ALBERT

Beschreibung: Seismic event detection and phase picking are the base of many seismological workflows. In recent years, several publications demonstrated that deep learning approaches significantly outperform classical approaches, achieving human-like performance under certain circumstances. However, as studies differ in the datasets and evaluation tasks, it is unclear how the different approaches compare to each other. Furthermore, there are no systematic studies about model performance in cross-domain scenarios, i.e., when applied to data with different characteristics. Here, we address these questions by conducting a large-scale benchmark. We compare six previously published deep learning models on eight datasets covering local to teleseismic distances and on three tasks: event detection, phase identification and onset time picking. Furthermore, we compare the results to a classical Baer-Kradolfer picker. Overall, we observe the best performance for EQTransformer, GPD and PhaseNet, with a small advantage for EQTransformer on teleseismic data. Furthermore, we conduct a cross-domain study, analyzing model performance on datasets they were not trained on. We show that trained models can be transferred between regions with only mild performance degradation, but models trained on regional data do not transfer well to teleseismic data. As deep learning for detection and picking is a rapidly evolving field, we ensured extensibility of our benchmark by building our code on standardized frameworks and making it openly accessible. This allows model developers to easily evaluate new models or performance on new datasets. Furthermore, we make all trained models available through the SeisBench framework, giving end-users an easy way to apply these models.

Beschreibung: Machine-learning (ML) methods have seen widespread adoption in seismology in recent years. The ability of these techniques to efficiently infer the statistical properties of large datasets often provides significant improvements over traditional techniques when the number of data are large (millions of examples). With the entire spectrum of seismological tasks, for example, seismic picking and detection, magnitude and source property estimation, ground-motion prediction, hypocenter determination, among others, now incorporating ML approaches, numerous models are emerging as these techniques are further adopted within seismology. To evaluate these algorithms, quality-controlled benchmark datasets that contain representative class distributions are vital. In addition to this, models require implementation through a common framework to facilitate comparison. Accessing these various benchmark datasets for training and implementing the standardization of models is currently a time-consuming process, hindering further advancement of ML techniques within seismology. These development bottlenecks also affect “practitioners” seeking to deploy the latest models on seismic data, without having to necessarily learn entirely new ML frameworks to perform this task. We present SeisBench as a software package to tackle these issues. SeisBench is an open-source framework for deploying ML in seismology—available via GitHub. SeisBench standardizes access to both models and datasets, while also providing a range of common processing and data augmentation operations through the API. Through SeisBench, users can access several seismological ML models and benchmark datasets available in the literature via a single interface. SeisBench is built to be extensible, with community involvement encouraged to expand the package. Having such frameworks available for accessing leading ML models forms an essential tool for seismologists seeking to iterate and apply the next generation of ML techniques to seismic data.

Beschreibung: Sardinia is a seismically quiet region: for this reason, it has been proposed as an excellent location to host fundamental physics experiments requiring low seismic ambient noise such as the Einstein Telescope, the third-generation gravitational wave observatory. In the framework of the instrumental deployment to characterise the formerly lead and zinc mine of Sos Enattos, currently dismissed and converted to a low-noise laboratory, we focus on the link between the records from seismic stations in different locations and the meteorological records collected in their vicinity. To do this, we use a scattering network, a convolutional neural network with wavelet filters, to extract relevant spectro-temporal features at different time scales of the signal. We then use a dimensionality reduction algorithm to reduce the features' dimensions and apply a hierarchical clustering algorithm to identify patterns in the continuous seismic data. We choose hierarchical clustering because it allows us to understand the inter-cluster similarity. We finally investigate the link between these clusters and the external meteorological data collected nearby and reveal the mutual information between the two datasets.

Beschreibung: The task of association of seismic phases into events is particularly challenging in every workflow dealing with seismically active regions where dense networks are usually deployed. Additionally, recents developments of picking algorithms based on deep learning frameworks provide an increased number of phase onsets with respect to standard approach based on the computation of characteristic functions of the seismic waveforms. Especially the number of S-picks has been increased by orders of magnitude, which now requires the simultaneous association of P- and S-wave arrivals or even detecting events only based on S-wave arrivals. We present an improved version of HEX (Hyperbolic Event eXtractor), a new technique based on the logic of Random Sample Consensus here applied to association of seismic phases. Since this algorithm is particularly effective in dealing with high noise in the input data, we show the benefits of HEX2.0 to analyze picks from deep learning methods. These datasets are characterized by either detections of small amplitude earthquakes that, according to network geometry, may not show at a sufficient number of seismic stations to declare an event or, sometimes, to a high rate of false positives. The application of HEX2.0 on real data from a seismic sequence of Sannio-Matese (Southern Apennines, Italy) occurred in 2013-2014 show that: i) resulting events show a high number of phases compared to previous catalogs; ii) few phases are discarded in the event location. HEX2.0 provides an accurate, easy-to-use and computationally effective solution to the seismic phase association problem.