updated README file

README.md

@@ -1,3 +1,47 @@
-# DUG Seis
+![](./docs/static/dug_seis_logo.svg)
 
-[Documentation](https://dugseis.readthedocs.io/)
+# Software for processing and visualization of micro-seismic data
+
+The DUGSeis software package was developed to manage, process and visualize continuous, high-frequency seismic data. It can be used to create earthquake catalogs in real-time,
+as well as in post-processing and directly visualize the event waveforms and event locations in a graphical user interface. The software is Python-based therefore, users can easily
+add their own processing routines.
+
+Detailed information can be found in the [Documentation](https://dugseis.readthedocs.io/).
+
+## Installation
+
+The `conda` Python distribution is recommended, but you can use any Python distribution you see fit.
+
+### Installing `conda`
+
+1. Install `miniconda` for your operating system: [https://docs.conda.io/en/latest/miniconda.html](https://docs.conda.io/en/latest/miniconda.html)
+2. Create a new environment:
+
+```bash
+conda create -n dug_seis python=3.11
+conda activate dug_seis
+```
+
+**Make sure the `dug_seis` environment is active when using `DUGSeis` and for
+all the following steps on this page!**
+
+### Install DUGSeis
+
+Clone DUGSeis
+
+```bash
+git clone https://github.com/swiss-seismological-service/DUGseis.git
+```
+
+
+```bash
+cd DUGseis
+conda activate dug_seis
+pip install -e .
+```
+
+% add example dataset on zenodo
+
+## Example
+An example dataset to test the software can be accessed with the [Zenodo DOI](https://doi.org/10.5281/zenodo.10598393).\
+The dataset consists of a python run-file and its associated configuration file and the needed waveform and station xml files. 

paper/paper.md

@@ -51,7 +51,7 @@ bibliography: paper.bib
 Detecting earthquakes and compiling these to earthquake catalogs are fundamental tasks in seismology. Acoustic emission sensors allow detecting tiniest so called picoseismic events representing fractures on mm, cm or dm scale (-6<Magnitude<0). Such picoseismic events have corner frequencies of 1kHz-1MHz and cannot be handled by standard seismic processing softwares that deal with signals <500 Hz. Other commercial software for monitoring picoseismicity for structural health monitoring applications, e.g. in mines exists, but are only trigger-based. For large-scale experiments in underground laboratories (e.g., hydraulic stimulation, earthquake nucleation, nuclear waste disposal) continuous recordings of the seismicity data streams in MHz range are needed to study the rock response in great detail. The DUGseis software package is filling this gap. It was developed to manage, process and visualize continuous, high-frequency seismic data. The package can be used to create earthquake catalogs in real-time, as well as in post-processing, and directly visualize their event waveforms and locations in a graphical interface. Since the software is python based, users can easily add their own processing routines.
 
 # Statement of need
-The open source, python-based DUGseis package is designed to align with the functionalities of SeisComP [@seiscomp], a standard software used in microseismic-large scale earthquake processing (<500Hz, M>-0.5). DUGseis is tailored to picoseismic events (-6<M<0) with much higher frequency ranges (kHz-MHz), as recorded by acoustic emission sensors (AE sensors). High-frequency seismic data processing is common in mining environments to monitor tunnel stability, and became very popular in underground laboratories. Until now, these projects have employed trigger-based recordings, meaning that the incoming waveform data is only saved to disk if a pre-set trigger threshold is reached by a recorded event.  One disadvantage of the triggered recording strategy is the so-called dead time. After an event is triggered, no additional event can be triggered until the processing of the first triggered event is completed. The dead time can be a multiple of the recording time, meaning important events can be missed if another event happened just before. Removing these dead times plays a significant role if high event rates are expected. With DUGseis it is possible to record and store [@DugSeisAcqui] continuous waveform data in the MHz range and directly process the data, removing these dead times.  Being Python-based, DUGseis offers a high flexibility for the researchers to complement the processing with their own Python-based codes, adjusted to the project needs. 
+The open source, Python-based DUGseis package is designed to align with the functionalities of SeisComP [@seiscomp], a standard software used in microseismic-large scale earthquake processing (<500Hz, M>-0.5). DUGseis is tailored to picoseismic events (-6<M<0) with much higher frequency ranges (kHz-MHz), as recorded by acoustic emission sensors (AE sensors). High-frequency seismic data processing is common in mining environments to monitor tunnel stability, and became very popular in underground laboratories. Until now, these projects have employed trigger-based recordings, meaning that the incoming waveform data is only saved to disk if a pre-set trigger threshold is reached by a recorded event.  One disadvantage of the triggered recording strategy is the so-called dead time. After an event is triggered, no additional event can be triggered until the processing of the first triggered event is completed. The dead time can be a multiple of the recording time, meaning important events can be missed if another event happened just before. Removing these dead times plays a significant role if high event rates are expected. With DUGseis it is possible to record and store [@DugSeisAcqui] continuous waveform data in the MHz range and directly process the data, removing these dead times.  Being Python-based, DUGseis offers a high flexibility for the researchers to complement the processing with their own Python-based codes, adjusted to the project needs. 
 
 # Functionality and Features
 The DUGseis software is a Python-based package with the main focus to process continuous high-frequency data, extract picoseismic event waveforms and create an earthquake catalog. To make its usage and its outputs more easily accessible for seismologists, some features use modules and functions of the ObsPy package [@Beyreuther2010], a popular package in seismology.\