**Subduction zones are ubiquitous features on Earth, and an integral part of plate tectonics. They are known to have a very important role in modulating climate on Earth, and are believed to have played an essential part in making the Earth’s surface habitable, a role that extends to present-day. This week, Antoniette Greta Grima writes about the ongoing debate on how subduction zones form and persist for millions of years, consuming oceanic lithosphere and transporting water and other volatiles to the Earth’s mantle.**

Before we can start thinking about how subduction zones form, we need to be clear on what we mean by the term **subduction zone**. In the most generic sense, this term has been described by White et al. (1970) as “an abruptly descending or formerly descended elongate body of lithosphere, together with an existing envelope of plate deformation”. In simple words this defines subduction zones as places where pieces of the Earth’s lithosphere bend downwards into the Earth’s interior. This definition however, does not take into account the spatio-temporal aspect of subduction zone formation. It also, does not differentiate between temporary, episodic lithosphere ‘peeling’ or ‘drips’, thought to precede the modern-day ocean plate-tectonic regime (see van Hunen and Moyen, 2012; Crameri et al., 2018; Foley, 2018, and references therein) and the rigid self-sustaining subduction, which we see on the present-day Earth (Gurnis et al., 2004).

A **self-sustaining subduction zone** is one where the total buried, **rigid slab length** extends deep into the **upper mantle** and is accompanied at the surface by **back-arc spreading** (Gurnis et al., 2004). The latter is an important surface observable indicating that the slab has overcome the resistive forces impeding its subduction and is falling quasi-vertically through the mantle. Gurnis et al. (2004) go on to say that if one or the other of these defining criteria is missing then subduction is forced rather than self-sustaining. Forced or induced subduction (Stern, 2004; Leng and Gurnis, 2011; Stern and Gerya, 2017; Baes et al., 2018) is described by Gurnis et al. (2004) as a juvenile, early stage system, that cannot be described as a fully fledged subduction zone. These forced subduction zones are characterised by incipient margins, short trench-arc distance, narrow trenches and a volcanically inactive island arc and/or trench. Furthermore, although these juvenile systems might be seismically active they will lack a well defined Benioff Zone. Examples of forced subduction include the Puysegur-Fiordland subduction along the Macquarie Ridge Complex, the Mussau Trench on the eastern boundary of the Caroline plate and the Yap Trench south of the Marianas, amongst others. On the other hand, Cenozoic (<66 Ma) subductions, shown in figure 2, are by this definition self-sustaining and mature subduction zones. These subduction zones including the Izu-Bonin-Mariana, Tonga-Kermadec and Aleutians subduction zones, are characterised by their extensive and well defined trenches (see figure 2) (Gurnis et al., 2004). However, despite their common categorization subduction zones can originate through various mechanisms and from very different tectonic settings.

We know from the geological record that the formation of subduction zones is an ongoing process, with nearly half of the present day active subduction zones initiating during the Cenozoic (<66 Ma) (see Gurnis et al., 2004; Dymkova and Gerya, 2013; Crameri et al., 2018, and references therein). However, it is less clear how subduction zones originate, nucleate and propagate to pristine oceanic basins.

Crameri et al. (2018, and references therein) list a number of mechanisms, some which are shown in figure 3, that may work together to weaken and break the lithosphere including:

- Meteorite impact
- Sediment loading
- Major episode of delamination
- Small scale convection in the sub-lithospheric mantle
- Interaction of thermo-chemical plume with the overlying lithosphere
- Plate bending via surface topographic variations
- Addition of water or melt to the lithosphere
- Pre-existing transform fault or oceanic plateau
- Shear heating
- Grain size reduction

Some of these mechanisms, particularly those listed at the beginning of the list are more appropriate to early Earth conditions while others, such as inherited weaknesses or fracture zones, transform faults and extinct spreading ridges are considered to be prime tectonic settings for subduction zone formation in the Cenozoic (<66 Ma) (Gurnis et al., 2004). As the oceanic lithosphere grows denser with age, it develops heterogeneity which facilitates its sinking into the mantle to form new subduction zones. However, it is important to keep in mind that without inherited, pre-existing weaknesses, it is extremely difficult to form subduction zones at passive margins. This is because as the oceanic lithosphere cools and becomes denser, it also becomes stronger and therefore harder to bend into the mantle that underlies it (Gurnis et al., 2004; Duarte et al., 2016). Gurnis et al. (2004) note that the formation of new subduction zones alters the force balance on the plate and suggest that the strength of the lithosphere during bending is potentially the largest resisting component in the development of new subduction zones. Once that resistance to bending is overcome, either through the negative buoyancy of the subducting plate and/or through the tectonic forces acting on it, a shear zone extending through the plate develops (Gurnis et al., 2004; Leng and Gurnis, 2011). This eventually leads to plate failure and subduction zone formation.

From our knowledge of the geological record, observations of on-going subduction, and numerical modelling (Baes et al., 2011; Leng and Gurnis, 2011; Baes et al., 2018; Beaussier et al., 2018) we think that subduction zone initiation primarily occurs through the following:

An intra-oceanic setting refers to a subduction zone forming right within the oceanic plate itself. Proposed weakening mechanisms include weakening of the lithosphere due to melt and/or hydration (e.g., Crameri et al., 2018; Foley, 2018, and references therein), localised lithospheric shear heating (Thielmann and Kaus, 2012) and density variations within oceanic plate due to age heterogeneities, where its older and denser portions flounder and sink (Duarte et al., 2016). Another mechanism proposed by Baes et al. (2018) suggests that intra-oceanic subduction can also be induced by mantle suction flow. These authors suggest that mantle suction flow stemming from either slab remnants and/or from slabs of active subduction zones can act on pre-existing zones of weakness, such as STEP (subduction-transfer edge propagate) faults to trigger a new subduction zone, thus facilitating spontaneous subduction initiation (e.g. figure 3) (Stern, 2004). The Sandwich and the Tonga-Kermadec subduction zones are often cited as prime examples of intra-oceanic subduction zone formation due to mantle suction forces (Baes et al., 2018). Ueda et al. (2008) and Gerya et al. (2015) also suggest that thermochemical plumes can break the lithosphere and initiate self-sustaining subduction, provided that the overlying lithosphere is weakened through the presence of volatiles and melt (e.g. figure 3). This mechanism can explain the Venusian corona and could have facilitated the initation of plate tectonics on Earth (Ueda et al., 2008; Gerya et al., 2015). Similarly Burov and Cloetingh (2010) suggest that in the absence of plate tectonics, mantle lithospheric interaction through plume-like instabilities, can induce spontaneous downwelling of both continental and oceanic lithosphere.

Subduction polarity reversal describes a process where the trench jumps from the subducting plate to the overriding one, flipping its polarity in the process (see figure 3). This can result from the arrival at the trench of continental lithosphere (McKenzie, 1969) or young positively buoyant lithosphere (Crameri and Tackley, 2015). Subduction polarity reversal is often invoked to explain and justify the two juxtaposed Wadati-Benioff zones and their opposite polarities, in the Solomon Island Region (Cooper and Taylor, 1985). Indications of a polarity reversal are also exhibited below the Alpine and Apennine Belts (Vignaroli et al., 2008). Furthermore, Crameri and Tackley (2014) also suggest that the continental connection between South America and the Antarctic peninsula has been severed through a subduction polarity reversal, resulting in the lateral detachment of the South Sandwich subduction zone.

Subduction zones can also initiate at ancient, inherited zones of weakness such as old fracture zones, transform faults, extinct subduction boundaries and extinct spreading ridges (Gurnis et al., 2004). Gurnis et al. (2004) suggest that the Izu-Bonin-Mariana subduction zone initiated at a fracture zone, while the Tonga-Kermadec subduction initiated at an extinct subduction boundary. The same study also proposes that the incipient Puysegur-Fiordland subduction zone nucleated at an extinct spreading centre.

In conclusion, we can say that subduction zone formation is a complex and multi layered process that can stem from a variety of tectonic settings. However, it is clear that our planet’s current convection style, mode of surface recycling and its ability to sustain life are interlinked with subduction zone formation. Therefore, to understand better how subduction zones form is to better understand what makes the Earth the planet it is today.

]]>References:Baes, M., Govers, R., and Wortel, R. (2011). Subduction initiation along the inherited weakness zone at the edge of a slab: Insights from numerical models. Geophysical Journal International, 184(3):991–1008. Baes, M., Sobolev, S. V., and Quinteros, J. (2018). Subduction initiation in mid-ocean induced by mantle suction flow. Geophysical Journal International, 215(3):1515–1522. Beaussier, S. J., Gerya, T. V., and Burg, J.-p. (2018). 3D numerical modelling of the Wilson cycle: structural inheritance of alternating subduction polarity. Fifty years of the Wilson Cycle concept in plate tectonics, page First published online. Burov, E. and Cloetingh, S. (2010). Plume-like upper mantle instabilities drive subduction initiation. Geophys. Res. Lett., 37(3). Cooper, P. and Taylor, B. (1985). Polarity reversal in the Solomon Island Arc. Nature, 313(6003):47–48. Crameri, F., Conrad, C. P., Mont ́esi, L., and Lithgow-Bertelloni, C. R. (2018). The dynamic life of an oceanic plate. Tectonophysics. Crameri, F. and Tackley, P. J. (2014). Spontaneous development of arcuate single-sided subduction in global 3-D mantle convection models with a free surface. Journal of Geophysical Research: Solid Earth, 119(7):5921–5942. Crameri, F. and Tackley, P. J. (2015). Parameters controlling dynamically self-consistent plate tectonics and single-sided subduction in global models of mantle convection. Journal of Geophysical Research: Solid Earth, 3(55):1–27. Duarte, J. C., Schellart, W. P., and Rosas, F. M. (2016). The future of Earth’s oceans: Consequences of subduction initiation in the Atlantic and implications for supercontinent formation. Geological Magazine, 155(1):45–58. Dymkova, D. and Gerya, T. (2013). Porous fluid flow enables oceanic subduction initiation on Earth. Geophysical Research Letters, 40(21):5671–5676. Foley, B. J. (2018). The dependence of planetary tectonics on mantle thermal state : applications to early Earth evolution. 376. Gerya, T. V., Stern, R. J., Baes, M., Sobolev, S. V., and Whattam, S. A. (2015). Plate tectonics on the Earth triggered by plume-induced subduction initiation. Nature, 527(7577):221–225. Gurnis, M., Hall, C., and Lavier, L. (2004). Evolving force balance during incipient subduction. Geochemistry, Geophysics, Geosystems, 5(7). Leng, W. and Gurnis, M. (2011). Dynamics of subduction initiation with different evolutionary pathways. Geochemistry, Geophysics, Geosystems, 12(12). McKenzie, D. P. (1969). Speculations on the Consequences and Causes of Plate Motions. Geophys. J. R. Astron. Soc., 18(1):1–32. Stern, R. J. (2004). Subduction initiation: spontaneous and induced. Earth and Planetary Science Letters, 226(3-4):275–292. Stern, R. J. and Gerya, T. (2017). Subduction initiation in nature and models: A review. Tectonophysics. Thielmann, M. and Kaus, B. J. (2012). Shear heating induced lithospheric-scale localization: Does it result in subduction? Earth Planet. Sci. Lett., 359-360:1–13. Ueda, K., Gerya, T., and Sobolev, S. V. (2008). Subduction initiation by thermal-chemical plumes: Numerical studies. Phys. Earth Planet. Inter., 171(1-4):296–312. van Hunen, J. and Moyen, J.-F. (2012). Archean Subduction: Fact or Fiction? Annual Review of Earth and Planetary Sciences, 40(1):195–219. Vignaroli, G., Faccenna, C., Jolivet, L., Piromallo, C., and Rossetti, F. (2008). Subduction polarity reversal at the junction between the Western Alps and the Northern Apennines, Italy. Tectonophysics, 450(1-4):34–50. Waldron, J. W., Schofield, D. I., Brendan Murphy, J., and Thomas, C. W. (2014). How was the iapetus ocean infected with subduction? Geology, 42(12):1095–1098. White, D. A., Roeder, D. H., Nelson, T. H., and Crowell, J. C. (1970). Subduction. Geological Society of America Bulletin, 81(October):3431–3432. W.K, H. and E.H., C. (2003). Earth’s Dynamic Systems. Prentice Hall; 10 edition.

Tomography… wait, isn’t that what happens in your CAT scan? Although the general public might associate tomography with medical imaging, Earth scientists are well aware that ‘seismic tomography’ has enabled us to peer deeper, and with more clarity, into the Earth’s interior (Fig. 1). What are some of the ways we can download and display tomography to inform our scientific discoveries? Why has seismic tomography been a valuable tool for plate reconstructions? And what are some new approaches for incorporating seismic tomography within plate tectonic models?

**Downloading and displaying seismic tomography**

Seismic tomography is a technique for imaging the Earth’s interior in 3-D using seismic waves. For complete beginners, IRIS (Incorporated Research Institutions for Seismology) has an excellent introduction that compares seismic tomography to medical CT scans.

A dizzying number of new, high quality seismic tomographic models are being published every year. For example, the IRIS EMC-EarthModels catalogue currently contains 64 diverse tomographic models that cover most of the Earth, from global to regional scales. From my personal count, at least seven of these models have been added in the past half year – about one new model a month. Aside from the IRIS catalog, a plethora of other tomographic models are also publicly-available from journal data suppositories, personal webpages, or by an e-mail request to the author.

Downloading a tomographic model is just the first step. If one does not have access to custom workflows and scripts to display tomography, consider visiting an online tomography viewer. I have listed a few of these websites at the end of this blog post. Of these websites, a personal favourite of mine is the Hades Underworld Explorer built by Douwe van Hinsbergen and colleagues at Utrecht University, which uses a familiar Google Maps user interface. By simply dragging a left and right pin on the map, a user can display a global tomographic section in real time. The displayed tomographic section can be displayed in either a polar or Cartesian view and exported to a .svg file. Another tool I have found useful are tomographic ‘vote maps’, which provide indications of lower mantle slab imaging robustness by comparison of multiple tomographic models (Shephard et al., 2017). Vote maps can be downloaded from the original paper above or from the SubMachine website (Hosseini et al. (2018); see more in the website list below).

**Using tomography for plate tectonic reconstructions**

Tomography has played an increasing role in plate tectonic studies over the past decades. A major reason is because classical plate tectonic inputs (e.g. seafloor magnetic anomalies, palaeomagnetism, magmatism, geology) are independent from the seismological inputs for tomographic images. This means that tomography can be used to augment or test classic plate reconstructions in a relatively independent fashion. For example, classical plate tectonic models can be tested by searching tomography for slab-like anomalies below or near predicted subduction zone locations. These ‘conventional’ plate modelling workflows have challenges at convergent margins, however, when the geological record has been significantly destroyed from subduction. In these cases, the plate modeller is forced to describe details of past plate kinematics using an overly sparse geological record.

**A ‘tomographic plate modelling’ workflow** (Fig. 2) was proposed by Wu et al. (2016) that essentially reversed the conventional plate modelling workflow. In this method, slabs are mapped from tomography and unfolded (i.e. retro-deformed) (Fig. 2a). The unfolded slabs are then populated into a seafloor spreading-based global plate model. Plate motions are assigned in a hierarchical fashion depending on available kinematic constraints (Fig. 2b). The plate modelling will result in either a single unique plate reconstruction, or several families of possible plate models (Fig. 2c). The final plate models (Fig. 2c) are fully-kinematic and make testable geological predictions for magmatic histories, palaeolatitudes and other geological events (e.g. collisions). These predictions can then be systematically compared against remnant geology (Fig. 2d), which are independent from the tomographic inputs (Fig. 2a).

The proposed** 3D slab mapping workflow **of Wu et al. (2016) assumed that the most robust feature of tomographic slabs is likely the slab center. The slab mapping workflow involved manual picking of a mid-slab ‘curve’ along hundreds (and sometimes thousands!) of variably oriented 2D cross-sections using software GOCAD (Figs. 3a, b). A 3-D triangulated mid-slab surface is then constructed from the mid-slab curves (Fig. 3c). Inspired by 3D seismic interpretation techniques from petroleum geoscience, the tomographic velocities can be extracted along the mid-slab surface for further tectonic analysis (Fig. 3d).

For relatively undeformed upper mantle slabs, a pre-subduction slab size and shape can be estimated by unfolding the mid-slab surface to a spherical Earth model, minimizing distortions and changes to surface area (Fig. 3e). Interestingly, the slab unfolding algorithm can also be applied to shoe design, where there is a need to flatten shoe materials to build cut patterns (Bennis et al., 1991). The three-dimensional slab mapping within GOCAD allows a self-consistent 3-D Earth model of the mapped slabs to be developed and maintained. This had advantages for East Asia (Wu et al., 2016), where many slabs have apparently subducted in close proximity to each other (Fig. 1).

**Web resources for displaying tomography**

Hades Underworld Explorer : http://www.atlas-of-the-underworld.org/hades-underworld-explorer/

Seismic Tomography Globe : http://dagik.org/misc/gst/user-guide/index.html

SubMachine : https://www.earth.ox.ac.uk/~smachine/cgi/index.php

]]>ReferencesBennis, C., Vezien, J.-M., Iglesias, G., 1991. Piecewise surface flattening for non-distorted texture mapping. Proceedings of the 18th annual conference on Computer graphics and interactive techniques 25, 237-246. Hosseini, K. , Matthews, K. J., Sigloch, K. , Shephard, G. E., Domeier, M. and Tsekhmistrenko, M., 2018. SubMachine: Web-Based tools for exploring seismic tomography and other models of Earth's deep interior. Geochemistry, Geophysics, Geosystems, 19. Li, C., van der Hilst, R.D., Engdahl, E.R., Burdick, S., 2008. A new global model for P wave speed variations in Earth's mantle. Geochemistry, Geophysics, Geosystems 9, Q05018. Shephard, G.E., Matthews, K.J., Hosseini, K., Domeier, M., 2017. On the consistency of seismically imaged lower mantle slabs. Scientific Reports 7, 10976. Wu, J., Suppe, J., 2018. Proto-South China Sea Plate Tectonics Using Subducted Slab Constraints from Tomography. Journal of Earth Science 29, 1304-1318. Wu, J., Suppe, J., Lu, R., Kanda, R., 2016. Philippine Sea and East Asian plate tectonics since 52 Ma constrained by new subducted slab reconstruction methods. Journal of Geophysical Research: Solid Earth 121, 4670-4741

I once tried to ski down a slope

as friends thought there might be hope

I was covered in snow

from my head to my toe

If they invite me again it’s a ‘nope’

So, as many of you might have guessed, winter sports (or any sports, really) are not entirely my thing. Particularly skiing did not go down well for me. However, as a true Dutch girl, I do really enjoy ice skating and can recommend it thoroughly! However, this year no winter sports at all for me: I flew towards the sun in an effort to actually destress from work (feeble attempt as I brought my laptop, but still, kudos for trying, right?). I hope everyone had a very nice holiday and relaxing break. May all your (academic) wishes come true in 2019!

In hemispheric defiance of the "wintersport" edition, I am currently back Down Under where I have replaced the (seemingly eternal) television coverage of cross-country skiing with cricket, swapped a toboggan for me 'togs', and exchanged a pull-over for some 'pluggers.' I wish all of our blog readers a very happy and safe end to the year that was, and a fabulous start to the next!

This year I spend winter in Berlin,

Where no snow has fallen and the ice is too thin.

So I drink myself heavy,

With hot chocolate and Pfeffi,

And wait for the fresh air of spring!

In the weeks before Christmas, Christmas markets dominate the streets of Berlin. Besides delicious food, they offer mulled wine and, as I discovered this year, hot chocolate with peppermintliqueur. A green version of the liqueur is made by Pfeffi, while a colorless Berlin-made peppermintliqueur is called Berliner Luft. It’s as clear and fresh as Berlin’s air according to the manufacturer. Although the freshness of Berlin’s air is debatable, the combination of chocolate and peppermint is delicious. I wish everybody a fresh start of the New Year with loads of hapiness!

]]>__Nailing your presentation__

• The art of the 15-minute talk: how to design the best 15-minute talk

• Presentation skills - 1. Voice: how to get the most out of your presentation voice

• Presentation skills - 2. Speech: how to stop staying 'uh'

__Making the best poster__

• Poster presentation tips: how to design the best poster layout

• The rainbow colour map (repeatedly) considered harmful: how to make the best scientific figures

When coming close to the final stages of a PhD life, many students reconsider whether they want to stay in academia or prefer to step over to industry or other non-academic jobs. This is surely not a simple decision to take, as it could strongly shape your future. In this blog post, I would like to report my industrial placement experience during my PhD and share a few thoughts on the topic.

The taste of industry life was an opportunity I had within the frame of my PhD project. Split into two terms, I spent four weeks at a medium-sized company developing optical imaging techniques (both software and equipment) to measure flow fields and deformation. The branch I worked in was "digital image correlation" (DIC) which measures strain on given surfaces purely by comparing successive images on an object (see figure below). This technique is used in both industry (crash tests, quality assessments, etc.) as well as in academia (analogue experiments, wind tunnels, engineering..), and has the substantial advantage of measuring physical properties precisely, without using any materials or affecting dynamical processes. DIC is not directly related to or used in my PhD (I do numerical modelling of subduction zones and continental collision), but surprisingly enough I was able to contribute more than expected - but more on that later.

The specific project I worked on was inspired by the analogue tectonics lab at GFZ Potsdam, that uses DIC measuring systems to quantify and measure the deformation of their sandbox experiments. Typical earthquake experiments like the figure below span periods of a few minutes to several days during which individual earthquakes occur in a couple of milliseconds. The experiment is continuously recorded by cameras to both monitor deformation visually and quantify deformation by using the optical imaging technique developed by my host company. To resolve the deformation related to individual earthquakes, high imaging rates are required which in turn produce a vast amount of data (up to 2TB per experiment). However, only a small fraction (max. 5%) of the entire dataset is of interest, as there is hardly any deformation during interseismic periods. The project I was involved in tried to tackle the issue of unnecessarily cluttered hard discs: the recording frequency should be linked to a measurable characteristic within the experiment, e.g. displacement velocities in these specific experiments, and controlled by the DIC software.

My general task during the internship was to develop this idea and the required software. We finally developed a 'live-extensometer' to calculate displacements between two given points of an image during recording and link its values to the camera's recording frequency. Therefore, restricting high imaging rates to large (and fast) displacements of earthquakes should result in reducing the total amount of data acquired for a typical earthquake experiment by 95%. However, we needed an actual experiment to verify this. So, I met up with the team at GFZ to test the developed feature.

The main experiment the GFZ team had in mind is sketched in the figure above: a conveyor belt modelling a subducting slab continuously creates strain in the 'orogenic wedge' which is released by earthquakes leading to surface deformation. Cameras above the experiment monitor the surface while software computes strain rates and displacement (see figure below). The developed feature of changing frequencies during the experiment depending on slip rates was included and worked surprisingly well. Yet freshly programmed software is seldom perfect: minor issues and bugs crept up during the experiments. My final contribution during the internship was to report these problems back to the company to be fixed.

My geodynamical background allowed me to contribute to various fields within the company and resulted in various individual tasks throughout the internship: coding experience helped with discussing ideal software implementations and testing the latest implemented software on small (physical) experiments. My knowledge of various deformation mechanisms and geosciences in general, with its numerous subdisciplines and methods, provided a solid base for searching further applications for the developed software within academia, but also in industry. Last but not least, pursuing my own large project (my PhD) strongly facilitated discussing possible future development steps.

The atmosphere at the company in general was very pleasant and similar to what I experienced at the university: relaxed handling, pared with discussion how to improve products or use of new techniques that might be applicable to a problem. To stay competitive, the company needs to further develop their products which requires a large amount of research, developments and innovative ideas. Meetings to discuss further improvements of certain products were thus scheduled on a (nearly) daily basis. On the one hand this adds pressure to get work done as quickly as possible, but working on a project as a team with many numerous areas of expertise is also highly exciting.

This internship help reveal the variability of possible jobs that geodynamicists can have in industry besides the 'classical' companies linked to exploration, tunnel engineering or geological surveys. The skill set acquired in a geodynamical PhD (coding, modelling, combining numerics, physics, and geosciences) makes a very flexible and adaptive employee which is attractive to companies who are so specialised, that there is (nearly) no classical education at university level. Jobs at small to medium-sized companies are often harder to find, but it's just as difficult for the companies to find suitable candidates for their open positions. Hence, it may be worth searching in-depth for a suitable job, if you are considering stepping out of academia and maybe even out of geoscience as well.

If PhD students are hesitant whether to stay in academia or change into industry, I would advise to do such a short internship with a company to get a taste of 'the other side'. During a PhD, we get to know academic life thoroughly but industry mostly remains alien. Besides giving a good impression of daily life at a company and how you can contribute, an industry internship might also widen your perspective of which areas might be relevant to you, your methodology and your PhD topic. In total, this internship was definitely a valuable experience for me and will help when deciding: academia or industry?

Here are a few links for more information:

Host company

Digital Image Correlation

TecLab at GFZ Potsdam

Previous EGU blog post interviews of former geoscientists

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One integral part of doing estimations on parameters is an uncertainty analysis. The aim of a general inverse problem is to find the value of a parameter, but it is often very helpful to indicate the measure of certainty. For example in the last figure of my previous post, the measurement values at the surface are more strongly correlated to the upper most blocks. Therefore, the result of an inversion in this set up will most likely be more accurate for these parameters, compared to the bottom blocks.

In linear deterministic inversion, the eigenvalues of the matrix system provide an indication of the resolvability of parameters (as discussed in the aforementioned work by Andrew Curtis). There are classes of methods to compute exact parameter uncertainty in the solution.

From what I know, for non-linear models, uncertainty analysis is limited to the computation of second derivatives of the misfit functional in parameter space. The second derivatives of X (the misfit function) are directly related to the standard deviations of the parameters. Thus, by computing all the second derivatives of X, a non-linear inverse problem can still be interrogated for its uncertainty. However, the problem with this is its linearisation; linearising the model and computing derivatives may not be truly how the model reacts in model space. Also, for strongly non-linear models many trade-offs (correlations) exist which influence the final solution, and these correlations may very strongly depending on the model to be inverted.

Enter reverend Thomas Bayes

This part-time mathematician (he only ever published one mathematical work) from the 18th century formulated the Bayes' Theorem for the first time, which combines knowledge on parameters. The mathematics behind it can be easily retrieved from our most beloved/hated Wikipedia, so I can avoid getting to caught up in it. What is important is that it allows us to combine two misfit functions or probabilities. Misfits and probabilities are directly interchangeable; a high probability of a model fitting our observations corresponds to a low misfit (and there are actual formulas linking the two). Combining two misfits allows us to accurately combine our pre-existing (or commonly: prior) knowledge on the Earth with the results of an experiment. The benefits of this are two-fold: we can use arbitrarily complex prior knowledge and by using prior knowledge that is *bounded* (in parameter space) we can still invert underdetermined problems without extra regularisation. In fact, the prior knowledge *acts* as regularisation.

Let's give our friend Bayes a shot at our non-linear 1D bread. We have to come up with our prior knowledge of the bread, and because we did not need that before I'm just going to conjure something up! We suddenly find the remains of a packaging of 500 grams of flour

This is turning in quite the detective story!

However, the kitchen counter that has been worked on is also royally covered in flour. Therefore, we estimate that probably this pack was used; about 400 grams of it, with an uncertainty (standard deviation) of 25 grams. Mathematically we can formulate our prior knowledge as a Gaussian distribution with the aforementioned standard deviation and combine this with our misfit of the inverse problem (often called the likelihood). The result is given here:

One success and one failure!

First, we successfully combined the two pieces of information to make an inverse problem that is no longer non-unique (which was a happy coincidence of the prior: it is not guaranteed). However, we failed to make the problem more tractable in terms of computational requirements. To get the result of our combined misfit, we still have to do a systematic grid search, or at least arrive at a (local) minimum using gradient descent methods.

We can do the same in 2D. We combine our likelihood (original inverse problem) with rather exotic prior information, an annulus in model space, to illustrate the power of Bayes' theorem. The used misfit functions and results are shown here:

This might also illustrate the need for non-linear uncertainty analysis. Trade-offs at the maxima in model space (last figure, at the intersection of the circle and lines) distinctly show two correlation directions, which might not be fully accounted for by using only second derivative approximations.

Despite this 'non-progress' of still requiring a grid search even after applying probability theory, we can go one step further by combining the application of Bayesian inference with the expertise of other fields in *appraising* inference problems...

Up to now, using a probabilistic (Bayesian) approach has only (apparently) made our life more difficult! Instead of one function, we now have to perform a grid search over the prior *and* the original problem. That doesn't seem like a good deal. However, a much used technique in statistics deals with exactly the kind of problems we are facing here: given a very irregular and high dimensional function

How do we extract interesting information (preferably without completely blowing our budget on supercomputers)?

Let's first say that with interesting information I mean minima (not necessarily restricted to global minima), correlations, and possibly other statistical properties (for our uncertainty analysis). One answer to this question was first applied in Los Alamos around 1950. The researches at the famous institute developed a method to simulate equations of state, which has become known as the Metropolis-Hastings algorithm. The algorithm is able to draw samples from a complicated probability distribution. It became part of a class of methods called Markov Chain Monte Carlo (MCMC) methods, which are often referred to as samplers (technically they would be a subset of all available samplers).

The reason that the Metropolis-Hastings algorithm (and MCMC algorithms in general) is useful, is that a complicated distribution (e.g. the annulus such as in our last figure) does not easily allow us to generate points proportional to its misfit. These methods overcome this difficulty by starting at a certain point in model space and traversing a random path through it - *jumping around* - but visiting regions only proportional to the misfit. So far, we have only considered directly finding optima to misfit functions, but by generating samples from a probability distribution proportional to the misfit functions, we can readily compute these minima by calculating statistical modes. Uncertainty analysis subsequently comes virtually for free, as we can calculate any statistical property from the sample set.

I won't try to illustrate any particular MCMC sampler in detail. Nowadays many great tools for visualising MCMC samplers exist. This blog by Alex Rogozhnikov does a beautiful job of both introducing MCMC methods (in general, not just for inversion) and illustrating the Metropolis Hastings Random Walk algorithm as well as the Hamiltonian Monte Carlo algorithm. Hamiltonian Monte Carlo also incorporates gradients of the misfit function, thereby even accelerating the MCMC sampling. Another great tool is this applet by Chi Feng. Different target distributions (misfit functions) can be sampled here by different algorithms.

The field of geophysics has been using these methods for quite some time (Malcom Sambridge writes in 2002 in a very interesting read that the first uses were 30 years ago), but they are becoming increasingly popular. However, strongly non-linear inversions and big numerical simulations are still very expensive to treat probabilistically, and success in inverting such a problem is strongly dependent on the appropriate choice of MCMC sampler.

In the third part of this blog we saw how to combine any non-linear statistical model, and how to sample these complex functions using MCMC samplers. The resulting sample sets can be used to do an inversion and compute statistical moments of the inverse problem.

If you reached this point while reading most of the text, you have very impressively worked your way through a huge amount of inverse theory! Inverse theory is a very diverse and large field, with many ways of approaching a problem. What's discussed here is, to my knowledge, only a subset of what's being used 'in the wild'. These ramblings of a aspiring seismologist might sound uninteresting to the geodynamiscists at the other side of the geophysics field. Inverse methods seem to be not nearly discussed as much in geodynamics as they are in seismology. Maybe it's the terminology that differs, and that all these concepts are well known and studied under different names and you recognise some of the methods. Otherwise, I hope I have given an insight in the wonderful and sometimes ludicrous mathematical world of (some) seismologists.

Interested in playing around with inversion yourself? You can find a toy code about baking bread here.

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The idea of inversion is to literally invert the forward problem. For the aforementioned problems our knowns become unknowns, and vice versa. We wish to infer physical system parameters from measurements. Note that in our forward problem there are multiple classes of knowns; we have the forcing parameters, the material properties and the boundary conditions. All of these parameters could be inverted for in an inversion, but it is typically one only inverts for one class. Most of the time, medium parameters are the target of inversions. As we will go through the examples, I will gradually introduce some inversion methods. Every new method is marked in **blue**.

Now, let's consider our first example: the recipe for a bread. Let's say we have a **0.5 kilogram bread**. For our first case (case **1a**), we will assume that the amount of water is ideal. In this case, we have *one* free variable to estimate (the used amount of flour), from *one* observable (the resulting amount of bread). We have an analytical relationship that is both **invertible** and **one-to-one**. Therefore, we can use the** direct inversion ** of the relationship to compute the amount of flour. The process would go like this:

1. Obtain the forward expression G(m) = d;

2. Solve this formula for G^-1 (d) = m;

3. Find m by plugging d into the inverse function.

Applying this direct inversion shows us that **312.5 grams of flour** must have been used.

The two properties of the analytical relationship (**invertible** and **one-to one**) are very important for our choice of this inversion method. If our relationship was sufficiently complex, we couldn't solve the formula analytically (though the limitation might then lie by the implementer 😉 ). If a function is not one-to-one, then two different inputs could have the same output, so we cannot analytically invert for a **unique** solution.

In large linear forward models (as often obtained in finite difference and finite element implementations), we have a matrix formula similar to

A **m** = **d**.

In these systems, **m** is a vector of model parameters, and **d** a vector of observables. If the *matrix is invertible*, we can directly obtain the model parameters from our observables. The invertibility of the matrix is a very important concept, which I will come back to in a bit.

Let's see what happens when the first condition is not satisfied. I tried creating a non-linear function that at least *I* could not invert. Someone might be able to do it, but that is not the aim of this exercise. This new relationship is given here:

The actual forward model is included in the script at the bottom of the post. When we cannot analytically solve the forward model, one approach would be to iteratively optimise the input parameters such that the end result becomes as close as possible to the observed real world. But, before we talk about how to propose inputs, we need a way to compare the outputs of our model to the observables!

When we talk about our bread, we see one end-product. It is very easy to compare the result of our simulation to the real world. But what happens when we make a few breads, or, similarly, when we have a lot of temperature observations in our volcanic model? One (common) way to combine observables and predictions into a single measure of discrepancy is by defining a misfit formula which takes into account all observations and predictions, and returns a single - you guessed it - **misfit**.

The choice of misfit directly influences which data is deemed more important in an inversion scheme, and many choices exist. One of the most intuitive is the L2-norm (or L2 misfit), which calculates the vector distance between the predicted data (from our forward model) with the observed data as if in Euclidean space. For our bread it would simply be

X = | predicted bread - observed bread |

Let's try to create a misfit function for our 1D bread model. I use the relationship in the included scripts. Again, we have 500 grams of bread. By calculating the quantity | G(m) - 500 |, we can now make a graph of how the misfit varies as we change the amount of flour. I created a figure which shows the observed and predicted value:

and a figure which shows the actual misfit at each value of m

Looking at the two previous figures may result in some questions and insights to the reader. First and foremost, it should be obvious that this problem does **not** have a unique solution. Different amounts of flour give exactly the same end result! It is thus impossible to say with certainty how much flour was used.

We have also recast our inversion problem as an optimisation function. Instead of thinking about fitting observations we can now think of our problem as a minimization of some function X (the misfit). This is very important in inversion.

Iterative optimizations schemes such as **gradient descent methods (and the plethora of methods derived from it) work in the following fashion:**

1. Pick a starting point based on the best prior knowledge (e.g., a 500 gram bread could have been logically baked using 500 grams of flour);

2. Calculate the misfit (X) at our initial guess;

3. If X is not low enough (compared to some arbitrary criterion):

• Compute the gradient of X;

• Do a step in the direction of the steepest gradient;

• Recompute X;

• Repeat from 2.

4. If X is low enough:

• Finalise optimisation, with the last point as solution.

These methods are **heavily influenced by the starting point** of the inversion scheme. If I would have started on the left side of the recipe-domain (e.g. 100 grams of flour), I might well have ended up in a different solution. Gradient descents often get 'stuck' in local solutions, which might not even be the optimal one! We will revisit this non-uniqueness in the 2D problem, and give some strategies to mitigate creating more than one solution. Extra material can be found here.

One thing that often bugged me about the aforementioned gradient descent methods is the seemingly complicated approach for such simple problems. Anyone looking at the figures could have said

Well, duh, there's 3 solutions, here, here and here!

Why care about such an complicated way to only get one of them?

The important realisation to make here is that I have precomputed all possible solution for this forward model in the 0 - 700 grams range. This precomputation on a 1D domain was very simple; at a regular interval, compute the predicted value of baked bread. Following this, I could have also programmed my Python routine to extract all the values with a sufficiently low misfit as solutions. This is the basis of a ** grid search**.

Let's perform a grid search on our second model (**1b**). Let's find all predictions with 500 grams of bread as the end result, plus-minus 50 grams. This is the result:

The original 312.5 grams of flour as input is part of the solution set. However, the model actually has infinitely many solutions (extending beyond the range of the current grid search)! The reason that a grid search might not be effective is the inherent computational burden. When the forward model is sufficiently expensive in numerical simulation, exploring a model space completely with adequate resolution might take very long. This burden increases with model dimension; if more model parameters are present, the relevant model space to irrelevant model space becomes very small. This is known as the **curse of dimensionality** (very well explained in Tarantola's textbook).

Another reason one might want to avoid grid searches is our inability to appropriately process the results. Performing a 5 dimensional or higher grid searches is sometimes possible on computational clusters, but visualizing and interpreting the resulting data is very hard for humans. This is partly why many supercomputing centers have in-house departments for data visualisation, as it is a very involved task to visualise complex data well.

Now: towards solving our physical inversions!

One big problem in inversion is non-uniqueness: the same result can be obtained from different inputs. The go-to way to combat this is to add extra information of any form to the forward problem. In our bread recipe we could think of adding extra observables to our problem, such as the consistency of the bread, its taste, color, etc. Another option could be to add constraints on the parameters, such as using the minimum amount of ingredients. This is akin to asking the question: given this amount of bread, how much flour and water was *minimally* used to make it?

Diffusion type problems are notorious for their non-uniqueness. Many different subsurface heat conduction distributions might result in the observations (imagine differently inclined volcanic conduits). An often used method of regularisation (not limited to diffusion type studies!) is spatial smoothing. This method requires that among equally likely solutions, the smoothest solutions are favoured, for it is more 'natural' to have smoothly varying parameters. Of course, in many geoscientific settings one would definitely expect sharp contrasts. However, in 'underdetermined' problems (i.e., you do not have enough observations to constrain a unique solution), we favour Occam's Razor and say

The simplest solution must be assumed

When dealing with more parameters than observables (non-uniqueness) in linear models it is interesting to regard the forward problem again. If one would parameterize our volcanic model using 9 parameters for the subsurface and combine that with the 3 measurements at the surface, the result would be an underdetermined inverse problem.

This forward model (the Laplace equation) can be discretised by using, for example, finite differences. The resulting matrix equation would be A**m** = **d**, with A a 3 x 9 matrix, **m** a 9 dimensional vector and **d** a 3 dimensional vector. As one might recall from linear algebra classes, for a matrix to have an inverse, it has to be square. This matrix system is not square, and therefore not invertible!

Aaaaahhh! But don't panic: there is a solution

By adding either prior information on the parameters, smoothing, or extra datapoints (e.g., taking extra measurements in wells) we can make the 3 x 9 system a perfect 9 x 9 system. By doing this, we condition our system such that it is invertible. However, many times we end up overdetermining our system which could result in a 20 x 9 system, for example. Note that although neither the underdetermined nor the overdetermined systems have an exact matrix inverse, both *do* have pseudo-inverses. For underdetermined systems, I have not found these to be particularly helpful (but some geophysicists do consider them). Overdetermined matrix systems on the other hand have a very interesting pseudo-inverse: the **least squares solution**. Finding the least squares solution in linear problems is the same as minimising the L2 norm! **Here, two views on inversion come together: solving a specific matrix equation is the same as minimising some objective functional** (at least in the linear case). Other concepts from linear algebra play important roles in linear and weakly non-linear inversions. For example, matrix decompositions offer information on how a system is probed with available data, and may provide insights on experimental geophysical survey design to optimise coverage *(see "Theory of Model-Based Geophysical Survey and Experimental Design Part A – Linear Problems" by Andrew Curtis)*.

I would say it is common practice for many geophysicists to pose an inverse problem that is typically underdetermined, and keep adding regularization until the problem is solvable in terms of matrix inversions. I do not necessarily advocate such an approach, but it has its advantages towards more agnostic approaches, as we will see in the post on probabilistic inversion next week!

We've seen how the forward model determines our inversion problem, and how many measurements can be combined into a single measure of fit (the misfit). Up to now, three inversion strategies have been introduced:

• **Direct inversion**: analytically find a solution to the forward problem. This method is limited to very specific simple cases, but of course yields near perfect results.

• **Gradient descent methods**: a very widely used class of algorithms that iteratively update solutions based on derivatives of the misfit function. Their drawbacks are mostly getting stuck in local minima, and medium computational cost.

• **Grid searches**: a method that searches the entire parameter space systematically. Although they can map all the features of the inverse problem (by design), they are often much too computationally expensive.

What might be even more important, is that we have seen how to reduce the amount of possible solutions from infinitely many to at least a tractable amount using regularisation. There is only one fundamental piece still missing... Stay tuned for the last blog post in this series for the reveal of this mysterious missing ingredient!

Interested in playing around with inversion yourself? You can find a toy code about baking bread here.

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Inversion methods are at the core of many physical sciences, especially geosciences. The term itself is used mostly by geophysicists, but the techniques employed can be found throughout investigative sciences. Inversion allows us to infer our surroundings, the physical world, from observations. This is especially helpful in geosciences, where one often relies on observations of physical phenomena to infer properties of the (deep) otherwise inaccessible subsurface. Luckily, we have many, many algorithms at our disposal! (...cue computer scientists rejoicing in the distance.)

The process of moving between data and physical parameters goes by different names in different fields. In geophysics we mostly use **inversion** or **inference**. Inversion is intricately related to optimization in control theory and some branches of machine learning. In this small series of blog posts, I will try to shed a light on how these methods work, and how they can be used in geophysics and in particular geodynamics! In the end, I will focus a little on Bayesian inference, as it is an increasingly popular method in geophysics with growing potential. Be warned though:

Math ahead!

(although as little as possible)

First, for people who are serious about learning more about inverse theory, I strongly suggest Albert Tarantola's “Inverse Problem Theory and Model Parameter Estimation”. You can buy it or download it (for free!) somewhere around here: http://www.ipgp.fr/~tarantola/.

I will discuss the following topics in this blog series (roughly in order of appearance):

1. Direct inversion

2. Gradient descent methods

3. Grid searches

4. Regularization

5. Bayesian inference

6. Markov chain Monte Carlo methods

Rather backwards, I will start the first blog post with a component of inversion which is needed about halfway through an inversion algorithm. However, this piece determines the physics we are trying to investigate. Choosing the forward 'problem' or model is posing the question of how a physical system will evolve, typically in a deterministic setting. This can be any relationship. The forward problem is roughly the same as asking:

I start with these materials and those initial conditions, what will happen in this system?

In this post I will consider two examples of forward problems (one of which is chosen because I have applied it everyday recently. Hint: it is not the second example...)

1. **The baking of a bread** with **known** quantities of water and flour, but the mass of the end product is **unknown**. A *recipe is available* which predicts how much bread will be produced (let's say under all ratios of flour to water).

2. **Heat conduction** through a volcanic zone where geological and thermal properties and base heat influx are **known**, but surface temperatures are **unknown**. A *partial differential equation is available* which predicts the surface temperature (heat diffusion, Laplace's equation).

Both these systems are forward problems, as we have all the necessary ingredients to simulate the real world. Making a prediction from these 'input' variables is a forward model run, a forward simulation or simply a forward solution. These simulations are almost exclusively deterministic in nature, meaning that no randomness is present. Simulations that start exactly the same, will always yield the same, *predictable* result.

For the first system, the recipe might indicate that 500 grams of flour produce 800 grams of bread (leaving aside the water for a moment, making case **1a**), and that the relationship is linear in flour. The forward model becomes:

flour * 800 / 500 = bread

This is a plot of that relationship:

Of course, if we add way too much water to our nice bread recipe, we mess it up! So, the correct amount of water is also important for making our bread. Hence, our bread is influenced by both quantities, and the forward model is a function of 2 variables:

bread = g(flour, water)

Let's call this case **1b**. This relationship is depicted here:

It is important to note that the distinction between these two cases creates two different physical systems, in terms of invertibility.

Modelling a specific physical system is a more complicated task. Typically, physical phenomena can be described by set of (partial) differential equations. Other components that are required to predict the end result of a physical deterministic system are

1. The domain extent, suitable discretisation and parameter distribution in the area of investigation, and the relevant **material properties (sometimes parameters)** at every location in the medium

2. The boundary conditions, and when the system is time varying (transient), additional initial conditions

3. A simulation method

As an example, the relevant property that determines heat conduction is the heat conductivity at every location in the medium. The parameters control much of how the physical system behaves. The boundary and initial conditions, together with the simulation methods are very important parts of our forward model, but are not the focus of this blog post. Typically, simulation methods are **numerical methods** (e.g., finite differences, finite elements, etc.). The performance and accuracy of the numerical approximations are very important to inversion, as we'll see later.

The result of the simulation of the volcanic heat diffusion is the temperature field throughout the medium. We might however be only interested in the temperature at real-world measurement points. We consider the following conceptual model:

The resulting temperatures at the measurement locations will be different for each point, because they are influenced by the heterogeneous subsurface. Real-world observations would for example look something like this

Now that you have a good grasp of the essence of the forward problem, I will discuss the inverse problem in the next blog post. Of course, the idea of inversion is to literally invert the forward problem, but if it were as easy as that, I wouldn't be spending 3 blog posts on it, now would I?

]]>Let's start with the basics. What the hell is CIDER? Well, CIDER stands for the Cooperative Institute for Dynamic Earth Research. One of it's main focusses is the interdisciplinary training of early career scientists. To that end, they organise a summer school every year (usually in June/July) that lasts for 4 weeks.

4 weeks?!

Again, you heard that correctly. You are very good at listening!

The first two weeks of the summer school are dedicated to getting up to speed on the topic of the summer school by means of lectures, tutorials, a little field trip, etc. During the last two weeks you will work together in groups on a project of your choosing. The projects are determined during the first two weeks, when you figure out where the knowledge gaps are and you start making teams (no worries, nobody will be left out). You will come up with possible project topics yourself, so you can imagine that there can be quite some lobbying going on to make sure your team gets sufficient members to pursue your favourite project!

Together with your team of students and postdocs, you will confer with established experts in the field to make your project a success. After two weeks, you can probably show some reasonable first results during the final presentation in front of everyone.

If you want to continue working on your project with your team afterwards, you can even write a small proposal to CIDER to request some funding to meet up again and turn your project into a paper. Although they can't reimburse intercontinental flights, it is still a pretty awesome opportunity!

The topic of the summer school changes every year and alternates between a 'deep' topic and a 'shallow' topic. I attended the CIDER 2017 summer school with the topic '**Subduction zone structure and dynamics**' - a shallow topic. This year (2018), the topic was '**Relating Geophysical and Geochemical Heterogeneity in the Deep Earth**' - clearly a deep topic. If you want to know more about this year's summer school, our Blog Reporter Diogo wrote about it here. Students from all kinds of different disciplines are encouraged to apply: geology, geochemistry, seismology, geodynamics, mineral physics, etc. The more diversity the better, because you need to learn from each other!

Now that we have all the details out of the way, I can properly start to convince you to apply! Did I already mention that the summer school is in an exotic place in California, USA? In 2017, the summer school was in Berkeley and this year it was in Santa Barbara. These locations are always fixed, with the 'shallow' topics being held in Berkeley, and the deep topics being held in Santa Barbara. Maybe this can act as your guide for finding out which kind of topic to ultimately pursue in your career.

Also, can you imagine? Four weeks, in beautiful, sunny California for 'work'? Because, yes, it is work, technically, but it won't feel like it. Actually, it's kind of like being transported to one of those American high school / college movies. Does anyone else watch those? Nope, just me? Okay then. You will get the full American student experience, as you will sleep in an actual dorm with all your fellow students and go to the dining hall religiously for breakfast, lunch, and dinner each day and every day! Yes, also in the weekends, because it's free and you're a poor student! Minor side-effect is that you want be able to look at - let alone stomach - burgers, fries, pizzas, and hotdogs for at least a year, but it's totally worth it for this all-American movie-like experience. Obviously, sharing a dorm with all your fellow students and complaining about the food will forge bonds that will last far longer than the duration of the summer school and you are guaranteed to have a lot of fun during the summer school also after the lectures.

Although the program is pretty packed, you will have free evenings (during which you might catch up on your actual work) and you will have some days off during the weekends. Of course, you can't have *all* weekend days off, because it wouldn't be a proper summer school experience if you don't return completely exhausted, right? However, on your precious days off, you can go and explore beyond the campus and do some nice day trips to a nearby city or nature reserve. You can of course also use your free evenings and weekends to sample some of the night life of whatever Californian city you are staying in!

I thoroughly enjoyed my own CIDER experience in Berkeley, 2017. I learned loads of things about subduction zones and a lot of my knowledge was refreshed, specifically on geochemistry, mineral physics and geology. It was great fun to live on an American campus (I mean, I really did feel as if I'd stumbled into an American teen movie) and we did some pretty cool things besides the summer school! There was a lovely field trip to learn a bit more about rocks and it was also a great opportunity to see something of the landscape and enjoy incredible views over San Francisco. Of course, San Francisco itself was also visited during one of our days off and I finally saw the Golden Gate bridge up close and ate crab at Fisherman's Wharf. Unforgettable experience. Best day of the summer school. I cannot recommend it enough! We also went out for dinner and drinks on occasion in the city centre of Berkeley and we even snuck in a visit to the musical 'Monsoon Wedding' at Berkely Rep.

After the summer school, our project group applied for funding to meet up again (I just couldn't get enough of the American vibe) and lo and behold, we actually got the funding! So this spring, I found myself in Austin, Texas, to work on our project.

Howdy y'all!

It was pretty amazing to have an opportunity like that, and I can assure you that we also had lots of fun in Austin. I mean, it's Texas, what did you expect? I was already over the moon by the fact that I had the possibility of spotting men wearing cowboy boots for real and not just for carnival!

All in all, I can thoroughly recommend the CIDER summer school as a great learning experience and opportunity for meeting fellow scientists interested in your topic of choice.

Next year, the topic will be '**Volcanoes**', so if you have any interest in that, be sure to apply! There is also always a one-day pre-AGU workshop, where you can get a little taste of the summer school, as the progress on the projects of the previous year is reported and lectures anticipating the coming topic are held.

So, are you going to apply to CIDER next year? I mean, who doesn't lava volcanoes?!

]]>Go. To. Edinburgh. Seriously: Edinburgh is the place to be for anyone who has an affinity with the Earth sciences. In this beautiful, historic city, James Hutton - the founder of modern geology, who originated the idea of uniformitarianism - lived and died. Everywhere in the city you can find little reminders indicating this iconic scientist lived there. You could, for example, visit his grave, and hike to his geological section on Edinburgh's Salisbury Crags. There are also little plaques spread around the city that mark significant James Hutton places and events. The city itself is also steeped in a mix of geology and history: Edinburgh Castle, situated on the impressive volcanic Castle Rock, boasts an 1100-year-old history and towers over the city. Directly across from the castle, connected by the charming Royal Mile is Holyrood Palace, where you can soak up even more history - Mary Queen of Scots lived here for a while. Nearby, there is Holyrood Park where you can find the group of hills that hosts Hutton's Section and a 350 million year old volcano named Arthur's Seat. Climb it when the weather is nice and you will have the most amazing view of Edinburgh. The whole park is perfect for day hikes and picknicks.

Even if you (or your travel buddy) are not that into Earth Sciences (or history), Edinburgh has plenty of other attractions. It is the perfect place for book and literature lovers with the large International Book Festival every August and a very rich literary history with iconic writers such as Walter Scott (Ivanhoe), Robert Louis Stevenson (Strange Case of Dr Jekyll and Mr Hyde; Treasure Island), Arthur Conan Doyle (Sherlock Holmes), and - more recently - J. K. Rowling (Harry Potter). Theatre fans will also love Edinburgh, particularly during August when it hosts the Edinburgh Festival Fringe - the largest arts festival in the world.

I totally should've booked a trip to Edinburgh this year... Learn from my mistakes and enjoy it in my stead!

My recommendation? I vote for the Aeolian Islands! Smouldering volcanoes, bubbling mud baths and steaming fumaroles make these tiny islands north of Sicily a truly hot destination. This is the best place to practice the joys of "*dolce far niente*": eat, sleep, and play. The Aeolian Arc is a volcanic structure, about 200 km long, located on the internal margin of the Calabrian-Peloritan Arc. The arc is formed by seven subaerial volcanic edifices (Alicudi, Filicudi, Salina, Lipari, Vulcano, Panarea, and Stromboli) and by several volcanic seamounts which roughly surround the Marsili Basin. The subduction-related volcanic activity showed the same eastward migration going from the Oligo-Miocene Sardinian Arc to the Pliocene Anchise-Ponza Arc and, at last, to the Pleistocene Aeolian Arc. My favourite island, Stromboli, is one of the few volcanoes on earth displaying continuous eruptive activity over a period longer than a few years or decades. I like Stromboli because it conforms perfectly to one's childhood idea of a volcano, with its symmetrical, smoking silhouette rising from the sea. Most of this activity is of a very moderate size, consisting of brief and small bursts of glowing lava fragments to heights of rarely more than 150 m above the vents. Occasionally, there are periods of stronger, more continuous activity, with fountaining lasting several hours, violent ejection of blocks and large bombs, and, still more rarely, lava outflow. I can’t quite explain what made it so special to me. It may be because Stromboli itself is an island, and all the time during the hike I enjoyed splendid sea views (with a beer in my hand). It may be the all encompassing experience, where I could see, hear and literally feel the lava explosions. It was simply fantastic.

My geo-holiday-destination: Montenegro!

A summer without beach-time is not a summer to me (already got one beach-day in this year, phew). Being Dutch, a proper holiday also requires some proper mountains – or hills at least. And no trip is complete without cultural and culinary highlights to explore.

Montenegro is a country that ticks all the boxes. Situated along the Adriatic Sea it hosts a score of picture-perfect beaches; quiet or taken over by the jet-set, intimate coves or long stretches of white sand, take your pick.

Further inland, you reach the Dinarides orogenic chain, the product of 150 My of contractional tectonics and later collapse during the Miocene. Traversing the chain into neighboring Serbia will lead you past complete ophiolite sequences, syn-orognic magma intrusions and major detachment zones of the extensional orogenic collapse.

Visit the centuries old fortified coastal cities of Budva or Kotor or one of the many churches and frescoed monasteries spread around the countryside. For more bodily sustenance, enjoy the fresh fish dishes, rich meats or the regional cheeses and yoghurts. Seasonal fruits are eaten for dessert or, even better, turned into wine and rakija. Ehm, why I am not going there again this year – this time in summer?

This year, my favourite geodynamical destination is CIDER 2018! It’s far from holidays... but it’s really cool! For the last three weeks (one week to go), we have been intensely learning about heterogeneity in the Earth, and trying to understand it in an interdisciplinary perspective with contributions from geochemistry, geodynamics, and seismology. Quite an intense schedule and a lot of information to process, but I think we are all learning a lot, and hopefully in the future we will use more constraints coming from other fields into our own work. Oh, and did I mention that it is happening in Santa Barbara? Great Californian weather, beautiful coastal landscapes, barbecues by the beach, and swimming in the ocean, all sprinkled with scientific discussions! Quite the geodynamical destination, no?

Geoscientists are no strangers to travelling to exotic places and many of us take the opportunity to turn a work-related trip into potential holiday scouting. My suggested destination is most probably the northernmost point you can quite easily travel to on this planet - Svalbard.

Svalbard is an Arctic archipelago located around between 74-81°N latitude. It is sometimes confused with Spitsbergen, which is actually the name of the largest island where the main settlements, including Longyearbyen and Barentsburg, are situated. The islands are part of Norwegian sovereignty, though with some interesting taxation and military restrictions (the Svalbard Treaty of 1920 makes for some pretty interesting reading). Svalbard is host to a stream of tourists and scientific researchers year-round, and this week I will travel back to Longyearbyen as a lecturer for an Arctic tectonics, volcanism and geodynamics course at the University Centre in Svalbard (UNIS).

Geologically speaking, Svalbard makes for a very interesting destination. It offers a diverse range of rock ages and types; having experienced orogenic deformation events, widespread magmatism, and extensive sedimentary and glacial processes.

If you're after a more usual tourist package amongst the draw cards are of course iconic polar bears (though please keep your distance), stumpy reindeer, arctic foxes, whales, birds and special flora. There are many glaciers - in fact around 60% of Svalbard is covered in ice - as well as fjords and mountains, former coal mining settlements... the list goes on. You are even spoilt for choice between midnight sun or midday darkness, depending on the time of year, so prioritise your activities wisely. Plus, did I mention those miles and miles of unvegetated, uninterrupted rock exposures to keep any geology enthusiast happy?... if you're lucky you might come across some incredible fossil sites.