scholarly journals DELINEATION OF SUBSIDENCE IN THE ALTERATION ZONE IN ULUBELU GEOTHERMAL FIELD BASED ON INTERFEROMETRY SYNTHETIC APERTURE RADAR TIME SERIES

2020 ◽  
Vol 6 (3) ◽  
pp. 190-196
Author(s):  
Ahmad Zaenudin ◽  
Ilham Tri Putra Sofiyadin ◽  
Rachmat Arief
Keyword(s):  
Time Series ◽  
Temporal Analysis ◽  
Geological Structure ◽  
Geothermal Field ◽  
Disaster Mitigation ◽  
Alteration Zone ◽  
Surface Loading ◽  
Subsidence Area ◽  
Multi Temporal ◽  
Geothermal Region

Ulubelu is a geothermal region managed by Pertamina Geothermal Energy (PGE) located in Mount Kukusan, Sula, Kabawok, Kuripan and Rindingan. The learning of subsidence is required for disaster mitigation. This research use 33 SAR data of Sentinel1-A which bundled for generate an APS and then using multi temporal analysis to generate InSAR time series from 2018 September to 2017 April. InSAR time series can detect indication of subsidence practically and quickly. Decrease velocity of subsidence in the Ulubelu geothermal region is an average of 3,3 mm/yr (X=453.558, Y=9.412.437, 48 S zone. This subsidence is domination by altered rocks compaction. In the other hand, the geological structure (faults) and lithology also surface loading affected the subsidence. Pekon Gn. Tiga, Karang Rejo, Muara Dua and Pagar Alam are the worst of subsidence area. Mitigation must to be clear early for areas that have the worst subsidence in Ulubelu geothermal field.

scholarly journals Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests

2020 ◽  
Vol 12 (11) ◽  
pp. 1876 ◽  
Author(s):  
Katsuto Shimizu ◽  
Tetsuji Ota ◽  
Nobuya Mizoue ◽  
Hideki Saito
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Forest Monitoring ◽  
Series Data ◽  
Landsat 8 ◽  
Multi Temporal ◽  
Sentinel 2

Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using Sentinel-2 and Landsat 8 data. Random forest (RF) models using a single composite, multi-seasonal composites, and time-series data were investigated at different spatial resolutions of 3, 10, 20, and 30 m. The highest prediction accuracy was obtained by the PlanetScope multi-seasonal composites at 3 m (relative root mean squared error: 51.3%) and Sentinel-2 multi-seasonal composites at the other spatial resolutions (40.5%, 35.2%, and 34.2% for 10, 20, and 30 m, respectively). The results show that RF models using multi-seasonal composites are 1.4% more accurate than those using harmonic metrics from time-series data in the median. PlanetScope is recommended for canopy height mapping at finer spatial resolutions. However, the unique characteristics of PlanetScope data in a spatial and temporal context should be further investigated for operational forest monitoring.

scholarly journals Spatial and Temporal Spread of the COVID-19 Pandemic Using Self Organizing Neural Networks and a Fuzzy Fractal Approach

2021 ◽  
Vol 13 (15) ◽  
pp. 8295
Author(s):  
Patricia Melin ◽  
Oscar Castillo
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Temporal Trends ◽  
Main Idea ◽  
Temporal Analysis ◽  
Virus Propagation ◽  
Self Organizing Maps ◽  
Fractal Approach ◽  
The Times ◽  
Self Organizing

In this article, the evolution in both space and time of the COVID-19 pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries considered in this study. Self-organizing neural networks possess the capability to cluster countries in the space domain based on their similar characteristics, with respect to their COVID-19 cases. This form enables the finding of countries that have a similar behavior, and thus can benefit from utilizing the same methods in fighting the virus propagation. In order to validate the approach, publicly available datasets of COVID-19 cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of the time series of the countries considered in this study. Then, a hybrid combination, using fuzzy rules, of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient coronavirus disease 2019 (COVID-19) forecasting of the countries. Relevant conclusions have emerged from this study that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. Many of the existing works concerned with COVID-19 look at the problem mostly from a temporal viewpoint, which is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant for improving the forecasting ability. The main idea of this article is combining neural networks with a self-organizing nature for clustering countries with a high similarity and the fuzzy fractal approach for being able to forecast the times series. Simulation results of COVID-19 data from countries around the world show the ability of the proposed approach to first spatially cluster the countries and then to accurately predict in time the COVID-19 data for different countries with a fuzzy fractal approach.

Analysis of Metallogenic Conditions of Geothermal Resources in Heiyu Lake of Daqing

2012 ◽  
Vol 550-553 ◽  
pp. 2472-2477
Author(s):  
Yu Chun Bai ◽  
Yong Li Li ◽  
Fu Li Qi ◽  
Feng Long Zhang
Keyword(s):  
Transitional Zone ◽  
Geological Structure ◽  
Major Elements ◽  
Geothermal Field ◽  
Geophysical Data ◽  
Geothermal Resources ◽  
Lake Zone ◽  
Geological Conditions ◽  
Geothermal Drilling ◽  
Geological Background

Heiyu Lake zone of Daqing is located in the southwest hollow borderland of Heiyu Lake and on the arching transitional zone of Daqing placanticline. Based on the geological background of Heiyu Lake, this paper analyzes the landform, the regional geological structure, the formation lithology and the irruptive rock and other metallogenic conditions in detail. The indispensable geological conditions for forming geothermal field in layers were summed up. Combining with the development characteristics and geophysical data of formation, the bore hole site of geothermal well and target stratum were ascertained. The four major elements of forming geothermal resources in this region were confirmed by carrying out geothermal drilling.

scholarly journals Greenhouse Crop Identification from Multi-Temporal Multi-Sensor Satellite Imagery Using Object-Based Approach: A Case Study from Almería (Spain)

2018 ◽  
Vol 10 (11) ◽  
pp. 1751 ◽  
Author(s):  
Abderrahim Nemmaoui ◽  
Manuel A. Aguilar ◽  
Fernando J. Aguilar ◽  
Antonio Novelli ◽  
Andrés García Lorca
Keyword(s):  
Time Series ◽  
Binary Classification ◽  
Free Access ◽  
Landsat 8 ◽  
Horticultural Crops ◽  
Command Line Tool ◽  
Multi Temporal ◽  
Tree Models ◽  
Sentinel 2A

A workflow headed up to identify crops growing under plastic-covered greenhouses (PCG) and based on multi-temporal and multi-sensor satellite data is developed in this article. This workflow is made up of four steps: (i) data pre-processing, (ii) PCG segmentation, (iii) binary pre-classification between greenhouses and non-greenhouses, and (iv) classification of horticultural crops under greenhouses regarding two agronomic seasons (autumn and spring). The segmentation stage was carried out by applying a multi-resolution segmentation algorithm on the pre-processed WorldView-2 data. The free access AssesSeg command line tool was used to determine the more suitable multi-resolution algorithm parameters. Two decision tree models mainly based on the Plastic Greenhouse Index were developed to perform greenhouse/non-greenhouse binary classification from Landsat 8 and Sentinel-2A time series, attaining overall accuracies of 92.65% and 93.97%, respectively. With regards to the classification of crops under PCG, pepper in autumn, and melon and watermelon in spring provided the best results (Fβ around 84% and 95%, respectively). Data from the Sentinel-2A time series showed slightly better accuracies than those from Landsat 8.

Multi-temporal analysis of the role of check dams in a debris-flow channel: Linking structural and functional connectivity

Geomorphology ◽  
2019 ◽  
Vol 345 ◽  
pp. 106844 ◽  
Author(s):  
Sara Cucchiaro ◽  
Federico Cazorzi ◽  
Lorenzo Marchi ◽  
Stefano Crema ◽  
Alberto Beinat ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Debris Flow ◽  
Temporal Analysis ◽  
Flow Channel ◽  
Check Dams ◽  
Multi Temporal

scholarly journals Spatial and temporal multiplet analysis for identification of dominant fluid migration path at The Geysers geothermal field, California

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
M. Staszek ◽  
Ł. Rudziński ◽  
G. Kwiatek
Keyword(s):  
Temporal Analysis ◽  
Geothermal Field ◽  
Fluid Injection ◽  
Fluid Migration ◽  
Multiple Events ◽  
Spatial And Temporal Analysis ◽  
Hydraulic Conditions ◽  
The Geysers ◽  
Multiplet Analysis

AbstractMultiplet analysis is based on the identification of seismic events with very similar waveforms which are used then to enhance seismological analysis e.g. by precise relocation of sources. In underground fluid injection conditions, it is a tool frequently used for imaging of subsurface fracture system. We identify over 150 repeatedly activated seismic sources within seismicity cluster induced by fluid injection in NW part of The Geysers geothermal field (California). Majority of multiple events (ME) occur along N–S oriented planar structure which we interpret as a fault plane. Remaining ME are distributed along structures interpreted as fractures, forming together a system of interconnected cracks enabling fluid migration. Temporal analysis reveals that during periods of relatively low fluid injection the proportion of ME to non-multiple events is higher than during periods of high injection. Moreover, ME which occur within the fault differ in activity rate and source properties from ME designating the fractures and non-multiple events. In this study we utilize observed differences between ME occurring within various structures and non-multiple events to describe hydraulic conditions within the reservoir. We show that spatial and temporal analysis of multiplets can be used for identification and characterization of dominant fluid migration paths.

scholarly journals Determination of variation uncertainty in runoff time series at multi-temporal scales

2021 ◽  
Author(s):  
Yan Ye ◽  
Jinping Zhang ◽  
Xunjian Long ◽  
Lihua Ma ◽  
Yong Ye
Keyword(s):  
Time Series ◽  
Annual Runoff ◽  
Important Variable ◽  
Temporal Scales ◽  
Flood Season ◽  
Mode Function ◽  
Mode Decomposition ◽  
Multi Temporal ◽  
Emd Method

Abstract In order to survey the possible periodic, uncertainty and common features in runoff with multi-temporal scales, the empirical mode decomposition (EMD) method combined with the set pair analysis (SPA) method was applied, with data observed at Zhangjiashan hydrological station. The results showed that the flood season and annual runoff time series consisted of four intrinsic mode function (IMF) components, and the non-flood season time series exhibited three IMF components. Moreover, based on the different coupled set pairs from the time series, the identity, discrepancy, and contrary of different periods at multi-temporal scales were determined by the SPA method. The degree of connection μ between the flood season and annual runoff periods were the highest, with 0.94, 0.77, 0.7 and 0.73, respectively, and the μ between the flood periods and the non-flood periods were the lowest, with 0.66, 0.46, 0.24 and 0.24, respectively. Third, the maximum μ of each SPA appeared in the first mode function. In general, the different extractive periods decomposed by EMD method can reflected the average state of Jinghe River. Results also verified that runoff suffered from seasonal and periodic fluctuations, and fluctuations in the short-term corresponded to the most important variable. Therefore, the conclusions draw in this study can improve water resources regulation and planning.

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