#EarthquakeDetection
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puretech.news
“Like putting on glasses for the first time”—how AI improves earthquake detection #Technology #EmergingTechnologies #ArtificialIntelligence #AI #EarthquakeDetection #EmergingTech
“Like putting on glasses for the first time”—how AI improves earthquake detection
On January 1, 2008, at 1:59 AM in Calipatria, California, an earthquake happened. You haven’t heard of this earthquake; even if you had been living in Calipatria, you wouldn’t have felt anything....
puretech.news
October 10, 2025 at 12:31 PM
themestimes.bsky.social
Google has created a groundbreaking earthquake detection system by using motion sensors from over two billion Android phones worldwide.

#Google #EarthquakeDetection
July 22, 2025 at 4:51 PM
puretech.news
Android Phones Can Detect Earthquakes Before the Ground Starts Shaking #Technology #ConsumerTechnology #Smartphones #Android #EarthquakeDetection
Android Phones Can Detect Earthquakes Before the Ground Starts Shaking
Electronic messages travel faster than seismic waves, Gizmodo points out — meaning some people near an earthquake receive an Android Earthquake Alert
puretech.news
July 20, 2025 at 6:30 AM
purescience.news
“Like putting on glasses for the first time”—how AI improves earthquake detection #Science #EarthSciences #Seismology #EarthquakeDetection #ArtificialIntelligence
“Like putting on glasses for the first time”—how AI improves earthquake detection
On January 1, 2008, at 1:59 AM in Calipatria, California, an earthquake happened. You haven’t heard of this earthquake; even if you had been living in Calipatria, you wouldn’t have felt anything....
purescience.news
October 10, 2025 at 5:42 PM
purescience.news
Billions of phones can detect and warn about nearby earthquakes #Science #EarthSciences #Seismology #EarthquakeDetection #SeismicTechnology #SmartphoneInnovation
Billions of phones can detect and warn about nearby earthquakes
Google’s Android Earthquake Alerts program is a globe-spanning earthquake early-warning system that uses billions of phone sensors to detect seismic shaking and alert those at risk
purescience.news
July 18, 2025 at 1:01 PM
dataandai.bsky.social
💥 Real-time earthquake detection made easier!
🚀 MCU-Quake integrates AI with IoT for fast, energy-efficient seismic signal analysis.
🌍 Global applications, from natural earthquakes to explosions!
#AI #IoT #EarthquakeDetection #TechInnovation

www.nature.com/articles/s43...
Real-time discrimination of earthquake signals by integrating artificial intelligence technology into IoT devices - Communications Earth & Environment
Tiny deep learning applied to microcontroller units may be used to discriminate seismological signals from ambient noise in real-time and explosions, using low-cost electric power microcontrollers and...
www.nature.com
January 31, 2025 at 8:02 AM
purescience.news
Earthquake or Explosion? Scientists Shut Down the Viral Nuclear Weapons Test Hoax #Science #EarthSciences #Seismology #EarthquakeDetection #NuclearSafety #ScienceVerification
Earthquake or Explosion? Scientists Shut Down the Viral Nuclear Weapons Test Hoax
A scientific investigation dismantles the widespread claim that an Iranian earthquake was actually a secret nuclear test. Researchers at Johns Hopkins University analyzed seismic data and confirmed the...
purescience.news
February 4, 2025 at 2:00 PM
nishthasrivastv.bsky.social
🚨Pleased to share the preprint resulted from the collaboration between my team and Prof. Carlos Moraila's team from University of Sinaloa. We’re exploring how anomaly detection on HR GNSS data can help identify large EQs. #EarthquakeDetection #earthquake #ai4science

arxiv.org/abs/2412.00264
High Magnitude Earthquake Identification Using an Anomaly Detection Approach on HR GNSS Data
Earthquake early warning systems are crucial for protecting areas that are subject to these natural disasters. An essential part of these systems is the detection procedure. Traditionally these system...
arxiv.org
December 6, 2024 at 2:11 PM
caltechseismo.bsky.social
Exciting progress at #SCSN! We're now using #MachineLearning tools PhaseNet & GaMMA in post-processing to refine EQ insights after detection. Trained on thousands of events, these tools enhance automatic solutions in our seismically active state! #EarthquakeDetection
November 14, 2024 at 8:56 PM
squaredtech.bsky.social
Google Uses Two Billion Android Phones to Detect Earthquakes!
#Google #Android #EarthquakeDetection #TechInnovation #PrivacyConcerns
www.squaredtech.co/google-earth...
Google Uses Two Billion Android Phones To Detect Earthquakes—And It’s Shaking Up The Seismology Industry
Google’s Android Earthquake Alerts system taps two billion android phones to detect tremors faster than traditional tools, raising new questions about accuracy and privacy.
www.squaredtech.co
July 22, 2025 at 11:47 AM
ivojuri.bsky.social
Detecting earthquakes or just passing cars? 🌍🚗 A research uses deep learning to distinguish seismic signals from earthquakes, vehicles, and noise with 99% accuracy!📡🔬

#MachineLearning #TrafficMonitoring #VehicleDetection #EarthquakeDetection
https://link.springer.com/article/10.1007/s10950-024-10267-8 Figure 2: Frequency Analysis of Noise, Vehicle, and Earthquake Signals Axes Explanation: X-Axis (Frequency in Hz): Represents the frequency content of the seismic signals. Frequency (Hertz, Hz) refers to the number of oscillations per second. Y-Axis (Amplitude, not explicitly labeled but inferred): Represents the strength or power of the seismic signal at different frequencies. Color Representation: Green: Noise signal components. Blue: Vehicle-related seismic signals. Red: Earthquake-related seismic signals. Types of Lines (Dotted vs. Solid): Each color has three dotted lines, corresponding to the East, North, and Vertical components of motion. - East, North, and Vertical refer to the three axes of ground motion measured by a seismometer. - Vertical component measures up-down movement. - East and North components measure horizontal movements in their respective directions. Interpretation & Relevance: Noise (Green): Dominates at high frequencies (>30 Hz), which indicates environmental or anthropogenic disturbances. Vehicle (Blue): Peaks between 5 Hz and 20 Hz, showing the frequency range of seismic signals generated by passing vehicles. Earthquake (Red): Stronger in the lower frequency range (0-10 Hz), which is typical for seismic events as earthquake waves have lower frequencies and longer wavelengths. Relevance to the Study: Helps in distinguishing between different seismic sources by analyzing their frequency characteristics. Relevance to Real Life: Understanding frequency differences is crucial for earthquake detection, noise filtering, and distinguishing between natural and human-made seismic events. Figure 6: Model Prediction for Noise, Earthquake, and Vehicle Seismic Signals Over Time Axes Explanation: X-Axis (Time in Minutes, 0–60 min): Represents an hour-long seismic recording. Y-Axis (Seismic Signal & Model Prediction): Represents seismic activity in three orientations: - East-West (Top Panel) - North-South (Middle Panel) - Vertical (Bottom Panel) Color Representation: Red (Earthquake): Indicates moments when the model predicts earthquake activity. Blue (Vehicle): Shows periods when the model detects seismic signals from vehicles. Green (Noise): Represents background noise detected by the model. Black (Seismic Data): The actual recorded seismic signal. Interpretation & Relevance: The model successfully identifies different sources of seismic activity. Noise (Green) is persistent and appears frequently in the vertical component. Vehicles (Blue) appear intermittently in the North-South component, matching expected vehicle movement. Earthquakes (Red) are rare and appear as spikes in the East-West component. Relevance to the Study: Demonstrates the model's capability to classify seismic events accurately. Relevance to Real Life: Helps in automatic seismic monitoring, reducing false alarms, and improving earthquake early warning systems. Figure 9: Impact of Time-Shifted Vehicle Noise on Earthquake Seismic Signals and Their Frequency Response X and Y Axes Left Side (Time-Domain Signal) - X-axis: Time (in seconds) → Shows how the signal changes over time. - Y-axis: Amplitude (measured in acceleration or velocity) → Represents the strength of the signal. A strong earthquake will have a high-amplitude waveform, while weaker signals will have lower amplitudes. Right Side (Frequency-Domain - FFT Representation) - X-axis: Frequency (Hz) → Represents how often a certain vibration occurs per second. - Y-axis: Amplitude (Intensity in frequency domain) → Shows how strong the signal is at different frequencies. FFT helps separate different sources of vibrations: - Low frequencies (~0-5 Hz): Typically associated with earthquakes. - Higher frequencies (~10-20 Hz): More likely caused by vehicles or human activity. Colors Red Line: Represents the original earthquake signal. Blue Line: Represents the vehicle signal (vibrations caused by a vehicle). Black Line: Represents the combined signal (earthquake + vehicle noise). By comparing these colors, researchers can determine how vehicle noise affects earthquake signal detection. Type of Lines Used Solid lines are used for all signals, but different colors distinguish earthquake signals, vehicle-induced noise, and their combination. The presence of overlapping or diverging lines indicates interference between signals. Explanation of Technical Words & Abbreviations FFT (Fast Fourier Transform): A mathematical technique that converts a time-domain signal into a frequency-domain signal. It helps analyze the different sources contributing to the signal. Why This Figure is Important (Scientific & Practical Relevance) For Earthquake Studies: Helps understand how human activity (e.g., vehicle movement) interferes with seismic readings. This can improve earthquake detection and help seismologists filter out noise. For Urban Planning. For Public Safety.
February 25, 2025 at 2:41 AM