Wyniki wyszukiwania - Biblioteka Nauki

Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

Ograniczanie wyników

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Parallel and distributed seismic wave-field modeling

100%

Danek T. , Franczyk A.

tom Vol. 8, No 4

573-581

Elastic or acoustic wave-field modeling is an important part of seismic exploration. It can be used during the planning, processing and interpretation stages of seismic investigation. First attempts of using wave-field modeling were undertaken in the seventies by Alford, Kelly and others (Alford R M, Kelly K R and Boore D M 1974 Geophysics 39 (6) 834; Kelly K R, Ward R W, Treitel S and Alford R M 1976 Geophysics 41 2). These attempts were restricted by the limitations of computers at that time. Even now, computation for models of the standard exploration scale could last many hours, and many days in case of longer recording times. One of the best methods to overcome this disadvantage is parallelization of computations (Niccanna C and Bean C J 1997 Computers and Geosciences 23 (7) 771; Villareal A and Scales J A 1997 Computers in Physics 11 (4) 388). This paper presents the results of distributed parallelization of elastic and acoustic wave-field modeling based on a Parallel Virtual Machine.

A Big Data processing strategy for hybrid interpretation of flood embankment multisensor data

80%

Chuchro M. , Franczyk A. , Dwornik M. , Leśniak A.

2016

tom Vol. 42, no. 3

269--277

The assessment of flood embankments is a key component of a country’s comprehensive flood protection. Proper and early information on the possible instability of a flood embankment can make it possible to take preventative action. The assessment method proposed by the ISMOP project is based on a strategy of processing huge data sets (Big Data). The detection of flood embankment anomalies can take two analysis paths. The first involves the computation of numerical models and comparing them with real data measured on a flood embankment. This is the path of model-driven analysis. The second solution is data-driven, meaning time series are analysed in order to detect deviations from average values. Flood embankments are assessed based on the results of model-driven and data-driven analyses and information from preprocessing. An alarm is triggered if a critical value is exceeded in one or both paths of analysis. Tests on synthetic data demonstrate the high efficiency of the chosen methods for assessing the state of flood embankments.