Locating induced earthquakes with a network of seismic stations in Oklahoma via a deep learning method.
Reads0
Chats0
TLDR
In this paper, a 3D volume of the event location probability in the Earth is estimated, and the output of the system can be used to distinguish interfered events or events out of the monitoring zone based on the output probability.Abstract:
The accurate and automated determination of small earthquake (ML ML ≥ 0.5), and the output is a 3D volume of the event location probability in the Earth. The prediction results suggest that the mean epicenter errors of the testing events (ML ≥ 1.5) vary from 3.7 to 6.4 km, meeting the need of the traffic light system in Oklahoma, but smaller events (ML = 1.0, 0.5) show errors larger than 11 km. Synthetic tests suggest that the accuracy of ground truth from catalog affects the prediction results. Correct ground truth leads to a mean epicenter error of 2.0 km in predictions, but adding a mean location error of 6.3 km to ground truth causes a mean epicenter error of 4.9 km. The automated system is able to distinguish certain interfered events or events out of the monitoring zone based on the output probability estimate. It requires approximately one hundredth of a second to locate an event without the need for any velocity model or human interference.read more
Citations
More filters
Journal ArticleDOI
Deep learning for geophysics: Current and future trends
Siwei Yu,Jianwei Ma +1 more
TL;DR: A new data-driven technique, i.e., deep learning (DL), has attracted significantly increasing attention in the geophysical community and the collision of DL and traditional methods has had an impact on traditional methods.
Journal ArticleDOI
Real-time determination of earthquake focal mechanism via deep learning.
TL;DR: In this paper, the authors proposed a novel deep learning method named Focal Mechanism Network (FMNet) to address the problem of real-time source focal mechanism prediction in earthquakes.
Posted ContentDOI
INSTANCE – the Italian seismic dataset for machine learning
Alberto Michelini,S. Cianetti,Sonja Gaviano,Sonja Gaviano,Carlo Giunchi,Dario Jozinović,Dario Jozinović,Valentino Lauciani +7 more
TL;DR: The waveform dataset is accompanied by metadata consisting of more than 100 parameters providing comprehensive information on the earthquake source, the recording stations, the trace features, and other derived quantities, which allows the users to target the data selection for their own purposes.
Book ChapterDOI
Seismic signal augmentation to improve generalization of deep neural networks
TL;DR: This paper presents augmentation methods appropriate for seismic waveforms and demonstrates their ability to reduce bias and increase performance and can be applied to a wide range of deep learning applications designed for seismic data.
Journal ArticleDOI
Real-Time Earthquake Early Warning With Deep Learning: Application to the 2016 M 6.0 Central Apennines, Italy Earthquake
TL;DR: In this paper, a deep learning earthquake early warning system that utilizes fully convolutional networks to simultaneously detect earthquakes and estimate their source parameters from continuous seismic waveform streams is presented.
References
More filters
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Journal ArticleDOI
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
TL;DR: This work addresses the task of semantic image segmentation with Deep Learning and proposes atrous spatial pyramid pooling (ASPP), which is proposed to robustly segment objects at multiple scales, and improves the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models.
Journal ArticleDOI
Fully Convolutional Networks for Semantic Segmentation
TL;DR: Fully convolutional networks (FCN) as mentioned in this paper were proposed to combine semantic information from a deep, coarse layer with appearance information from shallow, fine layer to produce accurate and detailed segmentations.
Related Papers (5)
Convolutional neural network for earthquake detection and location
P Wave Arrival Picking and First-Motion Polarity Determination With Deep Learning
PhaseNet: a deep-neural-network-based seismic arrival-time picking method
Weiqiang Zhu,Gregory C. Beroza +1 more