. Description, analysis and predictions of sea floor roughness using spectral models. Ocean bottom; Submarine geology. BAND PASS 10 BAND PASS 9 â¦# â *"â¦Â»'#â¦â¦!⦠#»<»»'iNNt<iiH|«» 'Ik BAND PASS 5. *iff^\^\i^iiWH'*fii^i'i^^^ INPUT SIGNAL Figure B-5 Example of province picker output with "white noise" input illustrated in Figure B-4. The input signal is shown at the bottom, above which are the output of ten band-pass filters convolved with the data. The lowest signal (Band Pass 1) is the lowest frequency pass, while the highest (Band Pass 10) is the highest frequenc
. Description, analysis and predictions of sea floor roughness using spectral models. Ocean bottom; Submarine geology. BAND PASS 10 BAND PASS 9 â¦# â *"â¦Â»'#â¦â¦!⦠#»<»»'iNNt<iiH|«» 'Ik BAND PASS 5. *iff^\^\i^iiWH'*fii^i'i^^^ INPUT SIGNAL Figure B-5 Example of province picker output with "white noise" input illustrated in Figure B-4. The input signal is shown at the bottom, above which are the output of ten band-pass filters convolved with the data. The lowest signal (Band Pass 1) is the lowest frequency pass, while the highest (Band Pass 10) is the highest frequency pass. Above each band-passed sig- nal is the energy envelope calculated by the Hilbert Trans- form method. Notice the large variability in energy for each band despite the constant amplitude input. At the top, the estimated spectral exponent (slope of log-transformed spec- tra) and the band-limited RMS energy calculated along the profile are plotted. Standard deviation of the slope par- ameter is .2. The algorithm was designed to minimize this "natural" variability. 144. Please note that these images are extracted from scanned page images that may have been digitally enhanced for readability - coloration and appearance of these illustrations may not perfectly resemble the original Fox, Christopher Gene; United States. Naval Oceanographic Office. Bay St. Louis, Miss. : Naval Oceanographic Office
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Keywords: ., bookcentury1900, bookcollectionbiodivers, booksubjectoceanbottom
