seisbench-model-api

Expose the SeisBench WaveformModel API for seismic data processing.

4|Updated Feb 22, 2026

Search Tags:#machine-learning #seismic #annotate #waveform #pretrained-models #phase-picking #earthquake-detection

Ready to run

npx skills add https://github.com/GeneralReasoning/env-skillsbench --skill seisbench-model-api-generalreasoning

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: seisbench-model-api
Source: https://github.com/GeneralReasoning/env-skillsbench/tree/main/seismic-phase-picking/environment/skills/seisbench-model-api
Command: npx skills add https://github.com/GeneralReasoning/env-skillsbench --skill seisbench-model-api-generalreasoning

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The SeisBench Model API provides a structured interface to train and apply ML models to seismic data, enabling easy conversion of waveforms to obspy streams and seamless integration with pretrained weights for tasks like phase picking, earthquake detection, and denoising.

Core Features & Use Cases

Abstract WaveformModel interface for consistent model integration and evaluation.
Loading and applying pretrained weights via from_pretrained utilities.
Guidance for deploying models in seismic data pipelines and research workflows.

Quick Start

Load a pretrained SeisBench model and run annotate on an obspy stream to obtain waveform annotations.

Frequently Asked Questions about seisbench-model-api

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I apply pretrained machine learning models to seismic waveforms for phase picking?▼

To apply pretrained models to seismic waveforms, load the model via the from_pretrained utility and run the annotate function on an obspy stream to obtain waveform annotations for phase picking.

What is the SeisBench WaveformModel API used for in earthquake detection workflows?▼

The SeisBench WaveformModel API provides a structured interface to train and apply machine learning models to seismic data, enabling seamless integration with pretrained weights for earthquake detection and classification workflows.

Can I use pretrained SeisBench models for denoising and earthquake detection with obspy streams?▼

Yes, you can use pretrained SeisBench models for denoising and earthquake detection by loading the weights and applying the annotate function to convert and process your obspy streams.

How do I load pretrained weights for seismic data processing tasks?▼

You load pretrained weights for seismic data processing using the from_pretrained utility provided by the WaveformModel API, which handles the integration of model weights for tasks like phase picking.

What is the best way to integrate machine learning models into a seismic data pipeline?▼

The best way to integrate machine learning models into a seismic data pipeline is using the abstract WaveformModel interface, which ensures consistent model loading, annotation, and evaluation across real-world workflows.

Are there limitations when using the WaveformModel API to annotate seismic data?▼

The WaveformModel API requires checking for dependencies and components before annotating seismic data, and it relies on the availability of pretrained weights to successfully execute phase picking and denoising tasks.