Can My Seismic Resolve That Fault?

Geophysicists get squirrely when pressed about what they can and cannot see on reflection seismic data…. or is it just me???

Quiz: Can you see a 12 meter interval of diagenetically altered carbonate sandwiched between a shale and unaltered carbonate? Answer: It depends!

It depends on the data, the geology, and what is meant by “see”. Do you have 3D seismic or just a 2D line? What’s the frequency bandwidth of the data? How noisy is the data? Is the impedance of the altered carbonate distinct from the layers above and below? Are the rock properties of a layer homogeneous? Do you want to see the depth to the altered carbonate layer? Or do you want its thickness, degree of alteration, or indication if it’s present at all?

Gamers Edge II

My last post introduced Valeria Card Kingdoms, a game that I’ve been playing with my family recently. The game involves rolling dice, so I explored the role of probability in the game. You’ll recall that the results of each dice roll activate certain citizens. Activated citizens produce a certain amount of resources, and by design, the citizens with a lower probability of being activated provide more resources. Though probability is important, I also need to consider reward. So if I’m going to find an edge in Valeria Card Kingdoms, I need to consider probability and reward simultaneously.

Gamers Edge

I’ve always enjoyed playing games. I have early memories of playing Uno, Sorry!, Mille Bournes, and Cribbage at my grandparents’ house. I always loved the competition and the challenge of breaking down a game to find winning strategies.

An Advent of Code

Knowing my interest in Python coding, Evan Saltman pointed me to the 2018 Advent of Code event. Like other Advent calendars, AoC supplies something new every day, but instead of a chocolate, there’s a new computer programming challenge to tackle. There is no “right” way to get a solution — use any approach in any programming language to pass the challenge and earn gold stars.

Building a Fantasy Hockey Beater

I convinced a couple friends to join me in a quest to wield Machine Learning to predict hockey player performance. It was a promising idea, but I needed their programming help and hockey insight to make it a reality. Knowing the outcome of the season before it’s played would assure success for our fantasy hockey teams! Just throw a bunch of stats into the black box of an ML algorithm and… poof! Championships! Right? We weren’t that naive (but still: naive). And at least we were aware of our naivety. Our combined experience with ML was 0.00, so if nothing else, we’d have infinitely more experience by the end.

Geeking to Fantasy Hockey Domination – Part 2

I’ve been applying some data analysis to gain an edge in fantasy hockey leagues. Part 1 of this series explained how I managed to get each team’s performance data into Python so I can work with it. In this post, we’ll have a look at some of the useful analyses I’ve cooked up over the past few years.

Geeking My Way to Fantasy Hockey Domination – Part 1

I like hockey. Watching, playing, chatting about the NHL. Like so many sports enthusiasts, my interest in professional sports turned into an obsession with fantasy sports. Ten years later, I’ve been involved in multiple leagues with multiple formats, often several at a time. I’m even a league commissioner. The draft is an exciting way to tee off the season, and the competitive aspects keep interest in the game throughout the year. I think it’s a great way to get to know about the entire NHL, and not just the hometown team (GO OILERS!).

For some, success in fantasy sports comes through in-depth knowledge of each player and team. Hours watching games, listening to sports talk shows, and reading scouting reports. I’m not one of those guys. I take a numbers approach to fantasy hockey.

Mild or Wild

Another post in the series on sketch2model. We’re highlighting a key issue that came up in our project, and describing what how we tackled it. Matteo’s post on Morphological Filtering does a great job of explaining what we implemented in sketch2model. I’ll build on his post to explain the why and how. In case you need a refresher on sketch2model, look back at sketch2model, Sketch Image Enhancement, Linking Edges with Geomorphological Filtering.

sketch2model – Linking Edges with Mathematical Morphology

This guest post (first published here) is by Matteo Niccoli, author of the blog MyCarta. It is the third post in a series of collaborative articles about sketch2model, a project from the 2015 Calgary Geoscience Hackathon organized by Agile Geoscience.

Introduction

As written by Elwyn in the first post of this series, sketch2model was conceived at the 2015 Calgary Geoscience Hackathon as a web and mobile app that would turn an image of geological sketch into a geological model, and then use Agile Geoscience’s modelr.io to create a synthetic seismic model.

sketch2model – Sketch Image Enhancements

This guest post (first published here) is by Matteo Niccoli, author of the blog MyCarta. It is the second post in a series of collaborative articles about sketch2model, a project from the 2015 Calgary Geoscience Hackathon organized by Agile Geoscience.