• Geophysics and Geophysical Exploration
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• v.9 no.1
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• pp.50-59
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• 2006

The risk of fault reactivation in the Gippsland Basin was calculated using the FAST (Fault Analysis Seal Technology) technique, which determines fault reactivation risk by estimating the increase in pore pressure required to cause reactivation within the present-day stress field. The stress regime in the Gippsland Basin is on the boundary between strike-slip and reverse faulting: maximum horizontal stress $({\sim}\;40.5\;Mpa/km)$ > vertical stress (21 Mpa/km) ${\sim}$ minimum horizontal stress (20 MPa/km). Pore pressure is hydrostatic above the Campanian Volcanics of the Golden Beach Subgroup. The NW-SE maximum horizontal stress orientation $(139^{\circ}N)$ determined herein is broadly consistent with previous estimates, and verifies a NW-SE maximum horizontal stress orientation in the Gippsland Basin. Fault reactivation risk in the Gippsland Basin was calculated using two fault strength scenarios; cohesionless faults $(C=0;{\mu}=0.65)$ and healed faults $(C=5.4;\;{\mu}=0.78)$. The orientations of faults with relatively high and relatively low reactivation potential are almost identical for healed and cohesionless fault strength scenarios. High-angle faults striking NE-SW are unlikely to reactivate in the current stress regime. High-angle faults oriented SSE-NNW and ENE-WSW have the highest fault reactivation risk. Additionally, low-angle faults (thrust faults) striking NE-SW have a relatively high risk of reactivation. The highest reactivation risk for optimally oriented faults corresponds to an estimated pore pressure increase (Delta-P) of 3.8 MPa $({\sim}548\;psi)$ for cohesionless faults and 15.6 MPa $({\sim}2262\;psi)$ for healed faults. The absolute values of pore pressure increase obtained from fault reactivation analysis presented in this paper are subject to large errors because of uncertainties in the geomechanical model (in situ stress and rock strength data). In particular, the maximum horizontal stress magnitude and fault strength data are poorly constrained. Therefore, fault reactivation analysis cannot be used to directly measure the maximum allowable pore pressure increase within a reservoir. We argue that fault reactivation analysis of this type can only be used for assessing the relative risk of fault reactivation and not to determine the maximum allowable pore pressure increase a fault can withstand prior to reactivation.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.265-282
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• 2022

Sea surface temperature (SST) is a factor that greatly influences ocean circulation and ecosystems in the Earth system. As global warming causes changes in the SST near the Korean Peninsula, abnormal water temperature phenomena (high water temperature, low water temperature) occurs, causing continuous damage to the marine ecosystem and the fishery industry. Therefore, this study proposes a methodology to predict the SST near the Korean Peninsula and prevent damage by predicting abnormal water temperature phenomena. The study area was set near the Korean Peninsula, and ERA5 data from the European Center for Medium-Range Weather Forecasts (ECMWF) was used to utilize SST data at the same time period. As a research method, Long Short-Term Memory (LSTM) algorithm specialized for time series data prediction among deep learning models was used in consideration of the time series characteristics of SST data. The prediction model predicts the SST near the Korean Peninsula after 1- to 7-days and predicts the high water temperature or low water temperature phenomenon. To evaluate the accuracy of SST prediction, Coefficient of determination (R²), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) indicators were used. The summer (JAS) 1-day prediction result of the prediction model, R²=0.996, RMSE=0.119℃, MAPE=0.352% and the winter (JFM) 1-day prediction result is R²=0.999, RMSE=0.063℃, MAPE=0.646%. Using the predicted SST, the accuracy of abnormal sea surface temperature prediction was evaluated with an F1 Score (F1 Score=0.98 for high water temperature prediction in summer (2021/08/05), F1 Score=1.0 for low water temperature prediction in winter (2021/02/19)). As the prediction period increased, the prediction model showed a tendency to underestimate the SST, which also reduced the accuracy of the abnormal water temperature prediction. Therefore, it is judged that it is necessary to analyze the cause of underestimation of the predictive model in the future and study to improve the prediction accuracy.