Machine Learning for Volcano-Seismic Signals Challenges and perspectives

被引：108

作者：

Malfante, Marielle ^{[1
]}

Dalla Mura, Mauro ^{[2
]}

Metaxian, Jean-Philippe ^{[2
,4
,5
,6
]}

Mars, Jerome I. ^{[3
]}

Macedo, Orlando ^{[7
,8
,9
]}

Inza, Adolfo ^{[10
]}

机构：

[1] Grenoble Inst Technol, Automat Classificat Nat Signals Environm Monitori, Grenoble, France

[2] Grenoble Inst Technol, Grenoble, France

[3] Grenoble Images Speech Signals & Automat Lab, Grenoble, France

[4] Inst Rech Dev, Montpellier, France

[5] Inst Phys Globe Paris, Geophys Volcanoes Team, Inst Sci Terre Lab, Paris, France

[6] Inst Phys Globe Paris, Seismol Team, Paris, France

[7] Inst Geofis Peru, Dept Volcanol, Lima, Peru

[8] Observ Volcanol South Peru, Arequipa, Peru

[9] Univ Nacl San Agustin Arequipa, Geophys, Arequipa, Peru

[10] IGP, Lima, Peru

来源：

IEEE SIGNAL PROCESSING MAGAZINE | 2018年 / 35卷 / 02期

关键词：

SOUFRIERE-HILLS-VOLCANO; FLUID-DRIVEN CRACK; LONG-PERIOD EVENTS; AUTOMATIC CLASSIFICATION; NEURAL-NETWORKS; TREMOR; ERUPTIONS; IDENTIFICATION; RECOGNITION; MONTSERRAT;

D O I：

10.1109/MSP.2017.2779166

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Environmental monitoring is a topic of increasing interest, especially concerning the matter of natural hazards prediction. Regarding volcanic unrest, effective methodologies along with innovative and operational tools are needed to monitor, mitigate, and prevent risks related to volcanic hazards. In general, the current approaches for volcanoes monitoring are mainly based on the manual analysis of various parameters, including gas leaps, deformations measurements, and seismic signals analysis. However, due to the large amount of data acquired by in situ sensors for long-term monitoring, manual inspection is no longer a viable option. As in many big data situations, classic machinelearning approaches are now considered to automatize the analysis of years of recorded signals, thereby enabling monitoring on a larger scale. © 1991-2012 IEEE.

引用

页码：20 / 30

页数：11

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