scholarly journals GemPy 1.0: open-source stochastic geological modeling and inversion

2019 ◽  
Vol 12 (1) ◽  
pp. 1-32 ◽  
Author(s):  
Miguel de la Varga ◽  
Alexander Schaaf ◽  
Florian Wellmann
Keyword(s):  
Open Source ◽  
Code Generation ◽  
Raw Material ◽  
Reproducible Research ◽  
Geological Modeling ◽  
Fault Surface ◽  
Wide Range ◽  
Geological Models ◽  
Density Values ◽  
Efficient Code

Abstract. The representation of subsurface structures is an essential aspect of a wide variety of geoscientific investigations and applications, ranging from geofluid reservoir studies, over raw material investigations, to geosequestration, as well as many branches of geoscientific research and applications in geological surveys. A wide range of methods exist to generate geological models. However, the powerful methods are behind a paywall in expensive commercial packages. We present here a full open-source geomodeling method, based on an implicit potential-field interpolation approach. The interpolation algorithm is comparable to implementations in commercial packages and capable of constructing complex full 3-D geological models, including fault networks, fault–surface interactions, unconformities and dome structures. This algorithm is implemented in the programming language Python, making use of a highly efficient underlying library for efficient code generation (Theano) that enables a direct execution on GPUs. The functionality can be separated into the core aspects required to generate 3-D geological models and additional assets for advanced scientific investigations. These assets provide the full power behind our approach, as they enable the link to machine-learning and Bayesian inference frameworks and thus a path to stochastic geological modeling and inversions. In addition, we provide methods to analyze model topology and to compute gravity fields on the basis of the geological models and assigned density values. In summary, we provide a basis for open scientific research using geological models, with the aim to foster reproducible research in the field of geomodeling.

scholarly journals GemPy 1.0: open-source stochastic geological modeling and inversion

2018 ◽  
Author(s):  
Miguel de la Varga ◽  
Alexander Schaaf ◽  
Florian Wellmann
Keyword(s):  
Open Source ◽  
Code Generation ◽  
Raw Material ◽  
Reproducible Research ◽  
Geological Modeling ◽  
Fault Surface ◽  
Wide Range ◽  
Geological Models ◽  
Density Values ◽  
Efficient Code

Abstract. The representation of subsurface structures is an essential aspect of a wide variety of geoscientific investigations and applications: ranging from geofluid reservoir studies, over raw material investigations, to geosequestration, as well as many branches of geoscientific research studies and applications in geological surveys. A wide range of methods exists to generate geological models. However, especially the powerful methods are behind a paywall in expensive commercial packages. We present here a full open-source geomodeling method, based on an implicit potential-field interpolation approach. The interpolation algorithm is comparable to implementations in commercial packages and capable of constructing complex full 3-D geological models, including fault networks, fault-surface interactions, unconformities, and dome structures. This algorithm is implemented in the programming language Python, making use of a highly efficient underlying library for efficient code generation (theano) that enables a direct execution on GPU's. The functionality can be separated into the core aspects required to generate 3-D geological models and additional assets for advanced scientific investigations. These assets provide the full power behind our approach, as they enable the link to Machine Learning and Bayesian inference frameworks and thus a path to stochastic geological modeling and inversions. In addition, we provide methods to analyse model topology and to compute gravity fields on the basis of the geological models and assigned density values. In summary, we provide a basis for open scientific research using geological models, with the aim to foster reproducible research in the field of geomodeling.

scholarly journals Automating Finite Element Methods for Geodynamics via Firedrake

2022 ◽  
Author(s):  
D. Rhodri Davies ◽  
Stephan C. Kramer ◽  
Siavash Ghelichkhan ◽  
Angus Gibson
Keyword(s):  
Finite Element ◽  
Code Generation ◽  
Automated System ◽  
Plate Motion ◽  
Reproducible Research ◽  
Surface Boundary ◽  
Problem Size ◽  
Mantle Dynamics ◽  
Research Software ◽  
Wide Range

Abstract. Firedrake is an automated system for solving partial differential equations using the finite element method. By applying sophisticated performance optimisations through automatic code-generation techniques, it provides a means to create accurate, efficient, flexible, easily extensible, scalable, transparent and reproducible research software, that is ideally suited to simulating a wide-range of problems in geophysical fluid dynamics. Here, we demonstrate the applicability of Firedrake for geodynamical simulation, with a focus on mantle dynamics. The accuracy and efficiency of the approach is confirmed via comparisons against a suite of analytical and benchmark cases of systematically increasing complexity, whilst parallel scalability is demonstrated up to 12288 compute cores, where the problem size and the number of processing cores are simultaneously increased. In addition, Firedrake's flexibility is highlighted via straightforward application to different physical (e.g. complex nonlinear rheologies, compressibility) and geometrical (2-D and 3-D Cartesian and spherical domains) scenarios. Finally, a representative simulation of global mantle convection is examined, which incorporates 230 Myr of plate motion history as a kinematic surface boundary condition, confirming its suitability for addressing research problems at the frontiers of global mantle dynamics research.

scholarly journals Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization

Solid Earth ◽  
2018 ◽  
Vol 9 (2) ◽  
pp. 385-402 ◽  
Author(s):  
Evren Pakyuz-Charrier ◽  
Mark Lindsay ◽  
Vitaliy Ogarko ◽  
Jeremie Giraud ◽  
Mark Jessell
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Input Data ◽  
Structural Data ◽  
Uncertainty Estimation ◽  
Data Sets ◽  
Geological Modeling ◽  
Input Uncertainty ◽  
Wide Range ◽  
Geological Models

Abstract. Three-dimensional (3-D) geological structural modeling aims to determine geological information in a 3-D space using structural data (foliations and interfaces) and topological rules as inputs. This is necessary in any project in which the properties of the subsurface matters; they express our understanding of geometries in depth. For that reason, 3-D geological models have a wide range of practical applications including but not restricted to civil engineering, the oil and gas industry, the mining industry, and water management. These models, however, are fraught with uncertainties originating from the inherent flaws of the modeling engines (working hypotheses, interpolator's parameterization) and the inherent lack of knowledge in areas where there are no observations combined with input uncertainty (observational, conceptual and technical errors). Because 3-D geological models are often used for impactful decision-making it is critical that all 3-D geological models provide accurate estimates of uncertainty. This paper's focus is set on the effect of structural input data measurement uncertainty propagation in implicit 3-D geological modeling. This aim is achieved using Monte Carlo simulation for uncertainty estimation (MCUE), a stochastic method which samples from predefined disturbance probability distributions that represent the uncertainty of the original input data set. MCUE is used to produce hundreds to thousands of altered unique data sets. The altered data sets are used as inputs to produce a range of plausible 3-D models. The plausible models are then combined into a single probabilistic model as a means to propagate uncertainty from the input data to the final model. In this paper, several improved methods for MCUE are proposed. The methods pertain to distribution selection for input uncertainty, sample analysis and statistical consistency of the sampled distribution. Pole vector sampling is proposed as a more rigorous alternative than dip vector sampling for planar features and the use of a Bayesian approach to disturbance distribution parameterization is suggested. The influence of incorrect disturbance distributions is discussed and propositions are made and evaluated on synthetic and realistic cases to address the sighted issues. The distribution of the errors of the observed data (i.e., scedasticity) is shown to affect the quality of prior distributions for MCUE. Results demonstrate that the proposed workflows improve the reliability of uncertainty estimation and diminish the occurrence of artifacts.

scholarly journals NeuroPycon: An open-source Python toolbox for fast multi-modal and reproducible brain connectivity pipelines

2019 ◽  
Author(s):  
David Meunier ◽  
Annalisa Pascarella ◽  
Dmitrii Altukhov ◽  
Mainak Jas ◽  
Etienne Combrisson ◽  
...  
Keyword(s):  
Open Source ◽  
Brain Connectivity ◽  
Reproducible Research ◽  
Link Type ◽  
Large Sets ◽  
Current Implementation ◽  
Wide Range ◽  
Neuroimaging Software ◽  
Brain Data ◽  
Automatic Removal

AbstractRecent years have witnessed a massive push towards reproducible research in neuroscience. Unfortunately, this endeavor is often challenged by the large diversity of tools used, project-specific custom code and the difficulty to track all user-defined parameters. NeuroPycon is an open-source multi-modal brain data analysis toolkit which provides Python-based template pipelines for advanced multi-processing of MEG, EEG, functional and anatomical MRI data, with a focus on connectivity and graph theoretical analyses. Importantly, it provides shareable parameter files to facilitate replication of all analysis steps. NeuroPycon is based on the NiPype framework which facilitates data analyses by wrapping many commonly-used neuroimaging software tools into a common Python environment. In other words, rather than being a brain imaging software with is own implementation of standard algorithms for brain signal processing, NeuroPycon seamlessly integrates existing packages (coded in python, Matlab or other languages) into a unified python framework. Importantly, thanks to the multi-threaded processing and computational efficiency afforded by NiPype, NeuroPycon provides an easy option for fast parallel processing, which critical when handling large sets of multi-dimensional brain data. Moreover, its flexible design allows users to easily configure analysis pipelines by connecting distinct nodes to each other. Each node can be a Python-wrapped module, a user-defined function or a well-established tool (e.g. MNE-Python for MEG analysis, Radatools for graph theoretical metrics, etc.). Last but not least, the ability to use NeuroPycon parameter files to fully describe any pipeline is an important feature for reproducibility, as they can be shared and used for easy replication by others. The current implementation of NeuroPycon contains two complementary packages: The first, called ephypype, includes pipelines for electrophysiology analysis and a command-line interface for on the fly pipeline creation. Current implementations allow for MEG/EEG data import, pre-processing and cleaning by automatic removal of ocular and cardiac artefacts, in addition to sensor or source-level connectivity analyses. The second package, called graphpype, is designed to investigate functional connectivity via a wide range of graph-theoretical metrics, including modular partitions. The present article describes the philosophy, architecture, and functionalities of the toolkit and provides illustrative examples through interactive notebooks. NeuroPycon is available for download via github (https://github.com/neuropycon) and the two principal packages are documented online (https://neuropycon.github.io/ephypype/index.html. and https://neuropycon.github.io/graphpype/index.html). Future developments include fusion of multi-modal data (eg. MEG and fMRI or intracranial EEG and fMRI). We hope that the release of NeuroPycon will attract many users and new contributors, and facilitate the efforts of our community towards open source tool sharing and development, as well as scientific reproducibility.

scholarly journals Monte Carlo Simulations for Uncertainty Estimation in 3D Geological Modeling, A Guide for Disturbance Distribution Selection and Parameterization

2017 ◽  
Author(s):  
Evren Pakyuz-Charrier ◽  
Mark Lindsay ◽  
Vitaliy Ogarko ◽  
Jeremie Giraud ◽  
Mark Jessell
Keyword(s):  
Monte Carlo ◽  
Input Data ◽  
Uncertainty Estimation ◽  
Data Sets ◽  
Geological Modeling ◽  
Data Set ◽  
Input Uncertainty ◽  
3D Geological Modeling ◽  
Wide Range ◽  
Geological Models

Abstract. Three-dimensional (3D) geological modeling aims to determine geological information in a 3D space using structural data (foliations and interfaces) and topological rules as inputs. They are necessary in any project where the properties of the subsurface matters, they express our understanding of geometries in depth. For that reason, 3D geological models have a wide range of practical applications including but not restrained to civil engineering, oil and gas industry, mining industry and water management. These models, however, are fraught with uncertainties originating from the inherent flaws of the modeling engines (working hypotheses, interpolator’s parameterization) combined with input uncertainty (observational-, conceptual- and technical errors). Because 3D geological models are often used for impactful decision making it is critical that all 3D geological models provide accurate estimates of uncertainty. This paper’s focus is set on the effect of structural input data uncertainty propagation in implicit 3D geological modeling using GeoModeller API. This aim is achieved using Monte Carlo simulation uncertainty estimation (MCUE), a heuristic stochastic method which samples from predefined disturbance probability distributions that represent the uncertainty of the original input data set. MCUE is used to produce hundreds to thousands of altered unique data sets. The altered data sets are used as inputs to produce a range of plausible 3D models. The plausible models are then combined into a single probabilistic model as a means to propagate uncertainty from the input data to the final model. In this paper, several improved methods for MCUE are proposed. The methods pertain to distribution selection for input uncertainty, sample analysis and statistical consistency of the sampled distribution. Pole vector sampling is proposed as a more rigorous alternative than dip vector sampling for planar features and the use of a Bayesian approach to disturbance distribution parameterization is suggested. The influence of inappropriate disturbance distributions is discussed and propositions are made and evaluated on synthetic and realistic cases to address the sighted issues. The distribution of the errors of the observed data (i.e. scedasticity) is shown to affect the quality of prior distributions for MCUE. Results demonstrate that the proposed workflows improve the reliability of uncertainty estimation and diminishes the occurrence of artefacts.

scholarly journals The metaRbolomics Toolbox in Bioconductor and beyond

Metabolites ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 200 ◽  
Author(s):  
Jan Stanstrup ◽  
Corey Broeckling ◽  
Rick Helmus ◽  
Nils Hoffmann ◽  
Ewy Mathé ◽  
...  
Keyword(s):  
Experimental Data ◽  
Data Processing ◽  
Open Source ◽  
User Interfaces ◽  
Workflow Management ◽  
Biochemical Network ◽  
Reproducible Research ◽  
Resonance Spectroscopy ◽  
Major Interest ◽  
Wide Range

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.

scholarly journals Discontinuous mechanical analysis of manifold element strain of rock slope based on open source Gempy

2021 ◽  
Vol 248 ◽  
pp. 03084
Author(s):  
Jie Wu ◽  
Bingyang Sun
Keyword(s):  
Rock Mass ◽  
Stability Analysis ◽  
Open Source ◽  
Interpolation Method ◽  
Data Use ◽  
Three Dimensional ◽  
Geological Model ◽  
Geological Modeling ◽  
Fault Surface ◽  
Interactive Operation

Three-dimensional geological modeling is an important means to transparently study the three-dimensional geological spatial structure and change trend of major complex rock mass projects, and plays a very important role in the decision-making and mining of major projects. Today’s modeling methods rely too much on the required data, the process is cumbersome and the time is relatively long. Therefore, based on the above discussion, this article uses a complete open source code geological modeling method here, based on the implicit potential field interpolation method of the open source GemPy package, using this interpolation algorithm can be relatively simple to build a complex full three-dimensional geological model, Including fault network, fault surface interaction, unconformity and dome structure. The article describes in detail how to construct the faults and strata of the 3D geological model, and how to add topographic maps, and use the generated model as input data, use MOOSE to voxelize it, and export the required data, use NMM Perform stability analysis. This method has the advantages of simple operation, fast modeling speed, and visual interactive operation. The establishment of a three-dimensional geological model of the fractured rock mass was very effective, laying a solid foundation for the subsequent stability analysis.

Automation of Process Control in Chemical Plant

2015 ◽  
Vol 2 (1) ◽  
pp. 6-12
Author(s):  
Agus Sugiarta ◽  
Houtman P. Siregar ◽  
Dedy Loebis
Keyword(s):  
Process Control ◽  
Pid Control ◽  
Control Algorithm ◽  
Chemical Engineering ◽  
Chemical Plant ◽  
Raw Material ◽  
Flow Rates ◽  
Material Supply ◽  
Wide Range ◽  
Inherent Problem

Automation of process control in chemical plant is an inspiring application field of mechatronicengineering. In order to understand the complexity of the automation and its application requireknowledges of chemical engineering, mechatronic and other numerous interconnected studies.The background of this paper is an inherent problem of overheating due to lack of level controlsystem. The objective of this research is to control the dynamic process of desired level more tightlywhich is able to stabilize raw material supply into the chemical plant system.The chemical plant is operated within a wide range of feed compositions and flow rates whichmake the process control become difficult. This research uses modelling for efficiency reason andanalyzes the model by PID control algorithm along with its simulations by using Matlab.

scholarly journals Bio-Based Alternatives to Phenol and Formaldehyde for the Production of Resins

Polymers ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2237 ◽  
Author(s):  
P. R. Sarika ◽  
Paul Nancarrow ◽  
Abdulrahman Khansaheb ◽  
Taleb Ibrahim
Keyword(s):  
Raw Materials ◽  
Phenol Formaldehyde ◽  
Raw Material ◽  
Wood Industry ◽  
Suitable Material ◽  
The Past ◽  
Wide Range ◽  
Recent Developments ◽  
Sustainable Materials ◽  
Materials Used

Phenol–formaldehyde (PF) resin continues to dominate the resin industry more than 100 years after its first synthesis. Its versatile properties such as thermal stability, chemical resistance, fire resistance, and dimensional stability make it a suitable material for a wide range of applications. PF resins have been used in the wood industry as adhesives, in paints and coatings, and in the aerospace, construction, and building industries as composites and foams. Currently, petroleum is the key source of raw materials used in manufacturing PF resin. However, increasing environmental pollution and fossil fuel depletion have driven industries to seek sustainable alternatives to petroleum based raw materials. Over the past decade, researchers have replaced phenol and formaldehyde with sustainable materials such as lignin, tannin, cardanol, hydroxymethylfurfural, and glyoxal to produce bio-based PF resin. Several synthesis modifications are currently under investigation towards improving the properties of bio-based phenolic resin. This review discusses recent developments in the synthesis of PF resins, particularly those created from sustainable raw material substitutes, and modifications applied to the synthetic route in order to improve the mechanical properties.

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