How thick is a fault? Fault displacement-thickness scaling revisited

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Impact of mechanical heterogeneity on joint density in a welded ignimbrite

Joints are conduits for groundwater, hydrocarbons and hydrothermal fluids. Robust fluid flow models rely on accurate characterisation of joint networks, in particular joint density. It is generally assumed that the predominant factor controlling joint density in layered stratigraphy is the thickness of the mechanical layer where the joints occur. Mechanical heterogeneity within the layer is considered a lesser influence on joint formation. We analysed the frequency and distribution of joints within a single 12-m thick ignimbrite layer to identify the controls on joint geometry and distribution. The observed joint distribution is not related to the thickness of the ignimbrite layer. Rather, joint initiation, propagation and termination are controlled by the shape, spatial distribution and mechanical properties of fiamme, which are present within the ignimbrite. The observations and analysis presented here demonstrate that models of joint distribution, particularly in thicker layers, that do not fully account for mechanical heterogeneity are likely to underestimate joint density, the spatial variability of joint distribution and the complex joint geometries that result. Consequently, we recommend that characterisation of a layer’s compositional and material properties improves predictions of subsurface joint density in rock layers that are mechanically heterogeneous

Fault fictions : systematic biases in the conceptualization of fault zones

Mental models (i.e. a human’s internal representation of the real world) have an important role in the way a human understands and reasons about uncertainties, explores potential options, and makes decisions. However, they are susceptible to biases. Issues associated with mental models have not yet received much attention in geosciences, yet systematic biases can affect the scientific process of any geological investigation; from the inception of how the problem is viewed, through selection of appropriate hypotheses and data collection/processing methods, to the conceptualisation and communication of results. This article draws on findings from cognitive science and system dynamics, with knowledge and experiences of field geology, to consider the limitations and biases presented by mental models in geoscience, and their effect on predictions of the physical properties of faults in particular. We identify a number of biases specific to geological investigations and propose strategies for debiasing. Doing so will enhance how multiple data sources can be brought together, and minimise controllable geological uncertainty to develop more robust geological models. Critically, we argue that there is a need for standardised procedures that guard against biases, permitting data from multiple studies to be combined and communication of assumptions to be made. While we use faults to illustrate potential biases in mental models and the implications of these biases, our findings can be applied across the geoscience discipline

Microseismic monitoring illuminates phases of slope failure in soft soils

The role of microseismic monitoring in rock slope stability has been long established: large microseismic events associated with rock failure can be detected by seismometers, even at distances of a few kilometres from the source. This is a favourable characteristic for the monitoring of mountainous areas prone to failure. We show that microseismic monitoring, using short-period arrays and a sufficiently high sampling rate, can also record weak precursory signals, that could represent early phases of a larger scale slope failure in soft soils. We validate this hypothesis with field observations. We find that, even in high attenuation material such as clays, it is possible to record and detect in the frequency domain, soil failures at source-to-receiver distances up to 10 m for crack formation/propagation to more than 43 m for small (less than 2.5 m3) events. Our results show for the first time, an extended frequency range (10 Hz to 380 Hz) where small soil failures can be detected at short monitoring distances, even at sites with high background noise levels. This is the first published study focusing on ground-truthed only, slope failure induced seismic signals in soft soils at field scale and within the seismic frequency range (1–500 Hz). We suggest that microseismic monitoring could complement existing monitoring techniques to characterize the response and structural integrity of earth structures, such as embankments, where the monitoring distances are a few 10s of metres, with the potential to detect any material deterioration at the very early stages. This study does not focus on automatic classification of slope failure signals, however, our observations and methodology could form the basis for the future development of such an approach

Coupled virus - bacteria interactions and ecosystem function in an engineered microbial system

Viruses are thought to control bacterial abundance, affect community composition and influence ecosystem function in natural environments. Yet their dynamics have seldom been studied in engineered systems, or indeed in any system, for long periods of time. We measured virus abundance in a full-scale activated sludge plant every week for two years. Total bacteria and ammonia oxidising bacteria (AOB) abundances, bacterial community profiles, and a suite of environmental and operational parameters were also monitored. Mixed liquor virus abundance fluctuated over an order of magnitude (3.18 × 108 – 3.41 × 109 virus’s mL-1) and that variation was statistically significantly associated with total bacterial and AOB abundance, community composition, and effluent concentrations of COD and NH4+- N and thus system function. This suggests viruses play a far more important role in the dynamics of activated sludge systems than previously realised and could be one of the key factors controlling bacterial abundance, community structure and functional stability and may cause reactors to fail. These finding are based on statistical associations, not mechanistic models. Nevertheless, viral associations with abiotic factors, such as pH, make physical sense giving credence to these findings and highlighting the role that physical factors play in virus ecology. Further work is needed to identify and quantify specific bacteriophage and their hosts to enable us to develop mechanistic models of the ecology of viruses in wastewater treatment systems. However, since we have shown that viruses can be related to effluent quality and virus quantification is simple and cheap, practitioners would probably benefit from quantifying viruses now

Sediment management in sustainable urban drainage system (SUDS) ponds

Since removal and disposal of sustainable urban drainage system (SUDS) sediment can incur high maintenance costs, assessments of sediment volumes, quality and frequency of removal are required. Sediment depth and quality were surveyed annually from 1999-2003 in three ponds and one wetland in Dunfermline, Scotland, UK. Highest sediment accumulation occurred in Halbeath Pond, in the most developed watershed and with no surface water management train. From comparison of measured potentially toxic metal concentrations (Cd, Cr, Cu, Fe, Ni, Pb, Zn) with standards, the average sediment quality should not impair aquatic ecosystems. 72-84% of the metal flux into the SUDS was estimated to be associated with coarse sediment (>500 μm diameter) suggesting that management of coarse sediment is particularly important at this site. The timing of sediment removal for these SUDS is expected to be determined by loss of storage volume, rather than by accumulation of contaminants. If sediment removal occurs when 25% of the SUDS storage volume has infilled, it would be required after 17 years in Halbeath Pond, but only after 98 years in Linburn Pond (which has upstream detention basins). From the quality measurements, sediment disposal should be acceptable on adjacent land within the boundaries of the SUDS studied

Using mathematical modelling to inform on the ability of stormwater ponds to improve the water quality of urban runoff

This paper concerns the mathematical modelling of flow and solute transport through stormwater ponds. The model is based on appropriate lumped system conservation equations that are solved using standard numerical techniques. The model was used to route a first flush pollution scenario through a cylindrical pond for 16 combinations of elevation and diameter of a submerged pipe outlet, in conjunction with a high level weir. Higher pipe elevations and smaller pipe diameters created larger pond volumes and hence led to greater dilution of the pollutant. In contrast, lower pipe elevations created larger storage volumes, leading to better flow attenuation. Interestingly, larger pipe diameters improved peak flow attenuation, even though the storage used decreased

Determination of the seismic signatures of landslides in soft soils : a methodology based on a field scale shear box

We present a novel field experimental setup that can be used for studying the characteristics of landslide seismicity. The setup consists of a concrete, filled with soil, cylinder that moves along a surficial soil corridor. The emitted seismic signals are due to soil friction. The cylinder acts as an upscaled sheer-box allowing control over a number of parameters: the magnitude of normal stress on the failure plane, the degree of saturation and the type of soil. This allows for the simulation of soil friction within, or between, different geological layers under different conditions. Results are site specific, but can be easily reproduced for any geological environment. We validate this methodology by comparing the spectral characteristics of the signals emitted by the movement of the cylinder to those induced by a controlled failure of a 2.5 m high vertical face at a nearby site with very similar geology. We find a very good agreement between the two. This methodology can be used as a site investigation tool for the optimization of the deployment geometry of seismic networks for landslide monitoring, as well as to inform machine learning algorithms on automatic detection and classification of recorded signals during seismic monitoring of landslides