Toward Knowledge Extraction in Classification of Volcano-Seismic Events: Visualizing Hidden States in Recurrent Neural Networks

ACKNOWLEDGMENT The authors would like to thank the Instituto Andaluz de Geofísica for providing us with the Decepction Island dataset and invaluable geophysical insight.Understanding how deep hierarchical models build their knowledge is a key issue in the usage of artificial intelligence to interpret the reality behind data. Depending on the discipline and models used, such knowledge may be represented in ways that are more or less intelligible for humans, limiting further improvements on the performance of the existing models. In order to delve into the characterization and modeling of volcano-seismic signals, this article emphasizes the idea of deciphering what and how recurrent neural networks (RNNs) model, and how this knowledge can be used to improve data interpretation.The key to accomplishing these objectives is both analyzing the hidden state dynamics associated with their hidden units as well as pruning/trimming based on the specialization of neurons. In this article, we process, analyze, and visualize the hidden states activation maps of two RNN architectures when managing different types of volcano-seismic events. As a result, the class-dependent discriminative behavior of most active neurons is analyzed, thereby increasing the comprehension of the detection and classification tasks. Arepresentative dataset fromthe deception island volcano (Antarctica), containing volcano-tectonic earthquakes, long period events, volcanic tremors, and hybrid events, is used to train the models. Experimental analysis shows how neural activity and its associated specialization skills change depending on the architecture chosen and the type of event analyzed.MINECO under Grant PID2019-106260GB-I00 FEMALEFEDER/Junta de Andalucia-Consejería de Economía y Conocimiento/ Proyecto A-TIC-215- UGR18

Estimating the maximum earthquake magnitude in the Iranian Plateau

The Iranian Plateau has been subjected to destructive earthquakes throughout its history. Reliable assessment of the seismic hazard in this earthquake-prone region is therefore essential. Our study focuses on estimating the maximum earthquake magnitude as one of the main parameters of seismic hazard analysis. We implemented two quantitative approaches, namely, probabilistic and deterministic. The probabilistic method allows combining the historical (i.e. incomplete) and the instrumental parts of a catalogue with different levels of completeness and considers the uncertainties in earthquake magnitude determination. In this study, we used a unified, declustered, and complete catalogue of earthquakes in Iran, covering the period from the fourth century BC to 2019. We calculated the maximum possible magnitudes for hundreds of grid points by using the seismicity data in a 200-km radial region around each grid point. The maximum possible earthquake was observed to vary between 6.0 and 8.2, and the highest values were found in the Alborz-Azarbayejan seismotectonic province, Kopeh-Dagh, central east Iran, Makran, and the southeast Zagros. The lowest mmax values were found in the Persian Gulf, Arabian Platform, Esfahan-Sirjan region, and the Dasht-e-Kavir Desert in central Iran. As a second part to this study, we calculated the maximum credible earthquakes for 1103 identified major faults by using five empirical magnitude-scaling relationships. Our results were consistent with both the observed earthquakes and the seismic potential of the various seismogenic zones of Iran. The study results can be used in future seismic hazard analyses and have fundamental implications for mitigating seismic risk in Iran.http://link.springer.com/journal/10950hj2022Geolog

Ranking of ground-motion models (Gmms) for use in probabilistic seismic hazard analysis for iran based on an independent data set

We apply three data-driven selection methods, log-likelihood (LLH), Euclidean distance-based ranking (EDR), and deviance information criterion (DIC), to objectively evaluate the predictive capability of 10 ground-motion models (GMMs) developed from Iranian and worldwide data sets against a new and independent Iranian strong-motion data set. The data set includes, for example, the 12 November 2017 Mw 7.3 Ezgaleh earthquake and the 25 November 2018 Mw 6.3 Sarpol-e Zahab earthquake and includes a total of 201 records from 29 recent events with moment magnitudes 4:5 ≤ Mw ≤ 7:3 with distances up to 275 km. The results of this study show that the prior sigma of the GMMs acts as the key measure used by the LLH and EDR methods in the ranking against the data set. In some cases, this leads to the resulting model bias being ignored. In contrast, the DIC method is free from such ambiguity as it uses the posterior sigma as the basis for the ranking. Thus, the DIC method offers a clear advantage of partially removing the ergodic assumption from the GMM selection process and allows a more objective representation of the expected ground motion at a specific site when the ground-motion recordings are homogeneously distributed in terms of magnitudes and distances. The ranking results thus show that the local models that were exclusively developed from Iranian strong motions perform better than GMMs from other regions for use in probabilistic seismic hazard analysis in Iran. Among the Next Generation Attenuation-West2 models, the GMMs by Boore et al. (2014) and Abrahamson et al. (2014) perform better. The GMMs proposed by Darzi et al. (2019) and Farajpour et al. (2019) fit the recorded data well at short periods (peak ground acceleration and pseudoacceleration spectra at T 0:2 s). However, at long periods, the models developed by Zafarani et al. (2018), Sedaghati and Pezeshk (2017), and Kale et al. (2015) are preferable

A provisional seismic source zonation of Iceland for the ESHM20 based on new physics-based bookshelf fault system models and a new revised earthquake catalogue

The earthquake hazard in Iceland is highest in its two transform zones, the South Iceland Seismic Zone in the South and the Tjörnes Fracture Zone in the North and the reliable probabilistic seismic hazard assessment (PSHA) is the prerequisite for the codified aseismic design of structures and mitigation of seismic risk. The three fundamental aspects of a reliable PSHA, the proper specification of the seismic sources, in particular in the transform zones, their activity rates, and the use of acceptable forms of ground motion models that characterize the rapid attenuation of Icelandic strong-motion, need to be based on the latest state-of-the-art information and methods. In this study, we present a new and provisional subdivision of Iceland into seismic area-source zones on the basis of new physics-based fault system models as well as parameter set for each zone based on new revised and harmonised earthquake catalogue for Iceland. The zonation is guided by the systematic spatial distribution of the predominant types of earthquake faulting mechanisms in Iceland, consistent with the volcanic and transform zones in the country. Moreover, the new physics-based estimates of activity rates in the transform zones effectively explain the historical seismicity and allow the specification of subzone activity rates. On the basis of this new zonation finite-fault earthquake catalogues can be simulated for long-time intervals that are consistent with the time-independent estimates of seismicity. The provisional seismic zonation model can therefore both serve as the basis for the revision of the PSHA of Iceland using conventional engineering approaches and lays the foundation for physics-based earthquake rupture simulation approaches to the time-independent PSHA. For the time being however, this provisional model has been provided to the harmonized efforts of PSHA in Europe (ESHM20).The Iceandic Centre for Research (Grant no. 196089).Peer Reviewe