Direct monitoring of active geohazards: emerging geophysical tools for deep-water assessments

Seafloor networks of cables, pipelines, and other infrastructure underpin our daily lives, providing communication links, information, and energy supplies. Despite their global importance, these networks are vulnerable to damage by a number of natural seafloor hazards, including landslides, turbidity currents, fluid flow, and scour. Conventional geophysical techniques, such as high-resolution reflection seismic and side-scan sonar, are commonly employed in geohazard assessments. These conventional tools provide essential information for route planning and design; however, such surveys provide only indirect evidence of past processes and do not observe or measure the geohazard itself. As such, many numerical-based impact models lack field-scale calibration, and much uncertainty exists about the triggers, nature, and frequency of deep-water geohazards. Recent advances in technology now enable a step change in their understanding through direct monitoring. We outline some emerging monitoring tools and how they can quantify key parameters for deepwater geohazard assessment. Repeat seafloor surveys in dynamic areas show that solely relying on evidence from past deposits can lead to an under-representation of the geohazard events. Acoustic Doppler current profiling provides new insights into the structure of turbidity currents, whereas instrumented mobile sensors record the nature of movement at the base of those flows for the first time. Existing and bespoke cabled networks enable high bandwidth, low power, and distributed measurements of parameters such as strain across large areas of seafloor. These techniques provide valuable new measurements that will improve geohazard assessments and should be deployed in a complementary manner alongside conventional geophysical tools

Regionally aggregated, stitched and de‐drifted CMIP‐climate data, processed with netCDF‐SCM v2.0.0

The world's most complex climate models are currently running a range of experiments as part of the Sixth Coupled Model Intercomparison Project (CMIP6). Added to the output from the Fifth Coupled Model Intercomparison Project (CMIP5), the total data volume will be in the order of 20PB. Here, we present a dataset of annual, monthly, global, hemispheric and land/ocean means derived from a selection of experiments of key interest to climate data analysts and reduced complexity climate modellers. The derived dataset is a key part of validating, calibrating and developing reduced complexity climate models against the behaviour of more physically complete models. In addition to its use for reduced complexity climate modellers, we aim to make our data accessible to other research communities. We facilitate this in a number of ways. Firstly, given the focus on annual, monthly, global, hemispheric and land/ocean mean quantities, our dataset is orders of magnitude smaller than the source data and hence does not require specialized ‘big data’ expertise. Secondly, again because of its smaller size, we are able to offer our dataset in a text-based format, greatly reducing the computational expertise required to work with CMIP output. Thirdly, we enable data provenance and integrity control by tracking all source metadata and providing tools which check whether a dataset has been retracted, that is identified as erroneous. The resulting dataset is updated as new CMIP6 results become available and we provide a stable access point to allow automated downloads. Along with our accompanying website (cmip6.science.unimelb.edu.au), we believe this dataset provides a unique community resource, as well as allowing non-specialists to access CMIP data in a new, user-friendly way

Global data set of long-term summertime vertical temperature profiles in 153 lakes

peer reviewedClimate change and other anthropogenic stressors have led to long-term changes in the thermal structure, including surface temperatures, deepwater temperatures, and vertical thermal gradients, in many lakes around the world. Though many studies highlight warming of surface water temperatures in lakes worldwide, less is known about long-term trends in full vertical thermal structure and deepwater temperatures, which have been changing less consistently in both direction and magnitude. Here, we present a globally-expansive data set of summertime in-situ vertical temperature profiles from 153 lakes, with one time series beginning as early as 1894. We also compiled lake geographic, morphometric, and water quality variables that can influence vertical thermal structure through a variety of potential mechanisms in these lakes. These long-term time series of vertical temperature profiles and corresponding lake characteristics serve as valuable data to help understand changes and drivers of lake thermal structure in a time of rapid global and ecological change. © 2021, The Author(s)

Seismic inversion for site characterization: When, where and why should we use it?

The application of seismic inversion techniques to the foundation and drilling top hole zones has garnered significant interest in recent years. The shift towards more geologically complex and deeper water sites, combined with the global economic climate, has driven a requirement for more cost-effective site characterisation. More often used by the exploration industry, seismic inversion has been touted as a potentially valuable tool for quantifying the spatial and depth variability in sediment properties. In doing so, this approach can reduce the risk of encountering unforeseen ground conditions and the need for excessive over-design. Despite its potential, the inversion of high-resolution seismic data has yet to see widespread use, leaving unanswered questions regarding how and where this tool can best fit into the site characterization work flow. We test the potential usefulness of seismic inversion using a range of existing site investigation data sets. We apply several different inversion methods, including acoustic impedance and seismic quality factor inversion, as well as artificial neural network multi-attribute regression, to tackle end-member potential uses. First, explore early-phase potential uses, showing how seismic quality factor and acoustic impedance inversion can be used to capture the spatial variability in facies architecture and bulk sediment properties that could be used in appraisal and pre-FEED studies to optimize borehole and penetrometer (CPT) depths/locations and to ensure effective site-wide characterization. Second, we apply a combined acoustic impedance and artificial neural network workflow to link seismic properties with CPT profiles. These results demonstrate the potential late-phase use of seismic inversion for short-range interpolation/extrapolation of more complex geotechnical properties through the generation of synthetic CPT profiles useful for infrastructure design and micro-siting late in the development cycle. While not a comprehensive list of applications, together these examples illustrate how seismic inversion can be utilized throughout the development cycle. If the required objectives are clearly defined and an appropriate inversion workflow developed, seismic inversion can help to reduce uncertainty in site-wide characterization and drive efficiencies in layout and design studies throughout a project lifetime