Multiple sub-array beamspace CAATI algorithm for multi-beam bathymetry system

This paper extends CAATI （Computed Angle-of-Arrival Transient Imaging） technique of Multi-angle Swath Bathymetry Sidesean Sonar （MSBSS） into Multi-Beam Bathymetry Sonar （MBBS） and presents a new Multiple Sub-array Beamspaee - CAATI （MSB-CAATI） algorithm. The method not only can achieve high resolution seafloor mapping in the whole wide swath, but also can work well in complex acoustic environments or geometries. Simulation results and processing results of sea-experiment data prove the validity and superiority of the algorithm.

A VGGNet-based correction for satellite altimetry-derived gravity anomalies to improve the accuracy of bathymetry to depths of 6500 m

Understanding the topographic patterns of the seafloor is a very important part of understanding our planet.Although the science involved in bathymetric surveying has advanced much over the decades,less than 20%of the seafloor has been precisely modeled to date,and there is an urgent need to improve the accuracy and reduce the uncertainty of underwater survey data.In this study,we introduce a pretrained visual geometry group network(VGGNet)method based on deep learning.To apply this method,we input gravity anomaly data derived from ship measurements and satellite altimetry into the model and correct the latter,which has a larger spatial coverage,based on the former,which is considered the true value and is more accurate.After obtaining the corrected high-precision gravity model,it is inverted to the corresponding bathymetric model by applying the gravity-depth correlation.We choose four data pairs collected from different environments,i.e.,the Southern Ocean,Pacific Ocean,Atlantic Ocean and Caribbean Sea,to evaluate the topographic correction results of the model.The experiments show that the coefficient of determination(R~2)reaches 0.834 among the results of the four experimental groups,signifying a high correlation.The standard deviation and normalized root mean square error are also evaluated,and the accuracy of their performance improved by up to 24.2%compared with similar research done in recent years.The evaluation of the R^(2) values at different water depths shows that our model can achieve performance results above 0.90 at certain water depths and can also significantly improve results from mid-water depths when compared to previous research.Finally,the bathymetry corrected by our model is able to show an accuracy improvement level of more than 21%within 1%of the total water depths,which is sufficient to prove that the VGGNet-based method has the ability to perform a gravity-bathymetry correction and achieve outstanding results.

Predicting bathymetry based on vertical gravity gradient anomaly and analyses for various influential factors

The prediction of bathymetry has advanced significantly with the development of satellite altimetry.However,the majority of its data originate from marine gravity anomaly.In this study,based on the expression of vertical gravity gradient(VGG)of a rectangular prism,the governing equations for determining sea depths to invert bathymetry.The governing equation is solved by linearization through an iterative process,and numerical simulations verify its algorithm and its stability.We also study the processing methods of different interference errors.The regularization method improves the stability of the inversion process for errors.A piecewise bilinear interpolation function roughly replaces the low-frequency error,and numerical simulations show that the accuracy can be improved by 41.2%after this treatment.For variable ocean crust density,simulation simulations verify that the root-mean-square(RMS)error of prediction is approximately 5 m for the sea depth of 6 km if density is chosen as the average one.Finally,two test regions in the South China Sea are predicted and compared with ship soundings data,RMS errors of predictions are 71.1 m and 91.4 m,respectively.

Highlighting relationships between sand thicknesses,reservoir-seal pairs and paleobathymetry from a sequence stratigraphic perspective:An example from Tortonian Serravallian deposits,onshore Niger Delta Basin

The utilization of sequence stratigraphic concepts in identifying sands and their spatial continuity in distinct gross depositional settings is key,especially in frontier settings where data paucity is a common challenge.In the Baka field,onshore Niger Delta,detailed reservoir correlation guided by sequence stratigraphic framework analysis showed the distribution of sand and shale units constituting reservoirseal pairs(RSP)correlatable across the field.Within the 3rd-order packages,it is observed that the lowstand systems tract(LST)and highstand systems tract(HST)contain more RSPs and thicker 4th-and 5th-order sands than the transgressive systems tract(TST).In terms of bathymetry,it is noted that irrespective of systems tracts,the RSP Index(RI)decreases from the proximal shallow/inner shelf settings to the more distal outer shelf areas.Amongst all three systems tracts,intervals interpreted as lowstand prograding complexes contain the best developed sands and highest RSP.Sand development within the LSTs has been controlled by a pronounced growth fault regime accompanied by high subsidence and sedimentation rates.This is linked to the basinward migration of the sands during prolonged sea-level fall,creating significant accommodation space for sand deposition.On the other hand,the TSTs known to mark periods of progressive sea-level rise and landward migration of sandy facies,show thinner sands enclosed in much thicker,laterally extensive,and better-preserved deeper marine shales.Interpreted seismic sections indicate intense growth faulting and channelization that influenced the syn-and postdepositional development of the sand packages across the field.The initial timing of deformation of subregional faults in this area coincides with periods of abrupt falls in sea level.This approach could be useful for predicting sand-prone areas in frontier fields as well as possible reservoir-seal parameters required for some aspects of petroleum system analysis and quick-look volume estimation.

Evolution and bathymetry of glacial lake at the lowest elevation in Nepal Himalaya

One of the prominent impacts of climate change induced glacier retreat in the Himalayas is the formation and expansion of glacial lakes. The newly formed glacial lakes are mostly located in higher altitudinal regions(4200-5800 m) of Himalaya,however, a new glacial lake(Kapuche, 28.446° N and 84.116° E) have been reported to be emerged in the relatively low elevation area of ~2450 m above sea level(masl) in the Nepal Himalaya. This short communication presents the remote sensing-based evolution and field-based bathymetry of Kapuche lake, and further discusses its formation process and lake type for being a glacial lake at the lowest elevation in Nepal Himalaya.

Shallow water bathymetry based on a back propagation neural network and ensemble learning using multispectral satellite imagery

The back propagation(BP)neural network method is widely used in bathymetry based on multispectral satellite imagery.However,the classical BP neural network method faces a potential problem because it easily falls into a local minimum,leading to model training failure.This study confirmed that the local minimum problem of the BP neural network method exists in the bathymetry field and cannot be ignored.Furthermore,to solve the local minimum problem of the BP neural network method,a bathymetry method based on a BP neural network and ensemble learning(BPEL)is proposed.First,the remote sensing imagery and training sample were used as input datasets,and the BP method was used as the base learner to produce multiple water depth inversion results.Then,a new ensemble strategy,namely the minimum outlying degree method,was proposed and used to integrate the water depth inversion results.Finally,an ensemble bathymetric map was acquired.Anda Reef,northeastern Jiuzhang Atoll,and Pingtan coastal zone were selected as test cases to validate the proposed method.Compared with the BP neural network method,the root-mean-square error and the average relative error of the BPEL method can reduce by 0.65–2.84 m and 16%–46%in the three test cases at most.The results showed that the proposed BPEL method could solve the local minimum problem of the BP neural network method and obtain highly robust and accurate bathymetric maps.

Coastal bathymetry inversion using SAR-based altimetric gravity data:A case study over the South Sandwich Island

The global bathymetry models are usually of low accuracy over the coastline of polar areas due to the harsh climatic environment and the complex topography.Satellite altimetric gravity data can be a supplement and plays a key role in bathymetry modeling over these regions.The Synthetic Aperture Radar(SAR)altimeters in the missions like CryoSat-2 and Sentinel-3A/3B can relieve waveform contamination that existed in conventional altimeters and provide data with improved accuracy and spatial resolution.In this study,we investigate the potential application of SAR altimetric gravity data in enhancing coastal bathymetry,where the effects on local bathymetry modeling introduced from SAR altimetry data are quantified and evaluated.Furthermore,we study the effects on bathymetry modeling by using different scale factor calculation approaches,where a partition-wise scheme is implemented.The numerical experiment over the South Sandwich Islands near Antarctica suggests that using SARbased altimetric gravity data improves local coastal bathymetry modeling,compared with the model calculated without SAR altimetry data by a magnitude of 3:55 m within 10 km of offshore areas.Moreover,by using the partition-wise scheme for scale factor calculation,the quality of the coastal bathymetry model is improved by 7.34 m compared with the result derived from the traditional method.These results indicate the superiority of using SAR altimetry data in coastal bathymetry inversion.

A True-Time-Delay Array Architecture for Wideband Multi-Beam Tracking

The true-time delay(TTD)units are critical for solving beam squint and frequency selective fading inWideband Large-Scale Antenna Systems(LSASs).In this work,we propose a TTD array architecture for wideband multi-beam tracking that eliminates the beam squint phenomenon and filters out interference signals by applying a spatial filter and time delay estimations(TDEs).The paper presents a novel approach to spatial filter design by introducing a transformation matrix that can optimize the beam response in a specific direction and at a specific frequency.Using the variable fractional delay(VFD)filters,we propose a TDE algorithm with a Newton-Raphson iteration update process that corrects the arrival time delay difference between sensors.Simulations and examples have demonstrated that the proposed architecture can achieve beam tracking within 10 ms at the low signalto-noise ratio(SNR)and demodulation loss is less than 0.5 dB in wideband multi-beam scenarios.

遥感水深反演海道测量业务化应用中的评估与思考

水深是海道测量最重要的地理要素,反映海底地貌形态和航区通航能力,在海道测量中占有重要的地位,遥感水深反演(SDB)对于补充传统声学测深系统效率不足具有重要意义。本文通过对现有研究总结,从实地因素、影像因素和反演方法3个维度分析了反演考量的要素,对照海道测量精度要求和业务化需求两个维度对水深反演适用性进行了评估。结果表明,精度方面,SDB水平精度已经满足要求,大部分多光谱反演相对垂直精度约达20%,但受水体浑浊程度影响较大,距海道测量业务化应用尚有不足,海底覆盖方面已经有了很好的实践。最后,从不确定性可控性、反演模型外推和多源数据同化方面分析了SDB应用所面临的问题,并给出了一些思考和建议。

东亚江豚全球适宜栖息地分布预测

东亚江豚是国家二级重点保护野生动物,IUCN红色名录濒危EN物种,主要分布于中国、日本和韩国水域,目前的关注度较少,科学家还未对其栖息地进行过相关系统的评价.本研究基于MaxEnt模型和ArcGIS平台以及物种分布位点和6种环境变量数据,构建了适宜栖息地的模型,模型的训练集和测试集的AUC分别为0.973和0.965,表明模型的预测能力良好.模型预测适合度大于0.5的中高度适宜栖息地约213110.2 km^(2),主要分布在台湾海峡以北的中国沿岸海域(159865.4 km^(2))、朝鲜半岛西南部海域(37863.3 km^(2))和日本的东南部海域(4615 km^(2));适合度大于0.7的高适宜区主要分布于浙江北部至江苏南通,盐城到连云港附近,山东威海以北的渤海海域,韩国的江华湾及周边海域.通过分析因子贡献率、排列重要性和刀切法检验,水深、盐度以及海表温度在东亚江豚栖息地选择中起到比较重要的作用.基于本研究结果,建议对长江口及渤海湾增加现有保护区的面积或建立新的保护区,管控区内的人类活动,促进东亚江豚的生存和发展.

智联无人船内河水深测量与抗水流能力评估

利用无人船无验潮测深与网络RTK技术开展内河水深测量试验,并从测深精度、路径跟踪及最大抗水流能力三个方面评估该自主研发无人船性能。结果表明,无人船水深测量精度高,满足相关规范要求,且无人船在水流速<2.0 m/s条件下,抗水流速能力强,路径跟踪精度良好,外业作业最大抗水流速安全阈值为5.0 m/s。

基于等效声速剖面原理的测深误差改正方法

水下地形测量中,水中声速误差的存在,造成在多波束测深数据中边缘波束区域测深误差显著增大,严重影响了多波束测深精度。对此,本文提出一种基于等效声速剖面原理的测深误差改正方法。针对相邻测线重叠区域的测深不符值序列,以声速误差、整体测深偏差、随机测深误差在等效声速剖面框架结构下构建函数模型,基于稳健估计原理对声速误差进行估计,进而实现测深偏差的整体改正。基于广东红海湾浅水测量数据和西太平洋深海测量数据的实验结果表明,本文所提方法可有效削弱声速导致的测深误差,改正后测深精度分别提升33.3%和32.6%。

多波束测深声呐自主数据质量监测及门限控制

为满足水面无人艇等海洋无人平台的需求,确保多波束测深声呐系统在无人监控和参数调节下具备可靠自主工作能力,本文提出一种数据质量在线监测及门限参数控制方法。通过建立基于质量和异常因子的监测模型,结合粒子滤波技术,提高海底地形跟踪门限控制抗差性。仿真和湖上试验显示该方法能判定数据有效性、适应地形变化,并提供合理深度门限,适用于多波束测深声呐系统的在线数据质量监测和无人自主控制。

基于数据-知识驱动的高精度海底地形绘制:以南海为例

海底地形具有非常重要的商业、工程、军事和科学研究价值。目前,常用重力场数据反演海底地形,如自由空气重力异常和垂直重力梯度。然而,由于现有方法反演海底地形具有较强的多解性,仍然无法准确获取高精度的海底地形。该文提出了重力-密度法与随机森林结合的数据-知识驱动新方法,以重建准确的海底地形。该方法在中国南海海域进行了测试,并与重力-密度法、随机森林以及现有的SIO模型进行了对比分析。反演结果显示,数据-知识驱动提供了更好的反演性能,随机森林和重力-密度法次之,SIO模型最差。相比于重力-密度法,数据-知识驱动的平均绝对误差、平均相对误差和均方根误差分别降低了21%、25%和7%;而相比于随机森林,它们分别也降低了20%、20%和20%。此外,数据-知识驱动模型与船载测深数据具有较高的一致性,其差值大约有72%分布在±10 m范围内,占比高于其他三种模型。该结果证明了数据-知识驱动方法在海底地形反演中的可行性和有效性,有助于加快高精度海底地形的绘制。

小孔径多波束声呐测深算法改进

多波束测深声呐采用多重信号分类算法能够在较小阵列尺寸的情况下保证较高的角度分辨率。然而,由于水底回波的边缘波束信噪比较低,多重信号分类算法伪谱中较强的伪峰干扰会导致后续测深算法的深度估计结果出现错误。针对该问题,提出一种基于最大信噪比的修正峰值搜索算法。该算法假定相邻波束的水底地形具有缓变性,选取回波信噪比最大的时刻,利用多重信号分类算法进行波达方向估计以获取信号角度、时间参考值。然后,分别向左右两侧利用时间期望值对回波信号进行加权与峰值搜索,实现更精确的测深。最后,通过实测数据验证了该文提出算法的有效性,该算法在特定条件下能够将边缘波束的最大测深误差减小25.85%,平均测深误差减小8.02%。

天津大神堂海洋牧场人工鱼礁沉陷现状调查研究

人工鱼礁建设可为海洋生态系统改善和渔业资源增殖做出重要贡献,但由于海床底质、水流风浪等因素影响,鱼礁礁体可能发生沉陷现象,严重影响其生态功能。本研究聚焦天津大神堂海洋牧场,基于侧扫声呐、单波束测深仪、差分式GPS、智能无人船、ArcGIS等设备和软件,对海洋牧场人工鱼礁沉陷状况进行了调查和分析,并对沉陷的影响因素进行了初步探讨。结果表明,天津大神堂海洋牧场中人工鱼礁保存现状较好,大多数礁体呈现竖置状态,平均沉陷深度为0.38 m,沉陷比例约25.43%,生态修复和渔业资源增殖功能未受重大影响;人工鱼礁沉陷情况与礁体坐底时长、海床底质密切相关。本研究采用的人工鱼礁沉陷调查方式具有较强的可行性,研究成果可为海洋牧场人工鱼礁建设的规划选址提供科学依据和技术支撑。

基于区域划分的多波束测线布设

研究了海底地形为坡面和不规则面的多波束测线布设,包括测线方向和测线间距.针对坡面海域,利用数学证明得出沿平行等深线走向进行测线布设是最优方向,并根据多波束测线布设的基本原则,建立了满足测线总长度最小、重叠率在规定范围内的递推公式,利用该公式求解测线间距;针对不规则海域,将海底地形划分为若干个近似为坡面的海域,用最小二乘法将各区域拟合为坡面海域,并对拟合效果不佳的区域进行进一步划分.按照坡面海域的求解公式分别得到各个海域的测线布设,最终汇总得到了该不规则海域的测线布设方案.

综合物探对多煤层采空区综合勘查对比研究

为了探明多煤层采空区分布,利用地震波频率谐振勘探法及可控源音频大地电磁测深法进行综合勘查。本文阐述了地震波频率谐振勘探法及可控源音频大地电磁法的方法原理以及采空区的地球物理特征,结合实际案例,从波阻抗断面图及视电阻率断面图,分析了采空区的异常特征并圈定采空区范围,后经钻探验证,推断结果与实际情况基本吻合,并对两种勘探方法的优缺点进行了分析。本次研究将地震波频率谐振勘探技术应用于多煤层采空区的探测取得了良好的效果,对多煤层采空区的探测有很重要的借鉴和指导意义。

Near-Field Beam Training for Holographic MIMO Communications: Typical Methods, Challenges and Future Directions

Holographic multiple-input multiple-output(HMIMO)has become an emerging technology for achieving ultra-high frequency spectral efficiency and spatial resolution in future wireless systems.The increasing antenna aperture leads to a more significant characterization of the spherical wavefront in near-field communications in HMIMO scenarios.Beam training as a key technique for wireless communication is worth exploring in this near-field scenario.Compared with the widely researched far-field beam training,the increased dimensionality of the search space for near-field beam training poses a challenge to the complexity and accuracy of the proposed algorithm.In this paper,we introduce several typical near-field beam training methods:exhaustive beam training,hierarchical beam training,and multi-beam training that includes equal interval multi-beam training and hash multi-beam training.The performances of these methods are compared through simulation analysis,and their effectiveness is verified on the hardware testbed as well.Additionally,we provide application scenarios,research challenges,and potential future research directions for near-field beam training.

多波束测深合理探测方案的设计及效果分析

以2023年“高教社杯”全国大学生数学建模竞赛B题为背景,建立二维平面上多波束测深的覆盖宽度及条带重叠率的数学模型,并将其扩展至三维空间,设计了基于贪心思想的测线设计方案.进一步,在复杂海底测线方案设计时,通过改进飞蛾火焰算法,给出了具体的路径搜索流程,从简单地形测线的设计推进到复杂真实海底测量策略的优化,解决了多波束测深系统最优路线方案的设计问题,并且给出具体的图像和精确数据.本文结果对使用多波束测深系统进行实际海底地形测绘有一定参考价值.