Displacement Analysis of Geothermal Field Based on PSInSAR And SOM Clustering Algorithms A Case Study of Brady Field, Nevada—USA

Cavur, Mahmut; Moraga, Jaime; Duzgun, H. Sebnem; Soydan, Hilal; Jin, Ge

doi:10.3390/rs13030349

Displacement Analysis of Geothermal Field Based on PSInSAR And SOM Clustering Algorithms A Case Study of Brady Field, Nevada—USA

Journal Article · Wed Jan 20 00:00:00 EST 2021 · Remote Sensing

DOI:https://doi.org/10.3390/rs13030349· OSTI ID:1761260

Cavur, Mahmut; ; Duzgun, H. Sebnem; Soydan, Hilal; Jin, Ge

The availability of free and high temporal resolution satellite data and advanced SAR techniques allows us to analyze ground displacement cost-effectively. Our aim was to properly define subsidence and uplift areas to delineate a geothermal field and perform time-series analysis to identify temporal trends. A Persistent Scatterer Interferometry (PSI) algorithm was used to estimate vertical displacement in the Brady geothermal field located in Nevada by analyzing 70 Sentinel-1A Synthetic-Aperture Radar (SAR) images, between January 2017 and December 2019. To classify zones affected by displacement, an unsupervised Self-Organizing Map (SOM) algorithm was applied to classify points based on their behavior in time, and those clusters were used to determine subsidence, uplift, and stable regions automatically. Finally, time-series analysis was applied to the clustered data to understand the inflection dates. The maximum subsidence is –19 mm/yr with an average value of –6 mm/yr within the geothermal field. The maximum uplift is 14 mm/yr with an average value of 4 mm/yr within the geothermal field. The uplift occurred on the NE of the field, where the injection wells are located. On the other hand, subsidence is concentrated on the SW of the field where the production wells are located. The coupling of the PSInSAR and the SOM algorithms was shown to be effective in analyzing the direction and pattern of the displacements observed in the field.

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Research Organization:: Colorado School of Mines, Golden, CO (United States)

Sponsoring Organization:: USDOE; USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: EE0008760

OSTI ID:: 1761260

Alternate ID(s):: OSTI ID: 1848871

Journal Information:: Remote Sensing, Journal Name: Remote Sensing Journal Issue: 3 Vol. 13; ISSN RSBSBZ; ISSN 2072-4292

Publisher:: MDPI AGCopyright Statement

Country of Publication:: Switzerland

Language:: English

References (37)

Subsidence rates at the southern Salton Sea consistent with reservoir depletion: SALTON SEA SUBSIDENCE Barbour, Andrew J.; Evans, Eileen L.; Hickman, Stephen H. Journal of Geophysical Research: Solid Earth, Vol. 121, Issue 7 https://doi.org/10.1002/2016JB012903	journal	July 2016
Self-organized formation of topologically correct feature maps Kohonen, Teuvo Biological Cybernetics, Vol. 43, Issue 1 https://doi.org/10.1007/BF00337288	journal	January 1982
Model-Based Clustering for Image Segmentation and Large Datasets via Sampling Wehrens, Ron; Buydens, Lutgarde M. C.; Fraley, Chris Journal of Classification, Vol. 21, Issue 2 https://doi.org/10.1007/s00357-004-0018-8	journal	September 2004
DInSAR analysis of ALOS PALSAR images for the assessment of very slow landslides: the Tena Valley case study García-Davalillo, Juan Carlos; Herrera, Gerardo; Notti, Davide Landslides, Vol. 11, Issue 2 https://doi.org/10.1007/s10346-012-0379-8	journal	January 2013
Quantitative hazard and risk assessment for slow-moving landslides from Persistent Scatterer Interferometry Lu, Ping; Catani, Filippo; Tofani, Veronica Landslides, Vol. 11, Issue 4 https://doi.org/10.1007/s10346-013-0432-2	journal	September 2013
Coupling of GPS/GNSS and radar interferometric data for a 3D surface displacement monitoring of landslides Komac, Marko; Holley, Rachel; Mahapatra, Pooja Landslides, Vol. 12, Issue 2 https://doi.org/10.1007/s10346-014-0482-0	journal	March 2014
Three years of mining subsidence monitored by SAR interferometry, near Gardanne, France Carnec, C.; Delacourt, C. Journal of Applied Geophysics, Vol. 43, Issue 1 https://doi.org/10.1016/S0926-9851(99)00032-4	journal	January 2000
Chapter One - 70 years of machine learning in geoscience in review Sören Dramsch, Jesper Advances in Geophysics https://doi.org/10.1016/bs.agph.2020.08.002	book	September 2020
Fuzzy clustering of time series data using dynamic time warping distance Izakian, Hesam; Pedrycz, Witold; Jamal, Iqbal Engineering Applications of Artificial Intelligence, Vol. 39 https://doi.org/10.1016/j.engappai.2014.12.015	journal	March 2015
Investigating landslides with space-borne Synthetic Aperture Radar (SAR) interferometry Colesanti, Carlo; Wasowski, Janusz Engineering Geology, Vol. 88, Issue 3-4 https://doi.org/10.1016/j.enggeo.2006.09.013	journal	December 2006
Semi-automatic mapping of linear-trending bedforms using ‘Self-Organizing Maps’ algorithm Foroutan, M.; Zimbelman, J. R. Geomorphology, Vol. 293 https://doi.org/10.1016/j.geomorph.2017.05.016	journal	September 2017
Time-series analysis of surface deformation at Brady Hot Springs geothermal field (Nevada) using interferometric synthetic aperture radar Ali, S. T.; Akerley, J.; Baluyut, E. C. Geothermics, Vol. 61 https://doi.org/10.1016/j.geothermics.2016.01.008	journal	May 2016
Persistent Scatterer Interferometry: A review Crosetto, Michele; Monserrat, Oriol; Cuevas-González, María ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 115 https://doi.org/10.1016/j.isprsjprs.2015.10.011	journal	May 2016
A shallow-dipping dike fed the 1995 flank eruption at Fernandina Volcano, Galápagos, observed by satellite radar interferometry Jónsson, Sigurjón; Zebker, Howard; Cervelli, Peter Geophysical Research Letters, Vol. 26, Issue 8 https://doi.org/10.1029/1999GL900108	journal	April 1999
Persistent scatterer interferometric synthetic aperture radar for crustal deformation analysis, with application to Volcán Alcedo, Galápagos Hooper, A.; Segall, P.; Zebker, H. Journal of Geophysical Research, Vol. 112, Issue B7 https://doi.org/10.1029/2006JB004763	journal	January 2007
Open radar interferometry software for mapping surface Deformation Sandwell, David; Mellors, Rob; Tong, Xiaopeng Eos, Transactions American Geophysical Union, Vol. 92, Issue 28 https://doi.org/10.1029/2011EO280002	journal	July 2011
The perceptron: A probabilistic model for information storage and organization in the brain. Rosenblatt, F. Psychological Review, Vol. 65, Issue 6 https://doi.org/10.1037/h0042519	journal	January 1958
Application of L‐band differential SAR interferometry to subsidence rate estimation in reclaimed coastal land Kim, S. ‐W.; Lee, C. ‐W.; Song, K. ‐Y. International Journal of Remote Sensing, Vol. 26, Issue 7 https://doi.org/10.1080/01431160512331326620	journal	April 2005
Characterizing volumetric strain at Brady Hot Springs, Nevada, USA using geodetic data, numerical models and prior information Reinisch, Elena C.; Cardiff, Michael; Feigl, Kurt L. Geophysical Journal International, Vol. 215, Issue 2 https://doi.org/10.1093/gji/ggy347	journal	August 2018
Radar interferometry: limits and potential Massonnet, D.; Rabaute, T. IEEE Transactions on Geoscience and Remote Sensing, Vol. 31, Issue 2 https://doi.org/10.1109/36.214922	journal	March 1993
Permanent scatterers in SAR interferometry Ferretti, A.; Prati, C.; Rocca, F. IEEE Transactions on Geoscience and Remote Sensing, Vol. 39, Issue 1 https://doi.org/10.1109/36.898661	journal	January 2001
Glacier motion estimation using SAR offset-tracking procedures Strozzi, T.; Luckman, A.; Murray, T. IEEE Transactions on Geoscience and Remote Sensing, Vol. 40, Issue 11 https://doi.org/10.1109/TGRS.2002.805079	journal	November 2002
A New Algorithm for Processing Interferometric Data-Stacks: SqueeSAR Ferretti, Alessandro; Fumagalli, Alfio; Novali, Fabrizio IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, Issue 9 https://doi.org/10.1109/TGRS.2011.2124465	journal	September 2011
The Parallel SBAS Approach for Sentinel-1 Interferometric Wide Swath Deformation Time-Series Generation: Algorithm Description and Products Quality Assessment Manunta, Michele; De Luca, Claudio; Zinno, Ivana IEEE Transactions on Geoscience and Remote Sensing, Vol. 57, Issue 9 https://doi.org/10.1109/TGRS.2019.2904912	journal	September 2019
Uplift around the geothermal power plant of Landau (Germany) as observed by InSAR monitoring Heimlich, Christine; Gourmelen, Noël; Masson, Frédéric Geothermal Energy, Vol. 3, Issue 1 https://doi.org/10.1186/s40517-014-0024-y	journal	January 2015
Unsupervised Fuzzy ARTMAP Classification of Hyperspectral Hyperion Data for Savanna and Agriculture Discrimination in the Brazilian Cerrado Filippi, Anthony M.; Brannstrom, Christian; Dobreva, Iliyana GIScience & Remote Sensing, Vol. 46, Issue 1 https://doi.org/10.2747/1548-1603.46.1.1	journal	January 2009
Wide-Area InSAR Survey of Surface Deformation in Urban Areas and Geothermal Fields in the Eastern Trans-Mexican Volcanic Belt, Mexico Cigna, Francesca; Tapete, Deodato; Garduño-Monroy, Víctor Hugo Remote Sensing, Vol. 11, Issue 20 https://doi.org/10.3390/rs11202341	journal	October 2019
Comparing DInSAR and PSI Techniques Employed to Sentinel-1 Data to Monitor Highway Stability: A Case Study of a Massive Dobkovičky Landslide, Czech Republic Fárová, Kateřina; Jelének, Jan; Kopačková-Strnadová, Veronika Remote Sensing, Vol. 11, Issue 22 https://doi.org/10.3390/rs11222670	journal	November 2019
Geodetic Measurements and Numerical Models of Deformation at Coso Geothermal Field, California, USA, 2004–2016 Reinisch, Elena C.; Ali, S. Tabrez; Cardiff, Michael Remote Sensing, Vol. 12, Issue 2 https://doi.org/10.3390/rs12020225	journal	January 2020
Landslide Mapping and Monitoring Using Persistent Scatterer Interferometry (PSI) Technique in the French Alps Aslan, Gokhan; Foumelis, Michael; Raucoules, Daniel Remote Sensing, Vol. 12, Issue 8 https://doi.org/10.3390/rs12081305	journal	April 2020
Ground Surface Response to Geothermal Drilling and the Following Counteractions in Staufen im Breisgau (Germany) Investigated by TerraSAR-X Time Series Analysis and Geophysical Modeling Lubitz, Christin; Motagh, Mahdi; Kaufmann, Hermann Remote Sensing, Vol. 6, Issue 11 https://doi.org/10.3390/rs61110571	journal	October 2014
Exploitation of Amplitude and Phase of Satellite SAR Images for Landslide Mapping: The Case of Montescaglioso (South Italy) Raspini, Federico; Ciampalini, Andrea; Del Conte, Sara Remote Sensing, Vol. 7, Issue 11 https://doi.org/10.3390/rs71114576	journal	November 2015
Space-Borne and Ground-Based InSAR Data Integration: The Åknes Test Site Bardi, Federica; Raspini, Federico; Ciampalini, Andrea Remote Sensing, Vol. 8, Issue 3 https://doi.org/10.3390/rs8030237	journal	March 2016
Land Subsidence Detection by PSInSAR^TM Based on TerraSAR-X Images Jia, Hong Liang; Yu, Bing; Zhang, Rui Advanced Materials Research, Vol. 301-303 https://doi.org/10.4028/www.scientific.net/AMR.301-303.641	journal	July 2011
Surface deformation study for a geothermal operation field Wang, Wenli; Diessl, Julia; Bruno, Michael S. Advances in Geosciences, Vol. 45 https://doi.org/10.5194/adgeo-45-243-2018	journal	January 2018
Deformation Measurement Using Sentinel-1a/B Imagery Crosetto, M.; Monserrat, O.; Devanthéry, N. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XLII-2/W7 https://doi.org/10.5194/isprs-archives-XLII-2-W7-597-2017	journal	January 2017
PSInSAR and DInSAR methodology comparison and their applicability in the field of surface deformations–A case of NW Slovenia Oštir, Krištof; Komac, Marko Geologija, Vol. 50, Issue 1 https://doi.org/10.5474/geologija.2007.007	journal	June 2007

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