Local earthquakes detection: A benchmark dataset of 3-component seismograms built on a global scale  

作　　者：Fabrizio Magrini Dario Jozinovic Fabio Cammarano Alberto Michelini Lapo Boschi 

机构地区：[1]Department of Science,UniversitàDegli Studi Roma Tre,Italy [2]Istituto Nazionale di Geofisica e Vulcanologia(INGV),Rome,Italy [3]Dipartimento di Geoscienze,UniversitàDegli Studi di Padova,Italy [4]Sorbonne Université,CNRS,INSU,Institut des Sciences de La Terre de Paris,ISTeP UMR 7193,F-75005,Paris,France [5]Istituto Nazionale di Geofisica e Vulcanologia,Bologna,Italy

出　　处：《Artificial Intelligence in Geosciences》2020年第1期1-10,共10页地学人工智能（英文）

摘　　要：Machine learning is becoming increasingly important in scientific and technological progress,due to its ability to create models that describe complex data and generalize well.The wealth of publicly-available seismic data nowadays requires automated,fast,and reliable tools to carry out a multitude of tasks,such as the detection of small,local earthquakes in areas characterized by sparsity of receivers.A similar application of machine learning,however,should be built on a large amount of labeled seismograms,which is neither immediate to obtain nor to compile.In this study we present a large dataset of seismograms recorded along the vertical,north,and east components of 1487 broad-band or very broad-band receivers distributed worldwide;this includes 629,0953-component seismograms generated by 304,878 local earthquakes and labeled as EQ,and 615,847 ones labeled as noise(AN).Application of machine learning to this dataset shows that a simple Convolutional Neural Network of 67,939 parameters allows discriminating between earthquakes and noise single-station recordings,even if applied in regions not represented in the training set.Achieving an accuracy of 96.7,95.3,and 93.2% on training,validation,and test set,respectively,we prove that the large variety of geological and tectonic settings covered by our data supports the generalization capabilities of the algorithm,and makes it applicable to real-time detection of local events.We make the database publicly available,intending to provide the seismological and broader scientific community with a benchmark for time-series to be used as a testing ground in signal processing.

关 键 词：Benchmark dataset Earthquake detection algorithm Supervised machine leaming SEISMOLOGY 

分 类 号：P31[天文地球—固体地球物理学]