Constraining the geotherm beneath the British Isles from Bayesian inversion of Curie depth: integrated modelling of magnetic, geothermal, and seismic data

Abstract

Abstract. Curie depth offers a valuable constraint on the thermal structure of the lithosphere, based on its interpretation as the depth to 580 ∘C, but current methods underestimate the range of uncertainty. We formulate the estimation of Curie depth within a Bayesian framework to quantify its uncertainty across the British Isles. Uncertainty increases exponentially with Curie depth but this can be moderated by increasing the size of the spatial window taken from the magnetic anomaly. The choice of window size needed to resolve the magnetic thickness is often ambiguous but, based on our chosen spectral method, we determine that significant gains in precision can be obtained with window sizes 15–30 times larger than the deepest magnetic source. Our Curie depth map of the British Isles includes a combination of window sizes: smaller windows are used where the magnetic base is shallow to resolve small-scale features, and larger window sizes are used where the magnetic base is deep in order to improve precision. On average, the Curie depth increases from Laurentian crust (22.2±5.3 km) to Avalonian crust (31.2±9.2 km). The temperature distribution in the crust, and associated uncertainty, was simulated from the ensemble of Curie depth realizations assigned to a lower thermal boundary condition of a crustal model (sedimentary thickness, Moho depth, heat production, thermal conductivity), constructed from various geophysical and geochemical datasets. The uncertainty in the simulated heat flow field substantially increases from ±10 mW m−2 for shallow Curie depths at ∼15 km to ±80 mW m−2 for Curie depths >40 km. Surface heat flow observations are concordant with the simulated heat flow field except in regions that contain igneous bodies. Heat flow data within large batholiths in the British Isles exceed the simulated heat flow by ∼25 mW m−2 as a result of their high rates of heat production (4–6 µW m−3). Conversely, heat refraction around thermally resistive mafic volcanics and thick sedimentary layers induce a negative heat flow misfit of a similar magnitude. A northward thinning of the lithosphere is supported by shallower Curie depths on the northern side of the Iapetus Suture, which separates Laurentian and Avalonian terranes. Cenozoic volcanism in Northern Britain and Ireland has previously been attributed to a lateral branch of the proto-Icelandic mantle plume. Our results show that high surface heat flow (>90 mW m−2) and shallow Curie depth (∼15 km) occur within the same region, which supports the hypothesis that lithospheric thinning occurred due to the influence of a mantle plume. The fact that the uncertainty is only ±3–8 km in this region demonstrates that Curie depths are more reliable in hotter regions of the crust where the magnetic base is shallow.

Introduction: Handling uncertainty in the geosciences: identification, mitigation and communication

“…Reflecting this importance, two papers in the special issue focus on the uncertainties in understanding aspects of subsurface carbon storage. In the first, Miocic et al (2019) employ various models to estimate the sealing capacity of faults in the context of CO 2 capture and storage systems (CCSs). Since deployment of CCS technology is very im-mature in comparison to that of the petroleum industry, the available data on CO 2 storage are limited in comparison to the wealth of knowledge generated over many decades of hydrocarbon exploration and production.…”

“…Thus, geoscientists need to use knowledge and techniques developed by the petroleum industry to inform and predict the behaviour of CO 2 in the subsurface. In their paper, Miocic et al (2019) translate standard uncertainty modelling techniques from the oil and gas industry to calculate the maximum supported column heights of CO 2 , given different fault sealing parameters. This is essential to be confident that a fault will not leak CO 2 after its injection into a reservoir.…”

“…This special issue on uncertainty encompasses 12 articles covering the quantification and management of uncertainty in a broad range of geological disciplines, including seismic interpretation (Alcalde et al, 2019;Schaaf and Bond, 2019), mantle dynamic models (Bodur and Rey, 2019;Mather and Fullea, 2019), field geology (Andrews et al, 2019;Bárbara et al, 2019;Pakyuz-Charrier et al, 2019;Stamm et al, 2019;Wilson et al, 2019), plate kinematic modelling (Causer et al, 2020) and subsurface resource evaluation (Miocic et al, 2019;Wilkinson and Polson, 2019). This collection of original research contributions, some of which were presented at the aforementioned EGU 2018 session, highlights the importance of understanding uncertainty, often neglected by interpreters, geomodellers and experimentalists.…”

PyCurious: A Python module for computing the Curie depth from the magnetic anomaly.

“…The former selects portions of the radial power spectrum in the low and high frequency domain to compute the depth of magnetic sources, while the latter fits an analytical solution to the entire power spectrum. We cast the Bouligand et al (2009) method within a Bayesian framework to estimate the uncertainty of Curie depth calculations (Mather & Fullea, 2019). Common computational workflows and geospatial manipulation of magnetic data are covered in the Jupyter notebooks bundled with this package.…”

“…Mather et al, (2019). PyCurious: A Python module for computing the Curie depth from the magnetic anomaly.. Journal of Open Source Software, 4(39), 1544. https://doi.org/10.21105/joss.01544…”