Researchers from Tokyo Metropolitan University have applied machine-learning techniques to achieve fast, accurate estimates of local geomagnetic fields using data taken at multiple observation points, potentially allowing detection of changes caused by earthquakes and tsunamis.
A deep neural network (DNN) model was developed and trained using existing data; the result is a fast, efficient method for estimating magnetic fields for unprecedentedly early detection of natural disasters. This is vital for developing effective warning systems that might help reduce casualties and widespread damage.
The devastation caused by earthquakes and tsunamis leaves little doubt that an effective means to predict their incidence is of paramount importance. Certainly, systems already exist for warning people just before the arrival of seismic waves; yet, it is often the case that the S-wave (or secondary wave), that is, the later part of the quake, has already arrived when the warning is given. A faster, more accurate means is sorely required to give local residents time to seek safety and minimize casualties.
It is known that earthquakes and tsunamis are accompanied by localized changes in the geomagnetic field. For earthquakes, it is primarily what is known as a piezo-magnetic effect, where the release of a massive amount of accumulated stress along a fault causes local changes in geomagnetic field; for tsunamis, it is the sudden, vast movement of the sea that causes variations in atmospheric pressure. This in turn affects the ionosphere, subsequently changing the geomagnetic field. Both can be detected by a network of observation points at various locations. The major benefit of such an approach is speed; remembering that electromagnetic waves travel at the speed of light, we can instantaneously detect the incidence of an event by observing changes in geomagnetic field.
However, how can we tell whether the detected field is anomalous or not? The geomagnetic field at various locations is a fluctuating signal; the entire method is predicated on knowing what the “normal” field at a location is.
Thus, Yuta Katori and Assoc. Prof. Kan Okubo from Tokyo Metropolitan University set out to develop a method to take measurements at multiple locations around Japan and create an estimate of the geomagnetic field at different, specific observation points. Specifically, they applied a state-of-the-art machine-learning algorithm known as a Deep Neural Network (DNN), modeled on how neurons are connected inside the human brain. By feeding the algorithm a vast amount of input taken from historical measurements, they let the algorithm create and optimize an extremely complex, multi-layered set of operations that most effectively maps the data to what was actually measured. Using half a million data points taken over 2015, they were able to create a network that can estimate the magnetic field at the observation point with unprecedented accuracy.
Given the relatively low computational cost of DNNs, the system may potentially be paired with a network of high sensitivity detectors to achieve lightning-fast detection of earthquakes and tsunamis, delivering an effective warning system that can minimize damage and save lives.
The Latest on: Deep neural networks
via Google News
The Latest on: Deep neural networks
- Outlook on the Global Deep Learning Market Size,on May 18, 2022 at 8:56 am
As per Zion Market Research study, The global deep learning market was worth around USD 11542.9 million by 2021 and is estimated to grow to ...
- Deep-learning AI algorithm accurately predicts severe aortic stenosison May 18, 2022 at 5:05 am
A machine learning algorithm detected severe aortic stenosis using audio files from a set of patients with accuracy similar to board-certified cardiologists, according to findings from a ...
- Imagination and Visidon partner for deep-learning-based super resolution technologyon May 18, 2022 at 1:14 am
Imagination’s IMG Series4 neural network accelerator (NNA) provides high compute performance and power efficiency for advanced AI image processing through its Tensor Tiling technology. Combined with ...
- Future is sparse: Prof Nir Shavit, Neural Magicon May 17, 2022 at 4:30 am
For this project, we had to run machine learning algorithms at very large scales to extract neural structures in mouse brains from electron microscopy images. Being multicore guys, we decided to ...
- 6. Convoyon May 17, 2022 at 1:00 am
Convoy develops on-demand technology that allows trucking companies and shippers to connect with each other directly via the company's mobile app.
- DeepSig Publishes Milestone Report on AI/ML Improved 5G Open vRANon May 16, 2022 at 5:02 am
DeepSig —experts in artificial intelligence (AI) and machine learning (ML) for wireless communications—today published “Amplifying 5G vRAN Performance with AI & Deep Learning.” Co-authored with a ...
- Using graph neural networks to measure the spatial homogeneity of road networkson May 9, 2022 at 6:30 am
Researchers at Purdue University and Peking University have recently carried out a study aimed at better understanding road networks in cities worldwide using machine-learning tools. Their paper, ...
- deep neural networkson May 6, 2022 at 5:00 pm
However, researchers have worked to simplify this task, by capturing nerve signals and allowing deep learning routines to figure the rest out. The prosthetic arm under test actually carries a ...
- LOEN: Lensless opto-electronic neural network empowered machine visionon May 6, 2022 at 7:41 am
has generated the rapid development of deep learning based on convolutional neural networks (CNN), leading to effective solutions for a variety of issues in artificial intelligence applications.
via Bing News