Emulation of vessel motion simulators for computationally efficient uncertainty quantification

Astfalck, Lachlan; Cripps, Edward; Gosling, John Paul; Milne, Ian

doi:10.1016/j.oceaneng.2018.11.059

Cited by 11 publications

(5 citation statements)

References 51 publications

(69 reference statements)

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“…As a probabilistic encoder of expert knowledge, formal elicitation procedures (O'Hagan et al, 2006) have contributed greatly to physical domain sciences where complex models are essential to our understanding of the underlying processes. From climatology, meteorology and oceanography (Kennedy et al, 2008) to geology and geostatistics (Walker andCurtis, 2014, andLark et al, 2015) to hydrodynamics and engineering (Astfalck et al, 2018(Astfalck et al, , 2019, the central role of expert elicitation is being increasingly recognised. The complexity and parameterisations of geophysical models, as well as the expert knowledge that resides within the geophysical community, suggest this domain should be no different.…”

Section: Expert Elicitationmentioning

confidence: 99%

Into the Noddyverse: a massive data store of 3D geological models for machine learning and inversion applications

Jessell

Guo

et al. 2022

Earth Syst. Sci. Data

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Abstract. Unlike some other well-known challenges such as facial recognition, where machine learning and inversion algorithms are widely developed, the geosciences suffer from a lack of large, labelled data sets that can be used to validate or train robust machine learning and inversion schemes. Publicly available 3D geological models are far too restricted in both number and the range of geological scenarios to serve these purposes. With reference to inverting geophysical data this problem is further exacerbated as in most cases real geophysical observations result from unknown 3D geology, and synthetic test data sets are often not particularly geological or geologically diverse. To overcome these limitations, we have used the Noddy modelling platform to generate 1 million models, which represent the first publicly accessible massive training set for 3D geology and resulting gravity and magnetic data sets (https://doi.org/10.5281/zenodo.4589883, Jessell, 2021). This model suite can be used to train machine learning systems and to provide comprehensive test suites for geophysical inversion. We describe the methodology for producing the model suite and discuss the opportunities such a model suite affords, as well as its limitations, and how we can grow and access this resource.

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Section: Expert Elicitationmentioning

confidence: 99%

Into the Noddyverse: a massive data store of 3D geological models for machine learning and inversion applications

Jessell

Guo

et al. 2022

Earth Syst. Sci. Data

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“…Gaussian Process (GP) emulators are non-parametric statistical regression models that flexibly represent chosen model output or performance metrics as a function of a subset of input parameters, together with an uncertainty on that prediction (Rasmussen and Williams, 2006;Astfalck et al, 2019). We describe the model output y as a function of a vector of input parameters 𝜃 expressed as:…”

Section: Statistical Design Of Multi-wave Ensemblementioning

confidence: 99%

Optimisation of the marine Nd isotope scheme in the ocean component of the FAMOUS general circulation model

Robinson

Ivanovic²,

Gregoire³

et al. 2022

Preprint

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Abstract. The neodymium (Nd) isotope composition (εNd) of seawater can be used to trace large-scale ocean circulation features. Yet, due to the elusive nature of marine Nd cycling, particularly in discerning non-conservative particle-seawater interactions, there remains considerable uncertainty surrounding a complete description of marine Nd budgets. Here, we present an optimisation of the Nd isotope scheme within the fast coupled atmosphere-ocean general circulation model (FAMOUS), using a statistical emulator to explore the parametric uncertainty and optimal combinations of three key model inputs relating to: (1) the efficiency of reversible scavenging, (2) the magnitude of the seafloor benthic flux, and (3) a riverine source scaling, accounting for release of Nd from river sourced particulate material. Furthermore, a suite of sensitivity tests provide insight on the regional mobilisation and spatial extent (i.e., testing a margin-constrained versus a seafloor-wide benthic flux) of certain reactive sediment components. In the calibrated scheme, the global marine Nd inventory totals 4.27 × 1012 g and has a mean residence time of 727 years. Atlantic Nd isotope distributions are represented well, and the weak sensitivity of North Atlantic Deep Water to highly unradiogenic sedimentary sources implies an abyssal benthic flux is of secondary importance in determining the water mass εNd properties under the modern vigorous circulation condition. On the other hand, Nd isotope distributions in the North Pacific are 3 to 4 εNd-units too unradiogenic compared to water measurements, and our simulations indicate that a spatially uniform flux of bulk sediment εNd does not sufficiently capture the mobile sediment components interacting with seawater. Our results of sensitivity tests suggest that there are distinct regional differences in how modern seawater acquires its εNd signal, in part relating to the complex interplay of Nd addition and water advection.

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“…where τ ≥ 6, c ∈ (0, π], (a) + = max(0, a), and d(i, j) is the geodesic distance between locations i and j. The C 4 -Wendland covariance function is commonly chosen so as to define a smooth process on the sphere; (see, for example Astfalck et al, 2019). Parameters are specified as κ 2 = 1.61, c = 0.92, and τ = 6; this represents the prior-beliefs of domain experts as to the behaviour of U X .…”

Section: Sea-surface Temperaturementioning

confidence: 99%

Coexchangeable process modelling for uncertainty quantification in joint climate reconstruction

Astfalck¹,

Williamson²,

Gandy³

et al. 2021

Preprint

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Any experiment with climate models relies on a potentially large set of spatiotemporal boundary conditions. These can represent both the initial state of the system and/or forcings driving the model output throughout the experiment. Whilst these boundary conditions are typically fixed using available reconstructions in climate modelling studies, they are highly uncertain, that uncertainty is unquantified, and the effect on the output of the experiment can be considerable. We develop efficient quantification of these uncertainties that combines relevant data from multiple models and observations. Starting from the coexchangeability model, we develop a coexchangable process model to capture multiple correlated spatio-temporal fields of variables. We demonstrate that further exchangeability judgements over the parameters within this representation lead to a Bayes linear analogy of a hierarchical model. We use the framework to provide a joint reconstruction of sea-surface temperature and sea-ice concentration boundary conditions at the last glacial maximum (19-23 ka) and use it to force an ensemble of ice-sheet simulations using the FAMOUS-Ice coupled atmosphere and ice-sheet model. We demonstrate that existing boundary conditions typically used in these experiments are implausible given our uncertainties and demonstrate the impact of using more plausible boundary conditions on ice-sheet simulation.

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Emulation of vessel motion simulators for computationally efficient uncertainty quantification

Cited by 11 publications

References 51 publications

Into the Noddyverse: a massive data store of 3D geological models for machine learning and inversion applications

Into the Noddyverse: a massive data store of 3D geological models for machine learning and inversion applications

Optimisation of the marine Nd isotope scheme in the ocean component of the FAMOUS general circulation model

Coexchangeable process modelling for uncertainty quantification in joint climate reconstruction

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