Inferring Crustal Temperatures Beneath Italy From Joint Inversion of Receiver Functions and Surface Waves

Abstract

Temperature distribution at depth is of key importance for characterizing the crust, defining its mechanical behavior and deformation. Temperature can be retrieved by heat flow measurements in boreholes that are sparse, shallow, and have limited reliability, especially in active and recently active areas. Laboratory data and thermodynamic modeling demonstrate that temperature exerts a strong control on the seismic properties of rocks, supporting the hypothesis that seismic data can be used to constrain the crustal thermal structure. We use Rayleigh wave dispersion curves and receiver functions, jointly inverted with a transdimensional Monte Carlo Markov Chain algorithm, to retrieve the VS and VP/VS within the crust in the Italian peninsula. The high values (>1.9) of VP/VS suggest the presence of filled‐fluid cracks in the middle and lower crust. Intracrustal discontinuities associated with large values of VP/VS are interpreted as the α−β quartz transition and used to estimate geothermal gradients. These are in agreement with the temperatures inferred from shear wave velocities and exhibit a behavior consistent with the known tectonic and geodynamic setting of the Italian peninsula. We argue that such methods, based on seismological observables, provide a viable alternative to heat flow measurements for inferring crustal thermal structure.

Deriving a New Crustal Model of Northern Adria: The Northern Adria Crust (NAC) Model

“…As pointed out by Diaferia et al (2019), applying an empirical relationship can be limiting, when the crust is heterogeneous, as shown by the high point dispersion (Figure 5). We tried to overcome this problem with the "regionalization" of the data, which resulted in the definition of two main domains, characterized by lithological differences.…”

“…In the remaining of the model, where igneous and metamorphic rocks are dominant, and at greater depths, we used the BRF relationship. ML would be more appropriate for this region, if also here there is a pervasive fluidfilled cracks distribution at depth, according to the results of Diaferia et al (2019) in the Apennines. The comparison with the available observation of V S values confirmed that we get greater effectiveness when empirical relationships to convert V P into V S , ρ, E, and µ are applied before the interpolation.…”

Geothermal Resources and ATES Potential of Mesozoic Reservoirs in the North German Basin

“…(12) We chose a minimum depth of more than 100 m below surface as the uppermost 100 m are frequently used for drinking water purposes, especially in urbanized areas, and we want to avoid any interference here. (13) The maximum depth for HIP was chosen to be 2500 m below surface as drilling costs and risks increase exponentially with depth, and this is a feasible cutoff point [45]. For the HSP, 750 m below surface was chosen in order to reduce drill costs and production costs, as the potential is higher, the shallower the storage site is located.…”

“…Alternatively, less cost-intensive, but more indirect and imprecise, estimations of the subsurface temperature are derived from other observations. These observations include seismics (e.g., [13]), satellite data (e.g., [14]) and magnetotellurics (e.g., [15]). Another means of deriving temperatures is numerical modeling.…”

WISTFUL: Whole‐Rock Interpretative Seismic Toolbox for Ultramafic Lithologies

“…(1996, 1997) built upon this methodology by incorporating the effects of anelasticity and partial melt to interpret the mantle seismic wave speeds beneath the Massif Central, France. The combination of thermodynamic modeling in conjunction with laboratory‐constrained mineral elastic moduli and densities has been used to interpret continental lower crust (e.g., Behn & Kelemen, 2003; Diaferia & Cammarano, 2017; Sammon et al., 2020), arc lower crust (e.g., Behn & Kelemen, 2006; Jagoutz & Behn, 2013), the mantle wedge at subduction zones (Hacker, Abers, & Peacock, 2003; Hacker, Peacock, et al., 2003), lunar mantle (Kuskov, Kronrod, Prokofyev, & Pavlenkova, 2014; Kuskov, Oleg, Kronrod, & Kronrod, 2014), cratonic lithospheric mantle (Kuskov et al., 2006; Kuskov, Kronrod, Prokofyev, & Pavlenkova, 2014; Kuskov, Oleg, Kronrod, & Kronrod, 2014), and continental geotherms and Moho temperatures (e.g., Diaferia et al., 2019; Schutt et al., 2018). To address the potential influence of sampling bias in compositional‐wave speed relations, Behn and Kelemen (2003) applied thermodynamic modeling and compiled elastic moduli for a synthetic database intended to span the full compositional space of anhydrous igneous and meta‐igneous rocks.…”