ITS4.3/NH1

How to cite: Zahs, V., Herfort, B., Kohns, J., Ullah, T., Anders, K., Stempniewski, L., Zipf, A., and Höfle, B.: 3D point cloud-based assessment of detailed building damage through a combination of machine learning, crowdsourcing and earthquake engineering, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1304, https://doi.org/10.5194/egusphere-egu21-1304, 2021.

Seismic datasets contain an enormous amount of information and a large variety of signals with different origins. We usually observe signatures of earthquakes, volcanic and non-volcanic tremors, rockfalls, road and air traffic, atmospheric perturbations and many other acoustic emissions. More and more seismic sensors are deployed worldwide and record the seismic wavefield in a continuous fashion, generating massive volumes of data that cannot be analyzed manually in decent times. Therefore, identifying classes of signals in seismic data with automatic strategies is a crucial stage towards the understanding of the underlying physics of geological objects. For that reason seismologists have developed different tools to detect and classify certain types of signals. Recently, machine learning gained much attention due to its ability to recognize patterns. While supervised learning is a great tool for detecting and classifying signals within already-known classes, it cannot be used to infer new classes of signals, and can be strongly biased by the labels we impose. We here propose to overcome this limitation with unsupervised learning. In this study, we present a new way to explore single-station continuous seismic data with a dendrogram produced by agglomerative clustering. Our method is motivated by the idea that labels in a seismic data set follow a hierarchical order with different levels of details. For example earthquakes belong to the larger class of stationary signals and can be also divided into subclasses with different focal mechanism or magnitudes. We first use a scattering network (a convolutional neural network that makes use of wavelet filers) in order to extract a multi-scale representation of the continuous seismic waveforms. We then select the most meaningful features by means of independent component analysis, and apply an agglomerative clustering on this representation. We finally explore the dendrogram in a systematic way in order to explore the different signal classes revealed by the strategy. We illustrate our method on seismic data continuously recorded in the vicinity of the North-Anatolian fault, in Turkey. During this time period, a seismic crisis with more than 200 micro-earthquakes occurred, together with many other anthropogenic and meteorological events. By exploring the classes revealed by the dendrogram with a posteriori signal features (occurrence, within-class correlations, etc.) we show that the strategy is capable of retrieving the seismic crisis as well as signals related to anthropogenic and meteorogical activities.

How to cite: Steinmann, R., Seydoux, L., and Campillo, M.: Hierarchical exploration of single station seismic data with unsupervised learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2788, https://doi.org/10.5194/egusphere-egu21-2788, 2021.

Time-series InSAR techniques, such as Stanford Method for Persistent Scatterers (StaMPS) are commonly used to measure time-series surface deformation. This study presents a novel approach of optimized time series deformation analysis based on a support vector regression (SVR) algorithm and optimization Hot-Spot Analysis on persistent scatterers (PS). To examine the performances of the optimized process in time-series, we generated a synthetic interferogram using a Mogi model equation to construct a simulated surface deformation phase. Topography errors simulated orbital error and atmospheric error phases have been added to synthetic interferogram construction. All the synthetic interferogram based on Sentinel-1 SAR Image acquisition dates over Seoul, Korea. An SVR algorithm was used to find an optimum measurement point and reduce error points in time-series analysis. Then, the OHSA approach was implemented on the optimum measurement point through the analysis of Getis-Ord Gi* statistics. As the result, the optimization measurement point indicates refined results in the mean velocity deformation map and time-series graph. In addition, the detection accuracy can be improved by more than 10% with synthetic data. Then, the correlation coefficient between the optimization result and the deformation model shows a good correlation (> 0.8). Also, the standard deviation of time-series results can be reduced by more than 7% after optimizing the process. The proposed method is useful to detect a low deformation rate and can be implemented for several deformation cases.   

How to cite: Fadhillah, M. F., Lee, S., and Lee, C.-W.: Optimization of the time series surface deformation analysis using machine learning algorithms on the interferogram simulation data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3755, https://doi.org/10.5194/egusphere-egu21-3755, 2021.

The estimation of earthquake source parameters, in particular magnitude and location, in real time is one of the key tasks for earthquake early warning and rapid response. In recent years, several publications introduced deep learning approaches for these fast assessment tasks. Deep learning is well suited for these tasks, as it can work directly on waveforms and can learn features and their relation from data.

A drawback of deep learning models is their lack of interpretability, i.e., it is usually unknown what reasoning the network uses. Due to this issue, it is also hard to estimate how the model will handle new data whose properties differ in some aspects from the training set, for example earthquakes in previously seismically quite regions. The discussions of previous studies usually focused on the average performance of models and did not consider this point in any detail.

Here we analyze a deep learning model for real time magnitude and location estimation through targeted experiments and a qualitative error analysis. We conduct our analysis on three large scale regional data sets from regions with diverse seismotectonic settings and network properties: Italy and Japan with dense networks (station spacing down to 10 km) of strong motion sensors, and North Chile with a sparser network (station spacing around 40 km) of broadband stations.

We obtained several key insights. First, the deep learning model does not seem to follow the classical approaches for magnitude and location estimation. For magnitude, one would classically expect the model to estimate attenuation, but the network rather seems to focus its attention on the spectral composition of the waveforms. For location, one would expect a triangulation approach, but our experiments instead show indications of a fingerprinting approach. Second, we can pinpoint the effect of training data size on model performance. For example, a four times larger training set reduces average errors for both magnitude and location prediction by more than half, and reduces the required time for real time assessment by a factor of four. Third, the model fails for events with few similar training examples. For magnitude, this means that the largest events are systematically underestimated. For location, events in regions with few events in the training set tend to get mislocated to regions with more training events. These characteristics can have severe consequences in downstream tasks like early warning and need to be taken into account for future model development and evaluation.

How to cite: Münchmeyer, J., Bindi, D., Leser, U., and Tilmann, F.: Insights into deep learning for earthquake magnitude and location estimation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4718, https://doi.org/10.5194/egusphere-egu21-4718, 2021.

The analysis of non-stationary signals is often performed on raw waveform data or on Fourier transformations of those data, i.e., spectrograms. However, the possibility of alternative time-frequency representations being more informative than spectrograms or the original data remains unstudied. In this study, we tested if alternative time-frequency representations could be more informative for machine learning classification of seismic signals. This hypothesis was assessed by training three well-established convolutional neural networks, using nine different time-frequency representations, to classify seismic waveforms as earthquake or noise. The results were compared to the base model, which was trained on the raw waveform data. The signals used in the experiment were seismogram instances from the LEN-DB seismological dataset (Magrini et al. 2020). The results demonstrate that Pseudo Wigner-Ville and Wigner-Ville time-frequency representations yield significantly better results than the base model, while Margenau-Hill performs significantly worse (P < .01). Interestingly, the spectrogram, which is often used in non-stationary signal analysis, did not yield statistically significant improvements. This research could have a notable impact in the field of seismology because the data that were previously hidden in the seismic noise are now classified more accurately. Moreover, the results might suggest that alternative time-frequency representations could be used in other fields which use non-stationary time series to extract more valuable information from the original data. The potential fields encompass different fields of geophysics, speech recognition, EEG and ECG signals, gravitational waves and so on. This, however, requires further research.

How to cite: Njirjak, M., Otović, E., Jozinović, D., Lerga, J., Mauša, G., Michelini, A., and Štajduhar, I.: Machine Learning Classification of Cohen's Class Time-Frequency Representations of Non-Stationary Signals: Effects on Earthquake Detection, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9670, https://doi.org/10.5194/egusphere-egu21-9670, 2021.

The International Telecommunication Union (ITU), World Meteorological Organization (WMO), and United Nations Environment Programme (UNEP) have recently partnered to establish the Focus Group on Artificial Intelligence for Natural Disaster Management (FG-AI4NDM). FG-AI4NDM is exploring the potential of AI-based algorithms to support data collection and handling, to improve modeling (i.e., reconstructions, forecasts, and projections) across spatiotemporal scales through extracting complex patterns (and gaining insights) from a growing volume of geospatial data, and to provide effective communication. To achieve these objectives, FG-AI4NDM is building an interdisciplinary, multi-stakeholder, and international community to explore specific natural disaster use cases. Special effort is made to support participation from low- and mid-income countries and those countries shown to be particularly impacted by these types of events. Here, we will explore: what is an ITU focus group, what are the objectives and planned deliverables of FG-AI4NDM, what progress has been made since its inception, and how members of the geoscience community can become involved.

How to cite: Kuglitsch, M.: ITU/WMO/UNEP Focus Group on AI for Natural Disaster Management: Introduction and call for participation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12791, https://doi.org/10.5194/egusphere-egu21-12791, 2021.