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updating myst file, thumbnail, slides and abstract #1

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4 changes: 2 additions & 2 deletions README.md
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# template-repository-conference
template repository for conference proceedings
# Comparing strategies for assessing uncertainty with geophysical inversions for mineral exploration

13 changes: 13 additions & 0 deletions abstract/abstract.md
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---
title: 'Processing potential fields data with Fatiando a Terra'
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It looks like this one has the wrong content for the abstract -- this one is Santi's abstract :) The content is here: https://docs.google.com/document/d/1ycMuS99pGvG4MWlw0DBzrCuVJQjLTazVtXbkc2Ob7BI/edit

Would you mind updating @tetikshajain?

(Also, it looks like there are 2 pull requests open on this repo, just let me know which one to pay attention to, thanks!)

abstract: |
Drilling plays a crucial role in exploration programs, and geophysical data often aid in choosing drill locations. Electromagnetic methods, sensitive to subsurface conductivity, are commonly used to create subsurface models for this purpose. However, geophysical inversions are inherently non-unique, as multiple Earth models can fit the data. Therefore, the uncertainty in the obtained models is of interest. However, formal uncertainty quantification poses a challenge, given the difficulty in translating the ultimate decision into a mathematical framework. Our work is to aim at using a blend of deterministic and Bayesian methods to assess uncertainty.

Deterministic inversion produces a single maximum likelihood model where one model is then used for the interpretation. In deterministic inversion, uncertainty estimates can be obtained from regularized methods via a local linearization around a reference model (Tarantola, 2005). Other regularized methods explore the model space via norms (Fournier, 2019).

The other approach is Bayesian where the prior is our choice of regularization which then influences the uncertainty estimates in a non-linear way. Standard approaches, such as Markov Chain Monte Carlo (MCMC) and Metropolis-Hastings, sample a posterior, but are computationally prohibitive, especially for 3D EM problems. A nice feature of these methods is that they are proven to converge with sufficient iterations. Recently, the Randomize Then Optimize (RTO) approach (Bardsley, 2014) is made less computationally expensive allowing parallel sampling. Although this method introduces a bias and does not necessarily converge, the bias has been shown to be small for 1D and 2D electromagnetic problems (Blatter, 2022). For RTO and other similar efficient randomized sampling algorithms we can get the benefits of both speed and to recover uncertainty estimations.

In this presentation, we will explore the assessment of uncertainty in inversions of magnetotelluric and DC resistivity data by utilizing various combinations of the above methods and priors. We will explore what information can be extracted from each and assess the strengths and weaknesses for addressing uncertainty-related questions relevant to mineral exploration.
---

![thumbnail](thumbnail.png)
41 changes: 18 additions & 23 deletions abstract/myst.yml
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version: 1
project:
# id: ubcgif-YEAR-AUTHORS-CONFERENCE
# title: TITLE
# description: DESCRIPTION
id: ubcgif-2023-kuttai-etal-agu
title: Comparing strategies for assessing uncertainty with geophysical inversions for mineral exploration
description: Talk presented in session SY41A-1036
authors:
# - name: AUTHOR 1
- name: Johnathan C. Kuttai
# email: EMAIL
# corresponding: true
corresponding: true
# orcid: ORCID
# affiliation:
# id: ubc
# name: Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Vancouver, British Columbia
# - name: Lindsey J. Heagy
# email: [email protected]
# corresponding: true
# orcid: 0000-0002-1551-5926
# affiliation: ubc
# - name: Douglas W. Oldenburg
# affiliation: ubc
# email: [email protected]
# orcid: 0000-0002-4327-2124
affiliation:
id: ubc
name: Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Vancouver, British Columbia
- name: Lindsey J. Heagy
email: [email protected]
orcid: 0000-0002-1551-5926
affiliation: ubc
# banner: banner.png
thumbnail: thumbnail.png
# subject: Electromagnetics
venue:
# title: CONFERENCE NAME
# url: CONFERENCE URL
title: AGU Fall Meeting
url: https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1409882
# license: CC-BY-4.0
# open_access: true
# doi: DOI
# date: DATE OF PRESENTATION
date: December 14, 2023
abbreviations:
# DC: direct current
# EM: electromagnetics
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site:
template: article-theme
actions:
# - title: pdf
# url: ARTICLE-NAME.pdf
# static: true
- title: poster
url: abstract/presentation/2023-AGU_poster-JK-LH.pdf
static: true
# - title: slides
# url: presentation/SLIDES-NAME.pdf
# static: true
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7 changes: 3 additions & 4 deletions abstract/presentation/presentation.md
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---
title: Presentation at CONFERENCE
title: Presentation at AGU
---

:::{note} Conference Presentation
This article was presented at the [CONFERENCE NAME](URL) in
LOCATION, DATE
This article was presented at the [AGU](https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1409882) on December 14, 2023
:::

[Download Presentation](NAME-OF-PRESENTATION.pdf)
[Download Presentation](abstract/presentation/2023-AGU_poster-JK-LH.pdf)
Binary file modified abstract/thumbnail.png
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