Lithology and Structure

The characterization of rock samples provides qualitative and quantitative data that are used for interpreting the origin of the rocks and their significance to the overall volcanic structure and geothermal properties. In addition, laboratory analyses of lithological character provide strong tests of field hypotheses. Appendix B outlines various rock classification methods.

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Before doing a structural analysis, it is extremely important to properly map faults, showing topographic effects that constrain their dip and strike. In addition to delineation and classification of faults and fractures, the overall volcanic structure must be evaluated. With this information, various rock associations, and the rocks' spatial and temporal variations, it it possible to constrain a probable underlying volcanic structure, as has been described in previous chapters of this book.

The following discussions cover basic aspects of the techniques employed in lithological and structural studies. More detailed information is available in popular petrographic and structural textbooks such as Williams et al . (1982) and Dennis (1972).

Lava Samples

Hand Sample Classification

Because many volcanic rocks are fine-grained, accurate hand-sample classification is often difficult. The color, texture, density, and mineral content are descriptive features that can be used to identify a sample. These macroscopic features are also invaluable for making the field identifications and correlations that are necessary for mapping.

Compositional classification is generally determined by color and phenocryst content, if any. An example of such a classification is shown in MacDonald (1972; p. 458). Mineralogical classification is greatly aided by the use of rock associations, as was described for rock families by Carmichael et al . (1974, pp. 32-37), including the basalts, andesite-rhyolite associations, trachybasalt-trachyandesites, trachyte-phonolites, lamprophyres, and nephelinites. In addition to the sample's phenocryst mineralogy, secondary mineralogy is employed to classify many volcanic rocks, especially those found in areas of geothermal activity. (This subject is discussed more thoroughly in Chapter 3.)

Textural features of samples lend a physical basis for classification to supplement the more chemical nature of mineralogical classifications. For example, textural features of lavas include vesicularity, phenocryst abundance and size, foliation and fracture, and secondary transformations such as hydration, devitrification, and weathering.

In general, a combination of the compositional and textural classifications of lava samples (for instance, aphyric rhyolite; pumiceous, hornblende-biotite dacite; flow-banded andesite) provides a satisfactory, unambiguous method of naming rocks for field and laboratory recognition.

Thin-Section Petrography

Analysis of rock samples by petrographic microscope is the most important laboratory procedure geologists use to supplement the field study; its value lies in part in the relatively simple preparation and facilities required. This work can be accomplished conveniently in the field area with a rock trim saw and lap, quick setting glues or epoxies (Hutchinson, 1974), and a polarizing, petrographic microscope. Petrographic methods, outlined in crystallography texts such as Heinrich (1965), as well as textural descriptions, well illustrated in other texts such as Williams et al . (1982), facilitate the analysis. This analysis usually includes textural classification (for example, aphyric, foliated, or vesicular) and modal analysis of the crystal content, which is quantified by point counts. One possible format for the analysis is shown in the sidebar on this page.

Petrographic analyses may also include scanning electron microscopy (SEM) of thin sections. This procedure requires sophisticated equipment that might not be readily available, but it can be extremely helpful in characterizing and interpreting phase mineralogy and textures in fine-grain samples, especially those that are pyroclastic or have been altered to secondary minerals. Etching samples with acids or by an ion beam greatly enhances poorly developed textures by

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selectively thinning the section according to mineral and glass hardnesses (Heiken et al ., 1989).

Whole-Rock and Mineral Chemistry

X-ray fluorescence spectrometry (XRF) and atomic absorption spectrophotometry (AA) are the most widely used methods for obtaining bulk chemical analyses of rock samples. (The methods and analytical problems are outlined by Hutchinson, 1974.) These data are very valuable when combined with petrographic descriptions to characterize volcanic stratigraphy and determine the nature of a magmatic source. For instance, Carmichael et al . (1974) reported that magma evolution through differentiation is revealed by the enrichment in the silica contents of erupted products with time. Samples for bulk chemical analysis must be carefully chosen to obtain a suite of samples for which analyses can be compared. Problems in discerning variable effects of secondary alterations and phenocryst contents can reduce the value of sample data for characterization and correlation.

Mineral compositions provide information to be used in detailed classification schemes that require specificity; for example, discrimination of the anorthite content of plagioclase. Mineral chemistry data also can be applied to calculation of geothermometers and geobarometers (Behen and Lindsley, 1987).

This type of information is typically obtained from thin sections by electron probe microanalysis (EPM); however, mineral separates, obtained when the sample is crushed and prepared for bulk chemical analyses (Hutchinson, 1974), can be readily analyzed by x-ray diffraction (XRD). These separates may be also useful for radiometric dating by mass spectrometry.

Alteration

Alteration mineralogy is an significant aspect of volcanic petrography in geothermal areas

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(see Chapter 3). Both the traditional XRD powder methods and the SEM are useful for identifying suites of alteration minerals such as clays and zeolites. These suites are typical of hydrothermal alteration environments and therefore can be employed to establish thermal regimes, the likely chemistry of the host rocks, and the nature of hydrothermal fluids. We recommend the review by Henley et al . (1983) for comprehensive instructions on this method.

Pyroclastic Samples

Field Classification

Several classification schemes are provided in Appendix B. Field descriptions include general grain size and sorting, bedding textures, color, and topographic effects on the pyroclastic deposit. More detailed descriptive aspects are discussed in Chapter 2.

Laboratory Analysis

Analysis of tephra samples in the laboratory involves several interdependent techniques that generally do not require elaborate analytical equipment. Figure A.8 is a flow chart that outlines laboratory treatment of pyroclastic samples, including both preparatory and analytical steps. The petrographic inspection follows procedures outlined above for lava samples and can be simply performed with a binocular scope on small sample splits or thin sections of epoxy-impregnated samples. Fundamental measurements comprise granulometry, mode and component analysis, grain shape and texture description, and mineral and glass chemical analysis.

Granulometric Analysis

Grain-size analysis of pyroclastic samples is a standard characterizing technique and, over the last 20 years, has been increasingly used to interpret the origin of samples (for instance, Sheridan, 1971; Walker, 1971; Wohletz, 1983). Granulometric characterization of samples is an especially important tool for correlation and classification in areas where many pyroclastic deposits are encountered. Interpretation is generally needed to determine the eruption and emplacement mechanisms for the deposits sampled.

Sieving is a practical approach for classifying samples in the range of ~16 to 0.064 mm, for which standard screens are readily available (see, for instance, Folk and Ward, 1957). Above this grain size, hand counts of individual fragments are useful; below this size, settling-tube measurements, based on either a pipette method (Folk, 1976) or optical methods such as fluid suspension absorbance measurements can extend the range to near 1 µm. The wide range easily analyzed by screen sieves provides enough data to adequately characterize and interpret most tephra samples. Table A.2 presents class size intervals for clastic sediments and pyroclastic rocks. Because of the broad range of grain sizes represented by pyroclastic materials, it is common to use a logarithmic transformation of grain diameters called the phi (f ) scale (Wentworth, 1922):

for which d_mm is the grain diameter in millimeters. Krumbein (1938) showed that on this scale transformation, plots of mass frequency vs phi size approximated a Gaussian distribution, which can be characterized by the use of log-normal statistics:

where d m/df = the mass per unit interval of f , K_s = a constant to normalize the distribution (usually K_s = 1), s_d = the standard deviation in log units, d = particle diameter, and d_m = the mode diameter of the distribution.

Tephra size data are useful for various types of interpretation. For example, Sparks et al .

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Fig. A.8
Flow chart for laboratory treatment of pyroclastic samples.

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(1978) discussed the importance of particle size to terminal fall velocity, which is useful in determining the amount of time required for pyroclasts to fall out of eruption plumes and clouds (Fig. A.9). Carey and Sparks (1986; Figs. 1.13 and 1.14) related maximum clast sizes to distance from source for eruptions of different magnitudes. A plot of median diameter vs distance from the source (Fig. A.10) shows the general fining of pyroclastic samples with distance for a number of different eruptions.

By using single-mode lognormal statistics, Walker (1971) characterized tephra samples of pyroclastic fall and flow origin. Wohletz (1983) described similar size data for pyroclastic surge samples. Sheridan and Wohletz (1983a) characterized size data for numerous samples of hydrovolcanic origin (see Fig. 2.20). Taken together and plotted on a sorting vs median diameter plot (Fig. A.11), these data provide a general interpretation scheme for tephra samples.

Another, more specific example illustrates the application of size data to a stratigraphic section of the Lathrop Wells scoria cone in Nevada that exhibits two main types of eruptive behavior (Wohletz, 1986): early

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hydrovolcanic explosions and later Strombolian eruptions (Fig. A.12). Three types of bedforms were recognizable: scoria fall, fine ash layers of undetermined origin, and pyroclastic surge. Figure A.13 is a sorting vs median diameter plot that nicely differentiates between the three bedforms. Because of their relatively poor sorting, it was assumed that the fine ash layers had been emplaced by pyroclastic surge. Furthermore, a plot of median diameter and weight percent of fine ash (Fig. A.14) correlated the fine ash layers with similar size distributions from early hydrovolcanic samples in the cone stratigraphy and thus permitted their classification as hydrovolcanic. This interpretation was supported by a later study of pyroclast constituents, morphology, and surface chemistry.

We believe that size analysis can provide even more information about the history of fragmentation and dispersal of pyroclastic samples through mathematical analysis of individual size-frequency distributions. Sheridan et al . (1987) discussed the typical polymodality of tephra size-frequency distributions and possible types of interpretations. Typically, size-frequency distributions are analyzed as lognormal-type distributions, in which, for any particular sample, one or more lognormal subpopulations may overlap to form the total observed distribution. Because the single-mode lognormal statistics are not strictly applicable to tephra samples, we advocate the subpopulation discrimination technique established by Sheridan et al . (1987), in which microcomputer software can be applied to sieve data for fully characterized sized distributions. More recently, Wohletz et al . (1989) developed a new mathematical distribution, the sequential fragmentation/transport model, that relates distribution shapes to physical

Fig. A.9
Terminal fall velocities for (a) pumice and (b) lithic fragments of varying radii in fluid of
several densities (for example, the lower two curves of each plot are for fallout in air at room
temperature and steam at 1300 K). Vertical dashed lines are shear velocities of 15 to 200 m/s,
assuming a drag coefficient of 0.01; these lines define the rate of fallout of tephra from
an eruption plume and velocities required to suspend the fragments in a pyroclastic flow.
(Adapted from Sparks et al., 1978.)

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Fig. A.10
Plot of median grain size vs distance from the source for various tephra deposits.
(Adapted from Fisher and Schmincke, 1984.)

processes of fragmentation and transport sorting, which allows a much more extensive analysis of size data. The distribution is given as

where the normalization constant (K_s ) and the transport distance factor (x/x_o ) are set to unity for frequency distributions totaling 100%, g_f = a parameter analogous in part to standard deviation, and g_f = g_f + 2 for fragmentation processes or g_f = 2 for transport processes. Because the distribution shapes for the fragmentation and transport forms of Eq. (A-3) are nearly identical and because almost all tephra samples have experienced some sorting by a transporting agent, the g_f = 2 form is most appropriate. Figures A. 15 through A.17 show the results when Eq. (A-3) is applied to several tephra samples. In Table A.3, we show observed ranges and expected values of g_f for volcanic fragmentation and transporting process.

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Fig. A.11
Plot of sorting (s_f ) vs median diameter (Md_f ), showing ranges of values as 1 and 8%
contours for fallout (dashed lines) and pyroclastic flows (solid lines).
(Adapted from Walker, 1971.)
The bold solid line encloses the range of values observed for pyroclastic surge samples
from observations referenced by Fisher and Schmincke (1984); the dotted line
surrounds values of cross-bedded surge deposits.
(Adapted from Fisher and Schmincke, 1984.)

Component Analysis

Tephra samples contain essential juvenile, (meaning new magma), accessory (older volcanic materials), and accidental (subvolcanic basement fragments) components. In juvenile components, fragments of glass, lava, and crystals vary in proportion in a complex fashion that is dependent on the magma composition and temperature as well as the mode of ejection and transport. Glass is often vesiculated and forms pumice or scoria. The three tephra components (glass, crystals, and lithic fragments) can be easily recognized with assistance of a hand lens or microscope. An example from Walker and Croasdale (1972) shows vertical and lateral changes of pyroclast constituents for the Fogo A tephra sampled southeast of Lake Fogo at Sao Miguel in the Azores (Fig. A.18).

An analysis of tephra components is especially important for identifying samples from deposits that have major nonjuvenile contributions. Abundant accidental and accessory lithic fragments are indicative of eruptions that have fractured and excavated rocks from around the magma conduit, as is the case for vent-opening and hydrovolcanic eruptions. A careful count of lithic-fragment abundances for the scoria cone at Lathrop Wells (Fig. A.12) showed the relative abundance of lithic fragments in pyroclastic deposits from hydrovolcanic phases (Fig. A.19). In addition, Fig. A.19 illustrates the relative increases of crystals in pyroclastic surge samples from the tuff ring and fine ashes from

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Fig. A.12
Stratigraphic section of the Lathrop Wells, Nevada, scoria cone,
showing sampled intervals.
(Adapted from Wohletz, 1986.)

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Fig. A.13
Plot of sorting vs median diameter for samples from the scoria cone described in Fig. A.12.

Fig. A.14
Plot median diameter (Md_f ) and wt% ash <1 mm vs the scoria cone
stratigraphy (shown in Fig. A.12). The peaks in median diameter and ash abundance
for the hydrovolcanic samples are unlike those for the Strombolian
samples, which are products of magmatic eruption.

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Fig. A.15
Plots of the size-frequency distribution for a
sample of a planar surge bed from Crater
Elegante in Sonora, Mexico. The upper plot
shows a cubic spline curve fit to the data
points (

), and the lower plot shows the
modeled distribution (solid curve) made
by adding three subpopulations of SFT
form (dashed curves).
(Adapted from Wohletz et al ., 1989.)

the early and late cone samples, which were interpreted as surge deposits. This increase in crystal concentration is a typical feature of surge-emplaced tephra.

Grain Shape and Texture

For most samples (except those with a high content of fine ash), a hand lens is sufficient to make vital observations about features such as those listed here.

· Vesicularity: the relative abundance and size of vesicles and whether they are spherical or elongated; in most cases, this is a measure of the contribution of magmatic gas

· Angularity: blocky grains with few vesicles—earmarks of phreatomagmatic or phreatic pyroclasts

Fig. A.16
Plots of the size-frequency distribution for
a sample of the proximal bedded deposits
of the Mount St. Helens May 18, 1980
pyroclastic flow. The upper plot shows
a cubic spline curve fit to the data points (

),
and the lower plot shows the modeled
distribution (solid curve) made by adding four
subpopulations of SFT form (dashed curves).
(Adapted from Wohletz et al ., 1989.)

· Rounding: used to determine the relative degree of transport abrasion and reworking by epiclastic processes

· Surface alteration: indicates weathering or hydrothermal processes; phreatomagmatic pyroclasts from wet surge deposits show abundant alteration coatings.

Grain-shape analysis can be further developed by using an SEM; methods for analysis and interpretation are discussed by Heiken (1972), Wohletz (1983; 1986), and Heiken and Wohletz (1985). The SEM micrographs in Fig. A.20 reveal prominent textural features. Figure A.21 plots the variation of grain textures that proved useful in distinguishing between Strombolian (magmatic) and hydrovolcanic samples from the scoria cone at Lathrop Wells (Fig. A.12).

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Fig. A.17
Plots of the size-frequency distribution for a
sample of gray pumice fall from the AD 79
eruption of Vesuvius. The upper plot
shows a cubic spline curve fit to the
data points (

), and the lower plot shows
the modeled distribution (solid curve) made
by adding three subpopulations of
sequential fragmentation/transport (SFT)
form (dashed curves).
(Adapted from Wohletz et al ., 1989.)

Tephra Chemistry and Alteration

Bulk chemical analyses can be obtained by x-ray fluorescence (XRF) and atomic absorption (AA) methods, as discussed for lava samples earlier; however, such analyses are not commonly performed because of the secondary alterations typical found in tephra samples. Glass silica content can easily be determined by index of refraction measurements with a petrographic microscope (see Fig. B.1). An x-ray analyzer attached to the scanning electron microscope (SEM) also provides a rapid means by which to obtain relative chemical compositions; using this technique, it is possible to analyze small alteration crystals that cover individual pyroclast surfaces (Fig. A.22). Many types of pyroclastic deposits show variations of glass surface chemistry with stratigraphic position (Fig. A.23). These variations can be interpreted with respect to the degree of secondary alteration (an essential measurement for porosity and permeability), the degree of water interaction during hydrovolcanic eruption, and changes in magma chemistry. In the Fig. A.24 plot of major-element variations for the Lathrop Wells scoria cone (Fig. A.12), the strongest variations occur in hydrovolcanic samples, as is the case for palagonitic constituents and surface alteration textures (Figs. A.19 and A.20, respectively).

Structural Analysis

Identification of geologic structures is a crucial component of field work. Frequently, these structures are most readily observed from stereo pairs of aerial photographs. In the field, definition of fault and vent structures requires careful correlation of rock units and close inspection of outcrop fabrics.

Regional Tectonic Control

Regional structures are related to past and present tectonic conditions such as crustal compression (thrusting and folding) and extension (block faulting, graben formation, and strike-slip movement). In general, regional structure exerts some control over volcanic vent locations and, to some degree, the type of volcanic complex that evolves (for instance, composite cones from compressional regions and scoria cone fields from extensional environments).

Stratigraphic Correlation and Volcano-Tectonic Models

As we discussed earlier, preparation of a detailed stratigraphy is one of the most important aspects of field work. Stratigraphic units in volcanic fields consist of old basement rocks, which are relatively large areally as a result of their sedimentary, intrusive, or metamorphic origin; rocks of petrologic consanguinty, such as older mafic rocks buried by younger intermediate extrusives; and widespread pyroclastic units.

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Correlation of these units can generally be accomplished in the field by examining out-crop textures and alteration, phenocryst contents, erosion surfaces, and overall rock type. For widespread pyroclastic units, discontinuities in surface elevation and thickness (for example, large thickness variations seen across graben- or caldera-bounding faults) are often used to identify fault locations. Composite cones can show facies such as near-vent intercalations of lava and pyroclastic units that change laterally to distal laharic deposits. Other typical stratigraphic successions for various volcanic field types are discussed in previous chapters (see also Cas and Wright, 1987).