Articles | Volume 12, issue 7
https://doi.org/10.5194/gmd-12-2941-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-2941-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
15 Jul 2019
Development and technical paper |
| 15 Jul 2019
| 15 Jul 2019
Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success
Richard Scalzo
CORRESPONDING AUTHOR
Centre for Translational Data Science, University of Sydney,
Darlington NSW 2008, Australia
David Kohn
Sydney Informatics Hub, University of Sydney,
Darlington NSW 2008, Australia
Hugo Olierook
School of Earth and Planetary Sciences, Curtin University,
Bentley WA 6102, Australia
Gregory Houseman
School of Earth and Environment, University of Leeds,
Leeds, LS2 9JT, UK
Rohitash Chandra
Centre for Translational Data Science, University of Sydney,
Darlington NSW 2008, Australia
School of Geosciences, University of Sydney,
Darlington NSW 2008, Australia
Mark Girolami
The Alan Turing Institute for Data Science,
British Library, 96 Euston Road, London, NW1 2DB, UK
Department of Mathematics,
Imperial College London, London, SW7 2AZ, UK
Sally Cripps
Centre for Translational Data Science, University of Sydney,
Darlington NSW 2008, Australia
School of Mathematics and Statistics,
University of Sydney, Darlington NSW 2008, Australia
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This paper addresses numerical challenges in reasoning about geological models constrained by sensor data, especially models that describe the history of an area in terms of a sequence of events. Our method ensures that small changes in simulated geological features, such as the position of a boundary between two rock layers, do not result in unrealistically large changes to resulting sensor measurements, as occur presently using several popular modeling packages.
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Richard Scalzo, Mark Lindsay, Mark Jessell, Guillaume Pirot, Jeremie Giraud, Edward Cripps, and Sally Cripps
Geosci. Model Dev., 15, 3641–3662, https://doi.org/10.5194/gmd-15-3641-2022, https://doi.org/10.5194/gmd-15-3641-2022, 2022
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This paper addresses numerical challenges in reasoning about geological models constrained by sensor data, especially models that describe the history of an area in terms of a sequence of events. Our method ensures that small changes in simulated geological features, such as the position of a boundary between two rock layers, do not result in unrealistically large changes to resulting sensor measurements, as occur presently using several popular modeling packages.
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Earth Syst. Sci. Data, 14, 381–392, https://doi.org/10.5194/essd-14-381-2022, https://doi.org/10.5194/essd-14-381-2022, 2022
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To robustly train and test automated methods in the geosciences, we need to have access to large numbers of examples where we know
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Solid Earth Discuss., https://doi.org/10.5194/se-2019-4, https://doi.org/10.5194/se-2019-4, 2019
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Younghun Kang and Ethan J. Kubatko
Geosci. Model Dev., 17, 1603–1625, https://doi.org/10.5194/gmd-17-1603-2024, https://doi.org/10.5194/gmd-17-1603-2024, 2024
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Hui Wan, Kai Zhang, Christopher J. Vogl, Carol S. Woodward, Richard C. Easter, Philip J. Rasch, Yan Feng, and Hailong Wang
Geosci. Model Dev., 17, 1387–1407, https://doi.org/10.5194/gmd-17-1387-2024, https://doi.org/10.5194/gmd-17-1387-2024, 2024
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Christopher J. Vogl, Hui Wan, Carol S. Woodward, and Quan M. Bui
Geosci. Model Dev., 17, 1409–1428, https://doi.org/10.5194/gmd-17-1409-2024, https://doi.org/10.5194/gmd-17-1409-2024, 2024
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Tom Keel, Chris Brierley, and Tamsin Edwards
Geosci. Model Dev., 17, 1229–1247, https://doi.org/10.5194/gmd-17-1229-2024, https://doi.org/10.5194/gmd-17-1229-2024, 2024
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Amir Golparvar, Matthias Kästner, and Martin Thullner
Geosci. Model Dev., 17, 881–898, https://doi.org/10.5194/gmd-17-881-2024, https://doi.org/10.5194/gmd-17-881-2024, 2024
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Stefan Hergarten
Geosci. Model Dev., 17, 781–794, https://doi.org/10.5194/gmd-17-781-2024, https://doi.org/10.5194/gmd-17-781-2024, 2024
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Arjun Babu Nellikkattil, Danielle Lemmon, Travis Allen O'Brien, June-Yi Lee, and Jung-Eun Chu
Geosci. Model Dev., 17, 301–320, https://doi.org/10.5194/gmd-17-301-2024, https://doi.org/10.5194/gmd-17-301-2024, 2024
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Mohammad Mortezazadeh, Jean-François Cossette, Ashu Dastoor, Jean de Grandpré, Irena Ivanova, and Abdessamad Qaddouri
Geosci. Model Dev., 17, 335–346, https://doi.org/10.5194/gmd-17-335-2024, https://doi.org/10.5194/gmd-17-335-2024, 2024
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Boris Gailleton, Luca C. Malatesta, Guillaume Cordonnier, and Jean Braun
Geosci. Model Dev., 17, 71–90, https://doi.org/10.5194/gmd-17-71-2024, https://doi.org/10.5194/gmd-17-71-2024, 2024
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This contribution presents a new method to numerically explore the evolution of mountain ranges and surrounding areas. The method helps in monitoring with details on the timing and travel path of material eroded from the mountain ranges. It is particularly well suited to studies juxtaposing different domains – lakes or multiple rock types, for example – and enables the combination of different processes.
Denise Degen, Daniel Caviedes Voullième, Susanne Buiter, Harrie-Jan Hendricks Franssen, Harry Vereecken, Ana González-Nicolás, and Florian Wellmann
Geosci. Model Dev., 16, 7375–7409, https://doi.org/10.5194/gmd-16-7375-2023, https://doi.org/10.5194/gmd-16-7375-2023, 2023
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In geosciences, we often use simulations based on physical laws. These simulations can be computationally expensive, which is a problem if simulations must be performed many times (e.g., to add error bounds). We show how a novel machine learning method helps to reduce simulation time. In comparison to other approaches, which typically only look at the output of a simulation, the method considers physical laws in the simulation itself. The method provides reliable results faster than standard.
Carlos Spa, Otilio Rojas, and Josep de la Puente
Geosci. Model Dev., 16, 7237–7252, https://doi.org/10.5194/gmd-16-7237-2023, https://doi.org/10.5194/gmd-16-7237-2023, 2023
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This paper develops a calibration methodology of all absorbing techniques typically used by Fourier pseudo-spectral time-domain (PSTD) methods for geoacoustic wave simulations. The main contributions of the paper are (a) an implementation and quantitative comparison of all absorbing techniques available for PSTD methods through a simple and robust numerical experiment, and (b) a validation of these absorbing techniques in several 3-D seismic scenarios with gradual heterogeneity complexities.
Michael Hillier, Florian Wellmann, Eric A. de Kemp, Boyan Brodaric, Ernst Schetselaar, and Karine Bédard
Geosci. Model Dev., 16, 6987–7012, https://doi.org/10.5194/gmd-16-6987-2023, https://doi.org/10.5194/gmd-16-6987-2023, 2023
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Neural networks can be used effectively to model three-dimensional geological structures from point data, sampling geological interfaces, units, and structural orientations. Existing neural network approaches for this type of modelling are advanced by the efficient incorporation of unconformities, new knowledge inputs, and improved data fitting techniques. These advances permit the modelling of more complex geology in diverse geological settings, different-sized areas, and various data regimes.
Tor Nordam, Ruben Kristiansen, Raymond Nepstad, Erik van Sebille, and Andy M. Booth
Geosci. Model Dev., 16, 5339–5363, https://doi.org/10.5194/gmd-16-5339-2023, https://doi.org/10.5194/gmd-16-5339-2023, 2023
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We describe and compare two common methods, Eulerian and Lagrangian models, used to simulate the vertical transport of material in the ocean. They both solve the same transport problems but use different approaches for representing the underlying equations on the computer. The main focus of our study is on the numerical accuracy of the two approaches. Our results should be useful for other researchers creating or using these types of transport models.
Mathieu Gravey and Grégoire Mariethoz
Geosci. Model Dev., 16, 5265–5279, https://doi.org/10.5194/gmd-16-5265-2023, https://doi.org/10.5194/gmd-16-5265-2023, 2023
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Multiple‐point geostatistics are widely used to simulate complex spatial structures based on a training image. The use of these methods relies on the possibility of finding optimal training images and parametrization of the simulation algorithms. Here, we propose finding an optimal set of parameters using only the training image as input. The main advantage of our approach is to remove the risk of overfitting an objective function.
Siting Li, Ping Wang, Hong Wang, Yue Peng, Zhaodong Liu, Wenjie Zhang, Hongli Liu, Yaqiang Wang, Huizheng Che, and Xiaoye Zhang
Geosci. Model Dev., 16, 4171–4191, https://doi.org/10.5194/gmd-16-4171-2023, https://doi.org/10.5194/gmd-16-4171-2023, 2023
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Optimizing the initial state of atmospheric chemistry model input is one of the most essential methods to improve forecast accuracy. Considering the large computational load of the model, we introduce an ensemble optimal interpolation scheme (EnOI) for operational use and efficient updating of the initial fields of chemical components. The results suggest that EnOI provides a practical and cost-effective technique for improving the accuracy of chemical weather numerical forecasts.
Thomas Richter, Véronique Dansereau, Christian Lessig, and Piotr Minakowski
Geosci. Model Dev., 16, 3907–3926, https://doi.org/10.5194/gmd-16-3907-2023, https://doi.org/10.5194/gmd-16-3907-2023, 2023
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Sea ice covers not only the pole regions but affects the weather and climate globally. For example, its white surface reflects more sunlight than land. The oceans around the poles are therefore kept cool, which affects the circulation in the oceans worldwide. Simulating the behavior and changes in sea ice on a computer is, however, very difficult. We propose a new computer simulation that better models how cracks in the ice change over time and show this by comparing to other simulations.
Emma J. MacKie, Michael Field, Lijing Wang, Zhen Yin, Nathan Schoedl, Matthew Hibbs, and Allan Zhang
Geosci. Model Dev., 16, 3765–3783, https://doi.org/10.5194/gmd-16-3765-2023, https://doi.org/10.5194/gmd-16-3765-2023, 2023
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Earth scientists often have to fill in spatial gaps in measurements. This gap-filling or interpolation can be accomplished with geostatistical methods, where the statistical relationships between measurements are used to inform how these gaps should be filled. Despite the broad utility of these methods, there are few freely available geostatistical software applications. We present GStatSim, a Python package for performing different geostatistical interpolation methods.
Ian Madden, Simone Marras, and Jenny Suckale
Geosci. Model Dev., 16, 3479–3500, https://doi.org/10.5194/gmd-16-3479-2023, https://doi.org/10.5194/gmd-16-3479-2023, 2023
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To aid risk managers who may wish to rapidly assess tsunami risk but may lack high-performance computing infrastructure, we provide an accessible software package able to rapidly model tsunami inundation over real topography by leveraging Google's Tensor Processing Unit, a high-performance hardware. Minimally trained users can take advantage of the rapid modeling abilities provided by this package via a web browser thanks to the ease of use of Google Cloud Platform.
Youtong Rong, Paul Bates, and Jeffrey Neal
Geosci. Model Dev., 16, 3291–3311, https://doi.org/10.5194/gmd-16-3291-2023, https://doi.org/10.5194/gmd-16-3291-2023, 2023
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A novel subgrid channel (SGC) model is developed for river–floodplain modelling, allowing utilization of subgrid-scale bathymetric information while performing computations on relatively coarse grids. By including adaptive artificial diffusion, potential numerical instability, which the original SGC solver had, in low-friction regions such as urban areas is addressed. Evaluation of the new SGC model through structured tests confirmed that the accuracy and stability have improved.
Xiaqiong Zhou and Hann-Ming Henry Juang
Geosci. Model Dev., 16, 3263–3274, https://doi.org/10.5194/gmd-16-3263-2023, https://doi.org/10.5194/gmd-16-3263-2023, 2023
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The National Centers for Environmental Prediction Global Forecast System version 16 experienced model instability failures in real-time runs resolved by increasing the minimum thickness depth parameter. Further investigation revealed that the issue was caused by the advection of geopotential heights at the model's layer interfaces. By replacing high-order boundary conditions with zero-gradient boundary conditions for interface-wind reconstruction, the instability was effectively addressed.
Grant T. Euen, Shangxin Liu, Rene Gassmöller, Timo Heister, and Scott D. King
Geosci. Model Dev., 16, 3221–3239, https://doi.org/10.5194/gmd-16-3221-2023, https://doi.org/10.5194/gmd-16-3221-2023, 2023
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Due to the increasing availability of high-performance computing over the past few decades, numerical models have become an important tool for research. Here we test two geodynamic codes that produce such models: ASPECT, a newer code, and CitcomS, an older one. We show that they produce solutions that are extremely close. As methods and codes become more complex over time, showing reproducibility allows us to seamlessly link previously known information to modern methodologies.
Mohammad Kazem Sharifian, Georges Kesserwani, Alovya Ahmed Chowdhury, Jeffrey Neal, and Paul Bates
Geosci. Model Dev., 16, 2391–2413, https://doi.org/10.5194/gmd-16-2391-2023, https://doi.org/10.5194/gmd-16-2391-2023, 2023
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This paper describes a new release of the LISFLOOD-FP model for fast and efficient flood simulations. It features a new non-uniform grid generator that uses multiwavelet analyses to sensibly coarsens the resolutions where the local topographic variations are smooth. Moreover, the model is parallelised on the graphical processing units (GPUs) to further boost computational efficiency. The performance of the model is assessed for five real-world case studies, noting its potential applications.
Matt J. Fischer
EGUsphere, https://doi.org/10.5194/egusphere-2023-643, https://doi.org/10.5194/egusphere-2023-643, 2023
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In paleoclimate research, proxy system models or PSMs model chemical or biological systems which receive environmental inputs, and output e.g. geochemical signals. The environmental inputs are rarely noiseless, which causes problems when calibrating multi-input PSMs. Here a PSM is developed which includes generalized noise in both model inputs and outputs, and prior information. A quasi-Bayesian method enhances the stability of the solution of the Measurement Error Proxy System Model.
Bruno K. Zürcher
Geosci. Model Dev., 16, 1697–1711, https://doi.org/10.5194/gmd-16-1697-2023, https://doi.org/10.5194/gmd-16-1697-2023, 2023
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We present a novel algorithm to efficiently compute Barnes interpolation, which is a method for transforming data values recorded at irregularly spaced points into a corresponding regular grid. In contrast to naive implementations with an algorithmic complexity that depends on the product of the number of sample points and the number of grid points, our approach reduces this dependency to their sum.
David H. Marsico and Paul A. Ullrich
Geosci. Model Dev., 16, 1537–1551, https://doi.org/10.5194/gmd-16-1537-2023, https://doi.org/10.5194/gmd-16-1537-2023, 2023
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Climate models involve several different components, such as the atmosphere, ocean, and land models. Information needs to be exchanged, or remapped, between these models, and devising algorithms for performing this exchange is important for ensuring the accuracy of climate simulations. In this paper, we examine the efficacy of several traditional and novel approaches to remapping on the sphere and demonstrate where our approaches offer improvement.
Moritz Liebl, Jörg Robl, Stefan Hergarten, David Lundbek Egholm, and Kurt Stüwe
Geosci. Model Dev., 16, 1315–1343, https://doi.org/10.5194/gmd-16-1315-2023, https://doi.org/10.5194/gmd-16-1315-2023, 2023
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In this study, we benchmark a topography-based model for glacier erosion (OpenLEM) with a well-established process-based model (iSOSIA). Our experiments show that large-scale erosion patterns and particularly the transformation of valley length geometry from fluvial to glacial conditions are very similar in both models. This finding enables the application of OpenLEM to study the influence of climate and tectonics on glaciated mountains with reasonable computational effort on standard PCs.
James Kent, Thomas Melvin, and Golo Albert Wimmer
Geosci. Model Dev., 16, 1265–1276, https://doi.org/10.5194/gmd-16-1265-2023, https://doi.org/10.5194/gmd-16-1265-2023, 2023
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This paper introduces the Met Office's new shallow water model. The shallow water model is a building block towards the Met Office's new atmospheric dynamical core. The shallow water model is tested on a number of standard spherical shallow water test cases, including flow over mountains and unstable jets. Results show that the model produces similar results to other shallow water models in the literature.
Anthony Gruber, Max Gunzburger, Lili Ju, Rihui Lan, and Zhu Wang
Geosci. Model Dev., 16, 1213–1229, https://doi.org/10.5194/gmd-16-1213-2023, https://doi.org/10.5194/gmd-16-1213-2023, 2023
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This work applies a novel technical tool, multifidelity Monte Carlo (MFMC) estimation, to three climate-related benchmark experiments involving oceanic, atmospheric, and glacial modeling. By considering useful quantities such as maximum sea height and total (kinetic) energy, we show that MFMC leads to predictions which are more accurate and less costly than those obtained by standard methods. This suggests MFMC as a potential drop-in replacement for estimation in realistic climate models.
Piyoosh Jaysaval, Glenn E. Hammond, and Timothy C. Johnson
Geosci. Model Dev., 16, 961–976, https://doi.org/10.5194/gmd-16-961-2023, https://doi.org/10.5194/gmd-16-961-2023, 2023
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We present a robust and highly scalable implementation of numerical forward modeling and inversion algorithms for geophysical electrical resistivity tomography data. The implementation is publicly available and developed within the framework of PFLOTRAN (http://www.pflotran.org), an open-source, state-of-the-art massively parallel subsurface flow and transport simulation code. The paper details all the theoretical and implementation aspects of the new capabilities along with test examples.
Lucas Schauer, Michael J. Schmidt, Nicholas B. Engdahl, Stephen D. Pankavich, David A. Benson, and Diogo Bolster
Geosci. Model Dev., 16, 833–849, https://doi.org/10.5194/gmd-16-833-2023, https://doi.org/10.5194/gmd-16-833-2023, 2023
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We develop a multi-dimensional, parallelized domain decomposition strategy for mass-transfer particle tracking methods in two and three dimensions, investigate different procedures for decomposing the domain, and prescribe an optimal tiling based on physical problem parameters and the number of available CPU cores. For an optimally subdivided diffusion problem, the parallelized algorithm achieves nearly perfect linear speedup in comparison with the serial run-up to thousands of cores.
John Mern and Jef Caers
Geosci. Model Dev., 16, 289–313, https://doi.org/10.5194/gmd-16-289-2023, https://doi.org/10.5194/gmd-16-289-2023, 2023
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In this work, we formulate the sequential geoscientific data acquisition problem as a problem that is similar to playing chess against nature, except the pieces are not fully observed. Solutions to these problems are given in AI and rarely used in geoscientific data planning. We illustrate our approach to a simple 2D problem of mineral exploration.
Ziqi Gao, Yifeng Wang, Petros Vasilakos, Cesunica E. Ivey, Khanh Do, and Armistead G. Russell
Geosci. Model Dev., 15, 9015–9029, https://doi.org/10.5194/gmd-15-9015-2022, https://doi.org/10.5194/gmd-15-9015-2022, 2022
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While the national ambient air quality standard of ozone is based on the 3-year average of the fourth highest 8 h maximum (MDA8) ozone concentrations, these predicted extreme values using numerical methods are always biased low. We built four computational models (GAM, MARS, random forest and SVR) to predict the fourth highest MDA8 ozone in Southern California using precursor emissions, meteorology and climatological patterns. All models presented acceptable performance, with GAM being the best.
Zhihao Wang, Jason Goetz, and Alexander Brenning
Geosci. Model Dev., 15, 8765–8784, https://doi.org/10.5194/gmd-15-8765-2022, https://doi.org/10.5194/gmd-15-8765-2022, 2022
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A lack of inventory data can be a limiting factor in developing landslide predictive models, which are crucial for supporting hazard policy and decision-making. We show how case-based reasoning and domain adaptation (transfer-learning techniques) can effectively retrieve similar landslide modeling situations for prediction in new data-scarce areas. Using cases in Italy, Austria, and Ecuador, our findings support the application of transfer learning for areas that require rapid model development.
Cited articles
Agostinetti, N. P. and Malinverno, A.: Receiver function inversion by
trans-dimensional Monte Carlo sampling, Geophys. J. Int.,
181, 858–872, https://doi.org/10.1111/j.1365-246X.2010.04530.x, 2010. a, b, c
Anand, R. R. and Butt, C. R. M.: A guide for mineral exploration through the
regolith in the Yilgarn Craton, Western Australia, Aust. J.
Earth Sci., 57, 1015–1114, 2010. a
Bodin, T., Sambridge, M., Tkalcic, H., Arroucau, P., Gallagher, K., and
Rawlinson, N.: Transdimensional inversion of receiver functions and surface
wave dispersion, Solid Earth, 117, B02301, https://doi.org/10.1029/2011JB008560, 2012. a, b, c
Calcagno, P., Chilès, J., Courrioux, G., and Guillen, A.: Geological modelling
from field data and geological knowledge: Part I. Modelling method coupling
3D potential-field interpolation and geological rules, Phys. Earth
Planet. Int., 171, 147–157, recent Advances in Computational
Geodynamics: Theory, Numerics and Applications, 2008. a
Chandra, R., Azam, D., Müller, R. D., Salles, T., and Cripps, S.:
BayesLands: A Bayesian inference approach for parameter uncertainty
quantification in Badlands, Comput. Geosci., 131, 89–101, https://doi.org/10.1016/j.cageo.2019.06.012, 2019. a, b
Chib, S. and Greenberg, E.: Understanding the Metropolis-Hastings Algorithm,
The American Statistician, 49, 327–335, 1995. a
Cramer, H.: Mathematical methods of statistics, Princeton University Press,
1946. a
de la Varga, M. and Wellmann, J. F.: Structural geologic modeling as an
inference problem: A Bayesian perspective, Interpretation, 4, SM1–SM16,
2016. a
Duane, S., Kennedy, A. D., Pendleton, B. J., and Roweth, D.: Hybrid Monte
Carlo, Phys. Lett. B, 195, 216–222, 1987. a
Fichtner, A., Bunge, H.-P., and Igel, H.: The adjoint method in seismology: I. Theory, Phys. Earth Planet. Int., 157, 86–104,
2006a. a
Fichtner, A., Bunge, H.-P., and Igel, H.: “The adjoint method in seismology:
II. Applications: travel times and sensitivity functionals”, Phys.
Earth Planet. Int., 157, 105–123, 2006b. a
Gelman, A. and Rubin, D.: Inference from Iterative Simulation Using Multiple
Sequences, Stat. Sci., 7, 457–472, 1992. a
Geyer, C. J. and Thompson, E. A.: Annealing Markov Chain Monte Carlo with
Applications to Ancestral Inference, J. Am. Stat.
Assoc., 90, 909–920, 1995. a
Giraud, J., Pakyuz-Charrier, E., Ogarko, V., Jessell, M., Lindsay, M., and
Martin, R.: Impact of uncertain geology in constrained geophysical inversion,
ASEG Extended Abstracts, 2018, 1, 2018. a
Goodman, J. and Weare, J.: Ensemble samplers with affine invariance,
Commun. Appl. Mathe. Comput. Sci., 5, 65–80,
2010. a
Green, P. J., Łatuszyński, K., Pereyra, M., and Robert, C. P.:
Bayesian computation: a perspective on the current state, and sampling
backwards and forwards, Stat. Comput., 25, arXiv:1502.01148,
2015. a
Gupta, V. K. and Grant, F. S.: 30. Mineral-Exploration Aspects of Gravity and
Aeromagnetic Surveys in the Sudbury-Cobalt Area, Ontario, 392–412,
Society of Exploration Geophysicists, 1985. a
Hastings, W. K.: Monte Carlo sampling methods using Markov chains and their
applications, Biometrika, 57, 97–109, 1970. a
Hoffman, M. D. and Gelman, A.: The No-U-turn sampler: adaptively setting path
lengths in Hamiltonian Monte Carlo, J. Mach. Learn. Res.,
15, 1593–1623, 2014. a
Jessell, M.: Three-dimensional geological modelling of potential-field data,
Comput. Geosci., 27, 455–465, 3D reconstruction, modelling &
visualization of geological materials, 2001. a
Jin, Y.: Surrogate-assisted evolutionary computation: Recent advances and
future challenges, Swarm Evol. Comput., 1, 61–70, 2011. a
Köpke, C., Irving, J., and Elsheikh, A. H.: Accounting for model error in
Bayesian solutions to hydrogeophysical inverse problems using a local basis
approach, Adv. Water Resour., 116, 195–207, 2018. a
Lajaunie, C., Courrioux, G., and Manuel, L.: Foliation fields and 3D
cartography in geology: Principles of a method based on potential
interpolation, Math. Geol., 29, 571–584, https://doi.org/10.1007/BF02775087,
1997. a, b
Lan, S., Bui-Thanh, T., Christie, M., and Girolami, M.: Emulation of
higher-order tensors in manifold Monte Carlo methods for Bayesian Inverse
Problems, J. Comput. Phys., 308, 81–101, 2016. a
Lindsay, M., Jessell, M., Ailleres, L., Perrouty, S., de Kemp, E., and Betts,
P.: Geodiversity: Exploration of 3D geological model space, Tectonophysics,
594, 27–37, 2013. a
MacCarthy, J. K., Borchers, B., and Aster, R. C.: Efficient stochastic
estimation of the model resolution matrix diagonal and generalized
cross–validation for large geophysical inverse problems, J.
Geophys. Res., 116, B10304, https://doi.org/10.1029/2011JB008234, 2011. a
McCalman, L., O'Callaghan, S. T., Reid, A., Shen, D., Carter, S., Krieger, L.,
Beardsmore, G. R., Bonilla, E. V., and Ramos, F. T.: Distributed bayesian
geophysical inversions, Proceedings of the Thirty-Ninth Workshop on
Geothermal Reservoir Engineering, Stanford University, 1–11, 2014. a, b, c, d, e, f, g
Menke, W.: Geophysical Data Analysis (Revised Edition), Elsevier Ltd, 2018. a
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and
Teller, E.: Equation of state calculations by fast computing machines,
J. Chem. Phys., 21, 1087–1092, 1953. a
Miasojedow, B., Moulines, E., and Vihola, M.: An adaptive parallel tempering
algorithm, J. Comput. Graph. Stat., 22, 649–664,
2013. a
Mockus, J.: Bayesian approach to global optimization: theory and applications,
Kluwer Academic Press, 2013. a
Nabighian, M. N., Ander, M. E., Grauch, V. J. S., Hansen, R. O., LaFehr, T. R.,
Li, Y., Pearson, W. C., Peirce, J. W., Phillips, J. D., and Ruder, M. E.:
Historical development of the gravity method in exploration, Geophysics, 70,
63ND–89ND, 2005a. a
Nabighian, M. N., Grauch, V. J. S., Hansen, R. O., LaFehr, T. R., Li, Y.,
Peirce, J. W., Phillips, J. D., and Ruder, M. E.: The historical development
of the magnetic method in exploration, Geophysics, 70, 33ND–61ND,
2005b. a
Olierook, H. K. H., Timms, N. E., Wellmann, J. F., Corbel, S., and Wilkes,
P. G.: 3D structural and stratigraphic model of the Perth Basin, Western
Australia: Implications for sub-basin evolution, Aust. J. Earth
Sci., 62, 447–467, 2015. a
Pakyuz-Charrier, E., Giraud, J., Ogarko, V., Lindsay, M., and Jessell, M.:
Drillhole uncertainty propagation for three-dimensional geological modeling
using Monte Carlo, Tectonophysics, 747-748, 16–39,
https://doi.org/10.1016/j.tecto.2018.09.005, 2018a. a, b
Pall, J., Chandra, R., Azam, D., Salles, T., Webster, J., and Cripps, S.:
BayesReef: A Bayesian inference framework for modelling reef growth in
response to environmental change and biological dynamics, arXiv preprint
arXiv:arXiv:tba, 2018. a
Rao, C. R.: Information and the accuracy attainable in the estimation of
statistical parameters, B. Cal. Math. Soc., 37,
81–89, 1945. a
Roberts, G. O. and Rosenthal, J. S.: Coupling and ergodicity of adaptive
Markov chain Monte Carlo algorithms, J. Appl. Prob., 44,
458–475, 2007. a
Roberts, G. O., Gelman, A., Gilks, W. R., et al.: Weak convergence and optimal
scaling of random walk Metropolis algorithms, Ann. Appl. Prob.,
7, 110–120, 1997. a
Sabins, F. F.: Remote sensing for mineral exploration, Ore Geol. Rev., 14, 157–183, 1999. a
Salama, W., Anand, R. R., and Verrall, M.: Mineral exploration and basement
mapping in areas of deep transported cover using indicator heavy minerals and
paleoredox fronts, Yilgarn Craton, Western Australia, Ore Geol. Rev.,
72, 485–509, 2016. a
Sambridge, M.: Exploring multidimensional landscapes without a map, Inverse
Problems, 14, 427–440, https://doi.org/10.1088/0266-5611/14/3/005, 1998. a
Scalzo, R., Kohn, D., OĆallaghan, S., McCalman, L., and Simpson-Young, B.:
rscalzo/obsidian: 0.1.2-beta, https://doi.org/10.5281/zenodo.2580422, 2019. a, b, c
Shannon, C. E.: A mathematical theory of communication, Bell Syst. Tech.
J., 27, 379–423, 1948. a
Silva, J. B. C. and Cutrim, A.: A robust maximum likelihood method for gravity
and magnetic interpretation, Geoexploration, 26, 1-–31, 1989. a
Sóbester, A., Forrester, A. I. J., Toal, D. J. J., Tresidder, E., and
Tucker, S.: Engineering design applications of surrogate-assisted
optimization techniques, Opt. Eng., 15, 243–265, 2014. a
Strangway, D. W., C. M. Swift, J., and Holmer, R. C.: The application of
audio-frequency magnetotellurics (AMT) to mineral exploration, Geophysics,
38, 1159–1175, 1973. a
Tarantola, A.: Inverse problem theory and methods for model parameter
estimation, Vol. 89, siam, 2005. a
Titsias, M. and Lawrence, N.: Bayesian Gaussian Process Latent Variable
Model, Artificial Intelligence, 9, 844–851, arXiv: 1309.6835, ISBN
978-1-4503-1285-1, 2010. a
Wilson, A. G., Knowles, D. A., and Ghahramani, Z.: Gaussian Process
Regression Networks, International Conference on Machine Learning, p. 17, arXiv: 1110.4411, 2012. a
Wrona, T., Pan, I., Gawthorpe, R. L., and Fossen, H.: Seismic facies analysis
using machine learning, Geophysics, 83, O83–O95, 2018. a
Short summary
Producing 3-D models of structures under the Earth's surface based on sensor data is a key problem in geophysics (for example, in mining exploration). There may be multiple models that explain the data well. We use the open-source Obsidian software to look at the efficiency of different methods for exploring the model space and attaching probabilities to models, leading to less biased results and a better idea of how sensor data interact with geological assumptions.
Producing 3-D models of structures under the Earth's surface based on sensor data is a key...
