7.3 Interpretation of gravity and magnetic anomalies

Gravity and magnetic anomalies offer clues about hidden structures beneath Earth's surface. By analyzing their shape, size, and patterns, geophysicists can deduce what's lurking underground. It's like solving a puzzle with pieces you can't see directly.

Interpreting these anomalies isn't straightforward, though. Multiple explanations can fit the same data, and deeper structures are harder to resolve. Combining gravity and magnetic data with other geophysical methods and geological info helps paint a clearer picture of what's below.

Anomaly Analysis for Subsurface Structures

Interpreting Anomaly Characteristics

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• Analyze the shape, amplitude, and wavelength of gravity and magnetic anomalies to infer subsurface structures
• Lateral variations in density or magnetic susceptibility within the Earth's subsurface cause gravity and magnetic anomalies
• The geometry and orientation of the causative body (spherical, cylindrical, or planar) are indicated by the shape of an anomaly
• The amplitude of an anomaly relates to the magnitude of the density or magnetic susceptibility contrast between the causative body and the surrounding rocks
• The wavelength of an anomaly is influenced by the depth and size of the causative body, with deeper and larger bodies producing longer wavelength anomalies (large, deep intrusion vs. shallow, localized ore body)

Identifying Subsurface Structure Boundaries

• The half-width of an anomaly, measured at half its maximum amplitude, estimates the depth to the top of the causative body using the half-width rule
• Analyze the horizontal gradient of the anomaly to identify the edges of subsurface structures, with maximum gradients occurring over the edges
• Steep gradients indicate sharp boundaries between contrasting lithologies (fault contact), while gradual gradients suggest gradational transitions (sedimentary facies change)
• Inflection points in the anomaly profile mark the edges of the causative body, allowing the lateral extent to be determined

Modeling Techniques for Source Estimation

Forward and Inverse Modeling Approaches

• Apply forward and inverse modeling techniques to estimate the depth, geometry, and properties of sources causing anomalies
• Forward modeling calculates the expected gravity or magnetic response of a hypothetical subsurface model and compares it with the observed data
• Inverse modeling determines the subsurface distribution of physical properties that best explains the observed anomalies
• Iterative forward modeling refines the initial model until a satisfactory match between the calculated and observed anomalies is achieved
• Inversion algorithms (least-squares, neural networks) automatically adjust the model parameters to minimize the misfit between the observed and calculated data

Estimating Source Parameters

• Estimate the depth to the top of a causative body using techniques such as Euler deconvolution, Werner deconvolution, and the Peters' half-slope method
• Approximate the geometry of a causative body by simple shapes (spheres, cylinders, prisms) or more complex polygonal or triangulated models
• Estimate the density or magnetic susceptibility of a causative body by comparing the observed anomaly amplitude with the calculated response of a model with known properties
• Non-uniqueness is a fundamental challenge in potential field interpretation, as multiple subsurface models can produce similar anomalies

Integrating Potential Field Data

Combining Geophysical Methods

• Integrate gravity and magnetic data with other geophysical methods (seismic reflection, seismic refraction, electromagnetic surveys) to constrain the interpretation
• Joint inversion of multiple geophysical datasets provides a more robust and consistent subsurface model by simultaneously honoring all available data
• Seismic data constrain the geometry and depth of subsurface interfaces, while potential field data provide information on the physical properties of the layers
• Electromagnetic data (magnetotellurics) complement potential field data by imaging conductive structures and estimating the electrical properties of the subsurface

Incorporating Geological Information

• Integrate geological information (surface geology, drill hole data, tectonic context) to guide the interpretation and reduce ambiguity
• Petrophysical data (density, magnetic susceptibility measurements from rock samples) help link the geophysical anomalies to specific lithologies or formations
• Outcrop patterns and structural measurements (bedding, foliation) provide constraints on the subsurface geometry and orientation of geological units
• Consider the regional tectonic framework and geodynamic processes when interpreting potential field anomalies, as they influence the distribution and geometry of subsurface structures

Limitations of Potential Field Interpretation

Non-Uniqueness and Resolution

• Potential field data are inherently non-unique, meaning multiple subsurface models can produce similar anomalies, leading to ambiguity in interpretation
• The resolution of potential field data decreases with depth, making it challenging to resolve small-scale features or subtle variations at greater depths
• Shallow, high-frequency anomalies may mask deeper, lower-frequency signals, requiring careful separation and filtering techniques
• Increasing the data coverage and incorporating additional constraints from other geophysical methods or geological information can help reduce the non-uniqueness and improve the resolution

Assumptions and Uncertainties

• The presence of noise in the data (measurement errors, terrain effects, cultural interference) can obscure or distort the anomalies, affecting the interpretation
• The interpretation of potential field data relies on assumptions about the subsurface (homogeneity, isotropy, absence of remanent magnetization), which may not always be valid
• Uncertainty in the density or magnetic susceptibility values assigned to the subsurface model can lead to variations in the estimated geometry and depth of the causative bodies
• Accompany the interpretation of potential field data with a quantitative assessment of the uncertainties (sensitivity analysis, inversion with different starting models) to evaluate the robustness of the results