I do think it's possible that thorough exploration of the data that do exist can yield broader patterns that apply to many regions, but earthquake behavior has a lot of complexities and different fault systems may behave differently.

A lot of the hope is for coupling physical simulators to ML and the existing datasets to better understand the physics and then work from there, but this is typically cutting-edge HPC work, which limits the pace of research and the number of researchers.

These are quite common. Hot Springs National Park in Arkansas is a notable example. Most of the hot springs in Idaho are on fault lines, as are the hot springs along Highway 395 on the east side of the Sierras (though there is Quaternary volcanism in the region, it's not the source of heat), and through much of the rest of the Intermountain West.

This site seems to be unique for both the volume and the temperature of water, though. I'd be curious to know how far away the water originated, and how deep it goes before being heated. The crust in the area is not known to be particularly hot, as far as I know, unlike the Basin and Range of the western US where tectonic stretching and thinning of the crust has brought the hot mantle much closer to the surface and increased the geothermal gradient.

I don't know whether John Arnold is spread too thin or not, but he's certainly top caliber and does a lot to measure progress before/during investment in various causes (including education). He also seems to be more agnostic on what the most appropriate solution may be at the beginning of the process.

I very regularly need to interact with my work through a python interpreter. My work is scientific programming. So the variables might be arrays with millions of elements. In order to debug, optimize, verify, or improve in any way my work, I cannot rely on any other methods than interacting with the code as it's being run, or while everything is still in memory. So if I want to really leverage LLMs, especially to allow them to work semi-autonomously, they must be able to do the same.

I'm not going to dump tens of GB of stuff to a log file or send it around via pipes or whatever. Why is there a nan in an array that is the product of many earlier steps in a code that took an hour to run? Why are certain data in a 200k-variable system of equations much harder to fit than others, and which equations are in tension with each other to prevent better convergence?

Are interpreters and pdb not great, previously-existing tools for this kind of work? Does a new tool that lets LLMs/agents use them actually represent some sort of hack job because better solutions have existed for years?

The issue is that these are generally reported in the literature from local investigations of one or two faults, yielding a few events. These studies are done wherever there are earthquakes on land, so we have a global scope and language issues. Even limiting the results to the English peer-reviewed literature, however, it's a huge distributed search.

I estimate that there are on the order of 10,000 published events, and a mean of 2-3 events per publication.

For my immediate use of the database, it is very important for the database to be as complete as possible--I'm not looking for a sort of statistically representative sample. The literature itself is quite incomplete of course, but we're limited to what exists for now.

Starting with the first step of collating publications, what tools would one use? I have access to most journals through various university affiliations. Are there particular APIs? Web scraping tools? LLMs?

Thanks!

Comments URL: https://news.ycombinator.com/item?id=47140588

Points: 3

# Comments: 3

A few years later, the gravitational deflection of the Himalayas on a plumb line by Airy proved less than expected, which suggested that mountains have 'roots' that extend below them, displacing more dense rock--like icebergs more or less.

I used the gravitational force of the Longmenshan range to calculate the perturbations in the elastic stress field of the Earth's crust in Sichuan province, China, to estimate the tectonic forces in the region, which caused the 2008 Wenchuan earthquake: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/201...

Sequoia developed the Cherokee syllabary (where symbols represent syllables instead of vowels/consonants) in the 1800s after seeing white men reading, and figuring out what they were doing (he spoke little English and could not read it). This is the first real written indigenous language in the Americas.

The Skeena characters shown here are obviously derived from European characters, as was the Cherokee syllabary. I think most written forms of native languages in the Americas are similar.

The Cree have a script which is far from European characters but was nonetheless developed for the Cree by a missionary in the 1800s. The Inuit have modified it for their language.

I don't know much about indigenous languages in the rest of the world.

Volcanic glass (eg obsidian) is also shiny and by no means rare in the solar system.

Many asteroids are also metallic, and perhaps metal crystals or fracture planes could produce reflectors of the right size.

But maybe it’s just aliens.

Or, you have sheets of a few different colors, each paired to a comforter with a different weight that is changed seasonally, or biweekly, depending on the preferences of you and your bedmate.

Time and time again I would send my friend Dave Walker an email declaring that Javascript (or something else) was utterly broken, incapable of executing the simplest program without errors. Dave would ask to see the source code and I would present it to him with detailed notes proving that my code was perfect and Javascript was broken. He’d call me, we’d discuss it, and eventually he’d say something like, “Where did you terminate the loop beginning at line 563?” There would be a long silence, followed by the tiniest “Oh” from me. I’d thank him for his help and hang up. A week later, I’d be fuming again about another fundamental flaw in Javascript.

Many of us are stubborn and will work hard and long, without much positive external feedback, under the assumption that our vision is correct and the audience, if one even exists, is wrong. Much fundamental progress has been made this way: Faraday, Einstein, Jobs, etc. But of course many times one simply is wrong and refusing to see it means throwing years away, and whatever else with it (money, relationships, etc.). It's a hard balance, especially for the monomaniacal without much interest in balance. Finding out how to make solid (public, peer-reviewed, evidence-based, whatever) incremental progress towards the paradigm shift seems to be the way if one can manage.

Furthermore I think most blue collar American workers, and many white collar workers, are used to the concept of sudden and arbitrary termination.