Petersberg laccolith. There are three strongly altered zones apparent from the cores (at the surface, at
100 m, and at the lower contact of the rhyolite at 300 m, Fig. 3). The country rock below the lower contact consists of a succession of grey silt- and mudstones with several fine sandstone beds and coal seams (Wettin beds, Fig. 3a) and a fluvio-limnic succession of reddish grey conglomerates, siltstones, clays and sandstones with abundant volcaniclastics (Halle beds in Fig. 3a; Kampe & Remy, 1960; Knoth et al., 1998). A short distance to the west, these sediments crop out at the surface with a steep dip. They form a thin band of tilted sediments between the Petersberg and Löbejün laccoliths (Fig. 3a, see also Fig. 1b). The Petersberg laccolith forms the largest of the laccoliths in the HVC with an estimated volume of 60 km3. Flow structures (Fig. 2), and carapace facies (spherulitic groundmass texture) found at the summit of the Petersberg hill suggest that the level of erosion at the drill site lies within the upper third of the laccolith. |
METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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Data acquisition and analysis techniques
Samples of up to 30 cm in length were taken from the 20 cm diameter drill core Petersberg 9, located in the core depository of the Geological Survey of Sachsen-Anhalt in Halle (drill hole location indicated in Fig. 1). The relative stratigraphic position of the samples is indicated in Fig. 3b. Two or three plane faces ranging from 7000 to 20 000 mm2 in area were cut vertically and/or horizontally from each sample for image analysis (see Table 1).
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The rhyolite is chloritized and partly albitized and haematized. Quartz crystals are unaltered and appear black, greyish or clear depending on the remaining size of the crystal after cutting and its background in the rock. Orthoclase crystals retain a pinkish red colour but have abundant clear parts. Plagioclase crystals appear greenish grey with abundant black inclusions. Twinning is abundant among the feldspars, zoning is not. Automatic image classification of scans from the plane faces was not possible because of ambiguous values in the RGB colour scheme for the various phenocrysts. Staining of the samples was considered impractical because of large sample sizes and alteration. Therefore, orthoclase (OR), plagioclase (PL) and quartz (QZ) phenocrysts were outlined by hand on a transparency superimposed on each rock slab, assigning different, unambiguous colours to each phase. The transparencies were then scanned at a resolution of 180 d.p.i. The digital images were double checked with the rock slabs to ensure correct identification of each phenocryst, that individual phenocrysts were separated from each other in the image, and that the colours in the digital image were suitable for automatic image analysis (Fig. 4a). The smallest grain size measurable with this technique is given by the width of the pen used to trace the phenocrysts on the transparency. It lies in the range of 0·1–0·5 mm. Figure 4b shows three examples of the characteristic texture of the samples as monochrome images to emphasize the apparent similarity throughout the core.
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The digital images were analysed using image analysis software KS300 (Rel. 2.0) by KONTRON ELEKTRONIK Imaging System, to provide the area, the lengths of the long and short axes, their orientation expressed as an angle from a horizontal line in the digital image, and grain centre coordinates of the phenocrysts. Phenocrysts intersected by the edge of the sample slab were excluded from the analysis.
Quantitative petrography
Petrographic characteristics, such as the modal abundance of mineral phases and their grain size, have been used extensively in rock classification. Recent developments in the textural analysis of rocks provide additional petrographic tools for the quantitative investigation of magmatic rocks, which can be used in conjunction with geochemical studies to help understand their origin and evolution. In this study we focus particularly on the size and spatial distribution of the crystal population, and apply circular statistics to quantify trends in orientation data.
Crystal size distributions
As minerals grow from a melt the actual size distribution of crystals can provide valuable information about the origin of the rock. Crystal size distribution (CSD) studies originated in engineering (e.g. Randolph & Larson, 1988) and were introduced to igneous petrology by Marsh (1988) and Cashman & Marsh (1988). Marsh (1998) summarized the theory of CSD as applied to the textures of igneous rocks. In the case of a log–linear CSD in a steady-state open system, the population density (n) of the items in question (crystals, vesicles, etc.) is linked to their size (L), with growth rate (G) times residence time (
) and final nucleation density (n0) being constants for one particular CSD, in the following equation:
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Linear regression analysis of the CSD curve—a plot of crystal size (L) vs logarithmic population density of that size [ln(n)]—provides a measure of growth rate/residence time (slope) and nucleation density (intercept). Additionally, the shape of the CSD curve can reveal the operation of different processes during the crystallization of magma batches (Marsh, 1998; Zieg & Marsh, 2002). CSD analysis might also be used to characterize variations within apparently homogeneous igneous bodies.
In this study, the size distribution of the long axes of phenocrysts of orthoclase, plagioclase and quartz within each slab were corrected for two-dimensional–three-dimensional (2D–3D) effects using the method and software of Higgins (2000): CSDCorrections 1.2. The 2D–3D effects are the intersection probability (a random section is more likely to intersect larger grains or crystals than smaller ones) and the cut section effect (one grain or crystal can produce different-sized sections in differently orientated cuts through a sample).
Higgins (2000) developed this method of 2D–3D correction following the work of Saltykov (1967) and Sahagian & Proussevitch (1998) on stereological conversion of particle size distributions from 2D sections to actual 3D distributions. This method uses a quantification of the above-mentioned 2D–3D effects to calculate a 3D size distribution from the 2D intersection distribution. It is a non-parametric method that does not require the shape of the distribution to be assumed beforehand, as opposed to Peterson's (1996) parametric solution.
Spatial distribution pattern
Understanding how crystals and grains in rock textures are orientated spatially in relation to each other is fundamental in interpreting their history (Kretz, 1969; Jerram et al., 1996; Jerram & Cheadle, 2000). A method to quantify the spatial distribution pattern (SDP) of grains in thin section was developed byJerram et al. (1996). This applies the technique of cluster analysis for automatic, objective and consistent classification of particles. Based on R-values, it provides a measure of how clustered, random or ordered a distribution of particles is; that is, the ratio of the mean nearest neighbour distance (NND) of all particles in a sample to the predicted mean NND for a random distribution of points, given by
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the density of the observed distribution (N divided by the sample area), and r the NND of one particular grain. This variation in the packing arrangement can be quantified in a matrix vs R-value diagram (Jerram et al., 1996), and it can also be used to distinguish touching from non-touching frameworks (Jerram et al., 2003). This plot compares 2D sections through 3D textures with 2D sections through 3D models of randomly packed spheres and it can thus be used to quantify the variation in the spatial packing arrangement of those 2D sections from 2D sections of random textures and from other 2D sections of 3D rock textures. Matrix vs R-value plots are discussed in a subsequent section. The positions of the centres of phenocrysts, determined by image analysis, were used to calculate an R-value [equation (2)] as a measure of the SDP in each slab. We used a FORTRAN77 program for calculating a mean NND for all the crystal centres in one slab. This was then divided by the mean NND expected for a random distribution of points of the same population size and density (see above) to give the R-value. An R-value for each slab as a whole was calculated using all the grain centre data for all the different phenocrysts. In a second step, R-values were calculated for each crystal phase separately.
Circular statistics of orientation
Statistical tests were performed on orientation data following the procedures outlined by Davis (1986) and Capaccioni et al. (1997). The mean orientation angle is defined as
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i is the orientation angle of one crystal's long axis. If the orientation of the sample is known, this is a good estimate of the flow structure's orientation where flow structures are manifested by the alignment of phenocrysts' long axes (see also the section ‘Geological Setting’ above). The mean resultant length of the mean orientation angle on a unit circle (
) is a measure of the dispersion of the data and hence a measure of the reliability of the flow structure statistics. It is defined as
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) and can be used as a test variable to look for a preferred orientation within a dataset. According to Davis (1986), the 95% significance level for n > 50 observations lies at 0·244. If the value of the test variable falls below this threshold, the crystals are orientated randomly. If it is larger than the threshold value, the crystals have a preferred orientation. |
RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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Modal abundance, shape of phenocrysts, and CSD
The sum of the areas of the phenocrysts of one phase expressed as a percentage of the slab's area is a valid estimate of the modal abundance (volumetric proportion) of that phase (Higgins, 2002
, and references therein). The relative modal abundances of OR, PL and QZ were calculated for each of the 16 slabs (Table 1) from the results of image analysis as described above. They are very similar in all samples, with 41 ± 4·8% OR, 37 ± 4·3% PL and 22 ± 3·7% QZ (mean relative modal abundances ± one standard deviation). There is no apparent change with depth in the intrusion or intra-sample variation. The uppermost sample (90499), however, is richer in quartz by 4% (Fig. 5).
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Higgins (2000)
suggested that the I/L ratio of the crystal dimensions [longest (L), intermediate (I) and shortest (S)] can be estimated statistically by adding 0·5 to the skewness of the width/length ratio distribution. The skewness is then taken to be the mean minus the mode divided by the standard deviation of that distribution. The S/I ratio is taken to be the mode of the width/length ratio distribution. Values obtained by this method were either unrealistically large (e.g. 1:3:50) or gave a value for L smaller than the value for I. The crystal dimensions for the input into the software used for 2D–3D correction in this study were estimated to be 1:2:3 for OR and PL, and 1:1·5:2 for QZ after careful examination of the samples using the recommendations of Higgins (1994). In Table 2, the mean ratios of all the crystals' long and short axes are listed. As observed qualitatively on the slabs, QZ exhibits more equant shapes (crystals are rounded) than the other two phenocrysts and also always has higher axial ratios (the difference between long and short axes is small as expected for equant shapes), whereas OR and PL have lower axial ratios with OR forming the lower limit. The different dimensions of QZ and the feldspars are justified by the different mean width/length ratios of these phenocrysts.
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Following the general convention, CSDs are plotted as linear crystal size (L [mm]) vs logarithmic population density {ln(n) [mm-4]; Marsh, 1998
; Higgins, 2000}. The size according to Higgins (2000) is equal to the longest dimension of each crystal in 3D. The CSDs for the phenocrysts are all straight and exhibit a power-law distribution over the range of grain sizes investigated in this study (Fig. 6). QZ has a smaller maximum crystal size than the two feldspars, and its CSDs are steeper. Linear regression coefficients (R2) are very close to unity for all CSDs (Table 2).
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For a linear CSD in a steady-state open system, the slope is a measure of growth rate and/or growth time (characteristic length), and the intercept represents the final nucleation density of the crystals. These parameters of the CSD are shown in Fig. 7. There is a linear relationship between final nucleation density and characteristic length, meaning that the more nuclei form, the slower the crystals grow and vice versa. This relation does not vary consistently with depth in the drill core nor even within differently orientated slabs of a single sample. However, there is a difference between the two feldspars and QZ, as becomes also obvious from the CSD plots. QZ has a higher final nucleation density (larger intercepts) and a lower product of growth rate and residence time (steeper slopes of the CSDs). Therefore, apart from being more equant, QZ also has a different growth history (Fig. 7). Higgins (2002)
suggested the use of a diagram of volumetric phase proportion vs characteristic length to provide more information on magmatic processes (Fig. 8). In this diagram, the variation within each sample is almost as large as the variation between samples. Nevertheless, the uppermost sample (90499) in Fig. 8a and the lowermost sample (92860) in Fig. 8b plot at opposite ends of the ‘cloud' of sample points. This indicates a very weak trend of open-system textural coarsening towards the bottom of the laccolith for the feldspars (see discussion for details). Any systematic in situ growth variations such as textural coarsening within the laccolith cannot be seen in Figs 7 and 8.
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Intra-slab variability
We carried out CSD analysis on sub-areas of the largest slab of the set of samples (92508 c, 20171 mm2) to unravel small-scale variability of the CSDs (Fig. 9). An image of 10·5 cm x 7 cm was cut out of the original image by a frame at one end of the sample slab. Then the frame was moved by
2 cm and another image was cut out. In this fashion, 10 overlapping images were retrieved from sample 92508 c (Fig. 9a) and subsequently analysed in the way described above. CSDs obtained from the different sub-images are shown in Fig. 9b–d. Apparently, they do not differ for each of the phenocrysts. On a diagram of modal abundance vs characteristic length, however, plagioclase shows a significant difference of the top and the bottom part of the slab, whereas the other two phenocrysts show a limited spread that is not significant if the 1 error bars are considered (Fig. 9e).
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Spatial distribution of phenocrysts
The modal abundances of the phenocrysts as well as the CSDs do not show systematic variation between samples within the intrusive body. The SDP, however, does show a systematic variation. According to the R-value method, crystals can be distributed either randomly, in an ordered manner, or clustered. Random distribution is represented by the random sphere distribution line (RSDL) in Fig. 10 (Jerram et al., 1996
). Populations of grains plotting above this line are ordered, populations below this line are clustered.
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The crystals of the samples of this study are generally randomly distributed (Fig. 10). In the uppermost sample (90499), the phenocrysts are slightly more clustered (R-value reduced by 0·1 compared with the samples at the bottom of the drill core). In the lower parts of the intrusion, crystals are more randomly distributed and even plot beyond the RSDL to suggest a slightly ordered pattern. Therefore, there is a trend of clustering of crystals towards the top of the intrusion. This becomes even more apparent in a plot of R-value vs depth in the drill core. Sample 91671 exhibits a remarkable deviation from this trend that we interpret as marking a zone between two magma pulses (Fig. 11). The significance of this will be elaborated in the discussion section below.
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OR always has higher R-values than the other two phases (Fig. 12). R-values of PL and QZ do not have a consistent relationship within one sample or throughout the drill core. Also, the two samples 90499 and 92860 are distinctly different from the others, forming opposite ends of the array on the R-value vs matrix plot, supporting the observed trend of Fig. 10.
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Circular statistics of orientation data
The results of the statistical analysis of orientation data are presented in Table 3. Most of the vector lengths (r) exceed the threshold value of 0·244 (italics in Table 3, second column). Therefore, there is a preferred orientation of the long axes of the phenocrysts in the rhyolites as a conjugate set of mean angles at
45° and at 135°. These values do not depend on the orientation of the slab (horizontal cuts are in italics in Table 3, third column), nor are they a reflection of changing trends in the flow structures with depth.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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In the following discussion we would like to address some possible physical properties influencing the spatial and size arrangement of phenocrysts in a magma during various stages of its development. We will use data of this study to constrain the importance of those for the case of the Petersberg laccolith.
Influence of magma flow on the spatial distribution of crystals
In addition to crystal nucleation and growth, flow and shear in a rising magma batch will influence the spatial distribution of phenocrysts. Let us consider a dyke: provided that flow is laminar, which is very likely for viscosities of 1012 Pa s and more typical for rhyolitic melts, crystals are distributed according to the velocity profile that develops across the dyke (Fig. 13a). Large crystals are transported towards the interior of the dyke analogous to grains affected by laminar flow in a pipe (see Leeder, 1982). Thus, the CSDs there become flatter and the maximum crystal sizes larger. Also, the R-value increases whereas the matrix proportion decreases following the compaction trend in Figs 10 and 11. Conversely, increased clustering (reduced R-values) can be expected at the walls or edges of an intrusion, between successive batches of magma, and close to flow foliation planes (Fig. 2). It remains to be established whether flow parameters such as flow velocity, viscosity of the magma, size of the phenocrysts, and bed shear stress are in the right range for these sorting mechanisms to be strongly effective. In zones of focused shear (Fig. 13d), random distributions of crystals become spatially modified by shear flow. This brings the crystals closer together in one direction and thus lowers the R-value (see Jerram et al., 1996). The decreasing R-values can be seen on the R-value vs depth plot for the Petersberg samples (Fig. 11).
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Additionally, flow of viscous magma should cause more effective redistribution of crystals with high aspect ratios than of equant crystals. Although there are no significant changes in the CSD characteristics with depth in the drill core through the Petersberg laccolith, trends in the characteristic length vs modal abundance plots and the SDP of phenocrysts are more noticeable for the feldspars (higher aspect ratios) than for QZ (Figs 8 and 12).
Flow sorting during emplacement
When the magma reaches the level of intrusion, the direction of flow of the magma is turned 90° to form a sill. Two end-member scenarios may take place: (1) the redirection of flow homogenizes the inherited spatial distribution of crystals in the magma or (2) the conditions prevailing in the dyke are turned by 90° and the spatial pattern of phenocrysts is preserved from the dyke, i.e. large crystals continue to be concentrated in the interior of the sill (Fig. 13b). In the former case, the size-dependent redistribution of crystals by flow will start anew in the direction of sill intrusion; in the latter case, the inherited spatial distribution of crystals will be enhanced.
If the sill transforms to a laccolith by inflation, i.e. several batches of magma intrude each other (Corry, 1988), a pattern of crystal distributions equivalent to several sill-like spatial arrangements should be found in a drill core through the laccolith (Fig. 13c). In the case of homogenization of the spatial arrangement of crystals, the trends in the CSD and the SDP might be obscure.
In the Petersberg samples, the phenocrysts display a preferred orientation. They show a ± conjugate set of mean angles (Table 3). This is evidence for the importance of flow sorting of phenocrysts in the intruding magma. If there was no preferred orientation of phenocrysts, it would be a strong indication for vigorous homogenization of the phenocryst texture and thus detailed textural investigations of this nature would be futile. Contrarily, the occurrence of preferred orientations provides a base for the considerations at the beginning of this section.
The data of this study show that the SDP in the Petersberg laccolith is changing from a random to a more clustered distribution with no significant change in the CSD. The difference in the SDP can be ascribed either to the physical movement of the magma as described above or to a change in the spacing of nucleation. In the latter case, one would expect a noticeable difference in the CSDs between samples. As that is not the case, a physical movement of a pre-existing crystal population is preferred. Additionally, the rather insignificant trend in the CSD characteristics (Fig. 8) advocates a degree of homogenization of the spatial pattern of phenocrysts upon intrusion despite a lack of turbulent conditions, so that the SDP evidence for the two-pulse model becomes even more significant.
Unfortunately, a general lack of exposure in and around the Petersberg laccolith inhibits recovery of further evidence for multiple pulses of intrusion. Many better exposed subvolcanic laccoliths do show evidence for such intrusive mechanisms; for example, the Donnersberg in the Saar–Nahe basin, Germany (Haneke, 1987) and the Henry Mountains, Utah, USA (Corry, 1988; Friedman & Huffman, 1998). Therefore, the SDP pattern observed in the Petersberg laccolith can be explained by such an interpretation. Furthermore, the simple, linear CSDs suggest that these pulses took place during the same crystallization period.
Comparison with CSDs from othersystems
While a magma is cooling and crystallizing, a number of circumstances will affect the CSD. Changes in growth and nucleation rate away from constant conditions will alter the slope and intercept of the CSD of a population of crystals. At later stages of crystallization larger crystals will grow at the expense of smaller ones, with the effect that smaller crystals are destroyed and the CSD kinks downwards at the small size end. This is known as Ostwald ripening, textural coarsening, or annealing (Higgins, 1998; Marsh, 1998). Also, at the margins of an intrusion, where cooling is more effective, nucleation should become more important than growth and the CSD becomes steeper with a smaller maximum and characteristic crystal size than in the interior of the intrusion. Mixing of populations of phenocrysts with different CSDs might be noticeable by changes of slope in the resultant composite CSD.
Two examples of the above-mentioned processes are given. Plagioclase phenocrysts in dacites from Kameni volcano, Greece, yielded strongly curved CSDs that were interpreted to stem from the mixing of two magmas with differing populations of crystals (Higgins, 1996b). CSDs of K-feldspar megacrysts in the Cathedral Peak granodiorite, California, show evidence of textural coarsening (Higgins, 1999). These two examples represent extreme end-members of possible time–temperature–crystallinity paths of magmas, the former leading to eruption as lava, the latter being buffered by earlier and later intrusions of different material. The simple CSDs of the phenocrysts of the Petersberg laccolith display an intermediate position: there is no indication of magma mixing in the CSDs nor in the chemistry (Romer et al., 2001), i.e. the laccolith formed by several intrusive batches derived shortly after one another from the same magma; and there is only a very weak indication of annealing (Fig. 8), i.e. the magma forming the Petersberg laccolith had only one population of phenocrysts that nucleated and grew continuously until a substantial increase of undercooling led to nucleation and growth of the groundmass phases after emplacement. The phenocryst populations were thus not affected by post-emplacement processes.
In the light of these considerations, the emplacement model for the Petersberg laccolith is far from being complete. It is evident, however, that detailed textural investigations of this kind are a powerful tool to reveal mechanisms important in igneous petrology and mechanics.
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CONCLUSIONS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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In this study, textural analysis techniques have been applied to quantify the evolution of a very homogeneous rhyolitic laccolith from the HVC. Specifically, we focused on the growth history of different phenocrysts as reflected in their CSD. Furthermore, we employed SDP analysis and statistics of phenocryst orientation to determine different packing arrangements of crystals and the influence of flow sorting. We reach the following conclusions:
- The CSDs suggest that crystals grew continuously in the magma during ascent and emplacement of the Petersberg unit. Systematic variation of crystal growth related to in situ cooling has not been detected.
- In spite of little acicularity, feldspar phenocrysts are orientated with a preferred orientation of their long axes by flow shearing in the intruding magma body.
- Redistribution of crystals as a result of flow is reflected in the spatial distribution pattern, as R-values change with sample position in the laccolith.
- The SDP also strongly suggests that the emplacement of the laccolith involved successive pulses of magma intruding each other.
- Size distributions are affected by the flow on a centimetre to decimetre scale, but are not different in subsequent magma pulses.
- Laccoliths can potentially be fed by pulses of magma with little or no major cooling between batches as indicated by the uniformity of the CSDs throughout the drill core.
- Internal heterogeneities of apparently homogeneous magmatic bodies can be measured and interpreted by detailed, quantitative textural analysis.
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ACKNOWLEDGEMENTS |
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We thank Michael Higgins for providing the CSDCorrections software and Michael Magnus for introducing KS300 to A.M. Bruce Charlier is thanked for comments on an earlier version of this manuscript. Thanks are due to B.-C. Ehling and the Landesamt für Geologie und Bergwesen Sachsen-Anhalt in Halle/Saale for the opportunity to take samples from the coal exploration drill core Petersberg 9. Reviews by Philip Candela, Michael Higgins and Michael Zieg are much appreciated. Most parts of this work were done with the help of grant Br 997/18-1 from the Deutsche Forschungsgemeinschaft.
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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