Estimating the amplitude scintillation index from sparsely sampled phase screen data
نویسندگان
چکیده
منابع مشابه
Index Models for Sparsely Sampled Functional Data
The regression problem involving functional predictors has many important applications and a number of functional regression methods have been developed. However, a common complication in functional data analysis is one of sparsely observed curves, that is predictors that are observed, with error, on a small subset of the possible time points. Such sparsely observed data induces an errors-in-va...
متن کاملMicro-earthquake monitoring with sparsely-sampled data
Micro-seismicity can be used to monitor the migration of fluids during reservoir production and hydro-fracturing operations in brittle formations or for studies of naturally occurring earthquakes in fault zones. Micro-earthquake locations can be inferred using wave-equation imaging under the exploding reflector model, assuming densely sampled data and known velocity. Seismicity is usually monit...
متن کاملInterferometric Seismic Imaging of Sparsely-sampled Data
Micro-seismicity induced by fluid migration can be used to monitor the migration of fluids during reservoir production and hydro-fracturing operations. The seismicity is usually monitored with sparse networks of seismic sensors. The sparsity of the sensor networks degrades the accuracy of the estimated event locations. This inaccuracy often makes it impossible to infer the fluid pathways at the...
متن کاملClustering for sparsely sampled functional data
We develop a flexible model-based procedure for clustering functional data. The technique can be applied to all types of curve data but is particularly useful when individuals are observed at a sparse set of time points. In addition to producing final cluster assignments, the procedure generates predictions and confidence intervals for missing portions of curves. Our approach also provides many...
متن کاملLearning Regulatory Networks from Sparsely Sampled Time Series Expression Data
We present a probabilistic modeling approach to learning gene transcriptional regulation networks from time series gene expression data that is appropriate for the sparsely and irregularly sampled time series datasets currently available. We use a clustering algorithm based on statistical splines to estimate continuous probabilistic models for clusters of genes with similar time expression prof...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Radio Science
سال: 2004
ISSN: 0048-6604
DOI: 10.1029/2002rs002792