The July 2019 Ridgecrest, California, earthquake sequence produced cross‐fault ruptures from a M_w6.4 left‐lateral foreshock and a M_w7.1 right‐lateral mainshock. We use interferometric synthetic aperture radar and satellite optical imagery to characterize the surface displacements and subsurface fault slip characteristics of the sequence. We document ~46 km of surface rupture and peak slip values of ~5 m associated with the M_w7.1 and evidence that the two ruptures crossed each other. We additionally document evidence of triggered creep along 20–25 km of the central Garlock fault. Static stress change analysis shows that the foreshock sequence systematically promoted slip at the M_w7.1 hypocenter. Moreover, we find static stress changes promoted slip on the Garlock fault only in locations where we document surface creep, strongly indicating that the Garlock fault is sensitive to static stress changes. A potential rupture of the Garlock fault where slip was promoted could produce a M_w6.7–7.0 earthquake.

The 6 February 2023 $Mw$ 7.8 Pazarcık and subsequent $Mw$ 7.5 Elbistan earthquakes generated strong ground shaking that resulted in catastrophic human and economic loss across south‐central Türkiye and northwest Syria. The rapid characterization of the earthquakes, including their location, size, fault geometries, and slip kinematics, is critical to estimate the impact of significant seismic events. The U.S. Geological Survey National Earthquake Information Center (NEIC) provides real‐time monitoring of earthquakes globally, including rapid source characterization and impact estimates. Here, we describe the seismic characterization products generated and made available by the NEIC over the two weeks following the start of the earthquake sequence in southeast Türkiye, their evolution, and how they inform our understanding of regional seismotectonics and hazards. The kinematics of rupture for the two earthquakes was complex, involving multiple fault segments. Therefore, incorporating observations from rupture mapping was critical for characterizing these events. Dense local datasets facilitated robust source characterization and impact assessment once these observations were obtained and converted to NEIC product input formats. We discuss how we may improve the timeliness of NEIC products for rapid assessment of future seismic hazards, particularly in the case of complex ruptures.

The 12 November 2017 M_w 7.3 Ezgeleh‐Sarpolzahab earthquake is the largest instrumentally recorded earthquake in the Zagros Simply Folded Belt by a factor of ∼10 in seismic moment. Exploiting local, regional, and teleseismic data and synthetic aperture radar interferometry imagery, we characterize the rupture, its aftershock sequence, background seismicity, and regional tectonics. The mainshock ruptured slowly (∼2 km/s), unilaterally southward, for ∼40 km along an oblique (dextral‐thrust) fault that dips ∼14°E beneath the northwestern Lurestan arc. Slip is confined to basement depths of ∼12–18 km, resolvably beneath the sedimentary cover which is ∼8 km thick in this area. The gentle dip angle and basement location allow for a broad slip area, explaining the large magnitude relative to earthquakes in the main Fars arc of the Zagros, where shallower, steeper faults are limited in rupture extent by weak sedimentary layers. Early aftershocks concentrate around the southern and western edges of the mainshock slip area and therefore cluster in the direction of rupture propagation, implying a contribution from dynamic triggering. A cluster of events ∼100 km to the south near Mandali (Iraq) reactivated the ∼50° dipping Zagros Foredeep Fault. The basement fault responsible for the Ezgeleh‐Sarpolzahab earthquake probably accounts for the ∼1 km elevation contrast between the Lurestan arc and the Kirkuk embayment but is distinct from sections of the Mountain Front Fault that define frontal escarpments elsewhere in the Zagros. It may be related to a seismic interface underlying the central and southern Lurestan arc, and a key concern is whether or not the more extensive regional structure is also seismogenic.

The availability and frequency of synthetic aperture radar (SAR) imagery are rapidly increasing. This surge of data presents new opportunities to constrain surface deformation that spans various spatial and temporal scales. This expansion also introduces common challenges associated with large volumes of data, including best practices for analyzing these data. In recent years, machine learning techniques have been at the forefront of big data challenges, as an efficient methodology for automatically classifying large volumes of data. Convolutional Neural Networks (CNNs), in particular, have achieved strong levels of performance on image classification problems. Here we present SarNet, a CNN developed to detect, locate, and classify the presence of co‐seismic‐like surface deformation within an interferogram. We trained SarNet using 4 × 10⁶ synthetic interferograms, including both wrapped and unwrapped forward modeled co‐seismic‐like surface deformation with synthetic noise representative of the atmospheric and topographic noise found in interferograms. The results show that SarNet obtains an overall accuracy of 99.74% on a validation data set. We use class activation maps (CAMs) to show that SarNet returns the location of surface deformation within the interferogram. We employ a transfer learning method to translate the accuracy of SarNet trained on synthetic data to real interferograms with manually classified co‐seismic surface displacement. We train SarNet on 32 interferograms containing labeled co‐seismic surface deformation as well as noise. The results show that, through transfer learning, SarNet obtains an overall accuracy of 85.22% on a real InSAR data set, and that SarNet returns the location of the surface deformation within the interferogram.