LCANet: Lightweight Context-aware Attention Networks for Earthquake Detection and Phase Picking on IoT Edge Devices

Citation Author(s):
Yu
Zhao
beihang university
Submitted by:
Pan Deng
Last updated:
Tue, 05/17/2022 - 22:18
DOI:
10.21227/kkhw-3m75
Data Format:
*.jpg; *.csv; *.txt; *.h5
Research Article Link:
LCANet: Lightweight Context-Aware Attention Networks for Earthquake Detection and Phase-Picking on IoT Edge Devices
License:
Creative Commons Attribution
498 Views
Categories:
Artificial Intelligence
IoT
Machine Learning
Digital signal processing
Keywords:
Edge Computing, Earthquake signal detection, Arrival time picking, Lightweight deep learning model, Jetson Nano.
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Abstract 

This data set is the result of model test trained on the basis of the Stanford earthquake dataset (stead): a global data set of seismic signals for AI, which can effectively get the seismic signal and the arrival time of seismic phase from the image, so as to prove the effectiveness of this model

Instructions: 

The Train detail folder includes all training information, including training history information, training loss curve, training parameter settings, and detailed model parameters

The test result folder includes all the test information, including the complete content of the test set, CSV, test result image, and model parameters.The test result folder includes all the test information, including the complete content of the test set, CSV, test result image, and model parameters.

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