Enter Zoom Meeting

NH4.3

EDI
Pattern recognition and statistical models applied to earthquake occurrence

New models based on seismicity patterns, considering their physical meaning and their statistical significance, shed light on the preparation process of large earthquakes and on the evolution in time and space of clustered seismicity.
Opportunities for improved model testing are being opened by the increasing amount of earthquake data available on local to global scales, together with accurate assessments of the catalogues’ reliability in terms of location precision, magnitude of completeness and coherence in magnitude determination.
Moreover, it is possible to reliably integrate the models with additional information, like geodetic deformation, active fault data, source parameters of previously recorded seismicity, fluid contents, tomographic information, or laboratory and numerical experiments of rock fracture and friction. Such integration allows a detailed description of the system and hopefully an improved forecasting of the future distribution of seismicity in space, time and magnitude.
In this session, we invite researchers to submit their latest results and insights on the physical and statistical models and machine learning approaches for the space, time and magnitude evolution of earthquake sequences. Particular emphasis will be placed on:

• physical and statistical models of earthquake occurrence;
• analysis of earthquake clustering;
• spatial, temporal and magnitude properties of earthquake statistics;
• quantitative testing of earthquake occurrence models;
• reliability of earthquake catalogues;
• time-dependent hazard assessment;
• methods for earthquake forecasting;
• data analyses and requirements for model testing;
• pattern recognition in seismology;
• machine learning applied to seismic data; and
• methods for quantifying uncertainty in pattern recognition and machine learning.

Co-organized by SM8
Convener: Stefania Gentili | Co-conveners: Rita Di Giovambattista, Álvaro GonzálezECSECS, Filippos Vallianatos
Presentations
| Thu, 26 May, 08:30–11:49 (CEST), 13:20–14:05 (CEST)
 
Room 1.61/62

Thu, 26 May, 08:30–10:00

Chairperson: Stefania Gentili

08:30–08:31
Introduction

08:31–08:32
Earthquake detection

08:32–08:39
|
EGU22-11098
|
ECS
|
On-site presentation
Yonggyu Choi et al.

In recent years, machine learning techniques have been widely applied in seismological data processing such as seismic event detection, phase picking, location, magnitude estimation, and further data analysis for determining source mechanisms. Especially in earthquake location, deep learning methods are used to reduce location errors compared to conventional algorithms.

In this study, we present a deep learning based epicentral distance estimation with two separate models using seismic data from two stations as input data. The first model is the P- and S-wave arrival time picking model and the second is the epicentral distance estimation model. Since the traditional epicentral distance estimation methods uses the difference in arrival times between P- and S-waves, the P- and S-wave arrival times were first predicted from three-component seismic data so that this information could be directly used as the next input data. This picking information is used as input data along with the station location in the epicentral distance estimation model to output the final epicentral distance. Since this method uses data from two stations, it has higher accuracy than epicentral distance estimation using data from a single station.

The P- and S-wave arrival time picking model was modified by referring to the ResUNet (Diakogiannis et al., 2020) structure to improve performance based on the seismic detection and phase picking model from the three-component acceleration data developed by Mousavi et al. (2020). This modified model performs feature extraction for P- and S-phase picking and includes a residual block and skip connection. The model for estimating the distance from the epicenter was constructed using a basic artificial neural network (ANN) architecture. As input data, a total of eight features were used by adding six combinations of the difference in arrival times of P-wave and S-wave in each component of the two stations and two values of the latitude and longitude difference between two stations. The ANN architecture consists of four hidden layers and the epicentral distances of the two stations are final output.

The STEAD data were used as training data and test data. The STEAD is a seismogram dataset recorded from about 450,000 global earthquakes, and among them, data with magnitudes greater than 2.5 and epicentral distances less than 400 km were selected and used. As a result of applying the trained model to the test data, the mean absolute error of the predicted epicentral distance was 6.5 km, which showed improved performance compared to the previous results. Also, since this method uses six time-differences as input data, it can provide more robust results even in the presence of random noise at the picked times.

How to cite: Choi, Y., Bae, S., Song, Y., Seol, S. J., and Byun, J.: P- and S-wave arrival picking and epicentral distance estimation of earthquakes using convolutional neural networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11098, https://doi.org/10.5194/egusphere-egu22-11098, 2022.

08:39–08:46
|
EGU22-4799
|
ECS
|
On-site presentation
Nooshin Najafipour and Christian Sippl

As the number of seismic stations and experiments greatly increases due to ever greater availability of instrumentation, automated data processing becomes more and more necessary and important. Machine Learning (ML)methods are becoming widespread in seismology, with programsthat identify signals and patterns or extract features that can eventually improve our understanding of ongoing physical processes. We here focus on comparing and testing a selection of currently available methods for machine-learning-based seismic event detection and arrival time picking, performing a comparative study of the two autopickers EQTransformer and GPD with seismic data from the IPOC deployment in Northern Chile within the open-source Seisbench framework.

As a small benchmark dataset, we chose a random day for which we handpicked all visually discernible events on the 16 IPOC stations, which led to 200 events from 450 extracted, comprising 1493 P and 1163 S-phases. These events cover a large range of hypocentral depths (surface to >200 km) as well as magnitudes (<1.5 to 4.5).

We present first results from the application of the two autopickers EQTransformer and GPD, which have been shown to be most suitable for our type of dataset in a recent study by Münchmeyer et al. (2021), to IPOC data. We use our small benchmark dataset to evaluate detection rate (missed events, false detections) as well as picking accuracy (residuals to handpicks), and also investigate the effect of using different training datasets.

The present study is the first step towards the design of an automated workflow that comprises event detection and phase picking, phase association and event location and will be used to evaluate subduction zone microseismicity in different locations.

How to cite: Najafipour, N. and Sippl, C.: Comparing machine-learning based picking algorithms in a subduction setting, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4799, https://doi.org/10.5194/egusphere-egu22-4799, 2022.

08:46–08:53
|
EGU22-4843
|
ECS
|
On-site presentation
Jorge Antonio Puente Huerta and Christian Sippl

Seismic phase association plays an important role in earthquake detection and location workflows as it links together seismic phases detected on different seismometers into individual earthquakes. Together with improved phase picking algorithms, a phase association algorithm can generate large earthquake phase data sets and earthquake catalogs when applied to dense permanent or temporary seismic networks. Recently, many efforts have been made on improving seismic phase association performance, such as developing machine learning approaches that are trained on millions of synthetic sequences of P and S arrival times, to generate more precise and complete catalogs including more small earthquakes.

As part of project MILESTONE, which aims at the automatic creation of large microseismicity catalogs in subduction settings, the present study evaluates the performance of the deep-learning based phase association algorithm PhaseLink (Ross et al. 2019) by comparison with a traditional grid-based method and a small handpicked benchmark dataset.

We used seismic data from the IPOC (Integrated Plate boundary Observatory Chile) permanent deployment of broadband stations in Northern Chile, dedicated to the study of earthquakes and deformation at the continental margin of Chile.

For an initial calibration, we manually picked P and S phases of raw waveforms on 15 stations on two randomly chosen days. All events that were visually recognizable were picked and located, which led to a dataset of 251 events comprising 1823 P and 1468 S picks, spanning a depth range from the surface down to 240 km. We use this handpicked dataset as ‘ground truth’, and evaluate the performance of PhaseLink and the grid-based method coupled with a STA/LTA trigger against this benchmark, considering both the numbers of (correctly/falsely) associated events and the number of constituent picks per event.

In a second experiment, we compare PhaseLink and conventional phase associator using a much larger set of STA/LTA alerts from the same region, but without the additional ground truth.

The presented research represents first steps towards an integrated automated workflow for detecting, picking, associating and locating microseismicity in subduction zone settings.

How to cite: Puente Huerta, J. A. and Sippl, C.: Exploring the performance of phase association algorithms, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4843, https://doi.org/10.5194/egusphere-egu22-4843, 2022.

08:53–09:00
|
EGU22-2874
|
ECS
|
On-site presentation
Josipa Majstorovic et al.

In recent years, it became clear that the seismological community is adopting deep learning (DL) models for many diverse tasks such as problems of discrimination and classification of seismic events, earthquake detection and phase picking, generalised phase detection, earthquake early warning etc. Many models that have been developed and tested reach quite high accuracy values. However, it has been showed that their performances depend on the DL architecture, on the training hyperparameters and on the datasets that are used for training. To help the community to understand how final results and a model’s performance depend on each of these different aspects, we propose implementing some techniques that target the black-box nature of DL models. In this study we applied three visualisation technique to a convolutional neural network (CNN) classification model for the earthquake detection. The implemented techniques are: feature map visualisation, backward optimisation and layer-wise relevance propagation methods. These can help us answer questions such as: How is an earthquake represented within a CNN model? What is the optimal earthquake signal according to a CNN? Which parts of the earthquake signal are more relevant for the model to correctly classify an earthquake sample? These findings can help us understand why the model might fail, how to build better model architectures, but also whether there is a physical meaning embedded in a model from training samples. The CNN used in this study had been trained for single-station detection, where an input sample is a 25 seconds long three-component waveform. The model outputs a binary target: an earthquake (positive) and a noise (negative) class. Following our two output classes, our training database contains a balanced number of samples from both classes. The positive samples span a wide range of earthquakes, from local to teleseismic, with a focus on the local and regional ones. Our analysis showed that the CNN model correctly identifies earthquakes within the sample window, while the position of the earthquake in the window is not explicitly given (based on the high relevance values). The model handles well earthquakes of different distance and magnitude values, without having any physical information about them during the training process. Thus, the model constructs highly abstract latent space where different earthquakes can eventually fit (can be shown by visualising feature maps). We also notice that having non-filtered training samples with low signal to noise ratio does not disrupt the model to generate distinct feature maps, which is crucial for the successful earthquake detection process. Finally, interpretation techniques proved to be useful for having an insight of how the CNN model treats input samples, which is beneficial for understanding whether the architecture is well designed for this task. 

How to cite: Majstorovic, J., Giffard-Roisin, S., and Poli, P.: Post hoc visual interpretation of convolutional neural network model for earthquake detection using feature maps, optimal solutions, and relevance values , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2874, https://doi.org/10.5194/egusphere-egu22-2874, 2022.

09:00–09:01
Earthquake characterisation

09:01–09:08
|
EGU22-295
|
ECS
|
|
On-site presentation
Mario Arroyo Solórzano et al.

Central America is a seismically active region where five tectonic plates interact (North America, Caribbean, Coco, Nazca, and South America) in a subduction zone with transform faults and near to triple points. This complex tectonic setting makes the estimation of the seismic potential (maximum magnitude) a very important task. There are a series of empirical formulas and diagrams by means of which the seismic potential of faults can be estimated from rupture earthquake geometry parameters. In this study, some of these formulas were applied to approximate the magnitude of earthquakes occurred in Central America, comparing the estimated magnitudes with those observed instrumentally. This has been accomplished based on the most complete data set of relevant and better characterized earthquakes generated by faults in the region. The data set consists in a compilation of the seismic events and its relatively well-established rupture parameters (length, width, area, slip, magnitude) and characteristics (location, faults, or possible associated faults, as well as localized aftershocks). The slip rate was incorporated, when available, but considering the current lack of information and, in some cases, the high uncertainty in its estimation, the use of other simpler rupture parameters is more practical and applicable for the region. Based on this, we identified which of the current available formulas developed worldwide, estimate magnitudes in a better way for the Central American seismotectonic context. The preliminary results show a better fit with the instrumental data, when the empirical equations were used with the segmented fault length. These outcomes were specifically validated for lengths between 10 and 30 km, in which the database presents good information coverage. We found that some empirical relationships fit quite well the observed data, including the classical Wells & Coppersmith (1994) equations. Finally, according to our data set compilation, we will try to propose a new empirical specific earthquake scaling relationship for Central America to be included in future seismic hazard studies. Is recommended, when possible, complement these approaches with more detailed historical seismicity review and paleoseismological, geodetic and neotectonic studies, to determine more precisely and realistically the fault’s maximum magnitude. Also, we suggest make estimates of the magnitude using the maximum and the segmented fault length and differentiating between ruptures at depth in the seismogenic zone (smaller) and ruptures in surface (larger). This approach is relevant due to the selection of an earthquake scaling relationships for a specific region is typically an abbreviated component of seismic-hazard analysis, being an important issue for the definition of source models which are one of the main inputs in the hazard estimation.

How to cite: Arroyo Solórzano, M., Benito, B., Alvarado, G., and Climent, Á.: Analysis and proposal of empirical magnitude scaling relationships for the seismic potential of earthquakes in Central America: its application for seismic hazard studies, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-295, https://doi.org/10.5194/egusphere-egu22-295, 2022.

09:08–09:15
|
EGU22-5805
|
ECS
|
Virtual presentation
Martina Orlando et al.

An Mw=7.3 earthquake occurred on June 15, 2019 in New Zealand, Kermadec Islands (30.644° S, 178.100° W, 46 km depth), in correspondence with the Tonga-Kermadec subduction zone, and was characterized by a tectonic setting of shallow reverse faulting.

We investigated the preparatory phase from a seismological point of view, focusing on the analysis of seismic data in the period between January 1, 2018 and June 14, 2019 in an area limited by the Dobrovolsky strain radius. Specifically, the data from the global United States Geological Survey (USGS) and the national New Zealand (GEONet) earthquake catalogues are used in this study.

To characterize the seismicity trend in terms of magnitude distribution variations and strain release with time, we made a two-step analysis. The first one was to calculate the magnitude of completeness (Mc), which is an important parameter when estimating b-values (Wiemer and Wyss, B. Seism. Soc. Am., 2000). After this preliminary step, we observed that the seismicity accelerated during the preparation phase of the earthquake through the Revised Accelerated Moment Release (R-AMR) method (De Santis et al., Tectonophysics, 2015).

Finally, we found that the seismological research of the preparation phase of this earthquake helped to understand, together with other observations from ground and satellite, the so-called Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) phenomena prior to the mainshock.

How to cite: Orlando, M., Cianchini, G., De Santis, A., Perrone, L., Arquero Campuzano, S., D'Arcangelo, S., Di Mauro, D., Marchetti, D., Piscini, A., Sabbagh, D., and Soldani, M.: Seismological investigation of Mw=7.3 Karmedec Islands (New Zealand) earthquake occurred on June 15, 2019, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5805, https://doi.org/10.5194/egusphere-egu22-5805, 2022.

09:15–09:22
|
EGU22-6650
|
Virtual presentation
Gilbert Mao et al.

Deep earthquakes, 300 to 700 km deep, have been observed for decades and shown to originate from major mineral transformations occurring at these depths, including phase transitions of olivine and pyroxenes. Yet, we still do not fully grasp their mechanism. Although transformational faulting in the rim of the metastable olivine wedge (MOW) is hypothesized as a triggering mechanism of deep-focus earthquakes, there is no direct seismic evidence of such rim. Variations of b-value – slope of the Gutenberg-Richter distribution – have been used to decipher triggering and rupture mechanisms of earthquakes. However, regarding deep-focus earthquakes the detection limit prevents full understanding of rupture nucleation at all sizes.

With one of the most complete catalogs, the Japan Meteorological Agency (JMA) catalog, we estimate the b values of deep-focus earthquakes (> 300 km) of four clusters in the NW Pacific Plate based on unsupervised machine learning. The applied K-means, Spectral and Gaussian Mixture Models Clustering algorithms divide the events into four clusters. For the first time, we observe kinks in the b values with abrupt reductions from 1.5–1.8 down to 0.7–1.0 at a threshold Mw of 3.7–3.8 for the Honshu and Izu clusters, while normal constant b values (0.9–1.0) are observed for the Bonin and Kuril clusters.

The four clusters found by the algorithms actually correspond to events within four different segments of the sinking Pacific lithosphere, characterized by significant differences in hydration state prior to subduction. High b values (1.5–1.8) at low magnitudes (Mw < 3.7–3.8) correlate with highly hydrated slab portions. The hydrous defects would enhance the nucleation of small earthquakes via transformational faulting within the rim. Such mechanism operates for small events with a rupture length of less than 1 km, which would correspond to the thickness of the MOW rim.

Combining with the b-value analysis from the latest CMT catalog, the kink at Mw 6.7 suggests that the thermal runaway mechanism operates for larger earthquakes rupturing through and possibly propagating outside the MOW, with increased heterogeneity in the new rupture domain. The changes of controlling mechanism and rupture domain heterogeneity due to the slab hydrous state and thermal state can explain the spatially varying b values.

How to cite: Mao, G., Ferrand, T., Li, J., Zhu, B., Xi, Z., and Chen, M.: Nature of Deep Earthquakes in the Pacific Plate from Unsupervised Machine Learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6650, https://doi.org/10.5194/egusphere-egu22-6650, 2022.

09:22–09:29
|
EGU22-10382
|
Virtual presentation
András Horváth et al.

Earthquake detection and phase picking are central problems of seismic activity analysis. Traditional approaches [1] and machine learning methods [2] are applied in this domain, typically performing well on commonly investigated standard datasets reaching above 99% accuracy in seismic activity detection.

 

Unfortunately, most databases in the literature contain only earthquake data as detectable activities and spurious activities such as mining are not included in these datasets. We have investigated a recently published deep neural network-based method [3] and found that these detectors are fooled by mining activity.

 

To solve this problem, we have created a complex dataset that contains 1200 independently recorded mining and earthquake activities from Central Europe. Our dataset poses a more complex problem than commonly investigated datasets such as the STanford EArthquake Dataset and can be viewed as an extension of that.

 

We have trained a convolutional neural network containing five convolutional and three fully-connected layers to classify these signals on this dataset and reached a 94% classification accuracy, which demonstrates that the categorization of mining activity and earthquakes is possible with modern machine learning approaches.



[1] Galiana-Merino, J. J., Rosa-Herranz, J. L., & Parolai, S. (2008). Seismic P Phase Picking Using a Kurtosis-Based Criterion in the Stationary Wavelet Domain. IEEE Transactions on Geoscience and Remote Sensing, 46(11), 3815-3826.

 

[2] Zhu, W., & Beroza, G. C. (2019). PhaseNet: a deep-neural-network-based seismic arrival-time picking method. Geophysical Journal International, 216(1), 261-273.

 

[3] Mousavi, S. M., Ellsworth, W. L., Zhu, W., Chuang, L. Y., & Beroza, G. C. (2020). Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nature communications, 11(1), 1-12.

How to cite: Horváth, A., Timkó, M., Kiszely, M., Bozóki, T., Bozsó, I., and Kuslits, L.: Classifying earthquakes and mining activity with deep neural networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10382, https://doi.org/10.5194/egusphere-egu22-10382, 2022.

09:29–09:30
b values and magnitude of completeness

09:30–09:37
|
EGU22-11532
|
ECS
|
|
On-site presentation
Vanille Ritz et al.

Induced seismicity is a hot topic within geo-applications, however the physical mechanisms driving the induced ruptures is yet to be fully understood. The injection of fluid in the subsurface in particular has been shown to cause changes in the stress field leading to the induction of eqarthquakes. Recent events in Switzerland (Basel, Sankt-Gallen) and Korea (Pohang) have shown that such injection operations can have dramatic consequences. The hazard associated with these earthquakes thus needs to be managed to prevent infrastructure damages and protect both the population and viability of the project.

The Gutenberg-Richter b-value has been used as a proxy for the state of stress in the subsurface. The temporal evolution of the b-value provides statistical tools to estimate the seismic hazard posed by an earthquake sequence. Thus, monitoring and forecasting changes in the b-value could be used as a proxy in a near-real-time mitigation context (Adaptive Traffic Light System). Several studies have looked at the evolution of the b-value both in time and space, for example in Basel, where the observed b-value dropped before shut-in and further away from the injection well.

We present a numerical approach coupling a fluid flow simulator with a geomechanical-stochastic formulation (TOUGH2-Seed) to simulate injection-induced seismicity sequences. We model a Hot Dry Rock-type setting, and we investigate the variation of b-value during injection-induced seismic sequences with different injection scenarios and levels of complexity as to the geological features.

How to cite: Ritz, V., Rinaldi, A. P., and Wiemer, S.: Evaluating injection strategies for EGS from the temporal evolution of the Gutenberg-Richter b-value., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11532, https://doi.org/10.5194/egusphere-egu22-11532, 2022.

09:37–09:44
|
EGU22-11800
|
ECS
|
On-site presentation
Sebastian von Specht et al.

As a population parameter, reliable estimation of the b-value is intrinsically complicated, particularly when spatial variability is considered. We approach this issue by treating the spatial b-value distribution as a non-stationary Gauss process for the underlying earthquake-realizing Poisson process. For Gauss process inference the covariance—which describes here the spatial correlation of the b-value—must be specified a priori. We base the covariance on the local fault structure, i.e. the covariance is anisotropic: elongated along the dominant fault strike and shortened when normal to the fault trace. This adaptive feature captures the geological structure better than an isotropic covariance or similarly defined and commonly used running-window estimates of the b-value.We demonstrate the Bayesian inference of the Gauss process b-value estimation for southern California based on the SCEDC earthquake catalog and with the covariance calibrated with the USGS fault model.Our model provides a continuous b-value estimate which reflects the local fault structure to a very high degree. Therefore, we are able to associate the b-value with the local seismicity distribution and can link it to the major Californian faults and geothermal areas. This technique, in its general formulation, can be applied to other non-stationary seismicity parameters, e.g. Omori’s law.

How to cite: von Specht, S., Holschneider, M., Ferrat, K., Zöller, G., Molkenthin, C., and Hainzl, S.: Spatial distribution of the b-value in Southern California based on Gauss process inference with a geologically defined prior, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11800, https://doi.org/10.5194/egusphere-egu22-11800, 2022.

09:44–09:51
|
EGU22-11803
Matteo Taroni et al.

The b-value of the Gutenberg-Richter law is one of the most widely studied parameters regarding the distribution of earthquakes’ magnitude. The estimation of such a parameter and its uncertainty is critical, and it may become complex in the case of seismic catalogs with a non-uniform magnitude of completeness, or when different shapes of the Gutenberg-Richter relation (e.g. the tapered one) are adopted. Here we review recent results on the b-value estimation, also in the case of catalogs with multiple completeness levels, including the application of the weighted likelihood methodology, a method particularly suitable for spatial b-value mapping and for studying its temporal variations. These new techniques are applied to both global and regional seismic catalogs, in order to unveil the peculiarities of the b-value.

How to cite: Taroni, M., Selva, J., Marzocchi, W., and Zhuang, J.: Everything you always wanted to know about b-value* (*but were afraid to ask), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11803, https://doi.org/10.5194/egusphere-egu22-11803, 2022.

09:51–09:58
|
EGU22-6663
|
ECS
|
Virtual presentation
Álvaro González

Levels of artificial seismic noise are typically lower at night, when road and train traffic, industries and other human activities are decreased. Such a variable noise hampers detection of small earthquakes preferentially during daytime, so typically they are more frequently recorded at night. Small earthquakes are recorded in higher numbers during the weekends too, also due to the lower artificial noise. Daily variations of earthquake frequencies might also have natural causes, but higher numbers of earthquakes recorded during weekends are unequivocally artificial.

These variations of detection capabilities are usually not well taken into account when looking for natural periodicities of earthquake frequencies, for example when searching for correlations of earthquake occurrence with diurnal or semidiurnal tides.

Featuring examples from different, regional, earthquake catalogues, this presentation shows that using a magnitude of completeness (Mc) calculated from the whole catalogue can be misleading. The reason is that such a value is actually an average between lower Mc values reached during the night (and weekends) and higher ones reached during the day (and working days).

The solution proposed here is to use a high enough Mc value such as the artificial (daily and weekly) periodicities of earthquake frequencies are removed, considering not only the best estimate of Mc but also its uncertainty range.

How to cite: González, Á.: On artificial daily and weekly periodicities of recorded earthquake frequencies, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6663, https://doi.org/10.5194/egusphere-egu22-6663, 2022.

09:58–10:00
Discussion

Thu, 26 May, 10:20–11:50

Chairperson: Stefania Gentili

10:20–10:21
Regional seismicity patterns

10:21–10:28
|
EGU22-9181
|
ECS
|
On-site presentation
Lili Czirok et al.

The SE-Carpathians indicate significant geodynamic activity, especially in the external part due to the current subduction processes. This part is the so-called Vrancea-zone where the distribution of the seismic events is quite dense considering the relatively small area (around 30*70 km).

The authors have carried out cluster analyses of the focal mechanism solutions and their inversions to support the recent and previously published studies in this region. They have applied different pre-existing clustering methods – e.g. HDBSCAN (hierarchical density-based clustering for applications with noise) and agglomerative hierarchical analysis – considering the geographical coordinates, focal depths and parameters of the focal mechanism solutions of the used seismic events, as well. Moreover, they have attempted to improve a fully-automated algorithm for the clustering of the earthquakes for the estimations. This algorithm has only one optional hyper-parameter which is eligible to detect the outliers from the input dataset. Due to this, it is possible to reduce the running time and subjectivity. In all cases, the calculated stress tensors are in close agreement with the previously published results.

How to cite: Czirok, L., Kuslits, L., Bozsó, I., and Gribovszki, K.: Studying the stress field variations in the Vrancea-zone using clustering-based stress inversions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9181, https://doi.org/10.5194/egusphere-egu22-9181, 2022.

10:28–10:35
|
EGU22-4721
|
ECS
|
On-site presentation
Gabriel Pană et al.

We report detailed statistical results on the structure of motifs in seismic networks covering four distinct terrain regions. Our main finding is that all seismic networks under investigation display motifs which have a distinct scale-free-like structure. 

The seismic networks were constructed from public seismic data using a standard procedure which relies on splitting the seismic region into equally sized cubes, which are the nodes of the seismic network. Then, placing each earthquake, in chronological order, into the cube corresponding to its epicenter, we define a link between two nodes as a series of two subsequent earthquakes with epicenters in different cubes. Using these seismic networks we study the occurrence of 3-node and 4-node motifs, which are triangles and tetrahedrons of the network, and report a scale-free-like behavior of the area or volume of these motifs weighted by the total energy released by the earthquakes contained in the nodes of the motif.

The statistical properties of motifs, in particular the scaling exponents of the aforementioned scale-free-like distributions, can be used to assess the differences and similarities between different seismic regions, without taking into account the inner workings of the plate tectonics. Our approach is fully customizable as all relevant parameters of the network (e.g., size of the cubes, magnitude of earthquakes, considered timeframe, coordinates of epicenters) can be changed to accommodate virtually all seismic regions.  

How to cite: Pană, G., Băran, V., and Nicolin, A.: Structure of Motifs in Seismic Networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4721, https://doi.org/10.5194/egusphere-egu22-4721, 2022.

10:35–10:42
|
EGU22-7510
|
Virtual presentation
Dragana Chernih-Anastasovska et al.
Two moderate earthquakes with magnitude ML5.0 happened on the 11th of November 2020 near the Mavrovo lake in northwestern Macedonia. Mavrovo lake is an artificial lake with a dam built between 1947 and filled by 1953. Its maximum length is 10km, width is 5km and depth is 50m. We try to investigate the factors which might be causing earthquakes, for example, local geology and seismotectonic regime in the region.
Seismic events of such size can have various sequences of foreshocks and aftershocks, which mostly depend on the earthquake mechanism. In this case study, a numerical analysis was done for the first time from the list of events reported by the Skopje Seismological observatory, that occurred some six months prior to the main events and one year after, till November 2021.
 
A list of 180 earthquakes registered by the local and regional stations with magnitudes equal or greater than ML1.7 was analyzed in more detail in terms of temporal and spatial distribution around the lake, in a polygon area defined by geological features. No statistically significant clustering of events was noticed in the foreshock period from July 2020. In the aftershock period, the most numerous events lasted about a month after the main events. However, there was another period of increased seismicity during March 2021, followed by a gradual decrease onwards.

The preliminary distribution of epicenters was mainly along the terrain of Radika river and close monitoring continues to establish possible longer-term variations of seismicity. Comparative analysis with various periods will be also considered in order to determine any patterns of seismicity.

How to cite: Chernih-Anastasovska, D., Drogreshka, K., Najdovska, J., Pekevski, L., and Sinadinovski, C.: Pattern analysis of seismicity around Mavrovo lake: a case study for the period July 2020 - November 2021, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7510, https://doi.org/10.5194/egusphere-egu22-7510, 2022.

10:42–10:49
|
EGU22-11461
|
ECS
|
Virtual presentation
Lyuba Dimova et al.

Seismogenic nodes, able to locate earthquakes with magnitudes M equal or higher than 6 (M6+), are identified for the territory of Bulgaria and adjacent areas. Definition of nodes is based on morphostructural zonation. Pattern recognition algorithm Cora-3 is applied to identify the seismogenic nodes, characterized by specific geological, geophysical and morphological data. The pattern recognition algorithm is trained on information for 30 seismic events M6+ for the period 29 B.C. – 2020, selected from historical and instrumental Bulgarian earthquake catalogues. These events are associated with 16 "training" nodes. Totally we have recognized 56 seismogenic nodes, most of them in southwestern Bulgaria.

The analysis of the identified seismic nodes shows that in addition to the initial 16 "training" nodes, about 20 ones may be associated with the already observed seismicity. Some of these nodes may be related with documented seismicity, which is not taken into account to select the “training” nodes. Other seimogenic nodes are close to (but do not include) some historical earthquakes, whose location or magnitude is not precise enough.

There has not been registered seismic activity M6+ in the vicinity of other 20 seismogenic nodes. To consider a certain inaccuracy in the determination of the earthquake magnitudes, in the analysis of these seismogenic nodes we take into account earthquakes with M higher than 5.8. In such a way the number of “inactive” until now seismogenic nodes decrease further. Several nodes can be related to seismic activity in the past, established by geological research. But a significant part of seismic nodes remains, which cannot be associated with any seismic manifestations. This is a sign of possible future earthquakes M6+ near these nodes.

It should be noted that 3 of the earthquakes M6+ in the twentieth century were not close to any seismogenic node. This may be due to several reasons among which are: hidden tectonic structures, gaps in the morphostructural zonation or an inaccurate magnitude of the earthquake’s catalogue data.

Acknowledgements. This study is partly funded by Russian Foundation of Basic Research (RFBR) according to the research projects 20-55-18008 and by Bulgarian National Science Fund, research project KP-06-Russia-29/16.12.2020.

How to cite: Dimova, L., Gorshkov, A., Novikova, O., Dimitrova, S., and Raykova, R.: Seismogenic nodes in the Bulgarian territory, defined by pattern recognition, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11461, https://doi.org/10.5194/egusphere-egu22-11461, 2022.

10:49–10:56
|
EGU22-10503
|
On-site presentation
Sergei Lebedev et al.

Stable continental areas—those largely unaffected by currently active plate-boundary processes—undergo little deformation and feature low seismicity rates. Notable exceptions, such as the well-known large earthquakes in the central United States or the Fennoscandian Craton, are rare but highlight the importance of understanding the seismicity in low-strain regions. One long-standing question, debated for over a century, relates to the seismicity of Ireland. Why is it much lower than that in the neighbouring Britain, even though they were assembled in the same Caledonian orogeny, share many of the ancient tectonic boundaries, and are subjected to similar tectonic stresses? Our new catalogue of Ireland’s seismicity, produced using the greatly improved seismic station coverage of the island over the last decade, shows many more micro-earthquakes than known previously but confirms the much lower seismicity rates in Ireland compared to Britain.

Comparing the distribution of seismicity with high-resolution, surface-wave tomography (performed using the abundant new data) we observe that areas with thicker, colder lithosphere feature lower seismicity than those with thinner lithosphere. This must be because the thicker and colder lithosphere is mechanically stronger and less likely to deform, compared to the thinner and weaker lithosphere under the same tectonic stress. According to the new tomography, Ireland has thicker lithosphere than most of Britain, which can explain its lower seismicity rates. The thinnest lithosphere in Ireland is found in the north of the island, in Co Donegal, and this is where most of Ireland’s micro-seismicity occurs. A similar relationship between the lithospheric thickness and seismicity rates is observed in Britain, with the London Platform in the southeast of the island showing thick lithosphere and low seismicity.

Together, lithospheric tomography and seismicity maps thus offer a solution to a seismo-tectonic puzzle first formulated in the 19-th century. Evidence of the lithospheric mantle controls on earthquake occurrence can be seen elsewhere around the world as well. The improving accuracy of the tomographic imaging of the lithosphere presents a useful new line of evidence on the mechanisms that control the regional distributions of intraplate earthquakes.

How to cite: Lebedev, S., Arroucau, P., Grannell, J., and Bonadio, R.: Intraplate seismicity controlled by lithospheric-mantle strength: the Ireland and Britain case study, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10503, https://doi.org/10.5194/egusphere-egu22-10503, 2022.

10:56–10:57
Earthquake precursors

10:57–11:04
|
EGU22-479
|
ECS
|
On-site presentation
Antonio Giovanni Iaccarino and Matteo Picozzi

What happens just before and generates a moderate to large earthquake is still on debate. Two different models are usually proposed. The first considers the main event as triggered by cascading effect from multiple random small earthquakes. The other one proposes the existence of a preparatory phase in which the seismicity slowly migrates towards the hypocentral zone loading stress on it, until the main event occurs.

In this work, we want to identify the preparatory process from catalogue data. We use data from The Geysers, a geothermal area in California (USA). Many studies showed that the seismicity of the area is triggered by the human activities related to the extraction of geo-energy.

Following the work done in Picozzi and Iaccarino (2021), we compute different features related to the seismicity around moderate events (M>3.5) and we use them as time-series. In this study, the features are computed following a fully causal procedure that make this analysis suitable for a real-time application. We apply both a supervised machine learning technique (LSTM Recurrent Neural Network) and an unsupervised clustering technique (K-means) to highlight the preparatory phase with respect to the background seismicity.

We show that, with both techniques, it is possible to identify a change in the seismicity just before most of the events studied. This confirms the existence, at least in some cases, of a preparatory phase for induced earthquakes at The Geysers.

How to cite: Iaccarino, A. G. and Picozzi, M.: Detecting the preparatory phase of induced earthquake at The Geysers (California) using K-means and LSTM, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-479, https://doi.org/10.5194/egusphere-egu22-479, 2022.

11:04–11:11
|
EGU22-7239
|
Virtual presentation
Jun Hu et al.

The seismicity rate in the Southern Sichuan Basin, China (SSBC), has increased by orders of magnitude within the past a few years accompanying the rapidly growing hydraulic fracturing (HF) operations. However, there is currently no appropriate method to directly determine whether a HF platform has induced seismicity and to quantitatively describe its potential of inducing earthquakes. In this study, by taking advantage of a more complete seismic catalog constructed from temporary short-period seismic stations and broadband stations for two adjacent well pads in the SSBC, we investigate a new statistical metrics for detailed studies of earthquake clusters on the "pre- and post-fracture" time scales to characterize their earthquake-inducing capacity. After declustering the earthquake catalog, a small-scale “Spatiotemporal Association Filter (SAF)” is designed to obtain seismic data closely associated with six independent fracturing well groups, and an “Unit Seismic Energy Release (USER)” index is established to evaluate the potential to induce earthquakes. Comparing the differences in the index before and after fracturing, as well as the nonparametric statistical test of each well group’s “Interevent time (IET)” and historical IET, four of the well groups are considered “induced-seismic”, and the other two are “anti-seismic”. The HF well with the largest USER value has the largest inducing capacity. The paired result of the nonparametric test shows that the p values are less than 0.001, indicating significant statistical differences between the IET series before and after the HF process around the four induced-seismic wells. To sum up, our method can conveniently distinguish the earthquake-inducing capacity of different HF wells, and thus offer practical advice for HF operation in the SSBC.

How to cite: Hu, J., Yang, H., Zhang, H., and Tan, Y.: A method for characterizing the earthquake-inducing capacity of hydraulic fracturing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7239, https://doi.org/10.5194/egusphere-egu22-7239, 2022.

11:11–11:18
|
EGU22-2043
|
ECS
|
|
On-site presentation
Davide Zaccagnino et al.

Assessing the stability state of faults is a crucial issue not only for seismic hazard, but also for understanding how the earthquake machine works. A possible approach consists in perturbing fault systems and studying how seismicity changes after additional stress is provided: if the starting energy state is stable, it will oscillate around it; otherwise, the background seismic rate will be modified. Tides provide natural stress sources featured by a wide range of frequencies and amplitudes, which make them a suitable candidate for our needs.  Analyses prove that the brittle crust becomes more and more sensible to stress modulations as the critical breaking point comes close.  Especially, the correlation between the variation of Coulomb failure stress induced by tidal loading, ΔCFS, and seismic energy rate progressively increases as long as seismic stability is kept; conversely, abrupt drops are observed as foreshocks and preslip occur. A preparatory phase, featured by increasing correlation, is usually detected before large and intermediate (Mw > 5) shallow (depth < 50 km) earthquakes. The duration of the anomaly, T, is suggested to be related to the seismic moment M of the impending mainshock by T ∝ M^(1/3) for M < 10^19  N m. The same power exponent characterizes seismic nucleation scaling of single earthquakes. This analogy may be explained assuming that the physical mechanism behind both these phenomena is the same. Consequently, the anomalies we measure might be interpreted as diffuse nucleation phases throughout the crust. The scaling relation becomes T ∝ M^0.1 for M > 10^19 N m, probably because of preparation processes occurring contemporaneously in interacting faults.  We apply this method to dozens of seismic sequences which hit California, Greece, Iceland, Italy and New Zealand, we also analysed seismic activity jointly with slow slip events in the Cascadia subduction zone, Manawatu region and in the Nankai Trough. Even though it is unlikely that our results may ever be of practical use for seismic hazard, the procedure could illuminate slow hidden processes of destabilization taking place within the brittle crust.                                                             

How to cite: Zaccagnino, D., Telesca, L., and Doglioni, C.: Assessing crustal stability via fault stress perturbation analysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2043, https://doi.org/10.5194/egusphere-egu22-2043, 2022.

11:18–11:25
|
EGU22-3136
|
Virtual presentation
Ilya Zaliapin and Yehuda Ben-Zion

We discuss recent results aimed at robust identification and quantification of space-time variations of earthquakes, with the ultimate goal of tracking preparation processes of large earthquakes. The first part focuses on progressive localization of seismicity, which corresponds to mechanical evolution of deformation from distributed failures in a rock volume to localized shear zones, culminating in generation of primary slip zones and large earthquakes. We present a methodology for estimation of localization using earthquake catalogs and acoustic emission experimental data, and showcase its applications to tracking localization processes of large failure events. This analysis is performed with declustered catalogs. The second part describes a technique to assess the degree of regional clustering of earthquakes, and justifies the need for declustering in localization and other analyses of seismicity. We demonstrate that events included in the existing short-duration instrumental catalogs are concentrated strongly within a very small fraction of the space-time volume, which is highly amplified by activity associated with the largest recorded events. The earthquakes that are included in instrumental catalogs are unlikely to be fully representative of the long-term behavior of regional seismicity, creating a bias in a range of seismicity analyses. Methodologically, both discussed topics are based on using the Receiver Operating Characteristic (ROC) framework. We demonstrate how this unified framework is adopted for diverse tasks, including assessment of coupled space-time clustering after controlling for space and time marginal inhomogeneities of earthquake rates, and tracking time-dependent transformations of a highly inhomogeneous earthquake space distribution. The examined data include crustal seismicity in California, Alaska and other regions, synthetic catalogs of the ETAS model, and acoustic emission data of laboratory fracturing experiments.

How to cite: Zaliapin, I. and Ben-Zion, Y.: Space-time variations of seismicity: quantitative assessment and systematic changes before large earthquakes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3136, https://doi.org/10.5194/egusphere-egu22-3136, 2022.

11:25–11:32
|
EGU22-10324
|
ECS
|
On-site presentation
Hamed Ali Diab Montero et al.

Our ability to forecast future earthquakes is hampered by the very limited information on the fault slip that produce them. In particular the current state of stress, strength, and parameters governing the slip of the faults are highly uncertain. Ensemble data-assimilation methods provide a means to estimate these variables by combining physics-based models and observations while considering their uncertainties. Perfect model experiments with an Ensemble Kalman Filter (EnKF), connected with one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) earthquake cycle models, demonstrate the ability to estimate the state variables of shear stress, slip velocity, and state (θ) of a straight fault governed by rate-and-state friction surrounded by a homogeneous elastic medium. The models represent a direct-shear laboratory setup in one, two and three dimensions, with an array of shear-strain gauges and piezoelectric transducers located at a small distance to the fault. In this research, we compare the recurrence interval and earthquake occurrence of the EnKF across the different models to better understand the challenges associated with a space-time systems with increasing dimensions and increasingly complex earthquake sequences. The assimilation of synthetic shear-stress and slip-rate observations improves in particular the estimates of shear stress and slip rate on the fault, despite the very low accuracy of the observations. We get reasonable estimates when modelling long-duration earthquakes or slow slip events . Interestingly, we also obtain very good estimates when simulating earthquakes with fast slip rates (up to m/s). The large, nonlinear, changes in stress and velocitiy  during the fast transition from the interseismic to the coseismic phase cause the distributions of the state variables to become bi-modal. The EnKF still provides a reasonable estimate of the time of occurrence of the earthquakes in the synthetic experiments, despite the inherent assumption on the Gaussianity of these distributions.

How to cite: Diab Montero, H. A., Li, M., van Dinther, Y., and Vossepoel, F. C.: An Ensemble Kalman Filter for Estimating Future Slow Slip Events and Earthquakes on 1D, 2D and 3D Synthetic Experiments , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10324, https://doi.org/10.5194/egusphere-egu22-10324, 2022.

11:32–11:42
|
EGU22-12022
|
solicited
|
Virtual presentation
Chris Marone

I summarize a broad suite of laboratory data sets showing that stick-slip failure events –lab earthquakes– are commonly preceded by both measurable changes in fault zone properties and acoustic emission (AE) events that foretell catastrophic failure.  These works show that both types of data can be used to predict labquakes with machine learning (ML) methods and deep learning (DL) approaches.  The first works used continuous measurements of AE to predict the timing of labquakes and the fault zone shear stress. Subsequent studies showed that catalogs of AE events could also predict labquakes and that ML approaches could also predict stress drop, peak fault slip velocity and the duration of failure. Recently, DL has been used to predict and autoregressively forecast labquakes and fault zone shear stress. Consistent with previous works, we see that seismic b-value begins to decrease as faults unlock and start to creep.  This provides a sensible connection between the ML-based predictions, fault zone elastic properties, and the physics of failure.  In the lab, AE events represent a form of foreshock and, not surprisingly, the rate of foreshock activity correlates with fault slip rate and its acceleration toward failure.  Our work shows precursory changes in wave speed prior to labquakes, consistent with many well known past studies, but the early studies did not provide a method to predict impending failure.  ML and DL predicts with fidelity the time of impending failure and other aspects of it. This suggests the possibility of physics-based models for prediction. We are working to connect ML prediction of labquakes with the evolution of fault zone elastic properties, frictional contact mechanics and constitutive laws.  A central goal is to learn from lab earthquake prediction to improve forecasts of earthquake precursors and tectonic faulting.

How to cite: Marone, C.: Machine Learning for Understanding Lab Earthquake Prediction and Precursors , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12022, https://doi.org/10.5194/egusphere-egu22-12022, 2022.

11:42–11:49
Discussion

Thu, 26 May, 13:20–14:50

Chairperson: Stefania Gentili

13:20–13:21
Probabilistic earthquake forecasting

13:21–13:28
|
EGU22-5907
|
ECS
|
On-site presentation
Muhammad Asim Khawaja et al.

The Collaboratory for the Study of Earthquake Predictability (CSEP) is an international effort to independently evaluate earthquake forecasting models and to provide the cyber-infrastructure together with a suite of testing methods. For global forecasts, CSEP defines a grid-based format to describe the expected rate of earthquakes, which is composed of 6.48 million cells for a 0.1º spacing. The spatial performance of the forecast is tested using the Spatial test (S-test), based on joint log-likelihood evaluations. The high-resolution grid combined with sparse and inhomogeneous earthquake distributions leads to many empty cells that may never experience an earthquake, biasing the S-test results. To explore this issue, we conducted a global earthquake forecast experiment. We tested a spatially uniform forecast model, which is non-informative and should be rejected by the S-test. However, it is not rejected by the S-test when the spatial resolution is high enough to allocate each observed earthquake in individual cells, thus raising questions about the test statistical power.

The number of observed earthquakes used to evaluate global forecasts is usually only a few hundred, in contrast to the millions of spatial cells. Our analysis shows that for such disparity, the statistical power of tests for single-resolution grids also depends on the number of earthquakes available to evaluate a model. With few earthquakes, the S-test does not allow powerful testing.

We propose to use a multi-resolution grid to generate and test earthquake forecast models, in which the resolution can be set freely based on available data, e.g., by the number of earthquakes per cell. Data-driven multi-resolution grids demonstrate the ability to reject the uniform forecast, contrary to a high-resolution grid. Furthermore, multi-resolution grids offer powerful testing with as minimum as four earthquakes available in the test catalog. Therefore, we propose to use multi-resolution grids in future CSEP global forecast experiments and to further study its application in regional and local experiments, where such sparsity of observations is present.

How to cite: Khawaja, M. A., Hainzl, S., Iturrieta, P., and Schorlemmer, D.: Effects of Spatial Grid Resolution on the Statistical Power of Testing Earthquake Forecast Models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5907, https://doi.org/10.5194/egusphere-egu22-5907, 2022.

13:28–13:35
|
EGU22-7674
|
|
On-site presentation
Christian Grimm et al.

Earthquake sequences typically show distinct spatiotemporal patterns, characterized by a power-law decay
of aftershock times and elongated aftershock distributions around the (extended) rupture. A prominent approach to
model seismic clustering in space and time is the Epidemic Type Aftershock Sequence (ETAS) model that differentiates
an independent background seismicity process from a branching tree process for triggered events. The conventional
ETAS approach shows three substantial biases: (1) The assumption of isotropic spatial distributions of aftershock
locations stands in contrast to observations and geophysical models for large mainshocks. (2) The unlimited spatial
distribution allows small events to trigger aftershocks at unrealistically large distances. (3) Short-term incomplete
event records after large mainshock events suggest supposedly smaller aftershock productivity and cluster sizes. The
above biases can lead to an underestimation of the aftershock productivity of strong events, and in consequence to
underpredicted cluster sizes, and to a miss-specification of the spatial aftershock distribution in the case of clearly ex-
tended ruptures. Here, we combine an ETAS-Incomplete model, accounting for short-term aftershock incompleteness,
with an ETAS approach applying anisotropic, spatially restricted distributions of aftershock locations. We evaluate
the benefits of these models by running forecast experiments for the 2019 Ridgecrest sequence and analyzing the oc-
currence frequencies of so-called Earthquake Doublets, i.e., sequences of two or more similarly strong earthquakes
within a small time-space window. The new model provides more realistic sequence forecasts and doublet predictions
and might be of particular interest for (short term) risk assessment units.

How to cite: Grimm, C., Hainzl, S., Käser, M., Pagani, M., and Küchenhoff, H.: Advancing the ETAS Model to Improve Forecasts of Earthquake Sequences and Doublets, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7674, https://doi.org/10.5194/egusphere-egu22-7674, 2022.

13:35–13:42
|
EGU22-10718
|
ECS
|
Virtual presentation
Leila Mizrahi et al.

When developing next-generation earthquake forecasting models, the key is to more carefully account for the real world (which has fault systems with different properties, site-specific properties, swarm-like episodes of temporally elevated seismicity, etc.), without constructing overly complicated models that are hard to comprehend and even harder to use. Finding the sweet spot between simplicity and accuracy is what constitutes the art of modelling. Epidemic-Type Aftershock Sequence (ETAS) models, despite being introduced over three decades ago, are still the undisputed reference for earthquake forecasting methods – be it as a benchmark when testing novel forecasting techniques, or as the model of choice for operational earthquake forecasting around the world. ETAS models accurately describe the average behavior of aftershock triggering as a self-exciting point process based on few simple empirical principles, including the Omori-Utsu and Gutenberg-Richter laws.

With this in mind, we are proposing a new model which naturally captures the diversity of conditions under which earthquakes take place. Within the ETAS statistical framework, we relax the assumptions of parametrically defined aftershock productivity and background earthquake rates. Instead, both productivity and background rates are calibrated with data such that their variability is optimally represented by the model. This allows for an impartial view on the behavior of background and triggered seismicity in different regions, different time periods, or different sequences. We perform pseudo-prospective forecasting experiments for Southern California to evaluate models based on their accuracy at forecasting the next event. These experiments reveal when, where, and under which conditions our proposed model yields better forecasts than the standard ETAS null model. 

How to cite: Mizrahi, L., Nandan, S., Savran, W., Wiemer, S., and Ben-Zion, Y.: Relaxing ETAS’s Assumptions to Better Capture the Real Behavior of Seismicity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10718, https://doi.org/10.5194/egusphere-egu22-10718, 2022.

13:42–13:49
|
EGU22-7506
|
Virtual presentation
Marcello Chiodi et al.
Seismic activity can be often described by a space-time ETAS (Epidemic Type Aftershock Sequences) model, which is composed of a background seismicity component (large scale) and a triggering one (small scale). Typically the large-scale component is a spatial inhomogeneous Poissonian process, whose intensity is usually estimated through non-parametric techniques: in the case of Chilean seismicity, the majority of events, with a greater magnitude, occur along the Nazca plate, due to the subduction process, so that the  anisotropic kernel estimates should better describe the background seismicity than the classical isotropic kernel estimates. Similar considerations could be made for triggered events.
In previous papers, we used the ETAS model, with the Forward Likelihood Predictive approach (FLP), with the triggered seismicity modeled with a parametric space-time function, using also some covariates together with the magnitude of the triggering events. From a statistical point of view, a forecast of triggered seismicity can be made in the days following a big event. In this work, we will explore the predictive properties of a new proposal of anisotropic ETAS model, with an extension of the semiparametric approach of etasFLP proposed by Chiodi, et al. (2021).
We used open-source software (R package etasFLP, Chiodi and Adelfio 
(2017, 2020)) to perform the semiparametric estimation of the ETAS model with covariates.
 

How to cite: Chiodi, M., Nicolis, O., Adelfio, G., Marcon, G., Gonzalez, A., and D'Alessandro, A.: Predictive properties of an anisotropic ETAS Space-time model applied to Chilean seismicity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7506, https://doi.org/10.5194/egusphere-egu22-7506, 2022.

13:49–13:56
|
EGU22-7735
Marisol Monterrubio-Velasco and Natalia Zamora

Forecasting spatio-temporal occurrence of earthquakes is not a trivial step for the seismic and tsunami hazard assessments. Estimating earthquake rates depends on information of a nonlinear system that is poorly known including the source dimensions. Thus, these assessments rely on e.g. seismic catalogues, or geophysical and geological data that could portray the statistical and physical behaviour of the seismogenic zones. In particular, earthquakes could rupture along asperities or areas of the seismogenic zone with high stress accumulation.Those areas have different physical properties than the surrounding area, such as a high frictional strength and larger stress drop (e.g. Madariaga 1979, Corbi et al., 2017). In this work, we apply the TREMOL code (Monterrubio-Velasco et al., 2019), based on the Fiber Bundle Model, to validate it as a tool to reproduce the seismicity occurring by the rupture of large, in some cases, single asperities. We have selected four regions where large earthquakes have occurred: M8.8 Maule 2010 (Chile) earthquakes, M9.1 Tohoku 2011 (Japan), M7.6 Nicoya 2012 (Costa Rica) and M8.3 Coquimbo 2015 (Chile). In these tectonic regions, earthquake sequences are generated based on a discrete model of material failure used in TREMOL. One of the most notable results is that the maximum earthquakes of the real sequences are achieved. Also, in most cases, the magnitude - frequency distribution is similar to those of real data. While the outcomes of TREMOL are given in rupture areas, several area-magnitude scaling laws are employed to obtain moment magnitudes. By carrying out a sensitivity analysis of different scaling laws, we show the bias in the synthetic catalogues which is a critical input in seismic hazard assessment. It is shown that the synthetic seismicity using the Ramirez-Gaytan scaling law (Ramirez-Gaytan et al. 2014) is the best to fit the magnitude of the real series in most of the cases. Following the validation of TREMOL, we provide a new seismic scenario generator of future events to assist e.g. the Probabilistic Seismic/Tsunami Hazard Assessment (PSHA/PTHA) complementing the seismic forecast with other well known statistical tools. 

 

References

 

Corbi, F., Funiciello, F., Brizzi, S., Lallemand, S., and Rosenau, M. (2017). Control of asperities size and spacing on seismic behavior of subduction megathrusts, Geophys. Res. Lett., 44, 8227– 8235, doi:10.1002/2017GL074182.



Madariaga, R. (1979). On the relation between seismic moment and stress drop in the presence of stress and strength heterogeneity, J. Geophys. Res.-Sol. Ea., 84, 2243–2250.





Monterrubio-Velasco et al., (2019). A stochastic rupture earthquake code based on the fiber bundle model (TREMOL v0.1): application to Mexican subduction earthquakes. Geosci. Model Dev., 12, 1809–1831.

Ramírez-Gaytán, A., Aguirre, J., Jaimes, M. A., and Huérfano, V. (2014). Scaling relationships of source parameters of M w 6.9–8.1 earthquakes in the Cocos–Rivera–North American subduction zone, Bulletin of the Seismological Society of America, 104, 840–854.

 

How to cite: Monterrubio-Velasco, M. and Zamora, N.: Sensitivity analysis using the TREMOL code for seismicity forecasting , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7735, https://doi.org/10.5194/egusphere-egu22-7735, 2022.

13:56–14:03
|
EGU22-3959
|
On-site presentation
Stefania Gentili et al.

NESTORE (Next STrOng Related Earthquake) is a recently developed algorithm (Gentili & Di Giovambattista 2017, 2020) to recognize clusters in which a strong mainshock is followed by an aftershock of similar magnitude. Specifically, NESTORE labels clusters as type A if the magnitude difference between the mainshock and its strongest aftershock is less than or equal to 1, otherwise as type B. After an intense earthquake, the prediction of strong following events is strategic for civil protection purposes. In fact, already weakened structures may suffer further damage, increasing the risk of collapse and casualties. The goal of NESTORE is a near real-time estimation of the probability that the ongoing cluster is type A. The software is based on a set of parameters (features) of seismic clusters calculated at increasing time intervals after the mainshock. In particular, the algorithm exploits a training procedure with a feature-based machine learning approach. The features are related to the evolution of the number of events and their space-magnitude distribution over time. To make NESTORE a suitable software for online sharing, we optimized its structure. Specifically, some functions have been improved, further ones have been added, and a new name structure has been introduced to better characterize the three independent modules of NESTORE (cluster identification, training, and testing). This software renovation has been developed in the frame of project “Analysis of seismic sequences for strong aftershock forecasting” funded by a grant from the Italian Ministry of Foreign Affairs and International Cooperation within the collaboration in science and technology between Italy and Japan. We applied this new version of NESTORE to Italian seismicity and in particular to North-Eastern Italy, and obtained information on the features with best performances in terms of type A and B cluster discrimination.

 

Funded by a grant from the Italian Ministry of Foreign Affairs and International Cooperation

How to cite: Gentili, S., Brondi, P., and Di Giovambattista, R.: An optimized online version of NESTORE software package for the forecasting of strong aftershocks: an application to Italian clusters , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3959, https://doi.org/10.5194/egusphere-egu22-3959, 2022.

14:03–14:05
Discussion