TRANSFORM 2020

• Simple math, logic and flow control
• Modelling Darcy's law with a function
• Computing fold wavelengths with the Ramberg-Biot equation
• Geophysical wavelets with bruges
• Mapping DEM's with matplotlib
• Data wrangling and QC well data with welly
• Scanning a seismic cube with segyio

1. spatiotmeporal context,
2. geoscience information integration, 
3. accounting for spatial data and model scales
4. uncertainty models

1. Introduce the Package: Expand familarity with the functionality of the GeostatsPy package
2. Add it to Your Toolbox: Provide experience with a variety of spatial data analytics workflows
3. You're Welcome: Invite community contributions to the GeostatsPy open source project

1. lectures: a limited amount time for overview lectures to provide basic theory
2. demonstrations: walk throughs of well-documented workflows
3. exercises: hands-on challenges for experiential learning

1. Variogram Calculation
2. Variogram Modeling
3. Spatial Estimation

1. Anaconda / Jupyter Notebooks
2. GeostatsPy (available at PyPi, use 'pip install GeostatsPy' to install)

• Times are in UTC and the location is virtual.
• You can reach me at mpyrcz@austin.utexas.edu.

• load those data into SimPEG 
• construct a survey object that contains the geometry of the sources and receivers
• set up and run a forward simulation 
• define the inverse problem consisting of a data misfit and regularization
• run an inversion and discuss inversion strategies

• Intro Slides
• Course Notebooks

• Resources on Geophysics and Inversions

  • Geophysics for Practicing Geoscientists

  • EM GeoSci

  • Lectures from EOSC 350: Exploration & Environmental Geophysics (2017, 2018)

  • DISC 2017 lectures (Mexico City)

• Resources on SimPEG

  • Docs

  • Discourse

  • Slack

  • This Tutorial

• Loading some data
• Generating and handling coordinates and projections (using pyproj)
• Splitting training and testing data for validation
• Data decimation with blocked means/medians to avoid aliasing
• 2D trend estimation
• Gridding with bi-harmonic splines
• Combining everything into a data processing pipeline
• Cross-validation of data distrubuted spatially on the Earth (including parallel execution)

• Introduction to the DSL - Expressing PDEs in Devito
• Introduction to Devito operators
• Building wave-propagators
• Expressing boundary conditions

• Overview / Introduction
• Seamount Detection Example
  • Thresholding
  • Filtering
  • Segmentation
  • Simplification
• Slope and Hillshade of Topographic Data
  • Gradients
• Grain Detection in Thin Sections
  • Using gradients for more than slope
• Lineament Analysis from Aerial Photography
  • Edge detection
  • Hough Transform
  • Structure Tensor
• What is color?
  • Satellite data examples

1. Las mud log: https://drive.google.com/file/d/1Lws7PztcgZlKT4TQNfmojDesKSOIZ56X/view?usp=sharing
2. Lithology report: https://drive.google.com/file/d/1splcE2E_SXjsRq4olHmlqFPHwDNtSLZO/view?usp=sharing

1. Las Mud Log: https://drive.google.com/file/d/1j1sLFl8Z31qQTfrGoDU6gHsX0yx1ShKA/view?usp=sharing
2. Lithology report: https://drive.google.com/file/d/1w4OJln6mzw-hDCe3YTRVVAsTLI5RDIcv/view?usp=sharing

• Communicating your code with interactive visuals  - what visualizations do you pick and why?  How can you establish trust with non-coding colleages by using good visuals?  We will discuss points on familiarity, colors, storytelling, and decisions on interactivity for you to understand which visuals will help you deliver your message best.
• Data formatting for ease in visualization based on the points above.
• Simple wrapper python packages - working off a provided dataset loaded into pandas, we will take a look at some one liners of code that allow you to visualize your data in some pretty cool ways with minimum effort.  Great for those off the cuff requests to show something with short notice or to look cool in hackathons in little time.  We will go through the functionalities in both plotly_express and hvplot and some of the key attributes that we can change to make the plots more effective.
• Dashboarding in Jupyter - with all these different visuals you can make, how can you combine them to be interactive together?  We will go through making a simple dashboard using Holoviews in Jupyter and discuss/experiment with how to best format the interactivity to deliver a message or give a live demo, bringing it back to how you establish trust in your code with your visualizations.

You are a Cloud Consultant who is beginning an assignment at Unicorn Reservoir Charachterization (URC) * a new tool for geological analysis that "will change how much your rocks rock!"

In this session, you will meet with the URC team, and design a scalable architecture that meets their requirements. What are their requirements? What will this system do? How does the data go from a rock to a data science notebook?

Well, to answer those questions, you simply have to show up and ask them.

In this session, you will be an Architect responsible to translating business requirements to technical requirements, white boarding out an architectural diagram, and then figuring out how to take your great ideas and translate them into actionable next steps.

In this session, you'll meet some of the key stakeholders at URC, learn about their goals, and design a system to meet their vision of "Being the Rock Stars of the Reservoir Characterization World"

• Note that Unicorn Reservoir Characterization (URC) is a fictitious company and any overlap with real companies and products is purely coincidental

• Understand the basics of translating business requirements to technical requirements
• Knowledge about key AWS Services and where to learn more
• Techniques for whiteboarding successfully
• What the AWS Well-Architected Framework is and how to use it
• How to take those great ideas and architecture and do something with them

Data engineering and systems architecture is a necessary precursor to delivering data-driven subsurface insights. As a geologist or geophysicist, systems design typically isn’t something you learn in school, yet it can be a necessary skill set to progress your career and drive meaningful change.

This session will present an overview of the key concepts and design principles for building a scalable and cloud-optimized infrastructure in line with the AWS Well-Architected Framework, with a specific focus on Energy-Industry use cases and accompanying challenges for subsurface professionals. Attendees will leave this session with techniques for outlining technical requirements, knowledge of key AWS services and solutions, an overview of the AWS Well-Architected Framework, and an understanding of how to design for scalable and resilient solutions that can be implemented to improve subsurface workflows.

• Working backwards: User stories to drive technical requirements
• Services, solutions, and scalability
• Whiteboarding as a superpower
• The AWS Well-Architected Framework
• Now what? How to communicate and drive architectural improvements

• File I/O and GeoDataFrames
  
• Plotting Data
• Spatial Selections and subsetting
  
• Neighbours and distances
• Basemaps (Demo, more than a tutorial)
  
• Spatial Joins (time permitting)