The vibrant evolutionary patterns made by earthquake swarms are incompatible with standard, effectively two-dimensional (2D) models for general fault architecture. We leverage advances in earthquake monitoring with a deep-learning algorithm to image a fault zone hosting a 4-year-long swarm in southern California. We infer that fluids are naturally injected into the fault zone from below and diffuse through strike-parallel channels while triggering earthquakes. A permeability barrier initially limits up-dip swarm migration but ultimately is circumvented. This enables fluid migration within a shallower section of the fault with fundamentally different mechanical properties. Our observations provide high-resolution constraints on the processes by which swarms initiate, grow, and arrest. These findings illustrate how swarm evolution is strongly controlled by 3D variations in fault architecture.

Objective. Ultrasound scans have become a routine part of antenatal care. Recent developments have meant that ultrasound has gone beyond monitoring the pregnancy and diagnosis of major abnormalities. Its routine use now includes screening for subtle fetal abnormalities and indications of chromosomal abnormality. Unfortunately ultrasound screening for chromosomal abnormalities has a low positive predictive value (similar to maternal serum screening), and only about 2% of pregnancies identified as ‘high risk’ will be diagnosed with a chromosomal abnormality. The other 98% of ‘high risk’ results are termed ‘false positive’. The aim of this paper is to elucidate the range of subjective experiences of women in response to having these results. Method. The approach employed was interpretative phenomenological analysis. Semi‐structured interviews were carried out with 24 pregnant women after ‘false positive’ ultrasound results. These were subjected to a qualitative analysis. Results. Participants perceived the ultrasound scan primarily as a social, non‐medical event and were unprepared for the result the scan elicited. Two‐thirds described residual feelings of anxiety even after normal diagnostic results were known, sometimes attributed to continued fears about fetal abnormality, but more often attributed to a generalized feeling that something else unexpected could happen to threaten the pregnancy. Conclusions. The paper has pointed to important discrepancies in the experiences of women undergoing ultrasound screening and the aims of informed choice. We would suggest consideration should be given to providing information on both the medical purposes of screening and on possible outcomes from it. The study also points to the possible need for counselling support for the majority of women who go on to receive confirmation of a negative diagnosis, counselling addressed at helping women to readjust to having a ‘low risk’ pregnancy.

The recent deep learning revolution has created enormous opportunities for accelerating compute capabilities in the context of physics-based simulations. In this article, we propose EikoNet, a deep learning approach to solving the Eikonal equation, which characterizes the first-arrival-time field in heterogeneous 3-D velocity structures. Our grid-free approach allows for rapid determination of the travel time between any two points within a continuous 3-D domain. These travel time solutions are allowed to violate the differential equation-which casts the problem as one of optimization-with the goal of finding network parameters that minimize the degree to which the equation is violated. In doing so, the method exploits the differentiability of neural networks to calculate the spatial gradients analytically, meaning that the network can be trained on its own without ever needing solutions from a finite-difference algorithm. EikoNet is rigorously tested on several velocity models and sampling methods to demonstrate robustness and versatility. Training and inference are highly parallelized, making the approach wellsuited for GPUs. EikoNet has low memory overhead and further avoids the need for travel-time lookup tables. The developed approach has important applications to earthquake hypocenter inversion, ray multipathing, and tomographic modeling, as well as to other fields beyond seismology where ray tracing is essential. Index Terms-Geophysics, partial differential equations (PDEs), ray tracing, travel time. I. INTRODUCTIONT HE 3-D ray tracing is a fundamental component of modern seismology, having direct applications to earthquake hypocenter inversions [8], seismic tomography [29], and earthquake source properties [4]. These derived products further form the basis for many downstream seismological applications. The Eikonal equation is a well-known nonlinear partial differential equation (PDE) that characterizes the firstarrival-time field for a given source location in a 3-D medium [17]. The Eikonal formulation can be solved with several finite-difference algorithms [18], [20], [27], with varying computational demands and stabilities to the solutions.

SUMMARY The Groningen gas reservoir, situated in the northeast of the Netherlands, is western Europe’s largest producing gas field and has been in production since 1963. The gas production has induced both subsidence and seismicity. Seismicity is detected and located using the Koninklijk Nederlands Meteorologisch Instituut shallow-borehole array for the period 2015–2017, incorporating the back projection techniques of QuakeMigrate and the nonlinear location procedure to constrain earthquake locations and depths. The uncertainties on the estimated depths are estimated taking into account velocity model, changes in station array geometry and uncertainties in the measurement of arrival times of the P and S waves. We show that the depth distribution of seismicity is consistent with nucleation within the reservoir (28 per cent) or in the overburden (60 per cent) within ∼500 m from the top of the reservoir. Earthquakes with hypocentres in the overburden likely originate from overlying Zechstein anhydrite caprock. Based on their depth distribution, it seems like the earthquakes are primarily driven by the elastic strain in the reservoir and overburden, induced by the reservoir compaction. We estimate the probability of earthquakes nucleating beneath the reservoir in the underlying Carboniferous limestone and basement, to be no more than 12 per cent.