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Polished blocks measuring 25 mm in diameter were made from a total of 6 samples covering the Los Chihuidos, Barda González, La Cuprosa, El Porvenir and Tordillos Cu deposits. These included a grain mount of calcite 3 from Los Chihuidos and 5 sediment blocks. These blocks were selected to cover all the localities and calcite generations available in the sample suite, The polished blocks were carbon coated and analysed using a Zeiss EVO SEM at the NHM, London. Each block was BSE montaged and sites of interest selected before SEM-CL imaging was conducted. The BSE analytical conditions were 20Kv acceleration voltage, 10 mm working distance and 1.5 nA probe current. For SEM-Cl imaging, the conditions were 20 kv acceleration voltage, 1 mm working distance from the CL detector and the probe current increased to 3 nA. Images were processed using Zeiss Digiscan II software. Following BSE and SEM-Cl imaging, each block was sliced to create a polished thick section and thin section. The thin sections were analysed using standard polarising microscope techniques to establish mineralogy and cement textures. Each thick section was BSE scanned using a Leo 1450VP SEM equipped with an Oxford Instruments X-Act 10mm2 detector at the National Oceanography Centre, Southampton prior to CL imaging. Analytical conditions were 20 kv acceleration voltage, 2 nA probe current and 19 mm working distance. Cl Imaging was conducted on a Lumin hot-stage Cl microscope at the NHM, London. The acceleration voltage used was 10 kv with a beam current kept at ~0.2 nA. Sites identified on the CL microscope were subsequently overlain onto the BSE scan for sample navigation. After fully imaging and characterising each thick section, the samples were sent to the BGS, Nottingham for in-situ U-Pb calcite geochronology. Laser ablation was performed using a New Wave Research 193UC excimer laser coupled to a Nu Instruments Attom single collector sector field mass spectrometer. A spot size of 50 µm was used. The full analytical setup for the laser and mass spectrometer are provided in Roberts and Walker (2016). Standard sample bracketing utilised the NIST614 glass standard (Jochum et al., 2011) to normalise the 207Pb/206Pb ratio (Pbc) whilst the calcite standard WC-1 (Roberts et al., 2017) was used to correct the 206Pb/238U ratio. Data uncertainty and propagation protocols follow Horstwood et al., (2016). The U-Pb heterogeneity of current carbonate mineral standards are often equal to or larger than the analytical uncertainties, meaning that significant excess variance will be present in the reference material U-Pb measurements (Roberts et al., 2017). If propagated into the sample point uncertainties as a within-session excess variance, as recommended for zircon in Horstwood et al., (2016), the data point uncertainties will be overestimated and will mask any small-scale real geological scatter in the sample and lead to ages with erroneously high precision. As per the recommendations of (Roberts et al., 2020), session-based reproducibility, accuracy and precision was performed on more homogeneous standards such as zircon or NIST glass standard. Accuracy of the method and secondary reference material is provided in thesis, doi:10.5258/SOTON/D3219, section 3.5.2. All results are plotted using a Tera-Wasserburg concordia diagram, taking the lower intercept as the age of the analysis. No common Pb correction was applied to the data.","langeng":"Laser Ablation U-Pb analyses at the NERC Laboratories Keyworth. Polished blocks measuring 25 mm in diameter were made from a total of 6 samples covering the Los Chihuidos, Barda González, La Cuprosa, El Porvenir and Tordillos Cu deposits. These included a grain mount of calcite 3 from Los Chihuidos and 5 sediment blocks. These blocks were selected to cover all the localities and calcite generations available in the sample suite, The polished blocks were carbon coated and analysed using a Zeiss EVO SEM at the NHM, London. Each block was BSE montaged and sites of interest selected before SEM-CL imaging was conducted. The BSE analytical conditions were 20Kv acceleration voltage, 10 mm working distance and 1.5 nA probe current. For SEM-Cl imaging, the conditions were 20 kv acceleration voltage, 1 mm working distance from the CL detector and the probe current increased to 3 nA. Images were processed using Zeiss Digiscan II software. Following BSE and SEM-Cl imaging, each block was sliced to create a polished thick section and thin section. The thin sections were analysed using standard polarising microscope techniques to establish mineralogy and cement textures. Each thick section was BSE scanned using a Leo 1450VP SEM equipped with an Oxford Instruments X-Act 10mm2 detector at the National Oceanography Centre, Southampton prior to CL imaging. Analytical conditions were 20 kv acceleration voltage, 2 nA probe current and 19 mm working distance. Cl Imaging was conducted on a Lumin hot-stage Cl microscope at the NHM, London. The acceleration voltage used was 10 kv with a beam current kept at ~0.2 nA. Sites identified on the CL microscope were subsequently overlain onto the BSE scan for sample navigation. After fully imaging and characterising each thick section, the samples were sent to the BGS, Nottingham for in-situ U-Pb calcite geochronology. Laser ablation was performed using a New Wave Research 193UC excimer laser coupled to a Nu Instruments Attom single collector sector field mass spectrometer. A spot size of 50 µm was used. The full analytical setup for the laser and mass spectrometer are provided in Roberts and Walker (2016). Standard sample bracketing utilised the NIST614 glass standard (Jochum et al., 2011) to normalise the 207Pb/206Pb ratio (Pbc) whilst the calcite standard WC-1 (Roberts et al., 2017) was used to correct the 206Pb/238U ratio. Data uncertainty and propagation protocols follow Horstwood et al., (2016). The U-Pb heterogeneity of current carbonate mineral standards are often equal to or larger than the analytical uncertainties, meaning that significant excess variance will be present in the reference material U-Pb measurements (Roberts et al., 2017). If propagated into the sample point uncertainties as a within-session excess variance, as recommended for zircon in Horstwood et al., (2016), the data point uncertainties will be overestimated and will mask any small-scale real geological scatter in the sample and lead to ages with erroneously high precision. As per the recommendations of (Roberts et al., 2020), session-based reproducibility, accuracy and precision was performed on more homogeneous standards such as zircon or NIST glass standard. Accuracy of the method and secondary reference material is provided in thesis, doi:10.5258/SOTON/D3219, section 3.5.2. All results are plotted using a Tera-Wasserburg concordia diagram, taking the lower intercept as the age of the analysis. No common Pb correction was applied to the data."},"format":["Microsoft Excel (XLSX)"],"OrgForDistributionObject":{"default":"British Geological Survey","langeng":"British Geological Survey"},"distributorOrgForDistributionObject":{"default":"British Geological Survey","langeng":"British Geological Survey"},"contactForDistribution":[{"organisationObject":{"default":"British Geological Survey","langeng":"British Geological Survey"},"role":"distributor","email":"not available","website":"","logo":"","individual":"Enquiries","position":"","phone":"","address":"United 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Suigetsu, Japan"},"creationDateForResource":["2025-10-15"],"creationYearForResource":"2025","creationMonthForResource":"2025-10","resourceDate":[{"type":"creation","date":"2025-10-15T00:00:00.000Z"}],"resourceTemporalDateRange":[{"gte":"2025-10-15T00:00:00.000Z","lte":"2025-10-15T00:00:00.000Z"},{"gte":"2024-01-01T00:00:00.000Z","lte":"2025-08-01T00:00:00.000Z"}],"resourceIdentifier":[{"code":"http://data.bgs.ac.uk/id/dataHolding/13608553","codeSpace":"","link":""}],"resourceAbstractObject":{"default":"This data was collected in order to investigate the level of evidence for: A) a relationship between the strength of the geodynamo and Earth surface UV-B irradiance; B) increased surface UV-B irradiance during the Laschamps Event (the last major geomagnetic excursion, ca. 41ka BP). The study considered subfossil Pinus Diploxylon (Pinus D.) pollen grains obtained from the annually laminated Japanese Lake Suigetsu sediment record (Bronk Ramsey et al., 2020; Nakagawa et al., 2003). The primary response variable is the abundance of para-coumaric acid (a UV-B absorbing compound) in the sporopollenin of the subfossil pollen grains. The secondary response variable is the probability of grain malformation. There are three key spreadsheets: 1) GCMS_pCA_day_dependent_calbration_simple.csv - estimated abundances of para-coumaric acid in the subfossil Pinus Diploxylon pollen at different depths/times of the Lake Suigetsu sediment core. 2) Suigetsu_malformations_simple.csv - malformations counts in the Lake Suigetsu subfossil Pinus Dipoxylon pollen 3) gc_cultivar_data_simple.csv - estimated abundance of para-coumaric acid in Pinus pollen in a controlled cultivar experiment (considering response to UV-B flux) Descriptor files (with corresponding names) are also provided. This dataset contains data generated by NERC Grant NE/Y002652/1:Reconstructing UV radiation over the Laschamps Geomagnetic Excursion.","langeng":"This data was collected in order to investigate the level of evidence for: A) a relationship between the strength of the geodynamo and Earth surface UV-B irradiance; B) increased surface UV-B irradiance during the Laschamps Event (the last major geomagnetic excursion, ca. 41ka BP). The study considered subfossil Pinus Diploxylon (Pinus D.) pollen grains obtained from the annually laminated Japanese Lake Suigetsu sediment record (Bronk Ramsey et al., 2020; Nakagawa et al., 2003). The primary response variable is the abundance of para-coumaric acid (a UV-B absorbing compound) in the sporopollenin of the subfossil pollen grains. The secondary response variable is the probability of grain malformation. There are three key spreadsheets: 1) GCMS_pCA_day_dependent_calbration_simple.csv - estimated abundances of para-coumaric acid in the subfossil Pinus Diploxylon pollen at different depths/times of the Lake Suigetsu sediment core. 2) Suigetsu_malformations_simple.csv - malformations counts in the Lake Suigetsu subfossil Pinus Dipoxylon pollen 3) gc_cultivar_data_simple.csv - estimated abundance of para-coumaric acid in Pinus pollen in a controlled cultivar experiment (considering response to UV-B flux) Descriptor files (with corresponding names) are also provided. This dataset contains data generated by NERC Grant NE/Y002652/1:Reconstructing UV radiation over the Laschamps Geomagnetic Excursion."},"OrgForResourceObject":[{"default":"University of Leeds","langeng":"University of Leeds"},{"default":"University of Bergen","langeng":"University of Bergen"},{"default":"University of Oxford","langeng":"University of Oxford"},{"default":"University of Bergen","langeng":"University of Bergen"},{"default":"British Geological Survey","langeng":"British Geological Survey"},{"default":"British Geological Survey","langeng":"British Geological Survey"}],"originatorOrgForResourceObject":[{"default":"University of Leeds","langeng":"University of Leeds"},{"default":"University of Bergen","langeng":"University of Bergen"},{"default":"University of Oxford","langeng":"University of Oxford"},{"default":"University of Bergen","langeng":"University of Bergen"}],"contactForResource":[{"organisationObject":{"default":"University of Leeds","langeng":"University of 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These are intended to update the current maps for UK waters published in 2002. We developed a comprehensive catalogue of earthquake activity across the region by combining existing earthquake catalogues and data from regional and local monitoring agencies. We modelled earthquake occurrence across the region using a seismic source characterisation (SSC) model that consists of a series of zones, where seismicity is considered to be homogeneous, based on tectonics, geology and seismicity of the study area. We use four different seismic source zone models within the SSC to capture the epistemic uncertainty in different rupture scenarios. A logic tree approach was used to account for the epistemic uncertainty in earthquake activity rates, maximum magnitude, earthquake depth distribution, and faulting style. Ground motions are estimated for different rupture scenarios using a ground motion characterisation (GMC) model that consists of five multiple ground motion prediction equations considered to be applicable to the region. The GMPEs are included in a logic tree where the weights are informed by the fit between observed and modelled ground motions. The GMC model also includes the host-to-target adjustments and a single-station sigma model. Hazard is calculated at 4585 individual points spaced at 0.125° in latitude and 0.25° in longitude for peak ground acceleration (PGA) and spectral acceleration at 0.2 s (SA0.2 s) and 1.0 s (SA1.0 s) for 5% damping and rock conditions and the return periods of 95, 475, 1100, 2475, and 5000 years. This is the first time that maps of the seismic hazard at short (0.2 s) and long periods (1.0 s) have been produced for UK waters. Hazard curves, uniform hazard spectra, and disaggregation analysis have been calculated for three offshore carbon capture and storage sites (Endurance, Acorn, and HyNet North West). New offshore hazard maps for the UK waters will support UK decarbonisation by providing owners and operators of offshore structures with robust estimates of seismic hazard for new and existing sites. The results will also inform regulatory decisions to ensure safe operating practices in the industry and help identify areas of higher hazard where further site-specific studies might be needed. Finally, it will provide a robust baseline for tectonic seismic activity in the North Sea that can be used to help discriminate any seismicity induced by operations, such as CCS, in the event it occurs. The technical report by Mosca et al. 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We use four different seismic source zone models within the SSC to capture the epistemic uncertainty in different rupture scenarios. A logic tree approach was used to account for the epistemic uncertainty in earthquake activity rates, maximum magnitude, earthquake depth distribution, and faulting style. Ground motions are estimated for different rupture scenarios using a ground motion characterisation (GMC) model that consists of five multiple ground motion prediction equations considered to be applicable to the region. The GMPEs are included in a logic tree where the weights are informed by the fit between observed and modelled ground motions. The GMC model also includes the host-to-target adjustments and a single-station sigma model. Hazard is calculated at 4585 individual points spaced at 0.125° in latitude and 0.25° in longitude for peak ground acceleration (PGA) and spectral acceleration at 0.2 s (SA0.2 s) and 1.0 s (SA1.0 s) for 5% damping and rock conditions and the return periods of 95, 475, 1100, 2475, and 5000 years. This is the first time that maps of the seismic hazard at short (0.2 s) and long periods (1.0 s) have been produced for UK waters. Hazard curves, uniform hazard spectra, and disaggregation analysis have been calculated for three offshore carbon capture and storage sites (Endurance, Acorn, and HyNet North West). New offshore hazard maps for the UK waters will support UK decarbonisation by providing owners and operators of offshore structures with robust estimates of seismic hazard for new and existing sites. The results will also inform regulatory decisions to ensure safe operating practices in the industry and help identify areas of higher hazard where further site-specific studies might be needed. Finally, it will provide a robust baseline for tectonic seismic activity in the North Sea that can be used to help discriminate any seismicity induced by operations, such as CCS, in the event it occurs. The technical report by Mosca et al. 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