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phasehunter

https://github.com/crimeacs/phasehunter

Science Score: 44.0%

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Repository

Basic Info
  • Host: GitHub
  • Owner: crimeacs
  • License: gpl-3.0
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 401 KB
Statistics
  • Stars: 9
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created over 2 years ago · Last pushed about 2 years ago
Metadata Files
Readme License Citation

README.md

PhaseHunter: Quickstart Guide

PhaseHunter

Open In Colab

Talk to PhaseHunter

Before you begin, we suggest you to talk to PhaseHunter - a friendly assistant that can answer any of your questions about this code as well as write code snippets. E.g. try asking it: text How to detect earthquakes in the radius of 1 mile from 35N -117S on 2019-05-01 from 2 am to 4 am.

Talk to PhaseHunter

How to install?

 1. Install Anaconda or Miniconda: If you haven't installed Anaconda or Miniconda, download and install it from [Anaconda's official website](https://www.anaconda.com/download) or [Miniconda's official website](https://docs.conda.io/projects/miniconda/en/latest/) respectively. 2. Create a New Environment: Open a terminal or Anaconda prompt and run the following command to create a new environment named phasehunter (you can choose a different name if you want): ```bash conda create --name phasehunter python=3.10 ``` 3. Activate the New Environment: ```bash conda activate phasehunter ``` 4. Install PyTorch and torchvision: Based on your system and CUDA version, install PyTorch and torchvision. Instructions can be found on the [PyTorch official site](https://pytorch.org/get-started/locally/). For example, for Linux use: ```bash conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia ``` 5. Install Required Libraries: ```bash conda install numpy pandas scikit-learn scipy tqdm pip install obspy pytorch-lightning lightning wandb pip install git+https://github.com/nikitadurasov/masksembles ``` 6. Install PhaseHunter ```bash pip install git+https://github.com/crimeacs/PhaseHunter ``` 7. Final Notes: * Remember to activate your environment (`conda activate phasehunter`) every time you work on this project. * It's a good idea to periodically update the packages to get the latest bug fixes and improvements. * If you encounter any compatibility issues or errors during installation, they might be due to version conflicts. In that case, you'll need to identify the versions that are compatible and specify them during installation. You should now have a working conda environment with the necessary packages for your project!

How to use PhaseHunter

PhaseHunter is designed to be straightforward to use, even for those new to seismic data processing. Here's a step-by-step guide:

  1. Load the Pre-trained Model: First, import PhaseHunter python import torch from phasehunter.model import PhaseHunter

Then load the pre-trained PhaseHunter model from a checkpoint (if on Linux):

```python

Auto select device, note that PhaseHunter works best on GPU

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model = PhaseHunter.loadfromcheckpoint('ckpts/30sSTEADdecay-epoch=196.ckpt') model = model.eval().to(device) ```

If on Mac, please use: ```python device = torch.device("mps") model = PhaseHunter.load_from_checkpoint('ckpts/30s_STEAD_decay-epoch=196.ckpt') model = model.eval().float().to(device) ```

At this time, 2 pre-trained models are available:

  • ckpts/30s_STEAD_decay-epoch=196.ckpt - 30s version of PhaseHunter trained on STEAD dataset for 200 epochs
  • ckpts/30s_ETHZ_decay-epoch=187.ckpt - 30s version of PhaseHunter trained on ETHZ dataset for 200 epochs

Please download them from HuggingFace Hub

  1. Download or use your own waveform data, e.g.:

python from obspy import read st = read('path_to_your_waveform_data') predictions = model.process_continuous_waveform(st) print(predictions)

process_continuous_waveform works with 3 channel Obspy streams of any length

For a more comprehansive tutorial try PhaseHunter in Google Colab

Open In Colab

How to train PhaseHunter

For training your own version of PhaseHunter on e.g. STEAD dataset, please follow following steps:

  1. Download data of your interest (e.g. from SeisBench)
  2. Convert it to the format required by PhaseHunter using training/convert_STEAD.py script
  3. Train on converted data using training/train_STEAD.py script

Evaluation

We provide evaluation pipeline used for the JGR paper in a separate notebook notebooks/PhaseHunter_EVAL.ipynb

Contact

Send me an email if you tryied Google Colab and already talked with a Bot and still need help: anovosel@stanford.edu

Owner

  • Name: Artemii Novoselov
  • Login: crimeacs
  • Kind: user
  • Location: Stanford
  • Company: Stanford

Postdoctoral Researcher @ Stanford University

Citation (CITATION.cff) 

cff-version: 1.1.0
message: "If you use this software, please cite it as below."
authors:
- family-names: Artemii
  given-names: Novoselov
orcid: https://orcid.org/0000-0002-4079-0807
title: PhaseHunter - deep learning tool for onset phase time picking with uncertainties
version: v0.2
date-released: 2023-10-13

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