Dataset Information

Gas and seismicity within the Istanbul seismic gap.

ABSTRACT: Understanding micro-seismicity is a critical question for earthquake hazard assessment. Since the devastating earthquakes of Izmit and Duzce in 1999, the seismicity along the submerged section of North Anatolian Fault within the Sea of Marmara (comprising the "Istanbul seismic gap") has been extensively studied in order to infer its mechanical behaviour (creeping vs locked). So far, the seismicity has been interpreted only in terms of being tectonic-driven, although the Main Marmara Fault (MMF) is known to strike across multiple hydrocarbon gas sources. Here, we show that a large number of the aftershocks that followed the M 5.1 earthquake of July, 25th 2011 in the western Sea of Marmara, occurred within a zone of gas overpressuring in the 1.5-5?km depth range, from where pressurized gas is expected to migrate along the MMF, up to the surface sediment layers. Hence, gas-related processes should also be considered for a complete interpretation of the micro-seismicity (~M?

SUBMITTER: Geli L

PROVIDER: S-EPMC5931589 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Gas and seismicity within the Istanbul seismic gap.

Géli L L Henry P P Grall C C Tary J-B JB Lomax A A Batsi E E Riboulot V V Cros E E Gürbüz C C Işık S E SE Sengör A M C AMC Le Pichon X X Ruffine L L Dupré S S Thomas Y Y Kalafat D D Bayrakci G G Coutellier Q Q Regnier T T Westbrook G G Saritas H H Çifçi G G Çağatay M N MN Özeren M S MS Görür N N Tryon M M Bohnhoff M M Gasperini L L Klingelhoefer F F Scalabrin C C Augustin J-M JM Embriaco D D Marinaro G G Frugoni F F Monna S S Etiope G G Favali P P Bécel A A

Scientific reports 20180501 1

Understanding micro-seismicity is a critical question for earthquake hazard assessment. Since the devastating earthquakes of Izmit and Duzce in 1999, the seismicity along the submerged section of North Anatolian Fault within the Sea of Marmara (comprising the "Istanbul seismic gap") has been extensively studied in order to infer its mechanical behaviour (creeping vs locked). So far, the seismicity has been interpreted only in terms of being tectonic-driven, although the Main Marmara Fault (MMF) ...[more]

PMID: 29717139

Similar Datasets

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

Gas and seismicity within the Istanbul seismic gap.

Publications

Gas and seismicity within the Istanbul seismic gap.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets