Implicit geological modeling for the Einstein Telescope (Meuse-Rhine Euroregion)

2021 ◽  
Author(s):  
Nils Chudalla ◽  
Florian Wellmann ◽  
Alexander Jüstel ◽  
Jan von Harten
Keyword(s):  
Open Source ◽  
Probabilistic Methods ◽  
Target Area ◽  
Orientation Data ◽  
Optimal Position ◽  
Seismic Surveys ◽  
Einstein Telescope ◽  
Geological Models ◽  
Complex Engineering ◽  
Implicit Modeling

<p>Expectations for geological models for underground characterization rise with complex engineering tasks. In this project we examine a target area as a potential site for the gravitational-wave observatory “Einstein Telescope” in the Meuse-Rhine Euroregion (Netherlands, Belgium, Germany).  The Einstein Telescope will be the world’s most sensitive observatory of its kind. It consists of a triangular shaped facility connected by 10 km long arms in 200-300 m depth. A high accuracy 3-D structural geological model is required to constrain the best position of the Einstein Telescope with geophysical and geotechnical methods.</p><p>We use an implicit modeling approach based on surface points and orientation data for modeling. This data is extracted from seismic surveys and well logs available in the region. The application of probabilistic methods in this workflow allows to propagate uncertainty of the input data into a resulting model suite, allowing to define a measure of uncertainty for the final model. Specific local difficulties that were encountered during the modelling process, including data management, the representation of complex fault networks and scaling issues will be discussed.</p><p>We will show 3-D geological models for the Meuse-Rhine Eurogregion to significantly improve our geological understanding of the target area. This improved understanding is crucial for finding the optimal position for the Einstein Telescope. Data is managed using the open-source library <em>GemGIS</em>. Models are created using the open-source library <em>GemPy</em>.</p>

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 Actors, actions, and uncertainties: optimizing decision-making based on 3-D structural geological models

Solid Earth ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 2015-2043 ◽  
Author(s):  
Fabian Antonio Stamm ◽  
Miguel de la Varga ◽  
Florian Wellmann
Keyword(s):  
Decision Making ◽  
Loss Function ◽  
Probabilistic Methods ◽  
Stochastic Simulations ◽  
Critical Parameters ◽  
Model Parameters ◽  
Final Decision ◽  
Expected Loss ◽  
True Value ◽  
Geological Models

Abstract. Uncertainties are common in geological models and have a considerable impact on model interpretations and subsequent decision-making. This is of particular significance for high-risk, high-reward sectors. Recent advances allows us to view geological modeling as a statistical problem that we can address with probabilistic methods. Using stochastic simulations and Bayesian inference, uncertainties can be quantified and reduced by incorporating additional geological information. In this work, we propose custom loss functions as a decision-making tool that builds upon such probabilistic approaches. As an example, we devise a case in which the decision problem is one of estimating the uncertain economic value of a potential fluid reservoir. For subsequent true value estimation, we design a case-specific loss function to reflect not only the decision-making environment, but also the preferences of differently risk-inclined decision makers. Based on this function, optimizing for expected loss returns an actor's best estimate to base decision-making on, given a probability distribution for the uncertain parameter of interest. We apply the customized loss function in the context of a case study featuring a synthetic 3-D structural geological model. A set of probability distributions for the maximum trap volume as the parameter of interest is generated via stochastic simulations. These represent different information scenarios to test the loss function approach for decision-making. Our results show that the optimizing estimators shift according to the characteristics of the underlying distribution. While overall variation leads to separation, risk-averse and risk-friendly decisions converge in the decision space and decrease in expected loss given narrower distributions. We thus consider the degree of decision convergence to be a measure for the state of knowledge and its inherent uncertainty at the moment of decision-making. This decisive uncertainty does not change in alignment with model uncertainty but depends on alterations of critical parameters and respective interdependencies, in particular relating to seal reliability. Additionally, actors are affected differently by adding new information to the model, depending on their risk affinity. It is therefore important to identify the model parameters that are most influential for the final decision in order to optimize the decision-making process.

scholarly journals Ligo: An Open Source Application for the Management and Execution of Administrative Data Linkage

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Greg Lawrance ◽  
Raphael Parra Hernandez ◽  
Khalegh Mamakani ◽  
Suraiya Khan ◽  
Brent Hills ◽  
...  
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Administrative Data ◽  
Data Science ◽  
Population Data ◽  
Probabilistic Methods ◽  
Learning Approaches ◽  
Web Interface ◽  
Science Community ◽  
Comparison Algorithms

IntroductionLigo is an open source application that provides a framework for managing and executing administrative data linking projects. Ligo provides an easy-to-use web interface that lets analysts select among data linking methods including deterministic, probabilistic and machine learning approaches and use these in a documented, repeatable, tested, step-by-step process. Objectives and ApproachThe linking application has two primary functions: identifying common entities in datasets [de-duplication] and identifying common entities between datasets [linking]. The application is being built from the ground up in a partnership between the Province of British Columbia’s Data Innovation (DI) Program and Population Data BC, and with input from data scientists. The simple web interface allows analysts to streamline the processing of multiple datasets in a straight-forward and reproducible manner. ResultsBuilt in Python and implemented as a desktop-capable and cloud-deployable containerized application, Ligo includes many of the latest data-linking comparison algorithms with a plugin architecture that supports the simple addition of new formulae. Currently, deterministic approaches to linking have been implemented and probabilistic methods are in alpha testing. A fully functional alpha, including deterministic and probabilistic methods is expected to be ready in September, with a machine learning extension expected soon after. Conclusion/ImplicationsLigo has been designed with enterprise users in mind. The application is intended to make the processes of data de-duplication and linking simple, fast and reproducible. By making the application open source, we encourage feedback and collaboration from across the population research and data science community.

scholarly journals From photo-geology to 3D geological models. An open source approach

2016 ◽  
Author(s):  
Michele Santangelo ◽  
Ivan Marchesini ◽  
Francesco Mirabella ◽  
Francesco Bucci ◽  
Mauro Cardinali ◽  
...  
Keyword(s):  
Open Source ◽  
Central Italy ◽  
Three Dimensional ◽  
3D Models ◽  
Preliminary Evaluation ◽  
Three Dimensional Modeling ◽  
Dimensional Modeling ◽  
Geological Models ◽  
Open Source Gis ◽  
Gis Environment

Three-dimensional modeling of geological bodies is a useful tool for multiple applications. Such tasks are usually accomplished starting from field-collected data, which typically suffer from intrinsic limitations such as accessibility constraints and punctuality of data collected. In this work, we explore the reliability of photo-geological analyses starting from aerial photo-interpretation in providing data useful to build up 3D geological models, and validate them using exploration wells data in a lignite rich area in Umbria, central Italy. The procedure that produces 3D models from photo-geological data is a three-step open source GIS procedure developed using python in GRASS GIS environment and GNU-Linux OS. We maintain that this procedure can have potential broad applications in Earth Sciences, including geological and structural analyses, up to the preliminary evaluation of potential reservoirs.

scholarly journals From photo-geology to 3D geological models. An open source approach

2016 ◽  
Author(s):  
Michele Santangelo ◽  
Ivan Marchesini ◽  
Francesco Mirabella ◽  
Francesco Bucci ◽  
Mauro Cardinali ◽  
...  
Keyword(s):  
Open Source ◽  
Central Italy ◽  
Three Dimensional ◽  
3D Models ◽  
Geological Mapping ◽  
Aerial Photographs ◽  
Preliminary Evaluation ◽  
Three Dimensional Modeling ◽  
Geological Models ◽  
Open Source Gis

Three-dimensional modeling of geological bodies is a useful tool for multiple applications. Such tasks are usually accomplished starting from field-collected data, which typically suffer from intrinsic limitations such as accessibility constraints and punctuality of data collected. In this work, we explore the reliability of photo-geological mapping from interpretation of aerial photographs in providing data useful to build up 3D geological models. The test was conducted in a 15 km2 in Umbria, central Italy. The three-steps open source GIS procedure that outputs 3D models from photo-geological data was developed using python in GRASS GIS environment and GNU-Linux OS. We maintain that this procedure can have potential broad applications in Earth Sciences, including geological and structural analyses, up to the preliminary evaluation of potential reservoirs.

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 Spatial agents for geological surface modelling

2021 ◽  
Vol 14 (11) ◽  
pp. 6661-6680
Author(s):  
Eric A. de Kemp
Keyword(s):  
Building Blocks ◽  
Sparse Data ◽  
Spatial Gradient ◽  
Infrastructure Development ◽  
Geological Data ◽  
Orientation Data ◽  
Spatial Continuity ◽  
Gradient Information ◽  
Geological Models ◽  
Complex Geology

Abstract. Increased availability and use of 3D-rendered geological models have provided society with predictive capabilities, supporting natural resource assessments, hazard awareness, and infrastructure development. The Geological Survey of Canada, along with other such institutions, has been trying to standardize and operationalize this modelling practice. Knowing what is in the subsurface, however, is not an easy exercise, especially when it is difficult or impossible to sample at greater depths. Existing approaches for creating 3D geological models involve developing surface components that represent spatial geological features, horizons, faults, and folds, and then assembling them into a framework model as context for downstream property modelling applications (e.g. geophysical inversions, thermo-mechanical simulations, and fracture density models). The current challenge is to develop geologically reasonable starting framework models from regions with sparser data when we have more complicated geology. This study explores the problem of geological data sparsity and presents a new approach that may be useful to open up the logjam in modelling the more challenging terrains using an agent-based approach. Semi-autonomous software entities called spatial agents can be programmed to perform spatial and property interrogation functions, estimations and construction operations for simple graphical objects, that may be usable in building 3D geological surfaces. These surfaces form the building blocks from which full geological and topological models are built and may be useful in sparse-data environments, where ancillary or a priori information is available. Critical in developing natural domain models is the use of gradient information. Increasing the density of spatial gradient information (fabric dips, fold plunges, and local or regional trends) from geologic feature orientations (planar and linear) is the key to more accurate geologic modelling and is core to the functions of spatial agents presented herein. This study, for the first time, examines the potential use of spatial agents to increase gradient constraints in the context of the Loop project (https://loop3d.github.io/, last access: 1 October 2021​​​​​​​) in which new complementary methods are being developed for modelling complex geology for regional applications. The spatial agent codes presented may act to densify and supplement gradient as well as on-contact control points used in LoopStructural (https://www.github.com/Loop3d/LoopStructural, last access: 1 October 2021) and Map2Loop (https://doi.org/10.5281/zenodo.4288476, de Rose et al., 2020). Spatial agents are used to represent common geological data constraints, such as interface locations and gradient geometry, and simple but topologically consistent triangulated meshes. Spatial agents can potentially be used to develop surfaces that conform to reasonable geological patterns of interest, provided that they are embedded with behaviours that are reflective of the knowledge of their geological environment. Initially, this would involve detecting simple geological constraints: locations, trajectories, and trends of geological interfaces. Local and global eigenvectors enable spatial continuity estimates, which can reflect geological trends, with rotational bias, using a quaternion implementation. Spatial interpolation of structural geology orientation data with spatial agents employs a range of simple nearest-neighbour to inverse-distance-weighted (IDW) and quaternion-based spherical linear rotation interpolation (SLERP) schemes. This simulation environment implemented in NetLogo 3D is potentially useful for complex-geology–sparse-data environments where extension, projection, and propagation functions are needed to create more realistic geological forms.

The application of high‐resolution seismics in Polish coal mining

Geophysics ◽  
1992 ◽  
Vol 57 (1) ◽  
pp. 171-180 ◽  
Author(s):  
Kaja Pietsch ◽  
Ryszard Ślusarczyk
Keyword(s):  
High Resolution ◽  
Coal Mining ◽  
Seismic Wave ◽  
Theoretical Modeling ◽  
Coal Seams ◽  
Coal Basin ◽  
Seismic Surveys ◽  
Coal Deposits ◽  
Mining Conditions ◽  
Geological Models

Use of high‐resolution seismics were applied to the exploration and development of coal deposits in the Upper Silesian and the Lublin Coal Basins in Poland. High‐resolution seismic surveys were carried out in various geological and mining conditions. The objectives were to map commercial coal seams, locate coal‐less zones and faults, and solve some problems associated with mining conditions, e.g., determining the height of caving roof zone. Theoretical modeling studies of seismic wave response for several geological models representing the coal‐basin structures were used to address these problems. Results from these successful surveys helped improved designs of new mines and/or expand existing ones to provide more productive and safer coal mines.

scholarly journals Behavioral Activity Recognition Based on Gaze Ethograms

2020 ◽  
Vol 30 (07) ◽  
pp. 2050025 ◽  
Author(s):  
Javier De Lope ◽  
Manuel Graña
Keyword(s):  
Open Source ◽  
Activity Recognition ◽  
Tracking System ◽  
Video Clip ◽  
Behavioral Research ◽  
Behavioral Activity ◽  
Target Area ◽  
Gaze Tracking ◽  
The Gaze ◽  
Classifier Selection

Noninvasive behavior observation techniques allow more natural human behavior assessment experiments with higher ecological validity. We propose the use of gaze ethograms in the context of user interaction with a computer display to characterize the user’s behavioral activity. A gaze ethogram is a time sequence of the screen regions the user is looking at. It can be used for the behavioral modeling of the user. Given a rough partition of the display space, we are able to extract gaze ethograms that allow discrimination of three common user behavioral activities: reading a text, viewing a video clip, and writing a text. A gaze tracking system is used to build the gaze ethogram. User behavioral activity is modeled by a classifier of gaze ethograms able to recognize the user activity after training. Conventional commercial gaze tracking for research in the neurosciences and psychology science are expensive and intrusive, sometimes impose wearing uncomfortable appliances. For the purposes of our behavioral research, we have developed an open source gaze tracking system that runs on conventional laptop computers using their low quality cameras. Some of the gaze tracking pipeline elements have been borrowed from the open source community. However, we have developed innovative solutions to some of the key issues that arise in the gaze tracker. Specifically, we have proposed texture-based eye features that are quite robust to low quality images. These features are the input for a classifier predicting the screen target area, the user is looking at. We report comparative results of several classifier architectures carried out in order to select the classifier to be used to extract the gaze ethograms for our behavioral research. We perform another classifier selection at the level of ethogram classification. Finally, we report encouraging results of user behavioral activity recognition experiments carried out over an inhouse dataset.

scholarly journals From photo-geology to 3D geological models. An open source approach

2016 ◽  
Author(s):  
Michele Santangelo ◽  
Ivan Marchesini ◽  
Francesco Mirabella ◽  
Francesco Bucci ◽  
Mauro Cardinali ◽  
...  
Keyword(s):  
Open Source ◽  
Central Italy ◽  
Three Dimensional ◽  
3D Models ◽  
Geological Mapping ◽  
Aerial Photographs ◽  
Preliminary Evaluation ◽  
Three Dimensional Modeling ◽  
Geological Models ◽  
Open Source Gis

Three-dimensional modeling of geological bodies is a useful tool for multiple applications. Such tasks are usually accomplished starting from field-collected data, which typically suffer from intrinsic limitations such as accessibility constraints and punctuality of data collected. In this work, we explore the reliability of photo-geological mapping from interpretation of aerial photographs in providing data useful to build up 3D geological models. The test was conducted in a 15 km2 in Umbria, central Italy. The three-steps open source GIS procedure that outputs 3D models from photo-geological data was developed using python in GRASS GIS environment and GNU-Linux OS. We maintain that this procedure can have potential broad applications in Earth Sciences, including geological and structural analyses, up to the preliminary evaluation of potential reservoirs.

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