ALBERT

Description: Joint marine electromagnetic (EM) and seismic interpretation are widely used for offshore gas hydrate and petroleum exploration, produce better estimates of lithology and fluids, and decrease the risk of low gas saturation. However, joint data acquisition is not commonly employed. Current marine EM data acquisition depends on an ocean bottom electromagnetic receiver (OBEM) and current seismic exploration methods use seismometers. Joint simultaneous data acquisition can decrease costs and improve efficiency; yet conventional independent data receivers have several drawbacks, including large size, high costs, position errors, and low operational efficiency. To address these limitations, we developed a compact ocean bottom electromagnetic receiver and seismometer (OBEMS). Based on existing ocean bottom E-field receiver (OBE) specifications, including low noise levels, low power consumption, and low clock-drift error, we integrated two induction coils for the magnetic sensor and a three-axis omnidirectional geophone for the seismic sensor and assembled an ultra-short base line (USBL) transponder as the position sensor, which improved position accuracy and operational efficiency while reducing field data acquisition costs. The resulting OBEMS has a noise level of 0.1 nV/m/rt (Hz) at 1 Hz in E-field and 0.1 pT/rt (Hz) at 1 Hz in B-field and a 30 day battery lifetime. It also supports a WiFi interface for configuring data acquisition parameters and data download. Offshore acquisition was performed to evaluate the system’s field performance during offshore gas hydrate exploration. The OBEMS functioned effectively throughout operation and field testing. The OBEMS therefore functions as a low cost, compact, and highly efficient joint data acquisition method.

Description: Seismic exploration equipment has developed rapidly over the past few decades. One such piece of equipment is a centralized seismograph, which plays an important role in engineering, so improving its performance is of great scientific significance. In this research, the core part of general seismic data acquisition devices is packaged to develop a centralized seismic data acquisition system (Named as CUGB-CS48DAS) that has independent operating ability and high scalability, which can be used for seismic exploration in varies engineering usage. Furthermore, by extending and modifying the acquisition circuit and corresponding software, the function of electrical method data acquisition has also been achieved. Thus, the proposed CUGB-CS48DAS makes it possible for joint exploration of seismic and electrical data in a single acquisition station, which is implicitly of great convenience in engineering prospecting. The low-power-consumption computer of the system comprises a 24-bit Σ-△ modulation A/D converter and 48 sampling channels with an optional sampling rate of 50 Hz to 64 kHz, dynamic range ≥ 120 dB, synchronization accuracy better than 200 ns. With regard to the host computer, the architecture of the control software is smart, and it can integrate the multiple functions of data acquisition, preprocessing, and self-testing; clear interfaces reduce the complexity of development and migration. Field tests was implemented to prove that the system is stable and convenient to use, and the performance could meet the demand of high-precision joint exploration.

Description: This article describes the development of a mobile roadside survey procedure for obtaining corroboration data for the remote sensing of agricultural land use practices. The key objective was to produce a dataset of geo-referenced roadside digital images that can be used to compare to in-field photos for measuring agricultural land use and land cover associated with crop residue and cover cropping in the non-growing season. It was concluded that a very high level of correspondence (〉 90 % level of agreement) could be attained using a mobile survey vehicle, as presented in this research, to detailed in-field ground verification data. Classification correspondence was carried out against 114 field sites with a level of agreement at 93 %. The few discrepancies were in the differentiation of residue levels between 30–60 % and 〉 60 %, both of which may be considered as achieving conservation practice standards. The mobile roadside image capture has advantages of relatively low cost and insensitivity to cloudy days, which often limits optical remote sensing acquisitions during the study period of interest. We anticipate that this approach can be used to reduce associated field costs for ground surveys, while expanding coverage areas and may be of interest to industry, academic and government organizations for more routine surveys of agricultural soil cover during periods of seasonal cloud cover.

Description: Marine controlled-source electromagnetic method has more and more applications in ocean resources exploration. Electromagnetic transmitter sends electromagnetic wave to the underground, the receiver located on the seafloor receives the electromagnetic wave which carries the information of the geosphere. And the underground structure is obtained by inversion calculation. Data quality of electromagnetic transmitter and seafloor receivers is the most important part of this method. The quality level of transmitting current directly affects the signal-to-noise ratio (SNR) of the electromagnetic field data, as received by a multi-component electromagnetic receiver from the seabed. Although the transmitting current stability is sufficient under normal circumstances, the SNR of the received signal can change owing to factors such as outside noise. In some emergency cases such as instrument failure or a sudden increase in electromagnetic interference that we are not aware of, the frequency and properties of the transmitting current may change, such as its size and waveform. The traditional current monitoring and data playback tools fail to detect and evaluate the anomalies well and in a timely manner, which introduces considerable errors in the later data processing procedure. Pertaining to these issues, this paper proposes a comprehensive quality evaluation method for the transmitting current. The proposed algorithm, based on the analytic hierarchy process, is first used to analyse five current stability parameters: current frequency, positive amplitudes, negative amplitudes, discrepancy of ideal waveform, and waveform repetition and then to define the harmonic energy and calculate the quality of transmitting current (QTC) index of the final data to assess the quality of the transmitting current comprehensively. The results of a marine experiment performed in 2016 show that the algorithm can identify abnormal current data and quantitatively evaluate the current conditions. Under normal circumstances, the QTC index is within 2 %. However, after the simulation of anomalous mutations of the various attributes, the QTC index synchronized mutations to more than 4 % and some curvilinear features were observed. These results will provide a positive, significant guide for the evaluation and monitoring of transmitting current data in marine experiments.

Description: Karst systems that are characterized by a high subsurface heterogeneity are posing a challenge to study their complex recharge processes. Experimental methods to study karst processes mostly focus on characterizing the entire aquifer. Despite their important role for recharge processes, the limited focus has been given on studies of the soil and epikarst and most available research has been performed at sites of similar latitudes. In our study, we describe a new monitoring concept that allows the improvement of soil and epikarst processes understanding by covering different karst systems with different land cover at different climate regions. First, we describe the site selection and the experimental setup. Then we describe the five individual sites and their soil profiles. We also present some preliminary data and highlight the potential of the data for future research aimed at answering the relevant research questions: (1) How do the soil and epikarst heterogeneities influence water flow and storage processes in the karst vadose zone? (2) What is the impact of the land cover type on karstic groundwater recharge and evapotranspiration? (3) What is the impact of climate on karstic groundwater recharge and evapotranspiration? In order to answer these questions, we monitor soil moisture, which controls the partitioning of rainfall into infiltration, soil water storage, evapotranspiration, and groundwater recharge processes. We installed a soil moisture-monitoring network at five different climate regions: in Puerto Rico (tropical), Spain (Mediterranean), the United Kingdom (humid oceanic), Germany (humid mountainous), and Australia (dry semi-arid). At each of the five sites, we defined two 20 m × 20 m plots to install soil moisture probes under different land use types (forest and grassland). At each plot, 15 soil moisture profiles were installed with probes at different depths from the top soil to the epikarst (over 400 soil moisture probes were installed). Our first results show that the monitoring network provides new insights into the soil moisture dynamics of the five study sites and that significant differences prevail among forest and grassland sites. Some profiles are characterized by sequential reactions of soil moisture, i.e., the uppermost probe reacts first and the lowest probe reacts last, while at other profiles, we find non-sequential reactions that we interpret to result from preferential flow processes. While the former favours storage in the soil providing water for evapotranspiration, the latter can be seen as an indicator for the initiation of fast and preferential recharge into the karst system. Covering the spatiotemporal variability of these processes through a large number of installed probes, our monitoring network will allow to develop a new conceptual understanding of evapotranspiration and groundwater recharge processes in karst regions across different climate regions and land use types, and provide the base for quantitative assessment with physically-based modelling approaches in the future.

Description: Installation of modern highly sensitive magnetometric equipment at geophysical observatories requires location of places with a low level of magnetic noise. It is also required to perform regular control of noise environment at observatory instrument installation points. This work is aimed at testing one of the prototypes of magnetic noise measuring instruments, capable of performing fast areal measurements. The key features of this prototype are high sensitivity and linearity and capability of registration of magnetic noise in different frequency bands. This work was supported by the Russian Science Foundation (project No. 17-77-20034).

Description: The present availability of sub-decametre digital elevation models on Mars – crucial for the study of surface processes – is scarce. In contrast to the globally-available but low-resolution datasets, such models enable the study of landforms  3000 stereo pairs at 25 cm/pixel resolution, enabling the creation of high-resolution digital elevation models (1–2 m/pixel). However, only ~ 500 of these pairs have been processed and made publicly available to date. Existing pipelines for the production of digital elevation models from stereo-pairs, however, are built upon commercial software, rely upon sparsely-available intermediate data, or are reliant on proprietary algorithms. Here, we present and test the output of a new pipeline for producing digital elevation models from HiRISE stereo pairs that is built entirely upon the open source NASA Ames Stereo Pipeline photogrammetric software, making use of freely available data for cartographic rectification. This pipeline is implemented here on a research computing cluster, but can also be used on consumer-grade UNIX computers. The four output digital elevation models produced using the pipeline presented here are globally well-registered, with accuracy similar to those of multiple digital elevation models produced elsewhere.

Description: Soil CO2 efflux is the second largest carbon flux in terrestrial ecosystems. Its feedback to climate determines model predictions of the land carbon sink, which is crucial to understanding the future of the earth system. For understanding and quantification, however, observations by the most widely applied chamber measurement method need to be aggregated to larger temporal and spatial scales. The aggregation is hampered by random error that is characterized by occasionally large fluxes and variance heterogeneity that is not properly accounted for under the typical assumption of normally distributed fluxes. Therefore, we explored the effect of different distributional assumptions on the aggregated fluxes. We tested the alternative assumption of log-normally distributed random error in observed fluxes by aggregating one year of data of four neighbouring automatic chambers at a Mediterranean savanna-type site. With the lognormal assumption, problems with error structure diminished and more reasonable confidence intervals were obtained. While the differences between distributional assumptions diminished when aggregating data of single chambers to an annual value, differences were important at short time scales and were especially pronounced when aggregating across chambers to plot level. Hence we recommend as a good practice that researchers report plot-level fluxes with uncertainties based on the log-normal assumption. Model-data integration studies should compare predictions and observations of soil CO2 efflux at log scale. This study provides methodology and guidance that will improve the analysis of soil CO2 efflux observations and hence improve understanding of soil carbon cycling and climate feedbacks.

Description: We propose a deep learning method for Atmospheric Ozone Interpolation. Our method directly learns an end-to-end mapping between classically interpolated satellite ozone images and the real ozone measurements. The model's architecture represents a deep stack of convolutions (CNN) that takes the already interpolated images (Using the classical state-of-the-art interpolation method) as Input and outputs a more precise Interpolation of the Region of Interest. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art interpolation quality, and achieves optimal data processing latency (∆T) for production-ready near-real-time Atmospheric Image Interpolation, which has a big advantage over the state of the art classical interpolation algorithms. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. This method showcases the potential applications of deep learning in Remote Sensing and Climate Science.

Description: This study presents the first environmental monitoring field campaign of a newly developed Tethered And Navigated Air Blimp (TANAB) system to investigate the microclimate over a complex terrain. The use of a tethered balloon in complex terrains such as mines and tailings ponds is novel and the focus of the present study. The TANAB system was fully developed and launched at a mine facility in northern Canada in May 2018. This study describes the key design features, the sensor payload onboard, and the observations made by the TANAB system. The system measured meteorological conditions including wind speed in three directions, temperature, relative humidity, and pressure over the first few tens of meters of the atmospheric boundary layer. The system also performed earth surface thermal imaging, or temperature mapping, of the underlying surface. The measurements were made at two primary locations in the facility: i) near a tailings pond and ii) in a mine pit. TANAB measured the dynamics of the atmosphere at different diurnal times (e.g. day versus night) and locations (near tailings pond versus inside the mine). Such dynamics include mean and turbulence statistics pertaining to flow momentum and energy, and they are crucial in the understanding of emission fluxes from the facility in future studies. In addition, TANAB can provide boundary conditions and validation datasets to support mesoscale dispersion modelling or Computational Fluid Dynamics (CFD) simulations for various transport models.