Deep Crust Composition
The composition of the deep continental crust, despite its influence over crust evolution and deformation, remains a mystery. The middle and lower crust contribute critically to the temperature, structure, and stress state of the overlying continent and underlying lithospheric mantle. Due to its inaccessibility and poorly constrained major and trace element abundances, the origin and evolution of the deep crust have sparked decades of scientific debate. Varied tectonic regimes and widespread crustal heterogeneity have led to numerous geochemical and geophysical models that can explain local phenomena but struggle to produce a coherent global picture. Individual attempts among the geosciences to resolve the debate are limited by nonunique solutions and poorly quantified uncertainties. Thus, the deep crustal puzzle remains unsolved.
Because we cannot sample it or analyze the deep crust’s composition in situ (it is some twenty to forty kilometers beneath our feet), we study amphibolite and granulite massifs and xenoliths to understand the geochemical composition of the deep crust. As metamorphic facies, however, amphibolites (medium-grade) and granulites (high-grade) span a confounding range of mafic (< 52 wt.% SiO2) to felsic (> 68 wt.% SiO2) compositions. Such wide variation leads to nonunique solutions to the deep crust's composition and density structure. Bulk composition estimates for the deep crust therefore vary by > 10% SiO2 and heat production by 20%. Because of the relative scarcity and the compositional diversity of samples, it is difficult to constrain the bulk composition of the deep crust through geochemical or petrological measures alone.
Joint geochemical-geophysical crust models help alleviate the nonuniqueness in crust modeling. Using thermodynamic modeling softwares (such as Perple_X), we can determine a plausible range of physical and chemical properties for the deep crust. Compression or shear wave velocities can be compared to seismic measurements to inform us, in part, of the deep crust's composition and structure. This, in turn, helps us build a more complete picture of crust evolution, plate tectonics, and the distribution of trace elements on Earth.
Earth radiates 46 3 TW of heat, but the contribution from radioactive isotopes vs. leftover heat from planetary accretion is unknown. Estimates of radiogenic heat range from 10 TW to 30 TW in bulk silicate Earth (crust + mantle). However, the emerging field of neutrino geoscience and geoneutrinos offers insight into the abundance and distribution of heat producing elements (HPE: U, Th, and K).
Geoneutrinos are electron neutrinos or antineutrinos emitted from the beta decay of radionuclides within the earth. geoneutrinos are chargeless subatomic particles that travel through most matter without interacting. In that way, geoneutrinos from radioactive decays within the deep Earth travel unimpeded from their source. Only geoneutrinos from the 232Th and 238U decay chains are energetic enough to be captured by the current inverse beta decay (IBD) detection methods. With IBD detection, two flashes of light are recorded at specific energies, which are correlated in time and space, in kiloton-sized detectors of scintillating fluid. Geoneutrinos collide with free protons (p+, hydrogen nuclei) in the scintillating fluid to produce a positron (e+) and a neutron (n). These correlations in time, space, and energy allow for the unique identification of geoneutrinos. The number of geoneutrinos detected is directly proportional to the number of decays of U and Th.
The geoneutrino signal at a given detector, measured in TNU (terrestrial neutrino units), is a combination of crust-sourced and mantle-sourced geoneutrinos. Approximately 50% of the signal is produced from nearfield (< 500 km from the detector) and 50% from farfield (> 500 km from the detector) sources. Since geoneutrinos do not carry directional information, we must constrain the lithospheric signal in order to parse out mantle abundances of Th and U. Crustal HPE abundance models must be constructed for each detector location, accounting for the specific types and proportions of lithologies surrounding the detector. My research focuses on techniques for improving deep crust compositional models.