The complexity of seismogenesis requires the development of stochastic models, the application of which aims to improve our understanding on the seismic process and the associated underlying process. Semi Markov models are introduced for estimating the expected number of earthquake occurrences with the classification of the model states based on earthquake magnitudes. The instantaneous earthquake occurrence rate between the model states as well as the total earthquake occurrence rate can be calculated. Seismotectonic characteristic of the study area, incorporated in the model as important component of the model, increase the consistency between the model and the process of earthquake generation and support a classification that integrates magnitudes and style of faulting, thus being more effective for the seismic hazard assessment. For revealing the stress field underlying the earthquake generation, which is not accessible to direct observation, the hidden Markov models (HMMs) are engaged. The HMMs consider that the states correspond to levels of the stress field and its application aim to reveal these states. Different number of states may be examined, dependent upon the organization of observations, and the HMMs are capable to reveal the number of stress levels as well as the way in which these levels are associated with the occurrence of certain earthquakes. Even better results are obtained via the application of hidden semi–Markov models (HSMMs) considering that the stress field constitutes the hidden process and which, compared with HMMs, allow any arbitrary distribution for the sojourn times. The investigation of the interactions between adjacent areas is accomplished by means of the linked stress release model (LSRM), based upon the consideration that spatio–temporal stress changes and interactions between adjacent fault segments constitute the most important component in seismic hazard assessment, as they can alter the occurrence probability of strong earthquakes onto these segments. The LSRM comprises the gradual increase of the strain energy due to continuous tectonic loading and its sudden release during the earthquake occurrence. The modeling is based on the theory of stochastic point process, and it is determined by the conditional intensity function. In an attempt to identify the most appropriate parameterization that better fits the data and describes the earthquake generation process, a constrained “m–memory” point process is introduced, the Constrained–Memory Stress Release Model (CM–SRM) implying that only the m most recent arrival times are taken into account in the conditional intensity function, for some suitable mÎN, instead of the entire history of the process. The performance of the above mentioned models application are evaluated and compared in terms of information criteria and residual analysis.

How to cite: Papadimitriou, E.: Stochastic models for earthquake occurrence, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4169, https://doi.org/10.5194/egusphere-egu23-4169, 2023.

The Parkfield section of the San Andreas fault has a history of frequently occurring moderate (M~6) earthquakes with recurrence times ranging from 12 to 38 years. Since 1985, it has been extensively monitored as part of the experiment to predict the next moderate earthquake. Using the rich data resulting from the high-resolution monitoring, studies have revealed several interesting and consistent patterns of the frequency-magnitude distribution (FMD) of earthquakes, measured by the b-value of the Gutenberg-Richter law. The fault consists of patches of low b-values (b < 0.6) that correlate well with locked patches and with the areas that slipped in the 2004 M6 earthquake. High b-values (b > 1.3) were found to correlate with creeping section of the faults, and both observations support the hypothesis of an inverse relation between differential stress and b-values. Further, the b-value was found to increase during the aftershock periods of the 2004 earthquake, but so far, no gradual loading throughout the seismic cycle has been documented at Parkfield.

Here we revisit the b-values along the Parkfield section 19 years after the last M6 event, with the objectives to monitor and better understand the evolution of b-values in space and time as the segment approaches the next rupture. Our aim is first to benchmark and enhance approaches to map and monitor transients, to optimize uncertainty quantification, robustness, and resolving power of our statistical methods. This is best targeted by creating synthetics catalogues with known properties and then benchmarking different methods for spatial mapping and time-series analysis of b-values. We specifically investigate the recently introduced b-positive estimator and convert observed b-values and activity rates to earthquake probabilities. In a second step, we analyse the observed patterns in a context of gradual fault loading and repeated moderate events, to derive insights into the underlying physical processes. Finally, our aim to set up a ‘b-value’ observatory that will continuously monitor the space-time evolution of b-values and earthquake probabilities.

How to cite: Mirwald, A., Gulia, L., and Wiemer, S.: b-value variation through the seismic cycle: Revisiting Parkfield, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15321, https://doi.org/10.5194/egusphere-egu23-15321, 2023.

The Gutenberg-Richter b-value represents the relative proportion of small to large earthquakes in a scale-free population and is an important parameter used in earthquake hazard assessment. Discussion of the amount of data required to obtain a robust b-value has been extensive and is ongoing. To complement these analyses, we show the effect of the b-value with changes to the dynamic range – the difference between minimum magnitude (or magnitude of completeness) and maximum magnitude, which is inherently linked to the sample size, but not proportionately correlated. Additionally, we show that biases in high b-values are due to the bias in the mean magnitude of a catalogue, which asymptotically converges from below.

We derive and analytic expression for the bias that arises in the maximum likelihood estimate of b as a function of dynamic range r. Our theory predicts the observed evolution of the modal value of the mean magnitude in multiple random samples of synthetic catalogues at different r, including the bias to high b at low r and the observed trend to an asymptotic limit with no bias. In the case of a single sample in real catalogues, the situation is substantially more complicated due to the heterogeneity, magnitude uncertainty and lack of knowledge of the true b-value. We summarise that these results explain why the likelihood of large events and the associated hazard is often underestimated in small catalogues with low r, for example in some studies of volcanic and induced seismicity.

How to cite: Geffers, G.-M., Main, I. G., and Naylor, M.: Frequency-size parameters as a function of dynamic range – the Gutenberg-Richter b-value for earthquakes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17092, https://doi.org/10.5194/egusphere-egu23-17092, 2023.

The go-to models for developing time-dependent earthquake forecasts are Epidemic-Type Aftershock Sequence (ETAS) models. They model earthquake occurrence as a spatio-temporal self-exciting point process, using basic empirical laws such as the Omori-Utsu law for the temporal evolution of aftershock rate, the Gutenberg-Richter law to describe the size distribution of earthquakes, the exponential productivity law and so on. The main focus and core strength of ETAS lie in modelling aftershock occurrence. An important aspect which holds great potential for improvement is the modelling of background seismicity.

In this study, we focus on the data sets and expert solicitations acquired for building the European Seismic Hazard Model (ESHM) 2020. Since these data sets cover a wide range of space and time, the properties of the earthquake catalogs (completeness magnitude, magnitude resolution, time and space resolution, b-value) vary by region and time period. We address these issues using the model accounting for the time-varying completeness magnitude (Mizrahi et al., 2021) and other adjustments, then develop an ETAS model that allows the background seismicity rate to vary with space and be covariate-dependent. The expectation-maximisation-based algorithm allows for these rates to be given as an input, in our case based on fault locations, estimated long-term seismicity rates and area sources, or estimated during inversion for expert-defined zonations. We test the models retrospectively for self-consistency and pseudo-prospectively to identify the ones that lead to the best operational forecasting model for Europe.

How to cite: Han, M., Mizrahi, L., and Wiemer, S.: Towards an Operational Earthquake Forecasting Model for Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14001, https://doi.org/10.5194/egusphere-egu23-14001, 2023.

How to cite: Petrillo, G., Zhuang, J., and Lippiello, E.: Is the stress relaxation relevant for long term forecasting?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1035, https://doi.org/10.5194/egusphere-egu23-1035, 2023.

Due to the complexity and high dimensionality of seismic catalogues, the dimensional reduction of raw seismic data and the feature selections needed to decluster these catalogues into crisis and non-crisis events remain a challenge. To address this problem, we propose a two-level analysis.

First, an unsupervised approach based on an artificial neural network called self-organising map (SOM) is applied. The SOM is a machine learning model that performs a non-linear mapping of large input spaces into a two-dimensional grid, which preserves the topological and metric relationships of the data. It therefore facilitates visualisation and interpretation of the results obtained. Then, agglomerative clustering is used to classify the different clusters obtained by the SOM method as containing background events, aftershocks and/or swarms. To estimate the classification uncertainty and confidence level of our declustering results, we developed a probabilistic function based on the feature representation learned by the SOM (spatiotemporal distances between events, magnitude variations and event density).

We tested the two-level analysis on synthetic data and applied it to real data: three seismic catalogues (Corithn Rift, Taiwan and Central Italy) that differ in area size, tectonic regime, magnitude of completeness, duration and detection methods. We show that our unsupervised machine learning approach can accurately distinguish between crisis and non-crisis events without the need for preliminary assumptions and that it is applicable to catalogues of various sizes in time and space without threshold selection.

How to cite: Septier, A., Renouard, A., Déverchère, J., and Perrot, J.: Can Seismicity Declustering be solved by Unsupervised Artificial Intelligence ?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5934, https://doi.org/10.5194/egusphere-egu23-5934, 2023.

Artificial Neural Networks (ANNs) are well known for their ability to find hidden patterns in data. This technique has also been widely used for predicting the time, location, and magnitude of future earthquakes. Using various data and neural networks, previous works claimed that their models are effective for predicting earthquakes. However, these scores provided by the evaluation metrics with poor reference models, which are non-professional in statistical seismology, are not robust. In this work, we first take the Nighttime Light Map (NLM) as the input of Long-short Term Memory (LSTM) networks to predict the earthquakes with M>=5.0 for the whole Chinese Mainland, and NLM records the lumens of nighttime artificial light, and it is retrieved from the nighttime satellite imagery. The NLM is not physically related to earthquakes; however, the scores provided by Receiver Operating Characteristics curve, Precision-Recall plot, and Molchan diagram with spatial invariant Poisson model indicated that NLM is effective for predicting earthquakes. These results reaffirmed that researchers should be cautious when using these evaluation metrics with poor reference models to evaluate earthquake prediction models. Moreover, the original loss functions of ANNs, such as Cross Entropy (CE), Balanced Cross Entropy (BCE), Focal Loss (FL), and Focal Loss alpha (FL-alpha), contain no knowledge about seismology. To differentiate the hard and easy examples of earthquake prediction models during the training steps of ANNs, the punishment of CE, BCE, FL, and FL-alpha for positive examples will be further weighted by P₀ and the punishment for negative examples will be weighted by P₁, where P₁/P₀ is the prior probability provided by the reference model that at least one or no earthquakes will occur for the given example and P₁+P₀=1. The reference models are supposed to be as close to the real spatial-temporal distribution of earthquakes as possible, and the spatial variable Poisson (SVP) model is the simplest version which is also friendly to data mining experts. In this work, we choose the SVP as the reference model to revise these previous loss functions and take the estimated cumulative earthquake energy in the time-space unit (1 degree*1 degree*10 days) as the input of the LSTM to predict the earthquakes with M>=5.0 in the whole Chinese Mainland, and we use the Molchan diagram (SVP) and Area Skill Score (ASS) to evaluate the performance of these models. Results show that the majority of models (134 out of 144) trained by original loss functions are ineffective for predicting earthquakes; however, the scores of models trained using the revised loss functions have been obviously improved, and 83 out of 144 models are proved to be better than SVP in predicting earthquakes. Our results indicate that designing a more complex structure for ANN and neuron is not the only way to improve the performance of ANNs for predicting earthquakes, and how combining the professional knowledge of data mining experts and seismologists deserves more attention for the future development of ANN-based earthquake prediction models.

How to cite: Zhang, Y. and Huang, Q.: Improved ANN-based earthquake prediction system with reference model engaged in the evaluation metrics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11684, https://doi.org/10.5194/egusphere-egu23-11684, 2023.

The establishment of the High Sensitivity Seismograph Network (Hi-net) in Japan has led to the discovery of deep low-frequency tremors. Since such tremors are considered to be associated with large earthquakes adjacent to tremors on the same subducting plate interface, it is important in seismology to investigate these tremors before establishing modern seismograph networks that record seismic data digitally. We propose a deep-learning method to detect evidence of tremors from seismogram images recorded on paper more than 50 years ago. In this study, we trained a convolutional neural network (CNN) based on the Residual Network (ResNet) with seismogram images converted from real seismic data recorded by Hi-net. The CNN trained by fine-tuning achieved an accuracy of 98.64% for determining whether an input image contains tremors. The Gradient-weighted Class Activation Mapping (Grad-CAM) heatmaps for visualizing model predictions indicated that the CNN successfully detects tremors without being affected by teleseisms. The trained CNN was applied to the past seismograms recorded from 1966 to 1977 at the Kumano observatory, in southwest Japan, operated by Earthquake Research Institute, The University of Tokyo. The CNN showed potential for detecting tremors from past seismogram images for broader applications, such as publishing a new tremor catalog, although further training using data including more variables such as the thickness of the pen would be required to develop a universally applicable model.

How to cite: Nagao, H., Kaneko, R., Ito, S., Tsuruoka, H., and Obara, K.: Detection of Deep Low-Frequency Tremors from Continuous Paper Records at a Station in Southwest Japan About 50 Years Ago Based on Convolutional Neural Network, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13113, https://doi.org/10.5194/egusphere-egu23-13113, 2023.

Modern seismology can benefit from a rapid and reliable earthquake catalog preparation process. In recent years Deep Learning (DL)-based methods attracted seismologists’ attention to keep up with the constantly increasing seismic data for maximally processing and locating the recorded events. This study focuses on deploying an optimized workflow that integrates DL and waveform migration algorithms to achieve a comprehensive automated phase detection and earthquake location workflow. The goal is to deeply scan the seismic datasets for Pand S- phases, associate and locate the detected events, and improve the performance of DL algorithms in processing out-of-distribution and low signal-to-noise ratio data. The workflow consists of six steps, including the preparation of one-minute data segments by employing the framework of ObsPy, deep investigation of the recorded data for P- and S- phases by a low threshold EQTransformer (EQT), and a pair-input Siamese EQTransformer (S-EQT), phase association by Rapid Earthquake Association and Location (REAL) method, applying MIgration Location (MIL) to accurately locate the outputs of REAL, and calculating the local magnitude of the located earthquakes. Eighteen months of the Ghana Digital Seismic Network (GHDSN) dataset (2012-2014), is processed by this integrated and automatic workflow, and a catalog of 461 earthquakes is acquired. Although S-EQT and EQT, with the respective number of 758 and 423 earthquakes, show a figurative superiority in the number of detected events, they are scattered with inaccurate hypo-central depth. Conversely, the compiled catalog show high accordance with the previously interpreted seismogenic sources by Mohammadigheymasi et al. (2023), and a new seismogenic source is also delineated. This workflow significantly enhanced the seismic catalog compilation process and lowered the computational costs while increasing the accuracy of phase detection, association, and location processes. This work was supported by the European Union and the Instituto Dom Luiz(IDL) Project under Grant UIDB/50019/2020, and it uses computational resources provided by C4G (Collaboratory for Geosciences) (Ref. PINFRA/22151/2016). P. S. is supported by the DEEP project (http://deepgeothermal.org) funded through the ERANET CofundGEOTHERMICA (Project No. 200320-4001) from the European Commission. The DEEP project benefits from an exploration subsidy of the Swiss federal office of energy for the EGSgeothermal project in Haute-Sorne, canton of Jura (contract number MF-021-GEO-ERK), which is gratefully acknowledged.

How to cite: Mohammadigheymasi, H., Tavakolizadeh, N., Shi, P., Xiao, Z., Mousavi, S. M., and Fernandes, R.: An automated earthquake detection algorithm by combining pair-input deep learning and migration location methods, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15180, https://doi.org/10.5194/egusphere-egu23-15180, 2023.

A temporary seismic network consisting of 32 broadband seismic sensors was installed in Cameroon between March 2005 and December 2006 to study the seismic structure of the crust and upper mantle beneath the Cameroon Volcanic Line (CVL). This study aims to re-evaluate the seismicity in this period by processing this database and calculating an updated crustal velocity model for the region incorporating the acquired earthquake bulletin. 

The earthquake detection and location procedure applies hybrid deep learning (DL) and phase validation methods. We use an integrated workflow composed of Earthquake Transformer (EqT) and Siamese Earthquake Transformer (S-EqT) for initial earthquake detection and phase picking. Then, PickNet is used as a phase refinement step, and REAL for earthquake association and rough location. A set of thresholding parameters for earthquake detection and P- and S-picking equal to 0.2 and 0.07 are adjusted, respectively. By combining a set of 33282 P and 29251 S-picked phases associated with 743 earthquakes with 1.3 ≤ ML ≤ 4.6, we implement a joint inversion for estimating an updated 1D crustal velocity model. The obtained mode comprises thicknesses of 8, 12, 14, 20, and 30km, from the surface to a depth of 45km, with Vp = 6.1, 6.4, 6.6, 7.6, 8.25, and 8.5km/s, respectively. The newly detected events are primarily concentrated in three main clusters, 1) the east flank of Mount Cameroon, 2) an area between Mount Cameroon and Bioko Island, and 3) southern Bioko Island. The compiled catalog for this time interval is 1.7 times larger than the already reported catalog for this data set. Finally, we present a 3D time-lapsed animation of the detected earthquake sequences.

Acknowledgements: The authors would like to thank the support of the Instituto de Telecomunicações. This work is funded by FCT/MCTES through national funds and, when applicable co-funded EU funds under the project UIDB/50008/2020.

How to cite: Carvalho, L., Mohammadigheymasi, H., Crocker, P., Tavakolizadeh, N., Moradichaleshtori, Y., and Fernandes, R.: Earthquake Detection and Location in the Cameroon Temporary Network Data Using Deep Learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16438, https://doi.org/10.5194/egusphere-egu23-16438, 2023.

How to cite: Hourcade, C., Bonnin, M., and Beucler, É.: New CNN based tool to discriminate anthropogenic from natural low magnitude seismic events, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6343, https://doi.org/10.5194/egusphere-egu23-6343, 2023.

Distinguishing small earthquakes from man-made blasts at construction sites, in quarries and in mines is a non-trivial task during automatic event analysis and thus typically requires manual revision. We have developed station-specific classification models capable of both accurately assigning source type to seismic events in Sweden and filtering out spurious events from an automatic event catalogue. Our method divides all three components of the seismic records for each event into four non-overlapping time windows, corresponding to P-phase, P-coda, S-phase and S-coda, and computes the Root-Mean-Square (RMS) amplitude in each window. This process is repeated for a total of twenty narrow frequency bands. The resulting array of amplitudes is passed as inputs to fully connected Artificial Neural Network classifiers which attempt to filter out spurious events before distinguishing between natural earthquakes, industrial blasts and mining-induced events. The distinction includes e.g. distinguishing mining blasts from mining induced events, shallow earthquakes from blasts and differentiating between different types of mining induced events. The classifiers are trained on labelled seismic records dating from 2010 to 2021. They are already in use at the Swedish National Seismic Network where they serve as an aid to the routine manual analysis and as a tool for directly assigning preliminary source type to events in an automatic event catalogue. Initial results are promising and suggest that the method can accurately distinguish between different types of seismic events registered in Sweden and filter out the majority of spurious events.

How to cite: Eggertsson, G., Lund, B., Schmidt, P., and Roth, M.: Supervised Learning for Automatic Source Type Discrimination of Seismic Events in Sweden, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6202, https://doi.org/10.5194/egusphere-egu23-6202, 2023.

Recent employment of large seismic arrays and distributed fibre optic sensing cables leads to an overwhelming amount of seismic data. As a consequence, the need for reliable automatic processing and analysis techniques increases. Therefore, the number of machine learning applications for detection and classification of seismic signal augments too.

A challenge however, is that seismic datasets are highly class imbalanced, i.e. certain seismic classes are dominant while others are underrepresented. Unfortunately, a skewed dataset may lead to biases in the model and thus to higher uncertainties in the model predictions. In the machine learning literature, several strategies are described to mitigate this problem. In presented work we explore and compare those approaches.

For our application, we use catalogues and seismic continuous recordings of the RD network in France [RESIF, 2018]. Using a simple 3-layered convolutional neural network (CNN) we aim to differentiate between six seismic classes, which are based on hand-picked catalogues. The training set we obtained is highly skewed with earthquakes as the majority class, containing 77% of the samples.  The remaining classes (quarry blasts, marine explosions, suspected induced events, noise and earthquakes with unquantifiable magnitude) represent 2.1 - 7.5% of the dataset.

We compare four strategies to deal with an imbalanced datasets for a multi-class classification problem. The first strategy is to resample the dataset (i.e. reduction of the majority class). Another approach is the adaptation of the loss function by weighting the classes when penalizing the loss (i.e. increasing the weight of the minority classes). Those class weights can be adjusted either w.r.t. the reciprocal of class frequency [inspired by King and Zeng, 2001] or w.r.t. the effective number of samples [Cui et al., 2019]. Lastly, we have explored the use of a focal loss function [Lin et al., 2020].

Using balanced accuracy as a metric while minimizing the loss, we found that in our case adjusting the class weights in the loss function according to the reciprocal of the class frequency provides the best results.

References:

- RESIF, 2018: https://doi.org/10.15778/RESIF.RD

- King, G., & Zeng, L. (2001). Logistic regression in rare events data. Political analysis, 9(2), 137-163.

- Lin et al. (2020), Focal Loss for Dense Object Detection, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 42, NO. 2, FEBRUARY 2020

- Cui et al. (2019), Class-Balanced Loss Based on Effective Number of Samples, https://doi.org/10.48550/arXiv.1901.05555

How to cite: van Dinther, C., Malfante, M., Gaillard, P., and Cano, Y.: Increasing the reliability of seismic classification: A comparison of strategies to deal with class size imbalanced datasets., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16410, https://doi.org/10.5194/egusphere-egu23-16410, 2023.

Seismic stations record superpositions of the seismic signals generated by all kinds of seismic sources. In earthquake seismology, seismic noise sources can be natural events such as wind or anthropogenic events such as cars. In this study, we developed a machine learning (ML) based algorithm to remove the noise from earthquake data. This is important since the information related with the features of the seismic event may be overlapped by the noise. The presence of noise in the recordings can affect the performance of the seismic network, lowering its sensibility and increasing the magnitude of completeness of the seismic catalogue. To train ML model, 10000 thousand earthquake records with relatively low signal to noise ratio (SNR) are selected and contaminated by 25 noise records that are magnified up to 50% of peak amplitude of the earthquake signal and frequency content of those signals are stored as three component traces. The architecture used consists of an Attention U-Net, i.e. an encoder-decoder model using an attention gate within the skip connections: the encoder maps samples from input space (the waveform STFTs) to a latent space while the decoder maps the latent space to the output space (the signal-noise mask). Skip connections are introduced to recover, from previous layers, fine details lost in the encoding-decoding process. Attention gates identify salient regions and prune inputs to preserve only the ones relevant to the specific task. The use of attention gates in skip connections allows to pass "fine-detailed" information to high levels of the decoder that the model itself considers useful to the waveform segmentation. Trained model is tested with a new set of data to understand its capabilities. It is found that trained model can significantly improve the SNR of noise signals with respect to standard filtering methods. Hence, it can be considered as a strong candidate for seismic data filtering method. 

How to cite: Ertuncay, D., Fornasari, S. F., and Costa, G.: Seismic Signal Denoising with Attention U-Net, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1309, https://doi.org/10.5194/egusphere-egu23-1309, 2023.

Marsquake recordings by NASA’s InSight seismometer often have low signal-to-noise ratios (SNR) owing to low marsquake amplitudes - only a handful of events are over M3.5 and epicentral distances are large, due to the single station being located in a seismically quiet region, and highly fluctuating atmospheric, spacecraft and instrumental noise signals.

We have previously shown [1] how deep convolutional neural networks (CNN) can be used for 1) event detection - thereby producing an event catalogue consistent with the manually curated catalogue by the Marsquake Service (MQS) [2], and further extending it from 1297 to 2079 seismic events - as well as for 2) separating event and noise signals in time-frequency domain. Due to the low number of events readily-available for network training, we trained the CNN on synthetic event data combined with recorded InSight noise.

Here, we construct a semi-synthetic data set (with real marsquake & noise data) to assess the denoising performance of the CNN w.r.t. to various evaluation metrics such as SNR, signal-distortion-ratio, cross-correlation, and peak amplitude of the recovered event waveforms, and compare modifications of the CNN architecture and the training data set.

For a large number of identified events [1,2] no distance estimates are available (or only with high uncertainty), and for all but a small subset the back azimuth is unclear, as the relatively high background noise often obscures this information in the waveforms. We explore how the denoised waveforms can support the phase picking and polarisation analysis of marsquakes, and with that their localisation, as well as their general characterisation.

References:

[1] Dahmen et al. (2022), doi: 10.1029/2022JE007503

[2] Ceylan et al. (2022), doi: 10.1016/j.pepi.2022.106943

How to cite: Dahmen, N., Clinton, J., Meier, M.-A., Stähler, S., Ceylan, S., Charalambous, C., Kim, D., Stott, A., and Giardini, D.: Denoising InSight’s marsquake recordings with deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11985, https://doi.org/10.5194/egusphere-egu23-11985, 2023.

Progressive localization of deformation may signify a regional preparation process leading to large earthquakes [Ben-Zion & Zaliapin, GJI, 2020; Kato & Ben-Zion, Nat Rev Earth Environ. 2021]. The localization framework describes the evolution from distributed failures in a rock volume to localized system-size events. Ben-Zion & Zaliapin (2020) documented robust cycles of localization and de-localization of background earthquakes with M > 2 in Southern California that precede the M7 earthquakes within 2-4 years. This analysis has been done on regional scale, without posterior selection of the examined areas (e.g., around epicenters of large events). Similar results are observed before M7.8 earthquakes in Alaska using background seismicity with M > 4, and in laboratory acoustic emission experiments.

In this work we examine spatial characteristics of the localization process, identifying sub-regions that are responsible for the observed localization and delocalization. The analysis focuses on relative (with respect to other areas) changes in the background intensity. On sub decadal temporal scale, the observed relative seismic activity tends to concentrate on and switch between several subsets of the regional fault network. Within 2-10 years prior to a large event, there is relative activation in a large volume that not necessarily include the impending epicenter. This is followed by a prominent deactivation 2-3 years prior to a large event, reminiscent of the “Mogi donut”, potentially reflecting a transition to aseismic or small events. Some regions may experience multiple activation episodes before a large earthquake. The results emphasize the importance of examining small-magnitude events and joint analyses of seismic and geodetic data.

References:

• Ben-Zion, Y. and I. Zaliapin (2020) Localization and coalescence of seismicity before large earthquakes. Geophysical Journal International, 223(1), 561-583. doi:10.1093/gji/ggaa315
• Kato, A. and Y. Ben-Zion (2021) The generation of large earthquakes. Nat Rev Earth Environ 2, 26–39 https://doi.org/10.1038/s43017-020-00108-w

How to cite: Zaliapin, I. and Ben-Zion, Y.: Spatio-temporal localization of seismicity in relation to large earthquakes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9874, https://doi.org/10.5194/egusphere-egu23-9874, 2023.