Deep Learning Algorithms and Seismic Data
Emerging technologies are used in every aspect of our lives: healthcare, financial, and now, environmental. MIT Technology Review brought this information to our attention in their article, “A deep-learning algorithm could detect earthquakes by filtering out city noise.”
Noise is the enemy of seismic data detection. It is more than an inconvenience; it can become a deadly problem because it’s difficult to spot the telltale signal of an approaching earthquake in seismic sensor data amid the general human-generated vibrations typical of bustling cities, known as urban seismic noise.
Researchers have found a way to use a deep learning algorithm to get a clearer signal. They claim it improves the detection capacity of earthquake monitoring networks in cities and other built-up areas. By filtering out urban seismic noise, it can boost the overall signal quality and recover signals that may have previously been too weak to register.
Emerging technologies like deep learning are still a foreign concept to many organizations, even if they utilize the technology. Most organizations have little knowledge of how it works. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms.
Melody K. Smith
Sponsored by Data Harmony, harmonizing knowledge for a better search experience.
