Dataset Information

Seismicity of the Gargano promontory (Southern Italy) after 7 years of local seismic network operation: Data release of waveforms from 2013 to 2018.

ABSTRACT: The University of Bari (Italy), in cooperation with the National Institute of Geophysics and Volcanology (INGV) (Italy), has installed the OTRIONS micro-earthquake network to better understand the active tectonics of the Gargano promontory (Southern Italy). The OTRIONS network operates since 2013 and consists of 12 short period, 3 components, seismic stations located in the Apulian territory (Southern Italy). This data article releases the waveform database collected from 2013 to 2018 and describes the characteristics of the local network in the current configuration. At the end of 2018, we implemented a cloud infrastructure to make more robust the acquisition and storage system of the network through a collaboration with the RECAS-Bari computing centre of the University of Bari (Italy) and of the National Institute of Nuclear Physics (Italy). Thanks to this implementation, waveforms recorded after the beginning of 2019 and the station metadata are accessible through the European Integrated Data Archive (EIDA, https://www.orfeus-eu.org/data/eida/nodes/INGV/).

SUBMITTER: Filippucci M

PROVIDER: S-EPMC7841310 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Seismicity of the Gargano promontory (Southern Italy) after 7 years of local seismic network operation: Data release of waveforms from 2013 to 2018.

Filippucci Marilena M Miccolis Simona S Castagnozzi Angelo A Cecere Gianpaolo G de Lorenzo Salvatore S Donvito Giacinto G Falco Luigi L Michele Maddalena M Nicotri Stefano S Romeo Annalisa A Selvaggi Giulio G Tallarico Andrea A

Data in brief 20210121

The University of Bari (Italy), in cooperation with the National Institute of Geophysics and Volcanology (INGV) (Italy), has installed the OTRIONS micro-earthquake network to better understand the active tectonics of the Gargano promontory (Southern Italy). The OTRIONS network operates since 2013 and consists of 12 short period, 3 components, seismic stations located in the Apulian territory (Southern Italy). This data article releases the waveform database collected from 2013 to 2018 and descri ...[more]

PMID: 33537385

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Project description:Monitoring mining-induced seismicity (MIS) can help engineers understand the rock mass response to resource extraction. With a thorough understanding of ongoing geomechanical processes, engineers can operate mines, especially those mines with the propensity for rock-bursting, more safely and efficiently. Unfortunately, processing MIS data usually requires significant effort from human analysts, which can result in substantial costs and time commitments. The problem is exacerbated for operations that produce copious amounts of MIS, such as mines with high-stress and/or extraction ratios. Recently, deep learning methods have shown the ability to significantly improve the quality of automated arrival-time picking on earthquake data recorded by regional seismic networks. However, relatively little has been published on applying these techniques to MIS. In this study, we compare the performance of a convolutional neural network (CNN) originally trained to pick arrival times on the Southern California Seismic Network (SCSN) to that of human analysts on coal-mine-related MIS. We perform comparisons on several coal-related MIS data sets recorded at various network scales, sampling rates and mines. We find that the Southern-California-trained CNN does not perform well on any of our data sets without retraining. However, applying the concept of transfer learning, we retrain the SCSN model with relatively little MIS data after which the CNN performs nearly as well as a human analyst. When retrained with data from a single analyst, the analyst-CNN pick time residual variance is lower than the variance observed between human analysts. We also compare the retrained CNN to a simpler, optimized picking algorithm, which falls short of the CNN's performance. We conclude that CNNs can achieve a significant improvement in automated phase picking although some data set-specific training will usually be required. Moreover, initializing training with weights found from other, even very different, data sets can greatly reduce the amount of training data required to achieve a given performance threshold.

Dataset Information

Seismicity of the Gargano promontory (Southern Italy) after 7 years of local seismic network operation: Data release of waveforms from 2013 to 2018.

Publications

Seismicity of the Gargano promontory (Southern Italy) after 7 years of local seismic network operation: Data release of waveforms from 2013 to 2018.

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