Heat flow variations in the Antarctic Continent

Guimarães, Suze Nei Pereira; Vieira, None Fábio; Hamza, Valiya M.

doi:10.31214/ijthfa.v3i1.51

Cited by 4 publications

(4 citation statements)

References 28 publications

(41 reference statements)

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“…Within the central WARS, magnetic (Fox Maule et al, 2005) and seismic (An et al., 2015b; Shen et al., 2018) modeling suggested relatively lower geothermal heat flow. For much of this rift system involving lithospheric thicknesses of roughly 100 km or less, the heat flow resembles that of the US Basin and Range Province with estimates in the 60–70 mW/m 2 range and likely higher along the rift margins in Victoria Land and Marie Byrd Land (Guimarães et al., 2020; Wannamaker et al., 2017). This interpretation is also compatible with the seismic analysis of O'Donnell et al.…”

Section: Discussionmentioning

confidence: 93%

Satellite Gravity Constraints on the Antarctic Moho and Its Potential Isostatic Adjustments

Zhang

Frese

Shum

et al. 2020

Geochem Geophys Geosyst

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Section: Discussionmentioning

confidence: 93%

Satellite Gravity Constraints on the Antarctic Moho and Its Potential Isostatic Adjustments

Zhang

Frese

Shum

et al. 2020

Geochem Geophys Geosyst

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“…The global tomography model is too low resolution to be very useful, so it is excluded from our study. We include several more recent heat flux models in our analysis in addition to the two magnetic-based models, including the continent-wide seismic tomography-based model by An et al (2015), two statistical-based models by Shen et al (2020) and Stål et al (2021) and a thermal-based model by Guimarães et al (2020).…”

Section: Discussionmentioning

confidence: 99%

“…b. Heat flow is an ensemble average from the following sources: Maule (2005), An et al (2015), Martos et al (2017), Shen et al (2020), Guimarães et al (2020), Stål et al (2021). c. Ice thickness, d. bedrock elevation and f. bedrock slope have all been taken from Morlighem et al (2019).…”

Section: Introductionmentioning

confidence: 99%

“…This ensemble heat flux map may improve the accuracy of the average by reinforcing heat flux estimates where they agree and averaging out large contrasting anomalies where the models agree poorly (Supplemental Appendix). These models include a satellite-based magnetic Curie depth model (Maule 2005), a shear-wave tomography model (An et al 2015), a Curie airborne-based magnetic model (Martos et al 2017), an interpolated model based on current core heat flux measurements (Guimarães et al 2020) and two empirical seismic similarity models (Shen et al 2020, Stål et al 2021).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of subglacial lake melt source regions from site characteristics

Willcocks

Hasterok

2023

Antarctic Science

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Subglacial melt has important implications for ice-sheet dynamics. Locating and identifying subglacial lakes are expensive and time-consuming, requiring radar surveys or satellite methods. We explore three methods to identify source regions for lakes using seven continent-wide environmental characteristics that are sensitive to or influenced by ice-sheet temperature. A simple comparison of environmental properties at lake locations with their continent-wide distributions suggests a statistical relationship (high Kolmogorov-Smirnov statistic) between stable lake locations and ice thickness and surface temperatures, indicating melting under passive conditions. Active lakes, in contrast, show little correlation with direct thermally influenced parameters, instead exhibiting large statistical differences with horizontal velocity and bedrock elevation. More sophisticated techniques, including principal component analysis (PCA) and machine learning (ML) classification, provide better spatial identification of lake types. Positive PCA scores derived from the environmental characteristics correlate with stable lakes, whereas negative values correspond to active lakes. ML methods can also identify regions where subglacial lake melt sources are probable. While ML provides the most accurate classification maps, the combination of approaches adds deeper knowledge of the primary controls on lake formation and the environmental settings in which they are likely to be found.

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Applying machine learning to characterize and extrapolate the relationship between seismic structure and surface heat flow

Zhang,

Ritzwoller

2024

Geophysical Journal International

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Summary Geothermal heat flow beneath the Greenland and Antarctic ice sheets is an important boundary condition for ice sheet dynamics, but is rarely measured directly and therefore is inferred indirectly from proxies (e.g. seismic structure, magnetic Curie depth, surface topography). We seek to improve the understanding of the relationship between heat flow and one such proxy—seismic structure—and determine how well heat flow data can be predicted from the structure (the characterization problem). We also seek to quantify the extent to which this relationship can be extrapolated from one continent to another (the transportability problem). To address these problems, we use direct heat flow observations and new seismic structural information in the contiguous US and Europe, and construct three Machine Learning models of the relationship with different levels of complexity (Linear Regression, Decision Tree, Random Forest). We compare these models in terms of their interpretability, the predicted heat flow accuracy within a continent, and the accuracy of the extrapolation between Europe and the US. The Random Forest and Decision Tree models are the most accurate within a continent, while the Linear Regression and Decision Tree models are the most accurate upon extrapolation between continents. The Decision Tree model uniquely illuminates the regional variations of the relationship between heat flow and seismic structure. From the Decision Tree model, uppermost mantle shear wavespeed, crustal shear wavespeed and Moho depth together explain more than half of the observed heat flow variations in both the US (r2 ≈ 0.6 (coefficient of determination), RMSE ≈ 8mW/m2 (Root Mean Squared Error)) and Europe (r2 ≈ 0.5, RMSE ≈ 13mW/m2), such that uppermost mantle shear wavespeed is the most important. Extrapolating the US-trained models to Europe reasonably predicts the geographical distribution of heat flow (ρ = 0.48 (correlation coefficient)), but not the absolute amplitude of the variations (r2 = 0.17), similarly from Europe to the US (ρ = 0.66, r2 = 0.24). The deterioration of accuracy upon extrapolation is caused by differences between the continents in how seismic structure is imaged, the heat flow data, and intrinsic crustal radiogenic heat production. Our methods have the potential to improve the reliability and resolution of heat flow inferences across Antarctica and the validation and cross-validation procedures we present can be applied to heat flow proxies other than seismic structure, which may help resolve inconsistencies between existing subglacial heat flow values inferred using different proxies.

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Heat flow variations in the Antarctic Continent

Cited by 4 publications

References 28 publications

Satellite Gravity Constraints on the Antarctic Moho and Its Potential Isostatic Adjustments

Satellite Gravity Constraints on the Antarctic Moho and Its Potential Isostatic Adjustments

Prediction of subglacial lake melt source regions from site characteristics

Applying machine learning to characterize and extrapolate the relationship between seismic structure and surface heat flow

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