Separation of Gravity Anomaly Data Considering Statistical Independence of Source Signals
نویسندگان
چکیده
منابع مشابه
Statistical downscaling of GRACE gravity satellite-derived groundwater level data
With the continued threat from climate change, population growth and followed by increasing water demand, the need for hydrological data with high spatial resolution and proper time coverage to be felt more than ago. Therefore, having data such as terrestrial water storage changes and groundwater level changes with high resolution spatial helps to plan and make decisions for water resource mana...
متن کاملPreferential filtering for gravity anomaly separation
We present the preferential filtering method for gravity anomaly separation based on Green equivalent-layer concept and Wiener filter. Compared to the conventional upward continuation and the preferential continuation, the preferential filtering method has the advantage of no requirement of continuation height. The method was tested both on the synthetic gravity data of a model of multiple rect...
متن کاملStatistical Principles of Source Separation
Blind signal separation (BSS) is an emerging signal processing technique, aiming at recovering unobserved signals or`sources' from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption of mutual independence between the signals. The weakness of the assumptions makes it a powerful approach but requires to venture beyond familiar second order statistics....
متن کاملEnhancement of Source Independence for Blind Source Separation
When exploiting independent component analysis (ICA) to perform blind source separation (BSS), it is assumed that sources are mutually independent. However, in practice, the latent sources are usually dependent to some extent. Fortunately, if the sources are the same type of natural signals, they may be mutually independent in some frequency band, and dependent in other band. It is possible to ...
متن کاملBlind Source Separation of Compressively Sensed Signals
We present an approach to simultaneously separate and reconstruct signals from a compressively sensed linear mixture. We assume that the signals have a common sparse representation. The approach combines classical Compressive Sensing (CS) theory with a linear mixing model. Since Blind Source Separation (BSS) from a linear mixture is only possible up to permutation and scaling, factoring out the...
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: Journal of Japan Society of Civil Engineers, Ser. A1 (Structural Engineering ^|^ Earthquake Engineering (SE/EE))
سال: 2013
ISSN: 2185-4653
DOI: 10.2208/jscejseee.69.i_549