Evo Seismic Association

Community

Zhang-Henry

evo-seismic-association

Seismic phase association pipeline: loads MSEED waveforms and station CSV, runs SeisBench PhaseNet for P/S picking, associates picks into earthquake events using greedy time-clustering with travel-time corrections, and outputs event catalog CSV. Use when task involves earthquake detection, phase picking, or seismic event association.

Overview

Publisher	Zhang-Henry
Repository	`CoEvoSkills`
Skill name	`evo-seismic-association`
Stars	83
Forks	9
Bundled files	4
License	Apache-2.0
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
4 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by Zhang-Henry on GitHub. Read the source before you install it.

Installation

Install the Evo Seismic Association AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/Zhang-Henry/CoEvoSkills/tree/main/artifacts/skills/earthquake-phase-association/evo-seismic-association
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/Zhang-Henry/CoEvoSkills/tree/main/artifacts/skills/earthquake-phase-association/evo-seismic-association

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/Zhang-Henry/CoEvoSkills.git /tmp/CoEvoSkills
mkdir -p .claude/skills
cp -r /tmp/CoEvoSkills/artifacts/skills/earthquake-phase-association/evo-seismic-association .claude/skills/evo-seismic-association

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Evo Seismic Association in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Evo Seismic Association on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Evo Seismic Association is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Seismic Phase Association Skill

End-to-end pipeline for detecting earthquake events from seismic waveform data.

Components

data_loader.py: Load MSEED waveforms, parse station CSV, group traces by station
picker.py: Run SeisBench PhaseNet deep learning model for P/S phase picking
associator.py: Associate picks into events using greedy P-pick clustering with travel-time origin estimation
pipeline.py: Orchestrate the full workflow and validate output

Quick Start

python
import sys
sys.path.insert(0, '/app/environment/skills/evo-seismic-association/scripts')
from pipeline import run_pipeline, validate_results

# Run the full pipeline
events_df = run_pipeline(
    mseed_path='/root/data/wave.mseed',
    stations_csv_path='/root/data/stations.csv',
    output_csv_path='/root/results.csv',
    p_threshold=0.3,
    s_threshold=0.3,
    vp=6.0,
    vs_ratio=1.75,
    max_p_spread=10.0,
    min_stations=3,
    assumed_depth_km=8.0,
    dedup_window=5.0,
    model_name='instance'
)

# Validate output
validate_results('/root/results.csv')
print(f"Detected {len(events_df)} events")

Algorithm Details

Phase Picking

Uses SeisBench PhaseNet with pretrained weights
Processes each station independently with 3-component (E/N/Z) data
Channel preference: HH > HN > EH
Default thresholds: P=0.3, S=0.3

Association

Sort all P picks chronologically
Greedy grouping: consecutive P picks within max_p_spread seconds form a group
Filter groups requiring min_stations unique stations
For each group, estimate origin time by subtracting travel time (distance/vp) from each pick
Use median of origin time estimates as event time
Deduplicate events within dedup_window seconds, keeping the one with most stations

Travel Time Model

Uniform velocity: vp (default 6 km/s), vs = vp / vs_ratio
Distance: haversine great-circle + assumed depth (3D distance)
Origin time = pick_time - (distance_3d / velocity)

Parameters

Parameter	Default	Description
p_threshold	0.3	PhaseNet P-phase detection threshold
s_threshold	0.3	PhaseNet S-phase detection threshold
vp	6.0	P-wave velocity (km/s)
vs_ratio	1.75	vp/vs ratio
max_p_spread	10.0	Max seconds between first and last P pick in group
min_stations	3	Minimum stations for valid event
assumed_depth_km	8.0	Assumed source depth (km)
dedup_window	5.0	Seconds window for merging duplicate events

Output Format

CSV with columns:

time: Event origin time in ISO format without timezone (e.g., 2019-07-04T19:00:11.708)
n_stations: Number of stations that detected the event
n_picks: Number of P picks in the event group
mean_prob: Mean pick probability

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Evo Seismic Association AI skill do?

Why use Evo Seismic Association on TypingMind?

Because you install it once and use it with any model. Evo Seismic Association is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Evo Seismic Association in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Zhang-Henry/CoEvoSkills/tree/main/artifacts/skills/earthquake-phase-association/evo-seismic-association. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Evo Seismic Association?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Evo Seismic Association?

As many as you like. As long as a model supports skills, you can use Evo Seismic Association with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Evo Seismic Association AI skill free?

Yes. It is published on GitHub by Zhang-Henry under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Seismic Phase Association Skill

Components

Quick Start

Algorithm Details

Phase Picking

Association

Travel Time Model

Parameters

Output Format

Bundled files

Frequently asked questions

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