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Geodynamic Diagnostics, Scientific Visualisation and Staglab Geosci. Model Dev., 11, 2541–2562, 2018 https://doi.org/10.5194/gmd-11-2541-2018 © Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License. Geodynamic diagnostics, scientific visualisation and StagLab 3.0 Fabio Crameri Centre for Earth Evolution and Dynamics (CEED), University of Oslo, P.O. Box 1028 Blindern, 0315 Oslo, Norway Correspondence: Fabio Crameri ([email protected]) Received: 31 December 2017 – Discussion started: 20 February 2018 Revised: 24 May 2018 – Accepted: 7 June 2018 – Published: 29 June 2018 Abstract. Today’s geodynamic models can, often do and everyone, is written in MATLAB and adjustable for use with sometimes have to become very complex. Their underlying, geodynamic mantle convection codes. increasingly elaborate numerical codes produce a growing amount of raw data. Post-processing such data is therefore becoming more and more important, but also more challeng- ing and time-consuming. In addition, visualising processed 1 Overview data and results has, in times of coloured figures and a wealth of half-scientific software, become one of the weakest pillars The first basic numerical geodynamic models were devel- of science, widely mistreated and ignored. Efficient and au- oped in the early 1970s (e.g. Minear and Toksöz, 1970; Tor- tomated geodynamic diagnostics and sensible scientific vi- rance and Turcotte, 1971). Since then they have become sualisation preventing common pitfalls is thus more impor- more powerful and often more complex (see e.g. King, 2001; tant than ever. Here, a collection of numerous diagnostics for Gerya, 2011; Lowman, 2011; Coltice et al., 2017). Indeed, plate tectonics and mantle dynamics is provided and a case dynamically self-consistent geodynamic models used to re- for truly scientific visualisation is made. Amongst other diag- produce the first-order characteristics of the complex plate– nostics are a most accurate and robust plate-boundary identi- mantle system, like mobile surface plates, single-sided sub- fication, slab-polarity recognition, plate-bending derivation, duction and mantle plumes, that need a certain complexity surface-topography component splitting and mantle-plume (e.g. Crameri and Tackley, 2015). However, this complex- detection. Thanks to powerful image processing tools and ity often inhibits a simple understanding of the full inter- other elaborate algorithms, these and many other insight- play between all individual physical aspects of these models: ful diagnostics are conveniently derived from only a subset the models become too complicated to be easily explained of the most basic parameter fields. A brand new set of sci- or even fully understood. In addition, the more elaborate entific quality, perceptually uniform colour maps including numerical codes powering these models (e.g. Zhong et al., devon, davos, oslo and broc is introduced and made freely 2000; Gerya and Yuen, 2007; Moresi et al., 2007; Tackley, available (http://www.fabiocrameri.ch/colourmaps, last ac- 2008; Davies et al., 2011; Thieulot, 2014; Kaus et al., 2016; cess: 25 June 2018). These novel colour maps bring a signif- Heister et al., 2017) and the still increasing computational icant advantage over misleading, non-scientific colour maps power available for their execution produce more and more like rainbow, which is shown to introduce a visual error to raw data. The resulting amount of data to be processed easily the underlying data of up to 7.5 %. Finally, STAGLAB (http: exceeds the capability of a human scientist. Today, efficient, //www.fabiocrameri.ch/StagLab, last access: 25 June 2018) automated and intelligent geodynamic diagnostics are thus is introduced, a software package that incorporates the whole more important than ever to keep up with the advances made suite of automated geodynamic diagnostics and, on top of in numerical models. that, applies state-of-the-art scientific visualisation to pro- Moreover, conveying new findings to the community crit- duce publication-ready figures and movies, all in the blink ically depends on data visualisation as figures are pivotal to of an eye and all fully reproducible. STAGLAB, a simple, make raw data tangible, understandable and explainable (e.g. flexible, efficient and reliable tool made freely available to Gerya, 2010). However, it has also become increasingly dif- ficult to visualise research given the increasing complexity Published by Copernicus Publications on behalf of the European Geosciences Union. 2542 F. Crameri: StagLab 3.0 of models (e.g. high-resolution and 3-D geometry) and new 2.1 Generic flow diagnostics and improved visualisation techniques (e.g. coloured figures and movies). Worrisome visualisation pitfalls arise that make A suite of generic flow diagnostics is often calculated and figures confusing, unreadable or even misleading and hence exported to file directly by fluid-dynamics codes (see e.g. unscientific. The rainbow colour map strongly deteriorates, Zhong et al., 1998; Tackley, 2000). These include, for exam- for example, the underlying scientific data (Pizer and Zim- ple, root mean square (RMS) flow velocity, the Nusselt num- merman, 1983; Ware, 1988; Tufte, 1997) due to the inhomo- ber, which is the ratio of the total heat flux across a fluid layer geneous colour sensitivity of the human retina (Thomson and to its conductive value (Chandrasekhar, 1961), heat flow and Wright, 1947). On top of that, it is confusing to people with mean temperature. These are often either given as a func- some of the most common colour-vision deficiencies. Even tion of depth or as depth-average values. Other generic flow though all this has been known for awhile (see Rogowitz and diagnostics like plateness and mobility, both characterising Treinish, 1996, 1998; Light and Bartlein, 2004; Borland and the surface boundary layer of mantle convection, are men- Ii, 2007, and #endrainbow), the rainbow colour map is still tioned below in more detail in addition to a list of different commonly used by scientists and commonly accepted by sci- approaches to derive residual mantle temperatures. entific journals. In addition to the current need for more ad- vanced geodynamic diagnostics, it is thus clear that, today, 2.1.1 Plateness efficient scientific quality visualisation has also become more One of the key characteristics of ocean-plate tectonics is important than ever. wide, almost rigid plate interiors bounded by localised, weak Nevertheless, there is currently no tool available to per- plate boundaries (Crameri et al., 2018). Plateness is a mea- form key geodynamic diagnostics and scientific visualisation sure of how localised the surface deformation is and thus for efficiently and reliably. STAGLAB (http://www.fabiocrameri. how well a surface represents ocean-plate tectonics (Wein- ch/StagLab, last access: 25 June 2018) is the post-processing stein and Olson, 1992; Tackley, 2000). Plateness is derived and visualisation software that aims to fill this gap and ful- from the second invariant of the strain rate on an xy plane fil all the necessities mentioned above. Previous versions of spanning the surface STAGLAB (Crameri, 2013, 2017a) were designed to specif- q ically handle the raw data produced by the finite-difference, 2 2 2 "Psurf D "Pxx CP"yy C 2"Pxy; (1) finite-volume multi-grid mantle convection code StagYY (Tackley, 2008). STAGLAB 3.0 (Crameri, 2017b), however, from which the total integrated "Psurf and the surface-area offers potential compatibility with other geodynamic codes fraction f80, in which the highest 80 % of that deforma- as well and has, for example, already been tested successfully tion occurs, can be calculated (Tackley, 2000). For perfect with output from the finite-element code Fluidity (Davies plates, f80 would be zero, with deformation only taking et al., 2011). place within infinitely narrow zones, while for uniformly dis- In the following, I will present key geodynamic diagnos- tributed plates, f80 would be 0.8. A non-dimensional plate- tics and how to automate them, discuss scientific and non- ness parameter, P , is finally derived by scientific colour schemes and visualisation approaches, and f80 introduce STAGLAB, the all-in-one software package that P D 1 − ; (2) fc makes powerful geodynamic diagnostics and state-of-the-art scientific visualisation easily accessible to everyone. where fc is the characteristic surface-area fraction for a flow with given vigour and heating mode, and is, for example, fc D 0:6 for internally heated convection with a Rayleigh 6 2 Geodynamic diagnostics number of RaH D 10 (Tackley, 2000). In science, it is desirable to rely on quantitative measures 2.1.2 Mobility rather than on visual impression, also for reasons outlined in Sect.3. Here, I list a variety of key diagnostics for the dynam- Another key characteristic of ocean-plate tectonics is the mo- ics of the plate–mantle system that all can be automatised tion of the surface plate. Mobility is a measure of this motion and applied to digital data. While some of these diagnostics across a specified surface. It is simply defined by are already well known, others are significantly improved or vrms;surf M D ; (3) newly introduced here. The geodynamic diagnostics covered vrms;global here are listed in Table2 and explained in detail below and where v and v are the RMS velocities aver- can be predominantly applied to 2-D data or vertical slices rms;surf rms;global aged over the surface and over the whole domain, respec- of Cartesian 3-D data. All of the diagnostics are tested and tively (Tackley, 2000). Values of M > 1 (e.g. M D 1:3) indi- implemented in the software STAGLAB 3.0 (see Sect.4). cate plate-like motion, whereas a value of M D 1:0 would be measured for isoviscous mantle convection with no stiff top boundary layer. Geosci. Model Dev., 11, 2541–2562, 2018 www.geosci-model-dev.net/11/2541/2018/ F. Crameri: StagLab 3.0 2543 Table 1.
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