merph 0.1.12

Bayesian methods for inferring mass eruption rate for column height (or vice versa) for volcanic eruptions

MERPH

Bayesian methods for inferring mass eruption rate from column height (or vice versa) for volcanic eruptions

Introduction

MERPH uses Bayesian methods to explore the relationship between the mass eruption rate (Q) of a volcanic eruption and the height reached by the volcanic eruption column (H) that is produced.

The mass eruption rate is a quantity that is very important in volcanology and in the dispersion of volcanic ash in the atmosphere, but it is very difficult to measure directly.

Often the mass eruption rate is inferred from observations of the height of the volcanic eruption column, since the eruption column is often much easier to measure. The eruption column height is linked to the mass eruption rate through the fluid dynamics of turbulent buoyant plumes, but there are often external volcanological and atmospheric effects that contribute and complicate the relationship.

Datasets of the mass eruption rate and eruption column height have been compiled and used to determine an empirical relationship these quantities, using linear regression. This has then been used to infer the mass eruption rate from the plume height.

MERPH goes further, by using Bayesian methods to perform the regression. Bayesian methods:

• allow us to incorporate a range of uncertainties quantitatively into our model;
• provide a meaningful quantitative comparison of different models.

Main Features

MERPH is a python package containing:

• a data module containing MER--Plume height data sets:

  • Sparks
  • Mastin
  • Aubry
  • IVESPA

• a stats module that performs Bayesian linear regression of the data and posterior prediction

• example jupyter notebooks to illustrate the use

• a command line interface to perform regression and posterior predicition

• a user interface as a dash/plotly web-browser app

Advanced features

in development

• advanced regression methods using pymc

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Install

MERPH can be installed from pypi using pip.

It is recommended to create a virtual environment, e.g. with Anaconda/Miniconda (conda create --name merph "python=3.10"), and install in the environment.

Basic, light-weight set up

The basic modules (with no notebooks or app interface) can be installed with pip install merph

Other features

Install with example notebooks using pip install merph[nb]

Install with the app using pip install merph[app]

Install pymc features using pip install merph[mc]

Full install

Install with all features using pip install merph[full]

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How to use

I recommend looking first at the notebook examples, which show how the MERPH module can be used. After installing, the examples can be launched using the command line interface: merph-example 1 and merph-example 2.

The app can be launched using the command line interface merph-app.

The command merph launches a command line interface for basic use.

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Dependencies

Basic, light-weight set up

• argparse>=1.4.0
• matplotlib>=3.5.1
• Pint>=0.18
• numpy>=1.24.2
• pandas>=1.4.1
• scipy>=1.8.0
• seaborn>=0.11.2
• odfpy>=1.4.1
• openpyxl>=3.0.9
• click<8,>=4.0

Other features

App

• dash>=2.3.0
• dash-bootstrap-components >=1.0.3
• plotly>=5.6.0

Notebooks

• notebook

Advanced features

• pymc>4
• arviz>=0.13.0

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Contacts

Mark J. Woodhouse [e-mail]

Repository

https://bitbucket.org/markwoodhouse/merph