The Ms7.4 earthquake of 18 July, 1969 is the only strong earthquake in the Bohai Sea that has been recorded by seismometers. Arguments about this large earthquake are focused on its causative fault. To tackle this problem, three cruises in the epicenter region from 2005 to 2008 were executed and 3 kinds of acoustic data with high‐resolution, including shallow penetrated single channel seismic data, sidescan sonar data and CHIRP data were collected. This paper presents the results based on these new acoustic data. A small depression zone 2~3 m beneath the seabed is first found in the Holocene sediments in the epicenter region. The zone with an NE30‐ strike is 20 km long and 3 km wide. CHIRP data reveal that depositional border formed at about 5000 a B.P. has a throw of 1.5 m in the zone. The good match of the locations of the depression zone and the BZ28 fault and aftershocks distribution of the 1969 earthquake suggests that the depression zone is the result of tectonic activities of the BZ28 fault and the bottom deformation of sequence A which was formed since 150 a B.P. is caused by 1969 earthquake. The BZ28 fault with a NE30° strike, a branch fault of the Tan‐Lu Fault Zone, is the causative fault of the Ms7.4 earthquake. CHIRP data show that the latest faulting age is the late Holocene. Based on the forming age of stratigraphic sequences and vertical displacement of the fault, the vertical slip rate of the BZ28 fault since late Pleistocene is calculated. The results show that the rate during the Holocene is 0.3 mm/a, which is larger than 0.05 mm/a during the late Pleistocene‐Holocene, reflecting a trend of aggrandizement.
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.
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