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The normalized full gradient is based on the noticeable stability of the modulus of the analytic signal of downward potential fields. We have developed a new approach to the study of the normalized full gradient, based on assessing the entropy of the normalized modulus of the analytic signal at each level of continuation. The increased disorder of progressively downward continued fields implies an increase of the computed entropy. However, a local decrease of the entropy is expected at the source level, where the field gets singular, as entropy decreases when the information is concentrated. Thus, our method is based on a simple search for a minimum of the computed entropy versus depth curve, and the estimated depth will be that at which the minimum is attained. The method is sensitive to interference and other types of noise, and specific strategies to deal with these limitations are defined and tested on synthetic data. The depth estimate is obtained without the assumption of a specific source shape. The depth could correspond to the top or center in case of a simple, one-point source, or it may be related to an intermediate depth between the source top and its center in case of a finite, general source. We applied this method to real magnetic data from an unexploded ordnance survey, and it could verify a rather accurate depth-to-source estimate when compared with excavation results.

We present a nonlinear method to invert potential fields data, based on inverting the scaling function (τ) of the potential fields, a quantity that is independent on the source property, that is the mass density in the gravity case or the magnetic susceptibility in the magnetic case. So, no a priori prescription of the density contrast is needed and the source model geometry is determined independently on it. We assume Talwani's formula and generalize the Multi-HOmogeneity Depth Estimation (MHODE) method to the case of the inhomogeneous field generated by a general 2D source. The scaling function is calculated at different altitudes along the lines defined by the extreme points of the potential fields and the inversion of the scaling function yields the coordinates of the vertices of a multiple source body. Once the geometry is estimated, the source density is automatically computed from a simple regression of the scaling function of the gravity data vs. that generated from the estimated source body with a unit-density. We solve the above nonlinear problem by the Very Fast Simulated Annealing algorithm. The best performance is obtained when some vertices are constrained by either reasonable bounds or exact knowledge. In the salt-dome case we assumed that the top of the body is known from seismic observations and we solved for the lateral and bottom parts of the body. We applied the technique on three synthetic cases of complex sources and on the gravity anomalies over the Mors salt-dome (Denmark) and the Godavari Basin (India). In all these cases the method performed very well in terms of both geometrical and source-property definition. This article is protected by copyright. All rights reserved

Imaging of potential fields yields a fast 3D representation of the source distribution of potential fields. Imaging methods are all based on multiscale methods allowing the source parameters of potential fields to be estimated from a simultaneous analysis of the field at various scales or, in other words, at many altitudes. Accuracy in performing upward continuation and differentiation of the field has therefore a key role for this class of methods. We here describe an accurate method for performing upward continuation and vertical differentiation in the space-domain. We perform a direct discretization of the integral equations for upward continuation and Hilbert transform; from these equations we then define matrix operators performing the transformation, which are symmetric (upward continuation) or anti-symmetric (differentiation), respectively. Thanks to these properties, just the first row of the matrices needs to be computed, so to decrease dramatically the computation cost. Our approach allows a simple procedure, with the advantage of not involving large data extension or tapering, as due instead in case of Fourier domain computation. It also allows level-to-drape upward continuation and a stable differentiation at high frequencies; finally, upward continuation and differentiation kernels may be merged into a single kernel. The accuracy of our approach is shown to be important for multi-scale algorithms, such as the continuous wavelet transform or the DEXP (depth from extreme point method), because border errors, which tend to propagate largely at the largest scales, are radically reduced. The application of our algorithm to synthetic and real-case gravity and magnetic data sets confirms the accuracy of our space domain strategy over FFT algorithms and standard convolution procedures.

The estimation of the structural index and of the depth to the source is the main task of many popular methods used to analyze potential field data, such as Euler deconvolution. However, these estimates are unstable even in the presence of a weak amount of noise, and Euler deconvolution of noisy data leads to an underestimation of structural index and depth. We have studied how the structural index and depth estimates are affected by applying low-pass filtering to the data. Physically based low-pass filters, such as upward continuation and integration, have been shown to be the best choice over a range of altitudes (upward continuation) or orders (integration filters), mainly because their outputs have a well-defined physical meaning. In contrast, mathematical low-pass filters require that the filter parameters be tuned carefully by means of several trial tests to produce optimally smoothed fields. The C-norm criterion is a reliable strategy to produce a stabilized vertical derivative, and we discourage Butterworth filters because they tend to a vertical integral filter, for a high cutoff wavenumber, thus complicating the interpretation of the estimated structural index. We found that the estimated structural index and depth to source increase proportionally with the amount of smoothing, unless in the case of overfiltering. In that case, the severe distortion of the original field may cause a decrease of the estimated structural index and depth to source.

We used a fast method to interpret self-potential data: the depth from extreme points (DEXP) method. This is an imaging method transforming self-potential data, or their derivatives, into a quantity proportional to the source distribution. It is based on upward continuing of the field to a number of altitudes and then multiplying the continued data with a scaling law of those altitudes. The scaling law is in the form of a power law of the altitudes, with an exponent equal to half of the structural index, a source parameter related to the type of source. The method is autoconsistent because the structural index is basically determined by analyzing the scaling function, which is defined as the derivative of the logarithm of the self-potential (or of its [Formula: see text]th derivative) with respect to the logarithm of the altitudes. So, the DEXP method does not need a priori information on the self-potential sources and yields effective information about their depth and shape/typology. Important features of the DEXP method are its high-resolution power and stability, resulting from the combined effect of a stable operator (upward continuation) and a high-order differentiation operator. We tested how to estimate the depth to the source in two ways: (1) at the positions of the extreme points in the DEXP transformed map and (2) at the intersection of the lines of the absolute values of the potential or of its derivative (geometrical method). The method was demonstrated using synthetic data of isolated sources and using a multisource model. The method is particularly suited to handle noisy data, because it is stable even using high-order derivatives of the self-potential. We discussed some real data sets: Malachite Mine, Colorado (USA), the Sariyer area (Turkey), and the Bender area (India). The estimated depths and structural indices agree well with the known information.

We analyse two classes of methods widely diffused in the geophysical community, especially for studying potential fields and their related source distri- butions. The first is that of the homogeneous fractals random models and the second is that of the homogeneous source distributions called “one-point” distributions. As a matter of fact both are depending on scaling laws, which are used worldwide in many scientific and economic disciplines. However, we point out that their appli- cation to potential fields is limited by the simplicity itself of the inherent assumptions on such source distributions. Multifractals are the models, which have been used in a much more general way to account for complex random source distributions of density or susceptibility. As regards the other class, a similar generalization is proposed here, as a multi-homogeneous model, having a variable homogeneity degree versus the position. While monofractals or homogeneous functions are scaling functions, that is they do not have a specific scale of interest, multi-fractal and multi-homogeneous models are necessarily described within a multiscale dataset and specific techniques are needed to manage the information contained on the whole multiscale dataset.

Summary We propose a new approach to the study the Normalized Full Gradient, based on assessing the entropy of the normalized downward continued field at each level of continuation. We show that the estimated entropy may be efficiently used to obtain information about potential fields source depth. As entropy decreases when the information is concentrated, we should detect the source depth at the continuation level where the entropy has a local minimum. We test this idea on synthetic and real magnetic data and found rather accurate results.

Standard Euler deconvolution is applied to potential-field functions that are homogeneous and harmonic. Homogeneity is necessary to satisfy the Euler deconvolution equation itself, whereas harmonicity is required to compute the vertical derivative from data collected on a horizontal plane, according to potential-field theory. The analytic signal modulus of a potential field is a homogeneous function but is not a harmonic function. Hence, the vertical derivative of the analytic signal is incorrect when computed by the usual techniques for harmonic functions and so also is the consequent Euler deconvolution. We show that the resulting errors primarily affect the structural index and that the estimated values are always notably lower than the correct ones. The consequences of this error in the structural index are equally important whether the structural index is given as input (as in standard Euler deconvolution) or represents an unknown to be solved for. The analysis of a case history confirms serious errors in the estimation of structural index if the vertical derivative of the analytic signal is computed as for harmonic functions. We suggest computing the first vertical derivative of the analytic signal modulus, taking into account its nonharmonicity, by using a simple finite-difference algorithm. When the vertical derivative of the analytic signal is computed by finite differences, the depth to source and the structural index consistent with known source parameters are, in fact, obtained.

SUMMARY The discrete wavelet transform (dwt), using the good property of localization of wavelet bases has been used as a powerful tool in filtering and denoising problems. The continuous wavelet transform (cwt) exploits the upward continuation properties of the field horizontal derivative and allows the location of potential field singularities in a simple geometrical manner. Within the cwt space-scale framework, the lines formed by joining, at different scales, the modulus maxima of the wavelet coefficients (multiscale edge detection method) intersect each other at the position of the point source or along the edges of the causative body. As long as the multiscale edge detection method is applied to experimental data the procedure may, however, fail, since the observed anomalies are the superposition of effects of sources having different density contrast, geometrical size and depths. We show that wavelet transform modulus maxima lines attributed to deep sources do not converge toward the true depths, but yield completely erroneous solutions. On the other hand, use of nth-order derivatives of the potential field allows the enhancement of the shallowest source effects, preventing us from obtaining information on the deeper ones. In this paper we therefore try to overcome this problem by a joint application of cwt and dwt. A localized dwt filter coupled to compactness criterion allows the separation of the effects due to the deeper sources from those of the shallower ones. Hence, the multiscale edge detection method, applied separately to the original and the filtered signals enabled the estimation of the depth of shallower and deeper sources, respectively. This analysis, performed on the gravity anomalies of Sardinia (Italy), has given estimations of the depths to both the Campidano graben and the Moho discontinuity, in good agreement with previous interpretations of gravity and seismic data.

Many methods have been used to upward continue potential field data. Most techniques employ the Fast Fourier transform, which is an accurate, quick way to compute level-to-level upward continuation or spatially varying scale filters for level-to-draped surfaces. We here propose a new continuation approach based on the minimum-length solution of the inverse potential field problem, which we call Volume Continuation (VOCO). For real data the VOCO is obtained as the regularized solution to the Tikhonov problem. We tested our method on several synthetic examples involving all types of upward continuation and downward continuation (level-to-level, level-to-draped, draped-to-level, draped-to-draped). We also employed the technique to upward continue to a constant height (2500 m a.s.l.), the high-resolution draped aeromagnetic data of the Ischia Island in Southern Italy. We found that, on the average, they are consistent with the aeromagnetic regional data measured at the same altitude. The main feature of our method is that it does not only provide continued data over a specified surface, but it yields a volume of upward continuation. For example, the continued data refers to a volume and thus, any surface may be easily picked up within the volume to get upward continuation to different surfaces. This approach, based on inversion of the measured data, tends to be especially advantageous over the classical techniques when dealing with draped-to-level upward continuation. It is also useful to obtain a more stable downward continuation and to continue noisy data. The inversion procedure involved in the method implies moderate computational costs, which are well compensated by getting a 3D set of upward continued data to achieve high quality results.

In potential field inversion problems we usually solve underdetermined systems and realistic solutions may be obtained by introducing a depth-weighting function in the objective function. The choice of the exponent of such power-law is crucial. It was suggested to determine it from the field-decay due to a single source-block; alternatively it has been defined as the structural index of the investigated source distribution. In both cases, when k -order derivatives of the potential field are considered, the depth-weighting exponent has to be increased by k with respect that of the potential field itself, in order to obtain consistent source model distributions. We show instead that invariant and realistic source-distribution models are obtained using the same depth-weighting exponent for the magnetic field and for its k -order derivatives. A similar behavior also occurs in the gravity case. In practice we found that the depth weighting-exponent is invariant for a given source-model and equal to that of the corresponding magnetic field, in the magnetic case, and of the 1st derivative of the gravity field, in the gravity case. In the case of the regularized inverse problem, with depth-weighting and general constraints, the mathematical demonstration of such invariance is difficult, because of its non-linearity, and of its variable form, due to the different constraints used. However, tests performed on a variety of synthetic cases seem to confirm the invariance of the depth-weighting exponent. A final consideration regards the role of the regularization parameter; we show that the regularization can severely affect the depth to the source because the estimated depth tends to increase proportionally with the size of the regularization parameter. Hence, some care is needed in handling the combined effect of the regularization parameter and depth weighting.

In this paper we present a new method for source parameter estimation, based on the local wavenumber function. We make use of the stable properties of the Depth from EXtreme Points (DEXP) method, in which the depth to the source is determined at the extreme points of the field scaled with a power-law of the altitude. Thus the method results particularly suited to deal with local wavenumber of high-order, as it is able to overcome its known instability caused by the use of high-order derivatives. The DEXP transformation enjoys a relevant feature when applied to the local wavenumber function: the scaling-law is in fact independent of the structural index. So, differently from the DEXP transformation applied directly to potential fields, the Local Wavenumber DEXP transformation is fully automatic and may be implemented as a very fast imaging method, mapping every kind of source at the correct depth. Also the simultaneous presence of sources with different homogeneity degree can be easily and correctly treated. The method was applied to synthetic and real examples from Bulgaria and Italy and the results agree well with known information about the causative sources.

An analysis of the field scaling power spectrum yields useful information about the source distribution, but it is uncertain whether deterministic, random, fractal or mixed approaches have to be used for the interpretation. To this end, the scaling properties of potential field spectra are analysed for a number of different source models of geological interest. Besides the models of Naidu (purely random sources) and Spector and Grant (gross block statistical ensembles) we consider other types of density and magnetization distributions with spectral exponents in the fractal range, such as a single homogeneous body with a random white source distribution. Spectral slopes in the fractal range are obtained. We also study the effects of important natural sources, such as salt domes and sedimentary basins, representing them with simple Gaussians or combinations of Gaussian signals. The same spectral slopes as for gravity signals generated by 3-D fractal source distributions are found for them. Hence the power law decay of the field is not a characteristic only of fractal source models. If a 3-D fractal source distribution is assumed a priori, a way of verifying the goodness of the model is to examine the whitened field at source level. The probability that the whitened field derives from a random white population is estimated for synthetic and real anomalies by applying the usual statistical tests.

A simple and fast determination of the limiting depth to the sources may represent a significant help to the data interpretation. To this end we explore the possibility of determining those source parameters shared by all the classes of models fitting the data. One approach is to determine the maximum depth-to-source compatible with the measured data, by using for example the well-known Bott–Smith rules. These rules involve only the knowledge of the field and its horizontal gradient maxima, and are independent from the density contrast. Thanks to the direct relationship between structural index and depth to sources we work out a simple and fast strategy to obtain the maximum depth by using the semi-automated methods, such as Euler deconvolution or depth-from-extreme-points method (DEXP). The proposed method consists in estimating the maximum depth as the one obtained for the highest allowable value of the structural index ( N max ). N max may be easily determined, since it depends only on the dimensionality of the problem (2D/3D) and on the nature of the analyzed field (e.g., gravity field or magnetic field). We tested our approach on synthetic models against the results obtained by the classical Bott–Smith formulas and the results are in fact very similar, confirming the validity of this method. However, while Bott–Smith formulas are restricted to the gravity field only, our method is applicable also to the magnetic field and to any derivative of the gravity and magnetic field. Our method yields a useful criterion to assess the source model based on the (∂ f /∂ x ) max / f max ratio. The usefulness of the method in real cases is demonstrated for a salt wall in the Mississippi basin, where the estimation of the maximum depth agrees with the seismic information.

We illustrate a new imaging method to estimate the depth to the sources of potential fields and the structural index. The method consists of applying the Depth from EXtreme Point (DEXP) transformation to the ratio (R) between two different-order partial derivatives of the field. While the scaling function of the potential field depends on the structural index, we show that the scaling function of R merely depends on the difference between the two used orders of differentiation. This allows three main features to be established for the DEXP transformation of R: (1) it is independent from the structural index; (2) the estimation of the source depths is fully automatic, simply consisting in the search of position of the extreme points of the DEXP image and (3) the structural index of each source is finally determined from the scaling function or the extreme points using the estimated depth. Besides the well-known characteristics of the DEXP transformation, such as high-resolution and stability, the DEXP transformation of R enjoys one more relevant feature: it can be applied to multisource cases, yielding simultaneously correct estimations of structural index and depth for each source in the same image. However, while the DEXP transformation is a linear transformation of the field, the DEXP transformation of R is non-linear, and a procedure is described to circumvent the non-linear effects. The method is tested with synthetic examples and the estimated source parameters show a good agreement with the true values. The method was applied also to real magnetic data from the Pima copper mine, Arizona, USA, Hamrawien area, Egypt and Cataldere, Bala district of Turkey. The results are consistent with the known information about the causative sources.

We compare the effect of the smoothing of magnetic data on the estimation of the structural index by Euler deconvolution. We analyze physically-based as well as mathematical filters. In general, we found that low-pass filtering the data leads to an increase of the estimated structural index.

Several noniterative, imaging methods for potential field data have been proposed that provide an estimate of the 3D magnetization/density distribution within the subsurface or that produce images of quantities related or proportional to such distributions. They have been derived in various ways, using generalized linear inversion, Wiener filtering, wavelet and depth from extreme points (DEXP) transformations, crosscorrelation, and migration. We demonstrated that the resulting images from each of these approaches are equivalent to an upward continuation of the data, weighted by a (possibly) depth-dependent function. Source distributions or related quantities imaged by all of these methods are smeared, diffuse versions of the true distributions; but owing to the stability of upward continuation, resolution may be substantially increased by coupling derivative and upward continuation operators. These imaging techniques appeared most effective in the case of isolated, compact, and depth-limited sources. Because all the approaches were noniterative, computationally fast, and in some cases, produced a fit to the data, they did provide a quick, but approximate picture of physical property distributions. We have found that inherent or explicit depth-weighting is necessary to image sources at their correct depths, and that the best scaling law or weighting function has to be physically based, for instance, using the theory of homogeneous fields. A major advantage of these techniques was their speed, efficiently providing a basis for further detailed, follow-up modelling.

Summary We describe a multiscale method for source parameter estimations (depth to the source, homogeneity degree) based on a new theory, which we propose for studying explicitly inhomogeneous field. The theory consists in enlarging the set of homogeneous fields with those having a fractional homogeneity-degree and in relaxing the standard homogeneous equation to a local homogeneity equation. We show the validity of the method as applied to the inhomogeneous-degree gravity fields of a finite vertical cylinder.

A new method based on 3D multiridge analysis of potential fields assumes a 3D subset in the harmonic region and studies the behavior of potential field ridges, which are built by joining extreme points of the analyzed field computed at different altitudes. Three types of ridges are formed by searching for the zeros of the first horizontal and first vertical derivatives of the potential field (types I and II, respectively) and the zeros of the potential field itself (type III). This method uses a redundant set of ridges, called a multiridge set, to determine source type and location. For homogeneous potential fields generated by simple sources, all of the ridges are straight lines converging to the source position. This method analyzes the multiridges by using a geometric criterion to find the source position at the intersection of the multiridge set and by solving the three reduced Euler equations associated with ridge types I, II, and III. The reduced Euler type I and II equations are used to obtain the structural index and the vertical and horizontal source positions; equation type III estimates the horizontal and vertical source positions. Tests on synthetic as well as the Bishop model field yield good results even with noise-corrupted data. Results obtained using magnetic data collected over the wreck of a military ship in the Tyrrhenian Sea successfully determine its vertical and horizontal positions and the structural index.

Summary We present a method to determine the source properties of potential fields by exploring the field at multiple scales. At each scale the depth to the source and the structural index are determined by analyzing the scaling function τ, a dimensionless quantity which defines the scaling properties of the field, and the differential scaling function β. For homogeneous fields, the estimated results are scale invariant. The source properties are determined either by a geometrical method applied to the a single ridge or by solving for the zeros of β.

Euler deconvolution has become a standard tool in rapid, semi-automated interpretation of potential fields. Its success derives from its flexibility, providing the source position and a parameter describing the shape of the source (structural index, SI) for all the possible one point sources (point, horizontal or vertical line, dike, contact). The Euler algorithms are particularly prone to regional (even constant) fields and noise (e.g.: FitzGerald et al., 2004). The presence of regional fields was the main reason for the strategy proposed in the first formulation of the method, that considered a moving window approach in which the regional field was approximated as a constant. This constant background (B) and the structural index are coupled in the Euler interpretation formula, and it was noted that the simultaneous estimation of SI, B and z0 was not stable. For this reason Thompson (1982) suggested to input the value of the structural index to obtain the position (x0, z0) of the point-source and B. Anyway this approach suffers of a certain degree of subjectivity because the choice of SI influences directly the depth estimate. Thus different approaches were devised to deal with the problem of the background fields allowing to obtain the SI as an unknown (e.g.: Stavrev, 1997; Hsu, 2002; Nabighian and Hansen, 2001). Meanwhile, other methods recovering the same parameters (position and SI) were proposed (e.g.: Smith et al., 1998; Salem and Ravat, 2003). Among them, the Continuous Wavelet Transform method (e.g.: Hornby et al., 1999; Sailhac and Gibert, 2003) that uses the information coming from the field upward continued to a set of different altitudes. This method has reduced sensitivity to noise with respect to the Euler deconvolution, because in the upward continued field the highwavenumbers are naturally attenuated. This method, however, tries to recover the information about the source using simultaneously data at as much altitudes as possible, both with the geometrical method (to recover z0) and the linear regression approach (to recover z0 and SI). In this paper we present a formulation of the Euler deconvolution method that uses a vertical profile of data above an anomaly source. It can be applied to almost all the Euler algorithms to tackle the problem of the regional field and, using upward continued fields, it efficiently deals with the problem of noise. Method

Summary A new method is described to determine the depth to sources of potential fields. The theory is here developed for sources such as poles or dipoles, but it may be extended to lines of poles, line masses, lines of dipoles, dykes, ribbons and so on. The field is scaled at several altitudes following a specific law analytically determined. The depth to the source is finally obtained by determining the scaled field extreme points. The method is fast and stable. It may be applied to any vertical derivative of a Newtonian potential, by using theoretically determined scaling functions for each order of derivative.

We start from the general inverse problem for potential fields and discuss the validity and suitability of using not only horizontal variations of them, but also their vertical ones. Hence multilevel data sets are considered, being they obtained either from measured data at different levels or by measuring the field just at the lower level and then generating by upward continuation the data at the remaining upper levels. In the latter case the upward continued data must be considered as the true ones plus some errors due to experimental errors and to the fact that they are generated from a discrete data set known on a finite survey area. Several forms of a priori information, such as that assuming the source can be represented by a fault or a sheet sandwich model, may allow the information contained in multilevel data to be effectively used for the interpretation of gravity and magnetic data. Recently established techniques of analysis, such as the continuous wavelet transform, are applied successfully to potential fields using implicitly the information contained in multilevel data. Assuming a block model for the source domain, the upward continuation formula may help to improve the solution of either 1D, 2D or 3D inverse problems enlarging the system with the equations related to the vertical variations of the fields, thereby reducing the algebraic ambiguity. Meaningful and computationally suitable quantities, such as weighted averages of potential fields, are also closely related to the use of multilevel data, providing useful insights for the determination of the depth distribution of the sources.

Fractal dimension is widely used to give a measure of variability and roughness of curves, signals, objects, sta- tistical distributions, and so on, We found that an often used method, the so-called triangular prism surface- area method, for estimating the tractal dimension of fractal surfaces possesses some intrinsic mistake., in appli- cation. This note describes the misintel"pretation and suggests the proper application, that we calI Revised Tri- angular Prism Method (RTPM). To show its feasibility we apply RTPM to some synthetic Euclidean and frac- tal surfaces of known dimension.

Ambiguity of depth estimation of magnetic sources via spectral analysis can be reduced representing its field via a set of space-frequency atoms. This is obtained throughout a continuous wavelet transform using a Morlet analyzing wavelet. In the phase-plane representation even a weak contribution related to deep-seated sources is clearly distinguished with respect a more intense effect of a shallow source, also in the presence of a strong noise. Furthermore, a new concept of local power spectrum allows the depth to both the sources to be correctly interpreted. Neither result can be provided by standard Fourier analysis. Another method is proposed to reduce ambiguity by inversion of potential field data lying along the vertical axis. This method allows a depth resolution to gravity or the magnetic methods and below some conditions helps to reduce their inherent ambiguity. Unlike the case of monopoles, inversion of a vertical profile of gravity data above a cubic source gives correct results for the cube side and density.