Basics of Computational Geophysics

Cover
Basics of Computational Geophysics
Copyright
Contents
List of Contributors
Preface
Part I Computation and Geophysics
1 Synthetic ground motions of the October 8, 2005 Kashmir earthquake (Mw 7.6): An inference to the site response and seismi...
1.1 Introduction
1.2 Input parameters
1.2.1 Source and path parameters of the October 8, 2005 Kashmir earthquake (Mw 7.6)
1.2.2 Shear wave velocity (Vs30)
1.3 Methodology
1.4 Results
1.4.1 Synthetic seismograms at the bedrock level and at surface
1.4.2 Synthetic ground motions (PGA in g) at the bedrock level and at surface
1.5 Conclusions
References
2 Global particle swarm optimization technique in the interpretation of residual magnetic anomalies due to simple geo-bodie...
2.1 Introduction
2.2 Forward formulation for magnetic anomaly
2.3 Global particle swarm optimization method
2.4 Results and discussion
2.4.1 Theoretical examples
2.4.1.1 Sphere (noise free and noisy) - model 1
2.4.1.2 Horizontal cylinder (noise free and noisy) - model 2
2.4.1.3 Thin dyke (noise free and noisy) - model 3
2.4.1.4 Thin sheet (noise free and noisy) - model 4
2.4.2 Field examples
2.4.2.1 Pima copper deposit, Arizona, United States
2.4.2.2 Pishabo Lake anomaly, Ontario, Canada
2.4.2.3 Bankura anomaly, India
2.4.2.4 Parnaiba anomaly, Brazil
2.5 Conclusions
References
3 Emerging techniques to simulate strong ground motion
3.1 Introduction
3.2 Stochastic simulation technique
3.3 Empirical green’s function technique
3.4 Composite source modeling technique
3.5 Semi empirical technique
3.6 Discussion
3.7 Conclusions
References
4 Earthquakes: Basics of seismology and computational techniques
4.1 Introduction
4.2 Types of earthquakes
4.2.1 Classification based on origin or cause of an earthquake
4.2.1.1 Tectonic earthquake
4.2.1.2 Induced earthquake
4.2.1.3 Volcanic earthquake
4.2.2 Classification based on epicentral distance
4.2.3 Classification based on size or magnitude
4.3 Seismic waves
4.4 Location of earthquake
4.5 Earthquake magnitude and intensity
4.5.1 Different magnitude scales
4.5.1.1 Local Richter magnitude
4.5.1.2 Body wave magnitude (mb)
4.5.1.3 Surface wave magnitude (Ms)
4.5.1.4 Seismic moment magnitude
4.5.2 Intensity
4.6 Earthquake source characterization
4.6.1 Fault geometry
4.6.2 Rupture dimension
4.6.3 Types of faulting
4.6.3.1 Reverse/thrust fault
4.6.3.2 Normal fault
4.6.3.3 Strike-slip fault
4.6.3.4 Oblique fault
4.6.4 Beach ball presentation
4.7 Foreshocks, aftershocks, and swarm
4.8 Earthquakes aftereffects
4.8.1 Ground rupture
4.8.2 Liquefaction
4.8.3 Landslide
4.8.4 Tsunami
4.9 Earthquake precursory research
4.9.1 Earthquake forecasting
4.9.1.1 Seismic pattern recognition
4.9.1.2 Seismic activation
4.9.1.3 Seismic hidden periodicities
4.9.1.4 Region-time-length (RTL)
4.9.2 Earthquake prediction
4.9.3 Real-time warning
4.9.4 Earthquake prediction status
4.9.5 Induced seismicity
4.10 Introduction to earthquake data applications
4.10.1 Investigation shallow and deep subsurface structure of the earth
4.10.1.1 Shallow structure
4.10.1.2 Deep sub-surface structure
4.11 Seismo-tectonic linkage
4.12 Earthquake hazard investigation
4.12.1 Seismic hazard map of the region
4.12.2 Global seismicity map
References
5 Significance and limit of electrical resistivity survey for detection sub surface cavity: A case study from, Southern Wes...
5.1 Introduction
5.2 Study area
5.2.1 Geomorphology
5.2.2 Geology
5.2.3 Nature of the event
5.2.4 Methodology
5.2.5 Results
5.2.5.1 Profile 1
5.2.5.2 Profile 2
5.2.5.3 Profile 3
5.2.5.4 Profile 4
5.3 Discussion and conclusion
5.4 Feasibility and restriction of ERT for demarcating soil piping systems
5.5 Conclusions
Acknowledgments
References
6 A review on geophysical parameters comparison in Garhwal and Kumaun Himalaya region, India
6.1 Introduction
6.2 Tectonics setting and data source
6.3 Discussion and observations
6.3.1 Spatial variation of seismicity
6.3.2 GPS study
6.3.3 Attenuation characteristics
6.4 Conclusions
References
7 Liquefaction susceptibility of high seismic region of Bihar considering fine content
7.1 Introduction
7.2 Methodology
7.2.1 Indian standard code (IS 1893, 2016) “Criteria for Earthquake Resistant Design of Structures”
7.2.2 Multi-linear regression model (MLR model)
7.2.3 Reliability analysis approach
7.3 Result and discussion
7.3.1 Deterministic approach
7.3.2 Probabilistic approach
7.4 Conclusion
References
8 Evaluating the reliability of various geospatial prediction models in landslide risk zoning
8.1 Introduction
8.1.1 Background of the study area
8.1.2 Data preparation
8.2 Methodology for landslide susceptibility analysis
8.2.1 Analytical hierarchic process (AHP)
8.2.2 Information value method
8.2.3 Logistic regression
8.3 Results and discussions
8.4 Conclusion
References
9 Fractals and complex networks applied to earthquakes
9.1 Fractals
9.2 Dimension
9.3 Multifractal spectrum dimension
9.4 Complex networks
9.5 Visibility graph
References
10 Liquefaction as a seismic hazard: Scales, examples and analysis
10.1 Introduction
10.2 Liquefaction during earthquakes
10.3 Liquefaction potential analysis methodology
10.4 Liquefaction potential analysis of Kashmir basin, NW Himalaya
10.5 Conclusion
References
11 Landslide prediction and field monitoring for Darjeeling Himalayas: A case study from Kalimpong
11.1 Introduction
11.2 Study area
11.2.1 Geology of the study area
11.2.2 Geohydrological details of the area
11.3 Rainfall thresholds
11.3.1 Empirical methods
11.3.1.1 Intensity–duration thresholds for Kalimpong
11.3.1.2 Antecedent rainfall thresholds for Kalimpong
11.3.1.3 Event–duration thresholds for Kalimpong
11.3.2 Probabilistic methods
11.3.2.1 One dimensional Bayesian analysis for Kalimpong
11.3.2.2 Two dimensional Bayesian analysis for Kalimpong
11.3.3 Process based models
11.3.3.1 TRIGRS model for Kalimpong
11.4 Field monitoring
11.4.1 Remote sensing
11.4.2 Geotechnical monitoring
11.4.2.1 Real time field monitoring for Kalimpong using MEMS tilt sensors
11.5 Summary
References
12 Improvement of shear strength of cohesive soils by additives: A review
12.1 Introduction
12.2 Literature review
12.3 Methodology
12.3.1 Criteria for selecting the quantity of RHA & FA
12.3.2 Moisture content (ASTM D 2216)
12.3.3 Atterberg limits (ASTM D 4318)
12.3.4 Specific gravity (ASTM D 854)
12.3.5 Standard proctor (ASTM D 698)
12.3.6 Experimental setup
12.3.6.1 Apparatus
12.3.7 Procedure (ASTM D 2166)
12.4 A case study
12.4.1 Engineering properties of soil samples
12.4.2 Atterberg limits of soil after additives
12.4.2.1 Soil sample A
12.4.2.2 Soil sample B
12.4.3 Effect of additives on maximum dry density
12.4.3.1 RHA
12.4.3.1.1 Sample A
12.4.3.1.2 Sample B
12.4.3.2 FA
12.4.3.2.1 Sample A
12.4.3.2.2 Sample B
12.4.3.3 Relation between optimum moisture content and % additives
12.4.3.3.1 Results for unconfined compression test
12.4.3.4 RHA
12.4.3.5 FA
12.5 Results/findings
12.5.1 Qualitative results
12.5.2 Quantitative results
12.6 Conclusions
References
13 Static stress change from February 6, 2017 (M 5.8) earthquake Northwestern Himalaya, India
13.1 Introduction
13.2 Method
13.3 Interpretation of calculated static stress change in Northwestern Himalaya
13.3.1 Coulomb stress change due to the 2017 Rudraprayag earthquake and its dependency on the depth of computation
13.3.2 Coulomb stress change due to the 2017 Rudraprayag earthquake and its dependency on frictional coefficient (μ′)
13.3.3 Coulomb stress change calculated on the optimally oriented faults
13.3.4 Coulomb stress change calculated on the MHT detachment
13.4 Conclusions
Appendix I
References
14 Remote sensing for geology-geophysics
14.1 Overview of the remote sensing terminology
14.2 Milestones in remote sensing of the geological exploration
14.2.1 Multispectral remote sensing in geological exploration
14.2.2 Hyperspectral remote sensing in geological exploration
14.3 Remote sensing methods in geology
14.3.1 Atmospheric correction
14.3.1.1 Image-based methods
14.3.1.2 Radiative transfer model
14.3.1.3 Empirical line method
14.3.2 Ortho-rectification
14.3.3 Feature identification techniques used in multispectral data
14.3.3.1 Band ratio
14.3.3.2 Relative absorption band depth technique
14.3.3.3 Principle component analysis
14.3.3.4 Minimum noise fraction
14.3.4 Common tools and methods used for hyperspectral image analysis
14.3.4.1 Pixel purity index (PPI)
14.3.4.2 Spectral analyst
14.3.4.3 Continuum removal of spectra
14.3.4.4 Spectral polishing
14.3.4.5 Feature extraction and mapping techniques used in hyperspectral images
14.3.4.6 Spectral feature fitting
14.3.4.7 Spectral angle mapping
14.3.4.8 Constrained energy minimization
14.3.4.9 Adaptive coherence estimator
14.3.4.10 Matched filtering (MF)
14.3.5 Multispectral remote sensing application in geology
14.3.5.1 Iron detection by band ratio
14.3.5.2 Iron detection by band ratio combination
14.3.5.3 Hydrothermally altered zone detection using ASTER data and PCA (principle component analysis) technique
14.3.6 Hyperspectral remote sensing application in geology
14.3.6.1 Copper ore identification using spectral similarity measurement from hyperion image, mapping of porphyry copper mi...
14.3.6.2 Iron ore identification using hyperspectral remote sensing, A case study
References
Part II Computation & Geoscience Applications
15 Prediction of petrophysical parameters using probabilistic neural network technique
15.1 Introduction
15.2 Seismic attributes
15.3 Data and study area
15.4 Probabilistic neural network
15.4.1 General
15.4.2 Theoretical approach
15.4.3 Mathematical approach
15.5 Prediction of petrophysical parameters
15.6 Conclusions
References
16 Interpretation and resolution of multiple structures from residual gravity anomaly data and its application to subsurfac...
16.1 Introduction
16.2 Forward modeling
16.3 Very fast simulated annealing (VFSA) global optimization method
16.4 Results and discussion
16.4.1 Theoretical model examples
16.4.1.1 Sphere
16.4.1.2 Horizontal cylinder
16.4.1.3 Vertical cylinder
16.4.1.4 Multiple structures
16.4.1.5 Effect of variation of amplitude coefficient (k)-single structure
16.4.1.6 Effect of variation of depth (z)-single structure
16.4.1.7 Effect of variation of amplitude coefficient (k)-multiple structures
16.4.1.8 Effect of variation of depth (z)-multiple structures
16.5 Case study: field examples from uranium mineralization
16.5.1 Beldih Mine, Purulia, West-Bengal, India
16.5.2 Uranium ore, South Purulia Shear Zone, West-Bengal, India
16.6 Conclusions
References
17 On fractal based estimations of soil subsidence
17.1 Introduction
17.2 Definitions and denotations
17.3 Modeling subsidence of soils with fractal properties
17.4 Some fractal-based estimations of volumetric characteristics of soil subsidence
17.5 The results of experimental studies
References
18 A neural network to predict spectral acceleration
18.1 Introduction
18.2 Methodology
18.2.1 Simple linear regression
18.2.2 Artificial neural network
18.3 Strong ground-motion models
18.4 Database of strong-motions
18.5 Research objectives, methodology, and discussion
18.5.1 Residual analysis
18.5.2 Goodness-of-fit measures testing
18.6 Conclusion
References
19 Prediction of Earth tide
19.1 Tidal force
19.2 Love Number: definition
19.3 Computation of Love number
19.4 Solar and Lunar ephemerides
19.5 Computation of Earth tide
19.6 Further refinement in body tide prediction
19.7 Ocean tide model
19.8 Fluid Love number and permanent tide
19.9 Load Love number
19.10 Concluding remark and acknowledgment
References
20 Time series analysis of hydrometeorological data for the characterization of meltwater storage in glaciers of Garhwal Hi...
20.1 Introduction
20.2 Description of the study area
20.3 Data collection and methods
20.4 Results and discussion
20.4.1 Hydro-meteorological observations and analysis
20.4.1.1 Distribution and variability in rainfall
20.4.1.2 Distribution and variability in air temperature and relative humidity
20.4.1.3 Distribution and variability in wind speed and wind direction
20.4.1.4 Distribution and variability in discharge (Q)
20.4.1.5 Discharge autocorrelations (QACR)
20.4.1.6 Corrélations of discharge with meteorological data
20.4.1.7 Development of multivariate regression model (MLR)
20.5 Conclusions
Acknowledgments
References
21 Trends in frequency and intensity of tropical cyclones in the Bay of Bengal: 1972–2015
21.1 Introduction
21.2 Data and methodology
21.3 Results and discussion
21.3.1 Interannual variations
21.3.2 Monthly and seasonal variations
21.3.3 Interannual variations during two peak seasons
21.3.4 Intensity-wise variations
21.3.5 Intensified and rapidly intensified TC events
21.4 Summary and conclusions
References
22 Application of machine learning models in hydrology: Case study of river temperature forecasting in the Drava River usin...
22.1 Introduction
22.2 Study area and available data
22.3 Methodology
22.3.1 Linear and non-linear regression models
22.3.2 Adaptive neuro-fuzzy inference systems (ANFIS)
22.3.3 Wavelet analysis (WA)
22.3.3.1 Coupled WA and ANFIS model
22.3.3.2 Model performance indicators
22.4 Results and discussion
22.4.1 Linear regression model
22.4.2 Non-linear regression model
22.4.3 ANFIS model
22.4.4 WA-ANFIS hybrid model
22.5 Conclusions
References
Index