The IRIS Consortium: Community Based Facilities and Data Management for Seismology

Ingate, S. F.

doi:10.1007/978-1-4020-6815-7_8

Cited by 4 publications

(3 citation statements)

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“…The key role in the success of the learning process is played by the quality, completeness and size of the training data set to ensure the network is learning correctly and without any bias. In seismology this task is particularly facilitated by the fact that a fair amount of information is made publicly available by data providers (e.g., IRIS, FDSN, Ingate, 2008) and also by recent publications of extensive data sets ready for DL applications such as STEAD (Mousavi et al, 2019), LEN-DB (Magrini et al, 2020) and INSTANCE (Michelini et al, 2021).…”

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confidence: 99%

Comparison of Deep Learning Techniques for the Investigation of a Seismic Sequence: An Application to the 2019, Mw 4.5 Mugello (Italy) Earthquake

et al. 2021

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The increase of available seismic data prompts the need for automatic processing procedures to fully exploit them. A good example is aftershock sequences recorded by temporary seismic networks, whose thorough analysis is challenging because of the high seismicity rate and station density. Here, we test the performance of two recent Deep Learning algorithms, the Generalized Phase Detection and Earthquake Transformer, for automatic seismic phases identification. We use data from the December 2019 Mugello basin (Northern Apennines, Italy) swarm, recorded on 13 permanent and nine temporary stations, applying these automatic procedures under different network configurations. As a benchmark, we use a catalog of 279 manually repicked earthquakes reported by the Italian National Seismic Network. Due to the ability of deep learning techniques to identify earthquakes under poor signal‐to‐noise‐ratio (SNR) conditions, we obtain: (a) a factor 3 increase in the number of locations with respect to INGV bulletin and (b) a factor 4 increase when stations from the temporary network are added. Comparison between deep learning and manually picked arrival times shows a mean difference of 0.02–0.04 s and a variance in the range 0.02–0.07 s. The improvement in magnitude completeness is ∼0.5 units. The deep learning algorithms were originally trained using data sets from different regions of the world: our results indicate that these can be successfully applied in our case, without any significant modification. Deep learning algorithms are efficient and accurate tools for data reprocessing in order to better understand the space‐time evolution of earthquake sequences.

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confidence: 99%

Comparison of Deep Learning Techniques for the Investigation of a Seismic Sequence: An Application to the 2019, Mw 4.5 Mugello (Italy) Earthquake

et al. 2021

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“…Over the past decades, huge volumes of continuous seismic data have been collected [30,31]. With the availability of large datasets and advances in machine learning, the seismological community has also seen a rise in the use of machine and deep learning.…”

Section: Deep Learning For Seismic Analysismentioning

confidence: 99%

Graph neural networks for multivariate time series regression with application to seismic data

Bloemheuvel

Hoogen

Jozinović

et al. 2022

Int J Data Sci Anal

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Machine learning, with its advances in deep learning has shown great potential in analyzing time series. In many scenarios, however, additional information that can potentially improve the predictions is available. This is crucial for data that arise from e. g., sensor networks that contain information about sensor locations. Then, such spatial information can be exploited by modeling it via graph structures, along with the sequential (time series) information. Recent advances in adapting deep learning to graphs have shown potential in various tasks. However, these methods have not been adapted for time series tasks to a great extent. Most attempts have essentially consolidated around time series forecasting with small sequence lengths. Generally, these architectures are not well suited for regression or classification tasks where the value to be predicted is not strictly depending on the most recent values, but rather on the whole length of the time series. We propose TISER-GCN, a novel graph neural network architecture for processing, in particular, these long time series in a multivariate regression task. Our proposed model is tested on two seismic datasets containing earthquake waveforms, where the goal is to predict maximum intensity measurements of ground shaking at each seismic station. Our findings demonstrate promising results of our approach—with an average MSE reduction of 16.3%—compared to the best performing baselines. In addition, our approach matches the baseline scores by needing only half the input size. The results are discussed in depth with an additional ablation study.

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“…Over the past decades, huge volumes of continuous seismic data have been collected [34,35]. This data can be modeled as complex networks, i. e., graphs forming seismograph networks consisting of observations from multiple stations.…”

Section: Deep Learning For Seismic Analysismentioning

confidence: 99%

Graph Neural Networks for Multivariate Time Series Regression with Application to Seismic Data

Bloemheuvel,

Hoogen,

Jozinović

et al. 2022

Preprint

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Machine learning, with its advances in Deep Learning has shown great potential in analysing time series in the past. However, in many scenarios, additional information is available that can potentially improve predictions, by incorporating it into the learning methods. This is crucial for data that arises from e. g., sensor networks that contain information about sensor locations. Then, such spatial information can be exploited by modeling it via graph structures, along with the sequential (time) information. Recent advances in adapting Deep Learning to graphs have shown promising potential in various graph-related tasks. However, these

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The IRIS Consortium: Community Based Facilities and Data Management for Seismology

Cited by 4 publications

References 0 publications

Comparison of Deep Learning Techniques for the Investigation of a Seismic Sequence: An Application to the 2019, Mw 4.5 Mugello (Italy) Earthquake

Comparison of Deep Learning Techniques for the Investigation of a Seismic Sequence: An Application to the 2019, Mw 4.5 Mugello (Italy) Earthquake

Graph neural networks for multivariate time series regression with application to seismic data

Graph Neural Networks for Multivariate Time Series Regression with Application to Seismic Data

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