SM3.2

Conventionally, the way of storing and exchange numerical data depends mainly on binary data files in compressible form. In this era of the Big Data and machine learning systems and with the accumulation of data with different forms and types, it is important to find an alternative way for handling the data. The binary data are software dependent which does not exhibit its content and type without accessing the data by the proper software. In addition, it does not have any encryption ability. To solve this issue, we propose a new concept to handle the digital data in a descriptive, encrypted, compressed form, and able to be previewed. The idea is to pack the binary bits into a bitmap image with specific coding scheme. This approach employs the Steim scheme as a primary compression tool with a 128-bit encryption method then packs the encrypted codes into a WebP image file. The WebP image is featured by being an independent, web friendly, and highly compressed file. In order to make the file describing its contents, we reserved some pixels as coded descriptive pixels. By this way, the now packed data exhibits its contents and type during image preview.

It is proven that the Data-In-Image format, regardless of being encrypted, occupies the least amount of storage space among other image formats that can be easily handled, stored, and shared through clouds and devices safely with a lower cost. For seismic data,  the size of the WebP image comprises ~20% of the corresponding binary size with a bit-rate of ~5.6 b/s which is smaller than that of the Steim form, 27% and 8.9 b/s, respectively. Regarding the compression speed, it is found that the code compresses data with a rate of ~11,118 samples/s or ~ 44 Kbytes/s in average.

In addition, the data image is able to be digitally scanned and with some modifications can be remotely accessed like the quick response code, the thing that is not possible in the binary form. Moreover, the descriptive pixels in the image allow the implementations of smart tools to archive and classify data by machine learning and recognition algorithms.

How to cite: Abdelwahed, M.: Data-In-Image: a novel concept for data manipulation and encryption implementation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-309, https://doi.org/10.5194/egusphere-egu21-309, 2021.

In the last decades, the discovery of seismic signals other than those known as classical earthquakes have changed our understanding of the dynamic process of lithosphere fracturing with implications for seismic monitoring. These signals are hypothesized to be generated by slow slip on faults and/or by the motion of fluids in the crust. The earthquakes that occur in the Azores archipelago are thought to result from the interaction between a tectonic triple junction and a low-velocity (possibly hot) anomalous mantle. Although most of the seismic activity in this region is tectonic, there is also evidence of seismic activity related to hydrothermal and magmatic activity, which makes the Azores region a privileged natural observatory for studying different types of seismic signals. In this work we will then focus on the spatio-temporal evolution of the February 2018 seismic sequence which occurred in the island of São Miguel. We will carry out detection and preliminary location of seismic events using Lassie, an open-source software for earthquake detection. We will also perform waveform similarity and clustering analysis to understand the detailed spatio-temporal evolution of the crisis.

This work is funded by FCT through projects UIDB/50019/2020 – IDL and PTDC/CTAGEF/ 6674/2020 (RESTLESS).

How to cite: Soares, A., Custodio, S., and Cesca, S.: Tools for monitoring the spatio-temporal evolution of seismic sequences: An application to the Azores triple junction, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16378, https://doi.org/10.5194/egusphere-egu21-16378, 2021.

A novel geotomography technique has been applied at the epicentral area around capitol of Macedonia - Skopje, using selected earthquakes that occurred over a period of 57 years and were recorded on temporary and permanent seismograph stations. This study will test the tomography method for the first time in investigation of the crustal shape and structures in our tectonic environment using specially designed datasets covering 1964-1967 and 2016-2020 periods.

In the initial phase, the analysis will show the potential of the geotomography application in revealing detailed velocity perturbation in the lithosphere. Then, the events are relocated in the 3-D models and new cross-sections of the crust produced by a simultaneous approach. The images can help in constraining the velocity vs depth relationship and thus can contribute towards redefinition of the earthquake zones. The results are discussed in terms of general stress and seismic regime and their temporal changes.

Better understanding of the seismicity and tectonics processes in the Skopje region will lead to an overall improvement of the earthquake hazard assessment at local and national level, as well as further integration in research programs with other geophysical methods.

How to cite: Sinadinovski, C., Pekevski, L., Cernih, D., Drogreska, K., and Najdovska, J.: Seismicity Detection in Skopje Region Using Tomographic Methods and 3-D Modelling , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6950, https://doi.org/10.5194/egusphere-egu21-6950, 2021.

When the earthquake rupture is complex and ruptures of multiple fault segments contribute to the total energy release, the produced wavefield is the superposition of individual signals produced by single subevents. Resolving source complexity for large, shallow earthquakes can be used to improve ground shaking and surface slip estimations, and thus tsunami models. The 2018 Mw 7.9 Alaska earthquake showed such complexity: according to previous studies, the rupture initiated as a right-lateral strike-slip fault on a N-S oriented fault plane, but then jumped onto a left-lateral strike-slip fault oriented westward. Rupture complexity and presence of multiple subevents may characterize a number of other earthquakes. However, even when individual subevents are spatially and/or temporally separated, it is very difficult to identify them from far field recordings. In order to model complex earthquakes we have implemented a multiple double couple inversion scheme within Grond, a tool devoted to the robust characterization of earthquake source parameters included in the Pyrocko software. Given the large magnitude of the target earthquake, we perform our source inversions using broadband body waves data (P and S phases) at teleseismic distances. Our approach starts with a standard moment tensor inversion, which allows to get more insights about the centroid location and overall moment release. These values can then be used to constrain the double source inversion. We discuss the performance of the inversion for the Alaska earthquake, using synthetic and real data. First, we generated realistic synthetic waveforms for a two-subevents source, assuming double couple sources with the strike-slip mechanisms proposed for the Alaska earthquake. We model the synthetic dataset both using a moment tensor and a double double couple source, and demonstrate the stability of the double double couple inversion, which is able to reconstruct the two focal mechanisms, the moment ratio and the relative centroid locations of the two subevents. Synthetic tests show that the inversion accuracy can be in some cases reduced, in presence of noisy data and when the interevent time between subevents is short. A larger noise addition affects the retrieval of the focal mechanism orientations only in some cases, but in general all the parameters were well retrieved. Then, we test our tool using real data for the earthquake. The single source inversion shows that the centroid is shifted 27 s in time and 40 km towards NE with respect to the original assumed location retrieved from the gCMT catalogue. The following double double couple source inversion resolves two subevents with right-lateral and left-lateral strike-slip focal mechanisms and Mw 7.6 and 7.8 respectively. The subevent centroids are separated by less than 40 km in space and less than 20 s in time.

How to cite: Carrillo Ponce, A., Dahm, T., Cesca, S., Tilmann, F., Babeyko, A., and Heimann, S.: Bayesian multiple rupture plane inversion to assess rupture complexity: application to the 2018 Mw 7.9 Alaska earthquake , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1583, https://doi.org/10.5194/egusphere-egu21-1583, 2021.

The aim of this study is to implement at the CEA a grid search moment tensor inversion scheme (GRiD MT) for the rapid detection and characterization of seismic events, to monitor low to moderate magnitude earthquakes in France. Given the heterogeneity of the seismic network and of the crustal geology over the country, we propose to use a combination of source grids focusing on specific regions. The GRiD MT approach requires an extensive preliminary analysis to define the different inversion parameters (velocity model, frequency band, and stations) and grid spacing (in latitude, longitude and depth). Here, we present the advances made towards the GRiD MT implementation for France. Using recent earthquakes, such as the 2019 ML 5.4 Le Teil earthquake, near Montelimar in Southern France, we discuss the validity of this detection and characterization tool for earthquake routine. The GRiD MT results applied in the South-East region for moderate earthquakes are close to those published by other agencies (USGS, IPGP, OCA, INGV) in terms of location, magnitude and focal mechanism. Nonetheless, more care is needed for the smallest events. We also discuss the precision and the uncertainties in constraining the source parameters using this method, especially when considering the goodness of fit as the unique criterion to identify a potential source, and different Earth’s structures. In the end, we show that the GRiD MT approach may be an interesting tool to analyze seismic events in France within only a few minutes after their occurrence. However, their low magnitude range raises some challenging questions to be answered.

How to cite: Menager, M., Trilla, A., and Delouis, B.: Toward implementing a grid search moment tensor (GRiD MT) tool for the rapid detection and characterization of seismic events in Metropolitan France, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2000, https://doi.org/10.5194/egusphere-egu21-2000, 2021.

Automatic Moment Tensor (MT) determination for regional areas is essential for real-time seismological applications such as stress inversion, shakemap generation, and tsunami warning. In recent years, the combination of two powerful tools, SeisComP and ISOLA (Sokos and Zahradník, 2008), paved the way for the release of Scisola (Triantafyllis et al., 2016), an open-source Python-based software for automatic MT calculation of seismic events provided by SeisComP -a well-known software package-, in real-time. ISOLA is an extensively used manual MT retrieval utility, based on multiple-point source representation and iterative deconvolution, full wavefield is taken into consideration and Green's functions are calculated with the discrete wavenumber method as implemented in the Axitra Fortran code (Cotton and Coutant, 1997). MT of subevents is found by least-square minimization of misfit between observed and synthetic waveforms, while position and time of subevents is optimized through grid search. Scisola monitors SeisComP and passes the event information, the respective waveform data and the station information to the ISOLA software for the Green’s Functions and MT computation. Gisola is a highly evolved version of Scisola software, oriented for High-Performance Computing. Unlike Scisola, the new program applies enhanced algorithms for waveform data filtering via quality metrics such as signal-to-noise ratio, waveform clipping, data and meta-data inconsistency, long-period (“mouse”) disturbances, and current station evaluation based on comparison between its daily Power Spectral Density (PSD) and various reference metrics for the frequency bands of interest, featuring a CPU multiprocessing implementation for faster calculations. Concerning the Green’s Functions computation, Gisola operates a newer version of the Axitra, highlighting the power of simultaneous processing in the CPU domain. Likewise, the inversion procedure code has been intensively improved by exploiting the performance efficiency of GPU-based multiprocessing implementation (with an automatic fallback to CPU-based multiprocessing in case of GPU hardware absence) and by unifying sub-programs to minimize I/O operations. In addition, a fine-grained 4D (space-time) adjustable source grid search is available for more accurate MT solutions. Moreover, Gisola expands its seismic data input resources by interconnecting to the FDSN Web Services. Furthermore, the new software has the ability to export the results in the well-known QuakeML standard, and in this way, provide clients -such as SeisComP- with MT results attached to the seismic event information. Finally, the operator has full control of all calculation aspects, with an extensive and adapted to regional data, configuration. The program can be installed on any computer that operates a Linux OS and has access to the FDSN Web Services, while the source code will be open and free to the scientific community.

Cotton F. and Coutant O., 1997, Dynamic stress variations due to shear faults in a plane-layered medium, GEOPHYSICAL JOURNAL INTERNATIONAL,Vol 128, 676-688, doi: 10.1111/j.1365-246X.1997.tb05328.x.
Sokos, E. N., and J. Zahradník (2008). ISOLA a FORTRAN code and a MATLAB GUI to perform multiple-point source inversion of seismic data, Comp. Geosci. 34, no. 8, 967–977, doi: 10.1016/j.cageo.2007.07.005.
Triantafyllis, N., Sokos, E., Ilias, A., & Zahradník, J. (2016). Scisola: automatic moment tensor solution for SeisComP3. Seismological Research Letters, 87(1), 157-163, doi: 10.1785/0220150065.

How to cite: Triantafyllis, N., Venetis, I., Fountoulakis, I., Pikoulis, E.-V., Sokos, E., and Evangelidis, C.: Gisola: Real-Time Moment Tensor computation optimized for multicore and manycore architectures, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15888, https://doi.org/10.5194/egusphere-egu21-15888, 2021.

Seismic networks record vibrations that are captured by their stations. These vibrations can be coming from various sources, such as tectonic tremors, quarry blasts and anthropogenic sources. Separation of earthquakes from other sources may require human intervention and it can be a labor-intensive work. In case of lack of such a separation, seismic hazard may be miscalculated. Our goal is to discriminate earthquakes from quarry blasts by using artificial neural networks. To do that, we used two different databases for earthquake signals and quarry blasts. Neither of them have data from our study of interest, which is North-East of Italy. We used three channel signals from the stations as an input for the neural networks. Signals are used as both time series and their spectral characteristics and are fed to the neural networks with this information. We then separated earthquakes from quarry blasts in North-East Italy by using our algorithm. We conclude that our algorithm is able to discriminate earthquakes from quarry blasts with high accuracy and the database can be used in different regions with different earthquake and quarry blast sources in a large variety of distances.

How to cite: Ertuncay, D., De Lorenzo, A., and Costa, G.: Discrimination of quarry blasts from earthquakes using artificial neural networks, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1285, https://doi.org/10.5194/egusphere-egu21-1285, 2021.

Estimating fast earthquakes’ source mechanism is essential for near real time hazard assessments, which are based on shakemaps and further downstream analysis such as physics based aftershock probability calculations. The model and data uncertainties associated to the estimated source mechanism are also crucial. We propose a Baysian Machine Learning algorithm trained on normalized synthetic waveforms for estimating the full moment tensor of earthquakes almost instantaneously with associated source parameter uncertainties.

A prior assumption is an appropriate location of the earthquakes along with its associated uncertainties. Here, this is obtained by already established Machine learning based algorithms, where the training data set is computed by forward calculations of synthetic waveforms based on Green’s functions calculated for a specified 1-D velocity model using the Pyrocko software package. The learned labels, which are the information learned by the Machine Learning algorithm associated to the data, are the moment tensor components, described with only five unique parameters. For predefined locations in an area of interest we train a full independent Bayesian Convolutional Neural Network (BNN).

With variational inference the weights of the network are not scalar but represent a distribution of weights for the activation of neurons. Each evaluation of input data into our BNN yields therefore to a set of predictions with associated probabilities. This allows us to evaluate an ensemble of possible source mechanisms for each evaluation of input waveform data.

As a test set, we trained our models for an area south of the Coso geothermal field in California for a fixed set of broadband stations at maximum 150 km distance. We validate our approach with a subset of earthquakes from the Ridgecrest 2019-2020 sequence. For this data set we compare the results of the estimates of our Machine Learning based approach with independently determined focal mechanism and moment tensors. Overall, we benchmark our approach with data unseen during the training process of the Machine Learning models and show its capabilities for generating similar source mechanism estimations as independent studies within only a few seconds processing time per earthquake. We finally apply the method to seismic data of a research network monitoring the area around two south-german geothermal power plants. Our approach demonstrates the potential of Machine Learning for being implemented in operational frameworks for fast earthquake source mechanism estimation with associated uncertainties.

How to cite: Steinberg, A., Vasyura-Bathke, H., Gaebler, P., and Ceranna, L.: Estimating Seismic Moment Tensors based on Bayesian Machine Learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-707, https://doi.org/10.5194/egusphere-egu21-707, 2021.

Machine learning methods have seen widespread adoption within the seismological community in recent years due to their ability to effectively process large amounts of data, while equalling or surpassing the performance of human analysts or classic algorithms. In the wider machine learning world, for example in imaging applications, the open availability of extensive high-quality datasets for training, validation, and the benchmarking of competing algorithms is seen as a vital ingredient to the rapid progress observed throughout the last decade. Within seismology, vast catalogues of labelled data are readily available, but collecting the waveform data for millions of records and assessing the quality of training examples is a time-consuming, tedious process. The natural variability in source processes and seismic wave propagation also presents a critical problem during training. The performance of models trained on different regions, distance and magnitude ranges are not easily comparable. The inability to easily compare and contrast state-of-the-art machine learning-based detection techniques on varying seismic data sets is currently a barrier to further progress within this emerging field. We present SeisBench, an extensible open-source framework for training, benchmarking, and applying machine learning algorithms. SeisBench provides access to various benchmark data sets and models from literature, along with pre-trained model weights, through a unified API. Built to be extensible, and modular, SeisBench allows for the simple addition of new models and data sets, which can be easily interchanged with existing pre-trained models and benchmark data. Standardising the access of varying quality data, and metadata simplifies comparison workflows, enabling the development of more robust machine learning algorithms. We initially focus on phase detection, identification and picking, but the framework is designed to be extended for other purposes, for example direct estimation of event parameters. Users will be able to contribute their own benchmarks and (trained) models. In the future, it will thus be much easier to compare both the performance of new algorithms against published machine learning models/architectures and to check the performance of established algorithms against new data sets. We hope that the ease of validation and inter-model comparison enabled by SeisBench will serve as a catalyst for the development of the next generation of machine learning techniques within the seismological community. The SeisBench source code will be published with an open license and explicitly encourages community involvement.

How to cite: Woollam, J., Münchmeyer, J., Giunchi, C., Jozinovic, D., Diehl, T., Saul, J., Michelini, A., Haslinger, F., Lange, D., Tilmann, F., and Rietbrock, A.: SeisBench: A toolbox for benchmarking and applying machine learning in seismology., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12218, https://doi.org/10.5194/egusphere-egu21-12218, 2021.