Articles | Volume 17, issue 7
https://doi.org/10.5194/gmd-17-2783-2024
© Author(s) 2024. 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-17-2783-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
15 Apr 2024
Model description paper |
| 15 Apr 2024
| 15 Apr 2024
REHEATFUNQ (REgional HEAT-Flow Uncertainty and aNomaly Quantification) 2.0.1: a model for regional aggregate heat flow distributions and anomaly quantification
Malte Jörn Ziebarth
CORRESPONDING AUTHOR
GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Institute of Geosciences, University of Potsdam, Karl-Liebknecht-Str. 24–25, 14476 Potsdam, Germany
Sebastian von Specht
Institute of Mathematics, University of Potsdam, Karl-Liebknecht-Str. 24–25, 14476 Potsdam, Germany
Related authors
No articles found.
Arno Zang, Peter Niemz, Sebastian von Specht, Günter Zimmermann, Claus Milkereit, Katrin Plenkers, and Gerd Klee
Earth Syst. Sci. Data, 16, 295–310, https://doi.org/10.5194/essd-16-295-2024, https://doi.org/10.5194/essd-16-295-2024, 2024
Short summary
Short summary
We present experimental data collected in 2015 at Äspö Hard Rock Laboratory. We created six cracks in a rock mass by injecting water into a borehole. The cracks were monitored using special sensors to study how the water affected the rock. The goal of the experiment was to figure out how to create a system for generating heat from the rock that is better than what has been done before. The data collected from this experiment are important for future research into generating energy from rocks.
Sebastian von Specht, Ugur Ozturk, Georg Veh, Fabrice Cotton, and Oliver Korup
Solid Earth, 10, 463–486, https://doi.org/10.5194/se-10-463-2019, https://doi.org/10.5194/se-10-463-2019, 2019
Short summary
Short summary
We show the landslide response to the 2016 Kumamoto earthquake (Mw 7.1) in central Kyushu (Japan). Landslides are concentrated to the northeast of the rupture, coinciding with the propagation direction of the earthquake. This azimuthal variation in the landslide concentration is linked to the seismic rupture process itself and not to classical landslide susceptibility factors. We propose a new ground-motion model that links the seismic radiation pattern with the landslide distribution.
Milena Latinović, Volker Klemann, Christopher Irrgang, Meike Bagge, Sebastian Specht, and Maik Thomas
Clim. Past Discuss., https://doi.org/10.5194/cp-2018-50, https://doi.org/10.5194/cp-2018-50, 2018
Revised manuscript not accepted
Short summary
Short summary
By using geological samples we are trying to validate the models that are reconstructing the sea level in the past 20 000 years. We applied proposed statistical method using 4 types of shells that were found in the area of the Hudson Bay on 140 members of model ensemble. After the comparison of the the results with studies from this area, we concluded that the method is suitable for validation of model ensemble based sea-level change caused by land movement of the Earth due to ice-age burden.
Related subject area
Solid Earth
A new temperature–photoperiod coupled phenology module in LPJ-GUESS model v4.1: optimizing estimation of terrestrial carbon and water processes
High-precision 1′ × 1′ bathymetric model of Philippine Sea inversed from marine gravity anomalies
Deciphering past earthquakes from the probabilistic modeling of paleoseismic records – the Paleoseismic EArthquake CHronologies code (PEACH, version 1)
Benchmarking the accuracy of higher order particle methods in geodynamic models of transient flow
Modelling detrital cosmogenic nuclide concentrations during landscape evolution in Cidre v2.0
IMEX_SfloW2D v2: a depth-averaged numerical flow model for volcanic gas–particle flows over complex topographies and water
Simulation of a fully coupled 3D glacial isostatic adjustment – ice sheet model for the Antarctic ice sheet over a glacial cycle
AdaHRBF v1.0: gradient-adaptive Hermite–Birkhoff radial basis function interpolants for three-dimensional stratigraphic implicit modeling
PySubdiv 1.0: open-source geological modeling and reconstruction by non-manifold subdivision surfaces
Reconstructing tephra fall deposits via ensemble-based data assimilation techniques
ClinoformNet-1.0: stratigraphic forward modeling and deep learning for seismic clinoform delineation
Addressing challenges in uncertainty quantification: the case of geohazard assessments
DeepISMNet: three-dimensional implicit structural modeling with convolutional neural network
Towards automatic finite-element methods for geodynamics via Firedrake
MagmaFOAM-1.0: a modular framework for the simulation of magmatic systems
A global, spherical finite-element model for post-seismic deformation using Abaqus
SMAUG v1.0 – a user-friendly muon simulator for the imaging of geological objects in 3-D
CliffDelineaTool v1.2.0: an algorithm for identifying coastal cliff base and top positions
Capturing the interactions between ice sheets, sea level and the solid Earth on a range of timescales: a new “time window” algorithm
Structural, petrophysical, and geological constraints in potential field inversion using the Tomofast-x v1.0 open-source code
Spatial agents for geological surface modelling
RHEA v1.0: Enabling fully coupled simulations with hydro-geomechanical heterogeneity
Modelling of faults in LoopStructural 1.0
PALEOSTRIPv1.0 – a user-friendly 3D backtracking software to reconstruct paleo-bathymetries
LoopStructural 1.0: time-aware geological modelling
Sub3DNet1.0: a deep-learning model for regional-scale 3D subsurface structure mapping
Analytical solutions for mantle flow in cylindrical and spherical shells
Towards a model for structured mass movements: the OpenLISEM hazard model 2.0a
GO_3D_OBS: the multi-parameter benchmark geomodel for seismic imaging method assessment and next-generation 3D survey design (version 1.0)
PLUME-MoM-TSM 1.0.0: a volcanic column and umbrella cloud spreading model
HydrothermalFoam v1.0: a 3-D hydrothermal transport model for natural submarine hydrothermal systems
Synthetic seismicity distribution in Guerrero–Oaxaca subduction zone, Mexico, and its implications on the role of asperities in Gutenberg–Richter law
A new open-source viscoelastic solid earth deformation module implemented in Elmer (v8.4)
CobWeb 1.0: machine learning toolbox for tomographic imaging
pygeodyn 1.1.0: a Python package for geomagnetic data assimilation
IMEX_SfloW2D 1.0: a depth-averaged numerical flow model for pyroclastic avalanches
A multilayer approach and its application to model a local gravimetric quasi-geoid model over the North Sea: QGNSea V1.0
Development of an automatic delineation of cliff top and toe on very irregular planform coastlines (CliffMetrics v1.0)
Bayesian inference of earthquake rupture models using polynomial chaos expansion
Geodynamic diagnostics, scientific visualisation and StagLab 3.0
SaLEM (v1.0) – the Soil and Landscape Evolution Model (SaLEM) for simulation of regolith depth in periglacial environments
SILLi 1.0: a 1-D numerical tool quantifying the thermal effects of sill intrusions
The SPACE 1.0 model: a Landlab component for 2-D calculation of sediment transport, bedrock erosion, and landscape evolution
Ellipsoids (v1.0): 3-D magnetic modelling of ellipsoidal bodies
Global-scale modelling of melting and isotopic evolution of Earth's mantle: melting modules for TERRA
pynoddy 1.0: an experimental platform for automated 3-D kinematic and potential field modelling
Open-source modular solutions for flexural isostasy: gFlex v1.0
FPLUME-1.0: An integral volcanic plume model accounting for ash aggregation
PyXRD v0.6.7: a free and open-source program to quantify disordered phyllosilicates using multi-specimen X-ray diffraction profile fitting
r.randomwalk v1, a multi-functional conceptual tool for mass movement routing
Shouzhi Chen, Yongshuo H. Fu, Mingwei Li, Zitong Jia, Yishuo Cui, and Jing Tang
Geosci. Model Dev., 17, 2509–2523, https://doi.org/10.5194/gmd-17-2509-2024, https://doi.org/10.5194/gmd-17-2509-2024, 2024
Short summary
Short summary
It is still a challenge to achieve an accurate simulation of vegetation phenology in the dynamic global vegetation models (DGVMs). We implemented and coupled the spring and autumn phenology models into one of the DGVMs, LPJ-GUESS, and substantially improved the accuracy in capturing the start and end dates of growing seasons. Our study highlights the importance of getting accurate phenology estimations to reduce the uncertainties in plant distribution and terrestrial carbon and water cycling.
Dechao An, Jinyun Guo, Xiaotao Chang, Zhenming Wang, Yongjun Jia, Xin Liu, Valery Bondur, and Heping Sun
Geosci. Model Dev., 17, 2039–2052, https://doi.org/10.5194/gmd-17-2039-2024, https://doi.org/10.5194/gmd-17-2039-2024, 2024
Short summary
Short summary
Seafloor topography, as fundamental geoinformation in marine surveying and mapping, plays a crucial role in numerous scientific studies. In this paper, we focus on constructing a high-precision seafloor topography and bathymetry model for the Philippine Sea (5° N–35° N, 120° E–150° E), based on shipborne bathymetric data and marine gravity anomalies, and evaluate the reliability of the model's accuracy.
Octavi Gómez-Novell, Bruno Pace, Francesco Visini, Joanna Faure Walker, and Oona Scotti
Geosci. Model Dev., 16, 7339–7355, https://doi.org/10.5194/gmd-16-7339-2023, https://doi.org/10.5194/gmd-16-7339-2023, 2023
Short summary
Short summary
Knowing the rate at which earthquakes happen along active faults is crucial to characterize the hazard that they pose. We present an approach (Paleoseismic EArthquake CHronologies, PEACH) to correlate and compute seismic histories using paleoseismic data, a type of data that characterizes past seismic activity from the geological record. Our approach reduces the uncertainties of the seismic histories and overall can improve the knowledge on fault rupture behavior for the seismic hazard.
Rene Gassmöller, Juliane Dannberg, Wolfgang Bangerth, Elbridge Gerry Puckett, and Cedric Thieulot
EGUsphere, https://doi.org/10.5194/egusphere-2023-2765, https://doi.org/10.5194/egusphere-2023-2765, 2023
Short summary
Short summary
Numerical models that use simulated particles are a powerful tool for investigating flow in the interior of the Earth, but the accuracy of these models is not fully understood. We here present two new benchmarks, that allow to measure the model accuracy. We then document that better accuracy matters for applications like convection beneath an oceanic plate. Our benchmarks and methods are freely available to help the community develop better models.
Sébastien Carretier, Vincent Regard, Youssouf Abdelhafiz, and Bastien Plazolles
Geosci. Model Dev., 16, 6741–6755, https://doi.org/10.5194/gmd-16-6741-2023, https://doi.org/10.5194/gmd-16-6741-2023, 2023
Short summary
Short summary
We present the development of a code to simulate simultaneously the dynamics of landscapes over geological time and the evolution of the concentration of cosmogenic isotopes in grains throughout their transport from the slopes to the river outlets. This new model makes it possible to study the relationship between the detrital signal of cosmogenic isotope concentration measured in sediment and the erosion--deposition processes in watersheds.
Mattia de' Michieli Vitturi, Tomaso Esposti Ongaro, and Samantha Engwell
Geosci. Model Dev., 16, 6309–6336, https://doi.org/10.5194/gmd-16-6309-2023, https://doi.org/10.5194/gmd-16-6309-2023, 2023
Short summary
Short summary
We present version 2 of the numerical code IMEX-SfloW2D. With this version it is possible to simulate a wide range of volcanic mass flows (pyroclastic avalanches, lahars, pyroclastic surges), and here we present its application to transient dilute pyroclastic density currents (PDCs). A simulation of the 1883 Krakatau eruption demonstrates the capability of the numerical model to face a complex natural case involving the propagation of PDCs over the sea surface and across topographic obstacles.
Caroline J. van Calcar, Roderik S. W. van de Wal, Bas Blank, Bas de Boer, and Wouter van der Wal
Geosci. Model Dev., 16, 5473–5492, https://doi.org/10.5194/gmd-16-5473-2023, https://doi.org/10.5194/gmd-16-5473-2023, 2023
Short summary
Short summary
The waxing and waning of the Antarctic ice sheet caused the Earth’s surface to deform, which is stabilizing the ice sheet and mainly determined by the spatially variable viscosity of the mantle. Including this feedback in model simulations led to significant differences in ice sheet extent and ice thickness over the last glacial cycle. The results underline and quantify the importance of including this local feedback effect in ice sheet models when simulating the Antarctic ice sheet evolution.
Baoyi Zhang, Linze Du, Umair Khan, Yongqiang Tong, Lifang Wang, and Hao Deng
Geosci. Model Dev., 16, 3651–3674, https://doi.org/10.5194/gmd-16-3651-2023, https://doi.org/10.5194/gmd-16-3651-2023, 2023
Short summary
Short summary
We propose a Hermite–Birkhoff radial basis function (HRBF) formulation, AdaHRBF, with an adaptive gradient magnitude for continuous 3D stratigraphic potential field (SPF) modeling of multiple stratigraphic interfaces. In the linear system of HRBF interpolants constrained by the scattered on-contact attribute points and off-contact attitude points of a set of strata in 3D space, we add a novel optimization term to iteratively obtain the true gradient magnitude.
Mohammad Moulaeifard, Simon Bernard, and Florian Wellmann
Geosci. Model Dev., 16, 3565–3579, https://doi.org/10.5194/gmd-16-3565-2023, https://doi.org/10.5194/gmd-16-3565-2023, 2023
Short summary
Short summary
In this work, we propose a flexible framework to generate and interact with geological models using explicit surface representations. The essence of the work lies in the determination of the flexible control mesh, topologically similar to the main geological structure, watertight and controllable with few control points, to manage the geological structures. We exploited the subdivision surface method in our work, which is commonly used in the animation and gaming industry.
Leonardo Mingari, Antonio Costa, Giovanni Macedonio, and Arnau Folch
Geosci. Model Dev., 16, 3459–3478, https://doi.org/10.5194/gmd-16-3459-2023, https://doi.org/10.5194/gmd-16-3459-2023, 2023
Short summary
Short summary
Two novel techniques for ensemble-based data assimilation, suitable for semi-positive-definite variables with highly skewed uncertainty distributions such as tephra deposit mass loading, are applied to reconstruct the tephra fallout deposit resulting from the 2015 Calbuco eruption in Chile. The deposit spatial distribution and the ashfall volume according to the analyses are in good agreement with estimations based on field measurements and isopach maps reported in previous studies.
Hui Gao, Xinming Wu, Jinyu Zhang, Xiaoming Sun, and Zhengfa Bi
Geosci. Model Dev., 16, 2495–2513, https://doi.org/10.5194/gmd-16-2495-2023, https://doi.org/10.5194/gmd-16-2495-2023, 2023
Short summary
Short summary
We propose a workflow to automatically generate synthetic seismic data and corresponding stratigraphic labels (e.g., clinoform facies, relative geologic time, and synchronous horizons) by geological and geophysical forward modeling. Trained with only synthetic datasets, our network works well to accurately and efficiently predict clinoform facies in 2D and 3D field seismic data. Such a workflow can be easily extended for other geological and geophysical scenarios in the future.
Ibsen Chivata Cardenas, Terje Aven, and Roger Flage
Geosci. Model Dev., 16, 1601–1615, https://doi.org/10.5194/gmd-16-1601-2023, https://doi.org/10.5194/gmd-16-1601-2023, 2023
Short summary
Short summary
We discuss challenges in uncertainty quantification for geohazard assessments. The challenges arise from limited data and the one-off nature of geohazard features. The challenges include the credibility of predictions, input uncertainty, and assumptions’ impact. Considerations to increase credibility of the quantification are provided. Crucial tasks in the quantification are the exhaustive scrutiny of the background knowledge coupled with the assessment of deviations of assumptions made.
Zhengfa Bi, Xinming Wu, Zhaoliang Li, Dekuan Chang, and Xueshan Yong
Geosci. Model Dev., 15, 6841–6861, https://doi.org/10.5194/gmd-15-6841-2022, https://doi.org/10.5194/gmd-15-6841-2022, 2022
Short summary
Short summary
We present an implicit modeling method based on deep learning to produce a geologically valid and structurally compatible model from unevenly sampled structural data. Trained with automatically generated synthetic data with realistic features, our network can efficiently model geological structures without the need to solve large systems of mathematical equations, opening new opportunities for further leveraging deep learning to improve modeling capacity in many Earth science applications.
D. Rhodri Davies, Stephan C. Kramer, Sia Ghelichkhan, and Angus Gibson
Geosci. Model Dev., 15, 5127–5166, https://doi.org/10.5194/gmd-15-5127-2022, https://doi.org/10.5194/gmd-15-5127-2022, 2022
Short summary
Short summary
Firedrake is a state-of-the-art system that automatically generates highly optimised code for simulating finite-element (FE) problems in geophysical fluid dynamics. It creates a separation of concerns between employing the FE method and implementing it. Here, we demonstrate the applicability and benefits of Firedrake for simulating geodynamical flows, with a focus on the slow creeping motion of Earth's mantle over geological timescales, which is ultimately the engine driving our dynamic Earth.
Federico Brogi, Simone Colucci, Jacopo Matrone, Chiara Paola Montagna, Mattia De' Michieli Vitturi, and Paolo Papale
Geosci. Model Dev., 15, 3773–3796, https://doi.org/10.5194/gmd-15-3773-2022, https://doi.org/10.5194/gmd-15-3773-2022, 2022
Short summary
Short summary
Computer simulations play a fundamental role in understanding volcanic phenomena. The growing complexity of these simulations requires the development of flexible computational tools that can easily switch between sub-models and solution techniques as well as optimizations. MagmaFOAM is a newly developed library that allows for maximum flexibility for solving multiphase volcanic flows and promotes collaborative work for in-house and community model development, testing, and comparison.
Grace A. Nield, Matt A. King, Rebekka Steffen, and Bas Blank
Geosci. Model Dev., 15, 2489–2503, https://doi.org/10.5194/gmd-15-2489-2022, https://doi.org/10.5194/gmd-15-2489-2022, 2022
Short summary
Short summary
We present a finite-element model of post-seismic solid Earth deformation built in the software package Abaqus for the purpose of calculating post-seismic deformation in the far field of major earthquakes. The model is benchmarked against an existing open-source post-seismic model demonstrating good agreement. The advantage over existing models is the potential for simple modification to include 3-D Earth structure, non-linear rheologies and alternative or multiple sources of stress change.
Alessandro Lechmann, David Mair, Akitaka Ariga, Tomoko Ariga, Antonio Ereditato, Ryuichi Nishiyama, Ciro Pistillo, Paola Scampoli, Mykhailo Vladymyrov, and Fritz Schlunegger
Geosci. Model Dev., 15, 2441–2473, https://doi.org/10.5194/gmd-15-2441-2022, https://doi.org/10.5194/gmd-15-2441-2022, 2022
Short summary
Short summary
Muon tomography is a technology that is used often in geoscientific research. The know-how of data analysis is, however, still possessed by physicists who developed this technology. This article aims at providing geoscientists with the necessary tools to perform their own analyses. We hope that a lower threshold to enter the field of muon tomography will allow more geoscientists to engage with muon tomography. SMAUG is set up in a modular way to allow for its own modules to work in between.
Zuzanna M. Swirad and Adam P. Young
Geosci. Model Dev., 15, 1499–1512, https://doi.org/10.5194/gmd-15-1499-2022, https://doi.org/10.5194/gmd-15-1499-2022, 2022
Short summary
Short summary
Cliff base and top lines that delimit coastal cliff faces are usually manually digitized based on maps, aerial photographs, terrain models, etc. However, manual mapping is time consuming and depends on the mapper's decisions and skills. To increase the objectivity and efficiency of cliff mapping, we developed CliffDelineaTool, an algorithm that identifies cliff base and top positions along cross-shore transects using elevation and slope characteristics.
Holly Kyeore Han, Natalya Gomez, and Jeannette Xiu Wen Wan
Geosci. Model Dev., 15, 1355–1373, https://doi.org/10.5194/gmd-15-1355-2022, https://doi.org/10.5194/gmd-15-1355-2022, 2022
Short summary
Short summary
Interactions between ice sheets, sea level and the solid Earth occur over a range of timescales from years to tens of thousands of years. This requires coupled ice-sheet–sea-level models to exchange information frequently, leading to a quadratic increase in computation time with the number of model timesteps. We present a new sea-level model algorithm that allows coupled models to improve the computational feasibility and precisely capture short-term interactions within longer simulations.
Jérémie Giraud, Vitaliy Ogarko, Roland Martin, Mark Jessell, and Mark Lindsay
Geosci. Model Dev., 14, 6681–6709, https://doi.org/10.5194/gmd-14-6681-2021, https://doi.org/10.5194/gmd-14-6681-2021, 2021
Short summary
Short summary
We review different techniques to model the Earth's subsurface from geophysical data (gravity field anomaly, magnetic field anomaly) using geological models and measurements of the rocks' properties. We show examples of application using idealised examples reproducing realistic features and provide theoretical details of the open-source algorithm we use.
Eric A. de Kemp
Geosci. Model Dev., 14, 6661–6680, https://doi.org/10.5194/gmd-14-6661-2021, https://doi.org/10.5194/gmd-14-6661-2021, 2021
Short summary
Short summary
This is a proof of concept and review paper of spatial agents, with initial research focusing on geomodelling. The results may be of interest to others working on complex regional geological modelling with sparse data. Structural agent-based swarming behaviour is key to advancing this field. The study provides groundwork for research in structural geology 3D modelling with spatial agents. This work was done with NetLogo, a free agent modelling platform used mostly for teaching complex systems.
José M. Bastías Espejo, Andy Wilkins, Gabriel C. Rau, and Philipp Blum
Geosci. Model Dev., 14, 6257–6272, https://doi.org/10.5194/gmd-14-6257-2021, https://doi.org/10.5194/gmd-14-6257-2021, 2021
Short summary
Short summary
The hydraulic and mechanical properties of the subsurface are inherently heterogeneous. RHEA is a simulator that can perform couple hydro-geomechanical processes in heterogeneous porous media with steep gradients. RHEA is able to fully integrate spatial heterogeneity, allowing allocation of distributed hydraulic and geomechanical properties at mesh element level. RHEA is a valuable tool that can simulate problems considering realistic heterogeneity inherent to geologic formations.
Lachlan Grose, Laurent Ailleres, Gautier Laurent, Guillaume Caumon, Mark Jessell, and Robin Armit
Geosci. Model Dev., 14, 6197–6213, https://doi.org/10.5194/gmd-14-6197-2021, https://doi.org/10.5194/gmd-14-6197-2021, 2021
Short summary
Short summary
Fault discontinuities in rock packages represent the plane where two blocks of rock have moved. They are challenging to incorporate into geological models because the geometry of the faulted rock units are defined by not only the location of the discontinuity but also the kinematics of the fault. In this paper, we outline a structural geology framework for incorporating faults into geological models by directly incorporating kinematics into the mathematical framework of the model.
Florence Colleoni, Laura De Santis, Enrico Pochini, Edy Forlin, Riccardo Geletti, Giuseppe Brancatelli, Magdala Tesauro, Martina Busetti, and Carla Braitenberg
Geosci. Model Dev., 14, 5285–5305, https://doi.org/10.5194/gmd-14-5285-2021, https://doi.org/10.5194/gmd-14-5285-2021, 2021
Short summary
Short summary
PALEOSTRIP has been developed in the framework of past Antarctic ice sheet reconstructions for periods when bathymetry around Antarctica differed substantially from today. It has been designed for users with no knowledge of numerical modelling and allows users to switch on and off the processes involved in backtracking and backstripping. Applications are broad, and it can be used to restore any continental margin bathymetry or sediment thickness and to perform basin analysis.
Lachlan Grose, Laurent Ailleres, Gautier Laurent, and Mark Jessell
Geosci. Model Dev., 14, 3915–3937, https://doi.org/10.5194/gmd-14-3915-2021, https://doi.org/10.5194/gmd-14-3915-2021, 2021
Short summary
Short summary
LoopStructural is an open-source 3D geological modelling library with a model design allowing for multiple different algorithms to be used for comparison for the same geology. Geological structures are modelled using structural geology concepts and techniques, allowing for complex structures such as overprinted folds and faults to be modelled. In the paper, we demonstrate automatically generating a 3-D model from map2loop-processed geological survey data of the Flinders Ranges, South Australia.
Zhenjiao Jiang, Dirk Mallants, Lei Gao, Tim Munday, Gregoire Mariethoz, and Luk Peeters
Geosci. Model Dev., 14, 3421–3435, https://doi.org/10.5194/gmd-14-3421-2021, https://doi.org/10.5194/gmd-14-3421-2021, 2021
Short summary
Short summary
Fast and reliable tools are required to extract hidden information from big geophysical and remote sensing data. A deep-learning model in 3D image construction from 2D image(s) is here developed for paleovalley mapping from globally available digital elevation data. The outstanding performance for 3D subsurface imaging gives confidence that this generic novel tool will make better use of existing geophysical and remote sensing data for improved management of limited earth resources.
Stephan C. Kramer, D. Rhodri Davies, and Cian R. Wilson
Geosci. Model Dev., 14, 1899–1919, https://doi.org/10.5194/gmd-14-1899-2021, https://doi.org/10.5194/gmd-14-1899-2021, 2021
Short summary
Short summary
Computational models of Earth's mantle require rigorous verification and validation. Analytical solutions of the underlying Stokes equations provide a method to verify that these equations are accurately solved for. However, their derivation in spherical and cylindrical shell domains with physically relevant boundary conditions is involved. This paper provides a number of solutions. They are provided in a Python package (Assess) and their use is demonstrated in a convergence study with Fluidity.
Bastian van den Bout, Theo van Asch, Wei Hu, Chenxiao X. Tang, Olga Mavrouli, Victor G. Jetten, and Cees J. van Westen
Geosci. Model Dev., 14, 1841–1864, https://doi.org/10.5194/gmd-14-1841-2021, https://doi.org/10.5194/gmd-14-1841-2021, 2021
Short summary
Short summary
Landslides, debris flows and other types of dense gravity-driven flows threaten livelihoods around the globe. Understanding the mechanics of these flows can be crucial for predicting their behaviour and reducing disaster risk. Numerical models assume that the solids and fluids of the flow are unstructured. The newly presented model captures the internal structure during movement. This important step can lead to more accurate predictions of landslide movement.
Andrzej Górszczyk and Stéphane Operto
Geosci. Model Dev., 14, 1773–1799, https://doi.org/10.5194/gmd-14-1773-2021, https://doi.org/10.5194/gmd-14-1773-2021, 2021
Short summary
Short summary
We present the 3D multi-parameter synthetic geomodel of the subduction zone, as well as the workflow designed to implement all of its components. The model contains different geological structures of various scales and complexities. It is intended to serve as a tool for the geophysical community to validate imaging approaches, design acquisition techniques, estimate uncertainties, benchmark computing approaches, etc.
Mattia de' Michieli Vitturi and Federica Pardini
Geosci. Model Dev., 14, 1345–1377, https://doi.org/10.5194/gmd-14-1345-2021, https://doi.org/10.5194/gmd-14-1345-2021, 2021
Short summary
Short summary
Here, we present PLUME-MoM-TSM, a volcanic plume model that allows us to quantify the formation of aggregates during the rise of the plume, model the phase change of water, and include the possibility to simulate the initial spreading of the tephra umbrella cloud intruding from the volcanic column into the atmosphere. The model is first applied to the 2015 Calbuco eruption (Chile) and provides an analytical relationship between the upwind spreading and some characteristic of the volcanic column.
Zhikui Guo, Lars Rüpke, and Chunhui Tao
Geosci. Model Dev., 13, 6547–6565, https://doi.org/10.5194/gmd-13-6547-2020, https://doi.org/10.5194/gmd-13-6547-2020, 2020
Short summary
Short summary
We present the 3-D hydro-thermo-transport model HydrothermalFoam v1.0, which we designed to provide the marine geosciences community with an easy-to-use and state-of-the-art tool for simulating mass and energy transport in submarine hydrothermal systems. HydrothermalFoam is based on the popular open-source platform OpenFOAM, comes with a number of tutorials, and is published under the GNU General Public License v3.0.
Marisol Monterrubio-Velasco, F. Ramón Zúñiga, Quetzalcoatl Rodríguez-Pérez, Otilio Rojas, Armando Aguilar-Meléndez, and Josep de la Puente
Geosci. Model Dev., 13, 6361–6381, https://doi.org/10.5194/gmd-13-6361-2020, https://doi.org/10.5194/gmd-13-6361-2020, 2020
Short summary
Short summary
The Mexican subduction zone along the Pacific coast is one of the most active seismic zones in the world, where every year larger-magnitude earthquakes shake huge inland cities such as Mexico City. In this work, we use TREMOL (sThochastic Rupture Earthquake ModeL) to simulate the seismicity observed in this zone. Our numerical results reinforce the hypothesis that in some subduction regions single asperities are responsible for producing the observed seismicity.
Thomas Zwinger, Grace A. Nield, Juha Ruokolainen, and Matt A. King
Geosci. Model Dev., 13, 1155–1164, https://doi.org/10.5194/gmd-13-1155-2020, https://doi.org/10.5194/gmd-13-1155-2020, 2020
Short summary
Short summary
We present a newly developed flat-earth model, Elmer/Earth, for viscoelastic treatment of solid earth deformation under ice loads. Unlike many previous approaches with proprietary software, this model is based on the open-source FEM code Elmer, with the advantage for scientists to apply and alter the model without license constraints. The new-generation full-stress ice-sheet model Elmer/Ice shares the same code base, enabling future coupled ice-sheet–glacial-isostatic-adjustment simulations.
Swarup Chauhan, Kathleen Sell, Wolfram Rühaak, Thorsten Wille, and Ingo Sass
Geosci. Model Dev., 13, 315–334, https://doi.org/10.5194/gmd-13-315-2020, https://doi.org/10.5194/gmd-13-315-2020, 2020
Short summary
Short summary
We present CobWeb 1.0, a graphical user interface for analysing tomographic images of geomaterials. CobWeb offers different machine learning techniques for accurate multiphase image segmentation and visualizing material specific parameters such as pore size distribution, relative porosity and volume fraction. We demonstrate a novel approach of dual filtration and dual segmentation to eliminate edge enhancement artefact in synchrotron-tomographic datasets and provide the computational code.
Loïc Huder, Nicolas Gillet, and Franck Thollard
Geosci. Model Dev., 12, 3795–3803, https://doi.org/10.5194/gmd-12-3795-2019, https://doi.org/10.5194/gmd-12-3795-2019, 2019
Short summary
Short summary
The pygeodyn package is a geomagnetic data assimilation tool written in Python. It gives access to the Earth's core flow dynamics, controlled by geomagnetic observations, by means of a reduced numerical model anchored to geodynamo simulation statistics. It aims to provide the community with a user-friendly and tunable data assimilation algorithm. It can be used for education, geomagnetic model production or tests in conjunction with webgeodyn, a set of visualization tools for geomagnetic models.
Mattia de' Michieli Vitturi, Tomaso Esposti Ongaro, Giacomo Lari, and Alvaro Aravena
Geosci. Model Dev., 12, 581–595, https://doi.org/10.5194/gmd-12-581-2019, https://doi.org/10.5194/gmd-12-581-2019, 2019
Short summary
Short summary
Pyroclastic avalanches are a type of granular flow generated at active volcanoes by different mechanisms, including the collapse of steep pyroclastic deposits (e.g., scoria and ash cones) and fountaining during moderately explosive eruptions. We present IMEX_SfloW2D, a depth-averaged flow model describing the granular mixture as a single-phase granular fluid. Benchmark cases and preliminary application to the simulation of the 11 February pyroclastic avalanche at Mt. Etna (Italy) are shown.
Yihao Wu, Zhicai Luo, Bo Zhong, and Chuang Xu
Geosci. Model Dev., 11, 4797–4815, https://doi.org/10.5194/gmd-11-4797-2018, https://doi.org/10.5194/gmd-11-4797-2018, 2018
Short summary
Short summary
A multilayer approach is parameterized for model development, and the multiple layers are located at different depths beneath the Earth’s surface. This method may be beneficial for gravity/manget field modeling, which may outperform the traditional single-layer approach.
Andres Payo, Bismarck Jigena Antelo, Martin Hurst, Monica Palaseanu-Lovejoy, Chris Williams, Gareth Jenkins, Kathryn Lee, David Favis-Mortlock, Andrew Barkwith, and Michael A. Ellis
Geosci. Model Dev., 11, 4317–4337, https://doi.org/10.5194/gmd-11-4317-2018, https://doi.org/10.5194/gmd-11-4317-2018, 2018
Short summary
Short summary
We describe a new algorithm that automatically delineates the cliff top and toe of a cliffed coastline from a digital elevation model (DEM). The algorithm builds upon existing methods but is specifically designed to resolve very irregular planform coastlines with many bays and capes, such as parts of the coastline of Great Britain.
Hugo Cruz-Jiménez, Guotu Li, Paul Martin Mai, Ibrahim Hoteit, and Omar M. Knio
Geosci. Model Dev., 11, 3071–3088, https://doi.org/10.5194/gmd-11-3071-2018, https://doi.org/10.5194/gmd-11-3071-2018, 2018
Short summary
Short summary
One of the most important challenges seismologists and earthquake engineers face is reliably estimating ground motion in an area prone to large damaging earthquakes. This study aimed at better understanding the relationship between characteristics of geological faults (e.g., hypocenter location, rupture size/location, etc.) and resulting ground motion, via statistical analysis of a rupture simulation model. This study provides important insight on ground-motion responses to geological faults.
Fabio Crameri
Geosci. Model Dev., 11, 2541–2562, https://doi.org/10.5194/gmd-11-2541-2018, https://doi.org/10.5194/gmd-11-2541-2018, 2018
Short summary
Short summary
Firstly, this study acts as a compilation of key geodynamic diagnostics and describes how to automatise them for a more efficient scientific procedure. Secondly, it outlines today's key pitfalls of scientific visualisation and provides means to circumvent them with, for example, a novel set of fully scientific colour maps. Thirdly, it introduces StagLab 3.0, a software that applies such fully automated diagnostics and state-of-the-art visualisation in the blink of an eye.
Michael Bock, Olaf Conrad, Andreas Günther, Ernst Gehrt, Rainer Baritz, and Jürgen Böhner
Geosci. Model Dev., 11, 1641–1652, https://doi.org/10.5194/gmd-11-1641-2018, https://doi.org/10.5194/gmd-11-1641-2018, 2018
Short summary
Short summary
We introduce the Soil and
Landscape Evolution Model (SaLEM) for the prediction of soil parent material evolution following a lithologically differentiated approach. The GIS tool is working within the software framework SAGA GIS. Weathering, erosion and transport functions are calibrated using extrinsic and intrinsic parameter data. First results indicate that our approach shows evidence for the spatiotemporal prediction of soil parental material properties.
Karthik Iyer, Henrik Svensen, and Daniel W. Schmid
Geosci. Model Dev., 11, 43–60, https://doi.org/10.5194/gmd-11-43-2018, https://doi.org/10.5194/gmd-11-43-2018, 2018
Short summary
Short summary
Igneous intrusions in sedimentary basins have a profound effect on the thermal structure of the hosting sedimentary rocks. In this paper, we present a user-friendly 1-D FEM-based tool, SILLi, that calculates the thermal effects of sill intrusions on the enclosing sedimentary stratigraphy. The motivation is to make a standardized numerical toolkit openly available that can be widely used by scientists with different backgrounds to test the effects of magmatic bodies in a wide variety of settings.
Charles M. Shobe, Gregory E. Tucker, and Katherine R. Barnhart
Geosci. Model Dev., 10, 4577–4604, https://doi.org/10.5194/gmd-10-4577-2017, https://doi.org/10.5194/gmd-10-4577-2017, 2017
Short summary
Short summary
Rivers control the movement of sediment and nutrients across Earth's surface. Understanding how rivers change through time is important for mitigating natural hazards and predicting Earth's response to climate change. We develop a new computer model for predicting how rivers cut through sediment and rock. Our model is designed to be joined with models of flooding, landslides, vegetation change, and other factors to provide a comprehensive toolbox for predicting changes to the landscape.
Diego Takahashi and Vanderlei C. Oliveira Jr.
Geosci. Model Dev., 10, 3591–3608, https://doi.org/10.5194/gmd-10-3591-2017, https://doi.org/10.5194/gmd-10-3591-2017, 2017
Short summary
Short summary
Ellipsoids are the only bodies for which the self-demagnetization can be treated analytically. This property is useful for modelling compact orebodies having high susceptibility. We present a review of the magnetic modelling of ellipsoids, propose a way of determining the isotropic susceptibility above which the self-demagnetization must be considered, and discuss the ambiguity between confocal ellipsoids, as well as provide a set of routines to model the magnetic field produced by ellipsoids.
Hein J. van Heck, J. Huw Davies, Tim Elliott, and Don Porcelli
Geosci. Model Dev., 9, 1399–1411, https://doi.org/10.5194/gmd-9-1399-2016, https://doi.org/10.5194/gmd-9-1399-2016, 2016
Short summary
Short summary
Currently, extensive geochemical databases of surface observations exist, but satisfying explanations of underlying mantle processes are lacking. We have implemented a new way to track both bulk compositions and concentrations of trace elements in a mantle convection code. In our model, chemical fractionation happens at evolving melting zones. We compare our results to a semi-analytical theory relating observed arrays of correlated Pb isotope compositions to melting age distributions.
J. Florian Wellmann, Sam T. Thiele, Mark D. Lindsay, and Mark W. Jessell
Geosci. Model Dev., 9, 1019–1035, https://doi.org/10.5194/gmd-9-1019-2016, https://doi.org/10.5194/gmd-9-1019-2016, 2016
Short summary
Short summary
We often obtain knowledge about the subsurface in the form of structural geological models, as a basis for subsurface usage or resource extraction. Here, we provide a modelling code to construct such models on the basis of significant deformational events in geological history, encapsulated in kinematic equations. Our methods simplify complex dynamic processes, but enable us to evaluate how events interact, and finally how certain we are about predictions of structures in the subsurface.
A. D. Wickert
Geosci. Model Dev., 9, 997–1017, https://doi.org/10.5194/gmd-9-997-2016, https://doi.org/10.5194/gmd-9-997-2016, 2016
Short summary
Short summary
Earth's lithosphere bends beneath surface loads, such as ice, sediments, and mountain belts. The pattern of this bending, or flexural isostatic response, is a function of both the loads and the spatially variable strength of the lithosphere. gFlex is an easy-to-use program to calculate flexural isostastic response, and may be used to better understand how ice sheets, glaciers, large lakes, sedimentary basins, volcanoes, and other surface loads interact with the solid Earth.
A. Folch, A. Costa, and G. Macedonio
Geosci. Model Dev., 9, 431–450, https://doi.org/10.5194/gmd-9-431-2016, https://doi.org/10.5194/gmd-9-431-2016, 2016
Short summary
Short summary
We present FPLUME-1.0, a steady-state 1-D cross-section-averaged eruption column model based on the buoyant plume theory (BPT). The model accounts for plume bending by wind, entrainment of ambient moisture, effects of water phase changes, particle fallout and re-entrainment, a new parameterization for the air entrainment coefficients and a model for wet aggregation of ash particles in presence of liquid water or ice.
M. Dumon and E. Van Ranst
Geosci. Model Dev., 9, 41–57, https://doi.org/10.5194/gmd-9-41-2016, https://doi.org/10.5194/gmd-9-41-2016, 2016
Short summary
Short summary
This paper presents a FOSS model called PyXRD used to improve the quantification of complex mixed-layer phyllosilicate assemblages using X-ray diffraction. The novelty of this model is the ab initio incorporation of the multi-specimen method, making it possible to share phases and their parameters across multiple specimens. We present results from a comparison of PyXRD with Sybilla v2.2.2 and a number of theoretical experiments illustrating the use of the multi-specimen set-up.
M. Mergili, J. Krenn, and H.-J. Chu
Geosci. Model Dev., 8, 4027–4043, https://doi.org/10.5194/gmd-8-4027-2015, https://doi.org/10.5194/gmd-8-4027-2015, 2015
Short summary
Short summary
r.randomwalk is a flexible and multi-functional open-source GIS tool for simulating the propagation of mass movements. Mass points are routed from given release pixels through a digital elevation model until a defined break criterion is reached. In contrast to existing tools, r.randomwalk includes functionalities to account for parameter uncertainties, and it offers built-in functions for validation and visualization. We show the key functionalities of r.randomwalk for three test areas.
Cited articles
Agrawal, N., Bikineev, A., Borland, M., Bristow, P. A., Guazzone, M., Kormanyos, C., Holin, H., Lalande, B., Maddock, J., Miller, E., Murphy, J., Pulver, M., Råde, J., Sewani, G., Sobotta, B., Thompson, N., van den Berg, T., Walker, D., and Zhang, X.: Boost C++ Libraries, https://www.boost.org/doc/libs/master/libs/math/doc/html/math_toolkit/double_exponential/de_tanh_sinh.html, last access: 16 November 2022. a
Alefeld, G. E., Potra, F. A., and Shi, Y.: Algorithm 748: Enclosing Zeros of Continuous Functions, ACM Trans. Math. Softw., 21, 327–344, https://doi.org/10.1145/210089.210111, 1995. a, b
Badros, G. J., Borning, A., and Stuckey, P. J.: The Cassowary Linear Arithmetic Constraint Solving Algorithm, ACM Trans. Comput.-Hum. Interact., 8, 267–306, https://doi.org/10.1145/504704.504705, 2001. a
Baldi, P.: A Computational Theory of Surprise, Springer US, Boston, MA, 1–25, ISBN 978-1-4757-3585-7, https://doi.org/10.1007/978-1-4757-3585-7_1, 2002. a, b
Baldi, P. and Itti, L.: Of bits and wows: A Bayesian theory of surprise with applications to attention, Neural Networks, 23, 649–666, https://doi.org/10.1016/j.neunet.2009.12.007, 2010. a
Behnel, S., Bradshaw, R., Citro, C., Dalcin, L., Seljebotn, D. S., and Smith, K.: Cython: The Best of Both Worlds, Computing in Science & Engineering, 13, 31–39, https://doi.org/10.1109/MCSE.2010.118, 2011. a
Bemmaor, A. C.: Modeling the Diffusion of New Durable Goods: Word-of-Mouth Effect Versus Consumer Heterogeneity, 201–229, Springer Netherlands, Dordrecht, ISBN 978-94-011-1402-8, https://doi.org/10.1007/978-94-011-1402-8_6, 1994. a
Bercovici, D. and Mulyukova, E.: Mantle Convection, pp. 1059–1079, Springer International Publishing, ISBN 978-3-030-58631-7, https://doi.org/10.1007/978-3-030-58631-7_130, 2021. a
Berrut, J.-P. and Trefethen, L. N.: Barycentric Lagrange Interpolation, SIAM Review, 46, 501–517, https://doi.org/10.1137/S0036144502417715, 2004. a, b, c
Blackwell, D. D., Steele, J. L., and Brott, C. A.: The terrain effect on terrestrial heat flow, J. Geophys. Res.-Sol. Ea., 85, 4757–4772, https://doi.org/10.1029/JB085iB09p04757, 1980. a, b, c
Bringedal, C., Berre, I., Nordbotten, J. M., and Rees, D. A. S.: Linear and nonlinear convection in porous media between coaxial cylinders, Phys. Fluids, 23, 094109, https://doi.org/10.1063/1.3637642, 2011. a
Brune, J. N.: Heat Flow on the Creeping Section of the San Andreas Fault: A Localized Transient Perspective, in: AGU Fall Meeting Abstracts, 6–10 December 2002, San Francisco, California, USA, EID: S21A-0979, 2002. a
Byrd, R. H., Peihuang, L., and Nocedal, J.: A limited-memory algorithm for bound-constrained optimization, https://digital.library.unt.edu/ark:/67531/metadc666315/ (last access: 25 July 2023), 1996. a
Cacace, M., Scheck-Wenderoth, M., Noack, V., Cherubini, Y., and Schellschmidt, R.: Modelling the Surface Heat Flow Distribution in the Area of Brandenburg (Northern Germany), Energy Proced., 40, 545–553, https://doi.org/10.1016/j.egypro.2013.08.063, 2013. a, b
Christensen, U. R.: Core Dynamo, pp. 55–63, Springer Netherlands, Dordrecht, ISBN 978-90-481-8702-7, https://doi.org/10.1007/978-90-481-8702-7_38, 2011. a
Conn, A. R., Gould, N. I. M., and Toint, P. L.: Trust Region Methods, Society for Industrial and Applied Mathematics, https://doi.org/10.1137/1.9780898719857, 2000. a
Covo, S. and Elalouf, A.: A novel single-gamma approximation to the sum of independent gamma variables, and a generalization to infinitely divisible distributions, Electron. J. Stat., 8, 894–926, https://doi.org/10.1214/14-EJS914, 2014. a
Crameri, F.: Scientific colour maps, Zenodo [data set], https://doi.org/10.5281/zenodo.4491293, 2021. a
d'Alessio, M. A., Williams, C. F., and Bürgmann, R.: Frictional strength heterogeneity and surface heat flow: Implications for the strength of the creeping San Andreas fault, J. Geophys. Res.-Sol. Ea., 111, B05410, https://doi.org/10.1029/2005JB003780, 2006. a, b
David, F. N. and Johnson, N. L.: The Probability Integral Transformation When Parameters are Estimated from the Sample, Biometrika, 35, 182–190, 1948. a
Davies, J. H. and Davies, D. R.: Earth's surface heat flux, Solid Earth, 1, 5–24, https://doi.org/10.5194/se-1-5-2010, 2010. a
Dierckx, P.: An algorithm for smoothing, differentiation and integration of experimental data using spline functions, J. Comput. Appl. Math., 1, 165–184, https://doi.org/10.1016/0771-050X(75)90034-0, 1975. a
Ekström, M.: Alternatives to maximum likelihood estimation based on spacings and the Kullback–Leibler divergence, J. Stat. Plan. Infer., 138, 1778–1791, https://doi.org/10.1016/j.jspi.2007.06.031, 2008. a
Endres, S. C., Sandrock, C., and Focke, W. W.: A simplicial homology algorithm for Lipschitz optimisation, J. Global Optim., 72, 181–217, https://doi.org/10.1007/s10898-018-0645-y, 2018. a
Fousse, L., Hanrot, G., Lefèvre, V., Pélissier, P., and Zimmermann, P.: MPFR: A Multiple-Precision Binary Floating-Point Library with Correct Rounding, ACM Trans. Math. Softw., 33, 13–es, https://doi.org/10.1145/1236463.1236468, 2007. a
Fuchs, S., Norden, B., and International Heat Flow Commission: The Global Heat Flow Database: Release 2021, https://doi.org/10.5880/fidgeo.2021.014, 2021. a
GDAL/OGR contributors: GDAL/OGR Geospatial Data Abstraction software Library, Zenodo [code], https://doi.org/10.5281/zenodo.5884351, 2022. a
Giezeman, G.-J. and Wesselink, W.: 2D Polygons, in: CGAL User and Reference Manual, CGAL Editorial Board, 5.5.1 edn., https://doc.cgal.org/5.5.1/Manual/packages.html#PkgPolygon2 (last access: 25 March 2024), 2022. a
Gonnet, P.: A Review of Error Estimation in Adaptive Quadrature, ACM Comput. Surv., 44, 22, https://doi.org/10.1145/2333112.2333117, 2012. a
Goutorbe, B., Poort, J., Lucazeau, F., and Raillard, S.: Global heat flow trends resolved from multiple geological and geophysical proxies, Geophys. J. Int., 187, 1405–1419, https://doi.org/10.1111/j.1365-246X.2011.05228.x, 2011. a, b, c
Graham, R. L., Lubachevsky, B. D., Nurmela, K., and Östergård, P. R. J.: Dense packings of congruent circles in a circle, Discrete Mathematics, 181, 139–154, https://doi.org/10.1016/S0012-365X(97)00050-2, 1998. a
Granger, B. E. and Pérez, F.: Jupyter: Thinking and Storytelling With Code and Data, Comput. Sci. Eng., 23, 7–14, https://doi.org/10.1109/MCSE.2021.3059263, 2021. a
Granlund, T. and the GMP development team: GNU MP: The GNU Multiple Precision Arithmetic Library, https://gmplib.org/ (last access: 25 March 2024), version 6.2.1, 2020. a
Guennebaud, G., Jacob, B., and the Eigen development team: Eigen v3, http://eigen.tuxfamily.org (last access: 25 March 2024), 2010. a
Gupta, H. K., ed.: Encyclopedia of Solid Earth Geophysics, vol. 1, Springer, Dordrecht, ISBN 978-90-481-8701-0, https://doi.org/10.1007/978-90-481-8702-7, 2011. a
Harlé, P., Kushnir, A. R. L., Aichholzer, C., Heap, M. J., Hehn, R., Maurer, V., Baud, P., Richard, A., Genter, A., and Duringer, P.: Heat flow density estimates in the Upper Rhine Graben using laboratory measurements of thermal conductivity on sedimentary rocks, Geothermal Energy, 7, 18, https://doi.org/10.1186/s40517-019-0154-3, 2019. a
Henyey, T. L. and Wasserburg, G. J.: Heat flow near major strike-slip faults in California, J. Geophys. Res., 76, 7924–7946, https://doi.org/10.1029/JB076i032p07924, 1971. a, b, c
Hewitt, D. R.: Vigorous convection in porous media, Proceedings of the Royal Society A: Mathematical, Phys. Eng. Sci., 476, 20200111, https://doi.org/10.1098/rspa.2020.0111, 2020. a
Hunter, J. D.: Matplotlib: A 2D graphics environment, Comput. Sci. Eng., 9, 90–95, https://doi.org/10.1109/MCSE.2007.55, 2007. a
Jaupart, C. and Mareschal, J.-C.: Heat Flow and Thermal Structure of the Lithosphere, vol. 6 of Treatise on Geophysics, 217–251, https://doi.org/10.1016/B978-044452748-6.00104-8, 2007. a
Johansson, F.: Arbitrary-precision computation of the gamma function, Maple Transactions, 3, 14591, https://doi.org/10.5206/mt.v3i1.14591, 2023. a
Jordahl, K., Van den Bossche, J., Fleischmann, M., McBride, J., Wasserman, J., Richards, M., Garcia Badaracco, A., Snow, A. D., Gerard, J., Tratner, J., Perry, M., Ward, B., Farmer, C., Hjelle, G. A., Cochran, M., Taves, M., Gillies, S., Caria, G., Culbertson, L., Bartos, M., Eubank, N., Bell, R., Sangarshanan, Flavin, J., Rey, S., Albert, M., Bilogur, A., Ren, C., Arribas-Bel, D., and Mesejo-León, D.: geopandas/geopandas: v0.12.1, Zenodo [code], https://doi.org/10.5281/zenodo.7262879, 2022. a
Kano, Y., Mori, J., Fujio, R., Ito, H., Yanagidani, T., Nakao, S., and Ma, K.-F.: Heat signature on the Chelungpu fault associated with the 1999 Chi-Chi, Taiwan earthquake, Geophys. Res. Lett., 33, L14306, https://doi.org/10.1029/2006GL026733, 2006. a
Karney, C. F. F.: GeographicLib, version 1.51, https://geographiclib.sourceforge.io/C++/1.51 (last access: 26 March 2024), 2022. a
Kass, R. E. and Raftery, A. E.: Bayes Factors, J. Am. Stat. Assoc., 90, 773–795, https://doi.org/10.1080/01621459.1995.10476572, 1995. a, b, c
Kluyver, T., Ragan-Kelley, B., Pérez, F., Granger, B., Bussonnier, M., Frederic, J., Kelley, K., Hamrick, J., Grout, J., Corlay, S., Ivanov, P., Avila, D., Abdalla, S., Willing, C., and Jupyter development team: Jupyter Notebooks – a publishing format for reproducible computational workflows, in: Positioning and Power in Academic Publishing: Players, Agents and Agendas, edited by: Loizides, F. and Schmidt, B., IOS Press, the Netherlands, 87–90, https://doi.org/10.3233/978-1-61499-649-1-87, 2016. a, b
Kroese, D. P., Taimre, T., and Botev, Z. I.: Probability Distributions, chap. 4, 85–151, John Wiley & Sons, Ltd, ISBN 9781118014967, https://doi.org/10.1002/9781118014967.ch4, 2011. a
Kronrod, A. S.: Nodes and Weights of Quadrature Formulas: Sixteen-place Tables, Consultants Bureau, 1965. a
Kullback, S.: Information Theory and Statistics, John Wiley & Sons, Inc., 1959. a
Landström, O., Larson, S. Å., Lind, G., and Malmqvist, D.: Geothermal investigations in the Bohus granite area in southwestern Sweden, Tectonophysics, 64, 131–162, https://doi.org/10.1016/0040-1951(80)90266-8, 1980. a
Laurie, D. P.: Calculation of Gauss-Kronrod Quadrature Rules, Math. Comput., 66, 1133–1145, https://doi.org/10.1090/S0025-5718-97-00861-2, 1997. a
Leemis, L. M. and McQueston, J. T.: Univariate Distribution Relationships, The American Statistician, 62, 45–53, https://doi.org/10.1198/000313008X270448, 2008. a
López, C. O. and Beasley, J. E.: A heuristic for the circle packing problem with a variety of containers, Eur. J. Oper. Res., 214, 512–525, https://doi.org/10.1016/j.ejor.2011.04.024, 2011. a
Mareschal, J.-C. and Jaupart, C.: Energy Budget of the Earth, pp. 361–368, Springer International Publishing, ISBN 978-3-030-58631-7, https://doi.org/10.1007/978-3-030-58631-7_64, 2021. a
Meurer, A., Smith, C. P., Paprocki, M., Čertík, O., Kirpichev, S. B., Rocklin, M., Kumar, A., Ivanov, S., Moore, J. K., Singh, S., Rathnayake, T., Vig, S., Granger, B. E., Muller, R. P., Bonazzi, F., Gupta, H., Vats, S., Johansson, F., Pedregosa, F., Curry, M. J., Terrel, A. R., Roučka, V., Saboo, A., Fernando, I., Kulal, S., Cimrman, R., and Scopatz, A.: SymPy: symbolic computing in Python, PeerJ Computer Science, 3, e103, https://doi.org/10.7717/peerj-cs.103, 2017. a, b
Miller, R. B.: Bayesian Analysis of the Two-Parameter Gamma Distribution, Technometrics, 22, 65–69, https://doi.org/10.2307/1268384, 1980. a, b, c, d
Milner, K. R.: Third Uniform California Earthquake Rupture Forecast (UCERF3) Fault System Solutions, Zenodo [data set], https://doi.org/10.5281/zenodo.5519802, 2014. a, b, c
Minka, T. P.: Estimating a Gamma Distribution, https://www.microsoft.com/en-us/research/publication/estimating-gamma-distribution/ (last access: 26 March 2024), 2002. a
Molnar, P. and England, P.: Temperatures, heat flux, and frictional stress near major thrust faults, J. Geophys. Res.-Sol. Ea., 95, 4833–4856, https://doi.org/10.1029/JB095iB04p04833, 1990. a
Morgan, P.: Heat Flow, Continental, 573–582, Springer Netherlands, Dordrecht, ISBN 978-90-481-8702-7, https://doi.org/10.1007/978-90-481-8702-7_73, 2011. a, b
Moya, D., Aldás, C., and Kaparaju, P.: Geothermal energy: Power plant technology and direct heat applications, Renew. Sust. Energ. Rev., 94, 889–901, https://doi.org/10.1016/j.rser.2018.06.047, 2018. a
Mysovskikh, I. P.: Simpson formula, in: Encyclopedia of Mathematics, edited by: Hazewinkel, M., http://encyclopediaofmath.org/index.php?title=Simpson_formula&oldid=17966 (last access: 26 March 2024), 2006. a
Nakagami, M.: The m-Distribution – A General Formula of Intensity Distribution of Rapid Fading, in: Statistical Methods in Radio Wave Propagation, edited by Hoffman, W. C., 3–36, Pergamon, ISBN 978-0-08-009306-2, https://doi.org/10.1016/B978-0-08-009306-2.50005-4, 1960. a
Nelder, J. A. and Mead, R.: A Simplex Method for Function Minimization, The Computer Journal, 7, 308–313, https://doi.org/10.1093/comjnl/7.4.308, 1965. a
Norden, B., Förster, A., Förster, H.-J., and Fuchs, S.: Temperature and pressure corrections applied to rock thermal conductivity: impact on subsurface temperature prognosis and heat-flow determination in geothermal exploration, Geothermal Energy, 8, 1, https://doi.org/10.1186/s40517-020-0157-0, 2020. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.: Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011. a
Pérez, F. and Granger, B. E.: IPython: a System for Interactive Scientific Computing, Comput. Sci. Eng., 9, 21–29, https://doi.org/10.1109/MCSE.2007.53, 2007. a
Peña, C., Heidbach, O., Moreno, M., Bedford, J., Ziegler, M., Tassara, A., and Oncken, O.: Impact of power-law rheology on the viscoelastic relaxation pattern and afterslip distribution following the 2010 Mw 8.8 Maule earthquake, Earth Planet. Sc. Lett., 542, 116292, https://doi.org/10.1016/j.epsl.2020.116292, 2020. a
Pitman, J.: Probability, Springer, ISBN 978-0-387-94594-1, 1993. a
Pollack, H. N., Hurter, S. J., and Johnson, J. R.: Heat flow from the Earth's interior: Analysis of the global data set, Re. Geophys., 31, 267–280, https://doi.org/10.1029/93RG01249, 1993. a, b
Proinov, P. D.: Discrepancy and integration of continuous functions, J. Approx. Theory, 52, 121–131, https://doi.org/10.1016/0021-9045(88)90051-2, 1988. a
PROJ contributors: PROJ coordinate transformation software library, Zenodo [code], https://doi.org/10.5281/zenodo.5884394, 2022. a
Sass, J. H. and Beardsmore, G.: Heat Flow Measurements, Continental, pp. 569–573, Springer Netherlands, Dordrecht, ISBN 978-90-481-8702-7, https://doi.org/10.1007/978-90-481-8702-7_72, 2011. a
Scholz, C. H.: The Strength of the San Andreas Fault: A Critical Analysis, 301–311, American Geophysical Union (AGU), https://doi.org/10.1029/170GM30, 2006. a
Snow, A. D., Whitaker, J., Cochran, M., Van den Bossche, J., Mayo, C., Miara, I., Cochrane, P., de Kloe, J., Karney, C. F. F., Fernandes, F., Couwenberg, B., Lostis, G., Dearing, J., Ouzounoudis, G., Jurd, B., Gohlke, C., Hoese, D., Itkin, M., May, R., Little, B., de Bittencourt, H. P., Shadchin, A., Wiedemann, B. M., Barker, C., Willoughby, C., DWesl, Hemberger, D., Haberthür, D., and Popov, E.: pyproj4/pyproj: 3.4.1 Release, version 3.4.1, Zenodo [code], https://doi.org/10.5281/zenodo.7430570, 2022. a
Stevens, C. W., Mccaffrey, R., Bock, Y., Genrich, J. F., Pubellier, M., and Subarya, C.: Evidence for Block Rotations and Basal Shear in the World's Fastest Slipping Continental Shear Zone in Nw New Guinea, pp. 87–99, American Geophysical Union (AGU), ISBN 9781118670446, https://doi.org/10.1029/GD030p0087, 2002. a
Takahasi, H. and Mori, M.: Double Exponential Formulas for Numerical Integration, Publications of the Research Institute for Mathemtical Sciences, 9, 721–741, 1973. a
Takahasi, H. and Mori, M.: Double Exponential Formulas for Numerical Integration, Publ. Res. I. Math. Sci., 9, 721–741, https://doi.org/10.2977/prims/1195192451, 1974. a
Tanaka, K., Sugihara, M., Murota, K., and Mori, M.: Function Classes for Double Exponential Integration Formulas, Numer. Math., 111, 631–655, https://doi.org/10.1007/s00211-008-0195-1, 2009. a
The mpmath development team: mpmath: a Python library for arbitrary-precision floating-point arithmetic (version 1.3.0), http://mpmath.org/ (last access: 26 March 2024), 2023. a
The pandas development team: pandas-dev/pandas: Pandas, Zenodo [code], https://doi.org/10.5281/zenodo.7344967, 2022. a
Thomopoulos, N. T.: Probability Distributions, Springer International Publishing AG, 1st edn., ISBN 978-3-319-76042-1, https://doi.org/10.1007/978-3-319-76042-1, 2018. a, b
Thyng, K. M., Greene, C. A., Hetland, R. D., Zimmerle, H. M., and DiMarco, S. F.: True Colors of Oceanography: Guidelines for Effective and Accurate Colormap Selection, Oceanography, 29, 9–13, https://doi.org/10.5670/oceanog.2016.66, 2016. a
Torres, T., Anselmi, N., Nayeri, P., Rocca, P., and Haupt, R.: Low Discrepancy Sparse Phased Array Antennas, Sensors, 21, 7816, https://doi.org/10.3390/s21237816, 2021. a
van der Velden, E.: CMasher: Scientific colormaps for making accessible, informative and 'cmashing' plots, J. Open Source Softw., 5, 2004, https://doi.org/10.21105/joss.02004, 2020. a
van der Walt, S., Colbert, S. C., and Varoquaux, G.: The NumPy Array: A Structure for Efficient Numerical Computation, Comput. Sci. Eng., 13, 22–30, https://doi.org/10.1109/MCSE.2011.37, 2011. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., Vijaykumar, A., Bardelli, A. P., Rothberg, A., Hilboll, A., Kloeckner, A., Scopatz, A., Lee, A., Rokem, A., Woods, C. N., Fulton, C., Masson, C., Häggström, C., Fitzgerald, C., Nicholson, D. A., Hagen, D. R., Pasechnik, D. V., Olivetti, E., Martin, E., Wieser, E., Silva, F., Lenders, F., Wilhelm, F., Young, G., Price, G. A., Ingold, G.-L., Allen, G. E., Lee, G. R., Audren, H., Probst, I., Dietrich, J. P., Silterra, J., Webber, J. T., Slavič, J., Nothman, J., Buchner, J., Kulick, J., Schönberger, J. L., de Miranda Cardoso, J. V., Reimer, J., Harrington, J., Rodríguez, J. L. C., Nunez-Iglesias, J., Kuczynski, J., Tritz, K., Thoma, M., Newville, M., Kümmerer, M., Bolingbroke, M., Tartre, M., Pak, M., Smith, N. J., Nowaczyk, N., Shebanov, N., Pavlyk, O., Brodtkorb, P. A., Lee, P., McGibbon, R. T., Feldbauer, R., Lewis, S., Tygier, S., Sievert, S., Vigna, S., Peterson, S., More, S., Pudlik, T., Oshima, T., Pingel, T. J., Robitaille, T. P., Spura, T., Jones, T. R., Cera, T., Leslie, T., Zito, T., Krauss, T., Upadhyay, U., Halchenko, Y. O., Vázquez-Baeza, Y., and SciPy 1.0 Contributors: SciPy 1.0: fundamental algorithms for scientific computing in Python, Nature Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a, b, c, d, e, f
Wang, Q., Zhang, X., Zhang, Y., and Yi, Q.: AUGEM: Automatically generate high performance Dense Linear Algebra kernels on x86 CPUs, in: SC '13: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, 1–12, https://doi.org/10.1145/2503210.2503219, 2013. a
Wessel, P. and Smith, W. H. F.: A global, self-consistent, hierarchical, high-resolution shoreline database, J. Geophys. Res.-Sol. Ea., 101, 8741–8743, https://doi.org/10.1029/96JB00104, 1996 (data available at: https://www.soest.hawaii.edu/pwessel/gshhg/, last access: 26 March 2024). a
Williams, C. F., Grubb, F. V., and Galanis Jr., S. P.: Heat flow in the SAFOD pilot hole and implications for the strength of the San Andreas Fault, Geophys. Res. Lett., 31, https://doi.org/10.1029/2003GL019352, 2004. a
Ziebarth, M. J.: PDToolbox: a Python probability distribution toolbox, GFZ Data Services [code], https://doi.org/10.5880/GFZ.2.6.2022.002, 2022a. a
Ziebarth, M. J.: FlotteKarte – a Python library for quick and versatile cartography based on PROJ4-string syntax and using Matplotlib, NumPy, and PyPROJ under the hood, v. 0.2.2, GFZ Data Services [code], https://doi.org/10.5880/GFZ.2.6.2022.003, 2022b. a
Ziebarth, M. J.: REHEATFUNQ: A Python package for the inference of regional aggregate heat flow distributions and heat flow anomalies, v. 1.4.0, GFZ Data Services [code], https://doi.org/10.5880/GFZ.2.6.2023.002, 2023. a, b, c, d
Ziebarth, M. J.: REHEATFUNQ: REgional HEAT-Flow Uncertainty and aNomaly Quantification, Python Package Version 2.0.1, Zenodo [code], https://doi.org/10.5281/zenodo.10614892, 2024. a, b
Zondervan-Zwijnenburg, M., Peeters, M., Depaoli, S., and Van de Schoot, R.: Where Do Priors Come From? Applying Guidelines to Construct Informative Priors in Small Sample Research, Res. Hum. Dev., 14, 305–320, https://doi.org/10.1080/15427609.2017.1370966, 2017. a
Short summary
Thermal energy from Earth’s active interior constantly dissipates through Earth’s surface. This heat flow is not spatially uniform, and its exact pattern is hard to predict since it depends on crustal and mantle properties, both varying across scales. Our new model REHEATFUNQ addresses this difficulty by treating the fluctuations of heat flow within a region statistically. REHEATFUNQ estimates the regional distribution of heat flow and quantifies known structural signals therein.
Thermal energy from Earth’s active interior constantly dissipates through Earth’s surface. This...
