Advanced Processing and

Interpretation of Gravity

Data

Presented by: Tripti Kumari

Piyali Das Sonali Pattnaik

Shweta Arushi Pratap

Aparajita Nath

The presentation includes • the interpretation process itself, outlining the

importance of qualitative interpretation and the complementary roles that gravity datas offer.

• examples of the various types of enhancements (or transforms) applied to gravity datas to highlight particular characteristics or features to aid qualitative interpretation.

• additional advanced methods of quantitative processing in support of interpretation that can be applied to gravity datas, including 3D gravity inversion, depth to source estimation and 2D modelling.

The Interpretation Process As with all geophysical interpretation, the analysis of gravity

datas has two distinct aspects: qualitative and quantitative. • The qualitative process is largely map-based and

dominates the early stages of a study. Qualitative interpretation involves recognition of:

the nature of discrete anomalous bodies including intrusions, faults and lenticular intra-sedimentary bodies.

disruptive cross-cutting features such as strike-slip faults. effects of mutual interference relative ages of intersecting faults structural styles. unifying tectonic features/events that integrate seemingly

unrelated interpreted features.

Quantitative Process

The quantitative process: Putting lines on maps during the qualitative process is the start of quantitative phase. Refinement of these locations begins with the determination of z i.e. depth values. Gravity modelling (ideally seismically controlled) including forward and inversion approaches contribute significantly to location in x, y and z. Accurate results of all these rely upon sensible qualitative recognition of body types.

Processing and Interpretation of

Gravity Data

• Qualitative Interpretation Techniques includes:

Traditional Filtering: Filtering is a way of separating signals of different wavelength to

isolate and hence enhance anomalous features with a certain wavelength. A rule of thumb is that the wavelength of an anomaly divided by three or four is approximately equal to the depth at which the body producing the anomaly is buried. Thus filtering can be used to enhance anomalies produced by features in a given depth range.

Traditional filtering can be either low pass (Regional) or high pass (Residual). Thus the technique is sometimes referred to as Regional-Residual Separation. Bandpass filtering isolates wavelengths between user-defined upper and lower cut-off limits.

40km High Pass Filtering (Residual)

(Before filtering) (after filtering)

40km Low Pass Filtering (Regional)

(before filtering) (After filtering)

Pseudo-depth Slicing

A potential field grid may be considered to represent a series of components of different wavelength and direction. The logarithm of the power of the signal at each wavelength can be plotted against wavelength, regardless of direction, to produce a power spectrum. The power spectrum is often observed to be broken up into a series of straight line segments. Each line segment represents the cumulative response of a discrete ensemble of sources at a given depth. The depth is directly proportional to the slope of the line segment. Filtering such that the power spectrum is a single straight line can thus enhance the effects from sources at any chosen depth at the expense of effects from deeper or shallower sources.

It is a data-adaptive process involving spectral shaping. As such, it performs significantly better than arbitrary traditional filtering techniques described above. When gravity depth slices coincide it is a good indication that the causative bodies are one and the same.

First Vertical Derivative

This enhancement sharpens up anomalies over bodies and tends to reduce anomaly complexity , allowing a clearer imaging of the causative structures. The transformation can be noisy since it will amplify short wavelength noise. In our example it clearly delineates areas of different data resolution in the gravity grid. VDR= -∂A/∂z

before

after

Total Horizontal Derivative

This enhancement is also designed to look at fault and contact features. Maxima in the mapped enhancement indicate source edges. It is complementary to the filtered and first vertical derivative enhancements above. It usually produces a more exact location for faults than the first vertical derivative. Specific directional horizontal derivatives can also be generated to highlight features with known strikes. This technique can be applied to pseudo-depth slices to image structure at different depths.

Total Horizontal Derivative:

before after

Second Vertical Derivative

The second vertical derivative serves much the same purpose as

‘residual’ filtering in gravity map, in that it emphasizes the expressions of local features, and removes the effects of large anomalies or regional influences. The principal usefulness of this enhancement is that the zero value for gravity data in particular closely follows sub-vertical edges of intra-basement blocks, or the edges of supra-basement disturbances or faults. As with other derivative displays, it is particularly helpful in the processing stage where it can be used to highlight line noise or mislevelling..

before after

•gravity field satisfy the Laplace’s equation so it obtained from horizontal

derivative

•The SVD from the average value at various distance from the station is

where g₀= the gravity at the station where SVD determined ; are average over the surrounding circle of various radii; w₀, w₁….. are weighting coefficients such that ∑wᵢ=0; c =numerical factor; s=grid spacing;

Analytic Signal (Total Gradient)

The analytic signal, although often more discontinuous than the simple horizontal gradient, has the property that it generates a maximum directly over discrete bodies as well as their edges. The width of a maximum, or ridge, is an indicator of depth of the contact, as long as the signal arising from a single contact can be resolved.

before after

Quantitative Interpretation Techniques

Euler Deconvolution Euler deconvolution technique is widely used as an aid to interpreting profile or girded gravity survey data. This technique provides automatic estimates of source location and depth. Therefore, Euler deconvolution is both a boundary finder and depth estimationThe quality of depth estimation depends mainly on the choice of a structure index (SI). The SI can be interpreted as the integer exponent in a power law expressing the fall-off of filed strength versus distance from source.

where:

(x0, y0, z0): the position of a source whose total field T is detected at any point (x,y,z)

B: the background value of the total field

N: the degree of homogeneity, interpreted physically as the attenuation rate with distance, and geophysically as a structural index (SI):

Euler's homogeneity equation

Spectral Depth Method

The spectral method is based on the shape of the power spectrum for buried bodies with a density contrast. The depth to the center of mass of the body is easily found from the power spectrum of the gravity field. If the spectrum is plotted on semi-log paper, the slope of the spectrum is equal to the depth to the center of mass. Since the gravity field of the earth is a linear system, this can be applied to inverting for the depth to a surface containing a distribution of complex shapes.

3D Gravity Stripping and Inversion A grid of gravity can be inverted to yield a grid of the depth variation of a significant density boundary, commonly the base of a sedimentary basin. The technique involves stripping off the gravity effects of known layers, seismically determined, within the sub-surface before inverting for the structure of a deep density boundary. For example, the structure and densities of shallow sedimentary horizons in a basin may be well known from seismic and well data, but the basement may not be adequately imaged from the seismic data due to the presence of salt or volcanics. The gravity effect of each of the known sedimentary layers is calculated from forward modelling and subtracted from the observed gravity anomaly. The residual anomaly is then inverted to provide the depth to the top of the basement.

In the 3D gravity inversion, we combine fast iterative inversion with

constraint inversion to obtain unique and robust modelling results.

2D Profile Modeling

The GM-SYS modeling software is an interactive forward modeling program which calculates the gravity from a user defined hypothetical geologic model. Any differences between the model response and the observed gravity field are reduced by refining the model structure or properties (e.g. density of model components).

References

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