arXiv subjects

Leila Mizrahi

Publications and source records attributed to Leila Mizrahi.

5 recordsLinked to original sources

arXiv2026

SeismoStats: A Python Package for Statistical Seismology

We introduce SeismoStats, a Python package that enables essential statistical seismology analyses. The package provides user-friendly tools to download and manipulate earthquake catalogs, to visualize them, and to estimate the a- and b-value of the Gutenberg-Richter law, or the magnitude of completeness. This comprehensively tested, well-documented, and openly accessible Python package is intended to serve as the foundation of a long-term community effort, continually expanding in functionality through shared contributions. We invite seismologists and developers to contribute ideas and code to support and shape its future development.

physics.geo-ph↗

arXiv2024

Modeling the Asymptotic Behavior of Higher-Order Aftershocks with Deep Learning

Aftershocks of aftershocks - and their aftershock cascades - substantially contribute to the increased seismicity rate and the associated elevated seismic hazard after the occurrence of a large earthquake. Current state-of-the-art earthquake forecasting models therefore describe earthquake occurrence using self-exciting point processes, where events can recursively trigger more events according to empirical laws. To estimate earthquake probabilities within future time horizons of interest, a large number of possible realizations of a process are simulated, which is typically associated with long computation times that increase with the desired resolution of the forecast in space, time, or magnitude range. We here propose a machine learning approach to estimate the temporal evolution of the rate of higher-order aftershocks. For this, we train a deep neural network to predict the output of the simulation-based approach, given a parametric description of the rate of direct aftershocks. A comparison of the two approaches reveals that they perform very similarly in describing synthetic datasets generated with the simulation-based approach. Our method has two major benefits over the traditional approach. It is faster by several orders of magnitude, and it is not susceptible to being influenced by the presence or absence of individual `extreme' realizations of the process, and thus enables accurate earthquake forecasting in near-real-time.

physics.geo-ph↗

arXiv2022

Question-driven ensembles of flexible ETAS models

The development of new earthquake forecasting models is often motivated by one of the following complementary goals: to gain new insights into the governing physics and to produce improved forecasts quantified by objective metrics. Often, one comes at the cost of the other. Here, we propose a question-driven ensemble (QDE) modeling approach to address both goals. We first describe flexible ETAS models in which we relax the assumptions of parametrically defined aftershock productivity and background earthquake rates during model calibration. Instead, both productivity and background rates are calibrated with data such that their variability is optimally represented by the model. Then we consider 64 QDE models in pseudo-prospective forecasting experiments for Southern California and Italy. QDE models are constructed by combining model parameters of different ingredient models, where the rules for how to combine parameters are defined by questions about the future seismicity. The QDE models can be interpreted as models which address different questions with different ingredient models. We find that certain models best address the same issues in both regions, and that QDE models can substantially outperform the standard ETAS and all ingredient models. The best performing QDE model is obtained through the combination of models allowing flexible background seismicity and flexible aftershock productivity, respectively, where the former parameterizes the spatial distribution of background earthquakes and the partitioning of seismicity into background events and aftershocks, and the latter is used to parameterize the spatio-temporal occurrence of aftershocks.

physics.geo-ph↗

arXiv2021

Embracing Data Incompleteness for Better Earthquake Forecasting

We propose two methods to calibrate the parameters of the epidemic-type aftershock sequence (ETAS) model based on expectation maximization (EM) while accounting for temporal variation of catalog completeness. The first method allows for model calibration on long-term earthquake catalogs with temporal variation of the completeness magnitude, $m_c$. This calibration technique is beneficial for long-term probabilistic seismic hazard assessment (PSHA), which is often based on a mixture of instrumental and historical catalogs. The second method generalizes the concept of $m_c$, considering rate- and magnitude-dependent detection probability, and allows for self-consistent estimation of ETAS parameters and high-frequency detection incompleteness. With this approach, we aim to address the potential biases in parameter calibration due to short-term aftershock incompleteness, embracing incompleteness instead of avoiding it. Using synthetic tests, we show that both methods can accurately invert the parameters of simulated catalogs. We then use them to estimate ETAS parameters for California using the earthquake catalog since 1932. To explore how model calibration, inclusion of small events, and accounting for short-term incompleteness affect earthquakes' predictability, we systematically compare variants of ETAS models based on the second approach in pseudo-prospective forecasting experiments for California. Our proposed model significantly outperforms the ETAS null model, with decreasing information gain for increasing target magnitude threshold. We find that the ability to include small earthquakes for simulation of future scenarios is the primary driver of the improvement and that accounting for incompleteness is necessary. Our results have significant implications for our understanding of earthquake interaction mechanisms and the future of seismicity forecasting.

physics.geo-ph↗

arXiv2020

The Effect of Declustering on the Size Distribution of Mainshocks

Declustering aims to divide earthquake catalogs into independent events (mainshocks), and dependent (clustered) events, and is an integral component of many seismicity studies, including seismic hazard assessment. We assess the effect of declustering on the frequency-magnitude distribution of mainshocks. In particular, we examine the dependence of the b-value of declustered catalogs on the choice of declustering approach and algorithm-specific parameters. Using the catalog of earthquakes in California since 1980, we show that the b-value decreases by up to 30% due to declustering with respect to the undeclustered catalog. The extent of the reduction is highly dependent on the declustering method and parameters applied. We then reproduce a similar effect by declustering synthetic earthquake catalogs with known b-value, which have been generated using an Epidemic-Type Aftershock Sequence (ETAS) model. Our analysis suggests that the observed decrease in b-value must, at least partially, arise from the application of the declustering algorithm on the catalog, rather than from differences in the nature of mainshocks versus fore- or aftershocks. We conclude that declustering should be considered as a potential source of bias in seismicity and hazard studies.

physics.geo-ph↗