Reviving mB

We determine m_B, the original body wave magnitude developed by Gutenberg and Richter over the period 1942–1956, for about 3300 M_w ≥ 6 earthquakes for the period 1988–present using modern broad-band seismograms. The main objective is to extend the database of energy-related parameters by combining m_B databases for recent and old events. The radiated energy ER_B (in erg) computed from m_B using the Gutenberg & Richter relation loge_(R_B) = 2.4m_B + 5.8 agrees very well with E_R estimated with modern techniques, especially for large deep earthquakes. Thus, E_(R_B) is useful as a proxy for E_R to investigate the global diversity of earthquake characteristics and physics over an extended period of time

Pervasive Foreshock Activity Across Southern California

Foreshocks have been documented as preceding less than half of all mainshock earthquakes. These observations are difficult to reconcile with laboratory earthquake experiments and theoretical models of earthquake nucleation, which both suggest that foreshock activity should be nearly ubiquitous. Here we use a state‐of‐the‐art, high‐resolution earthquake catalog to study foreshock sequences of magnitude M4 and greater mainshocks in southern California from 2008–2017. This highly complete catalog provides a new opportunity to examine smaller magnitude precursory seismicity. Seventy‐two percent of mainshocks within this catalog are preceded by foreshock activity that is significantly elevated compared to the local background seismicity rate. Foreshock sequences vary in duration from several days to weeks, with a median of 16.6 days. The results suggest that foreshock occurrence in nature is more prevalent than previously thought and that our understanding of earthquake nucleation may improve in tandem with advances in our ability to detect small earthquakes

Slow-Growing and Extended-Duration Seismicity Swarms: Reactivating Joints or Foliations in the Cahuilla Valley Pluton, Central Peninsular Ranges, Southern California

Three prolific earthquake swarms and numerous smaller ones have occurred since 1980 in the Mesozoic igneous plutonic rocks of the Perris block of the Peninsular Ranges, Southern California. The major swarms occurred in 1980–1981, 1983–1984, and 2016–2018, with the latest swarm still ongoing. These swarms have no clear mainshock, with the largest events of M_L 3.6, M_L 3.7, and M_w 4.4. Each successive swarm had larger cumulative seismic moment release with about 314 and 411 events of M ≥ 1.5, while the third swarm has produced about 451 events of M ≥ 1.5 (as of September 2018). The concurrent strike‐slip faulting occurred on north to northwest striking planes but with no orthogonal northeast trending seismicity alignments. These shallow swarms are probably driven by intrablock Pacific‐North America plate boundary stress loading of the two bounding major late Quaternary strike‐slip faults, the Elsinore and San Jacinto faults. The state of stress within the Cahuilla Valley pluton has a ~40° angle between the maximum principal stress and the average trend of the swarms, suggesting that migrating pore fluid pressures aid in the formation and growth of zones of weakness. These swarms, which last more than 600 days each, exhibit clear bilateral spatial migration for distances of up to ~7–8 km and reach their full length in about 20 months. The slow spatial‐temporal development of the swarms corresponds to a fluid diffusivity of 0.006 to 0.01 m2/s, consistent with very low permeability rocks as expected for this block. There is no geodetic or other evidence for a slow slip event driving the swarms

Abundant off-fault seismicity and orthogonal structures in the San Jacinto fault zone

The trifurcation area of the San Jacinto fault zone has produced more than 10% of all earthquakes in southern California since 2000, including the June 2016 M_w (moment magnitude) 5.2 Borrego Springs earthquake. In this area, the fault splits into three subparallel strands and is associated with broad V_P/V_S anomalies. We synthesize spatiotemporal properties of historical background seismicity and aftershocks of the June 2016 event. A template matching technique is used to detect and locate more than 23,000 aftershocks, which illuminate highly complex active fault structures in conjunction with a high-resolution regional catalog. The hypocenters form dipping seismicity lineations both along strike and nearly orthogonal to the main fault, and are composed of interlaced strike-slip and normal faults. The primary faults change dip with depth and become listric by transitioning to a dip of ~70° near a depth of 10 km. The M_w 5.2 Borrego Springs earthquake and past events with M > 4.0 occurred on the main faults, whereas most of the low-magnitude events are located in a damage zone (several kilometers wide) at seismogenic depths. The lack of significant low-magnitude seismicity on the main fault traces suggests that they do not creep. The very high rate of aftershocks likely reflects the large geometrical fault complexity and perhaps a relatively high stress due to a significant length of time elapsed since the last major event. The results provide important insights into the physics of faulting near the brittle-ductile transition

EikoNet: Solving the Eikonal Equation With Deep Neural Networks

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 well-suited 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

Directivity Modes of Earthquake Populations with Unsupervised Learning

We present a novel approach for resolving modes of rupture directivity in large populations of earthquakes. A seismic spectral decomposition technique is used to first produce relative measurements of radiated energy for earthquakes in a spatially compact cluster. The azimuthal distribution of energy for each earthquake is then assumed to result from one of several distinct modes of rupture propagation. Rather than fitting a kinematic rupture model to determine the most likely mode of rupture propagation, we instead treat the modes as latent variables and learn them with a Gaussian mixture model. The mixture model simultaneously determines the number of events that best identify with each mode. The technique is demonstrated on four datasets in California, each with compact clusters of several thousand earthquakes with comparable slip mechanisms. We show that the datasets naturally decompose into distinct rupture propagation modes that correspond to different rupture directions, and the fault plane is unambiguously identified for all cases. We find that these small earthquakes exhibit unilateral ruptures 63–73% of the time on average. The results provide important observational constraints on the physics of earthquakes and faults