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Earthquakes and Co-seismic Mass Movements Remote Sensing: From Prediction to Crisis Management

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 40950

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Special Issue Editors

Prof. Dr. Christopher Gomez

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Laboratory of Sediment Hazards and Disaster Risk, Graduate School of Maritme Sciences, Kobe University, Kobe 658-0022, Japan
Interests: volcanic geomorphology and slope erosion; hazards and disaster risks; lahars; Japan; Indonesia and New Zealand
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Dr. Candide Lissak

E-Mail Website1 Website2
Guest Editor

Unicaen, CNRS, IDEES-Caen, Normandie Univ., 14000 Caen, France
Interests: geomorphology; landslide; sediment transfers; hazard & risk assessment GIS; remote sensing; modeling
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Dr. Danang Sri Hadmoko

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Guest Editor

1. Department of Environmental Geography, Faculty of Geography, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
2. Research Centre for Disasters, Universitas Gadjah Mada Indonesia, Yogyakarta, Indonesia
Interests: physical geography; geomorphological hazards

Special Issue Information

Dear Colleagues,

I would like to invite you to submit your research work—either as a technical, a scientific or a review paper—pertaining to the remote sensing of earthquakes and co-seismic mass movements. This issue is meant to provide a common platform that reflects on the recent progresses and case studies, as well as the difficulties that are still ahead of us. On a personal note: after working in and out of Indonesia for almost 20 years, and moving from New Zealand to Japan three years ago, the importance of co-seismic landslides and earthquakes is a daily reality that also motivates this publication.

If predicting earthquakes is still in the chimeric domain, multiple-platforms (from UAVs to satellite imagery) remote sensing of pre-cursor events, and of co-seismic mass movements and probable events has made tremendous progress in the last twenty years since the Chichi earthquake (1999) in Taiwan. This evolution has notably emerged from developments in computing capabilities and in solid-state electronics, providing a wide array of data ranging from near-real time satellite data to LiDAR (ALS and TLS) and low-cost UAV solutions. Those platforms have provided scientists and practitioners with unrivalled data for the Wenchuan Earthquake and debris flows in 2008 (China), the Tohoku Earthquake in 2011 (Japan), the Kaikoura Earthquake (New Zealand) in 2018, the Hokkaido Iburi-Tobu earthquake in 2018, and the co-seismic landslides in Lombok (Indonesia) in 2018.

Finally, this proposal is in line with the ethical concerns of the manifesto “Power, Prestige & Forgotten Values: A Disaster Studies Manifesto”, which encourages minorities and under-represented views to be heard, for whom a space will be provided.

Prof. Dr. Christopher Gomez
Dr. Candide Lissak
Dr. Danang Sri Hadmoko
Guest Editors

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Published Papers (10 papers)

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15 pages, 2883 KiB

Open AccessArticle

Deposits’ Morphology of the 2018 Hokkaido Iburi-Tobu Earthquake Mass Movements from LiDAR & Aerial Photographs

by Christopher Gomez and Norifumi Hotta

Remote Sens. 2021, 13(17), 3421; https://doi.org/10.3390/rs13173421 - 28 Aug 2021

Cited by 5 | Viewed by 2752

Abstract

On 6 September at 03:08 a.m. local time, a 33 km deep earthquake underneath the Iburi mountains triggered more than 7000 co-seismic mass movements within 25 km of the epicenter. Most of the mass movements occurred in complex terrain and became coalescent. However, a total of 59 mass movements occurred as discrete events and stopped on the semi-horizontal valley floor. Using this case study, the authors aimed to define planar and vertical parameters to (1) compare the geometrical parameters with rain-triggered mass movements and (2) to extend existing datasets used for hazards and disaster risk purposes. To reach these objectives, the methodology relies on LiDAR data flown in the aftermath of the earthquake as well as aerial photographs. Using a Geographical Information System (GIS), planform and vertical parameters were extracted from the DEM in order to calculate the relationship between areas and volume, between the Fahrböschung and the volume of the deposits, and to discuss the relationship between the deposit slope surface and the effective stress of the deposit. Results have shown that the relation

S = k {[V_{d}]}^{2 / 3}

(where S is the surface area of a deposit and V_d the volume, and k a scalar that is function of S) is k = 2.1842ln(S) − 10.167 with a R² of 0.52, with less variability in deposits left by valley-confined processes compared to open-slope processes. The Fahrböschung for events that started as valley-confined mass-movements was Fc = −0.043ln(D) + 0.7082, with a R² of 0.5, while for open-slope mass-movements, the Fo = −0.046ln(D) + 0.7088 with a R² of 0.52. The “T-values”, as defined by Takahashi (2014), are displaying values as high as nine times that of the values for experimental rainfall debris-flow, signifying that the effective stress is higher than in rain-triggered counterparts, which have an increased pore pressure due to the need for further water in the material to be moving. For co-seismic debris-flows and other co-seismic mass movements it is the ground acceleration that “fluidizes” the material. The maxima found in this study are as high as 3.75. Full article

(This article belongs to the Special Issue Earthquakes and Co-seismic Mass Movements Remote Sensing: From Prediction to Crisis Management )

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21 pages, 12656 KiB

Open AccessArticle

Satellite Measured Ionospheric Magnetic Field Variations over Natural Hazards Sites

by Christoph Schirninger, Hans U. Eichelberger, Werner Magnes, Mohammed Y. Boudjada, Konrad Schwingenschuh, Andreas Pollinger, Bruno P. Besser, Pier F. Biagi, Maria Solovieva, Jindong Wang, Bingjun Cheng, Bin Zhou, Xuhui Shen, Magda Delva and Roland Lammegger

Remote Sens. 2021, 13(12), 2360; https://doi.org/10.3390/rs13122360 - 17 Jun 2021

Cited by 4 | Viewed by 3019

Abstract

Processes and threats related to natural hazards play an important role in the evolution of the Earth and in human history. The purpose of this study is to investigate magnetic field variations measured at low Earth orbit (LEO) altitudes possibly associated with earthquakes, volcanic eruptions, and artificial outbursts. We focus on two missions with well equipped magnetometer packages, the China Seismo-Electromagnetic Satellite (CSES) and ESA’s three spacecraft Swarm fleet. After a natural hazards survey in the context of this satellites, and consideration of external magnetospheric and solar influences, together with spacecraft interferences, wavelet analysed spatio-temporal patterns in ionospheric magnetic field variations related to atmospheric waves are examined in detail. We provide assessment of the links between specific lithospheric or near surface sources and ionospheric magnetic field measurements. For some of the diverse events the achieved statistical results show a change in the pattern between pre- and post-event periods, we show there is an increase in the fluctuations for the higher frequency (smaller scales) components. Our results are relevant to studies which establish a link between space based magnetic field measurements and natural hazards. Full article

(This article belongs to the Special Issue Earthquakes and Co-seismic Mass Movements Remote Sensing: From Prediction to Crisis Management )

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14 pages, 6782 KiB

Open AccessCommunication

Spatio-Temporal Distribution of Ground Deformation Due to 2018 Lombok Earthquake Series

by Sandy Budi Wibowo, Danang Sri Hadmoko, Yunus Isnaeni, Nur Mohammad Farda, Ade Febri Sandhini Putri, Idea Wening Nurani and Suhono Harso Supangkat

Remote Sens. 2021, 13(11), 2222; https://doi.org/10.3390/rs13112222 - 6 Jun 2021

Cited by 19 | Viewed by 5277

Abstract

Lombok Island in Indonesia was hit by four major earthquakes (6.4 Mw to 7 Mw) and by at least 818 earthquakes between 29 July and 31 August 2018. The aims of this study are to measure ground deformation due to the 2018 Lombok earthquake series and to map its spatio-temporal distribution. The application of DinSAR was performed to produce an interferogram and deformation map. Time series Sentinel-1 satellite imageries were used as master and slave for each of these four major earthquakes. The spatio-temporal distribution of the ground deformation was analyzed using a zonal statistics algorithm in GIS. It focused on the overlapping area between the raster layer of the deformation map and the polygon layer of six observation sites (Mataram City, Pamenang, Tampes, Sukadana, Sembalun, and Belanting). The results showed that the deformation includes uplift and subsidence. The first 6.4 Mw foreshock hitting on 29 July 2018 produces a minimum uplift effect on the island. The 7.0 Mw mainshock on 5 August 2018 causes extreme uplift at the northern shore. The 6.2 Mw Aftershock on 9 August 2018 generates subsidence throughout the study area. The final earthquake of 6.9 Mw on 19 August 2018 initiates massive uplift in the study area and extreme uplift at the northeastern shore. The highest uplift reaches 0.713 m at the northern shore, while the deepest subsidence is measured −0.338 m at the northwestern shore. Dominant deformation on the northern area of Lombok Island indicates movement of Back Arc Trust in the north of the island. The output of this study would be valuable to local authorities to evaluate existing earthquake’s impacts and to design mitigation strategies to face earthquake-induced ground displacement. Full article

(This article belongs to the Special Issue Earthquakes and Co-seismic Mass Movements Remote Sensing: From Prediction to Crisis Management )

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24 pages, 3358 KiB

Open AccessArticle

Analyzing the Performance of GPS Data for Earthquake Prediction

by Valeri Gitis, Alexander Derendyaev and Konstantin Petrov

Remote Sens. 2021, 13(9), 1842; https://doi.org/10.3390/rs13091842 - 9 May 2021

Cited by 18 | Viewed by 5146

Abstract

The results of earthquake prediction largely depend on the quality of data and the methods of their joint processing. At present, for a number of regions, it is possible, in addition to data from earthquake catalogs, to use space geodesy data obtained with the help of GPS. The purpose of our study is to evaluate the efficiency of using the time series of displacements of the Earth’s surface according to GPS data for the systematic prediction of earthquakes. The criterion of efficiency is the probability of successful prediction of an earthquake with a limited size of the alarm zone. We use a machine learning method, namely the method of the minimum area of alarm, to predict earthquakes with a magnitude greater than 6.0 and a hypocenter depth of up to 60 km, which occurred from 2016 to 2020 in Japan, and earthquakes with a magnitude greater than 5.5. and a hypocenter depth of up to 60 km, which happened from 2013 to 2020 in California. For each region, we compare the following results: random forecast of earthquakes, forecast obtained with the field of spatial density of earthquake epicenters, forecast obtained with spatio-temporal fields based on GPS data, based on seismological data, and based on combined GPS data and seismological data. The results confirm the effectiveness of using GPS data for the systematic prediction of earthquakes. Full article

(This article belongs to the Special Issue Earthquakes and Co-seismic Mass Movements Remote Sensing: From Prediction to Crisis Management )

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27 pages, 31235 KiB

Open AccessArticle

Identification of Electromagnetic Pre-Earthquake Perturbations from the DEMETER Data by Machine Learning

by Pan Xiong, Cheng Long, Huiyu Zhou, Roberto Battiston, Xuemin Zhang and Xuhui Shen

Remote Sens. 2020, 12(21), 3643; https://doi.org/10.3390/rs12213643 - 6 Nov 2020

Cited by 25 | Viewed by 4392

Abstract

The low-altitude satellite DEMETER recorded many cases of ionospheric perturbations observed on occasion of large seismic events. In this paper, we explore 16 spot-checking classification algorithms, among which, the top classifier with low-frequency power spectra of electric and magnetic fields was used for ionospheric perturbation analysis. This study included the analysis of satellite data spanning over six years, during which about 8760 earthquakes with magnitude greater than or equal to 5.0 occurred in the world. We discover that among these methods, a gradient boosting-based method called LightGBM outperforms others and achieves superior performance in a five-fold cross-validation test on the benchmarking datasets, which shows a strong capability in discriminating electromagnetic pre-earthquake perturbations. The results show that the electromagnetic pre-earthquake data within a circular region with its center at the epicenter and its radius given by the Dobrovolsky’s formula and the time window of about a few hours before shocks are much better at discriminating electromagnetic pre-earthquake perturbations. Moreover, by investigating different earthquake databases, we confirm that some low-frequency electric and magnetic fields’ frequency bands are the dominant features for electromagnetic pre-earthquake perturbations identification. We have also found that the choice of the geographical region used to simulate the training set of non-seismic data influences, to a certain extent, the performance of the LightGBM model, by reducing its capability in discriminating electromagnetic pre-earthquake perturbations. Full article

(This article belongs to the Special Issue Earthquakes and Co-seismic Mass Movements Remote Sensing: From Prediction to Crisis Management )

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16 pages, 6366 KiB

Open AccessArticle

Thermal Infrared and Ionospheric Anomalies of the 2017 M_w6.5 Jiuzhaigou Earthquake

by Meijiao Zhong, Xinjian Shan, Xuemin Zhang, Chunyan Qu, Xiao Guo and Zhonghu Jiao

Remote Sens. 2020, 12(17), 2843; https://doi.org/10.3390/rs12172843 - 1 Sep 2020

Cited by 22 | Viewed by 3558

Abstract

Taking the 2017 M_w6.5 Jiuzhaigou earthquake as a case study, ionospheric disturbances (i.e., total electron content and TEC) and thermal infrared (TIR) anomalies were simultaneously investigated. The characteristics of the temperature of brightness blackbody (TBB), medium-wave infrared brightness (MIB), and outgoing longwave radiation (OLR) were extracted and compared with the characteristics of ionospheric TEC. We observed different relationships among the three types of TIR radiation according to seismic or aseismic conditions. A wide range of positive TEC anomalies occurred southern to the epicenter. The area to the south of the Huarong mountain fracture, which contained the maximum TEC anomaly amplitudes, overlapped one of the regions with notable TIR anomalies. We observed three stages of increasing TIR radiation, with ionospheric TEC anomalies appearing after each stage, for the first time. There was also high spatial correspondence between both TIR and TEC anomalies and the regional geological structure. Together with the time series data, these results suggest that TEC anomaly genesis might be related to increasing TIR. Full article

(This article belongs to the Special Issue Earthquakes and Co-seismic Mass Movements Remote Sensing: From Prediction to Crisis Management )

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14 pages, 3141 KiB

Open AccessArticle

Locating Seismo-Conductivity Anomaly before the 2017 M_W 6.5 Jiuzhaigou Earthquake in China Using Far Magnetic Stations

by Zhiqiang Mao, Chieh-Hung Chen, Suqin Zhang, Aisa Yisimayili, Huaizhong Yu, Chen Yu and Jann-Yenq Liu

Remote Sens. 2020, 12(11), 1777; https://doi.org/10.3390/rs12111777 - 1 Jun 2020

Cited by 13 | Viewed by 2671

Abstract

Changes in the underlying conductivity around hypocenters are generally considered one of the promising mechanisms of seismo-electromagnetic anomaly generation. Parkinson vectors are indicators of high-conductivity materials and were utilized to remotely monitor conductivity changes during the M_W 6.5 Jiuzhaigou earthquake (103.82°E, 33.20°N) on 8 August 2017. Three-component geomagnetic data recorded in 2017 at nine magnetic stations with epicenter distances of 63–770 km were utilized to compute the azimuths of the Parkinson vectors based on the magnetic transfer function. The monitoring and background distributions at each station were constructed by using the azimuths within a 15-day moving window and over the entire study period, respectively. The background distribution was subtracted from the monitoring distribution to mitigate the effects of underlying inhomogeneous electric conductivity structures. The differences obtained at nine stations were superimposed and the intersection of a seismo-conductivity anomaly was located about 70 km away from the epicenter about 17 days before the earthquake. The anomaly disappeared about 7 days before and remained insignificant after the earthquake. Analytical results suggested that the underlying conductivity close to the hypocenter changed before the Jiuzhaigou earthquake. These changes can be detected simultaneously by using multiple magnetometers located far from the epicenter. The disappearance of the seismo-conductivity anomaly after the earthquake sheds light on a promising candidate of the pre-earthquake anomalous phenomena. Full article

(This article belongs to the Special Issue Earthquakes and Co-seismic Mass Movements Remote Sensing: From Prediction to Crisis Management )

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21 pages, 5080 KiB

Open AccessArticle

Co-Seismic Magnetic Field Perturbations Detected by Swarm Three-Satellite Constellation

by Dedalo Marchetti, Angelo De Santis, Shuanggen Jin, Saioa A. Campuzano, Gianfranco Cianchini and Alessandro Piscini

Remote Sens. 2020, 12(7), 1166; https://doi.org/10.3390/rs12071166 - 5 Apr 2020

Cited by 12 | Viewed by 4410

Abstract

The first 5.3 years of magnetic data from three Swarm satellites have been systematically analyzed, and possible co-seismic magnetic disturbances in the ionosphere were investigated just a few minutes after the occurrence of large earthquakes. We preferred to limit the investigation to a subset of earthquakes selected in function of depth and magnitude. After a systematic inspection of the available data around (in time and space) the seismic events, we found 12 Swarm satellite tracks with co-seismic disturbances possibly produced by ten earthquakes from Mw5.6 to Mw6.9. The distance of the satellite to the earthquake epicenter corresponds to the measured distance-time arrival of the disturbance from the surface to the ionosphere, confirming that the identified disturbances are most likely produced by the seismic events. Secondly, we found a good agreement with a model that combined a propagation of the disturbance to the F2 ionospheric layer with an acoustic gravity wave at a velocity of about (2.2 ± 0.3) km/s and a second faster phenomenon that transmits the disturbance from F2 layer to the Swarm satellite with a velocity of about (16 ± 3) km/s as an electromagnetic scattering propagation. Full article

(This article belongs to the Special Issue Earthquakes and Co-seismic Mass Movements Remote Sensing: From Prediction to Crisis Management )

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8 pages, 3018 KiB

Open AccessTechnical Note

Analysis of Ocean Bottom Pressure Anomalies and Seismic Activities in the MedRidge Zone

by Hakan S. Kutoglu and Kazimierz Becek

Remote Sens. 2021, 13(7), 1242; https://doi.org/10.3390/rs13071242 - 24 Mar 2021

Cited by 2 | Viewed by 3376

Abstract

The Mediterranean Ridge accretionary complex (MAC) is a product of the convergence of Africa–Europe–Aegean plates. As a result, the region exhibits a continuous mass change (horizontal/vertical movements) that generates earthquakes. Over the last 50 years, approximately 430 earthquakes with M ≥ 5, including 36 M ≥ 6 earthquakes, have been recorded in the region. This study aims to link the ocean bottom deformations manifested through ocean bottom pressure variations with the earthquakes’ time series. To this end, we investigated the time series of the ocean bottom pressure (OBP) anomalies derived from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) satellite missions. The OBP time series comprises a decreasing trend in addition to 1.02, 1.52, 4.27, and 10.66-year periodic components, which can be explained by atmosphere, oceans, and hydrosphere (AOH) processes, the Earth’s pole movement, solar activity, and core–mantle coupling. It can be inferred from the results that the OBP anomalies time series/mass change is linked to a rising trend and periods in the earthquakes’ energy time series. Based on this preliminary work, ocean-bottom pressure variation appears to be a promising lead for further research. Full article

(This article belongs to the Special Issue Earthquakes and Co-seismic Mass Movements Remote Sensing: From Prediction to Crisis Management )

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13 pages, 4810 KiB

Open AccessLetter

Determination of Epicenters before Earthquakes Utilizing Far Seismic and GNSS Data: Insights from Ground Vibrations

by Chieh-Hung Chen, Li-Ching Lin, Ta-Kang Yeh, Strong Wen, Huaizhong Yu, Chen Yu, Yongxin Gao, Peng Han, Yang-Yi Sun, Jann-Yenq Liu, Cheng-Horng Lin, Chi-Chia Tang, Che-Min Lin, Hung-Hao Hsieh and Pin-Ji Lu

Remote Sens. 2020, 12(19), 3252; https://doi.org/10.3390/rs12193252 - 7 Oct 2020

Cited by 22 | Viewed by 4434

Abstract

Broadband seismometers, ground-based Global Navigation Satellite Systems (GNSS), and magnetometers that were located within an epicentral distance of approximately 150 km consistently observed the novel anomalous behaviors of the common-mode ground vibrations approximately 5–10 days before the M6.6 Meinong earthquake in Taiwan. The common-mode ground vibrations with amplitudes near 0.1 m at frequencies ranging from 8 × 10⁻⁵ to 2 × 10⁻⁴ Hz were generated near the region close to the epicenter of the impending earthquake. The common-mode vibrations were consistently observed in seismic and GNSS data associated with five other earthquakes in four distinct areas. The results reveal that the common-mode vibrations could be a typical behavior before earthquakes. The causal mechanism of common-mode vibrations can be attributed to crustal resonance excitations before fault dislocations occur. Potential relationships with other pre-earthquake anomalies suggest that the common-mode vibrations could be ground motion before earthquakes, which was investigated for a significant length of time. Full article

(This article belongs to the Special Issue Earthquakes and Co-seismic Mass Movements Remote Sensing: From Prediction to Crisis Management )

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