Analyzing satellite-derived 3D building inventories and quantifying urban growth towards active faults: a case study of Bishkek, Kyrgyzstan

Earth observation (EO) data can provide large scale, high-resolution, and transferable methodologies to quantify the sprawl and vertical development of cities and are required to inform disaster risk reduction strategies for current and future populations. We synthesize the evolution of Bishkek, Kyrgyzstan, which experiences high seismic hazard, and derive new datasets relevant for seismic risk modeling. First, the urban sprawl of Bishkek (1979–2021) was quantified using built-up area land cover classifications. Second, a change detection methodology was applied to a declassified KeyHole Hexagon (KH-9) and Sentinel-2 satellite image to detect areas of redevelopment within Bishkek. Finally, vertical development was quantified using multi-temporal high-resolution stereo and tri-stereo satellite imagery, which were used in a deep learning workflow to extract buildings footprints and assign building heights. Our results revealed urban growth of 139 km2 (92%) and redevelopment of ~26% (59 km2) of the city (1979–2021). The trends of urban growth were not reflected in all the open access global settlement footprint products that were evaluated. Building polygons that were extracted using a deep learning workflow applied to high-resolution tri-stereo (Pleiades) satellite imagery were most accurate (F1 score = 0.70) compared to stereo (WorldView-2) imagery (F1 score = 0.61). Similarly, building heights extracted using a Pleiades-derived digital elevation model were most comparable to independent measurements obtained using ICESat-2 altimetry data and field-measurements (normalized absolute median deviation < 1 m). Across different areas of the city, our analysis suggested rates of building growth in the region of 2000–10,700 buildings per year, which when combined with a trend of urban growth towards active faults highlights the importance of up-to-date building stock exposure data in areas of seismic hazard. Deep learning methodologies applied to high-resolution imagery are a valuable monitoring tool for building stock, especially where country-level or open-source datasets are lacking or incomplete

Significant Seismic Risk Potential From Buried Faults Beneath Almaty City, Kazakhstan, Revealed From High-Resolution Satellite DEMs

Major faults of the Tien Shan, Central Asia, have long repeat times, but fail in large (Mw 7+) earthquakes. In addition, there may be smaller, buried faults off the major faults which are not properly characterized or even recognized as active. These all pose hazard to cities along the mountain range front such as Almaty, Kazakhstan. Here, we explore the seismic hazard and risk for Almaty from specific earthquake scenarios. We run three historical-based earthquake scenarios (1887 Verny Mw 7.3, 1889 Chilik Mw 8.0 and 1911 Chon-Kemin Mw 8.0) on the current population and four hypothetical scenarios for near-field faulting. By making high-resolution Digital Elevation Models (DEMs) from SPOT and Pleiades stereo optical satellite imagery, we identify fault splays near and under Almaty. We assess the feasibility of using DEMs to estimate city building heights, aiming to better constrain future exposure datasets. Both Pleiades and SPOT-derived DEMs find accurate building heights of the majority of sampled buildings within error; Pleiades tri-stereo estimates 80% of 15 building heights within one sigma and has the smallest average percentage difference to field-measured heights (14%). A moderately sized Mw 6.5 earthquake rupture occurring on a blind thrust fault, under folding north of Almaty is the most damaging scenario explored here due to the modeled fault stretching under Almaty, with estimated 12,300±5,000 completely damaged buildings, 4,100 ± 3,500 fatalities and an economic cost of 4,700 ± Million US dollars (one sigma uncertainty). This highlights the importance of characterizing location, extent, geometry, and activity of small faults beneath cities

Earthquake Cycle Deformation Associated with the 2021 MW 7.4 Maduo (Eastern Tibet) Earthquake: An Intrablock Rupture Event on a Slow-Slipping Fault from Sentinel-1 InSAR and Teleseismic Data

In the continents, the importance of earthquakes that occur away from major block-bounding faults is still debated. The 21 May 2021 MW ∼7.4 Maduo earthquake occurred on a secondary fault away from previously-identified major block boundaries. Here we use seven years of Sentinel-1 InSAR time series (between October 2014 and November 2021) to determine the distribution of coseismic slip and early postseismic afterslip following the Maduo earthquake, and the preceding interseismic strain accumulation. We devised a 13-segment 3-D fault geometry constrained by the SAR range offsets and the distribution of relocated aftershocks and used a Bayesian method incorporating von Karman regularization to solve for coseismic slip and afterslip models. We also used teleseismic waveforms as a standalone inversion to show the rupture evolution in space and time during the earthquake, finding that it propagates bilaterally with three notable rupture episodes. Our preferred coseismic self-similar slip model shows a moderate shallow slip deficit, with the majority of moment release occurring in the depth interval of 1-10 km. The coseismic slip deficit is taken up in part by afterslip at shallow ( 10 km depths where afterslip grows logarithmically with time. We suggest that this heterogeneity is likely controlled by spatial variations in fault friction related to lithology. We discuss the implications for seismic hazard away from major tectonic block boundaries in light of our observations of the earthquake cycle on this intrablock fault