TY - JOUR
T1 - Earthquake forecasting model for Albania
T2 - the area source model and the smoothing model
AU - Xhafaj, Edlira
AU - Chan, Chung Han
AU - Ma, Kuo Fong
N1 - Publisher Copyright:
© 2024 Edlira Xhafaj et al.
PY - 2024/1/17
Y1 - 2024/1/17
N2 - We proposed earthquake forecasting models for Albania, one of the most seismogenic regions in Europe, to give an overview of seismic activity by implementing area source and smoothing approaches. The earthquake catalogue was first declustered to remove foreshocks and aftershocks when they are within the derived distance windows and time windows of mainshocks. Considering catalogue completeness, the events with M ≥ 4:1 during the period of 1960- 2006 were implemented for the learning forecast model. The forecasting is implemented into an area source model that includes 20 sub-regions and a smoothing model with a cell size of 0.2°times;0.2° to forecast the seismicity in Albania. Both models show high seismic rates along the western coastline and in the southern part of the study area, consistent with previous studies that discussed seismicity in the area and currently active regions. To further validate the forecast performance of the two models, we introduced the Molchan diagram to quantify the correlation between models and observations. The Molchan diagram suggests that the models are significantly better than a random distribution, confirming their forecasting abilities. Our results provide crucial information for subsequent research on seismic activity, such as probabilistic seismic hazard assessment.
AB - We proposed earthquake forecasting models for Albania, one of the most seismogenic regions in Europe, to give an overview of seismic activity by implementing area source and smoothing approaches. The earthquake catalogue was first declustered to remove foreshocks and aftershocks when they are within the derived distance windows and time windows of mainshocks. Considering catalogue completeness, the events with M ≥ 4:1 during the period of 1960- 2006 were implemented for the learning forecast model. The forecasting is implemented into an area source model that includes 20 sub-regions and a smoothing model with a cell size of 0.2°times;0.2° to forecast the seismicity in Albania. Both models show high seismic rates along the western coastline and in the southern part of the study area, consistent with previous studies that discussed seismicity in the area and currently active regions. To further validate the forecast performance of the two models, we introduced the Molchan diagram to quantify the correlation between models and observations. The Molchan diagram suggests that the models are significantly better than a random distribution, confirming their forecasting abilities. Our results provide crucial information for subsequent research on seismic activity, such as probabilistic seismic hazard assessment.
UR - http://www.scopus.com/inward/record.url?scp=85184067428&partnerID=8YFLogxK
U2 - 10.5194/nhess-24-109-2024
DO - 10.5194/nhess-24-109-2024
M3 - 期刊論文
AN - SCOPUS:85184067428
SN - 1561-8633
VL - 24
SP - 109
EP - 119
JO - Natural Hazards and Earth System Sciences
JF - Natural Hazards and Earth System Sciences
IS - 1
ER -