Stratigraphy, depositional history, and pore network of the Lower Cretaceous Sunniland carbonates in the South Florida basin

The South Florida Basin of the eastern Gulf of Mexico represents a vast, undisturbed carbonate system that extended from the Florida Keys through the Tampa-Sarasota Arch. In South Florida, extensive subsurface data and analogous modern environments provide an opportunity to unravel the evolution of this system from shoreline to shelf-margin. This study examines the changing facies and the pore network of the Latest Aptian-Early Albian Sunniland interval. Stratigraphic results are closely comparable with contemporary carbonate platform studies in the northern Gulf of Mexico. The Sunniland Formation was deposited during a major transgressive-regressive sequence. The Sunniland interval is divided into five third-fourth order, transgress-regressive depositional cycles (S-1 to S-5) in south Florida using sequence analysis of shelf-interior facies succession. In these sequences, facies proportion, faunal composition, and stratal geometries of the shelf-interior are found to be the result of the changing accommodation trends and ocean chemistry. As in the Comanche Platform in South Texas, the detrimental effects of oceanic anoxic event 1B may fundamentally drive the evolution of platform morphology in the eastern Gulf of Mexico as: • Rimmed shelf (crisis phase: S1) • Distally steepened ramp (anoxic/dysoxic phase: S2, recovery phase: S3, S4) • High-angle rimmed shelf (recovery to equilibrium phase: S5). Within this hydrocarbon-producing trend, the lowered sea level at the end of S4 enhances the reservoir quality in the high-energy settings including back-reef debris aprons, tidal shoal-complex and carbonate beach by dissolution. The tight sabkha-tidal flat facies in S5 forms the reservoir seal, whereas the medium-fine crystalline dolomites in S3 may not adversely affect and likely facilitate the migration of hydrocarbon self-sourced from the high TOC, argillaceous mudstone in S2.Energy and Earth Resource

SparseTrack: Multi-Object Tracking by Performing Scene Decomposition based on Pseudo-Depth

Exploring robust and efficient association methods has always been an important issue in multiple-object tracking (MOT). Although existing tracking methods have achieved impressive performance, congestion and frequent occlusions still pose challenging problems in multi-object tracking. We reveal that performing sparse decomposition on dense scenes is a crucial step to enhance the performance of associating occluded targets. To this end, we propose a pseudo-depth estimation method for obtaining the relative depth of targets from 2D images. Secondly, we design a depth cascading matching (DCM) algorithm, which can use the obtained depth information to convert a dense target set into multiple sparse target subsets and perform data association on these sparse target subsets in order from near to far. By integrating the pseudo-depth method and the DCM strategy into the data association process, we propose a new tracker, called SparseTrack. SparseTrack provides a new perspective for solving the challenging crowded scene MOT problem. Only using IoU matching, SparseTrack achieves comparable performance with the state-of-the-art (SOTA) methods on the MOT17 and MOT20 benchmarks. Code and models are publicly available at \url{https://github.com/hustvl/SparseTrack}.Comment: 12 pages, 8 figure

Generalizable Neural Voxels for Fast Human Radiance Fields

Rendering moving human bodies at free viewpoints only from a monocular video is quite a challenging problem. The information is too sparse to model complicated human body structures and motions from both view and pose dimensions. Neural radiance fields (NeRF) have shown great power in novel view synthesis and have been applied to human body rendering. However, most current NeRF-based methods bear huge costs for both training and rendering, which impedes the wide applications in real-life scenarios. In this paper, we propose a rendering framework that can learn moving human body structures extremely quickly from a monocular video. The framework is built by integrating both neural fields and neural voxels. Especially, a set of generalizable neural voxels are constructed. With pretrained on various human bodies, these general voxels represent a basic skeleton and can provide strong geometric priors. For the fine-tuning process, individual voxels are constructed for learning differential textures, complementary to general voxels. Thus learning a novel body can be further accelerated, taking only a few minutes. Our method shows significantly higher training efficiency compared with previous methods, while maintaining similar rendering quality. The project page is at https://taoranyi.com/gneuvox .Comment: Project page: http://taoranyi.com/gneuvo

Adaptive Fault-Tolerant Formation Control for Quadrotors with Actuator Faults

In this paper, we investigate the fault-tolerant formation control of a group of quadrotor aircrafts with a leader. Continuous fault-tolerant formation control protocol is constructed by using adaptive updating mechanism and boundary layer theory to compensate actuator fault. Results show that the desired formation pattern and trajectory under actuator fault can be achieved using the proposed fault-tolerant formation control. A simulation is conducted to illustrate the effectiveness of the method