Badlands: A parallel basin and landscape dynamics model

Salles, Tristan

doi:10.1016/j.softx.2016.08.005

Cited by 41 publications

(38 citation statements)

References 23 publications

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“…A modified version of the Basin and Landscape Dynamics (Badlands) model is used to calculate the spatial and temporal evolution of North American topography during the last 82 Ma together with discharge, denudation, and sedimentary flux (Salles, ). This landscape evolution model exploits a triangular mesh to solve the relevant geomorphic equations.…”

Section: Landscape Modelingmentioning

confidence: 99%

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Continental‐Scale Landscape Evolution: A History of North American Topography

2019

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The generation and evolution of continental topography are fundamental geologic and geomorphic concerns. In particular, the history of landscape development might contain useful information about the spatiotemporal evolution of deep Earth processes, such as mantle convection. A significant challenge is to generate observations and theoretical predictions of sufficient fidelity to enable landscape evolution to be constrained at scales of interest. Here, we combine substantial inventories of stratigraphic and geomorphic observations with inverse and forward modeling approaches to determine how the North American landscape evolved. First, stratigraphic markers are used to estimate postdepositional regional uplift. Present‐day elevations of these deposits demonstrate that >2 km of long‐wavelength surface uplift centered on the Colorado‐Rocky‐Mountain plateaus occurred in Cenozoic times. Second, to bridge the gaps between these measurements, an inverse modeling scheme is used to calculate the smoothest spatiotemporal pattern of rock uplift rate that yields the smallest misfit between 4,161 observed and calculated longitudinal river profiles. Our results suggest that Cenozoic regional uplift occurred in a series of stages, in agreement with independent stratigraphic observations. Finally, a landscape evolution model driven by this calculated rock uplift history is used to determine drainage patterns, denudation, and sedimentary flux from Late Cretaceous times until the present day. These patterns are broadly consistent with stratigraphic and thermochronologic observations. We conclude that a calibrated inverse modeling strategy can be used to reliably extract the temporal and spatial evolution of the North American landscape at geodynamically useful scales.

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Section: Landscape Modelingmentioning

confidence: 99%

“…By relating these histories to estimates of crustal temperatures, it is possible to extract denudational histories (see, e.g., Farley, 2002;Gallagher, 1995;Gleadow & Brown, 1999;Green et al, 1989). A key result from thermochronometers (Salles, 2015). Model was forced using uplift history calculated by inverse modeling (Figure 8).…”

Section: Denudational Historymentioning

confidence: 99%

Continental‐Scale Landscape Evolution: A History of North American Topography

2019

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“…The initial and final topographies for all three problems are shown in Figure . We note that Cr and CM have been adapted from examples of the Badlands model (Salles & Hardiman, ; Salles, ; Salles et al., ) and also used in our previous work (Chandra et al., ).…”

Section: Methodsmentioning

confidence: 99%

“…We use Badlands (Salles & Hardiman, ; Salles, ; Salles et al., ) to simulate regional to continental sediment deposition and associated sedimentary basin architecture (Salles et al., , ). In its basic formulation, the Earth surface elevation changes, denoted by

\frac{\partial z}{\partial t}

, where z is the elevation and t refers to time in years related to the interaction of three types of processes: the tectonic uplift rate ( U ), the incision rate by rivers ( I ), and the hillslope processes ( D ).…”

Section: Background and Related Workmentioning

confidence: 99%

Multicore Parallel Tempering Bayeslands for Basin and Landscape Evolution

Chandra

Müller

Azam

et al. 2019

Geochem Geophys Geosyst

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The Bayesian paradigm is becoming an increasingly popular framework for estimation and uncertainty quantification of unknown parameters in geophysical inversion problems. Badlands is a landscape evolution model for simulating topography evolution at a broad range of spatial and temporal scales. Our previous work presented Bayeslands that used the Bayesian inference for estimating unknown parameters in the Badlands model using Markov chain Monte Carlo sampling. Bayeslands faced challenges in terms of computational issues and convergence due to multimodal posterior distributions. Parallel tempering is an advanced Markov chain Monte Carlo method suited for irregular and multimodal posterior distributions. In this paper, we extend Bayeslands using parallel tempering with high‐performance computing to address previous limitations in Bayeslands. Our results show that parallel tempering Bayeslands not only reduces the computation time‚ but also provides an improvement in sampling multimodal posterior distributions, which motivates future application to continental scale landscape evolution models.

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“…Badlands (basin and landscape dynamics) [31] is an example of such a model that can be used to reconstruct landscape evolution and associated sediment fluxes [43,44]. We use Badlands [31,45,46] to simulate landscape evolution and sediment transport/deposition of selected areas in order to provide estimation with uncertainty quantification of the free parameters such as precipitation and erodibility.…”

Section: Badlands and Bayeslandsmentioning

confidence: 99%

Surrogate-assisted Bayesian inversion for landscape and basin evolution models

Chandra

Azam

Kapoor

et al. 2019

Preprint

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The complex and computationally expensive features of the forward landscape and sedimentary basin evolution models pose a major challenge in the development of efficient inference and optimization methods. Bayesian inference provides a methodology for estimation and uncertainty quantification of free model parameters. In our previous work, parallel tempering Bayeslands was developed as a framework for parameter estimation and uncertainty quantification for the landscape and basin evolution modelling software Badlands [1]. Parallel tempering Bayeslands features high-performance computing with dozens of processing cores running in parallel to enhance computational efficiency. Although parallel computing is used, the procedure remains computationally challenging since thousands of samples need to be drawn and evaluated. In large-scale landscape and basin evolution problems, a single model evaluation can take from several minutes to hours, and in certain cases, even days. Surrogate-assisted optimization has been with successfully applied to a number of engineering problems [2,3]. This motivates its use in optimisation and inference methods suited for complex models in geology and geophysics. Surrogates can speed up parallel tempering Bayeslands by developing computationally inexpensive surrogates to mimic expensive models. In this paper, we present an application of surrogate-assisted parallel tempering where that surrogate mimics a landscape evolution model including erosion, sediment transport and deposition, by estimating the likelihood function that is given by the model. We employ a machine learning model as a surrogate that learns from the samples generated by the parallel tempering algorithm and the likelihood from the model. The entire framework is developed in a parallel computing infrastructure to take advantage of parallelization. The results show that the proposed methodology is effective in lowering the overall computational cost significantly while retaining the quality of solutions.

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Badlands: A parallel basin and landscape dynamics model

Cited by 41 publications

References 23 publications

Continental‐Scale Landscape Evolution: A History of North American Topography

Continental‐Scale Landscape Evolution: A History of North American Topography

Multicore Parallel Tempering Bayeslands for Basin and Landscape Evolution

Surrogate-assisted Bayesian inversion for landscape and basin evolution models

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