Due to the need for accurate exploration of deep-sea scientific research, drilling techniques by combining the operational advantages of the Jiaolong manned submersible is considered one of the most feasible methods for deep-sea bedrock drilling. Based on deep sea bedrock cutting model and discrete element simulation, as well as efficient drilling as the design criterion, the development of a deep sea 7000 m electromechanical coring apparatus was carried out. The outstanding feature of this technology is that the bit load produced by the drill pressure is usually within the range 100–400 N while the recommended load for diamond drilling is 1–3 KN or even more. Therefore, searching for the drilling bits that can drill in extremely hard formations with minimal load and acceptable rates of penetration and rotary speed is the necessary step to prove the feasibility of electromechanical deep-sea drilling technology. A test has been designed and constructed to examine three types of drill bits. The results of experiments show that the new low-load polycrystalline diamond compact (PDC) bit has the highest penetration length of 138 mm/15 min under a 300 N load and 250 rpm rotary speed. Finally, field tests with the Jiaolong submersible were used to conduct deep sea experiments and verify the load model, which provides theoretical and technical data on the use of a low-load core sampling drill developed specifically for a deep sea submersible.

China’s 7000 m manned submersible JIAOLONG carried out an exploration cruise at the Mariana Trench from June to July 2016. The submersible completed nine manned dives on the north and south area of the Mariana Trench from the depth of 5500 to 6700 m, to investigate the geological, biological and chemical characteristics in the hadal area. During the cruise, JIAOLONG deployed a gas-tight serial sampler to collect the water near the sea bottom regularly. Five days later, the sub located the sampler in another dive and retrieved it successfully from the same location, which is the first time that scientists and engineers finished the high accuracy in-situ deployment and retrieval using a manned submersible with Ultra-Short Base Line (USBL) positioning system at the depth more than 6600 m. In this task, we used not only the USBL system of the manned submersible but also a compound strategy, including five position marks, the sea floor terrain, the depth contour, and the heading of the sub. This paper introduces the compound strategy of the target deployment and retrieval with the practical diving experience of JIAOLONG, and provides a promising technique for other underwater vehicles such as manned submersible or Remote Operated Vehicle (ROV) under similar conditions.

The complicated topographies of the deep sea pose significant challenges for the core drilling with the Jiaolong submersible manipulator. To address this p

This paper focuses on investigating the configuration and parameter selection of the silicone pipe in soft valve. According to the working principles of soft valve, five configurations and four structural parameters of silicone pipes are proposed and analyzed. The relationship between the pipe configuration and breakthrough pressure is investigated through experimental tests. The influence of the structural parameters on the breakthrough pressure is revealed by experiments as well. Based on the revealed design considerations, a three-way soft valve is designed, fabricated and tested. The experimental results show that the designed pipes have great stability and good sealability, which ensures the three-way soft valve possesses high breakthrough pressure. Finally, two application tests of the three-way soft valve are carried out, which further confirm the effectiveness of designed pipe and designed soft valve.

In the field of ocean observation, we often face the challenge of the contradiction between the vast ocean environment and limited ocean sensor observations, resulting in significant sparsity in the acquired ocean sensor data. This sparse ocean sensor data typically exhibits characteristics such as discrete spatial distribution, discontinuous observation time, and vertical stratification with water depth variations. Current methods primarily employ rule-based quality control, time series modeling, or traditional graph neural networks for processing. This paper addresses the characteristics of sparse ocean sensor data, building upon these methods by further utilizing topological correlation and hierarchical feature modeling on a topological basis. It proposes a depth-aware heterogeneous spatiotemporal graph neural network (DAHSGNN) to achieve efficient anomaly detection and data correction for this type of data. DAHSGNN integrates discrete observation data along the depth axis using a local graph construction method. It employs hierarchical feature engineering to characterize the vertical stratification of the ocean. A Gaussian Hidden Markov Model is used to segment the water layers, and intra- and inter-layer trend features are extracted using a water layer probability-guided Transformer encoder. Then, a bidirectional long short-term memory deep sequence encoder captures the local dynamic context, thereby achieving fine-grained modeling of the ocean’s vertical stratification features. Finally, a heterogeneous graph autoencoder is used to reconstruct the site-level data distribution. Experiments were conducted using multiple environmental variables from the International Seabed Authority (ISA) DeepData database. Results show that DAHSGNN exhibits good cross-variable generalization ability, achieves higher reconstruction accuracy than baseline methods, and significantly improves anomaly detection performance.