1:45 PM - 3:15 PM
[SVC33-P01] Noise level quantification of EDM applying atmospheric correction using numerical weather model
Keywords:EDM, atmospheric correction, ground deformation, Izu-Oshima volcano
Electro-optical Distance Measurement (EDM) is one of methos for observing ground deformation around volcanic area. EDM observes slope distance between an observation site and a reflector site. Observed slope distance should be corrected by atmospheric data. Takagi et al. (2010) reported that application of atmospheric correction with the use of a numerical weather model reduces the noise level. In this study, we derive a quantitative relation between noise level and slope distance.
We use a data set for 9 baselines composed by 2 observation sites and 8 reflector sites installed at Izu-Oshima volcano from 2010 to 2021. We use Japan Meteorological Agency (JMA) weather forecasting model mesoscale analysis (MA) as a numerical weather model.
As a result, noise levels are reduced for all baselines. Based on the theory of error propagation, we obtain a relational expression between monthly standard deviation and monthly average slope distance by regression analysis. In this case, remarkable dependence of vertical distance for noise level. In order to obtain more standard relation, we should analyze additionally including another data under different observation settings.
We use a data set for 9 baselines composed by 2 observation sites and 8 reflector sites installed at Izu-Oshima volcano from 2010 to 2021. We use Japan Meteorological Agency (JMA) weather forecasting model mesoscale analysis (MA) as a numerical weather model.
As a result, noise levels are reduced for all baselines. Based on the theory of error propagation, we obtain a relational expression between monthly standard deviation and monthly average slope distance by regression analysis. In this case, remarkable dependence of vertical distance for noise level. In order to obtain more standard relation, we should analyze additionally including another data under different observation settings.