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In this work, convolutional neural networks (CNN) are developed to detect and characterize sporadic E (E_s), demonstrating an improvement over current methods. This includes a binary classification model to determine ifE_sis present, followed by a regression model to estimate theE_sordinary mode critical frequency (foEs), a proxy for the intensity, along with the height at which theE_slayer occurs (hEs). Signal‐to‐noise ratio (SNR) and excess phase profiles from six Global Navigation Satellite System (GNSS) radio occultation (RO) missions during the years 2008–2022 are used as the inputs of the model. Intensity (foEs) and the height (hEs) values are obtained from the global network of ground‐based Digisonde ionosondes and are used as the “ground truth,” or target variables, during training. After corresponding the two data sets, a total of 36,521 samples are available for training and testing the models. The foEs CNN binary classification model achieved an accuracy of 74% and F1‐score of 0.70. Mean absolute errors (MAE) of 0.63 MHz and 5.81 km along with root‐mean squared errors (RMSE) of 0.95 MHz and 7.89 km were attained for estimating foEs and hEs, respectively, when it was known thatE_swas present. When combining the classification and regression models together for use in practical applications where it is unknown ifE_sis present, an foEs MAE and RMSE of 0.97 and 1.65 MHz, respectively, were realized. We implemented three other techniques for sporadic E characterization, and found that the CNN model appears to perform better.

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.