correcting typos, pre upload of final version

bibliography/phdt.bib

@@ -1801,7 +1801,7 @@ @Book{olea2012
 }
 
 @Article{ScheckWenderoth2014,
-  author    = {Scheck-wenderoth, Magdalena and Cacace, Mauro and Maystrenko, Yuriy Petrovich and Cherubini, Yvonne and Noack, Vera and Kaiser, Bj{\"{o}}rn Onno and Sippel, Judith and Bj{\"{o}}rn, Lewerenz},
+  author    = {{Scheck-Wenderoth}, Magdalena and Cacace, Mauro and Maystrenko, Yuriy Petrovich and Cherubini, Yvonne and Noack, Vera and Kaiser, Bj{\"{o}}rn Onno and Sippel, Judith and Bj{\"{o}}rn, Lewerenz},
   title     = {Models of heat transport in the Central European Basin System: Effective mechanisms at different scales},
   journal   = {Marine and Petroleum Geology},
   year      = {2014},
@@ -2245,7 +2245,7 @@ @Article{Jensen2002
 }
 
 @Article{Przybycin2015,
-  author    = {Przybycin, Anna M. and Scheck-Wenderoth, Magdalena and Schneider, Michael},
+  author    = {Przybycin, Anna M. and {Scheck-Wenderoth}, Magdalena and Schneider, Michael},
   title     = {{The 3D conductive thermal field of the North Alpine Foreland Basin: influence of the deep structure and the adjacent European Alps}},
   journal   = {Geothermal Energy},
   year      = {2015},

chapters/0100/phdt_0100.tex

@@ -22,11 +22,11 @@ \chapter{Introduction and motivation}
 Satellite-derived gravity (synthesised from global models or in the form of on-orbit gradiometry data) has been extensively employed in the last decade in inverse modelling of crustal thickness and density, at regional and global scales.
 The non-uniqueness of gravity inversion is usually overcome by integration of different geophysical observables.
 
-A variety of method have been developed to achieve this: through combination of models, e.g. \textcite{Eshagh2011} combined the seismological {CRUST2.0 model} \parencite{Bassin2000Crust20} with the gravimetric inversion of {EGM08} global gravity model \parencite{Pavlis2012EGM2008}, \textcite{Reguzzoni2015} used a mean-depth constrain based on geological provinces; or by constraining the a-priori parameters of gravity inversion using seismic estimates (where available) as ground truth for depths and density contrasts, e.g. resorting to iterative forward modelling \parencite{Ebbing2006}, cross-validation \parencite{Uieda2017}, and grid-searching for maximum correlation \parencite{Zhao2020}.
+A variety of methods have been developed to achieve this: through combination of models, e.g. \textcite{Eshagh2011} combined the seismological {CRUST2.0 model} \parencite{Bassin2000Crust20} with the gravimetric inversion of {EGM08} global gravity model \parencite{Pavlis2012EGM2008}, \textcite{Reguzzoni2015} used a mean-depth constrain based on geological provinces; or by constraining the a-priori parameters of gravity inversion using seismic estimates (where available) as ground truth for depths and density contrasts, e.g. resorting to iterative forward modelling \parencite{Ebbing2006}, cross-validation \parencite{Uieda2017}, and grid-searching for maximum correlation \parencite{Zhao2020}.
 Joint analysis of satellite gravity products and seismological models has been proven useful to assess the crust-mantle density contrast, as in the work by \textcite{Eshagh2016contrast}, which exploited the GOCE gradiometry data.
 
 Therefore, estimating the crustal contribution to the surface heat flow by assuming a `standard heat production' and scaling it with crustal thickness is tempting.
-Nevertheless, available evidence suggest against such a simple relationship \parencites{Mareschal2013}{Alessio2018deepRHP}.
+Nevertheless, available evidence suggests against such a simple relationship \parencites{Mareschal2013}{Alessio2018deepRHP}.
 However, crustal thickness variations on their own successfully isolate tectonothermal age groups, terranes, and geological provinces.
 An example of this was shown in \textcite{Grad2009}, through spatial filtering of their European Plate Moho model at different wavelengths.
 

chapters/0300/phdt_0300.tex

@@ -1398,7 +1398,7 @@ \subsection{Propagated uncertainty}
 
 % uncertainty on uncertainty
 Finally, it should be reiterated how the depth- and density-error models (described section~\ref{ss:SigIs:Impl:ErrorModel}) rely on a set of a strong assumptions, due to their simplicity.
-As already mentioned, this proposed strategy could be easily scaled to complex error models, based on data availability of sources and its associated error (e.g. due to different geophysical observables and inversion methods).
+As already mentioned, this proposed strategy could be easily scaled to complex error models, based on data availability of sources and their associated error (e.g. due to different geophysical observables and inversion methods).
 Still, there are biases that could affect such a kind of estimate, regardless of the error model.
 Two main issues suggest caution in the interpretation of uncertainty: systematic errors arising from modelling approximations, which propagate to a systematic "error on the uncertainty", and assumptions on the covariance between uncertainty at different grid nodes: if absence of spatial covariance (the case of this experiment) is indeed excessive, over optimistic smoothing may severely underestimate errors in areas where abrupt changes in geological features are expected.
 

chapters/0400/phdt_0400.tex

@@ -368,7 +368,7 @@ \subsection{Iterative fit of heat generation}
 
 The depth-wise distribution of radioactive elements is poorly modelled by simple functions \parencite{Jaupart2003} and is difficult to constrain from indirect observables.
 A decay with depth at large scale is commonly expected, owing to the progressive depletion of the lowermost crustal terms.
-Still, this model is challenged by evidence of heat-producing element rich bodies in the lower crust \parencite{Alessio2018deepRHP}, which suggest reconsidering the role attributed to crustal-scale differentiation.
+Still, this model is challenged by evidence of heat-producing element rich bodies in the lower crust \parencite{Alessio2018deepRHP}, which suggests reconsidering the role attributed to crustal-scale differentiation.
 I therefore resort to using one bulk heat production for the whole crystalline crust, using an initial value that I derive from the lithotype medians reported in \textcite{Vila2010} and the reference crustal column of \textcite{Wedepohl1995}.
 
 This initial value of bulk heat production is then fitted to the measured surface heat flow, in cells where it is available, using the following scheme.