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Traditional algorithms degrade under heterogeneous, noisy conditions, motivating complementary artificial intelligence (AI) approaches. However, most previous studies emphasized model accuracy alone; we provide a benchmark of modern deep-vision backbones within a standardized time\u2013frequency pipeline to characterize the accuracy\u2013latency trade-off and assess real-time feasibility for EEW event\/noise discrimination. We fine-tune and evaluate six pretrained vision models for event\/noise discrimination on 10\u00a0s vertical-component windows aligned so that the P onset occurs\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\sim$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    7\u20138\u00a0s into the window, i.e., only the first 2\u20133\u00a0s of post-onset P-wave signal are included. Each window is transformed to a continuous wavelet transform power scalogram and fed to pre-trained convolutional neural networks (CNNs; EfficientNet-B0, ResNet-18\/50, ShuffleNetV2) and vision transformers (ViT-B\/16, Swin-Tiny), fine-tuned for binary classification. Models are trained on the STanford EArthquake Dataset (STEAD, global) and externally evaluated on the Italian Seismic Dataset for Machine Learning (INSTANCE, Italy) and the Red S\u00edsmica del Noroeste de M\u00e9xico (RESNOM; the CICESE broadband network in northwestern Mexico), with decisions derived from either a fixed threshold or dataset-specific calibration. The models achieve near-ceiling discrimination on STEAD and strong but dataset-dependent transfer to independent networks, with external performance in the range MCC\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\approx$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    0.87\u20130.97 and F1\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\approx$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    0.93\u20130.98, depending on model, training regime, and thresholding strategy. The lightweight CNNs evaluate a single trace in only 0.6\u00a0ms, a latency compatible with real-time EEW requirements. This study introduces a unified framework for benchmarking early event\/noise discrimination based on short P-onset windows, explicitly incorporating end-to-end computational latency. By demonstrating cross-network generalization, millisecond-scale inference, and physically interpretable focus on the P onset, the results provide a realistic foundation for next-generation AI modules within operational early warning systems.\n                  <\/jats:p>","DOI":"10.1007\/s12145-026-02099-6","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:46:36Z","timestamp":1773801996000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Real-time seismic signal classification using deep vision architectures for earthquake early warning systems"],"prefix":"10.1007","volume":"19","author":[{"given":"Andr\u00e9s","family":"Navarro-Rodr\u00edguez","sequence":"first","affiliation":[]},{"given":"Oscar Alberto","family":"Castro-Artola","sequence":"additional","affiliation":[]},{"given":"Boris","family":"R\u00f6sler","sequence":"additional","affiliation":[]},{"given":"Enrique Efr\u00e9n","family":"Garc\u00eda-Guerrero","sequence":"additional","affiliation":[]},{"given":"Oscar Adrian","family":"Aguirre-Castro","sequence":"additional","affiliation":[]},{"given":"Jorge","family":"Galarza-Falfan","sequence":"additional","affiliation":[]},{"given":"Everardo","family":"Inzunza-Gonzalez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,18]]},"reference":[{"issue":"19","key":"2099_CR1","doi-asserted-by":"publisher","first-page":"10884","DOI":"10.3390\/app131910884","volume":"13","author":"L Agathos","year":"2023","unstructured":"Agathos L, Avgoustis A, Avgoustis N et al (2023) Identifying earthquakes in low-cost sensor signals contaminated with vehicular noise. 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