A new method of alternating shooting of two different seismic sources for deep geophysical surveys.

In the latest geophysical survey crossing the Ninety East Ridge of the Indian Ocean, a new method was employed to perform proportional double seismic source excitation and synchronously receive signals from the sea surface and the seabed. The two seismic sources used for excitation were two sets of gun arrays with different energies and dominant frequencies, a G gun array and a Bolt gun array. The G gun array consisted of 3 G.II guns with a total capacity of 450 in3 and a dominant frequency of 20-100 Hz. The Bolt gun array consisted of 4 Bolt 1500LL air guns with a total capacity of 6000 in3 and a dominant frequency of 10-40 Hz. The seismic receiving system comprised a 480-channel seismic streamer towed from the sea surface and 21 ocean bottom seismometers (OBS). During offshore operations, the integrated navigation system produced equidistant trigger signals at an interval of 50 m. The trigger signals were distributed to the G gun array and Bolt gun array at a ratio of 3:1 after passing through a pulse signal proportional distributor. The two sets of gun arrays fired alternatingly at a given ratio. The receiving equipment on the sea surface and seabed simultaneously received the seismic signals excited by the two sets of gun arrays. After targeted data processing, in addition to the seismic profile generated by the conventional G gun seismic source, the deep seismic profile generated by the Bolt gun seismic source and the survey profile of the active-source OBS were obtained simultaneously. The penetration depths of the three sets of profiles reach 2 km, 6 km, and 30 km, respectively, greatly improving the efficiency of offshore deep-sea seismic surveys.

Towards Characterizing and Developing Formation and Migration Cues in Seafloor Sand Waves on Topology, Morphology, Evolution from High-Resolution Mapping via Side-Scan Sonar in Autonomous Underwater Vehicles.

Sand waves constitute ubiquitous geomorphology distribution in the ocean. In this paper, we quantitatively investigate the sand wave variation of topology, morphology, and evolution from the high-resolution mapping of a side scan sonar (SSS) in an Autonomous Underwater Vehicle (AUV), in favor of online sequential Extreme Learning Machine (OS-ELM). We utilize echo intensity directly derived from SSS to help accelerate detection and localization, denote a collection of Gaussian-type morphological templates, with one integrated matching criterion for similarity assessment, discuss the envelope demodulation, zero-crossing rate (ZCR), cross-correlation statistically, and estimate the specific morphological parameters. It is demonstrated that the sand wave detection rate could reach up to 95.61% averagely, comparable to deep learning such as MobileNet, but at a much higher speed, with the average test time of 0.0018 s, which is particularly superior for sand waves at smaller scales. The calculation of morphological parameters primarily infer a wave length range and composition ratio in all types of sand waves, implying the possible dominant direction of hydrodynamics. The proposed scheme permits to delicately and adaptively explore the submarine geomorphology of sand waves with online computation strategies and symmetrically integrate evidence of its spatio-temporal responses during formation and migration.

Inversion of the physical properties of seafloor surface sediments based on AUV sub-bottom profile data in the northern slope of the South China Sea.

Based on the seafloor reflection coefficient obtained from autonomous underwater vehicle (AUV) sub-bottom profile survey data of the northern slope of the South China Sea, combined with the sample test data of seafloor surface sediments, we use the Biot-Stoll model to establish the equations relating the seafloor reflection coefficient to the porosity, density, and mean grain size of the sediments at the dominant frequency of 5 kHz (the dominant frequency of the AUV sub-bottom profiler). The physical property parameters such as the porosity, density, and mean grain size of seafloor surface sediments are further inverted. Comparison of inversion results with measured results shows that the overall deviation ratios of the inverted mean grain size, porosity, and density of the surface sediments are in the ranges of - 13.56 to 14.44%, - 6.15 to 8.06%, and - 10.85 to 0.46%, respectively. Among them, the mean grain size directly reflects the size of seafloor sediment particles, and the particles are finer in deeper water. Overall, the inversion results are basically consistent with the measured values and thus can well reflect the variation characteristics of the physical properties of seafloor surface sediments.