Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference

• URL: http://arxiv.org/abs/2006.12422v1
• Date: Mon, 22 Jun 2020 16:47:33 GMT
• Title: Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference
• Authors: Douglas Brinkerhoff, Andy Aschwanden, Mark Fahnestock
• Abstract summary: Basal motion is the primary mechanism for ice flux outside Antarctica, yet a widely applicable model for predicting it remains elusive. This is due to the difficulty in both observing small-scale bed properties and predicting a time-varying water pressure on which basal motion putatively depends. We take a Bayesian approach to these problems by coupling models of ice dynamics and subglacial hydrology and conditioning on observations of surface velocity in southwestern Greenland.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Basal motion is the primary mechanism for ice flux outside Antarctica, yet a widely applicable model for predicting it in the absence of retrospective observations remains elusive. This is due to the difficulty in both observing small-scale bed properties and predicting a time-varying water pressure on which basal motion putatively depends. We take a Bayesian approach to these problems by coupling models of ice dynamics and subglacial hydrology and conditioning on observations of surface velocity in southwestern Greenland to infer the posterior probability distributions for eight spatially and temporally constant parameters governing the behavior of both the sliding law and hydrologic model. Because the model is computationally expensive, classical MCMC sampling is intractable. We skirt this issue by training a neural network as a surrogate that approximates the model at a sliver of the computational cost. We find that surface velocity observations establish strong constraints on model parameters relative to a prior distribution and also elucidate correlations, while the model explains 60% of observed variance. However, we also find that several distinct configurations of the hydrologic system and stress regime are consistent with observations, underscoring the need for continued data collection and model development.

Related papers

• Machine learning-enabled velocity model building with uncertainty quantification [0.41942958779358674]
  Accurately characterizing migration velocity models is crucial for a wide range of geophysical applications. Traditional velocity model building methods are powerful but often struggle with the inherent complexities of the inverse problem. We propose a scalable methodology that integrates generative modeling, in the form of Diffusion networks, with physics-informed summary statistics.
  arXiv  Detail & Related papers  (2024-11-11T01:36:48Z)
• von Mises Quasi-Processes for Bayesian Circular Regression [57.88921637944379]
  We explore a family of expressive and interpretable distributions over circle-valued random functions. The resulting probability model has connections with continuous spin models in statistical physics. For posterior inference, we introduce a new Stratonovich-like augmentation that lends itself to fast Markov Chain Monte Carlo sampling.
  arXiv  Detail & Related papers  (2024-06-19T01:57:21Z)
• Physics-guided weak-form discovery of reduced-order models for trapped ultracold hydrodynamics [0.0]
  We study the relaxation of a highly collisional, ultracold but nondegenerate gas of polar molecules. The gas is subject to fluid-gas coupled dynamics that lead to a breakdown of first-order hydrodynamics. We present substantially improved reduced-order models for these same observables.
  arXiv  Detail & Related papers  (2024-06-11T17:50:04Z)
• A Floquet analysis perspective of driven light-matter interaction models [0.0]
  We analyze the harmonically driven Jaynes-Cummings and Lipkin-Meshkov-Glick models using both numerical integration of time-dependent Hamiltonians and Floquet theory. For a separation of time-scales between the drive and intrinsic Rabi oscillations in the former model, the driving results in an effective periodic reversal of time. Despite the chaotic nature of the driven Lipkin-Meshkov-Glick model, moderate system sizes can display qualitatively different behaviors under varying system parameters.
  arXiv  Detail & Related papers  (2024-03-26T16:53:32Z)
• Attention-Based Ensemble Pooling for Time Series Forecasting [55.2480439325792]
  We propose a method for pooling that performs a weighted average over candidate model forecasts. We test this method on two time-series forecasting problems: multi-step forecasting of the dynamics of the non-stationary Lorenz 63 equation, and one-step forecasting of the weekly incident deaths due to COVID-19.
  arXiv  Detail & Related papers  (2023-10-24T22:59:56Z)
• Modeling the space-time correlation of pulsed twin beams [68.8204255655161]
  Entangled twin-beams generated by parametric down-conversion are among the favorite sources for imaging-oriented applications. We propose a semi-analytic model which aims to bridge the gap between time-consuming numerical simulations and the unrealistic plane-wave pump theory.
  arXiv  Detail & Related papers  (2023-01-18T11:29:49Z)
• Kernel Learning for Explainable Climate Science [19.654936516882803]
  We propose non-stationary kernels to model precipitation patterns in the Upper Himalayas Indus Basin. We account for the spatial variation in precipitation with a non-stationary Gibbs kernel parameterised with an input dependent lengthscale. In ablation experiments we motivate each component of the proposed kernel by demonstrating its ability to model the spatial covariance, temporal structure and joint-temporal reconstruction.
  arXiv  Detail & Related papers  (2022-09-11T22:10:08Z)
• Variational Inference at Glacier Scale [0.0]
  We characterize the complete joint posterior distribution over spatially-varying basal traction and ice softness parameters of an ice sheet model. We find that posterior uncertainty in regions of slow flow is high regardless of the choice of observational noise model.
  arXiv  Detail & Related papers  (2021-08-16T17:56:43Z)
• Entropy Production and the Role of Correlations in Quantum Brownian Motion [77.34726150561087]
  We perform a study on quantum entropy production, different kinds of correlations, and their interplay in the driven Caldeira-Leggett model of quantum Brownian motion.
  arXiv  Detail & Related papers  (2021-08-05T13:11:05Z)
• Leveraging Global Parameters for Flow-based Neural Posterior Estimation [90.21090932619695]
  Inferring the parameters of a model based on experimental observations is central to the scientific method. A particularly challenging setting is when the model is strongly indeterminate, i.e., when distinct sets of parameters yield identical observations. We present a method for cracking such indeterminacy by exploiting additional information conveyed by an auxiliary set of observations sharing global parameters.
  arXiv  Detail & Related papers  (2021-02-12T12:23:13Z)
• Semiparametric Bayesian Forecasting of Spatial Earthquake Occurrences [77.68028443709338]
  We propose a fully Bayesian formulation of the Epidemic Type Aftershock Sequence (ETAS) model. The occurrence of the mainshock earthquakes in a geographical region is assumed to follow an inhomogeneous spatial point process.
  arXiv  Detail & Related papers  (2020-02-05T10:11:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.