The European-Mediterranean Seismological Centre (EMSC) is a non-profit scientific organization aiming at establishing and operating a rapid earthquake detection system globally and in particular in the European and Mediterranean regions as well as facilitating exchange between seismological institutes. The EMSC has been a pioneer in citizen seismology by collecting in-situ information on the earthquake impact directly from the witnesses.
The EMSC has been collecting citizen intensity felt reports at a global scale for many years via two channels: its websites and its “LastQuake” smartphone application. These felt reports are collected through a set of 12 cartoons representing the 12 levels of the European Macroseismic Scale (Grünthal, 1998). They provide rapid information on how the earthquake’s impact is felt by the local population. The EMSC felt reports were shown to be consistent with the USGS Did You Feel It? (Wald et al., 2011) responses and with manually derived macroseismic datasets (Bossu et al., 2017). Such felt reports are provided for a set of 36 earthquakes, each tagged with a unique ID number. They are only considered for intensity values of up to 10, since higher values are unrealistic.
Additionally, an interactive map of the aftershocks distribution is provided for each earthquake. These aftershocks are selected from the EMSC catalogue in the 14 days after the event and within 500km of the epicentre location. On each map, the beachball representing the two nodal planes as given by the Global Centroid Moment Tensor catalogue (Dziewonski et al., 1981; Ekstrom et al., 2012) is displayed at the epicentre location.
For each event (identified by unique id number), the first line indicates catalogue information on the earthquake (event_id, region, origin_time (UTC), latitude, longitude, depth, magnitude, strike angle from GCMT) Each following line is a felt report gathered by the EMSC including, the longitude, latitude, reported intensity and report time.
The REHEATFUNQ Python package helps to work with the (residual) scatter of surface heat flow even in small regions. REHEATFUNQ uses a stochastic model for regional aggregate heat flow distributions (RAHFD), that is, the collected set of heat flow measurements within a region marginalized to the heat flow dimension. The stochastic model is used in a Bayesian analysis that
(1) yields a posterior estimate of the RAHFD which captures the range of heat flow within the analysis region, and
(2) quantifies the magnitude of a surface heat flow anomaly within the region, for instance through the generating frictional power.
The stochastic model underlying REHEATFUNQ views heat flow data, uniformly sampled across the region of interest, as a random variable. A gamma distribution is used as a model for this random variable and information from the global data set of Lucazeau (2019) is introduced by means of a conjugate prior (Miller, 1980). The detailed science behind the model is described in Ziebarth et al. (202X).
The analysis by Ziebarth et al. (202X) can be reproduced through the Jupyter notebooks contained in the subdirectory “jupyter/REHEATFUNQ/”. The location specified in the map below covers the region to which REHEAFUNQ is applied in this analysis.
REHEATFUNQ is a Python package that uses a compiled Cython/C++ backend. Compiling REHEATFUNQ requires the Meson build system and a number of scientific libraries and Python packages (and their dependencies) that are listed in the documentation.
A Docker image “reheatfunq” is provided as an alternative means of installation. The Docker image comes in two flavors, specified in “Dockerfile” and “Dockerfile-stable”. The former is based on the current “python:slim” image and downloads further dependencies through the Debian package manager, leading to a short image generation time. The latter bootstraps the REHEATFUNQ dependencies from source, aiming to create a reproducible model. To do so, “Dockerfile-stable” depends on the sources contained in “vendor-1.3.3.tar.xz”. If you plan to build the stable image, download both “REHEATFUNQ-1.3.3.tar.gz” and “vendor-1.3.3.tar.xz”, and see the README contained in the latter. Later versions of the “REHEATFUNQ” archive are compatible with the latest “vendor” archive.
A quickstart introduction and the API documentation can be found in the linked documentation.
This archive contains datasets pertaining to the article "Crowdsourcing triggers rapid, reliable earthquake locations" by Steed et al. (2018). There is a dataset containing the European-Mediterranean Seismological Centre's detections of seismological events via crowdsourced methods (i.e. monitoring of internet traffic on the site www.emsc-csem.org, usage of the EMSC app LastQuake or monitoring of tweets containing earthquake related words). This dataset covers the years 2016 and 2017 and contains 2590 detections. The other dataset contains the raw results from testing the CsLoc system (Crowdseeded seismic Location) on the historical data of 2016 and 2017; this system is described in the article for which this dataset is supplemental material. This dataset was used for the creation of the results presented in the article. The archive contains more detailed descriptions of the datasets, which are stored in csv files, including the definition of column heads (*_dataset_description.csv).
List of files:
2018-068_Steed-et-al_README.txt
crowdsourced_detections_dataset.csv
crowdsourced_detections_dataset_descriptions.csv
crowdsourced_detections_auditting.txt
CsLoc_publication_dataset.csv
CsLoc_publication_dataset_descriptions.csv