The nimble node — Million-channel land recording systems have arrived

A new nodal land acquisition system is being developed with successful field tests in both cold- and hot-weather settings and is being compared against several existing industry acquisition systems. This nodal system differs from others in that it has been designed to deliver affordable high-quality seismic data with the lightest, smallest, and lowest-cost seismic channel system in the industry by significant margins. There are three main drivers for this innovation. First, it will significantly improve data quality by reducing the current cost barriers to acquiring high-density seismic surveys (i.e., full sampling in space, azimuth, and offset). Second, it will reduce the environmental footprint of land operations (less line clearance required). Third, it will improve the safety of land operations (fewer people and vehicles required per channel). All three have been achieved by dramatically reducing both the capital expenditure to build a one-million-channel highly portable recording system and the operational expenditure needed to operate such a system in the field.

Detecting dominant changes in irregularly sampled multivariate water quality data sets

Time series of groundwater and stream water quality often exhibit substantial temporal and spatial variability, whereas typical existing monitoring data sets, e.g. from environmental agencies, are usually characterized by relatively low sampling frequency and irregular sampling in space and/or time. This complicates the differentiation between anthropogenic influence and natural variability as well as the detection of changes in water quality which indicate changes in single drivers. We suggest the new term “dominant changes” for changes in multivariate water quality data which concern (1) multiple variables, (2) multiple sites and (3) long-term patterns and present an exploratory framework for the detection of such dominant changes in data sets with irregular sampling in space and time. Firstly, a non-linear dimension-reduction technique was used to summarize the dominant spatiotemporal dynamics in the multivariate water quality data set in a few components. Those were used to derive hypotheses on the dominant drivers influencing water quality. Secondly, different sampling sites were compared with respect to median component values. Thirdly, time series of the components at single sites were analysed for long-term patterns. We tested the approach with a joint stream water and groundwater data set quality consisting of 1572 samples, each comprising sixteen variables, sampled with a spatially and temporally irregular sampling scheme at 29 sites in northeast Germany from 1998 to 2009. The first four components were interpreted as (1) an agriculturally induced enhancement of the natural background level of solute concentration, (2) a redox sequence from reducing conditions in deep groundwater to post-oxic conditions in shallow groundwater and oxic conditions in stream water, (3) a mixing ratio of deep and shallow groundwater to the streamflow and (4) sporadic events of slurry application in the agricultural practice. Dominant changes were observed for the first two components. The changing intensity of the first component was interpreted as response to the temporal variability of the thickness of the unsaturated zone. A steady increase in the second component at most stream water sites pointed towards progressing depletion of the denitrification capacity of the deep aquifer.

Detecting dominant changes in irregularly sampled multivariate water quality data sets

Time series of catchment water quality often exhibit substantial temporal and spatial variability which can rarely be traced back to single causal factors. Numerous anthropogenic and natural drivers influence groundwater and stream water quality, especially in regions with high land use intensity. In addition, typical existing monitoring data sets, e.g. from environmental agencies, are usually characterized by relatively low sampling frequency and irregular sampling in space and/or time. This complicates the differentiation between anthropogenic influence and natural variability as well as the detection of changes in water quality which indicate changes of single drivers. Detecting such changes is of fundamental interest for water management purposes as well as for scientific analyses. We suggest the new term dominant changes for changes in multivariate water quality data that concern (1) more than a single variable, (2) more than one single site and (3) more than short-term fluctuations or single events and present an exploratory framework for the detection of such dominant changes in multivariate water quality data sets with irregular sampling in space and time. Firstly, we used a non-linear dimension reduction technique to derive multivariate water quality components. The components provide a sparse description of the dominant spatiotemporal dynamics in the multivariate water quality data set. In addition, they can be used to derive hypotheses on the dominant drivers influencing water quality. Secondly, different sampling sites were compared with respect to median component values. Thirdly, time series of the components at single sites were analysed for seasonal patterns and linear and non-linear trends. Spatial and temporal heterogeneities are efficiently used as a source of information rather than being considered as noise. Besides, non-linearities are considered explicitly. The approach is especially recommended for the exploratory assessment of existing long term low frequency multivariate water quality monitoring data. We tested the approach with a large data set of stream water and groundwater quality consisting of sixteen hydrochemical variables sampled with a spatially and temporally irregular sampling scheme at 29 sites in the Uckermark region in northeast Germany from 1998 to 2009. Four components were derived and interpreted as (1) the agriculturally induced enhancement of the natural background level of solute concentration, (2) the redox sequence from reducing conditions in deep groundwater to post oxic conditions in shallow groundwater and oxic conditions in stream water, (3) the mixing ratio of deep and shallow groundwater to the streamflow and (4) sporadic events of slurry application in the agricultural practice. Dominant changes were observed for the first two components. The changing intensity of the 1st component during the course of the observation period was interpreted as response to the temporal variability of the thickness of the unsaturated zone. A steady increase of the 2nd component throughout the monitoring period at most stream water sites pointed towards progressing depletion of the denitrification capacity of the deep aquifer.

Reliability Simulation Combining Kriging and Monte Carlo Radius-outside Importance Sampling in Space Structure Latch

Concerning the issue of high dimensions and low failure probabilities including implicit and highly non-linear limit state function (LSF), the approach of reliability simulation combing Kriging and Monte Carlo Radius-Outside Importance Sampling (MCROIS) is presented, and the Kriging model is to approximate the unknown LSF, then calculate the initial sampling radius of the sphere, and the optimal radius is gained through the iterative algorithm. As such, the joint probability density function of importance sampling is constructed, which ensures that sampling domain is restricted to values outside the sphere located in the design point, and the efficiency is improved. The numerical example of space structure latch demonstrates the efficiency, accuracy and robustness compared with Crude Mote Carlo (CMC), which is validated that the method is of particular interest in applications with a low failure probability of failure, and it realizes the comprehensive design of reliability and wear lifetime on-orbit, and makes contributions to the exploration of reliability and lifetime analysis for the complex aerospace equipment