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.
BEAT is an open-source software tool for the robust characterization of the temporal and spatial evolution of earthquake rupture processes. It uses kinematic rupture models that include low-parametric models like Moment Tensors but also complex high-parametric, finite-extent sources. In other words, BEAT allows studying earthquakes on a first-order level as points with location, size and mechanisms. In consecutive steps, the complexity of the source model may be increased by various details up to the potential to resolve rupture dimension, fault segmentation, slip-distribution and slip-history. The source model parameters and their uncertainties are estimated based on seismic waveforms, and/or geodetic observations like InSAR and GNSS data. Rapid forward modeling is enabled by using pre-computed Green's function databases, handled through the Pyrocko software library. Based on these, synthetic data are provided for arbitrary earthquake rupture models embedded in heterogeneous media. For an extensive exploration of the often high-dimensional model parameter space, BEAT offers a suite of sampling algorithms for high-standard Bayesian inference. The implementations of these sampling algorithms exploit the parallel architecture of modern computers for optimal performance. Finally, BEAT offers easy configuration and automatic visualization of relevant results. The software relies on functionality from PYROCKO (Heimann et al., 2017) and KITE (optionally, Isken et al., 2017).