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The Derbyshire Platform is a Mississippian aged flat-topped, steep sided platform that forms the westernmost expression of the Derbyshire-East Midlands Platform. On the south-east platform margin, 60 km2 of Visean limestone has been dolomitised, forming two distinct bodies. One of these bodies forms along a major NW–SE trending basement fault and smaller, associated, N–S trending faults and fractures. This study uses outcrop, petrographic and geochemical analysis to better constrain the timing and mechanism for this fault-controlled dolomitisation. Field relationships demonstrate dolomitisation was multi-phase and initiated after the main phase of matrix pore-occluding calcite cementation on the Derbyshire Platform and terminated prior to the main phase of mineralisation. Fluids are interpreted to have fluxed from adjacent basins, primarily along strike-slip crustal faults that were reactivated during basin inversion at the onset of the Variscan Orogeny. Fluid supply was episodic and progressively confined to fractures as matrix porosity became occluded. The study demonstrates the complex interplay between basin kinematics, host rock permeability and timing of fluid supply through seismic valving along faults that connect the carbonate platform to basin compartments. This ultimately controlled the position of dolomite geobodies along faults and provides a record of fluid flow during the transition from thermal subsidence to post-rift basin inversion. The findings have implications for the exploration of both minerals and hydrocarbon within dolomitised host rocks and can inform studies of fluid transfer and reaction on carbonate platforms within the burial realm.

Landslide susceptibility modelling in tropical climates is hindered by incomplete inventory due to rapid development and natural processes that obliterate field evidence, making validation a challenge. Susceptibility modelling was conducted in Kuala Lumpur, Malaysia using a new spatial partitioning technique for cross-validation. This involved a series of two alternating east-west linear zones, where the first zone served as the training dataset and the second zone was the test dataset, and vice versa. The results show that the susceptibility models have good compatibility with the selected landslide conditioning factors and high predictive accuracy. The model with the highest area under curve (AUC) values (SRC = 0.92, PRC = 0.90) was submitted to the City Council of Kuala Lumpur for land use planning and development control. Rainfall-induced landslides are prominent within the study area, especially during the monsoon period. An extreme rainfall event in December 2021 that triggered 122 landslides provided an opportunity to conduct retrospective validation of the model; the high predictive capability (AUC of PRC = 0.93) was reaffirmed. The findings proved that retrospective validation is vital for landslide susceptibility modelling, especially where the inventory is not of the best quality. This is to encourage wider usage and acceptance among end users, especially decision-makers in cities, to support disaster risk management in a changing climate.