Seismo-acoustic observations and video records of a lahar at Volcán de Fuego, Guatemala

This repository contains an open-access dense seismo-acoustic dataset acquired at Fuego volcano, Guatemala, along the Ceniza channel between 10 and 21 October 2025. The experiment was designed to document both volcanic activity and lahar occurrence within a single integrated monitoring deployment, combining continuous geophysical recordings with time-synchronized visual observations. The dataset includes seismic and acoustic waveform data from a dense linear network of geophones, with co-located microphones at selected stations, together with recordings from a small-aperture infrasound array. Instrument response metadata are provided to enable conversion of raw digital counts to physical units. In addition to the geophysical data, the repository contains synchronized visual observations acquired at two locations along the Ceniza channel. These include two timestamped 4K MP4 videos recorded at 25 fps in the upper channel at 14.43161° N, 90.92482° W, capturing lahar arrival, passage, and subsequent flow evolution, and a timestamped AVI time-lapse video from the lower channel at 14.43120° N, 90.92493° W, assembled from 1080p images acquired every 30 s. Waveform data are stored in standard seismological formats and organized according to the SDS (SeisComP Data Structure) convention, ensuring compatibility with widely used software packages such as ObsPy and SeisComP. The repository also includes analysis tools for reproducible data access, processing, and visualization. A Python Jupyter notebook demonstrates how to read waveform data from the SDS archive, optionally remove the instrument response, generate waveform plots, compute root-mean-square amplitude (RMSA), calculate spectrograms, and export derived products in .csv and .mat format. A complementary MATLAB script is also provided for single-station visualization and time-frequency analysis, including RMSA and spectrogram generation from calibrated waveform data. Processed outputs generated from the Python notebook, such as RMSA time series and spectrogram products, can be stored in dedicated output directories within the repository. The overall structure is intended to make the dataset easy to explore, reproduce, and reuse for different applications in volcano seismology and infrasound research.