FAST is an end-to-end and unsupervised earthquake detection pipeline. It is a useful tool for seismologists to extract more small earthquakes from continuous seismic data. FAST is able to run on different machines by using Google Colab, Linux, or Docker.

• To run FAST with Google Colab, click here for the tutorial.

• To run FAST with Linux, click here for the tutorial.

• To run FAST with Docker, click here for the tutorial.

Check out the user guide to learn more about FAST and how to use it on your own dataset.

1. FAST Overview
   Click here for a summary of the FAST algorithm and why you might want to use it on your seismic data.

2. Install
   Go here to learn how to install and run the FAST software on your computer.

   1. Google Colab

   2. Linux

   3. Docker

3. Tutorial
   Learn how FAST detects earthquakes on the Hector Mine data set.

4. How to Set Parameters
   Click here to learn how to test FAST on your own data sets.

   1. FAST Checklist

   2. Getting Seismic Data

   3. Input and Preprocessing

   4. Fingerprint

   5. Similarity Search

   6. Network Detection

   7. FAST Output

   8. Phase Picking

   9. Earthquake Location

   10. Example Parameters
       Click here to see data sets FAST has been used on to detect earthquakes.

5. References
   Read publications about FAST here.

You can find more details about the pipeline and guidelines for setting parameters in our extended user guide. You may also check out the following papers:

• FAST Overview: Earthquake detection through computationally efficient similarity search
• Fingerprint Overview: Scalable Similarity Search in Seismology: A New Approach to Large-Scale Earthquake Detection
• Fingerprint Benchmark: Earthquake Fingerprints: Extracting Waveform Features for Similarity-Based EarthquakeDetection
• Network Detection: Detecting Earthquakes over a Seismic Network using Single-Station Similarity Measures
• FAST Application: Seismicity During the Initial Stages of the Guy‐Greenbrier, Arkansas, Earthquake Sequence
• Implementation and Performance: Locality-Sensitive Hashing for Earthquake Detection: A Case Study Scaling Data-Driven Science