Advanced techniques for subsurface imaging Bayesian neural networks and Marchenko methods

Estimation of material properties such as density and velocity of the Earth’s subsurface are important in resource exploration, waste and CO2 storage and for monitoring changes underground. These properties can be used to create structural images of the subsurface or for resource characterisation. Seismic data are often the main source of information from which these estimates are derived. However the complex nature of the Earth, limitations in data acquisition and in resolution of images, and various types of noise all mean that estimates of material parameters also come with a level of uncertainty. The physics relating these material parameters to recorded seismic data is usually non-linear, necessitating the use of Monte Carlo inversion methods to solve the estimation problem in a fully probabilistic sense. Such methods are computationally expensive which usually prohibits their use over areas with many data, or for subsurface models that involve many parameters. Furthermore multiple unknown material parameters can be jointly dependent on each datum so trade-offs between parameters deteriorate parameter estimates and increase uncertainty in the results. In this thesis various types of neural networks are trained to provide probabilistic estimates of the subsurface velocity structure. A trained network can rapidly invert data in near real- time, much more rapidly than any traditional non-linear sampling method such as Monte Carlo. The thesis also shows how the density estimation problem can be reformulated to avoid direct trade-offs with velocity, by using a combination of seismic interferometry and Marchenko methods. First this thesis shows how neural networks can provide a full probability density function describing the uncertainty in parameters of interest, by using a form of network called a mixture density network. This type of network uses a weighted sum of kernel distributions (in our case Gaussians) to model the Bayesian posterior probability density function. The method is demonstrated by inverting localised phase velocity dispersion curves for shear-wave velocity profiles at the scale of a subsurface fluid reservoir, and is applied to field data from the North Sea. This work shows that when the data contain significant noise, including data uncertainties in the network gives more reliable mean velocity estimates. Whilst the post-training inversion process is rapid using neural networks, the method to estimate localised phase velocities in the first place is significantly slower. Therefore a computationally cheap method is demonstrated that combines gradiometry to estimate phase velocities and mixture density networks to invert for subsurface velocity-depth structure, the whole process taking a matter of minutes. This opens the possibility of real-time monitoring using spatially dense surface seismic arrays. For some monitoring situations a dense array is not available and gradiometry therefore cannot be applied to estimate phase velocities. In a third application this thesis uses mixture density networks to invert travel-time data for 2D localised velocity maps with associated uncertainty estimates. The importance of prior information in high dimensional inverse problems is also demonstrated. A new method is then developed to estimate density in the subsurface using a formulation of seismic interferometry that contains a linear dependence of seismic data on subsurface density, avoiding the usual direct trade-off between density and velocity. When wavefields cannot be measured directly in the subsurface, the method requires the use of a technique called Marchenko redatuming that can estimate the Green’s function from a virtual source or receiver inside a medium to the surface. This thesis shows that critical to implementing this work would be the development of more robust methods to scale the amplitude of Green’s function estimates from Marchenko methods. Finally the limitations of the methods presented in this thesis are discussed, as are suggestions for further research, and alternative applications for some of the methods. Overall this thesis proposes several new ways to monitor the subsurface efficiently using probabilistic machine learning techniques, discusses a novel way to estimate subsurface density, and demonstrates the methods on a mixture of synthetic and field data

Do You See What I See?:Quantifying Inter-Observer Variability in an Intertidal Marine Citizen Science Experiment

Citizen science represents an effective means of collecting ecological data; however, the quality/reliability of these data is often questioned. Quality assurance procedures are therefore important to determine the validity of citizen science data and to promote confidence in conclusions. Here, data generated by a marine citizen science project conducted at 12 sites across the United Kingdom was used to investigate whether the use of a simple, low-taxonomic-resolution field-monitoring protocol allowed trained citizen scientists to generate data comparable to those of professional scientists. To do this, differences between field estimates of algal percentage cover generated by different observer units (i.e., trained citizen scientists, professional scientists, and combined units), and digitally derived baseline estimates were examined. The results show that in the field, citizen scientists generated data similar to those of professional scientists, demonstrating that training, coupled with the use of a simple, low-taxonomic-resolution protocol can allow citizen scientists to generate robust datasets in which variability likely represents ecological variation/change as opposed to observer variation. The results also show, irrespective of observer unit, that differences between field and digital baseline estimates of algal percentage cover were greatest in plots with medium levels of algal cover, highlighting that additional/enhanced training for all participants could be beneficial in this area. The approach presented can serve as a guide for existing and future projects with similar protocols to assess their data quality, to strengthen participant training/protocols, and ultimately to promote the incorporation of robust citizen science datasets into environmental research and management

Patterns of abundance across geographical ranges as a predictor for responses to climate change:Evidence from UK rocky shores

Aim: Understanding patterns in the abundance of species across thermal ranges can give useful insights into the potential impacts of climate change. The abundant-centre hypothesis suggests that species will reach peak abundance at the centre of their thermal range where conditions are optimal, but evidence in support of this hypothesis is mixed and limited in geographical and taxonomic scope. We tested the applicability of the abundant-centre hypothesis across a range of intertidal organisms using a large, citizen science-generated data set. Location: UK. Methods: Species' abundance records were matched with their location within their thermal range. Patterns in abundance distribution for individual species, and across aggregated species abundances, were analysed using Kruskal–Wallis tests and quantile general additive models. Results: Individually, invertebrate species showed increasing abundances in the cooler half of the thermal range and decreasing abundances in the warmer half of the thermal range. The overall shape for aggregated invertebrate species abundances reflected a broad peak, with a cool-skewed maximum abundance. Algal species showed little evidence for an abundant-centre distribution individually, but overall the aggregated species abundances suggested a hump-backed abundance distribution. Main Conclusions: Our study follows others in showing mixed support for the abundant-centre hypothesis at an individual species level, but demonstrates an increased predictability in species responses when an aggregated overall response is considered

Combined Associations of a Polygenic Risk Score and Classical Risk Factors With Breast Cancer Risk.

We evaluated the joint associations between a new 313-variant PRS (PRS313) and questionnaire-based breast cancer risk factors for women of European ancestry, using 72 284 cases and 80 354 controls from the Breast Cancer Association Consortium. Interactions were evaluated using standard logistic regression and a newly developed case-only method for breast cancer risk overall and by estrogen receptor status. After accounting for multiple testing, we did not find evidence that per-standard deviation PRS313 odds ratio differed across strata defined by individual risk factors. Goodness-of-fit tests did not reject the assumption of a multiplicative model between PRS313 and each risk factor. Variation in projected absolute lifetime risk of breast cancer associated with classical risk factors was greater for women with higher genetic risk (PRS313 and family history) and, on average, 17.5% higher in the highest vs lowest deciles of genetic risk. These findings have implications for risk prevention for women at increased risk of breast cancer

Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer.

To identify common alleles associated with different histotypes of epithelial ovarian cancer (EOC), we pooled data from multiple genome-wide genotyping projects totaling 25,509 EOC cases and 40,941 controls. We identified nine new susceptibility loci for different EOC histotypes: six for serous EOC histotypes (3q28, 4q32.3, 8q21.11, 10q24.33, 18q11.2 and 22q12.1), two for mucinous EOC (3q22.3 and 9q31.1) and one for endometrioid EOC (5q12.3). We then performed meta-analysis on the results for high-grade serous ovarian cancer with the results from analysis of 31,448 BRCA1 and BRCA2 mutation carriers, including 3,887 mutation carriers with EOC. This identified three additional susceptibility loci at 2q13, 8q24.1 and 12q24.31. Integrated analyses of genes and regulatory biofeatures at each locus predicted candidate susceptibility genes, including OBFC1, a new candidate susceptibility gene for low-grade and borderline serous EOC

The Ethics of Matching: Mobile and web-based dating and hook up platforms

In this chapter, we argued that DHAs have the potential to be a socially and morally disruptive technology which is already and may continue to change the way people treat each other in the context of dating and hooking up, understand themselves in these contexts, and understand the practices in which they are engaging. The chapter does not commit to a broad assessment of DHAs as a whole. Instead, we highlighted several features of the technology that encourage certain uses, behaviors, and attitudes and brought out some of their moral dimensions, using mediation theory, affordances, and soft and hard impacts. Finally, we introduced design for values methodologies, especially VSD. The aim of introducing this methodology is to suggest that, if we have convinced the reader that DHAs can have significant moral impacts, then we may want to intentionally design them with the values that we explicitly endorse. Users and designers alike must dispel the myth that DHAs are merely a new way of communicating for lust and love that is value neutral. Only once we are all aware of the scripts, we are following can we question whether or not they reflect our values, goals, and conceptions of the good life in this context

The endosperm morphology of rice and its wild relatives as observed by scanning electron microscopy

While cultivated rice, Oryza sativa, is arguably the world’s most important cereal crop, there is little comparative morphological information available for the grain of rice wild relatives. In this study, the endosperm of 16 rice wild relatives were compared to O. sativa subspecies indica and O. sativa subspeciesjaponica using scanning electron microscopy. Although the aleurone, starch granules, protein bodies and endosperm cell shapes of the cultivated and non-cultivated species were similar, several differences were observed. The starch granules of some wild species had internal channels that have not been reported in cultivated rice. Oryza longiglumis, Microlaena stipoides and Potamophila parviflora, had an aleurone that was only one-cell thick in contrast to the multiple cell layers observed in the aleurone of the remainingOryza species. The similarity of the endosperm morphology of undomesticated species with cultivated rice suggests that some wild species may have similar functional properties. Obtaining a better understanding of the wild rice species grain ultrastructure will assist in identifying potential opportunities for development of these wild species as new cultivated crops or for their inclusion in plant improvement programmes

Patterns of abundance across geographical ranges as a predictor for responses to climate change: Evidence from UK rocky shores

Aim: Understanding patterns in the abundance of species across thermal ranges can give useful insights into the potential impacts of climate change. The abundant-centre hypothesis suggests that species will reach peak abundance at the centre of their thermal range where conditions are optimal, but evidence in support of this hypothesis is mixed and limited in geographical and taxonomic scope. We tested the applicability of the abundant-centre hypothesis across a range of intertidal organisms using a large, citizen science-generated data set. Location: UK. Methods: Species' abundance records were matched with their location within their thermal range. Patterns in abundance distribution for individual species, and across aggregated species abundances, were analysed using Kruskal–Wallis tests and quantile general additive models. Results: Individually, invertebrate species showed increasing abundances in the cooler half of the thermal range and decreasing abundances in the warmer half of the thermal range. The overall shape for aggregated invertebrate species abundances reflected a broad peak, with a cool-skewed maximum abundance. Algal species showed little evidence for an abundant-centre distribution individually, but overall the aggregated species abundances suggested a hump-backed abundance distribution. Main Conclusions: Our study follows others in showing mixed support for the abundant-centre hypothesis at an individual species level, but demonstrates an increased predictability in species responses when an aggregated overall response is considered