Sedimentary elements,evolutions and controlling factors of the Miocene channel system:a case study of the deep-water Taranaki Basin in New Zealand

Deep-water channel systems are important petroleum reservoirs,and many have been discovered worldwide.Understanding deep-water channel sedimentary elements and evolution is helpful for deep-sea petroleum exploration and development.Based on high-resolution 3D seismic data,the Miocene channel system in the deep-water Taranaki Basin,New Zealand,was analyzed by using seismic interpretation techniques such as interlayer attribute extraction and strata slicing.The channel system was divided into five composite channels(CC-I to CC-V)according to four secondary level channel boundaries,and sedimentary elements such as channels,slump deposits,inner levees,mass transport deposits,and hemipelagic drape deposits were identified in the channel system.The morphological characteristics of several composite channels exhibited stark variances,and the overall morphology of the composite channels changed from relatively straight to highly sinuous to relatively straight.The evolution of the composite channels involved a gradual and repeated process of erosion and filling,and the composite channels could be divided into three evolutionary stages:initial erosion-filling,later erosion-filling(multistage),and channel abandonment.The middle Miocene channel system may have formed as a consequence of combined regional tectonic activity and global climatic change,and its intricate morphological alterations may have been influenced by the channel's ability to self-regulate and gravity flow properties.When studying the sedimentary evolution of a large-scale deep-water channel system in the Taranaki Basin during the Oligocene-Miocene,which transitioned from a passive margin to plate convergence,it can be understood how tectonic activity affected the channel and can also provide a theoretical reference for the evolution of the deepwater channels in areas with similar tectonic conversion environments around the world.

Quantitative morphometric analysis of a deep-water channel in the Taranaki Basin, New Zealand

The morphological changes of deep-water channels have an important influence on the distributions of channel sand reservoirs,so it is important to explore the morphological change process of deep-water channel for the exploration and development of deep-water oil and gas.Based on a typical sinuous Quaternary channel(Channel I)in the Taranaki Basin,New Zealand,a variety of seismic interpretation techniques were applied to quantitatively characterize the morphological characteristics of the Channel I,and the relationships between the quantitative parameters and the morphological changes of the Channel I,as well as the controlling factors affecting those morphological changes,were discussed.The results are as follows:(1)in the quantitative analysis,six parameters were selected:the channel depth,width,sinuosity,and aspect ratio(width/depth),the channel swing amplitude(λ)and the channel bend frequency(ω);(2)according to the quantitative morphological parameters of the channel(mainly including three parameters such as channel sinuosity,ωandλ),the Channel I was divided into three types:the low-sinuous channel(LSC),the high-sinuous channel(HSC),the moderate-sinuous channel(MSC).U-shaped channel cross-sections developed in the LSC,V-shaped channel cross-sections developed in the HSC,including inclined-V and symmetric-V cross-sections,and dish-shaped channel cross-sections developed in the MSC;(3)the morphological characteristics of the LSC and MSC were related to their widths and depths,while the morphology of the HSC was greatly affected by the channel width,a change in depth did not affect the HSC morphology;(4)the morphological changes of the Channel I were controlled mainly by the slope gradient,the restricted capacity of the channel and the differential in fluid properties.

Sub-Terahertz Channel Sounder: Review and Future Challenges

Due to the large amount of unused and unexplored spectrum resources, the so-called subTerahertz(sub-THz) frequency bands from 100 to 300 GHz are seen as promising bands for the next generation of wireless communication systems. Channel modeling at sub-THz bands is essential for the design and deployment of future wireless communication systems. Channel measurement is a widely adopted method to obtain channel characteristics and establish mathematical channel models. Channel measurements depend on the design and construction of channel sounders. Thus, reliable channel sounding techniques and accurate channel measurements are required. In this paper, the requirements of an ideal channel sounder are discussed and the main channel sounding techniques are described for the subTHz frequency bands. The state-of-the-art sub-THz channel sounders reported in the literature and respective channel measurements are presented. Moreover, a vector network analyzer(VNA) based channel sounder, which supports frequency bands from 220 to330 GHz is presented and its performance capability and limitation are evaluated. This paper also discussed the challenge and future outlook of the sub-THz channel sounders and measurements.

Sound event localization and detection based on deep learning

Acoustic source localization(ASL)and sound event detection(SED)are two widely pursued independent research fields.In recent years,in order to achieve a more complete spatial and temporal representation of sound field,sound event localization and detection(SELD)has become a very active research topic.This paper presents a deep learning-based multioverlapping sound event localization and detection algorithm in three-dimensional space.Log-Mel spectrum and generalized cross-correlation spectrum are joined together in channel dimension as input features.These features are classified and regressed in parallel after training by a neural network to obtain sound recognition and localization results respectively.The channel attention mechanism is also introduced in the network to selectively enhance the features containing essential information and suppress the useless features.Finally,a thourough comparison confirms the efficiency and effectiveness of the proposed SELD algorithm.Field experiments show that the proposed algorithm is robust to reverberation and environment and can achieve higher recognition and localization accuracy compared with the baseline method.

A New Infrared Atmospheric Sounding Interferometer Channel Selection and Assessment of Its Impact on Met Office NWP Forecasts

A new set of Infrared Atmospheric Sounding Interferometer （IASI） channels was re-selected from 314 EUMETSAT channels. In selecting channels, we calculated the impact of the individually added channel on the improvement in the analysis outputs from a one-dimensional variational analysis （1D-Var） for the Unified Model （UM） data assimilation system at the Met Office, using the channel score index （CSI） as a figure of merit. Then, 200 channels were selected in order by counting each individual channel＇s CSI contribution. Compared with the operationally used 183 channels for the UM at the Met Office, the new set shares 149 channels, while the other 51 channels are new. Also examined is the selection from the entropy reduction method with the same 1D-Var approach, Results suggest that channel selection can be made in a more objective fashion using the proposed CSI method. This is because the most important channels can be selected across the whole IASI observation spectrum. In the experimental trial runs using the UM global assimilation system, the new channels had an overall neutral impact in terms of improvement in forecasts, as compared with results from the operational channels. However, upper-tropospheric moist biases shown in the control run with operational channels were significantly reduced in the experimental trial with the newly selected channels. The reduction of moist biases was mainly due to the additional water vapor channels, which are sensitive to the upper-tropospheric water vapor.

Spatial Channel Sounding Based on Bistatic Synthetic Aperture Radar Principles

In this paper, we propose a simplified spatial channel sounding method by utilizing bistatic synthetic aperture radar(BiSAR) principles. Despite the different deployment geometries compared with a conventional BiSAR system, the feasibility of the approach is established by 1) the proposed method achieves a better spatial resolution than conventional directional channel sounders and 2) reconstruction algorithms based on time-domain backprojection in conjunction with a digital elevation model provide a good imaging performance and are suitable for reconstructing the spatial distribution of scatterers. Simulations of a high-speed rail(HSR) scenario demonstrate that the estimated power delay profiles(PDPs) and power angle profiles(PAPs) are close to the actual values.

Long-Range VNA-Based Channel Sounder: Design and Measurement Validation at MmWave and Sub-THz Frequency Bands

With the increasing demand for high bandwidth wireless communication systems,and with a congested spectrum in the sub-6 GHz frequency bands,researchers have been looking into exploration of millimeter wave(mmWave)and sub-terahertz(subTHz)frequency bands.Channel modeling is essential for system design and performance evaluation of new wireless communication systems.Accurate channel modeling relies on reliable measured channel data,which is collected by high-fidelity channel sounders.Furthermore,it is of importance to understand to which extent channel parameters are frequency dependent in typical deployment scenario(including both indoor short-range and outdoor long-range scenarios).To achieve this purpose,this paper presents a stateof-art long-range 28 GHz and 300 GHz VNA-based channel sounder using optical cable solutions,which can support a measurement range up to 300 m and 600 m in principle,respectively.The design,development and validation of the long-range channel sounders at mmWave and sub-THz bands are reported,with a focus on their system principle,link budget,and backto-back measurements.Furthermore,a measurement campaign in an indoor corridor is performed using the developed 300 GHz system and 28 GHz channel sounding systems.Both measured channels at the 28 GHz and 300 GHz channels are shown to be highly sparse and specular.A higher number of Multi Path Components(MPC)are observed for the 28 GHz system,while the same main MPC are observed for both systems.

Inaudible Sound Covert Channel with Anti-Jamming Capability: Attacks vs. Countermeasure

When an inaudible sound covert channel(ISCC)attack is launched inside a computer system,sensitive data are converted to inaudible sound waves and then transmitted.The receiver at the other end picks up the sound signal,from which the original sensitive data can be recovered.As a forceful countermeasure against the ISCC attack,strong noise can be used to jam the channel and literally shut down any possible sound data transmission.In this paper,enhanced ISCC is proposed,whose transmission frequency can be dynamically changed.Essentially,if the transmitter detects that the covert channel is being jammed,the transmitter and receiver both will switch to another available frequency and re-establish their communications,following the proposed communications protocol.Experimental results show that the proposed enhanced ISCC can remain connected even in the presence of a strong jamming noise source.Correspondingly,a detection method based on frequency scanning is proposed to help to combat such an anti-jamming sound channel.With the proposed countermeasure,the bit error rate(BER)of the data communications over enhanced ISCC soars to more than 48%,essentially shutting down the data transmission,and thus neutralizing the security threat.

结合MGCC特征与多尺度通道注意力的环境声深度学习分类方法

环境声分类技术在家居安全监测、人机语声交互等领域具有关键作用。然而,声源的多样性与混合性给环境声分类方法设计带来了重大挑战。为提高分类准确率与节约计算资源,该文提出一种基于多尺度通道注意力机制的深度学习分类模型。所提模型由特征提取模块、多尺度卷积模块、高效通道注意力模块、输出层四部分组成。首先,通过引入加权型梅尔Gammatone频率倒谱系数(MGCC)挖掘环境声频谱幅值与相位结构信息;其次,融合多尺度卷积核与高效通道注意力机制优选出声频关键局部细节和通道特征;最后,在全连接层采用softmax函数映射特征并输出环境声类型的概率值。所提模型在6种环境声的iFLYTEK、10种环境声的Urbansound8k数据集上开展测试验证,分别取得了94%、76.52%、79.24%(iFLYTEK+Urbansound8k)的分类准确率。消融实验结果进一步表明:引入的多尺度卷积模块、通道注意力机制模块对分类准确率的提升贡献率分别接近于3.77%和1.89%。实验还详细对比了7种现有的深度学习分类方法,所提算法在分类准确率上排名第二;另外,在同级别算法中如ResNet18、GoogLeNet,所提算法在模型参数量和计算复杂度方面上实现了进一步的约减。

北极冰下水平变化双声道波导声传播特性

针对北极部分海域中的双声道波导现象,研究了冰层覆盖下水平变化双声道波导中的声传播。使用微扰法推导并确定了粗糙下表面的冰层反射系数,结合Bellhop射线模型,计算并分析了实测海域中双声道波导水平变化时的声传播特性,并研究了声源深度、声源入射角以及声源频率对水平变化的双声道波导中声传播的影响规律。结果表明,在北极,深海声道中的声传播大多被限制在深海声道的上、下边缘之间;声源与水平变化的深海声道轴处于同一深度时声传播损失较小,当声源位于深海声道边界以外时,水平变化的声速剖面相比于水平不变时具有更低的声传播损失;入射角对双声道波导中声传播影响较小;随着声源频率的增加,表面声道中声传播损失也随之增大,但是对深海声道影响不明显,在相同频率下水平变化的声速剖面更利于声传播。

HCN通道在听觉传导通路中的作用研究进展

超极化激活环核苷酸门控阳离子通(hyperpolarization-activated cyclic nucleotide-gated channel,HCN channel)广泛表达于中枢和周围神经系统,通过其介导的超极化激活阳离子电流(hyperpolarization-activated cation current,Ih)调节神经元的静息膜电位和兴奋性来影响听觉的精确加工和传导,对时间信息的精确分析起着至关重要的作用。因此,本文通过对HCN通道结构和分布以及电生理作用进行综述,进一步探讨HCN通道在正常及单侧聋或双侧聋情况下听觉传导通路中的作用及机制。

舞台演出《诗路风雅颂》声音设计

本文根据创作团队的要求,对声音现场呈现方面进行艺术加工和创作;在打破剧场常规镜框式舞台上下观演关系的演出中,用多点布置音箱的方式重构声场,实现声音的540°全方位覆盖;依靠强大的数字音频处理服务器,通过基于音频轨道和基于对象2种处理方式,实现声音在方向、响度、音色等方面的动态变化,给观众以身临其境的沉浸式观演感受。

基于通道和帧级特征注意力模型的环境声音识别

为了对环境声音进行更好的识别,提出基于通道和帧级特征注意力的环境声音识别卷积神经网络模型。该模型针对声音特征特点选取一维卷积以提高模型对声音特征信息的提取能力,并引入SE-Res2Net模块实现对声音特征细粒度上的全局感受并帮助模型关注特征通道间的信息,在全连接层前加入注意力统计池化模块,增强模型对表征不同声音类别的关键帧级特征的学习以提高模型识别性能。采用Urbansound8K数据集,实验结果表明:所提模型在测试集上的训练准确率达到94.5%,即模型可以有效学习声音特征中表征不同环境声音的关键信息并进行正确预测。对消融实验结果分析可得,所提模型的设计可使其分类错误率的下降率达到43.8%,表明模型对一维卷积的应用和各个模块的引入是有效的,可见所提环境声音识别模型性能优越。

多路音频传感信号低功耗实时采集方法设计

由于多路音频存在信号混合噪声干扰,以单路无线传感器网络(Wireless Sensor Networks, WSN)节点为基础的采集过程,淡化了不同路线上的噪声特征,以单一阈值为基础降噪,存在采集偏差大、丢包率高等问题,提出基于WSN节点的多路音频信号低功耗实时采集方法。利用五元麦克风声源定位模型,确定音频信号声源的方位、距离和角度等信息,引入传感器节点,引入静音区检测的多路降噪法,通过调整静音区幅度值为0达到对音频信号不同线路的降噪目的;通过计算合适的采样时间点和平均采样频率,实现多路音频信号低功耗采集。在对比实验测试中,所提方法取得的音频信号值最接近实际值,并且丢包率最高仅为2.5%,最高功耗仅为0.62 W,该方法有效提高了信号的准确性。

采用多视角注意力的声音事件定位与检测

近年来,基于深度学习的方法有效改进了声音事件定位与检测的性能,但当场景中存在多声源重叠时,准确的声源时空信息估计依然较为困难,声音事件定位与检测的性能存在较大提升空间。为充分挖掘多通道深层表示所包含的关键信息,本文提出了一种多视角注意力网络模型MVANet(Multi-View Attention Network)。首先,引入软参数共享网络架构实现不同任务之间的交互学习,计算多通道深层表示,在对比不同通道注意力结构的基础上,选择了一种轻量级的高效通道注意力模块ECA(Efficient Channel Attention)与多头自注意力模块MHSA(Multi-Head Self-Attention)结合,从通道、时间、频率三个视角关注深层表示中的关键特征,丰富高维特征信息。其次,对比了ECA模块和软参数共享架构在MVANet不同位置上的性能,确定了ECA模块和软参数共享在模型上的最佳实现位置,最大程度上提高模型对特征的挖掘能力。仿真结果表明,对于包含同类别重叠声事件的TAU-NIGENS Spatial Sound Events 2020数据集,本文提出的MVANet模型相比较于基线方法,检测和定位性能均得到了改善。在多声源场景下,检测错误率下降了0.03,定位误差下降了1.5°。

典型水声环境的浅海声信道多径特性研究

声纳的通信能力与水文条件密切相关,同样一部声纳在不同的海洋环境中具有不同的通信性能。针对船只经常活动的浅海区域,重点介绍3种典型的声速梯度,并利用仿真手段分析浅海水声传播机理,建立不同水文条件下的浅海多径信道。

Towards 6G: Paradigm of Realistic Terahertz Channel Modeling

Terahertz(THz)communications are envisioned as a key technology for the sixth-generation wireless communication system(6G).However,it is not practical to perform large-scale channel measurements with high degrees of freedom at THz frequency band.This makes empirical or stochastic modeling approaches relying on measurements no longer stand.In order to break through the bottleneck of scarce fulldimensional channel sounding measurements,this paper presents a novel paradigm for THz channel modeling towards 6G.With the core of high-performance ray tracing(RT),the presented paradigm requires merely quite limited channel sounding to calibrate the geometry and material electromagnetic(EM)properties of the three-dimensional(3D)environment model in the target scenarios.Then,through extensive RT simulations,the parameters extracted from RT simulations can be fed into either ray-based novel stochastic channel models or cluster-based standard channel model families.Verified by RT simulations,these models can generate realistic channels that are valuable for the design and evaluation of THz systems.Representing two ends of 6G THz use cases from microscopy to macroscopy,case studies are made for close-proximity communications,wireless connections on a desktop,and smart rail mobility,respectively.Last but not least,new concerns on channel modeling resulting from distinguishing features of THz wave are discussed regarding propagation,antenna array,and device aspects,respectively.

Complex Domain Wavelet-Based Denoising of Measured UHF Wireless Channel Power Delay Profiles

This work extends the use of wavelet-based denoising as an alternative processing scheme to improve measured mobile-radio channel power delay profiles. It has already been reported that, when applied on real domain data (amplitude only), denoising provides mainly a qualitative improvement. Here, phase content was also considered, leading to significant qualitative and quantitative improvement of the processed profiles. Signal-to-noise ratios and dynamic ranges improvements as high as 50 dB have been observed.

带百叶吸声体的轨道交通全封闭声屏障声学设计研究

依据相关设计规范,轨道交通全封闭声屏障应设置不少于水平投影响面积5%的自然排烟通道,这将直接导致全封闭声屏障顶部严重漏声。为减少全封闭声屏障顶部漏声,提出并设计了全封闭声屏障顶部开口处百叶吸声体。采用SoundPLAN噪声预测软件,模拟全封闭声屏障顶部开口位置安装百叶吸声体前后对周边敏感建筑的噪声影响。在全封闭声屏障顶部开口位置处安装百叶吸声体后,敏感点的噪声值比安装前明显降低;不同厚度和不同材质的百叶吸声体对全封闭声屏障整体降噪效果均有影响。结果表明,在全封闭声屏障顶部开口位置安装百叶吸声体可以提高声屏障降噪效果。

DenseNet结合空间通道注意力机制的环境声音分类

音乐信息识别(MIR)和自动语音识别(ASR)都是以结构化声音为特点的声音识别,环境声音识别在声音识别领域的难度很大。为了充分利用从环境声中提取的Log-Mel谱图的空间特征与通道特征,提出了一种基于密集连接卷积网络(DenseNet)的空间通道注意力机制。使用DenseNet对Log-Mel谱图进行特征提取,引入空间通道注意力机制使网络更加关注显著特征;为了解决数据不足导致的过拟合问题,将混合数据增强的方法应用于Log-Mel谱图,从而保证了数据的多样性;在2个公共数据集(ESC-50和ESC-10)验证所提方法的有效性。结果表明:所提的空间通道注意力机制模型能够使神经网络对环境声音的识别率分别达到79.3%(ESC-50)和94.3%(ESC-10)。