scholarly journals Deep-sea sediments of the global ocean

2020 ◽  
Author(s):  
Markus Diesing
Keyword(s):  
Machine Learning ◽  
Spatial Distribution ◽  
Deep Sea ◽  
Learning Algorithm ◽  
Marine Species ◽  
Global Ocean ◽  
Teaching Management ◽  
Sediment Distribution ◽  
Deep Sea Sediments ◽  
Seafloor Sediment

Abstract. Although the deep-sea floor accounts for more than 70 % of the Earth's surface, there has been little progress in relation to deriving maps of seafloor sediment distribution based on transparent, repeatable and automated methods such as machine learning. A new digital map of the spatial distribution of seafloor lithologies in the deep sea below 500 m water depth is presented to address this shortcoming. The lithology map is accompanied by estimates of the probability of the most probable class, which may be interpreted as a spatially-explicit measure of confidence in the predictions, and probabilities for the occurrence of seven lithology classes (Calcareous sediment, Clay, Diatom ooze, Lithogenous sediment, Mixed calcareous-siliceous ooze, Radiolarian ooze and Siliceous mud). These map products were derived by the application of the Random Forest machine learning algorithm to a homogenised dataset of seafloor lithology samples and global environmental predictor variables that were selected based on the current understanding of the controls on the spatial distribution of deep-sea sediments. The overall accuracy of the lithology map is 69.5 %, with 95 % confidence limits of 67.9 % and 71.1 %. It is expected that the map products are useful for various purposes including, but not limited to, teaching, management, spatial planning, design of marine protected areas and as input for global spatial predictions of marine species distributions and seafloor sediment properties. The map products are available at https://doi.org/10.1594/PANGAEA.911692 (Diesing, 2020).

scholarly journals Deep-sea sediments of the global ocean

2020 ◽  
Vol 12 (4) ◽  
pp. 3367-3381
Author(s):  
Markus Diesing
Keyword(s):  
Machine Learning ◽  
Spatial Distribution ◽  
Deep Sea ◽  
Learning Algorithm ◽  
Marine Species ◽  
Global Ocean ◽  
Teaching Management ◽  
Sediment Distribution ◽  
Deep Sea Sediments ◽  
Seafloor Sediment

Abstract. Although the deep-sea floor accounts for approximately 60 % of Earth's surface, there has been little progress in relation to deriving maps of seafloor sediment distribution based on transparent, repeatable, and automated methods such as machine learning. A new digital map of the spatial distribution of seafloor lithologies below 500 m water depth is presented to address this shortcoming. The lithology map is accompanied by estimates of the probability of the most probable class, which may be interpreted as a spatially explicit measure of confidence in the predictions, and probabilities for the occurrence of five lithology classes (calcareous sediment, clay, diatom ooze, lithogenous sediment, and radiolarian ooze). These map products were derived by the application of the random-forest machine-learning algorithm to a homogenised dataset of seafloor lithology samples and global environmental predictor variables that were selected based on the current understanding of the controls on the spatial distribution of deep-sea sediments. It is expected that the map products are useful for various purposes including, but not limited to, teaching, management, spatial planning, design of marine protected areas, and as input for global spatial predictions of marine species distributions and seafloor sediment properties. The map products are available at https://doi.org/10.1594/PANGAEA.911692 (Diesing, 2020).

Application of machine learning to map the global distribution of deep-sea sediments

2020 ◽  
Author(s):  
Markus Diesing
Keyword(s):  
Machine Learning ◽  
Deep Sea ◽  
Learning Algorithm ◽  
Predictor Variable ◽  
Global Ocean ◽  
Predictor Variables ◽  
Maximum Temperature ◽  
Sea Floor ◽  
Deep Sea Sediments ◽  
Calcareous Sediment

<p>The deep-sea floor accounts for >90% of seafloor area and >70% of the Earth’s surface. It acts as a receptor of the particle flux from the surface layers of the global ocean, is a place of biogeochemical cycling, records environmental and climate conditions through time and provides habitat for benthic organisms. Maps of the spatial patterns of deep-sea sediments are therefore a major prerequisite for many studies addressing aspects of deep-sea sedimentation, biogeochemistry, ecology and related fields.</p><p>A new digital map of deep-sea sediments of the global ocean is presented. The map was derived by applying the Random Forest machine-learning algorithm to published sample data of seafloor lithologies and environmental predictor variables. The selection of environmental predictors was initially based on the current understanding of the controls on the distribution of deep-sea sediments and the availability of data. A predictor variable selection process ensured that only important and uncorrelated variables were employed in the model. The three most important predictor variables were sea-surface maximum salinity, sea-floor maximum temperature and bathymetry. The occurrence probabilities of seven seafloor lithologies (Calcareous sediment, Clay, Diatom ooze, Lithogenous sediment, Mixed calcareous-siliceous ooze, Radiolarian ooze and Siliceous mud) were spatially predicted. The final map shows the most probable seafloor lithology and an associated probability value, which may be viewed as a spatially explicit measure of map confidence. An assessment of the accuracy of the map was based on a test set of observations not used for model training. Overall map accuracy was 69.5% (95% confidence interval: 67.9% - 71.1%). The sea-floor lithology map bears some resemblance with previously published hand-drawn maps in that the distribution of Calcareous sediment, Clay and Diatom ooze are very similar. Clear differences were however also noted: Most strikingly, the map presented here does not display a band of Radiolarian ooze in the equatorial Pacific.</p><p>The probability surfaces of individual seafloor lithologies, the categorical map of the seven mapped lithologies and the associated map confidence will be made freely available. It is hoped that they form a useful basis for research pertaining to deep-sea sediments.</p>

Spatial distribution and abundance of ammonia-oxidizing microorganisms in deep-sea sediments of the Pacific Ocean

2015 ◽  
Vol 108 (2) ◽  
pp. 329-342 ◽  
Author(s):  
Zhu-Hua Luo ◽  
Wei Xu ◽  
Meng Li ◽  
Ji-Dong Gu ◽  
Tian-Hua Zhong
Keyword(s):  
Spatial Distribution ◽  
Pacific Ocean ◽  
Deep Sea ◽  
The Pacific ◽  
Deep Sea Sediments ◽  
The Pacific Ocean

scholarly journals Changes in Ocean Ventilation Rates Over the Last 7000 Years Based on 14C Variations in the Atmosphere and Oceans

Radiocarbon ◽  
1989 ◽  
Vol 31 (03) ◽  
pp. 481-492 ◽  
Author(s):  
T-H Peng
Keyword(s):  
Age Differences ◽  
Benthic Foraminifera ◽  
Deep Sea ◽  
Ventilation Rate ◽  
Global Ocean ◽  
Production Rates ◽  
The Past ◽  
Ocean Ventilation ◽  
Deep Sea Sediments ◽  
Ventilation Rates

Changes in the ocean ventilation rate may be one of the causes for a net decrease of 100‰ Δ 14C in atmospheric CO2 over the last 8000 years. Ocean ventilation rates of the past can be derived from the 14C record preserved in planktonic and benthic foraminifera in deep-sea sediments. Results of 14C dating using accelerator mass spectrometry on deep sea sediments from the South China Sea show that the age differences between planktonic (G sacculifer) and benthic foraminifera increase from 1350 yr ca 7000 yr ago to 1590 yr at present. An 11-box geochemical model of global ocean circulation was used for this study. Both tree-ring-determined atmospheric 14C values and foraminifera 14C age differences are used as constraints to place limits on patterns of changes in ocean ventilation rates and in atmospheric 14C production rates. Results indicate: 1) 14C production rates in the atmosphere may have decreased by as much as 30% between 7000 and 3000 yr ago, and may have increased again by ca 15% in the past 2000 yr, and 2) the global ocean ventilation rate may not have been at steady state over the last 7000 yr, but may have slowed by as much as 35%.

scholarly journals Seafloor microplastic hotspots controlled by deep-sea circulation

Science ◽  
2020 ◽  
Vol 368 (6495) ◽  
pp. 1140-1145 ◽  
Author(s):  
Ian A. Kane ◽  
Michael A. Clare ◽  
Elda Miramontes ◽  
Roy Wogelius ◽  
James J. Rothwell ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Deep Sea ◽  
Biodiversity Hotspots ◽  
Bottom Currents ◽  
Ultimate Fate ◽  
Seafloor Sediment

Although microplastics are known to pervade the global seafloor, the processes that control their dispersal and concentration in the deep sea remain largely unknown. Here, we show that thermohaline-driven currents, which build extensive seafloor sediment accumulations, can control the distribution of microplastics and create hotspots with the highest concentrations reported for any seafloor setting (190 pieces per 50 grams). Previous studies propose that microplastics are transported to the seafloor by vertical settling from surface accumulations; here, we demonstrate that the spatial distribution and ultimate fate of microplastics are strongly controlled by near-bed thermohaline currents (bottom currents). These currents are known to supply oxygen and nutrients to deep-sea benthos, suggesting that deep-sea biodiversity hotspots are also likely to be microplastic hotspots.

Spatial distribution and activity of viruses in the deep-sea sediments of Sagami Bay, Japan

2006 ◽  
Vol 53 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Mathias Middelboe ◽  
Ronnie N. Glud ◽  
Frank Wenzhöfer ◽  
Kazumasa Oguri ◽  
Hiroshi Kitazato
Keyword(s):  
Spatial Distribution ◽  
Deep Sea ◽  
Sagami Bay ◽  
Deep Sea Sediments

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Design of English text-to-speech conversion algorithm based on machine learning

2020 ◽  
pp. 1-12
Author(s):  
Li Dongmei
Keyword(s):  
Machine Learning ◽  
Speech Synthesis ◽  
Feature Recognition ◽  
Learning Algorithm ◽  
Morphological Structure ◽  
English Text ◽  
Text To Speech ◽  
Part Of Speech ◽  
Modern Computer ◽  
Conversion Algorithm

English text-to-speech conversion is the key content of modern computer technology research. Its difficulty is that there are large errors in the conversion process of text-to-speech feature recognition, and it is difficult to apply the English text-to-speech conversion algorithm to the system. In order to improve the efficiency of the English text-to-speech conversion, based on the machine learning algorithm, after the original voice waveform is labeled with the pitch, this article modifies the rhythm through PSOLA, and uses the C4.5 algorithm to train a decision tree for judging pronunciation of polyphones. In order to evaluate the performance of pronunciation discrimination method based on part-of-speech rules and HMM-based prosody hierarchy prediction in speech synthesis systems, this study constructed a system model. In addition, the waveform stitching method and PSOLA are used to synthesize the sound. For words whose main stress cannot be discriminated by morphological structure, label learning can be done by machine learning methods. Finally, this study evaluates and analyzes the performance of the algorithm through control experiments. The results show that the algorithm proposed in this paper has good performance and has a certain practical effect.

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