Stochastic modelling of Krafla's magma bodies

The Krafla volcanic system in NE Iceland exhibits active bimodal basic and acidic magmatism, with high-temperature geothermal resources that have been exploited since 1977. Deep wells provide a unique insight into Krafla’s geological structure, including two intersections of acidic magma. It is thou...

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Bibliographic Details
Main Author: Catley, James William 1989-
Other Authors: Háskólinn í Reykjavík
Format: Thesis
Language:English
Published: 2018
Subjects:
Online Access:http://hdl.handle.net/1946/31408
id ftskemman:oai:skemman.is:1946/31408
record_format openpolar
spelling ftskemman:oai:skemman.is:1946/31408 2023-05-15T16:52:27+02:00 Stochastic modelling of Krafla's magma bodies Catley, James William 1989- Háskólinn í Reykjavík 2018-05 application/pdf http://hdl.handle.net/1946/31408 en eng http://hdl.handle.net/1946/31408 Orkuvísindi Meistaraprófsritgerðir Jarðhitakerfi Líkön Tækni- og verkfræðideild Sustainable energy Geothermal systems Models School of Science and Engineering Thesis Master's 2018 ftskemman 2022-12-11T06:54:10Z The Krafla volcanic system in NE Iceland exhibits active bimodal basic and acidic magmatism, with high-temperature geothermal resources that have been exploited since 1977. Deep wells provide a unique insight into Krafla’s geological structure, including two intersections of acidic magma. It is thought that shallow magma bodies play a significant role in heating the geothermal system, as well as being a volcanic hazard. The study evaluates available geoscience and engineering data; generating several conceptual models for the locations and morphology of current magma bodies. These include various combinations of dykes, sills, cone sheets, and magma chambers. Training images derived from these models form the basis for further stochastic simulation of the bodies using the DeeSse multiple point geostatistics algorithm. A supervised machine learning classification model was used to predict magma occurrence based on exhaustive geophysics data, with the aim of guiding local target probability in the simulations. Unfortunately, the data was inadequate to train a robust model, and excessive processing times also resulted when using a local target probability in DeeSse. The resulting realisations are therefore unconstrained by geophysics and quantities of magma in the models remain arbitrary. When analysed for uncertainty using information entropy, phi, and distance clustering – the simulations show that magma probability is defined mostly by body geometry and large-scale patterns constrained by the limited hard data. The study demonstrates the potential for DeeSse to reproduce complex geological patterns, as well as the difficulty in providing appropriate training images. Kvika eldstöðvakerfisins í Kröflu á norðaustur Íslandi er tvískipt, bæði súr og basísk. Hitastig jarðhitakerfisins er hátt. Þarna hefur verið framleitt rafmagn frá 1977. Djúpar borholur hafa gefið nýja sýn á jarðfræðilega skiptingu Kröflukerfisins og greina má tvö súr innskot. Talið er að grunnstæð kvikuhólf leiki stórt hlutverk bæði hvað varðar ... Thesis Iceland Skemman (Iceland) Krafla ENVELOPE(-16.747,-16.747,65.713,65.713)
institution Open Polar
collection Skemman (Iceland)
op_collection_id ftskemman
language English
topic Orkuvísindi
Meistaraprófsritgerðir
Jarðhitakerfi
Líkön
Tækni- og verkfræðideild
Sustainable energy
Geothermal systems
Models
School of Science and Engineering
spellingShingle Orkuvísindi
Meistaraprófsritgerðir
Jarðhitakerfi
Líkön
Tækni- og verkfræðideild
Sustainable energy
Geothermal systems
Models
School of Science and Engineering
Catley, James William 1989-
Stochastic modelling of Krafla's magma bodies
topic_facet Orkuvísindi
Meistaraprófsritgerðir
Jarðhitakerfi
Líkön
Tækni- og verkfræðideild
Sustainable energy
Geothermal systems
Models
School of Science and Engineering
description The Krafla volcanic system in NE Iceland exhibits active bimodal basic and acidic magmatism, with high-temperature geothermal resources that have been exploited since 1977. Deep wells provide a unique insight into Krafla’s geological structure, including two intersections of acidic magma. It is thought that shallow magma bodies play a significant role in heating the geothermal system, as well as being a volcanic hazard. The study evaluates available geoscience and engineering data; generating several conceptual models for the locations and morphology of current magma bodies. These include various combinations of dykes, sills, cone sheets, and magma chambers. Training images derived from these models form the basis for further stochastic simulation of the bodies using the DeeSse multiple point geostatistics algorithm. A supervised machine learning classification model was used to predict magma occurrence based on exhaustive geophysics data, with the aim of guiding local target probability in the simulations. Unfortunately, the data was inadequate to train a robust model, and excessive processing times also resulted when using a local target probability in DeeSse. The resulting realisations are therefore unconstrained by geophysics and quantities of magma in the models remain arbitrary. When analysed for uncertainty using information entropy, phi, and distance clustering – the simulations show that magma probability is defined mostly by body geometry and large-scale patterns constrained by the limited hard data. The study demonstrates the potential for DeeSse to reproduce complex geological patterns, as well as the difficulty in providing appropriate training images. Kvika eldstöðvakerfisins í Kröflu á norðaustur Íslandi er tvískipt, bæði súr og basísk. Hitastig jarðhitakerfisins er hátt. Þarna hefur verið framleitt rafmagn frá 1977. Djúpar borholur hafa gefið nýja sýn á jarðfræðilega skiptingu Kröflukerfisins og greina má tvö súr innskot. Talið er að grunnstæð kvikuhólf leiki stórt hlutverk bæði hvað varðar ...
author2 Háskólinn í Reykjavík
format Thesis
author Catley, James William 1989-
author_facet Catley, James William 1989-
author_sort Catley, James William 1989-
title Stochastic modelling of Krafla's magma bodies
title_short Stochastic modelling of Krafla's magma bodies
title_full Stochastic modelling of Krafla's magma bodies
title_fullStr Stochastic modelling of Krafla's magma bodies
title_full_unstemmed Stochastic modelling of Krafla's magma bodies
title_sort stochastic modelling of krafla's magma bodies
publishDate 2018
url http://hdl.handle.net/1946/31408
long_lat ENVELOPE(-16.747,-16.747,65.713,65.713)
geographic Krafla
geographic_facet Krafla
genre Iceland
genre_facet Iceland
op_relation http://hdl.handle.net/1946/31408
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