Seasonal variations in faunal distribution and activity across the continental slope of the goban spur area (NE Atlantic)

Density, biomass and community structure of macrofauna were estimated together with several sediment characteristics at seven stations ranging from 208 m to 4460 m water depth along the OMEX transect in the Goban Spur area (NE Atlantic) during three seasons (October 1993, May 1994, and August 1995). Median grain size decreased with increasing water depth and showed no differences between the seasons. The percentages of organic carbon and total nitrogen were highest at mid-slope depths (1000 to 1500 m), and were significantly higher in August at the upper part of the slope to a depth of 1500 m. The C:N ratio in the surface layer amounted to 7 to 8 in May, 10 to 12 in August and 14 to 17 in October at all stations (except the deepest at 4460 m, where it was 11 in May and August), indicating arrival of fresh phytodetritus in May, and therefore seasonality in food input to the benthos. Densities of macrofauna decreased exponentially with increasing water depth. Significantly higher densities of macrofauna were found in May at the upper part of the slope to a depth of 1500 m. These differences were mainly due to high numbers of postlarvae of echinoids at the shallowest station and ophiuroids at the deeper stations. Biomass values also decreased with increasing water depth, but biomass was relatively high at the 1000 m station and low at 1500 m, due to relatively high and low mean weights of the individual macrofaunal specimens. No significant differences in biomass were found between the seasons. Respiration was high (15 to 20 mgC·m⁻²·d⁻¹) in May at the upper part of the slope to a depth of 1000 m and low (1–3 mg C·m⁻²·d⁻¹) at the deeper part. At the shallowest stations to a depth of 1000 m respiration was highest in May, at the mid-slope stations (1400–2200 m) it was highest in August, whereas the deepest stations (3600 to 4500 m) did not show any differences in respiration rates. In conclusion; seasonal variation in organic input is reflected in denstiy, community structure and activity of the macrofauna along the continental slope in the NE Atlantic.     

罗源湾南岸上元—坑园牡蛎礁与海平面变化

上元-坑园牡蛎礁位于高潮位以下3m,主要成分为长牡蛎和近江牡蛎,其~(14)C年龄为3043±82a,B.P.。据此推算出罗源湾一带海岸处于缓慢上升状态,速率为1.15mm/a。根据上元-坑园牡蛎礁的上述特征,并结合断块差异升降运动的表现,作者认为这一地区中、晚全新世以来以“陆动型”海平面变化占主导地位。     

对虾封闭式工厂化养殖水环境要素的变化特征及其相互关系

根据2002年8月7～8日对虾工厂化养成中后期A、B代表池的实测数据，分析讨论了该时期养殖水体中环境要素的变化特征及其相互关系．结果表明：研究水体水温、盐度和溶解氧含量适宜．日变化幅度不大．但pH值略为偏低，而COD值明显偏高．与该时期投饵量大，残饵及排泄物明显增加有关．但对A池来说，生物作用的影响占主导地位，而对B池而言．则以化学过程影响为主．     

声波对海洋生物的影响与作用的研究

本文研究了声波对某些海洋生物的影响与作用。实验表明，适当的声波剂量对海洋生物的作用所产生的声学生物效应是积极的，可促使生物体向有利的方向产生变异，而强度过大的声波对海洋生物有效伤甚至致死的效应。     

1998年春夏南海温盐结构及其变化特征

利用1998年5～8月“南海季风试验”期间“科学1”号和“实验3”号科学考察船两个航次CTD资料,分析了1998年南海夏季风暴发前后南海主要断面的温盐结构及其变化特征.观测发现,南海腹地基本被典型的南海水团所控制,但在南海东北部尤其是吕宋海峡附近,表层和次表层水明显受到西太平洋水的影响.季风暴发以后,南海北部表面温度有显著升高,升幅由西向东递减,而南海中部和南部表面温度基本没变,这使得南海北部东西向温度梯度和整个海盆南北向温度梯度均减小.北部断面表层盐度普遍由34以上降低到34以下,混合层均有所发展,是季风暴发后降水和风力加剧的结果.观测期间黑潮水跨越吕宋海峡的迹象明显但变化剧烈.4～5月,黑潮次表层水除在吕宋海峡中北部出现外,在吕宋岛以西亦有发现,表明有部分黑潮水从吕宋海峡南端沿岸向西进而向南进入南海.6～7月,次表层高盐核在吕宋海峡中北部有极大发展,但在吕宋岛以西却明显萎缩;虽然看上去黑潮水以更强的流速进、出南海,但对南海腹地动力热力结构的影响未必更大.一个超过34.55的表层高盐水体于巴拉望附近被发现,似与通过巴拉望两侧水道入侵南海的西太平洋水有关.     

南海北部海区上层水体平均声速场的变化

对所收集到的南海北部海区的温度、盐度和深度历史资料进行了声速的计算、统计与分析,结果表明该海区上层水体的平均声速场有较明显的年际变化:表层声速的平面分布显示出沿岸水与外海水强弱交替变化的特征,声速等值线的走向几乎与岸线平行,等声速线的值自近岸向外海增加,大陆架外缘海区声速的水平梯度较大;下层声速的分布以环流和水体共同作用的形式出现,50m层声速平面分布的趋势除冬季与表层稍为相似外,其余季节与表层有明显的差别,春、夏季节50m层声速自西向东增加,而秋季与其表层分布相反,自近岸向外海减小;声速的垂直分布受海水升温与降温的影响显著,春、夏季表层海水升温,海表声速最大,声速自海表随深度的增加而减小;秋、冬季表层附近水层降温,声速稍偏低,普遍出现正梯度现象,最大声速移至表层以下的水层,这个深度随季节的改变而改变,随海区的不同而不同.该海区的平均声速有年波动现象,表层波幅最大,随深度的增加波幅变小,至100m水层其波动的位相几乎与表层相反.     

煤炭资源勘查误差理论研究

对煤炭资源勘查资源量与客观物质量之间差值的误差理论探讨,提出误差由真值误差、系统误差和离差3个基本部分组成,其中真值误差是不可消除的观测误差、系统误差是控制网度所决定的准确度标准、而离差是由地质构造及矿床形态的多解性产生的资源量波动。在此基础上,构筑了一个关于误差的数学模型,最后应用误差理论对现行勘查工作中常见的错误进行分析,并就如何避免错误及减少误差提出了见解。     

Methodology and use of tensor invariants for satellite gravity gradiometry

Although its use is widespread in several other scientific disciplines, the theory of tensor invariants is only marginally adopted in gravity field modeling. We aim to close this gap by developing and applying the invariants approach for geopotential recovery. Gravitational tensor invariants are deduced from products of second-order derivatives of the gravitational potential. The benefit of the method presented arises from its independence of the gradiometer instrument’s orientation in space. Thus, we refrain from the classical methods for satellite gravity gradiometry analysis, i.e., in terms of individual gravity gradients, in favor of the alternative invariants approach. The invariants approach requires a tailored processing strategy. Firstly, the non-linear functionals with regard to the potential series expansion in spherical harmonics necessitates the linearization and iterative solution of the resulting least-squares problem. From the computational point of view, efficient linearization by means of perturbation theory has been adopted. It only requires the computation of reference gravity gradients. Secondly, the deduced pseudo-observations are composed of all the gravitational tensor elements, all of which require a comparable level of accuracy. Additionally, implementation of the invariants method for large data sets is a challenging task. We show the fundamentals of tensor invariants theory adapted to satellite gradiometry. With regard to the GOCE (Gravity field and steady-state Ocean Circulation Explorer) satellite gradiometry mission, we demonstrate that the iterative parameter estimation process converges within only two iterations. Additionally, for the GOCE configuration, we show the invariants approach to be insensitive to the synthesis of unobserved gravity gradients.     

Efficient GOCE satellite gravity field recovery based on least-squares using QR decomposition

We develop and apply an efficient strategy for Earth gravity field recovery from satellite gravity gradiometry data. Our approach is based upon the Paige-Saunders iterative least-squares method using QR decomposition (LSQR). We modify the original algorithm for space-geodetic applications: firstly, we investigate how convergence can be accelerated by means of both subspace and block-diagonal preconditioning. The efficiency of the latter dominates if the design matrix exhibits block-dominant structure. Secondly, we address Tikhonov-Phillips regularization in general. Thirdly, we demonstrate an effective implementation of the algorithm in a high-performance computing environment. In this context, an important issue is to avoid the twofold computation of the design matrix in each iteration. The computational platform is a 64-processor shared-memory supercomputer. The runtime results prove the successful parallelization of the LSQR solver. The numerical examples are chosen in view of the forthcoming satellite mission GOCE (Gravity field and steady-state Ocean Circulation Explorer). The closed-loop scenario covers 1 month of simulated data with 5 s sampling. We focus exclusively on the analysis of radial components of satellite accelerations and gravity gradients. Our extensions to the basic algorithm enable the method to be competitive with well-established inversion strategies in satellite geodesy, such as conjugate gradient methods or the brute-force approach. In its current development stage, the LSQR method appears ready to deal with real-data applications.