• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.177-188
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• 2022

Recently, the application of distributed acoustic sensors (DAS), which can replace geophones and seismometers, has significantly increased along with interest in micro-seismic monitoring technique, which is one of the CO₂ storage monitoring techniques. A significant amount of temporally and spatially continuous data is recorded in a DAS monitoring system, thereby necessitating fast and accurate data processing techniques. Because event detection and seismic phase picking are the most basic data processing techniques, they should be performed on all data. In this study, a machine learning-based P, S wave phase picking algorithm was developed to compensate for the limitations of conventional phase picking algorithms, and it was modified using a transfer learning technique for the application of DAS data consisting of a single component with a low signal-to-noise ratio. Our model was constructed by modifying the convolution-based EQTransformer, which performs well in phase picking, to the ResUNet structure. Not only the global earthquake dataset, STEAD but also the augmented dataset was used as training datasets to enhance the prediction performance on the unseen characteristics of the target dataset. The performance of the developed algorithm was verified using K-net and KiK-net data with characteristics different from the training data. Additionally, after modifying the trained model to suit DAS data using the transfer learning technique, the performance was verified by applying it to the DAS field data measured in the Pohang Janggi basin.

• Journal of the Korean Society for Marine Environment & Energy
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• v.16 no.1
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• pp.9-16
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• 2013

An experiment was conducted to evaluate the biochemical adverse effect of increased carbon dioxide in seawater on marine polychaete, Perinereis aibuhitensis. We measured the available energy reserves, Ea (total carbohydrate, protein, and lipid content) and the energy consumption, Ec (electron transport activity) of Perinereis aibuhitensis exposed for 7-d to a range of $CO_2$ concentration such as 0.39 (control =390 ppmv), 3.03 (=3,030 ppmv), 10.3 (=10,300 ppmv), and 30.1 (=30,100 ppmv) $CO_2$ mM, respectively. The cellular energy allocation (CEA) methodology was used to assess the adverse effects of toxic stress on the energy budget of the test organisms. The results of a decrease in CEA effect of increased carbon dioxide in seawater from all individual in Ea and Ec. Increase of carbon dioxide reduced pH in seawater, significantly. The chemical changes in sea- water caused by increasing $pCO_2$ might cause stresses to test organisms and changes in the cellular energy allocations. Results of this study can be used to understand the possible influence of $CO_2$ concentration increased by the leakage from sub-sea bed storage sites as well as fossil fuel combustion on marine organisms.