Analysis of ocean internal waves imaged by multichannel reflection seismics, using ensemble empirical mode decomposition

Research on ocean internal waves using seismic oceanography is a frontier issue both for marine geophysicists and physical oceanographers. Images of the ocean water layer obtained by conventional processing of multichannel seismic reflection data can show the overall patterns of internal waves. However, in order to extract more information from the seismic data, new tools need to be developed. Here, we use the ensemble empirical mode decomposition (EEMD) method to decompose vertical displacement data from seismic sections and apply this method to a seismic section from the northeastern South China Sea, where clear internal waves are observed. Compared with the conventional empirical mode decomposition method, EEMD has greatly reduced the scale mixing problems induced in the decomposition results. The results obtained show that the internal waves in this area are composed of different characteristic wavelengths at different depths. The depth range of 200–1050 m contains internal waves with a wavelength of 1.25 km that are very well coupled in the vertical direction. The internal waves with a wavelength of 3 km, in the depth range of 200–600 m, are also well coupled, but in an oblique direction; this suggests that the propagation speed of internal waves of this scale changes with depth in this area. Finally, the internal waves with a wavelength of 6.5 km, observed in the depth range of 200–800 m, are separated into two parts with a phase difference of about 90◦, by a clear interface at a depth of 650 m; this allows us to infer an oblique propagation of wave energy of this scale.publishe

Design and Implementation of Intelligent Vegetable Recognition System based on MobileNet

With the rise of food safety traceability, unmanned supermarkets and autonomous shopping, the automatic identification technology of agricultural products such as vegetables in circulation and sales has become an urgent problem. This paper designs an intelligent vegetable identification system based on MobileNet to solve intelligent identification problem of vegetable sales in supermarkets. The system includes main control core, visual processing module, pressure sensor, voice broadcasting module and display module. When the system detects that there are vegetables to be weighed, the visual processing module completes the classification of vegetables, broadcasts the name, unit price and total price of vegetables by voice, and displays the weight, unit price and total price by OLED. The machine vision processing module is constructed by deep separable convolution (DSC). It realizes the separation of channels and regions, so it has high computing efficiency and is more suitable for embedded devices with low memory space. The experimental results show that the overall recognition rate of five vegetables reaches 97.33% under three kinds of illumination. The system has the advantages of stability, intelligence and convenience

Research on submesoscale eddy and front near the South Shetland Islands (Antarctic Peninsula) using seismic oceanography data

The submesoscale processes, including submesoscale eddies and fronts, have a strong vertical velocity, can thus make important supplements to the nutrients in the upper ocean. Using legacy multichannel seismic data AP25 of cruise EW9101 acquired northeast of the South Shetland Islands (Antarctic Peninsula) in February 1991, we identified an oceanic submesoscale eddy with the horizontal scale of ~4 km and a steep shelf break front that has variable dip angles from 5o to 10o. The submesoscale eddy is an anticyclonic eddy, which carries warm core water, can accelerate ice shelves melting. The upwelling induced by shelf break front may play an important role in transporting nutrients to the sea surface. The seismic images with very high lateral resolution may provide a new insight to understand the submesoscale and even small-scale oceanic phenomena in the interior