Search for: All records

Abstract We discuss general structural features of the Banning and Mission Creek strands (BF and MCF) of the southern San Andreas fault (SSAF) in the Coachella Valley, based on ambient noise and earthquake wavefields recorded by a seismic array with >300 nodes. Earthquake P arrivals show rapid changes in waveform characteristics over 20–40 m zones that coincide with the surface BF and MCF. These variations indicate that the BF and MCF are high-impedance contrast interfaces—an observation supported by the presence of seismic reflections. Another prominent but more diffuse change in SSAF structure is found ∼1 km northeast of the BF. This feature has average-to-low arrival times (P and S) and ambient noise levels (at <30 Hz), and likely represents a relatively fast velocity block sandwiched between broader MCF and BF zones. The maximal arrival delays (P ∼0.1 s and S ∼0.25 s) and the highest ambient noise levels (>2 times median) are consistently observed southwest of the BF—a combined effect of Coachella Valley sediments and rock damage on that side. Immediately northeast of the MCF, large S minus P delays suggest a broad high VP/VS zone associated with asymmetric rock damage across the SSAF. This general overview shows the BF and MCF as mature but distinctly different fault zones. Future analyses will further clarify these and other SSAF features in greater detail. 

The size and frequency of wildland fires in the western United States have dramatically increased in recent years. On high-fire-risk days, a small fire ignition can rapidly grow and become out of control. Early detection of fire ignitions from initial smoke can assist the response to such fires before they become difficult to manage. Past deep learning approaches for wildfire smoke detection have suffered from small or unreliable datasets that make it difficult to extrapolate performance to real-world scenarios. In this work, we present the Fire Ignition Library (FIgLib), a publicly available dataset of nearly 25,000 labeled wildfire smoke images as seen from fixed-view cameras deployed in Southern California. We also introduce SmokeyNet, a novel deep learning architecture using spatiotemporal information from camera imagery for real-time wildfire smoke detection. When trained on the FIgLib dataset, SmokeyNet outperforms comparable baselines and rivals human performance. We hope that the availability of the FIgLib dataset and the SmokeyNet architecture will inspire further research into deep learning methods for wildfire smoke detection, leading to automated notification systems that reduce the time to wildfire response. 

The Southern San Andreas Fault (SSAF) in California is one of the most thoroughly studied faults in the world, but its configuration at seismogenic depths remains enigmatic in the Coachella Valley. We use a combination of space geodetic and seismic observations to demonstrate that the relatively straight southernmost section of the SSAF, between Thousand Palms and Bombay Beach, is dipping to the northeast at 60–80° throughout the upper crust (<10 km), including the shallow aseismic layer. We constrain the fault attitude in the top 2–3 km using inversions of surface displacements associated with shallow creep, and seismic data from a dense nodal array crossing the fault trace near Thousand Palms. The data inversions show that the shallow dipping structure connects with clusters of seismicity at depth, indicating a continuous throughgoing fault surface. The dipping fault geometry has important implications for the long‐term fault slip rate, the intensity of ground shaking during future large earthquakes, and the effective strength of the southern SAF.

 

Understanding and modeling variability of ground motion is essential for building accurate and precise ground motion prediction equations, which can net site‐specific characterization and reduced hazard levels. Here, we explore the spatial variability in peak ground velocity (PGV) at Sage Brush Flats along the San Jacinto Fault in Southern California. We use data from a dense array (0.6 × 0.6 km², 1,108 geophones, station spacings 10–30 m) deployed in 2014 for ~1 month. These data offer an opportunity to study small‐scale variability in this region. We examine 38 earthquakes (2 ≤ M_L ≤ 4.2) within 200 km of the array. Fault strands and a small basin impact the ground motions, producing PGV variations up to 22% of the mean and a 40% reduction inPandSwave near‐surface velocities. We find along‐fault rupture directivity, source, and path effects can increase PGVs by 167%. Surface PGV measurements exceed the colocated borehole station (depth at 148 m) PGV by factors of 3–10, confirming the impact on PGV from near‐surface fault structures, basins, topography, and amplifications from soft sediments. Consistently, we find high PGVs within the basin structure. A pair of colocated GaM_L2.6 events produce repeatable PGV values with similar spatial patterns. The average corner frequencies of these two events are 11–16 Hz, and viable measurements of stress drop can differ by 6.45 MPa. Within this small array, the PGV values are variable implying spatial extrapolation of PGV to regions of known faults and basins, even across a small area, should be done with caution.

 

Proper classification of nontectonic seismic signals is critical for detecting microearthquakes and developing an improved understanding of ongoing weak ground motions. We use unsupervised machine learning to label five classes of nonstationary seismic noise common in continuous waveforms. Temporal and spectral features describing the data are clustered to identify separable types of emergent and impulsive waveforms. The trained clustering model is used to classify every 1 s of continuous seismic records from a dense seismic array with 10–30 m station spacing. We show that dominate noise signals can be highly localized and vary on length scales of hundreds of meters. The methodology demonstrates the complexity of weak ground motions and improves the standard of analyzing seismic waveforms with a low signal‐to‐noise ratio. Application of this technique will improve the ability to detect genuine microseismic events in noisy environments where seismic sensors record earthquake‐like signals originating from nontectonic sources.