SM4.1 | Earthquake Source Physics: Applications of Imaging, Numerical Simulations and Machine Learning

This session will focus on three approaches for investigating the physics of earthquakes: imaging, numerical simulations, and machine learning. We solicit abstracts on works to image rupture kinematics, simulate earthquake dynamics using numerical methods, and those using Machine Learning (ML) to improve understanding of the physics of earthquakes. We invite in particular works that aim to develop a deeper understanding of earthquake source physics by linking novel laboratory experiments to earthquake dynamics, and studies on earthquake scaling properties. We also encourage works that illuminate the physics behind and transferability to Earth of studies showing that acoustic emissions can be used to predict characteristics of laboratory earthquakes and identify precursors to labquakes. Other works show progress in imaging earthquake sources using seismic data and surface deformation measurements (e.g. GPS and InSAR) to estimate rupture properties on faults and fault systems.

We want to highlight strengths and limitations of each data set and method in the context of the source-inversion problem, accounting for uncertainties and robustness of the source models and imaging or simulation methods. Contributions are welcome that make use of modern computing paradigms and infrastructure to tackle large-scale forward simulation of earthquake process, but also inverse modeling to retrieve the rupture process with proper uncertainty quantification. We also welcome ML-based works on a broad range of issues in seismology and encourage seismic studies using data from natural faults, lab results and numerical approaches to understand earthquake physics.

Co-organized by NH4
Convener: Henriette Sudhaus | Co-conveners: Chris Marone, Alice-Agnes GabrielECSECS, Elisa Tinti, Paul Johnson, P. Martin Mai
Orals
| Fri, 28 Apr, 08:30–12:30 (CEST)
 
Room -2.47/48
Posters on site
| Attendance Fri, 28 Apr, 14:00–15:45 (CEST)
 
Hall X2
Posters virtual
| Attendance Fri, 28 Apr, 14:00–15:45 (CEST)
 
vHall GMPV/G/GD/SM
Orals |
Fri, 08:30
Fri, 14:00
Fri, 14:00

Orals: Fri, 28 Apr | Room -2.47/48

Chairpersons: Henriette Sudhaus, Chris Marone, Alice-Agnes Gabriel
08:30–08:35
Invited Talk
08:35–08:55
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EGU23-15437
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SM4.1
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solicited
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On-site presentation
Parisa Shokouhi, Prabhav Borate, Jacques Riviere, Ankur Mali, and Dan Kifer

Recent laboratory studies of fault friction have shown that deep learning can accurately predict the magnitude and timing of stick-slip sliding events, the laboratory equivalent of earthquakes, from the preceding acoustic emissions (AE) events or time-lapse active-source ultrasonic signals. While there are observations that provide insight into the physics of these predictions, the underlying precursory mechanisms are not fully understood. Furthermore, these purely data-driven models require a large amount of training data and may not generalize well outside their training domain. Here, we present a physics-guided machine learning approach - by incorporating the relevant physics directly in the prediction model architecture - with the objectives of enhancing model predictions and generalizability as well as reducing the amount of required training data. We use data from well-controlled double-direct shear laboratory friction experiments on Westerly granite blocks exhibiting numerous regular and irregular stick-slip cycles. Simultaneously, AEs are recorded while the faults are also regularly probed by ultrasonic waves transmitted through the fault zone to monitor the evolution of the contact stiffness during shearing. Our physics-guided ML models take features extracted from AE time series or time-lapse active source ultrasonic signals and predict the shear stress history, which gives both the timing and size of the laboratory earthquakes. The models are constrained by friction laws as well as simplified physical laws governing ultrasonic transmission and AE generation. Our findings indicate that physics-guided ML models outperform purely data-driven models in important ways; they provide accurate predictions even with little training data and transfer learning is greatly enhanced when physics constraints are incorporated. These findings have important implications for earthquake predictions in the field, where training data are scarce.  

How to cite: Shokouhi, P., Borate, P., Riviere, J., Mali, A., and Kifer, D.: Physics-guided machine learning for laboratory earthquake prediction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15437, https://doi.org/10.5194/egusphere-egu23-15437, 2023.

08:55–09:05
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EGU23-1967
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SM4.1
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On-site presentation
Danu Caus, Harsh Grover, Thomas H. Goebel, Grzegorz Kwiatek, and Tobias Weigel

Earthquake prediction relies on identification of distinctive patterns of precursory parameters that might precede a large earthquake. However, these patterns are typically not reliably observed in the field. Laboratory stick-slip experiments provide an analog of seismic cycle observed in nature in fully controlled conditions with the associated Acoustic Emission (AE) activity reproducing basic characteristics of seismicity preceding and following the large lab earthquake. Recent laboratory studies showed that the deployment of Machine Learning/Artificial Intelligence techniques has lead to new state of the art results in lab earthquake prediction on smooth faults while using simple statistical features derived from raw AE signals and AE-derived catalogs. However, not enough work has been done on explainability of earthquakes preparatory process on rough faults by leveraging deep learning techniques. In this work we attempt to mitigate this gap and analyze/grade a pool of  explainable seismo-mechanical features through the eyes of neural networks.

We used AE data from three laboratory stick-slip experiments performed in triaxial pressure vessel on Westerly Granite samples. Samples were first fractured at 75MPa confining pressure creating rough fault surfaces. The following stick-slip experiments were performed at constant displacement rate. The experimental procedure led to an extremely complex slip pattern composed of large and small slips of the whole surface, as well as the confined slips highlighted only with AE data and no externally measured slip. The AE catalog was used to extract temporal evolution of 16 seismo-mechanical and statistical features characterizing evolution of stress and damage in response to the axial stress change. The feature pool included clearly physically interpretable parameters such as AE rates, b-value, fractal dimension, AE localization, clustering and triggering properties, and features characterizing the variability of local stress field. 

We apply explainable AI techniques to identify what features are more important to forecast  axial stress and stress drop.  Our feature ranking and importance evaluation with the help of neural networks can serve as an indicator as to what research directions are more promising to take for further feature engineering efforts with an emphasis on explainability of earthquake phenomena.

How to cite: Caus, D., Grover, H., H. Goebel, T., Kwiatek, G., and Weigel, T.: Predicting fault stress level and stress drop using seismo-mechanical and statistical features derived from acoustic signals in laboratory stick-slip friction experiments and assesing feature importance via the derived models., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1967, https://doi.org/10.5194/egusphere-egu23-1967, 2023.

09:05–09:15
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EGU23-5398
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SM4.1
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ECS
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On-site presentation
Antonio Giovanni Iaccarino and Matteo Picozzi

The generation of strong earthquakes is a long-debated problem in seismology, and its importance is increased by the possible implications for earthquake forecasting. It is hypothesized that the earthquake generation processes are anticipated by several phenomena occurring within a nucleation region. These phenomena, also defined as preparatory processes, load stress on the fault leading it to reach a critical state. In this paper, we investigate the seismicity preceding 19 moderate (Mw≥3.5) earthquakes at The Geysers, Northern California, aiming to verify the existence of a preparatory phase before their occurrence. We apply an unsupervised K-means clustering technique to analyze time-series of physics-related features extracted from catalog information and estimated for events occurred before the mainshocks. Specifically, we study the temporal evolution of the b-value from the Gutenberg-Richter (b), the magnitude of completeness (Mc), the fractal dimension (Dc), the inter-event time (dt), and the moment rate (Mr). Our analysis shows the existence of a common preparatory phase for 11 events, plus other 5 events for which we can guess a preparatory phase but with different characteristics of previous ones, indicating different possible activation behavior. The duration of the preparatory process ranges between about 16 hours and 4 days. We find that the retrieved preparatory process involves a decrease of b, Mc, and Dc, and an increase of Mr, as found by many authors. Finally, we show a clear correlation between events showing a preparation phase and the location of injection’s wells, suggesting an important role of fluids in the preparatory process.

How to cite: Iaccarino, A. G. and Picozzi, M.: Detecting the preparatory phase of induced earthquakes at The Geysers (California) using K-means clustering, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5398, https://doi.org/10.5194/egusphere-egu23-5398, 2023.

09:15–09:25
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EGU23-7112
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SM4.1
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ECS
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On-site presentation
Wei-Fang Sun, Sheng-Yan Pan, Chun-Ming Huang, Zhuo-Kang Guan, I-Chin Yen, and Hao Kuo-Chen

The Longitudinal Valley in eastern Taiwan, the arc-collision boundary between the Eurasian and Philippine Sea plates, is one the most seismic active areas in the world. On September 18, 2022, the Mw 6.9 Chihshang earthquake struck the south half of the valley and caused severe damage. Since November 2021, we have installed a five-station permanent broadband seismic array with station spacings of 10-20 km around the Chihshang area, and right after the Mw 6.5 foreshock occurred, we further installed a 46-station temporary dense array of nodal seismometers with station spacings of 2-5 km for 35 days. We use SeisBlue, a deep-learning platform/package, to extract the whole earthquake sequence including the Mw 6.5 foreshock, the Mw 6.9 main shock, and over 5,000 aftershocks from the broadband array, and to obtain over 40,000 aftershocks from the dense nodal array. With the high quality and quantity of P- and S-wave arrival times, we apply the finite difference travel time tomography, developed by Roecker et al. (2006). The improved resolution at the shallow part of the crust (at depth < 10 km) provides new constraints to get detailed (with grid spacing 1 km) and reliable Vp, Vs, and Vp/Vs velocity models at the local scale for the first time. Combined with the high-resolution velocity models and the much more complete seismicity, our results clearly depict not only the Central Range fault and the Longitudinal fault but also several local, shallow tectonic structures that have not been observed along the southern Longitudinal Valley.

How to cite: Sun, W.-F., Pan, S.-Y., Huang, C.-M., Guan, Z.-K., Yen, I.-C., and Kuo-Chen, H.: Deep Learning-based earthquake catalog and tomography reveal the rupture process of the 2022 Mw 6.9 Chihshang earthquake sequence, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7112, https://doi.org/10.5194/egusphere-egu23-7112, 2023.

09:25–09:35
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EGU23-5810
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SM4.1
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ECS
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On-site presentation
Laura Laurenti, Gabriele Paoletti, Elisa Tinti, Fabio Galasso, Luca Franco, Cristiano Collettini, and Chris Marone

Fault zone properties can change significantly during the seismic cycle in response to stress changes, microcracking and wall rock damage. Lab experiments show consistent changes in elastic properties prior to and after lab earthquakes (EQ) and previous works show that machine learning/deep learning (ML/DL) techniques are successful for capturing such changes. Here, we apply DL techniques to assess whether similar changes occur during the seismic cycle of tectonic EQ. The main motivation is to generalize lab-based findings to tectonic faulting, to predict failure and identify precursors. The novelty is that we use EQ traces as probing signals to estimate the fault state.

We train DL model to distinguish foreshocks, aftershocks and time to failure of the Mw 6.5 2016 Norcia EQ in central Italy, October 30th 2016. We analyze a 25-second window of 3-component data around the P- and S-wave arrivals for events near the Norcia fault with M>0.5 and ±2 months before/after the Norcia mainshock. Normalized waveforms are used to train a Convolutional Neural Network (CNN). As a first task we divide events into two classes (foreshocks/aftershocks), and then refine the classification as a function of time-to-failure (TTF) for the mainshock. Our DL model perform very well for TTF classification into 2, 4, 8, or 9-classes for the 2 months before/after the mainshock. We explore a range of seismic ray paths near, through, and away from the Norcia mainshock fault zone. Model performance exceeds 90% for most stations. Waveform investigations show that wave amplitude is not the key factor; other waveform properties dictate model performance. Models derived from seismic spectra, rather than time-domain data, are equally good. We challenged the model in several ways to confirm the results. We found reduced performance in training the model with the wrong mainshock time and by omitting data immediately before/after the mainshock. Foreshock/aftershock identification is significantly degraded also by removing high frequencies (filtering seismic data above 25 Hz). We tested data from different years to understand seasonality at individual stations for the time period September to December and removed these effects. Comparing these seasonality effects defined from noise with our EQ results shows that foreshocks/aftershocks for the 2016 Norcia mainshock are well resolved. Training with data containing EQ offers a huge increase in classification performance over noise only, proving that EQ signals are the sole that enable assessing timing as a function of the fault status. To confirm our results and understand which stations are able to detect changes of fault properties we perform a further test cleaning the signals from the seasonality by confounding the DL with a shuffled noise (adversarial training).

We conclude that DL is able to recognize variations in the stress state and fracture during the seismic cycle. The model uses EQ-induced changes in seismic attenuation to distinguish foreshocks from aftershocks and time to failure. This is an important step in ongoing efforts to improve EQ prediction and precursor identification through the use of ML and DL.

How to cite: Laurenti, L., Paoletti, G., Tinti, E., Galasso, F., Franco, L., Collettini, C., and Marone, C.: Using Deep Learning to understand variations in fault zone properties: distinguishing foreshocks from aftershocks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5810, https://doi.org/10.5194/egusphere-egu23-5810, 2023.

09:35–09:45
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EGU23-13811
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SM4.1
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ECS
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On-site presentation
Daniele Trappolini, Laura Laurenti, Elisa Tinti, Fabio Galasso, Chris Marone, and Alberto Michelini

Seismic waves contain information about the earthquake (EQ) source and many forms of noise deriving from the seismometer, anthropogenic effects, background noise associated with ocean waves, and microseismic noise. Separating the noise from the EQ signal is a critical first step in EQ physics and seismic waveform analysis. However, this is difficult because optimal parameters for filtering noise typically vary with time and may strongly alter the shape of the waveform. A few recent works have employed Deep Learning (DL) model for seismic denoising, among which we have taken as a benchmark Deep Denoiser and SEDENOSS. These models turn the noisy trace into a  2D signal (spectrograms) within the model to denoise the traces, making the process pretty heavy. We propose a novel DL-powered seismic denoising algorithm based on Diffusion Models (DMs), keeping the signal in 1D. DMs are the latest trend in Machine Learning (ML), having revolutionized the application fields of audio and image processing for denoising (DiffWave), synthesis (Stable Diffusion), and sequence modeling (STARS). The training of DMs proceeds by polluting a signal with noise until the signal has completely vanished into noise, then reversing the process by iterative denoising, conditioned on the latent signal representation. This makes DMs the ideal tool for seismic traces cleaning, as the model naturally learns from seismic sequences by denoising, which aligns the ML training procedure and the final task objective. In a preliminary evaluation, we used the Stanford Earthquake Dataset (STEAD); our proposed Diffusion-based Seismic Denoiser (DiffSD) outperforms the state-of-the-art DL methods on the Signal Noise Ratio (SNR),  Scale-Invariant Source to Distortion Ratio (SI-SDR), and Source to Distortion Ratio (SDR) metrics. DiffSD also yields qualitatively pleasing EQ traces out of visual inspection in time and frequency. Finally, DiffSD proceeds from regenerating clean EQ signals from noise, which opens the way to data-driven EQ sequence generations, potentially instrumental to further study and dataset augmentations.

How to cite: Trappolini, D., Laurenti, L., Tinti, E., Galasso, F., Marone, C., and Michelini, A.: DiffSD: Diffusion models for seismic denoising, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13811, https://doi.org/10.5194/egusphere-egu23-13811, 2023.

09:45–09:55
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EGU23-12170
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SM4.1
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On-site presentation
Gianmarco Mengaldo, Adriano Gualandi, and Chris Marone

Friction is a complex phenomenon. This can be seen, for example, in laboratory experiments where stick-slip motion of various kind (i.e., slow and fast instabilities) can be produced when adapting the normal stress applied to the system. Similarly, natural earthquakes also produce
complex stick-slip behaviour. A first challenge in the description of friction comes from the potentially high number of degrees of freedom (dofs) involved in the description of the dynamics of the sliding surfaces. Nonetheless, it was shown that friction can be described with a reduced number of dofs or variables of the dynamics. These may include the shear stress, the relative sliding slip rate, and one or more variables that describe the state of the contact of the sliding surfaces. We investigate the possibility to extract directly from the data the governing equations of friction starting from a simplified synthetic example. We further study the laboratory data with the Hankel Alternative View Of Koopman (HAVOK) theory, a method rooted in dynamical system theory that leverages data driven techniques and produces a Reduced Order Model (ROM) to reconstruct a shadow of the attractor of a system from observational data. We finally compare the results obtained for the laboratory experiments with Cascadia slow earthquakes.

How to cite: Mengaldo, G., Gualandi, A., and Marone, C.: Data-driven slow earthquake dynamics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12170, https://doi.org/10.5194/egusphere-egu23-12170, 2023.

Applications of Imaging and Numerical Simulations
09:55–10:05
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EGU23-16782
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SM4.1
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ECS
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On-site presentation
Lucas Sawade, Liang Ding, Daniel Peter, Hom Nath Gharti, Qinya Liu, Meredith Nettles, Göran Ekström, and Jeroen Tromp

Currently, the accuracy of synthetic seismograms used for Global CMT inversion, which are based on modern 3D Earth models, is limited by the validity of the path-average approximation for mode summation and surface-wave ray theory. Inaccurate computation of the ground motion’s amplitude and polarization as well as other effects that are not modeled may bias inverted earthquake parameters. Synthetic seismograms of higher accuracy will improve the determination of seismic sources in the CMT analysis, and remove concerns about this source of uncertainty. Strain tensors, and databases thereof, have recently been implemented for the spectral-element solver SPECFEM3D (Ding et al., 2020) based on the theory of previous work (Zhao et al., 2006) for regional inversion of seismograms for earthquake parameters. The main barriers to a global database of Green functions have been storage, I/O, and computation. But, compression tricks and smart selection of spectral elements, fast I/O data formats for high-performance computing, such as ADIOS, and wave-equation solution on GPUs, have dramatically decreased the cost of storage, I/O, and computation, respectively. Additionally, the global spectral-element grid matches the accuracy of a full forward calculation by virtue of Lagrange interpolation. Here, we present our first preliminary database of stored Green functions for 17 seismic stations of the global seismic networks to be used in future 3-D centroid moment tensor inversions. We demonstrate the fast retrieval and computation of seismograms from the database.

How to cite: Sawade, L., Ding, L., Peter, D., Gharti, H. N., Liu, Q., Nettles, M., Ekström, G., and Tromp, J.: A Preliminary Green Function Database for Global 3-D Centroid Moment Tensor Inversions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16782, https://doi.org/10.5194/egusphere-egu23-16782, 2023.

10:05–10:15
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EGU23-12392
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SM4.1
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ECS
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On-site presentation
Francesco Mosconi, Elisa Tinti, Emanuele Casarotti, Alice Gabriel, Ravil Dorozhinskii, Luca Dal Zilio, Antonio Pio Rinaldi, and Massimo Cocco

Understanding the dynamics of microearthquakes is a timely challenge to solve current paradoxes in earthquake mechanics, such as the stress drop and fracture energy scaling with seismic moment. Dynamic modeling of microearthquakes induced by fluid injection is also relevant for studying rupture propagation following a stimulated nucleation. We study the main features of unstable dynamic ruptures caused by fluid injection on a target preexisting fault (50m x 50m) generating a Mw=1 event. The selected fault is located in the Bedretto Underground Laboratory (Swiss Alps) at ≈1000m depth. We perform fully dynamic rupture simulations and model seismic wave propagation in 3D by adopting a linear slip-weakening law. We use the distributed multi-GPU implementation of SeisSol on the supercomputer Marconi100.

 Stress field and fault geometry are well constrained by in-situ observations, allowing us to minimize the a priori imposed parameters. We investigate the scaling relations of stress drop, slip-weakening distance (Dc) and fracture energy (Gc) focusing on their role in governing dynamics of rupture propagation and arrest for a target Mw=1 induced earthquake. We explore different homogenous conditions of frictional parameters, and we show that the spontaneous arrest of the rupture is possible in the modeled stress regime, by assuming a high ratio between stress excess and dynamic stress drop (the fault strength parameter S), characterizing the fault before the fluid pressure change. The rupture arrest of modeled induced earthquakes depends on the heterogeneity of dynamic parameters due to the spatially variable effective normal stress. Moreover, for a fault with high S values (not ready to slip), small variations of Dc (0.5÷1.2 mm) can drive the rupture from self-arrested to run-away. Studying dynamic interactions (stress transfer) among slipping points on the rupturing fault provides insight on the breakdown process zone and shear stress evolution at the crack tip leading to failure. The inferred spatial dimension of the cohesive zone in our models is nearly ~0.3-0.4m, with a maximum slip of ~0.6 cm. Finally, we compare stress drop and fracture energy estimated from synthetic waveforms with assumed dynamic parameters. Our results suggest that meso-scale processes near the crack-tip affect rupture dynamics of micro-earthquakes.

How to cite: Mosconi, F., Tinti, E., Casarotti, E., Gabriel, A., Dorozhinskii, R., Dal Zilio, L., Rinaldi, A. P., and Cocco, M.: Modeling dynamic ruptures on extended faults for microearthquakes induced by fluid injection, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12392, https://doi.org/10.5194/egusphere-egu23-12392, 2023.

Coffee break
Chairpersons: Elisa Tinti, Paul Johnson, P. Martin Mai
10:45–10:50
Invited Talk
10:50–11:10
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EGU23-7571
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SM4.1
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ECS
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solicited
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On-site presentation
Sophie Giffard-Roisin, Tristan Montagnon, Erwan Pathier, Mauro Dalla Mura, Mathilde Marchandon, and James Hollingsworth

Recent advances in machine learning are having a revolutionizing effect on our understanding of the Solid Earth, in particular in the automatic detection of geophysical events and objects (such as volcano movements in InSAR [Anantrasirichai et al. 2018], landslides in optical satellite imaging [Mohan et al. 2021], and earthquakes in seismic recordings [Zhu et al. 2019]). Yet, the understanding of geophysical phenomena requires us to be able to accurately characterize them: automatizing such tasks by machine learning is the new challenge for future years. One main difficulty resides in the availability of a high quality labeled database, that is a database with both input data (such as remote sensing acquisitions) together with their ground truth (what we are looking for). In this context, the problem of ground deformation estimation by sub-pixel optical satellite image registration (or correlation) is a good example.

Precise estimation of ground displacement at regional scales from optical satellite imagery is fundamental for the understanding of earthquake ruptures. Current methods make use of correlation techniques between two image acquisitions in order to retrieve a fractional pixel shift [Rosu et al. 2014, Leprince et al. 2007]. However, the precision and accuracy of image correlation can be limited by various problems, such as differences in local lighting conditions between acquisitions, seasonal changes in image reflectance, stereoscopic and resampling artifacts, which can all bias the displacement estimate, especially in the sub-pixel domain.

Image correlation is a valuable and unique source of information on the coseismic strain particularly in the near-field of earthquake ruptures, where InSAR can often decorrelate. However, the correlation process can be limited by the underlying assumption of a locally homogenous displacement within the correlation window (typically 3x3 to 32x32 pixels wide), leading to a bias when the correlation window crosses a fault discontinuity. Data-driven methods may provide a way to overcome these errors. Yet, no ground truth displacement field exists in real world datasets. From the generation of a realistic simulated database based on Landsat-8 satellite image pairs, with added simulated sub-pixel shifts, we developed a Convolutional Neural Network (CNN) able to retrieve sub-pixel displacements. In particular, we show how to specifically design discontinuities in the training set in order to reduce the near-field bias where the correlation window crosses the fault. Comparisions are made with state-of-the-art correlations methods both on synthetic (and realistic) data, and on real images from the Ridgequest area.
This preliminary study provides an example of how to use realistic synthetic data generation (combining real data with synthetic numerical approaches) for training a machine learning model able to estimate fault displacement fields. Such an approach could be applied to other characterization tasks, e.g. when realistic numerical simulation data is available, while sufficient ground truth data is not.

How to cite: Giffard-Roisin, S., Montagnon, T., Pathier, E., Dalla Mura, M., Marchandon, M., and Hollingsworth, J.: Can deep learning help understand and characterize earthquakes? An example with deep learning optical satellite image correlation., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7571, https://doi.org/10.5194/egusphere-egu23-7571, 2023.

11:10–11:20
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EGU23-242
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SM4.1
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ECS
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On-site presentation
Ritsuya Shibata and Naofumi Aso

Fault rupture has various complexity in space and time. The temporal complexity could be expressed by the radiated energy enhancement factor (Ye et al., 2018), which is the ratio of the radiated energy to its theoretical minimum value. Regarding the spatial complexity, the rupture directivity has been well investigated from small scales (Boatwright 2007; Kane et al., 2013; Ross and Ben-Zion, 2016) to large scales (e.g. Ide and Takeo, 1997; Ruiz et al., 2016) by investigating the azimuthal dependency of dominant frequency or estimating the source process. While these studies focus on the spatiotemporal complexity of the entire fault rupture, the mesoscopic scale rupture complexity also exists through the rupture propagation, which is an important perspective of the rupture mechanics. Specifically, we can classify the rupture propagation into two endmembers: mode-II and -III ruptures. In this regard, we focused on the rupture propagations at the scale of subfault extracted from the waveform inversion.

In this study, we analyzed multiple M6-class inland earthquakes in Japan using waveform inversion with the radiation-corrected empirical Green’s functions (Shibata et al., 2022), which enable us to estimate slip distributions with slip directions by synthesizing the EGF waveforms for any focal mechanisms. Then, we introduced rupture-mode intensity to evaluate the rupture-mode preferences by comparing the rupture propagation direction with the slip direction for each earthquake. As a result, we confirmed that the rupture preferentially propagated parallel (mode II) or perpendicular (mode III) to the slip direction, which is expected from the fracture mechanics. In addition, the characteristic of rupture propagation at the early stage was similar to that during the entire rupture, implying that most rupture characteristics are determined at the early stage.

How to cite: Shibata, R. and Aso, N.: Rupture-mode preferences of crustal earthquakes in Japan, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-242, https://doi.org/10.5194/egusphere-egu23-242, 2023.

11:20–11:30
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EGU23-1726
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SM4.1
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ECS
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On-site presentation
Parva Shoaeifar and Katsuichiro Goda

The Denali Fault earthquake was one of the largest strike-slip earthquakes with significant surface ruptures that occurred in 2002 in Alaska, United States. Probabilistic fault displacement hazard assessment (PFDHA) plays an important role in post-earthquake disaster management. This is because critical facilities and infrastructures in the vicinity of active faults are prone to major damage, leading to suspension of service due to fault displacement. Hence, in the present study, a PFDHA due to the Denali earthquake is conducted using a new methodology of stochastic source-based fault displacement hazard analysis. In this method, the surface rupture can be evaluated by applying Okada equations to simulated earthquake source models. The main differences between the methodology of the present study with conventional fault displacement assessment practices are to utilize the stochastic source models instead of the empirical predictive relationship of surface fault displacement and to calculate the distribution for surface fault displacement at a site of interest using the Okada equations. The new methodology is more versatile than the existing methods in several characteristics. First, it is applicable to all faulting mechanisms (e.g., strike-slip, normal, and reverse) by specifying different rake angles of the ruptured fault. Second, it has the ability to consider multi-segment fault rupture. Third, the calculation of three translational displacements by the Okada equations for a given location is available. Lastly, it provides physically consistent fault displacement modelling at two locations for a given earthquake scenario, allowing estimating of the differential fault displacement at two sites. Then the capability of the method is evaluated by applying it to the historical case of the 2002 Denali Fault earthquake. The satisfactory match of the modelled fault displacement and the observations, such as surface offset, Global Positioning System (GPS), and Interferometric Synthetic Aperture Radar (InSAR) data, is achieved based on calculating a performance metric. Therefore, more realistic ground deformation assessments can be carried out. Importantly, the obtained results significantly contribute to the hazard in earthquake-prone areas and reduce potential fatality and casualty risks as well as the post-earthquake damage repair costs of the built environment.

How to cite: Shoaeifar, P. and Goda, K.: Stochastic Source Modelling of the 2002 Denali Earthquake for Fault Displacement Hazard Assessment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1726, https://doi.org/10.5194/egusphere-egu23-1726, 2023.

11:30–11:40
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EGU23-4934
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SM4.1
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ECS
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On-site presentation
Iva Lončar, Mathieu Causse, Martin Vallée, and Snježana Markušić

Seismological data from almost 100 broadband stations (70 < Δ < 420 km) from Croatia, Slovenia, Hungary, Italy, Austria, Bosnia and Hercegovina, Montenegro, and Slovakia have been used in the rupture analysis of the Petrinja (Croatia) MW6.4 earthquake, that occurred on the 29th of December 2020. Several foreshocks and aftershocks have been used as empirical Green’s function (EGF) to isolate source effects from propagation and local soil effects. First, P-wave mainshock seismograms are deconvolved from the EGF seismograms in the frequency domain to obtain the corner frequency (fc). Assuming Brune’s source model, the spectral analysis results in a large stress drop of 25 MPa. Second, using time-domain deconvolution of the Love wave time windows, apparent source time functions (ASTFs) have been computed and indicate an average source duration of 5 seconds. No significant directivity effects can be seen in both the fc values and source durations, whose weak variability suggests a bilateral rupture. Lastly, physical rupture parameters, such as rupture velocity, rupture dimensions, slip model and rise time, have been extracted from the ASTFs by two different techniques: (1) the Bayesian inversion method (Causse et al. 2017) and (2) the backprojection of the ASTFs on the isochrones (Király‐Proag et al. 2019). Both techniques indicate a slow rupture velocity (about 50% of the shear-wave velocity) and a rather short rupture length for an MW6.4 event (about 8 km), consistent with the obtained large seismological stress drop. Such features may be explained by the relatively complex and segmented fault system, typical of immature fault contexts.

 

References:

Causse, M., Cultrera, G., Moreau, L., Herrero, A., Schiappapietra, E. and Courboulex, F., 2017. Bayesian rupture imaging in a complex medium: The 29 May 2012 Emilia, Northern Italy, earthquake. Geophysical Research Letters44(15), pp.7783-7792.

Király‐Proag, E., Satriano, C., Bernard, P. and Wiemer, S., 2019. Rupture process of the M w 3.3 earthquake in the St. Gallen 2013 geothermal reservoir, Switzerland. Geophysical Research Letters46(14), pp.7990-7999.

How to cite: Lončar, I., Causse, M., Vallée, M., and Markušić, S.: Slow rupture propagation and large stress drop during the 2020 Mw6.4 Petrinja earthquake, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4934, https://doi.org/10.5194/egusphere-egu23-4934, 2023.

11:40–11:50
|
EGU23-6666
|
SM4.1
|
On-site presentation
Zacharie Duputel, Emmanuel Caballero, Cédric Twardzik, Luis Rivera, Emilie Klein, Junle Jiang, Cunren Liang, Lijun Zhu, Romain Jolivet, Eric Fielding, and Mark Simons

The 2015 Mw=8.3 Illapel earthquake is one of the largest megathrust earthquakes that has been recorded along the Chilean subduction zone. Given its magnitude, different rupture scenarios have been obtained. Previous studies show different amounts of shallow slip with some results showing almost no slip at the trench and others showing significant slip at shallow depths, up to 14 meters. In this work, we revisit this event by assembling a comprehensive data set including continuous and survey GNSS measurements corrected for post-seismic and aftershock signals, ascending and descending InSAR images of the Sentinel-1A satellite, tsunami data along with high-rate GPS, and doubly integrated strong-motion waveforms. We follow a Bayesian approach using the AlTar algorithm, in which we aim to obtain the posterior PDF of the joint inversion problem. In addition, we explore a new approach to account for forward problem uncertainties using a second-order perturbation approach. 

Results show a rupture with two main slip regions, and with significant slip at shallow depth that correlates with outer-rise aftershocks. Furthermore, kinematic models indicate that the rupture is encircling two regions updip of the hypocenter that remain unbroken during the mainshock and its aftershocks. These encircling patterns have been previously suggested by back-projection results but have not been observed in finite-fault slip models. We propose that the encircled regions correspond to barriers that can potentially be related to secondary fracture zones in the Chilean subduction zone.

How to cite: Duputel, Z., Caballero, E., Twardzik, C., Rivera, L., Klein, E., Jiang, J., Liang, C., Zhu, L., Jolivet, R., Fielding, E., and Simons, M.: Revisiting the 2015 Mw=8.3 Illapel earthquake: Unveiling complex fault slip properties using Bayesian inversion., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6666, https://doi.org/10.5194/egusphere-egu23-6666, 2023.

11:50–12:00
|
EGU23-8901
|
SM4.1
|
ECS
|
On-site presentation
Jonas Folesky, Rens Hofman, and Jörn Kummerow

We produced a comprehensive stress drop catalog for northern Chile. To improve reliability, we applied a combination of two different stress drop estimation approaches. The result is a mapped stress drop distribution for more than 30,000 events covering the subduction zone from the trench to a depth of about 150 km. The stress drops were computed on the basis of a recently updated version of the IPOC seismic catalog, now spanning the years 2007 to 2021, using the spectral stacking technique as well as the spectral ratio technique.
The resulting distribution reveals a segmentation of median stress drop values for different seismogenic parts of the subduction zone: We find the lowest stress drops for interface events and slightly increased values for the two parallel bands of seismicity below, which lie inside the subducting plate. The upper plate events, show higher stress drops and the intermediate depth events bear the highest median stress drop. The variation of the median stress drops between classes is small: from 1.3 MPa for interface events to about 3.2 MPa for intermediate depth events. This being the values of the spectral ratio results. Using spectral ratios we find the exact same order of median stress drops between the classes with a range of 2.0 MPa to 5.8 MPa for interface and intermediate depth events, respectively. Interestingly, there is no stress drop increase with dept in the uppermost ~80 km, i.e. within each of the classes except for the intermediate depth events.
Additionally, we observe spatial stress drop variability, a noticeable increase with distance from the plate interface, and temporal variability connected with the two megathrust events in the study region, the Mw7.6 2007 Tocopilla event and the Mw 8.1 Iquique event. 

How to cite: Folesky, J., Hofman, R., and Kummerow, J.: Stress Drop Segmentation in the Northern Chilean Subduction Zone: from Interface to Deep Seismicity., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8901, https://doi.org/10.5194/egusphere-egu23-8901, 2023.

12:00–12:10
|
EGU23-9037
|
SM4.1
|
ECS
|
On-site presentation
Catherine Hanagan and Richard Bennett

The 2019 Ridgecrest earthquake sequence is an exceptionally well-studied event, captured in nearly unprecedented geophysical detail. Three horizontal tensor borehole strainmeters (BSMs), ranging from ~2 to 30 kms near the trace of the rupture, offer a less-conventional and more sensitive measure of coseismic and postseismic deformation for the event. Historically, these instruments are noted as unreliable for measurements of coseismic strain because they are sensitive to small-scale, near-instrument heterogeneities, such as additional offsets triggered by dynamic strains or pore pressure effects. However, many studies compare the strains with pre-constrained forward models of slip. Our preliminary investigations show that we can better match the observed strains if we include BSM measurements in a joint inversion with GPS displacements for coseismic slip. Postseismically, the strainmeters record rapid, non-monotonic deformation that likewise does not match existing afterslip models with a single decay time. We present a new interpretation of co- and post-seismic deformation using BSM strains and GPS displacements at discrete time intervals marked by a change in sign or rate of strain accumulation. Our joint analysis resolves details of the co- and post-seismic slip history that remain otherwise hidden with more common satellite-based inversions from GPS and InSAR alone. 

How to cite: Hanagan, C. and Bennett, R.: Co- and post-seismic deformation resolved from joint inversion of GPS and borehole strainmeter measurements during the 2019 Ridgecrest earthquake sequence, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9037, https://doi.org/10.5194/egusphere-egu23-9037, 2023.

12:10–12:20
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EGU23-16374
|
SM4.1
|
ECS
|
Virtual presentation
Pulak Biswas and Sohom Ray

Mechanical models of slip development on geological faults and basal slip development in landslide or ice-sheets generally consider interfacial strength to be frictional and deformation of the surrounding medium to be elastic. The frictional strength is usually considered as sliding rate- and state-dependent. Their combination, elastic deformation due to differential slip and rate-state frictional strength, leads to nonlinear partial differential equations (PDEs) that govern the spatio-temporal evolution of slip. Here, we investigate how (synthetic) data on fault slip rate and traction can find the system of PDEs that governs fault slip development during the aseismic rupture phase and the slip instability phase. We first prepare (synthetic) data sets by numerically solving the forward problem of slip rate and fault shear stress evolution during a seismic cycle. We now identify the physical variables, for example, slip rate or frictional state variable, and apply nonlinearity identification algorithms within different time durations. We show that the nonlinearity identification algorithms can find the terms of the PDE that governs the slip rate evolution during the aseismic rupture phase and subsequent instability phase.

In particular, we use nonlinear dynamics identification algorithms (e.g., SINDy, Brunton et al., 2016) where we solve a regression problem, Ax=y. Here, y is the time derivative of the variable of interest, for example, slip rate. A is a large matrix (library) with all possible candidate functions that may appear in the slip rate evolution PDE. The entries in x, to be solved for, are coefficients corresponding to each library function in matrix A. We update A according to the solutions so that A's column space can span the dynamics we seek to find. To find the suitable column space for A, we encourage sparse solutions for x, suggesting that only a few columns in matrix are dominant, leading to a parsimonious representation of the governing PDE. 

We show that the algorithm successfully recovers the PDE governing quasi-static fault slip and basal slip evolution. Additionally, we could also find the frictional parameter, for example, a/b, where a and b, respectively, are the magnitudes that control direct and evolution effects. Moreover, the algorithm can also determine whether the associated state variable evolves as aging- or slip-law types or their combination. Further, with the data set prepared from distinct initial conditions, we show that the nonlinear dynamics identification algorithm can also determine the problem parameters’ spatial distributions (heterogeneities) from fault slip rate and shear stress data. 

How to cite: Biswas, P. and Ray, S.: Finding governing PDEs of quasistatic fault slip and basal slip evolution from (synthetic) slip rate and shear traction data., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16374, https://doi.org/10.5194/egusphere-egu23-16374, 2023.

12:20–12:30
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EGU23-9150
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SM4.1
|
On-site presentation
|
Martin Vallée, Yuqing Xie, Raphaël Grandin, Juan Carlos Villegas-Lanza, Jean-Mathieu Nocquet, Sandro Vaca, Lingsen Meng, Jean Paul Ampuero, Patricia Mothes, Paul Jarrin, Ciro Sierra Farfan, and Frédérique Rolandone

The 2019/05/26 Northern Peru earthquake (Mw=8) is a major intermediate-depth earthquake that occurred close to the eastern edge of the Nazca slab flat area. We analyze its rupture process using high-frequency back-projection and seismo-geodetic broadband inversion. Both imaging techniques provide a very consistent image of the peculiar space-time rupture process of this earthquake : its 60-second long rupture is characterized both by a main northward propagation (resulting in a rupture extent of almost 200km in this direction) and by a reactivation phase of the hypocentral area, particularly active 35s to 50s after origin time.

Given the depth of this earthquake (125-140km), the reactivation time window coincides with the arrival time of the surface-reflected elastic wavefield. Computed values of the dynamic Coulomb stresses associated with this wavefield are of the order of ten to several tens of kPa, in a range of values where dynamic triggering has already been observed. The reactivation phase of the Peru earthquake may thus originate from fault areas that were brought close to rupture by the initial rupture front before being triggered by stress increments provided by the reflected wavefield. Source time function complexity observed for other large intermediate-depth earthquakes further suggests that such a mechanism is not an isolated case. 

How to cite: Vallée, M., Xie, Y., Grandin, R., Villegas-Lanza, J. C., Nocquet, J.-M., Vaca, S., Meng, L., Ampuero, J. P., Mothes, P., Jarrin, P., Sierra Farfan, C., and Rolandone, F.: Free surface effects and rupture dynamics : insights from the 2019 Mw=8 northern Peru intraslab earthquake, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9150, https://doi.org/10.5194/egusphere-egu23-9150, 2023.

Posters on site: Fri, 28 Apr, 14:00–15:45 | Hall X2

Chairpersons: Henriette Sudhaus, Elisa Tinti, P. Martin Mai
X2.37
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EGU23-15105
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SM4.1
Carlo Giunchi, Matteo Bagagli, Spina Cianetti, Sonja Gaviano, Dario Jozinović, Valentino Lauciani, Anthony Lomax, Alberto Michelini, Léonard Seydoux, Luisa Valoroso, and Christopher Zerafa

Recent developments of machine learning (ML) algorithms and software-platforms (e.g. Keras, TensorFlow, PyTorch) have opened new frontiers for Earth sciences. In seismology, these advances have affected different aspects of the earthquake physics studies, such as ground motion prediction, seismic phase detection and identification, and seismic big-data analysis.

Within the project Pianeta Dinamico (Working Earth) of Istituto Nazionale di Geofisica e Vulcanologia, funded by the Italian Ministry of University and Research, in 2021 we applied to an internal call with the aim of developing and using existing state-of-the-art machine learning techniques, and delivering useful benchmarking dataset for earthquake analysis. The project is named SOME (Seismological Oriented Machine lEarning).

This multidisciplinary project tackles different tasks that highlight the potential and possible pitfalls of ML applications:

  • Earthquake monitoring: testing and applying Convolutional Neural Network (CNN) and Graph Neural Network (GNN) architectures to predict the intensity measurements (IM) of medium-size seismic events (2.9 < M ≤ 5.1) recorded from a regional network.
  • Seismic waveforms characterization: development of an unsupervised framework for hierarchical clustering of continuous data based on a deep scattering network (scatseisnet). A first application is aimed to detect and classify seismic data from a mainly aseismic region in NE Sardinia (Sos Enattos mining site) to assess the anthropogenic and natural noise levels.
  • Development of a new picking algorithm: implementation of U-NET model architecture of PhaseNet algorithm by using characteristic functions derived from FilterPicker software. This newly developed software is called Domain Knowledge PhaseNet (DKPN).
  • Creation of 2 ML dataset for earthquake studies: 1) INSTANCE dataset, containing the seismicity recorded between January 2005 and January 2020 by the national seismic network of INGV (~1.2 million three-component waveform traces), 2) AQUILA-2009 dataset containing the aftershock sequence of the 2009 Mw6.1  L’Aquila earthquake collected by a dense array of the permanent and temporary network deployed after the mainshock (>63,704 events, nearly >1.2 million 3C three-component traces).

The INSTANCE and AQUILA-2009 dataset are already used as training sets for new picking algorithms, and will be employed for additional statistical analysis in the near future (e.g. hazard assessment, shakemaps) and transfer-learning approaches. The GNN for IM shows promising results for future developments for ground-shaking forecasting applications. The unsupervised learning clusterization algorithm clearly detects signals that differ from purely seismic ones, proving to be a great tool for seeking new patterns and features in time-series records. The DKPN algorithm achieves better results compared  to the original PhaseNet architecture, even if trained with a small dataset (<15.000 3C traces), and shows improved performance for cross-domain application.

Overall, the SOME project has produced many deliverables, some of which have already been released. We also aimed to provide reproducibility of ML experiments, creating Docker applications suitable for ML-picking algorithms (e.g. EQ-Transformer, PhaseNet, GPD) and contributing to the improvement of existing libraries, like SeisBench, for benchmarking purposes. Indeed, reproducibility is an additional yet paramount issue that must be addressed by the seismological community when dealing with ML applications.

How to cite: Giunchi, C., Bagagli, M., Cianetti, S., Gaviano, S., Jozinović, D., Lauciani, V., Lomax, A., Michelini, A., Seydoux, L., Valoroso, L., and Zerafa, C.: Seismological Oriented Machine lEarning (SOME) project, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15105, https://doi.org/10.5194/egusphere-egu23-15105, 2023.

X2.38
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EGU23-2329
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SM4.1
|
ECS
Dimension Reduction Applied to Laboratory Earthquake Acoustic Emissions for Visual Analysis and Machine Learning
(withdrawn)
Rens Elbertsen, André Niemeijer Niemeijer, and Ivan Vasconcelos
X2.39
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EGU23-12453
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SM4.1
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ECS
Dinko Sindija, Jean-Baptiste Ammirati, Marija Mustac Brcic, Josip Stipcevic, and Gyorgy Hetenyi

Earthquake detection and phase picking are crucial steps in the analysis of earthquakes. With the increasing number of seismic instruments available, large amounts of seismic data are generated, requiring the use of automatic algorithms to process earthquake series and to include events that would not be discovered with manual approaches.

The Petrinja earthquake series started with local magnitude ML5.0 earthquake on December 28, 2020, followed by ML6.4 earthquake one day later. In the two years of this earthquake series, human analysts picked a total of 16,000 earthquakes smaller than M2.0, 1528 with magnitudes M2.0-2.9, 156 with magnitudes M3.0-3.9, 17 with magnitudes M4.0-4.9, 2 with magnitudes M5.0-5.9 and one earthquake with magnitude greater than M6.0. While the seismic network at the onset of this sequence counted only a few instruments in the epicentral area, the rapid aftershock deployment of 5 stations in the near vicinity of the fault zone, and the further gradual yet still remarkable growth of the seismic network to more than 50 instruments, produced an extraordinary amount of data, which are perfectly suited for employing machine learning (ML) methods for seismic phase picking and earthquake detection.

In this study we present application of various ML methods to the Petrinja earthquake series. We also compare how the results change when we train a model using a subset of data from this earthquake series. Our results show that these machine learning methods are promising approaches for accurately detecting and picking phases in such earthquake series, and also delineate tectonic features responsible for generating them.

How to cite: Sindija, D., Ammirati, J.-B., Mustac Brcic, M., Stipcevic, J., and Hetenyi, G.: The Petrinja earthquake series located and visualised using machine learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12453, https://doi.org/10.5194/egusphere-egu23-12453, 2023.

X2.40
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EGU23-2516
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SM4.1
|
ECS
Ke Gao

Predicting earthquakes has been a long-standing challenge. Recently, machine learning (ML) approaches have been employed to predict laboratory earthquakes using stick-slip dynamics data obtained from shear experiments. However, the data utilized are often acquired from only a few sensor points, thus insufficient in feature dimension and may limit the predictive power of ML. To address this issue, we adopt the combined finite-discrete element method (FDEM) to simulate a two-dimensional sheared granular fault system, from which abundant fault dynamics data (i.e., displacement and velocity) during stick-slip cycles are collected at 2203 “sensor” points densely placed in the numerical model. We then use the simulated data to train the LightGBM (Light Gradient Boosting Machine) models and predict the normalized gouge-plate shear stress (an indicator of stick-slips). Meanwhile, to optimize features, we build the importance ranking of input features and select those with top importance for prediction. We iteratively optimize and adjust the feature data, and finally reach a LightGBM model with an acceptable prediction accuracy (R2 = 0.91). The SHAP (SHapley Additive exPlanations) values of input features are also calculated to quantify their contributions to prediction. We show that when sufficient fault dynamics data are available, LightGBM, together with the SHAP value approach, is capable of accurately predicting the occurrence time and magnitude of laboratory earthquakes, and also has the potential to uncover the relationship between microscopic fault dynamics and macroscopic stick-slip behaviors. This work may shed light on natural earthquake prediction and open new possibilities to explore useful earthquake precursors using ML.

How to cite: Gao, K.: Predicting stick-slips in FDEM simulated sheared granular faults using machine learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2516, https://doi.org/10.5194/egusphere-egu23-2516, 2023.

X2.41
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EGU23-15640
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SM4.1
Alessandro Pignatelli, Adriano Nardi, and Elena Spagnuolo

Electromagnetic signals have been increasingly investigated in the last decade in association to natural earthquakes and laboratory rock fractures. Studies on this type of signals are hampered by  the lack of continuous recordings and, when data are available, the sampling rates (> kHz) is such to require an efficient and systematic processing of large data sets. Despite this limitation, previous studies performed under controlled conditions in the laboratory seem to suggest that electromagnetic signals exhibit characteristic patterns, called OIS - Ordered Impulsive Sequences, on a specific frequency band (the very low frequency, VLF) that correlate uniquely with the paroxistic rupture of rocks specimens under uniaxial tests. Importantly, these characteristic patterns were also detected in the atmosphere in association to moderate magnitude earthquakes occurring within a few days (up to 5) from their detection. The similarity of laboratory and atmospheric VLF offers a unique opportunity to study the relation between VLF and rock deformation on at least two different scales and to enlarge the dataset by combining laboratory and atmospheric data. Here, we deployed tools for a systematic monitoring of electromagnetic signals in the atmosphere and we show that the enlarged VLF dataset, which comprises both laboratory and natural electromagnetic signals, can be successfully processed using a neural network approach. Our neural network architecture was designed to deal with time series and is structured using a recurrent neural networks (RNN) and a Long Short Term memory (LSTM) as a state variable. After a careful data collection, signal sequences were classified as rock rupture precursors (“RUPTURE”) and some of them, including those composing the background noise, as no rupture precursors (“QUIET”). A deep BI-LSTM neural network with 1000 hidden units has been trained in order to fit the known classification and to implicitly acquire the most important features and cut offs to split the potential events to not events. Our main results are 1. laboratory and atmospheric OIS signals are similar and scalable; 2. the similarity is such that it can be successfully used to train a neural network for signal detection in the atmosphere; 3. the neural network is capable of detecting OIS from the huge data set which is made of all the atmospheric background; 4. the extracted signals are those which were typically recorded in association to earthquakes in a temporal window of a few days. The above results show that LSTM neural networks are effective “automatic detectors” for characteristic spectral patterns revealed in the VLF both in the laboratory and in atmospheric signals recorded in association with transient natural events involving fracturing of rock volumes (e.g. earthquakes). The above results suggest that the electromagnetic radiation in the very low frequency band is a promising and valuable signal to probe the deformation of the seismically active Earth crust.

How to cite: Pignatelli, A., Nardi, A., and Spagnuolo, E.: A neural network based approach to classify VLF signals as rock rupture precursors, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15640, https://doi.org/10.5194/egusphere-egu23-15640, 2023.

X2.42
|
EGU23-1238
|
SM4.1
Focal mechanism of intermediate and deep depth earthquakes at the Peru-Brazil-Bolivia border
(withdrawn)
Carmen Pro, Elisa Buforn, Hernando Tavera, Maurizio Mattesini, and Agustín Udías
X2.43
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EGU23-6521
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SM4.1
|
ECS
|
Duyuan Xu, Wenzheng Gong, Zhenguo Zhang, Houyun Yu, and Xiaofei Chen

A quantitative understanding of the factors that control earthquake rupture propagation is critical because it is helpful to estimate the eventual magnitude of an earthquake, which has significant implications for seismic hazard assessment. Previous studies suggest that the complex fault geometry and the heterogeneous material properties of the fault zone can slow and/or stop the rupture propagation. The 2016 Mw 5.9 Menyuan earthquake occurred near the Haiyuan fault system on the northeastern Tibetan plateau. Although some of the kinematic rupture properties of this earthquake have been known, the rupture process and some in-depth source properties remain to be understood. In this study, we first use the empirical Green's functions approach to reveal that the apparent source time functions (ASTFs) of this event display an approximately equal bell shape and have a total duration of about 3 s, which suggests that the rupture process of this earthquake is simple and exhibits no rupture directivity. Moreover, the spectra of ASTFs are very smooth and have no spectral holes. Then, we conduct two end-member spontaneous rupture models, namely the runaway rupture and the self-arresting rupture, to further explain the observed features of the ASTFs. We use the curved grid finite difference method (CG-FDM) to simulate the spontaneous rupture process with a linear slip-weakening friction law. Our results show that the synthetic data from the dynamic source fits well with the InSAR observations and strong ground motions, which indicates that the dynamic source captures the main features of this event. Significantly, the observed smooth spectra of ASTFs can be well explained by the self-arresting rupture process, which implies that this earthquake might be a self-arresting event. In other words, this earthquake may spontaneously stop before reaching the barriers. This finding suggests that some earthquake rupture processes may be deterministic by the initial stress state in which they nucleated. This work increases our understanding of what controls earthquake rupture propagation.

 

 

How to cite: Xu, D., Gong, W., Zhang, Z., Yu, H., and Chen, X.: A further explore the source features of the 2016 Mw 5.9 Menyuan earthquake by empirical Green's functions and dynamic simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6521, https://doi.org/10.5194/egusphere-egu23-6521, 2023.

X2.44
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EGU23-7939
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SM4.1
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ECS
|
Alex Saoulis, Ana Ferreira, Benjamin Joachimi, Alessio Spurio Mancini, and Davide Piras

Bayesian inference provides a pathway toward accurate predictions of source parameters (e.g., location and moment tensor), along with principled, well-calibrated uncertainty estimates. Unfortunately, standard Bayesian inference techniques can often require O(105) simulations per full waveform inversion, making the method infeasible when studying large numbers of events due to the high computational cost of seismological forward modelling. Machine Learning (ML) has emerged as a promising solution to this issue, with recent work demonstrating that ML-based emulators of the physics simulation can be used as rapid-executing surrogates of the forward model in the Bayesian inference workflow. It has been demonstrated that such models, in conjunction with an assumed likelihood model (e.g. Gaussian), can be used to efficiently perform Bayesian posterior inference over seismological source parameters.

 

This work explores an extension to the above method, often referred to as “likelihood-free” or “simulation-based” inference, which removes any assumptions about the likelihood model. This approach leverages a class of neural networks known as Neural Density Estimators (NDEs) to estimate the likelihood density directly given some representation of the observables. To simplify training of these NDEs, a compression technique that can reduce the observables (i.e., full seismograms) into a small set of parameters is required. This work investigates “classical” and ML-based compression techniques for creating a reduced dimension representation. It then demonstrates simulation-based inference on the problem of source location inversion applied to synthetic examples based on a recent seismic swarm on the São Jorge island in the Azores. Comparisons between the proposed approach and other inversion techniques are also presented.

How to cite: Saoulis, A., Ferreira, A., Joachimi, B., Spurio Mancini, A., and Piras, D.: Towards Bayesian Full-Waveform Source Inversion using Simulation-Based Inference, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7939, https://doi.org/10.5194/egusphere-egu23-7939, 2023.

X2.45
|
EGU23-13506
|
SM4.1
Elisa Tinti, Massimo Cocco, Stefano Aretusini, Chiara Cornelio, Stefan B. Nielsen, Elena Spagnuolo, Paul Selvadurai, and Giulio Di Toro

Geological observations reveal that earthquakes nucleate, propagate, and arrest in complex fault zones whose structural heterogeneity depends on the tectonic loading, geometry, lithology, rheology, presence of fluids, and strain localization processes. These fault zones can host a wide range of fault slip behaviors (e.g., creep, aseismic- and slow-slip events, afterslip, and earthquakes). This implies that the environment in which earthquakes occur is diverse, and that different physical and chemical processes can be involved during the coseismic dynamic rupture.

Earthquakes are generated by rupture propagation and slip within fault cores and dissipate the stored elastic and gravitational strain energy in fracture and frictional processes in the fault zone (from microscale - less than a millimeter - to macroscale - centimeters to kilometers) and in radiated seismic waves. Understanding this energy partitioning is fundamental in earthquake mechanics to describe dynamic fault weakening and causative rupture processes operating over different spatial and temporal scales.

The energy dissipated in earthquake rupture propagation is commonly called fracture energy (G) or breakdown work (Wb). Here we discuss these two parameters, and we review fracture energy estimates from seismological, modeling, geological, and experimental studies and show that fracture energy scales with fault slip and earthquake size. Our analysis confirms that seismological estimates of fracture energy and breakdown work are comparable and scale with seismic slip. The inferred scaling laws show modest deviations explained in terms of epistemic uncertainties. The original collection of fracture energy estimates from laboratory experiments confirms the scaling with slip over a slip range of more than 10 decades. Fracture energy associated with breaking of intact rocks is larger than for precut specimens and might suggest differences between the role of fracture and friction, or a different size of the rupture front zone. It is important to recall that fault products after deformation in the laboratory correspond to fault products observed in nature, and acoustic emissions recorded in the laboratory can be processed as seismic waves on a natural fault. We conclude that although material-dependent constant fracture energies are important at the microscale for fracturing grains of the fault zone, they are negligible with respect to the macroscale processes governing rupture propagation on natural faults.

In this study we discuss the scaling of fracture energy and breakdown work with slip, and we propose different interpretations relying on different processes characterizing complex fault zones. Our results suggest that, for earthquake ruptures in natural faults, the estimates of G and Wb are consistent with a macroscale description of the causative processes.

Reconciling observations and results from laboratory experiments and numerical modeling with geological observations can be done, provided that we accept the evidence that earthquakes can occur in a variety of geological settings and fault zone structures governed by different physical and chemical processes.

How to cite: Tinti, E., Cocco, M., Aretusini, S., Cornelio, C., Nielsen, S. B., Spagnuolo, E., Selvadurai, P., and Di Toro, G.: Fracture energy and breakdown work scaling with coseismic slip, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13506, https://doi.org/10.5194/egusphere-egu23-13506, 2023.

X2.46
|
EGU23-1165
|
SM4.1
Elisa Buforn, Agustín Udías, and Maurizio Mattesini

On 25th December 1884, a damaging earthquake shocked the Granada-Malaga (Spain) region, followed by a large number of aftershocks. This is the largest earthquake (Imax= IX-X, EMS-98, estimated magnitude 6.7) in southern Spain, with 750 persons killed and 1500 injured, and 4400 houses destroyed. After the occurrence of the main shock, a considerable number of reports on the damage caused by the catastrophic Andalusian earthquake were published mainly during the following year (1885) in several European journals, as well as in bulletins of scientific societies and books. A few of them were anonymous notes while others were signed by the most important geologists and seismologists from different European countries. Exceptional cases are the publications from the members of the three commissions (Spanish, French and Italian) that were specifically appointed to study this Andalusian earthquake, with the participation of prestigious seismologists, such as Macpherson, Mercalli, Taramelli, Fouqué, and Barrois. We present detailed information about the publications that appeared mainly during the following year (1885) of the occurrence of this earthquake. The prompt study of the Andalusian earthquake provided an opportunity for the scientific community at that time to present and disseminate new modern ideas about the nature of earthquakes and their relationship with the geodynamic processes and geology of the region abandoning the traditional explosive source.

How to cite: Buforn, E., Udías, A., and Mattesini, M.: Contemporary publications in Europe on the Spanish earthquake of 1884 , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1165, https://doi.org/10.5194/egusphere-egu23-1165, 2023.

X2.47
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EGU23-14611
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SM4.1
Çağrı Diner, Feyza Öztürk, and Mustafa Aktar

In this talk, a new geometric inversion method is proposed for obtaining the elastic parameters of an anisotropic focal region. More precisely, the eigenvalues of a vertical transversely isotropic (VTI) elasticity tensor of a focal region are obtained, up to a constant, for a given only one moment tensor, with an accuracy depending on the strength of anisotropy. The reason of using only one moment tensor is that although there occurs a lot of earthquakes in the same focal region, the orientation of the sources are similar. Hence one do not obtain independent equations from each earthquake, in order to use it in the inversion of elastic parameters of the focal region. Moreover, this method can be applied for real-time inversion once the moment tensor of an earthquake is evaluated.

The inversion method relies on the geometric fact that a moment tensor can be written as a linear combination of the eigenvectors of the anisotropic focal region's elasticity tensor. Then, in the inversion, we use the fact that each coefficient of this unique decomposition is proportional to the eigenvalues of the focal region's elasticity tensor. Two approximations are used in this inversion method; in particular for the potency and for the source orientations.

The strength of anisotropy of the focal region determines how accurate these approximations are and hence it also determines the resolutions of the inverted eigenvalues. Because of the anisotropy of the focal region, the errors in the inversion do depend on the orientations of the dip and rakes angles, but not the strike angle since the focal region is VTI. The accuracy of the inversion for the five parameters of VTI are shown on the steographic projection. The results are very promising along some orientations as shown in the figures. The last section of the talk deals with the inversion of eigenvalues, up to a constant, for a given set of moment tensors; not only one moment tensor. It turns out that the best fit corresponds to the average of inversion results obtained for different orientations of the sources.

How to cite: Diner, Ç., Öztürk, F., and Aktar, M.: Inversion for Eigenvalues of Focal Region’s Elasticity Tensor from a Moment Tensor, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14611, https://doi.org/10.5194/egusphere-egu23-14611, 2023.

Posters virtual: Fri, 28 Apr, 14:00–15:45 | vHall GMPV/G/GD/SM

vGGGS.4
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EGU23-1344
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SM4.1
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ECS
Tomohisa Okazaki, Takeo Ito, Kazuro Hirahara, and Naonori Ueda

Crustal deformation, which can be modeled by dislocation models, provides critical insights into the evolution of earthquake processes and future earthquake potentials. In this presentation, we introduce our recent work on a novel physics-informed deep learning approach for modeling coseismic crustal deformation (Okazaki et al. 2022). Physics-informed neural networks were proposed to solve both the forward and inverse problems by incorporating partial differential equations into loss functions (Raissi et al. 2019). The use of neural networks enables to represent continuous displacement fields in arbitrary geometrical structures and mechanical properties of rocks without discretization. To accurately model the displacement discontinuity on a fault, which cannot be directly approximated by neural networks composed of continuous functions, the polar coordinate system is introduced. We illustrate the validity and usefulness of the proposed approach through forward modeling of antiplane dislocations, which are used to model strike-slip faults. This approach would have considerable potential for extension to high-dimensional, anelastic, nonlinear, and inverse problems in a straightforward way.

Reference

Okazaki T, Ito T, Hirahara K, Ueda N, Physics-informed deep learning approach for modeling crustal deformation. Nature Communications, 13, 7092 (2022). https://doi.org/10.1038/s41467-022-34922-1

How to cite: Okazaki, T., Ito, T., Hirahara, K., and Ueda, N.: Physics Informed Deep Learning for Modeling Coseismic Crustal Deformation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1344, https://doi.org/10.5194/egusphere-egu23-1344, 2023.

vGGGS.5
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EGU23-379
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SM4.1
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ECS
Avinash Gupta and Ranjith Kunnath

Frictional sliding at bi-material interfaces (when contacting bodies possess different elastic properties) is important in context of earthquake dynamics. Dissimilarity in elastic materials across the interface give rises to complex rupture propagation phenomena and instabilities, compared to the case when the material is similar across the interface. This is due to the coupling between the normal stress and interfacial slip, which is absent in the homogenous case. In the literature, various numerical schemes have been proposed but still many aspects of bi-material ruptures are not well-understood such as the rupture mode, velocity selection and stability. The present work proposes a new numerical scheme to study frictional rupture at a bi-material interface governed by a rate- and state-dependent friction law. It uses a spectral form of the boundary integral equation method (BIEM) as derived in Ranjith (2015, 2022), to evaluate the field quantities at the interface. The BIEM approach computes elastodynamic convolution of traction over its temporal history at the interface only, without need to calculate in regions away from interface, making it numerically efficient, compared to other conventional approaches. In prior work, an alternative spectral form of BIEM was used by Breitenfeld and Geubelle (1998) for 2D in-plane elasticity and Morrissey and Geubelle (1997) for 2D antiplane elasticity. In their approach, time-convolution is performed of the displacement history at the interface. An advantage of Ranjith’s approach is that the convolution kernels for a bi-material interface can be expressed in closed form, whereas Breitenfeld and Geubelle (1998) had to obtain their convolution kernels numerically. Conversion between real space and spectral domain is done by the Fast Fourier Transform (FFT). Rupture propagation is studied for both in-plane and antiplane frictional sliding at a bi-material interface by coupling the BIEM with a rate- and state-dependent friction law. Such a friction law is known to be suitable for a bi-material interface because it gives rise to well-posed problems (Rice et al., 2001). In earlier studies, an alternative numerical scheme for rate- and state-dependent friction was proposed by Lapusta et al. (2001) to study earthquake sequences on a fault. The disadvantage of their approach is that convolution kernels need to be evaluated multiple times for higher order accuracy. In the present work, a simpler numerical scheme is proposed for bi-material interfaces following a rate- and state-dependent friction law which is computationally more efficient.

How to cite: Gupta, A. and Kunnath, R.: A numerical methodology for rupture propagation at bi-material interfaces with rate- and state-dependent friction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-379, https://doi.org/10.5194/egusphere-egu23-379, 2023.