Estimating depth and explosive charge weight for an extremely shallow underwater explosion of the ROKS Cheonan sinking in the Yellow Sea

We estimated the detonation depth and net explosive weight for a very shallow underwater explosion using cutoff frequencies and spectral analysis. With detonation depth and a bubble pulse the net explosive weight for a shallow underwater explosion could simply be determined. The ray trace modeling confirms the detonation depth as a source of the hydroacoustic wave propagation in a shallow channel. We found cutoff frequencies of the reflection off the ocean bottom to be 8.5 Hz, 25 Hz, and 43 Hz while the cutoff frequency of the reflection off the free surface to be 45 Hz including 1.01 Hz for the bubble pulse, and also found the cutoff frequency of surface reflection to well fit the ray-trace modeling. We also attempted to corroborate our findings using a 3D bubble shape modeling and boundary element method. Our findings led us to the net explosive weight of the underwater explosion offshore of Baengnyeong-do for the ROKS Cheonan sinking to be approximately 136 kg TNT at a depth of about 8 m within an ocean depth of around 44 m. © 2015 Elsevier B.V.

Understanding the importance of sediments to water ouality in coastal shallow lagoons

The undesirable enrichment of water by nutrients may be a problem, especially in areas with restricted exchange with the sea. The tidal regime flushes the system and contributes for the removal of phytoplankton, favouring phytobenthos as the target of enhanced nutrients. Water samples were collected during the years of 2006 and 2007-08 for nutrients, chlorophyll a and dissolved oxygen. Sediment sample s were also collected for pore water nutrients and benthic chlorophyll a. From comparison with previous work, a decrease in the nitrogen concentration in the water column can be pointed out, which may indicate an improvement of the water quality. Pore water DAIN represents approximately 75% of the total DAIN of the whole lagoon. Benthic chlorophyll a concentrations were much larger than in the water column, representing around 99% of the total chlorophyll existent in the lagoon. Benthic microalgae play a relevant role in this system and therefore standard monitoring programs of the WFD, which do not consider this component, may fail to track nutrient-driven changes in primary producers. Dissolved oxygen concentration could be near critical levels during the summer (early in the morning), especially in the inner channels.

A neural network seismic detector

This experimental study focuses on a detection system at the seismic station level that should have a similar role to the detection algorithms based on the ratio STA/LTA. We tested two types of neural network: Multi-Layer Perceptrons and Support Vector Machines, trained in supervised mode. The universe of data consisted of 2903 patterns extracted from records of the PVAQ station, of the seismography network of the Institute of Meteorology of Portugal. The spectral characteristics of the records and its variation in time were reflected in the input patterns, consisting in a set of values of power spectral density in selected frequencies, extracted from a spectro gram calculated over a segment of record of pre-determined duration. The universe of data was divided, with about 60% for the training and the remainder reserved for testing and validation. To ensure that all patterns in the universe of data were within the range of variation of the training set, we used an algorithm to separate the universe of data by hyper-convex polyhedrons, determining in this manner a set of patterns that have a mandatory part of the training set. Additionally, an active learning strategy was conducted, by iteratively incorporating poorly classified cases in the training set. The best results, in terms of sensitivity and selectivity in the whole data ranged between 98% and 100%. These results compare very favorably with the ones obtained by the existing detection system, 50%.

On-line operation of an intelligent seismic detector

This study describes the on-line operation of a seismic detection system to act at the level of a seismic station providing similar role to that of a STA /LTA ratio-based detection algorithms. The intelligent detector is a Support Vector Machine (SVM), trained with data consisting of 2903 patterns extracted from records of the PVAQ station, one of the seismographic network's stations of the Institute of Meteorology of Portugal (IM). Records' spectral variations in time and characteristics were reflected in the SVM input patterns, as a set of values of power spectral density at selected frequencies. To ensure that all patterns of the sample data were within the range of variation of the training set, we used an algorithm to separate the universe of data by hyper-convex polyhedrons, determining in this manner a set of patterns that have a mandatory part of the training set. Additionally, an active learning strategy was conducted, by iteratively incorporating poorly classified cases in the training set. After having been trained, the proposed system was experimented in continuous operation for unseen (out of sample) data, and the SVM detector obtained 97.7% and 98.7% of sensitivity and selectivity, respectively. The same type of ANN presented 88.4 % and 99.4% of sensitivity and selectivity when applied to data of a different seismic station of IM. © 2013 Springer-Verlag Berlin Heidelberg.

Assemblage structure and secondary production of mesozooplankton in shallow water volcanic CO2 vents of the Azores

Dissertação de mestrado, Biologia Marinha, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015