April 2016 Issue Table of Contents
The accumulation of sizeable volumes of magma in the upper crust may produce plutons and/or result in supereruptions. Geophysical observations provide constraints on the rates, volumes, and melt distributions in magmatic systems, but they suffer from limited resolution and inherent nonuniqueness. Different, yet complementary, geophysical approaches must be combined with petrological, laboratory, and geochemical measurements. We summarize the results from such a combined approach from the central Andes. Taking a global perspective on large silicic systems reveals that several have ~10% partial melt over large areas (10s of km2), and there may be localized zones with 50% or more.
Keywords: volcano deformation, supervolcano, seismology, tomography, InSAR, magnetotellurics, gravity.
Much of our understanding of the structure of the Earth’s interior has been gained from geophysical observations. Geophysical data from seismic, gravity, magnetotelluric, and geodetic investigations provide critical glimpses into the subsurface and are necessary to constrain the extent and size of the magma plumbing systems beneath active volcanoes. A fundamental outstanding question in the Earth sciences is, “What drives a large silicic magma body to go from a state of stable magma storage to a violent caldera-forming eruption?” The accumulation of large volumes of silicic melt in the upper crust is necessary for the development of continental crust and results in either the production of large granitic plutons (most commonly) and/or very infrequent but catastrophic events which erupt >1,000 km3 of material, better known as supereruptions. The link between these two outcomes is evasive, and many previous studies have focused on building the connection between the development of plutons and the volcanoes that produce supereruptions.
This issue of Elements seeks to address the enigmatic relationship between volcanoes and their plutonic counterparts. Supervolcano-size systems must accumulate melt over periods of hundreds of thousands to millions of years into bodies that can produce supereruptions which emplace hundreds to thousands of cubic kilometers of material (e.g. de Silva and Gregg 2014). As such, most of the lifetime of an active supereruptive system is potentially spent developing extensive mush or melt-rich zones throughout the crust. Given that partial melts must be in residence for such long time periods, it is logical to expect that their existence should be observable using geophysical techniques (Reid 2008). The alternative is that large magma systems may accumulate rapidly, making them more difficult to resolve (Reid 2008). To test these end-member ideas, we explore geophysical constraints on the number of magma chambers and the amount of melt using cutting-edge advancements in geophysical imaging of large silicic magma systems.
The discussion of geophysical observations in this article is divided into two main groups (Fig. 1): first, 3-D static observations, such as seismic and potential field; second, 4-D transient or dynamic observations, such as geodetic measurements of surface deformation from magma movement. The benefits of each geophysical approach are reviewed, as well as potential limitations such as nonuniqueness and discordant results. We then provide a case study in which multiple geophysical observations were used to infer partial melt in the Altiplano–Puna magma body (APMB; Figs. 1 and 2) of the central Andes. The APMB is the largest known zone of partial melt in the continental crust, spanning a region 200 km in diameter, 4–25 km below sea level (bsl): one estimate puts it at 500,000 km3 (Ward et al. 2014). Finally, we compare the APMB with other large crustal silicic centers worldwide and outline directions for future work to understand the location and amount of partial melt in the continents.
Geophysicists and geochemists often have different assumptions when using the term “magma chamber” or “magma body.” Although geophysicists sometimes use the term for any amount of partial melt, we will adopt the definitions used by Bachmann and Bergantz (2008): a magma chamber has eruptible magma with crystal content <50%; a crystal mush is a configuration of melt and crystals where the crystal content >50%; a magma reservoir contains chambers and mush. By these definitions the Altiplano–Puna “magma body” of the central Andes (Figs. 2, 3) could more properly be called a “mush body” (e.g. Ward et al. 2014). While in the latter case the acronym “APMB” remains, the body itself might more speculatively be termed a “magma reservoir.”
IMAGING CRUSTAL MAGMA SYSTEMS IN 3-D
As rocks melt, their physical properties change. Among those that change are the seismic velocities (pressure and shear waves: Vp and Vs, respectively), density, electrical and magnetic characteristics (e.g. resistivity), and the attenuation of Vp and Vs waves (governed by the quality factors for each; e.g. Iyer 1984). Geophysical observations can be used to infer a current snapshot of partial melt in the subsurface and arrays of these instruments potentially provide a 3-D view. However, because observations are always made many kilometers away from the region of interest, there are ambiguities in resolving and interpreting the signals.
Seismic waves are one of the most versatile tools for sensing partial melt because they travel through the subsurface and are refracted, attenuated, reflected, and/or converted from P to S waves when passing through areas of changing seismic velocity and density (e.g. Iyer 1984). Among the most popular methods is seismic tomography, which produces attractive 3-D views of subsurface variations in seismic velocity (Fig. 2D). Tomographic images can be created by using waves generated by earthquakes (earthquake tomography), manmade explosions, or from “ambient noise,” e.g. ocean waves or other signals that are measured at multiple stations within the array (Fig. 2D) (e.g. Brenguier et al. 2008; Ward et al. 2014). However, seismic tomographic images are often highly smoothed—large seismic velocity anomalies are reduced in amplitude and smeared out over a larger area than they cover in reality. The result is that seismic tomography typically underestimates the melt content of a partially molten region due to noise and to limitations in resolution (e.g. Lees 2007; Paulatto et al. 2012). Sharper images of abrupt seismic velocity and density anomalies (for example, at the edge of a region of partial melt) are possible by observing seismic waves that are reflected and/or converted from P to S waves at the interface by using the receiver function, seismic reflection, or other methods. These techniques have been used to discover the Socorro (New Mexico, USA) and other magma reservoirs (e.g. Iyer 1984; Zollo et al. 2008).
The method of magnetotellurics (MT) uses continuously recorded observations of the natural electric and magnetic fields at the surface of the Earth (e.g. Heise et al. 2010; Comeau et al. 2015). By analyzing the frequency content of the electromagnetic signals, the resistivity of the subsurface can be inferred (because longer wavelengths probe deeper depths). A network of MT stations can be used to determine the lateral resistivity structure. While MT measurements have been made for decades (e.g. Iyer et al. 1984), they are on the increase because of more reliable instrumentation being more widely available (for example, through the EarthScope Transportable Array), and there is the ability to develop 2-D and 3-D models using improved computational resources.
Different geophysical methods have different sensitivities for detecting partially molten rocks. In areas of potential melt, seismic velocity varies in extreme cases by a factor of 2 to 3 for Vs or Vp, whereas the density of a rock only changes by about 10% when melted. Yet, because the regions that melt sometimes span tens of kilometers, the integrated effect of density variations on the gravity field is pronounced and observable (e.g. del Potro et al. 2013). Alternatively, seismic wave attenuation and electrical resistivity change by orders of magnitude in the presence of partial melt (e.g. Schilling et al. 2006; Comeau et al. 2015); thus, attenuation tomography can be a very valuable tool for mapping melts, but is only rarely applied because it is noisier and more difficult than conventional seismic velocity tomography (Lees 2007). Geophysical observations on properties such as Vs, gravity, or resistivity can be converted to melt fraction by estimating the following parameters directly using laboratory samples or equations based on experiments (e.g. Chu et al. 2010; Comeau et al. 2015): composition (including silica, H2O, CO2, Na), temperature, pressure, density, melt connectivity (usually a range of values is assumed), and elastic moduli.
IMAGING CRUSTAL MAGMA SYSTEMS IN 4-D
Geophysical changes measured near a volcano provide critical information on how the system could be evolving towards eruption (e.g. Lowenstern et al. 2006). However, even without an eruption and allowing for some ambiguities in interpretation, these observations are valuable to constrain the location, geometry, and volume of moving melt. Critical observations include changes in the frequency, location, and type of earthquakes (e.g. Lowenstern et al. 2006), surface deformation measured from sensors on the ground (e.g. tiltmeters, or global navigation satellite system [GNSS]) or from space (e.g. interferometric synthetic aperture radar [InSAR]), and changes in gravity. An emerging area of research is the measurement of temporal changes in seismic velocity and anisotropy: the latter referring to the variation in seismic velocity that is dependent on the orientation of the wave relative to the crystal axes of any mineral alignments or cracks in the rock, also called the birefringence in optics (e.g. Brenguier et al. 2008).
Ideally, all of these measurements are made at the same time because they are highly complementary. For example, while InSAR provides images with millions of measurements of surface deformation (Fig. 4) even in remote areas without ground sensors, the measurements are only made when the satellite flies overhead (and the satellite is turned on). Ground observations are essential to measure rapid changes in deformation. Further, magma can move without causing surface deformation; but time-variable gravity is able to observe magma movements. To be clear, time-variable microgravity observations (dynamic surveys) are more sensitive than the typical field surveys that measure the regional gravity field (e.g. Singer et al. 2014). Further, gravity can resolve the density of the fluids in motion—is it a magma or a supercritical fluid?—while the other techniques cannot.
NONUNIQUENESS AND COMPARING GEOPHYSICAL TECHNIQUES
Every type of geophysical observation described above is nonunique, i.e. the observations can be explained equally well by different models. This fact has been recorded in an old joke that goes something like this: “A geologist, a geochemist, and a geophysicist were asked the weight of a 55-pound bag of salt. The geologist replied, ‘About 50 pounds.’ The geochemists said, ‘54 plus or minus 2 pounds.’ The geophysicist said ‘What number do you want me to say?’” Many geophysicists cringe at this joke and respond that it is difficult to use surface observations to make quantitative statements about conditions kilometers below the surface, but they also recognize the element of truth: multiple interpretations of the data exist. The real question is, “What do any given data reliably show?”
Each geophysical method is sensitive to particular variations in the material properties of rock. For example, gravity measurements provide information about density structure (Fig. 2E), while MT stations record variations in subsurface resistivity (Figs. 2B and 2C), which are related to partial melt and brines. We can take advantage of these different sensitivities to reduce and better understand nonuniqueness using several approaches. One approach is data inversions that use multiple types of data simultaneously (e.g. seismic and MT), and these are an active area of research. Another approach is comparing the results of each technique when analyzed independently, which allows geophysicists to assess the nonuniqueness inherent in each approach (Fig. 2).
Another way to reduce nonuniqueness is to improve the quantity/quality of data available. For example, with deformation data, the ambiguity between source depth, shape, and volume change can be reduced by combining observations of horizontal and vertical displacements (e.g. Fialko and Pearse 2012). Further reduction in nonuniqueness may be accomplished by making more complete use of the data available through sophisticated modeling. Seismic inversions that use the whole waveform (instead of just small segments or picks of P and S wave arrival times) have long been used in the oil and gas industry and show great potential, albeit at great computational cost (e.g. Morgan et al. 2013).
When interpreting a geophysical image or model (Fig. 2), one should always try to determine the family of models that are permitted by the data instead of a single model. For example, the red area in Figure 3B shows the models that fit the static gravity data of the central Andes. But not all models are physically realistic, and in this case it is Figure 2E that is geologically favored. In some cases, very different conceptual models can be consistent with the data, as is the case of the deformation data at Volcán Uturuncu (Fig. 2A) in the central Andes above the APMB. Ground deformation at Uturuncu can be explained either by stacked magma chambers or by a rising diapir model (Fig. 3A). More fundamentally, ground uplift can be caused by several processes, many of which do not require the injection of new magma, such as by pressurization of a reservoir by gas exsolving from a magma, or by supercritical fluids in a hydrothermal system, or by melting and heating of country rock.
CONSTRAINING MELT IN THE CENTRAL ANDES: THE PLUTONS PROJECT
Many investigators have identified the central Andes as a key modern location to study the silicic plutonic–volcanic connection because it is the site of the Altiplano–Puna Volcanic Complex (APVC), an ignimbrite flare-up that occurred over the past 10 My (e.g. Bachmann et al. 2007). This complex has a large partially molten region that has been observed in the mid-crust by seismology, deformation, and electrical resistivity (e.g. Schilling et al. 2006; Fialko and Pearse 2012; Henderson and Pritchard 2013). Recently, the interdisciplinary and international PLUTONS project (plutons.science.oregonstate.edu) collected geophysical (deformation, gravity, seismic and MT) and geochemical observations in this region in an effort to compare the current subsurface distribution and composition of partial melts with the surface volcanic record. Geochemical analysis showed that the APVC ignimbrites and the Uturuncu dacites were erupted from chambers that were, or are, 1 km above to 4 km below sea level (bsl) (e.g. Schilling et al. 2006; Muir et al. 2014). This is shallower than the APMB, whose depth from the different techniques we describe below. Some of the key PLUTONS geophysical findings are shown in Figure 2 and are summarized here:
All of the geophysical methods indicate significant anomalies near the APMB, but these results are not all consistent with each other. One must ask why does the depth to the top of the APMB from magnetotellurics (~13–14 km bsl; Comeau et al. 2015) differ by almost 10 km from that of the joint ambient noise tomography and receiver function solution (4–5 km bsl; Ward et al. 2014)? Answering this question is an area of active research, but two explanations have been offered. The first is to appeal to the low spatial resolving power of the ambient noise tomography, because of the long wavelengths of the seismic waves used, i.e. perhaps Figure 2D has merged together several low velocity pathways between the surface and AMPB (visible in the MT and gravity results Figs. 2B and 2E; Comeau et al. 2015). If true, higher resolution seismic tomography (e.g. West et al. 2013) could test this option. The second possibility is that the materials between 4–13 km bsl really do have a low seismic velocity but relatively high resistivity (compared to the APMB below).
Seismic and MT methods are not equally sensitive to partial melts, and this is one reason why it is useful to use both methods together because combining the observations might allow one to reduce the nonuniqueness in the geologic interpretation of the geophysical anomalies. MT data are very sensitive to the degree of fluid interconnectivity: some melt may not be detected if it isn’t connected enough to reduce resistivity. So, perhaps the seismic data senses fluids (partial melt and/or brines), but the MT cannot detect them because either they are not connected or the fluids have a high resistivity. Based on the high calculated resistivity of Uturuncu dacites (Comeau et al. 2015), it is unlikely that the low resistivity observed between 0 and 10 km bsl (C4 in Fig. 2C) is caused by dacite partial melts, but it could be from low resistivity brines, perhaps exsolved from the APMB below (e.g. Comeau et al. 2015). It is possible to test the idea that there are brines and not melt at 5 km bsl because the two scenarios have different implications for heat flow, but heat flow data are limited in the study area. If the low seismic velocities really are caused primarily by brines above 13 km bsl, it would reduce the high estimates of the inferred intrusive:extrusive ratio of 25–45:1 from Ward et al. (2014).
The amount of melt in the APMB is at least 4–25% based on combining the seismic and MT observations with laboratory-derived relations between Vs, resistivity, and melt percentage using appropriate pressure, temperature, and compositional inferences from volcanic samples (e.g. Schilling et al. 2006; Ward et al. 2014; Comeau et al. 2015). Melt volumes up to 80–90% are permitted by the gravity and MT data and may exist in small regions of the APMB (e.g. del Potro et al. 2013; Comeau et al. 2015) but bulk values above 35–50% are considered unlikely because they would not be physically stable and would produce an S-wave shadow zone, which has not been observed (e.g. Chu et al. 2010; Ward et al. 2014). It is possible that there are true magma chambers (or “reservoirs” using the definition above) at 13–25 km bsl, but they are not well resolved by the available data, and in any case, are 10–25 km deeper than the now-frozen magma chambers that erupted the ignimbrites and Uturuncu dacites.
GLOBAL PERSPECTIVE AND FUTURE EFFORTS
Returning to the question that motivated this work: “Are regions of partial melt such as the APMB globally common?” In Table 1, we update previous reviews of geophysical evidence for large magma reservoirs (e.g. Iyer 1984; Bachmann et al.; Lees, 2007). There are several volcanic regions that have >10% partial melt over large areas (tens of km2), and it is likely that there are localized zones of even higher melt in some places (e.g. the Campi Flegrie in Italy and the APMB), although the “smoking gun” of an S-wave shadow zone has not yet been found.
It is worth repeating that inferring partial melt from geophysical observations involves nonuniqueness in the data inversion and in using laboratory experiments to simulate real subsurface conditions. Some steps in this process may underestimate the amount of melt: for example, the smoothing of seismic tomography can underestimate melt by >20% (e.g. Paulatto et al. 2012). Other assumptions may lead to overestimates, such as when the resistivity of the melt is lower than assumed (e.g. Pommier and Garnero 2014).
While no certain evidence currently exists of large volumes of eruptible magma, we should not be satisfied that no such evidence can be found. To identify these zones with confidence, further work is needed in several areas: field deployments of dense networks of geophysical instruments using multiple methods in the same areas, especially at volcanoes that just had large silicic eruptions; improved inversion techniques, like full waveform inversion and joint inversions; combining geophysical observations with geochemistry, geodynamics, and numerical modeling techniques; and laboratory experiments to determine the subsurface characteristics that could match the geophysical observations.
We thank the guest editors, reviewers Pete LaFemina and Geoff Wadge, and we thank Matthew Comeau, Rodrigo del Potro, Scott Henderson, Kevin Ward, and Mike Poland for figures. Development of viscoelastic models for investigating large silicic systems was funded by a US National Science Foundation Postdoctoral Fellowship (EAR 0815101, Gregg) and an Oregon State University CEOAS Institutional Postdoc (Gregg). We thank all participants in the PLUTONS project for discussions over the years and the US National Science Foundation (EAR-0908281, Pritchard) and UK National Environmental Research Council for funding.
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