Note: Intro -- Preface -- Acknowledgments -- Contents -- Editor and Contributors -- 1 Analytical Methods in the Interpretation of Self-Potential Anomalies-A Comprehensive Review -- 1.1 General Introduction -- 1.2 Interpretation of SP Anomalies -- 1.3 Hilbert Transforms -- 1.4 Analytic Signal and Amplitude -- 1.5 2-D Horizontal Circular Cylinder -- 1.6 Spherical Structures -- 1.7 Hartley Spectral Analysis of SP Anomalies -- 1.8 Artificial Neural Network Analysis -- 1.9 Noise Analysis -- 1.10 Discussion -- References -- 2 Metaheuristics Inversion of Self-Potential Anomalies -- 2.1 Introduction -- 2.2 Forward SP Model -- 2.3 Optimization Methods -- 2.4 Inversion of Spontaneous Potential (SP) Anomalies -- 2.4.1 Ambiguity and Non-uniqueness of SP Inverse Solutions -- 2.4.2 Ambiguity Control -- 2.4.3 Formulation of the Objective/target Function for SP Problem -- 2.5 Metaheuristics Inversion of SP Anomalies -- 2.5.1 Evolutionary Algorithms (EAs) -- 2.5.2 When Using Metaheuristics? -- 2.5.3 Examples of the Metaphor Based Algorithms -- 2.6 Application to Synthetic Model with and Without Noise -- 2.6.1 Application to Simple Geometrical Models -- 2.6.2 Application to Thin Sheet Model -- 2.7 Application to Field Examples -- 2.7.1 Application to Field Data Approximated by Simple Geometrical Models -- 2.7.2 Anomalies Approximated by Thin Sheet Model -- 2.8 Conclusions -- References -- 3 Self-potential Inversion and Uncertainty Analysis via the Particle Swarm Optimization (PSO) Family -- 3.1 Introduction -- 3.2 The Streaming Potential Forward and Inverse Problems -- 3.3 The Inverse Problem and the Topology of the Cost Function -- 3.4 The PSO Family -- 3.5 PSO Design in the SP Case -- 3.6 Modeling the Bogoslovsky and Ogilvy Dataset -- 3.7 Conclusions -- References. , 4 A Comparison of the Model Parameter Estimations from Self-Potential Anomalies by Levenberg-Marquardt (LM), Differential Evolution (DE) and Particle Swarm Optimization (PSO) Algorithms: An Example from Tamış-Çanakkale, Turkey -- 4.1 Introduction -- 4.2 Materials and Methods -- 4.2.1 Formulation of the SP Anomaly -- 4.2.2 Algorithms -- 4.2.3 Differential Evolution (DE) Algorithm -- 4.2.4 Parameter Estimation Studies -- 4.2.5 Synthetic Examples -- 4.2.6 Field Example -- 4.3 Conclusions -- References -- 5 Estimation of the Buried Model Parameters from the Self-potential Data Applying Advanced Approaches: A Comparison Study -- 5.1 Introduction -- 5.2 Methodology -- 5.2.1 Forward Modeling -- 5.2.2 Least Squares Inversion Technique -- 5.2.3 Particle Swarm Optimization -- 5.2.4 Neural Network Algorithm -- 5.3 Synthetic Examples -- 5.3.1 Sphere Model -- 5.3.2 Horizontal Cylinder Model -- 5.4 Field Example -- 5.4.1 Malachite Mine, USA Real Data -- 5.5 Conclusions -- References -- 6 Determining the Structure Factor and Parameters of a Buried Polarized Structure from Self Potential Anomalies -- 6.1 Introduction -- 6.2 Theory of the Method -- 6.2.1 Determination of X0 and V(0) -- 6.3 Theoretical and Field Applications -- 6.4 Conclusion -- Appendix: Determination of Roots of Non-linear Equations -- Regula False Method -- Bisection Method -- References -- 7 Ensemble Kalman Inversion for Determining Model Parameter of Self-potential Data in the Mineral Exploration -- 7.1 Introduction -- 7.2 Methodology -- 7.2.1 Ensemble Kalman Inversion -- 7.2.2 Forward Modeling -- 7.2.3 Inversion Using EKI -- 7.3 Synthetic Model -- 7.3.1 EKI in Single Anomaly -- 7.3.2 EKI in Multiple Anomalies -- 7.4 Field Examples -- 7.4.1 Neem-Ka-Thana, India -- 7.4.2 Malachite Mine, Jefferson County, Colorado, USA -- 7.4.3 Surda Anomaly, Portugal -- 7.5 Conclusion -- References. , 8 Advanced Analysis of Self-potential Anomalies: Review of Case Studies from Mining, Archaeology and Environment -- 8.1 Introduction -- 8.2 Self-potential Observations: Common Disturbances -- 8.2.1 Different Kinds of Noise in SP Observations -- 8.3 Review of Quantitative Interpretation Methods -- 8.4 Some Common Aspects of Magnetic and SP Fields -- 8.4.1 Quantitative Analysis of SP Anomalies by the Use of Advanced Methodologies Developed in Magnetic Prospecting -- 8.4.2 SP Observations on an Inclined Profile -- 8.5 Quantitative Analysis of SP Anomalies -- 8.5.1 Testing on Theoretical Models -- 8.5.2 Mining Geophysics -- 8.5.3 Archaeological Sites -- 8.5.4 Environmental Geophysics -- 8.5.5 Technogenic Geophysics -- 8.5.6 Generalization of the Calculated Self-Potential Moments -- 8.6 Conclusions -- References -- 9 Preferential Water Flow Pathways Detection in Sinkholes Using Self-Potential (SP) Method. The Study Case of Anina Karst Region (Banat Mountains, Romania) -- 9.1 Introduction -- 9.2 Study Area and Investigation Sites Description -- 9.3 Self-Potential as Geophysical Method -- 9.4 Methodology -- 9.5 Results -- 9.5.1 Mărghitaș Plateau -- 9.5.2 Colonovăț Plateau -- 9.5.3 Cârneală Plateau -- 9.5.4 Brădet Plateau-Culmea Neagră Area -- 9.6 Discussions and Conclude Remarks -- References -- 10 Interpretation of Self-Potential (SP) Log and Depositional Environment in the Upper Assam Basin, India -- 10.1 Introduction -- 10.2 Geology -- 10.3 Methodology -- 10.3.1 The Determination of Formation Water Resistivity (Rw) -- 10.3.2 The Determination of Volume of Shale (Vsh) in the Formations -- 10.3.3 SP Log Shape Analysis -- 10.4 Estimation of Petrophysical Parameters -- 10.4.1 Estimation of Water Saturation in the Formations -- 10.4.2 Estimation of porosity Φ(s) -- 10.4.3 Estimating the Types of Shale -- 10.5 Results and Discussions. , 10.6 Depositional Environment -- 10.7 Conclusions -- References -- 11 High Resolution Electrical Resistivity Tomography and Self-Potential Mapping for Groundwater and Mineral Exploration in Different Geological Settings of India -- 11.1 Introduction -- 11.2 Interpretation of 2D Inverted Resistivity and Induced Polarization Models -- 11.2.1 Results and Discussions -- 11.3 Conclusions -- References.