Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models

Olierook, Hugo K.H.; Scalzo, R.; Kohn, David; Chandra, Rohitash; Farahbakhsh, Ehsan; Clark, Chris; Reddy, Steven M.; Müller, R. Dietmar

doi:10.1016/j.gsf.2020.04.015

Cited by 45 publications

(17 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multi‐view representation is a projection‐based network [25]. The traditional methodology for 3D view model uses quantitative technique blended with qualitative techniques [37]. Simple orthographic projections in engineering drawings are used to determine the 3D view of an object [14, 21].…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Two viewpoints based real‐time recognition for hand gestures

Kumar

Guo

2020

IET image process

View full text Add to dashboard Cite

It is extremely challenging to accomplish excellent accuracy for gesture recognition using an approach where complexity in computation time for recognition is less. This study compares accuracy in hand gesture recognition of a single viewpoint set‐up with proposed two viewpoint set‐up for different classification techniques. The efficacy of the presented approach is verified practically with various image processing, feature extraction and classification techniques. Two camera system make geometry learning and three‐dimensional (3D) view feasible compared to a single camera system. Geometrical features from additional viewpoint contribute to 3D view estimation of the hand gesture. It also improves the classification accuracy. Experimental results demonstrate that the proposed method show escalation in recognition rate compared to the single‐camera system, and also has great performance using simple classifiers like the nearest neighbour and decision tree. Classification within 1 s is considered as real‐time in this study.

show abstract

Section: Methodsmentioning

confidence: 99%

“…Major axis to minor axis ratio of an ellipse is 4:3 since this is the aspect ratio of images. Prior knowledge of the 3D view can be identified by the probability distribution of parameters [37]. Major parameter is the reference points r 0 .…”

Section: Methodsmentioning

confidence: 99%

Two viewpoints based real‐time recognition for hand gestures

Kumar

Guo

2020

IET image process

View full text Add to dashboard Cite

show abstract

“…The Obsidian distributed inversion code (McCalman et al, 2014) implemented a geological model of a layered sedimentary basin with explicit unit boundaries to explore geothermal potential (Beardsmore et al, 2016); it supported multiple geophysical sensors and used a distributed parallel-tempered MCMC sampler to draw from the multi-modal posteriors that may arise in underconstrained inverse problems. Obsidian has been extended with new within-chain MCMC proposals inside the parallel-tempered framework (Scalzo et al, 2019) and a new sensor likelihood for field observations of surface lithostratigraphy (Olierook et al, 2020); however, the limitations of its geological model make it an unlikely engine for general-purpose inversions. Wellmann et al (2017) present a more general workflow that uses GeoModeller to render the geology, Noddy for calculation of geophysical fields, and pymc2 (Patil et al, 2010) for MCMC sampling.…”

Section: Introductionmentioning

confidence: 99%

Blockworlds 0.1.0: A demonstration of anti-aliased geophysics for probabilistic inversions of implicit and kinematic geological models

Scalzo

Lindsay

Jessell

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. Parametric geological models such as implicit or kinematic models provide low-dimensional, interpretable representations of 3-D geological structures. Combining these models with geophysical data in a probabilistic joint inversion framework provides an opportunity to directly quantify uncertainty in geological interpretations. For best results, the projection of the geological parameter space onto the finite-resolution discrete basis of the geophysical calculation must be faithful within the power of the data to discriminate. We show that naively exporting voxelised geology as done in commonly used geological modeling tools can easily produce a poor approximation to the true geophysical likelihood, degrading posterior inference for structural parameters. We then demonstrate a numerical forward-modeling scheme for calculating anti-aliased rock properties on regular meshes for use with gravity and magnetic sensors. Finally, we explore anti-aliasing in the context of a kinematic forward model for simple tectonic histories, showing its impact on the structure of the geophysical likelihood for gravity anomaly.

show abstract

“…The Bayesian methodology provides a probabilistic approach for the estimation of unknown parameters in complex models (Sambridge, 1999;Neal, 1996;Chandra et al, 2019b). We can view a deterministic geophysical forward model as a probabilistic model via Bayesian inference, which is also known as Bayesian inversion, which has been used for landscape evolution (Chandra et al, 2019a, c), geological reef evolution models (Pall et al, 2020), and other geoscientific models (Sambridge, 1999(Sambridge, , 2013Scalzo et al, 2019;Olierook et al, 2020). Markov chain Monte Carlo (MCMC) sampling is typically used to implement Bayesian inference that involves the estimation and uncertainty quantification of unknown parameters (Hastings, 1970;Metropolis et al, 1953;Neal, 2012Neal, , 1996.…”

Section: Introductionmentioning

confidence: 99%

Surrogate-assisted Bayesian inversion for landscape and basin evolution models

et al. 2020

Self Cite

View full text Add to dashboard Cite

Abstract. The complex and computationally expensive nature of landscape evolution models poses significant challenges to the inference and optimization of unknown model parameters. Bayesian inference provides a methodology for estimation and uncertainty quantification of unknown model parameters. In our previous work, we developed parallel tempering Bayeslands as a framework for parameter estimation and uncertainty quantification for the Badlands landscape evolution model. Parallel tempering Bayeslands features high-performance computing that can feature dozens of processing cores running in parallel to enhance computational efficiency. Nevertheless, the procedure remains computationally challenging since thousands of samples need to be drawn and evaluated. In large-scale landscape evolution problems, a single model evaluation can take from several minutes to hours and in some instances, even days or weeks. Surrogate-assisted optimization has been used for several computationally expensive engineering problems which motivate its use in optimization and inference of complex geoscientific models. The use of surrogate models can speed up parallel tempering Bayeslands by developing computationally inexpensive models to mimic expensive ones. In this paper, we apply surrogate-assisted parallel tempering where the surrogate mimics a landscape evolution model by estimating the likelihood function from the model. We employ a neural-network-based surrogate model that learns from the history of samples generated. The entire framework is developed in a parallel computing infrastructure to take advantage of parallelism. The results show that the proposed methodology is effective in lowering the computational cost significantly while retaining the quality of model predictions.

show abstract

Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models

Cited by 45 publications

References 72 publications

Two viewpoints based real‐time recognition for hand gestures

Two viewpoints based real‐time recognition for hand gestures

Blockworlds 0.1.0: A demonstration of anti-aliased geophysics for probabilistic inversions of implicit and kinematic geological models

Surrogate-assisted Bayesian inversion for landscape and basin evolution models

Contact Info

Product

Resources

About