.. This file is generated by scripts/render_model_docs.py. Do not edit by hand.

EQNet
=====

Overview
--------

``eqnet`` extends the PyHazards earthquake benchmark stack with a lightweight attention-based picking model.

At a Glance
-----------

.. grid:: 1 2 4 4
   :gutter: 2
   :class-container: catalog-grid

   .. grid-item-card:: Hazard Family
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         Earthquake

      .. container:: catalog-stat-note

         Public catalog grouping used for this model.

   .. grid-item-card:: Maturity
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         Implemented

      .. container:: catalog-stat-note

         Catalog maturity label used on the index page.

   .. grid-item-card:: Tasks
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         1

      .. container:: catalog-stat-note

         Phase Picking

   .. grid-item-card:: Benchmark Family
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         :doc:`Earthquake Benchmark </benchmarks/earthquake_benchmark>`

      .. container:: catalog-stat-note

         Primary benchmark-family link used for compatible evaluation coverage.


Description
-----------

``eqnet`` extends the PyHazards earthquake benchmark stack with a lightweight attention-based picking model.

The implementation keeps the shared waveform input and two-pick output contract so it can be evaluated alongside ``phasenet`` and ``eqtransformer``.

Benchmark Compatibility
-----------------------

**Primary benchmark family:** :doc:`Earthquake Benchmark </benchmarks/earthquake_benchmark>`

**Mapped benchmark ecosystems:** :doc:`SeisBench </benchmarks/seisbench>`

External References
-------------------

**Paper:** `An End-To-End Earthquake Detection Method for Joint Phase Picking and Association Using Deep Learning <https://www.osti.gov/biblio/1978539>`_ | **Repo:** `Repository <https://github.com/AI4EPS/EQNet>`__

Registry Name
-------------

Primary entrypoint: ``eqnet``

Supported Tasks
---------------

- Phase Picking

Programmatic Use
----------------

.. code-block:: python

   import torch
   from pyhazards.models import build_model

   model = build_model(name="eqnet", task="regression", in_channels=3)
   picks = model(torch.randn(4, 3, 256))
   print(picks.shape)

Notes
-----

- Outputs are P- and S-arrival sample indices.