Diatremes act as fluid conduits for Zn-Pb mineralization in the SW Irish Ore field

Irish-type mineralization is commonly attributed to fault-controlled mixing of a seawater-derived, sulfur-rich fluid and basement-derived, metal-rich fluid. However, maar-diatreme volcanoes discovered in close spatial and temporal association with Zn-Pb mineralization at Stonepark in the Limerick basin (southwest Ireland) bring a new dimension to established geologic models and may increase the deposit-scale prospectivity in one of the world’s greatest Zn-Pb districts. Stonepark exhibits many incidences of dolomitic black matrix breccias with associated Zn-Pb mineralization, the latter typically occurring within 150 m of the diatremes. Highly negative δ34S pyrite values within country rock-dominated black matrix breccias (–12 to –34‰) are consistent with sulfide precipitation from bacteriogenic sulfur reduction in seawater-derived brines. However, δ34S values of Zn-Pb sulfides replacing black matrix breccias (–10 to 1‰) reflect multiple sulfur sources. Diatreme emplacement both greatly enhanced country rock fracture permeability and produced conduits that are filled with porous volcaniclastic material and extend down to basement rock types. Our δ34S data suggest that diatremes provide more efficient fluid pathways for basement-derived fluids. The diatremes introduce another potential sulfur source and facilitate a greater input of metal-rich basement-derived hydrothermal fluid into the system compared to other Irish-type deposits such as Navan and Lisheen, evidenced by Stonepark’s more positive modal δ34S value of –4‰. Irish-type deposits are traditionally thought to form in association with extensional basement faults and are considered unrelated to extensive Carboniferous magmatism. Our results indicate that a direct link exists between diatreme volcanism and Zn-Pb mineralization at Limerick, prompting a reevaluation of the traditional Irish-type ore formation model, in regions where mineralization is spatially associated with volcanic pipes.Natural Environment Research Council (IP-1397-1113); SUERC; Teck Ireland Ltd

Developing learned regularization for geophysical inversions

Geophysical inverse problems can be posed as the minimization of an objective function where one term (ϕ[sub d]) is a data misfit function and another (ϕ[sub m]) is a model regularization. In current practice, ϕ[sub m] is posed as a mathematical operator that potentially includes limited prior information on the model, m. This research focusses on the specification of learned forms of <pm from information on the model contained in a training set, M[sub T]. This is accomplished via three routes: probabilistic, deterministic (model based) and the Haber- Tenorio (HT) algorithm. In order to adopt a pure probabilistic method for finding a learned ϕ[sub m], equivalence between Gibbs distributions and Markov random fields is established. As a result, the prior probability of any given model is reduced to the interactions of cells in a local neighbourhood. Gibbs potentials are defined to represent these interactions. The case of the multivariate Gaussian is used due to its expressible form of normalization. ϕ[sub m] is parameterized by a set of coefficients, θ, and the recovery of these parameters is obtained via an optimization method given M[sub T]. For non-Gaussian distributions θ is recovered via Markov chain Monte Carlo (MCMC) sampling techniques and a strategy to compare different forms of ϕ[sub m] is introduced and developed. The model based deterministic route revolves around independent identically distributed (i.i.d.) assumptions on some filter property of the model, z = f(m). Given samples of z, two methods of expressing its corresponding ϕ[sub m] are developed. The first requires the expression of a generic distribution to which all the samples of z are assumed to belong. Methodology to translate z into usable data and recover the corresponding ϕ[sub m] is developed. Although there are ramifications of the statistical assumptions, this method is shown to translate significant information on z into ϕ[sub m]. Specifically, the shape of the ϕ[sub m] functional is maintained and, as a result, the deterministic ϕ[sub m] performs well in geophysical inversions. This method is compared with the parametrization of the generalized Gaussian (p-norm) for z. Agreement between the generic ϕ[sub m] and generalized Gaussian helps validate the specific choice of norm in the probabilistic route. The HT algorithm is based around the notion that the geophysical forward operator should help determine the form of ϕ[sub m]. The strategy of Haber and Tenorio [16] is introduced and an algorithm for the recovery of 9 is developed. Two examples are used to show a case where the HT algorithm is advantageous and one where it does not differ significantly from the probabilistic route. Finally, a methodology to invert geophysical data with generic learned regularization is developed and a simple example is shown. For this example, the generic deterministic method is shown to transfer the most information from the training set to the recovered model. Difficulties with extremely non-linear objective functions due to learned regularization are discussed and research into more effective search algorithms is suggested.Science, Faculty ofEarth, Ocean and Atmospheric Sciences, Department ofGraduat

An Evaluation of Experimental Sampling Strategies for Autonomous Empirical Research in Cognitive Science

In light of constraints inherent to empirical research, such as finite time and resources, there has been growing interest in using artificial intelligence to streamline the scientific process. However, despite advancements in automating scientific discovery, the implementation of strategies for sampling useful experiments remains a challenge. This metascientific study evaluates different experimental sampling strategies based on their effectiveness in advancing the discovery of linear models of human cognition based on synthetic data. We investigate the hypothesis put forth by Dubova et al. (2022) that random sampling of experiments is more effective than model-driven sampling. Indeed, the results of this study indicate that random sampling is more effective in a majority of cases, and that the underperformance of model-driven strategies can be attributed to a narrow sampling of the design space. Despite limitations in our approach, the work presented offers a novel framework for the metascientific study of autonomous empirical research

North Avenue Review [Volume 1, Issue 1]

This is the first issue of the North Avenue Review and includes a brief summary of the purpose and philosophy of the magazine