Postglacial strain rate – stress paradox, example of the Western Alps active faults

The understanding of the origins of seismicity in intraplate regions is crucial to better characterize seismic hazards. In formerly glaciated regions such as Fennoscandia North America or the Western Alps, stress perturbations from Glacial Isostatic Adjustment (GIA) have been proposed as a major cause of large earthquakes. In this study, we focus on the Western Alps case using numerical modeling of lithosphere response to the Last Glacial Maximum icecap. We show that the flexural response to GIA induces present-day stress perturbations of ca. 1–2 MPa, associated with horizontal extension rates up to ca. 2.5 × 10−9 yr−1. The latter is in good agreement with extension rates of ca. 2 × 10−9 yr−1 derived from high-resolution geodetic (GNSS) data and with the overall seismicity deformation pattern. In the majority of simulations, stress perturbations induced by GIA promote fault reactivation in the internal massifs and in the foreland regions (i.e., positive Coulomb Failure Stress perturbation), but with predicted rakes systematically incompatible with those from earthquake focal mechanisms. Thus, although GIA explains a major part of the GNSS strain rates, it tends to inhibit the observed seismicity in the Western Alps. A direct corollary of this result is that, in cases of significant GIA effect, GNSS strain rate measurements cannot be directly integrated in seismic hazard computations, but instead require detailed modeling of the GIA transient impact.

Study of Ground Movement in a Mining Area with Geological Faults Using FDM Analysis and a Stacking InSAR Method

Underground coal mining activities and ground movement are directly correlated, and coal mining-induced ground movement can cause damage to property and resources, thus its monitoring is essential for the safety and economics of a city. Fangezhuang coal mine is one of the largest coalfields in operation in Tangshan, China. The enormous amount of coal extraction has resulted in significant ground movement over the years. These phenomena have produced severe damages to the local infrastructure. This paper uses the finite difference method (FDM) 3D model and the stacking interferometric synthetic aperture radar (InSAR) method to monitor the ground movement in Fangezhuang coalfield during 2016. The FDM 3D model used calibrated Fangezhuang geological parameters and the satellite InSAR analysis involved the use of ascending C-band Sentinel-1A interferometric wide (IW) data for 2016. The results show that the most prominent subsidence signal occurs in mining panel 2553N and the area between panel 2553N and fault F0 with subsidence up to 57 cm. The subsidence observed for the FDM 3D model and stacking InSAR to monitor land deformation under the influence of fault are in close agreement and were verified using a two-sample t-test. It was observed that the maximum subsidence point shifted towards the fault location from the centre of the mining panel. The tectonic fault F0 was found to be reactivated by the coal mining and controls the spatial extent of the observed ground movement. The impact of dominant geological faults on local subsidence boundaries is investigated in details. It is concluded that ground movement in the study area was mainly induced by mining activities, with its spatial pattern being controlled by geological faults. These results highlight that the two methods are capable of measuring mining induced ground movement in fault dominated areas. The study will improve the understanding of subsidence control, and aid in developing preventive measures in Fangezhuang coalfield with fault reactivation.

Seismicity recorded in hematite fault mirrors in the Rio Grande rift

Exhumed fault rocks provide a textural and chemical record of how fault zone composition and architecture control coseismic temperature rise and earthquake mechanics. We integrated field, microstructural, and hematite (U-Th)/He (He) thermochronometry analyses of exhumed minor (square-centimeter-scale surface area) hematite fault mirrors that crosscut the ca. 1400 Ma Sandia granite in two localities along the eastern flank of the central Rio Grande rift, New Mexico. We used these data to characterize fault slip textures; evaluate relationships among fault zone composition, thickness, and inferred magnitude of friction-generated heat; and document the timing of fault slip. Hematite fault mirrors are collocated with and crosscut specular hematite veins and hematite-cemented cataclasite. Observed fault mirror microstructures reflect fault reactivation and strain localization within the comparatively weaker hematite relative to the granite. The fault mirror volume of some slip surfaces exhibits polygonal, sintered hematite nanoparticles likely created during coseismic temperature rise. Individual fault mirror hematite He dates range from ca. 97 to 5 Ma, and ~80% of dates from fault mirror volume aliquots with high-temperature crystal morphologies are ca. 25–10 Ma. These aliquots have grain-size–dependent closure temperatures of ~75–108 °C. A new mean apatite He date of 13.6 ± 2.6 Ma from the Sandia granite is consistent with prior low-temperature thermochronometry data and reflects rapid, Miocene rift flank exhumation. Comparisons of thermal history models and hematite He data patterns, together with field and microstructural observations, indicate that seismicity along the fault mirrors at ~2–4 km depth was coeval with rift flank exhumation. The prevalence and distribution of high-temperature hematite grain morphologies on different slip surfaces correspond with thinner deforming zones and higher proportions of quartz and feldspar derived from the granite that impacted the bulk strength of the deforming zone. Thus, these exhumed fault mirrors illustrate how evolving fault material properties reflect but also govern coseismic temperature rise and associated dynamic weakening mechanisms on minor faults at the upper end of the seismogenic zone.

New onshore insights into the role of structural inheritance during Mesozoic opening of the Inner Moray Firth Basin, Scotland

The Inner Moray Firth Basin (IMFB) forms the western arm of the North Sea trilete rift system that initiated mainly during the Late Jurassic-Early Cretaceous with the widespread development of major NE-SW-trending dip-slip growth faults. The IMFB is superimposed over the southern part of the older Devonian Orcadian Basin. The potential influence of older rift-related faults on the kinematics of later Mesozoic basin opening has received little attention, partly due to the poor resolution of offshore seismic reflection data at depth. New field observations augmented by drone photography and photogrammetry, coupled with U-Pb geochronology have been used to explore the kinematic history of faulting in onshore exposures along the southern IMFB margin. Dip-slip N-S to NNE-SSW-striking Devonian growth faults are recognised that have undergone later dextral reactivation during NNW-SSE extension. The U-Pb calcite dating of a sample from the syn-kinematic calcite veins associated with this later episode shows that the age of fault reactivation is 131.73 ± 3.07 Ma (Hauterivian). The recognition of dextral-oblique Early Cretaceous reactivation of faults related to the underlying and older Orcadian Basin highlights the importance of structural inheritance in controlling basin- to sub-basin-scale architectures and how this influences the kinematics of IMFB rifting.Supplementary material:https://doi.org/10.6084/m9.figshare.c.5635432

Assessing the Accuracy of Fault Interpretation using Machine Learning Techniques when Risking Faults for CO2 Storage Site Assessment

Generating an accurate model of the subsurface for the purpose of assessing the feasibility of a CO2 storage site is crucial. In particular, how faults are interpreted is likely to influence the predicted capacity and integrity of the reservoir; whether this is through identifying high risk areas along the fault, where fluid is likely to flow across the fault, or by assessing the reactivation potential of the fault with increased pressure, causing fluid to flow up the fault. New technologies allow users to interpret faults effortlessly, and in much quicker time, utilizing methods such as Deep Learning. These Deep Learning techniques use knowledge from Neural Networks to allow end-users to compute areas where faults are likely to occur. Although these new technologies may be attractive due to reduced interpretation time, it is important to understand the inherent uncertainties in their ability to predict accurate fault geometries. Here, we compare Deep Learning fault interpretation versus manual fault interpretation, and can see distinct differences to those faults where significant ambiguity exists due to poor seismic resolution at the fault; we observe an increased irregularity when Deep Learning methods are used over conventional manual interpretation. This can result in significant differences between the resulting analyses, such as fault reactivation potential. Conversely, we observe that well-imaged faults show a close similarity between the resulting fault surfaces when both Deep Learning and manual fault interpretation methods are employed, and hence we also observe a close similarity between any attributes and fault analyses made.