Ask Ogbon, and the Road On

Part 12, Part 12: The Modeling Lab

Learning objectives

  • Recognise what you can now do
  • Turn a goal in your own words into an engine, a workflow, and starter code with the Ask Ogbon advisor
  • Plan a first project on real data or in Python
  • Carry the fit-for-purpose question forward

You Have Finished

This is the end of the course. You started at the forward problem, the idea that a known earth produces knowable data, and you have built every engine that turns one into the other: the convolutional model, the finite-difference wave equation, elastic AVO with Gassmann, anisotropy and the Thomsen parameters, the fracture rock physics and azimuthal AVO, earth models at scale, and the Modeling Lab that ties them together. More than any one of these, you have learned to ask which of them a given question actually needs.

Ask Ogbon: which engine does your question need?is this bright spot a real gas DHI or lithology?Ask OgbonFor: test a direct hydrocarbon indicatorElastic (AVO + Gassmann)1Zoeppritz and Shuey: the AVO engine (6.3)2Gassmann-substitute the fluid, model brine vs gas (6.5)3Judge the deviation from the brine baseline (6.6)Cost: analytic curves and 1D convolutions, under a second anywhere.Copy codeDownload .pyPlayground.ipynbColabYour goal, in your words: engine, workflow, cost, and starter code.

The Advisor, and the Road On

The widget above is the Ask Ogbon advisor, the promise of this section kept. Describe your end use in your own words. The advisor matches the goal, asks a short interview about the few things that change the answer (baselines, azimuths, usable angles, compute budget), and returns the recommended engine with its fit-for-purpose reasoning, a workflow of course sections in order, a three-platform compute estimate, and a starter Python program with your numbers baked in. One more click copies that code, saves it as a .py file or a Colab-ready notebook, or prefills it into the Python Playground. It is the natural continuation of the course: from choosing the engine to building the whole study. Everything runs offline and rule-based; your text never leaves the page.

From here, the road on is open. Take a model you built in the Lab and run it in Python. Open a real dataset and try to reproduce a feature you can see. Scale a promising run up to Devito or SPECFEM. And whatever you model, keep asking the one question this whole course was built around, before you touch a solver: which engine does this actually need? Answer that well, and you are no longer running software. You are doing seismic modelling. Thank you for taking the course.

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