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At the Blanco transform fault system (BTFS) off Oregon, 138 local earthquakes and 84 double‐couple focal mechanisms from ocean‐bottom‐seismometer recordings jointly discussed with bathymetric features reveal a highly segmented transform system without any prominent fracture zone traces longer than 100 km. In the west, seismicity is focused at deep troughs (i.e., the West and East Blanco, and Surveyor Depressions). In the east, the BTFS lacks a characteristic transform valley and instead developed the Blanco Ridge, which is the most seismically active feature, showing strike‐slip and dip‐slip faulting. Sandwiched between the two main segments of the BTFS is the Cascadia Depression, representing a short intra‐transform spreading segment. Seismic slip vectors reveal that stresses at the eastern BTFS are roughly in line with plate motion. In contrast, stresses to the west are clockwise skewed, indicating ongoing reorganization of the OTF system. As we observed no prominent fracture zones at the BTFS, plate tectonic reconstructions suggest that the BTFS developed from non‐transform offsets rather than pre‐existing transform faults during a series of ridge propagation events. Our observations suggest that the BTFS can be divided into two oceanic transform systems. The eastern BTFS is suggested to be a mature transform plate boundary since ∼0.6 Ma. In contrast, the western BTFS is an immature transform system, which is still evolving to accommodate far‐field stress change. The BTFS acts as a natural laboratory to yield processes governing the development of oceanic transform faults. Plain Language Summary: The Blanco transform fault system (BTFS) northwest off the coast of Oregon is seismically very active. We used 1 year of ocean bottom seismometer data collected between September 2012 and October 2013 to locate 138 local earthquakes. The events align perfectly with the morphologic features of the BTFS, dividing the BTFS into five transform segments and two short intra‐transform spreading centers. Furthermore, we observe different seismotectonic behaviors of the western and eastern BTFS based on the along‐strike variation in morphology, magnetization, focal depth distribution, and strain partitioning. Although many segmented oceanic transform systems were formed from a single transform fault in response to rotations in plate motion, the BTFS turns out to be originated from non‐transform offsets between ridge segments, as we observed no prominent fracture zone traces neither in morphology nor gravity field data. A clockwise shift in the Juan de Fuca/Pacific pole of rotation at ∼5 Ma followed by a series of ridge propagation events initiated the formation of the BTFS, integrated each segment of the BTFS by shortening the ridge segments in between. Our observations suggest that the Blanco Ridge and the Gorda transform segment in the eastern BTFS were formed at ∼1.6 and 0.6 Ma, respectively, and ever since, the eastern BTFS became a mature transform boundary. In contrast, seismic slip vectors comparing to plate motion directions reveal that stresses in the western BTFS are systematically skewed, suggesting the immature transform plate boundary is still adjusting to the new stress regime. Key Points: Local seismicity of the Blanco transform fault system (BTFS) reveals along‐strike variations dominated by strike‐slip and oblique dip‐slipThe BTFS developed from non‐transform offsets rather than discrete transform faults in response to plate rotation and ridge propagationThe BTFS consists of a mature plate boundary in the east and an immature system in the west, separated by a central spreading center [ABSTRACT FROM AUTHOR]

Seismic event detection and phase picking are the base of many seismological workflows. In recent years, several publications demonstrated that deep learning approaches significantly outperform classical approaches, achieving human‐like performance under certain circumstances. However, as studies differ in the datasets and evaluation tasks, it is unclear how the different approaches compare to each other. Furthermore, there are no systematic studies about model performance in cross‐domain scenarios, that is, when applied to data with different characteristics. Here, we address these questions by conducting a large‐scale benchmark. We compare six previously published deep learning models on eight data sets covering local to teleseismic distances and on three tasks: event detection, phase identification and onset time picking. Furthermore, we compare the results to a classical Baer‐Kradolfer picker. Overall, we observe the best performance for EQTransformer, GPD and PhaseNet, with a small advantage for EQTransformer on teleseismic data. Furthermore, we conduct a cross‐domain study, analyzing model performance on data sets they were not trained on. We show that trained models can be transferred between regions with only mild performance degradation, but models trained on regional data do not transfer well to teleseismic data. As deep learning for detection and picking is a rapidly evolving field, we ensured extensibility of our benchmark by building our code on standardized frameworks and making it openly accessible. This allows model developers to easily evaluate new models or performance on new data sets. Furthermore, we make all trained models available through the SeisBench framework, giving end‐users an easy way to apply these models. Plain Language Summary: The first step in many seismological workflows is identifying if a signal contains an earthquake, and at which time which type of seismic wave arrived. These steps are known as event detection, phase identification and phase picking. In recent years, machine learning methods, in particular deep learning methods have been developed, showing promising performance on these tasks. However, so far these models have not been compared systematically in a quantitative way. Here we evaluate the performance of six deep learning models on eight datasets. Additionally, we compare them to a traditional picking algorithm not using machine learning. From our results we identify that the models EQTransformer, GPD and PhaseNet perform best. As in many use cases no picker trained on the target region will be available, we further evaluated how well models are transferable across regions. We identified that transfer across regions works well as long as the distance ranges stay similar. To foster application of the results, we make all our trained models available through the SeisBench framework. Key Points: We conducted a large scale benchmark of machine learning pickers using six models and eight datasetsBest overall performance is observed for EQTransformer, GPD and PhaseNet, with advantages for EQTransformer on teleseismic distancesModels transfer well between different regions with similar distances, but not between regional and teleseismic distances [ABSTRACT FROM AUTHOR]

Receiver functions (RF) have been used for several decades to study structures beneath seismic stations. Although most available stations are deployed on shore, the number of ocean bottom station (OBS) experiments has increased in recent years. Almost all OBSs have to deal with higher noise levels and a limited deployment time (∼1 year), resulting in a small number of usable records of teleseismic earthquakes. Here we use OBSs deployed as midaperture array in the deep ocean (4.5-5.5 km water depth) of the eastern mid-Atlantic. We use evaluation criteria for OBS data and beamforming to enhance the quality of the RFs. Although some stations show reverberations caused by sedimentary cover, we are able to identify the Moho signal, indicating a normal thickness (5-8 km) of oceanic crust. Observations at single stations with thin sediments (300-400 m) indicate that a probable sharp lithosphere-asthenosphere boundary (LAB) might exist at a depth of ∼70-80 km which is in line with LAB depth estimates for similar lithospheric ages in the Pacific. The mantle discontinuities at ∼410 km and ∼660 km are clearly identifiable. Their delay times are in agreement with PREM. Overall the usage of beam-formed earthquake recordings for OBS RF analysis is an excellent way to increase the signal quality and the number of usable events. [ABSTRACT FROM AUTHOR]