The exponent b of the log-linear frequency-magnitude relation for natural seismicity commonly takes values that are statistically indistinguishable from b=1.  There are some exceptions, notably with respect to focal mechanism and for volcanic and induced seismicity, but it is possible these could be explained at least in part by variability in the dynamic range of measurements between the minimum magnitude of complete reporting and the maximum magnitude, especially where the dynamic range of the statistical sample is small.  However, in laboratory experiments and in discrete element simulations a wide range of b-values for acoustic emissions are consistent (after accounting for systematic differences in the transducer response) with systematic variations in the range  as the stress intensity factor increases from its minimum to its maximum, critical value.  The question remains: why is  an attractor stationary state for large-scale seismicity?  Previous attempts to answer this question have relied on a simple geometric ‘tiling’ argument that is inconsistent with the spatial distribution of earthquake locations, or a hierarchical ‘triple-junction’ model that has not been validated by observation.  Here, we derive a closed analytical solution for the maximum entropy -value, conditional on the assumption that earthquake magnitude scales linearly with the logarithm of rupture area. In the limit of infinite dynamic range, the solution is .  The maximum entropy -value converges to this value asymptotically from above as dynamic range increases for large systems at steady state.  This is in contrast to a previous maximum entropy solution based on analysing the spectrum in ‘natural time’ of earthquake catalogues, where larger samples with greater dynamic range lead to a divergence from .  The new theory is consistent with the trend in b-value convergence from above towards an asymptotic limit of in b=1.027±0.015 at 95% confidence from the global CMT earthquake frequency-moment catalogue for data since 1990.

How to cite: Main, I. and Geffers, G.-M.: Why is  b=1?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6037, https://doi.org/10.5194/egusphere-egu24-6037, 2024.

Earthquake catalogues, vital for understanding earthquake dynamics, often grapple with incompleteness across varying time scales. Our research pioneers an innovative strategy to seamlessly integrate time-varying incompleteness into the Epidemic-Type Aftershock Sequence (ETAS) model. Leveraging the Bayesian prowess of inlabru package in R programming language, which is based on the Integrated Nested Laplace Approximation (INLA) method, we not only capture uncertainties but also forge a robust bridge between short-term to long-term gaps in records of earthquakes.

Our methodology, a fusion of the ETAS model and inlabru, provides a comprehensive framework that adapts to diverse scales of incompleteness. We address the complex nature of seismic patterns by considering both short-term gaps in early aftershocks (minutes to a few days) and long-term irregularities (years to centuries) in historical earthquake data records. Technically, the short-term incompleteness period arises from seismic network saturation during periods of high activity, resulting in the underrecording of small events, while the long-term incompleteness originates from sparse network coverage and inability to detect events over extended time. Bayesian foundation of inlabru enriches the model with posterior distributions, empowering us to navigate uncertainties and refine seismic hazard assessments. By utilising a combination of simulated synthetic data and real earthquake catalogues, our results showcase the impact of this approach on the ETAS model, markedly improving its predictive accuracy across various temporal scales of incompleteness.

In this study, we present an initiative in seismicity modelling that bridges temporal gaps, allowing the ETAS model to evolve with the ever-changing landscape of earthquake data incompleteness. This research not only enriches our understanding of spatiotemporal seismicity patterns but also lays the groundwork for more resilient and adaptive aftershock forecasting, ultimately equipping decision-makers with more reliable information about seismic hazards, and enhancing community resilience in the face of earthquakes.

How to cite: Kamranzad, F., Naylor, M., and Lindgren, F.: Bridging time scales for comprehensive ETAS modelling to accommodate short-term to long-term incompleteness of seismicity catalogues, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-401, https://doi.org/10.5194/egusphere-egu24-401, 2024.

Current models used for earthquake forecasting assume that the magnitude of an earthquake is independent of past earthquakes, i.e., the earthquake magnitudes are uncorrelated. Nevertheless, several studies have challenged this assumption by revealing correlations between the magnitude of subsequent earthquakes in a sequence. These findings could significantly improve earthquake forecasting and help in understanding the physics of the nucleation process.

We investigate this phenomenon for the foreshock sequence of the first 2019 Ridgecrest event (M_w6.4) using a high-resolution catalog; choosing this foreshock sequence has been guided by a low b-value (~0.68 ± 0.06 after converting local magnitudes to moment magnitudes) and a significant magnitude correlation, even when considering only earthquakes above the completeness level estimated with different methods. To disregard incomplete events in the b-value estimation, we apply the b-positive approach (van der Elst 2021), i.e., using only positive magnitude differences; those magnitude differences are uncorrelated and we obtain a markedly higher b-value (~0.9 ± 0.1). Apparently, the foreshock sequence contained substantial short-term aftershock incompleteness due to a M_w4.0 event.

We observe a similar behaviour for whole Southern California after stacking earthquake sequences. Finally, we generate synthetic catalogs and apply short-term incompleteness to demonstrate that common methods for estimating the completeness level still result in magnitude correlation, indicating hidden incompleteness.

Our findings highlight that (i) existing methods for estimating the completeness level have limited statistical power and the remaining incompleteness can significantly bias the b-value estimation; (ii) the magnitude correlation is the most powerful property to detect incompleteness, so it should supplement statistical analyses of earthquake catalogs.

How to cite: Corrado, P., Herrmann, M., and Marzocchi, W.: Magnitude correlation exposes hidden short-term earthquake catalog incompleteness, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4379, https://doi.org/10.5194/egusphere-egu24-4379, 2024.

Exploring the potential relationship between an earthquake’s onset and its final moment magnitude (M_w) is a fundamental question in earthquake physics. This has practical implications, as rapid and accurate magnitude estimation is essential for effective early warning systems.

This study employs a novel approach of a hybrid Convolutional Neural Network (CNN) - Recurrent Neural Network (RNN) models to estimate moment magnitude from just the first two seconds of source time functions (STFs), which is significantly shorter than the entire source duration. We use STFs of large earthquakes from the SCARDEC database, which applies a deconvolution method on teleseismic body waves, considering only events with a M_w > 7 and an initial STF value smaller than 10¹⁷ Nm/s to avoid potential bias. Additionally, we incorporate STFs from physics-based numerical simulations of earthquake cycles on nonplanar faults, varying in roughness levels and fault lengths. These simulations exhibit substantial variability in earthquake magnitude and slip behavior between events. The reported methodology uses the information contained in the initial characteristics of the STF, its temporal derivative, and the associated seismic moment, capturing the valuable insights present in the initial energy release about the final moment magnitude.

For the simulated data, the CNN-RNN model demonstrates a good correlation between the initial 2 seconds of the STF and the final event magnitude. Correlation coefficients close to 0.8 and root mean squared errors (RMSE) around 0.25 for magnitudes between 5 and 7.5 showcase the model’s ability to learn and generalize effectively from diverse earthquake scenarios. While results for natural earthquakes from the SCARDEC database remain promising (RMSE of 0.27), the correlation coefficient is lower (0.31), suggesting a weaker relationship than simulated data. This discrepancy might be attributed to the narrower band of magnitudes (7 to 7.5) within SCARDEC data used here, potentially limiting the model’s ability to discern subtle variations and establish a stronger correlation. Further, as an earthquake's fractional duration, 2 sec/source duration, increases, the model's error consistently decreases as expected. Finally, most predictions fall within a narrow range of 1% error, and nearly 90% of samples across diverse durations satisfy a set 5% error threshold. This consistent performance of the hybrid CNN-RNN model across varying source durations, magnitude ranges, and fault characteristics underscores the model's adaptability and robustness in handling diverse earthquake scenarios. While we mostly use here STFs from simulated earthquakes, continuous learning and refinement against reliable and diverse STFs obtained from teleseismic data, when available, are key to enhancing the potential of these CNN-RNN models for a better understanding of the onset-magnitude correlation in natural earthquakes.

How to cite: Rodda, G. K. and Tal, Y.: Analyzing Earthquake's Onset-Magnitude Correlation Using Machine Learning and Simulated and Seismic Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7459, https://doi.org/10.5194/egusphere-egu24-7459, 2024.

Seismic swarms are characterized by intense seismic activity strongly clustered in time and space and without the occurrence of a major event that can be considered as the mainshock. Such intense seismic activity is most commonly associated with external aseismic factors, as pore-fluid pressure diffusion, aseismic creep, or magmatic intrusion that can perturb the regional stresses locally triggering the observed seismicity. These factors can control the spatiotemporal evolution of seismic swarms, frequently exhibiting spatial expansion and migration of event hypocenters with time. This phenomenon, termed as earthquake diffusion, can be highly anisotropic and complex, with earthquakes occurring preferentially along fractures and zones of weakness within the heterogeneous crust, presenting anisotropic diffusivities that may locally vary over several orders of magnitude. The efficient modelling of the complex spatiotemporal evolution of seismic swarms, thus, represents a major challenge. Herein, we develop a stochastic framework based on the well-established Continuous Time Random Walk (CTRW) model, to map the spatiotemporal evolution of seismic swarms. Within this context, earthquake occurrence is considered as a point-process in space and time, with jump lengths and waiting times between successive earthquakes drawn from a joint probability density function. The spatiotemporal evolution of seismicity is then described with an appropriate master equation and the time-fractional diffusion equation (TFDE). The applicability of the model is demonstrated in the 2014 Long Valley Caldera (California) seismic swarm, which has been associated with a pore-fluid pressure triggering mechanism. Statistical analysis of the seismic swarm in the light of the CTRW model shows that the mean squared distance of event hypocenters grows slowly with time, with a diffusion exponent much lower than unity, as well as a broad waiting times distribution with asymptotic power law behavior. Such properties are intrinsic characteristics of anomalous earthquake diffusion and particularly subdiffusion. Furthermore, the asymptotic solution of the TFDE can successfully capture the main features of earthquake progression in time and space, showing a peak of event concentration close to the initial source of the stress perturbation and a stretched relaxation of seismicity with distance. Overall, the results demonstrate that the CTRW model and the TFDE can efficiently be used to decipher the complex spatiotemporal evolution of seismic swarms.

Acknowledgements

The research project was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “2nd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers” (Project Number: 00256). 

How to cite: Michas, G. and Vallianatos, F.: Spatiotemporal Evolution of Seismic Swarms in the light of the Continuous Time Random Walk Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9184, https://doi.org/10.5194/egusphere-egu24-9184, 2024.

Seismic activity clusters in space and time due to stress accumulation and static and dynamic triggering. Therefore, both moderate and large magnitude events can be preceded by smaller events and also seismic swarms can occur without being succeeded by major shocks – which represents the vast majority of cases.

Unveiling if seismic activity can forewarn mainshocks, being somewhat distinguished by swarms, is an issue of crucial importance for the development of short-term seismic hazard. The analysis of thousand clusters of seismicity before mainshocks in Southern California and Italy highlights that the surface over which selected seismic activity spreads is positively correlated with the magnitude of the impending mainshock, as well as the cumulative seismic moment, the number of earthquakes, the variance of magnitude and its entropy, while no significant difference is observed in the duration, seismic rate, and trends of magnitudes and interevent times between foreshocks and swarms. Our interpretation is that crustal volumes and fault interfaces host more and more correlated seismicity as they become unstable, and some properties of seismic clusters may mark their state of stability. For this reason, large mainshocks tend to occur in more extended correlated regions and because of the scaling of maximum magnitudes with the size of unstable faults. Considering this, the recording of more numerous and energetic cluster activity before mainshocks than during swarms is also reasonable.

In recent years, our ability to track seismic clusters has improved outstandingly, so that their structural and statistical characterization can be performed almost in real time. Therefore, it may be possible to compare the current features of the active seismic cluster with the cumulative distribution functions of past seismicity. However, we would like to stress that foreshocks should not be considered as precursors in the sense that neither they forewarn mainshocks, nor they are physically different from swarms: the precursor is not in seismic activity itself, but in the development of mechanical instability within crustal volumes.

How to cite: Zaccagnino, D., Vallianatos, F., Michas, G., Telesca, L., and Doglioni, C.: What do seismic clusters tell us about fault stability?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2615, https://doi.org/10.5194/egusphere-egu24-2615, 2024.

Tectonic earthquake swarms exhibit a distinct temporal and spatial pattern compared to mainshock-aftershock sequences. Unlike the latter ones, where the earthquake sequence typically starts with the largest earthquake that triggers an Omori-Utsu temporal decay of aftershocks, earthquake swarms show a unique increase in seismic activity without a clear mainshock. The largest earthquake(s) in a swarm sequence often occur(s) later, and the sequence consists of multiple earthquake bursts showing spatial migration. This erratic clustering behavior of earthquake swarms arises from the interplay between the long-term accumulation of tectonic elastic strain and short-term transient forces. Detecting and investigating earthquake swarms challenges the community and ideally requires an unsupervised approach, which has led in recent decades to the emergence of numerous algorithms for earthquake swarm identification.

In a comprehensive review of commonly used techniques for detecting earthquake clusters, we applied a blend of declustering algorithms and machine learning clustering techniques to synthetic earthquake catalogs produced with a state-of-the-art ETAS model, with a time-dependent background rate mimicking realistic swarm-like sequences. This approach enabled the identification of boundaries in the statistical parameters commonly used to distinguish earthquake cluster types, i.e., mainshock-aftershock clusters versus earthquake swarms. The results obtained from synthetic data helped to have a more accurate classification of seismicity clusters in real earthquake catalogs, as it is the case for the 2010-2014 Pollino Range (Italy) seismic sequence, the Húsavík-Flatey transform fault seismicity (Iceland), and the regional catalog of Utah (USA). However, the classification obtained through automated application of these findings to real cases depends on the clustering algorithm utilized, the statistical completeness of catalogs, the spatial and temporal distribution of earthquakes, and benefits of a posteriori manual inspection. Nevertheless, the systematic assessment and comparison of commonly used methods - benchmarked in this work to synthetics catalogs and real seismicity – allows the community to have clear and thorough guidelines to identify swarm-like seismicity.

How to cite: Passarelli, L., Cesca, S., Mizrahi, L., and Petersen, G.: Detect and characterize swarm-like seismicity, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15724, https://doi.org/10.5194/egusphere-egu24-15724, 2024.

From October to December 2019, the provinces of Cotabato and Davao del Sur in the Philippines experienced an earthquake sequence that involved five M~6 (Mw 6.4, 6.6, 5.9, 6.5, and 6.7) inland earthquakes. A deep-neural network-based phase picker, PhaseNet, was used to obtain the seismic phases of earthquake waveforms of stations within 200 km from the area of the events for 80 days from October 16 to December 31, 2019. The acquired seismic phases were initially associated and located using the Rapid Earthquake Association and Location (REAL). Subsequently, the initial hypocenter locations were adjusted through relocation utilizing VELEST, with further refinement achieved through a relative relocation technique hypoDD. By employing these methodologies, we successfully created an earthquake catalog that contains ~5,000 earthquakes for the corresponding period. The number of determined earthquakes through this method surpassed the ~3,000 event count reported in the original catalog by DOST-PHIVOLCS which depended solely on manually selected seismic phases. The spatial distribution of the relocated hypocenters reveals two seismic alignments: one trending in the SW-NE direction, parallel to the existing mapped active faults, and the other in the NW-SE direction. These lineaments intersect near the location of the Mw6.4 event, suggesting the presence of a conjugate fault or cross fault. The created earthquake catalog illuminates the spatial and temporal evolution of seismicity following each significant event, offering insights into the detailed patterns that characterize the clustering of aftershocks.

How to cite: Sawi, P., Kita, S., and Bürgmann, R.: Machine-Learning-based Relocation Analysis: Revealing the Spatiotemporal Changes in the 2019 Cotabato and Davao del Sur Earthquakes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3482, https://doi.org/10.5194/egusphere-egu24-3482, 2024.

We present a method for automatically identifying segmented fault surfaces through the clustering of earthquake hypocenters without prior information. Our approach integrates density-based clustering algorithms (DBSCAN and OPTICS) with principal component analysis (PCA). Using the spatial distribution of earthquake hypocenters, DBSCAN detects primary clusters, which represent areas with the highest density of connected seismic events. Within each primary cluster, OPTICS identifies nested higher-order clusters, providing information on their quantity and size. PCA analysis is then applied to the primary and higher-order clusters to assess eigenvalues, enabling the differentiation of seismicity associated with planar features and distributed seismicity that remains uncategorized. The identified planes are subsequently characterized in terms of their location and orientation in space, as well as their length and height. By applying PCA analysis before and after OPTICS, a planar feature derived from a primary cluster can be interpreted as a fault surface, while planes derived from high-order clusters can be interpreted as fault segments within the fault surface. The consistency between the orientation of illuminated fault surfaces and fault segments, and that of the nodal planes of earthquake focal mechanisms calculated along the same faults, supports this interpretation. We show applications of the method to earthquake hypocenter distributions from various seismically active areas (Italy, Taiwan, California) associated with faults exhibiting diverse kinematics.

How to cite: Piegari, E., Camanni, G., Mercurio, M., and Marzocchi, W.: A Machine Learning-based Method for Identifying Segmented Fault Surfaces Through Hypocenter Clustering, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8786, https://doi.org/10.5194/egusphere-egu24-8786, 2024.

Earthquake catalog is a key element in seism hazards. However, it may be contaminated by non-natural earthquake sources. Hence,
This study aims to discriminate natural and non-natural earthquakes through machine learning techniques and spatio-temporal distribution of the events. 
First, we propose a Convolutional Neural Network based on spectrograms to perform the waveform classification. It is targeted to applications in Madagascar. The approach consists of three main steps: (1) generation of the time–frequency representation of ground-motion recordings (spectrogram); (2) training and validation of the model using spectrograms of ground shaking; (3) testing and prediction. To measure the compatibility between output predictions and given ground truth labels, we adopt the commonly used loss function and accuracy measure. Given that the spatial distribution of the seismic data in Madagascar is non-uniform, we perform two-step analyses. First, we adopt a supervised approach for 6051 known events in the central part of Madagascar. Then, we use the outcome for the second step of training and perform the prediction for non-categorized events throughout the country. The results show that our model has the potential to separate earthquakes from mining-related events. For the supervised approach, among the 20% used for testing, 97.48% and 2.52% of the events give correct and incorrect labels, respectively. These pre-trained data are subsequently used to perform predictions for unlabeled events throughout Madagascar. Our results show that the model could learn the features of the classes even for data coming from different parts of Madagascar.
From the analyses of the spatio-temporal patterns of seismicity, we also found evidence of induced earthquakes associated with the heavy-oil exploration in Tsimiroro, Madagascar with an increase in the rate of earthquake occurrence in 2022.

How to cite: Razafindrakoto, H. N. T. and Rakotoarisoa, A. T.: Identification of Earthquakes and Anthropogenic Events in Madagascar, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20167, https://doi.org/10.5194/egusphere-egu24-20167, 2024.

Strong earthquakes are followed by countless smaller events, whose number decays with time: a posteriori, we call them aftershocks. Sometimes, this sequence is interrupted by a larger event, and the “aftershocks” turn out to be foreshocks. In 2019, Gulia and Wiemer have proposed traffic light tool, named the Foreshock Traffic Light System (FTLS), that can discriminate between foreshocks and aftershocks, by monitoring the size distribution of events closely. The model successfully passed the first near real-time test (Gulia et al., 2020). A new version of the code, that can run in real-time, has been recently developed; since testing is the essence of the scientific method and is fundamentally important in seismicity forecast evaluation, we here show the performance of the new version of the FTLS through pseudo-prospective and, when possible, real-time tests on the available seismic sequences between 2016 and 2024.

How to cite: Gulia, L., Wiemer, S., Biondini, E., Enescu, B., and Vannucci, G.: The performance of the Foreshock Traffic Light System for the period 2016-2024, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6097, https://doi.org/10.5194/egusphere-egu24-6097, 2024.

The use of machine learning (ML) methods for earthquake forecasting has recently emerged as a promising avenue, with several recent publications exploring the application of neural point processes. Such models, in contrast to those currently applied in practice, offer the flexibility to incorporate additional datasets alongside earthquake catalogs, indicating potential for enhanced earthquake forecasting capabilities in the future. However, with a forecasting performance that currently remains similar to that of the agreed-upon benchmark, the Epidemic-Type Aftershock Sequence (ETAS) model, the black-box nature of ML models poses a challenge in communicating forecasts to lay audiences. The ETAS model has stood the test of time and is relatively simple and comprehensively understood, with few empirically derived laws describing aftershock triggering behavior. A main drawback of ETAS is its reliance on large numbers of simulations of possible evolutions of ongoing earthquake sequences, which is typically associated with long computation times or resources required for parallelization.

In this study, we propose a deep learning approach to emulate the output of the well-established ETAS model, bridging the gap between traditional methodologies and the potential advantages offered by machine learning. By focusing on modeling the temporal behavior of higher-order aftershocks, our approach aims to combine the interpretability of the ETAS model with the computational efficiency intrinsic to deep learning.

Evaluated using commonly applied metrics of both the ML and earthquake forecasting communities, our approach and the traditional, simulation-based approach are shown to 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.

How to cite: Mizrahi, L. and Jozinović, D.: Deep Learning for Higher-Order Aftershock Forecasting in Near-Real-Time, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9366, https://doi.org/10.5194/egusphere-egu24-9366, 2024.

With earthquake disasters inflicting immense devastation worldwide, advancing reliable prediction models utilizing diverse data paradigms offers new perspectives to unlock practicable prediction solutions. As reliable earthquake forecasting remains a grand challenge amidst complex fault dynamics, we employ combined finite-discrete element method (FDEM) simulations to generate abundant laboratory earthquake data. We propose a multimodal features fusion model that integrates temporal sensor data and wavelet-transformed visual kinetic energy to predict laboratory earthquakes. Comprehensive experiments under varied stress conditions confirm the superior prediction capability over single modal approaches by accurately capturing stick slip events and patterns. Furthermore, efficient adaptation to new experiments is achieved through fine-tuning of a lightweight adapter module, enabling generalization. We present a novel framework leveraging multimodal features and transfer learning for advancing physics-based, data-driven laboratory earthquake prediction. As increasing multi-source monitoring data becomes available, the established modeling strategies introduced here will facilitate the development of reliable real world earthquake analysis systems.

How to cite: Gao, K.: Laboratory earthquake prediction via multimodal features, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3745, https://doi.org/10.5194/egusphere-egu24-3745, 2024.

In our recent study, we have developed an ETAS-based (Epidemic-Type Aftershock Sequence; Ogata, 1988) time-dependent earthquake forecasting model for Europe. Aside from inverting a basic set of parameters describing aftershock behaviour on a highly heterogeneous dataset, we have proposed several model variants, focusing on implementing the knowledge about spatial variations in the background rate inferred by ESHM20 already during the inversion of ETAS parameters, fixing the term dictating the productivity law to specific values to balance the more productive triggering by high-magnitude events (productivity law) with their much rarer occurrence (GR law), and using the b-positive method for the estimation of the b-value.

When testing the model variants, we apply the commonly used approach of performing retrospective tests on each model to check for self-consistency over long time periods and pseudo-prospective tests for comparison of models on one-day forecasting periods during seven years. While such pseudo-prospective tests reveal that some models indeed outperform others, for other model pairs, no significant performance difference was detected.

Here, we investigate in more detail the conditions under which performance differences of two competing models can be detected with statistical significance. Using synthetic tests, we investigate the effects of a catalog’s size and the magnitude range it covers on the significance of model performance difference. This will provide insight into whether recording many small events can, in this sense, replace having a large enough dataset of higher-magnitude events. Furthermore, due to the underrepresentation (or absence) of high-magnitude earthquakes in both training and testing data, both the models and tests are prone to overfitting to small events, potentially resulting in forecasts that underestimate both productivity of sequences with a high-magnitude main event and probabilities that a larger earthquake will follow such an event. We focus on defining metrics that highlight these properties as they are often of interest when applying time-dependent forecasting models to issuing operational earthquake forecasts.

How to cite: Han, M., Mizrahi, L., and Wiemer, S.: The Effect of Data Limitations on Earthquake Forecasting Model Selection, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17967, https://doi.org/10.5194/egusphere-egu24-17967, 2024.