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ISPRS Journal of Photogrammetry and Remote Sensing 66 (2011) 818–832

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ISPRS Journal of Photogrammetry and Remote Sensing

journal homepage: www.elsevier .com/ locate/ isprs jprs

Auto-detection and integration of tectonically significant lineaments from SRTMDEM and remotely-sensed geophysical data

Alaa A. Masoud a,⇑, Katsuaki Koike b,1

a Geology Department, Faculty of Science, Tanta University, 31527 Tanta, Egyptb Graduate School of Science and Technology, Kumamoto University, 2-39-1 Kurokami, Kumamoto 860-8555, Japan

a r t i c l e i n f o a b s t r a c t

Article history:Received 2 January 2010Received in revised form 15 August 2011Accepted 16 August 2011

Keywords:Adaptive shadingSegment tracingSegment groupingB-splineTectonic modelEgypt

0924-2716/$ - see front matter � 2011 Internationaldoi:10.1016/j.isprsjprs.2011.08.003

⇑ Corresponding author.E-mail address: [email protected] (A.A. Mas

1 Present address: Graduate School of Engineering, K

A set of techniques was developed for automatically detecting tectonic lineaments from multi-sourceremotely-sensed data at various scales. The techniques include adaptive shading of grid data to enhancelinear features, a segment-tracing algorithm to extract line segments from the shaded grid data, groupingof the segments by concatenating short segments, and connecting them by proximity and co-linearity cri-teria to form a lineament that represents significant tectonic structure. B-spline smoothing was adoptedfor lineament representation. Finally, a technique for assessing the orientations and styles of faulting(normal, reverse, and strike-slip types) was developed for use in characterizing the extrapolated fractureplanes. The applicability of the developed techniques was examined using 30 arc-second topography/bathymetry grids, 1-min gravity anomaly grids, and 2-min total field magnetic intensity grids coveringEgypt and its surroundings. Lineaments derived from data types so diverse in composition and from var-ious depths corresponded well with the referenced tectonic features over much of the region. Prominenttrends and faulting styles of lineaments provided important clues as to the timing of their developmentas well as strong support for a structural inheritance model. Results demonstrated the effectiveness of thedeveloped techniques combined with integration of remotely-sensed data in detecting regional fracturesystems accurately and in characterizing geodynamics over a long timeframe.� 2011 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier

B.V. All rights reserved.

1. Introduction

Lineaments are one of the essential topographic features used inexploring for resources such as groundwater, hydrocarbons, miner-als, and geothermal energy, as well as in mapping hazard suscep-tibility from earthquakes and landslides (e.g., Guild, 1974; Dixand Jackson, 1981; Sibson, 1986a,b; Boucher, 1995; Rowan andBowers, 1995; Rowland and Sibson, 2004; Masoud et al., 2007).In this regard, characteristics of lineaments such as spatial extent,density, intersection, and orientation have proved significant be-cause they indicate zones and trends of high permeability and/orlow pressure that may serve as pathways for migration, and thustargets for increased reserve. Lineaments may represent faults thatcontrol basin development and distribution of reservoirs (Warner,1997). Regional lineaments are commonly interpreted as surfaceexpressions of geologic weak zones at tectonic boundaries of ba-sins and plates, as well as faults and rock fractures (e.g., Oakey,1994; Fichler et al., 1999; Kudo et al., 2004; Salem et al., 2005;Milbury et al., 2007; Austin and Blenkinsop, 2008).

Society for Photogrammetry and R

oud).yoto University, Kyoto, Japan.

With recent advances in computer hardware and spatial-analy-sis techniques, computer-assisted lineament analysis on a largescale has become practical for characterizing geologic structuresand tectonics. Many methods of automatically extracting linea-ments from digital data have been proposed, such as from satelliteimages and the Digital Elevation Model (DEM). Such methods aremostly based on edge-detection techniques using spatial and mor-phological filters (e.g., Morris, 1991; Süzen and Toprak, 1998; Tri-pathi et al., 2000). In cases where the results are extrapolated usingfilters, the frequency and connectivity of the line segments isstrongly affected by the scale of the source data and the detectionparameters (Argialas and Mavrantza, 2004). This may preventplausible representation of tectonically significant lineaments rel-evant to long rock fractures and faults. Therefore, it is indispensibleto develop an auto-detection technique that can increase the fre-quency and connectivity of detected lineaments so that resultantlineament maps resemble fault maps by geologists.

Another important point to be considered for auto-detection oftectonically significant lineaments is the selection of suitable datasources. Generally, satellite images representing reflectance andbackscattering characteristics of the earth’s surface in response toelectromagnetic waves at various wavelengths are used for linea-ment extraction. However, linear artificial features unrelated to

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A.A. Masoud, K. Koike / ISPRS Journal of Photogrammetry and Remote Sensing 66 (2011) 818–832 819

fractures, such as land use boundaries and land cover, also tend tobe detected in satellite images. Instead of such data, it may be morepromising to use grids of data from multiple sources that encom-pass the widely varied composition and depths represented bythe topography and the subsurface geophysical attributes, espe-cially when integrated at various scales. This can lead to a betterunderstanding of the relationship between tectonic trends andanomalies from which they develop.

Based on the above background, this study has three purposes.The first is to develop a method that can enhance line features ingrid data. For this, we use grids of composite topography–bathym-etry data, gravity data, and aeromagnetic data. The second is to de-velop methods for detection and connection of lineaments. Thethird is an effective integration of lineaments from such multi-source data that describe different geologic attributes. The resul-tant lineaments are used in geological characterization to estimateorientations of fracture planes and fault types. We demonstrate,through a case study of a wide area around Egypt situated at theintersection of the African, Arabian, and Eurasian plate boundaries,that the techniques developed are useful for better understandingregional tectonics and geodynamics.

2. Methodology

With these purposes in mind, the methods developed forenhancement of line features in grid data, identification and group-ing of lineaments, calculating orientations of fracture planes, andfault type modeling are described below. The developed tech-niques are applicable not only to the DEM, but also to gravityand magnetic grid data, as was the case in the research presentedherein.

Fig. 1. Shaded DEM at constant tilt angle of 45� from the horizon with four illuminationSW Sinai Peninsula in Egypt. Location of the test DEM is shown in Fig. 7. White arrows

2.1. DEM shading

With the increase in regions for which elevation data has beenextrapolated using high spatial resolution DEM, geomorphologicand lineament analyses using DEM have also increased signifi-cantly. Because shading of DEM can enhance the linear features,shading has played an important role in such analyses. A varietyof shading methods have been proposed such as the Lambertianreflection (Yoëli, 1967; Foley et al., 1990; Horn, 1982), the Phongillumination (Bui-Tuong, 1975), the Blinn reflection (Blinn, 1977),and ray-tracing (Foley et al., 1990), which are generally based ona physical model for illumination at specified azimuth and tilt an-gles over a DEM. Assuming a constant albedo equal to unity andLambertian reflectance properties for the whole DEM surface, thegray shade intensity value, G (x, y), for a pixel located at (x, y) inthe DEM space becomes proportional only to the squared innerproduct of the directional vector along the illumination s, andthe surface normal vector t as follows:

Gðx; yÞ ¼ cðs � tÞ2 ¼ c�txsx � tysy þ szffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

tx2 þ ty2 þ 1p

!2

ðc : constantÞ ð1Þ

where:

s ¼ ½sx; sy; sz�T ð2Þ

and

t ¼ ½�tx;�ty;1�T ð3Þ

The horizontal components sx and sy control the illuminationazimuth, while the vertical sz defines the tilt angle. This shadeintensity at a given position varies with illumination azimuth

azimuths from west, north, east, and south, for the test DEM data (middle figure) indemarcate areas of suppression and shift in position of the linear features.

820 A.A. Masoud, K. Koike / ISPRS Journal of Photogrammetry and Remote Sensing 66 (2011) 818–832

and tilt. Therefore, linear features striking perpendicularly to theillumination azimuth are optimally emphasized, whereas theyare suppressed if oriented parallel to the illumination (Cooper,2003). This bias can be confirmed by the DEM for a part of ourstudy area located in southern Sinai, Egypt, where a flat plain(Qaa Plain) lies adjacent to high mountain ranges (Fig. 1). In thisfigure, the position of the intense linear shades resulting fromthe same topographic features varies greatly even for opposite illu-mination azimuths.

To reduce the bias from the illumination effect on the appear-ance of linear features, Zhou and Dorrer (1995) presented a meth-od consisting of a wavelet transform of the DEM and thenadjustment of the main illumination direction. Prechtel (2000)developed an alternative technique, identifying clusters of simi-larly-oriented cells from which a triangulation is derived for thedeflection of direction. Masoud and Koike (2006) applied a multi-directional technique that assigns the average of shade intensitiesfor several illumination directions to the kernel center for accentu-ating tectonic lineaments. However, the problem in the aboveshading methods is that the tilt of the illumination source is keptconstant. The tilt change should affect the shade intensities, sothe characteristics of the line segments extrapolated from suchshading may vary accordingly.

In order to address this problem, we propose a technique foradaptive-tilt multi-directional shading (ATMDS) to obtain themaximum shade intensity at each DEM grid node. ATMDS setsthe six northward illumination azimuths at 30� intervals clockwisefrom west and automatically sets the tilt angle defined by the vec-tor component sz in Eq. (1) to range from 0� to 45� upward from thehorizon as shown in Fig. 2. The reason for selecting these limiteddirections is because opposite southward illumination azimuthsand tilt angles greater than 45� suppress shade intensities ratherthan enhancing them. At each grid node, shade intensities for thesix illumination azimuths and the tilt angles in the defined rangeare calculated. Then, the maximum value is selected (Eq. (4)). Thiscalculation is repeated for all the grid nodes.

Gðx; yÞ ¼ max c�txsxi � tysyi þ szjffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

tx2 þ ty2 þ 1p

!( )2

where ði ¼ 0�;30�; . . . ;150�Þ and ðj ¼ 0� � 45�Þ ð4Þ

Figs. 1 and 3 are based on the same DEM, but Fig. 3 illustratesthe differences resulting from adopting the ATMDS method.Fig. 3 demonstrates the strong effect of applying the ATMDS meth-

Fig. 2. Schematic idea of the adaptive-tilt multi-directional shading (ATMDS) techniquranging from 0� to 45�. Base of this figure is a perspective view of the test DEM shown

od with tilt angles of 15�, 30�, and 45� on the multi-directionalshading (as compared to Fig. 1 where the ATMDS method wasnot applied). Next, linear features (line segments) were extractedfrom the DEM using the segment-tracing algorithm (STA: Koikeet al., 1995, 1998; Koike and Ichikawa, 2006) described below.The resultant extractions and rose diagrams showing the directionsof linear features are compared in Fig. 3. Table 1 summarizesdescriptive statistics of the length distributions of the line features.As clearly seen, high tilt angle (45�) is inferior to identify linear fea-tures in the flat and lowland areas such as those detected on theQaa Plain (Fig. 3c). The superiority of ATMDS can be demonstratedby the numerous linear features extrapolated thoroughly from allparts of the test area regardless of the slope of the topography(Fig. 3d). Table 1 also demonstrates the superiority of the ATMDSmethod in that the number of detected segments and the totallength are both greater compared with those detected applying15�, 30�, and 45� tilt angles.

2.2. Detection and extraction of linear features

Computer-assisted lineament detection and extraction basicallyrely on edge detectors that model edge points as extrema of datagradients over a threshold and their orientations in images. Theedge points that are straightly aligned or smoothly curved are con-nected to form a longer segment as an element of a lineament.Based on the review by Argialas and Mavrantza (2004) of thewidely used edge detectors, edge detectors can be classified intothree categories based on the principles they use: line segmentdetection based on the region-growing algorithm, spatial filteringtechniques, and the technique of directional detection of a segmentin a local area.

We tested the performance of these three categories of detec-tors that have been proved successful in many case studies: LSD(Grompone von Gioi et al., 2010), EDISON (RIUL, 2003), and STA.LSD is a parameterless linear-time line segment detector basedon the region-growing algorithm (Burns et al., 1986). In LSD, a linesegment is defined as a straight rectangular region whose edgepoints share roughly the same gradient angle. EDISON is a featureextraction tool that integrates confidence-based edge detection(Meer and Georgescu, 2001) and mean shift-based image segmen-tation (Comanicu and Meer, 2002). In EDISON, Canny Edge is ap-plied, and the line segment is adjusted to the candidate edgepoints based on thresholds for gradient, segment length, and hys-teresis criteria for type, rank, and confidence levels. STA defines a

e with six illumination azimuths at 30� interval from the northward and tilt anglein Fig. 1.

Fig. 3. Shaded DEM of the test area (DEM and the location are shown in Figs. 1 and 7, respectively) at varying tilt angles (left); (a) 15�, (b) 30�, (c) 45�, and (d) the adaptivelyset tilts, overlain with the detected segments (middle) derived by STA, and rose diagrams and counts of the segments (right).

Table 1Descriptive statistics of the length (km) of the segments by STA for the test area(shown in Fig. 3) to demonstrate sensitivity of tilt angle to detection accuracy ofsegments.

Tilt angle 15� 30� 45� ATMDS

ParameterMean 2.61 2.57 2.51 2.59Standard error 0.03 0.03 0.03 0.03Median 2.16 2.17 2.12 2.18Standard deviation 1.59 1.43 1.42 1.44Sample variance 2.53 2.06 2.02 2.08Range 19.03 16.56 15.97 16.55Minimum 0.18 0.723 0.72 0.36Maximum 19.21 17.28 16.69 16.91Sum 5638.23 5388.73 4937.75 6026.13Count 2159 2092 1965 2329


line composed of adjacent pixels as a vector element by examininglocal variance of the gray level using 11 � 11 kernel in the digitalimage, and connects retained line elements along their expecteddirections. Threshold values for the extraction and the linkage ofline elements are direction-dependent in order to avoid the illumi-nation bias. Some advantages of this method over other filteringmethods are its capabilities to (1) trace only continuous valleysand (2) extract more lineaments that are parallel to the illumina-tion azimuth as well as those located in shadow areas.

The three detectors were applied to the DEM for the test area(shown in Figs. 3(d) and 4(a)) that was shaded using the ATMDS.Fig. 4(b–d) are the line segments extracted by LSD, EDISON, andSTA, respectively. The performance of LSD depends on shade inten-sity; line segments were not extracted from low intensity (dark)zones. Connectivity of the line segments from LSD is the worst

Fig. 4. Line segments for the test area detected from (a) the shaded DEM by ATMDS using (b) LSD, (c) EDISON, and (d) STA, respectively. Yellow arrows point to remarkableridge features. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)


among the three detectors: most extracted segments do not followthe edges of those shade intensities that correspond with the longlinear valleys and the boundaries between the mountain and plain.Line segments from EDISON are higher in frequency comparedwith those derived from LSD. However, there are two drawbacks:low detection accuracy from low shade intensity zones andappearance of many curved segments with short length in areaswith rolling topography. Fracture information cannot be derivedfrom such curved segments. Another serious problem is that LSDand EDISON cannot distinguish between line segments corre-sponding to ridge and valley features. Fractures tend to form linearvalley features by selective erosion. More than 50% of the line seg-ments detected are located on ridge features, as marked in Fig. 4(band c). Conversely, STA can overcome the above problems as linesegments are extracted thoroughly even from low shade intensityzones, and their connectivity into straight or slightly curved fea-tures is higher. These advantages enable STA to produce a moreprecise lineament map. Consequently, we selected STA for the lin-eament analysis.

2.3. Lineaments produced from line segments

Line segments detected using STA (Figs. 3d and 4d) provide thepotential candidates for lineaments that correspond to fracturesand have tectonic significance. To identify such geologic linea-ments, the next step is to concatenate line segments to form onelong lineament based on co-linearity and proximity. We have im-proved the algorithm for concatenation in STA to produce long lin-eaments from the short line segments by using B-spline asdescribed below.

As the first step, segments are sorted according to their start Xand then Y coordinates from the top-left margin of the study area.Each line segment is nominated as a start segment in a group. The

algorithm searches for adjacent segments that cross-cut or aredisplaced with a dynamic threshold distance related to the startsegment geometry (Koike et al., 1995, 1998) and are co-linearat less than or equal to 10� angle. The longest searched segmentthat satisfies these criteria and give the largest cumulative lengthwill be listed in the group as the second candidate segment. Thecumulative length is calculated from the start point of the startsegment to the end point of the candidate segment. The processis repeated for the candidate segment searching for the next seg-ment in the group and so on until there would be no segmentsthat satisfy the conditions. After completing the first grouping,regrouping that merges cases of more than two groups is sequen-tially adopted using the same criteria and the average azimuthsof the groups. The co-linearity angle (Fig. 5) proved crucial inadjusting for length, frequency, and straightness of the resultinglineaments. Performance analysis of the grouping algorithmshowed that the smaller the co-linearity angle, the fewer seg-ments there are that satisfy the condition. Hence, short straightlineaments are more abundant, and vice versa. Selection of the10� co-linearity criteria is therefore based on the objective ofretaining the maximum achievable length, frequency, andstraightness, as well as minimizing the directional difference be-tween the original segments and the resultant extrapolatedlineament.

The second step is to approximate the grouped line segmentswith one smooth curve using B-spline (Fig. 5). This curve is re-garded as a lineament. We selected the cubic B-spline curve forrepresenting lineaments because it well preserves the geometriccontinuity of the line segments within the group and is indepen-dent of the number of control points (Masoud and Koike, 2009).Since B-spline is a well-known piecewise polynomial functionwidely used for curve generation (e.g., Prautzsch et al., 2002), itis only briefly explained herein.

P0

P2

P3

P4

u=0 u=1

N=6

P1B-spline Curve

Co-linearity angle

Pn B-spline curve control points

Start and end points of segments

Fig. 5. Connection of line segments and approximation of them into a smooth curve using B-spline.


We set the inter-group control points (n + 1) as P0, P1, P2, . . ., Pn

for n segments. P0 and P1 are the starting and midpoints of the firstsegment, and Pn�1 and Pn are the mid and end points of the lastsegment. The rest P2, . . ., Pn�2 are the midpoints of the other seg-ments. The B-spline curve of order u (degree u�1) is then definedby the control points and the knot vector u:

CðuÞ ¼Xnþ1

i¼1

Ni;pðuÞPi; 0 6 u 6 1 ð5Þ

where C(u) is a piecewise continuous function defining the curvewith N = n + 1 discrete control points Pi. Ni,p(u) is a basis functionthat blends the control points to form a smooth curve. The bound-ary condition of u is u = 0 at the first control point (i = 1) and u = 1 atthe last control point (i = n + 1).

2.4. Estimating fracture planes and fault modeling from extrapolatedlineaments

Under the assumption that a fracture along a fault plane has afinite dimension, a lineament, which is interpreted as a trace of afracture or fault plane on the terrain composed of concatenatedline segments, can be used to calculate the fault plane orientation(strike and dip). The trace curvature on the surface depends on thedip angle of the interpreted plane, which can be expressed by ageometrical relationship between the terrain and the plane. A nor-mal vector of a plane is denoted by n. The geometrical relationshipcan be described through vector analysis (Koike et al., 1998). Thistechnique has been applied successfully to model fault types inKyushu island in Japan and validated by structural and lithologicdata (Koike et al., 2001). In this technique, a slope made by theDEM and its normal vector is represented by ti (i = 1, 2, . . ., ms),where ms is the number of slopes through which the lineamentsclassified into the same group pass. Let the strike vector of the lin-eament in a group be li. Because li is equivalent to the intersection

(a)

T321 ),,( iiii llll

n

T321 ),,( iiii tttt

Lineament

SurfaceFault plane normal vecto

Fig. 6. (a) Definition of vectors used in estimating azimuth of ‘‘fracture’’ plane from linebetween the average of surface normal vectors and the normal vector of the interpreted

of the desired plane and the surface slope i (Fig. 6), li should beequal to the vector product of the two normal vectors:

li ¼ ti � n ði ¼ 1;2; . . . ;msÞ ð6Þ

The only unknown in this equation is n. It is prudent, therefore,to obtain n by applying the least-squares method and solving thenormal equation that is deduced from:

@

@n

Xms

i¼1

xiðli � ti � nÞTðli � ti � nÞ( )

¼ 0 ð7Þ

where xi is a weighting coefficient.Furthermore, directional relationship between n and the aver-

age vector of ti, t, is used to judge whether the obtained plane rep-resents a normal or reverse fault feature. Dipping criteria of theplane, its direction and angle of dip, are then estimated. We definethree features, normal, reverse, and strike-slip types depending onwhether the directions of n and t are the same (normal), opposite(reverse), and undetermined (strike-slip) as shown in Fig. 6. Be-cause strike-slip movements generally do not accompany down-throws on the fault plane, the fault type cannot be specified bythe simple geometrical relationship in Fig. 6. Therefore, the unde-termined type is assumed to have strike-slip components. Thetechnique is thought to be applicable to the gravity and magneticlineaments which depict very narrow low anomaly zones thatmay be caused by planes associated with faulting of different stylesand types of movement.

3. Study area and data

The techniques developed in our study were applied to detect-ing tectonically significant lineaments and characterizing the geo-logic structures for Egypt and adjacent areas (Fig. 7). The region isgeologically and structurally complex, composed of lithologies ofdifferent types and ages and showing varying tectonic styles andtrends. Better understanding of the kinematics and the spatial

Reverse fault

Average surface normal vectors

Normal fault

n

t

n

(b)

r

aments, and (b) schematic idea of judging fault type by the directional relationshipfracture plane.

Fig. 7. Geological and kinematic structural map of the study area modified from CGMW (2006). Structural features described in Fig. 2 of Guiraud and Bosworth (1999) areshown in pink and those described in Fig. 10 of (Keely, 1994) are shown in black.


extent of the dominant tectonic trends may have implications foridentifying the potential for natural resources in the area. The re-gion has been shaped through extension-dominated tectonismalternated with intermittent less-influential but intense compres-sive phases (Guiraud and Bosworth, 1999). Extensional tectonicslead to the Neotethys opening along the Mediterranean Basins inthe Permian (Guiraud and Bosworth, 1999). Polyphased extension,inversion, and folding resulted in the development of the SyrianArc belts, commenced in the latest Cretaceous, and continued asa series of pulses into the Late Oligocene (Keeley and Massoud,1998). The convergence and suturing of Africa–Arabia with Eurasiacame to effect since the late Cretaceous and proceeded to the pres-ent (Ziegler and Roure, 1999). In the Late Oligocene, the Gulf ofSuez–Red Sea rifting initiated (Omar and Steckler, 1995). During

the Middle Miocene, the Red Sea divergent plate boundary aban-doned the Gulf of Suez and broke through the Arabian plate viathe Gulf of Aqaba–Dead Sea transform fault, establishing the pres-ent-day plate kinematic framework (Guiraud and Bosworth, 1999).

Many studies have addressed the repeated reactivation of theolder structures by plate-scale stress fields along broad lineamentsin the region (e.g., Guiraud and Bellion, 1995; Guiraud and Bos-worth, 1999; Bumby and Guiraud, 2005; Abdeen and Greiling,2005). In these studies, lineaments played an important role inunderstanding the region’s geodynamic evolution. However, thesewere commonly generalized sketches reviewed from interpreta-tions of geologic observations and scarce geophysical surveyswhich lack spatial inter-relationships. Integrating lineamentsextrapolated from various spatial data describing surface and


subsurface features seems a more promising way to draw a de-tailed, reliable structural map of the region.

We used three grid data sets: the 30-arc second topography/bathymetry grid (SRTM30_PLUS) by Becker et al. (2009); the 1-arc minute gravity anomalies grid (mGal) by Sandwell and Smith(1997); and the 2-arc minute earth magnetic anomaly (nT) gridby Maus et al. (2007) and Maus et al. (2009). For short, topogra-phy/bathymetry is hereafter termed topography. Use of the gravitydata allowed an integrated response to density contrasts, and grav-ity data was closely related to topography by a linear transfer func-

Fig. 8. STA-derived line segments (left) overlaid on shaded grid data of (a) topography, (boverlaid on the shaded DEM (top), and the shaded gravity (middle) and magnetic data (

tion. Integrating gravity and topographic data is significant instudying crustal structures (McNutt, 1979; Smith and Sandwell,1994). Gravity and magnetic data are related to different rock attri-butes in the subsurface which provides a basis for joint interpreta-tion of both kinds of data. Coinciding gravity and magneticanomalies may suggest a common origin. Contrasting gravity andmagnetic anomalies may result from erasing the crust’s magnetismby heating (Milbury et al., 2007) or from rocks with low magneticcontents (Fichler et al., 1999). Magnetic intensities of rocksoccurring below the Curie isotherms can provide insight into the

) gravity, and (c) magnetic, and their corresponding lineaments by connection (right)bottom) as shown in color.

Table 2Descriptive length statistics (unit: km) of (a) segments and (b) lineaments derivedfrom the topographic, gravity, and magnetic data (shown in Fig. 8).

Data Topography Gravity Magnetic

(a)ParameterMean 13 58 60Standard error 0.03 0.32 0.31Median 11.4 45.6 49.5Standard deviation 7.71 40.84 38.66Sample variance 59.48 1668.66 1495.17Range 107.49 481.13 592.05Minimum 4 4 4Maximum 108.50 485.13 596.05Sum (km) 823,578 913,679 930,339Count (Number) 60,131 15,689 15,349(b)Mean 81 163 305Standard error 0.93 3.28 5.35Median 69.22 138.93 267.5Standard deviation 36.03 73.42 112.10Sample variance 1298.5 5390.5 12,567.9Range 262.16 493.7 691.15Minimum 50.01 100.11 200Maximum 312.17 593.81 891.15Sum (km) 120,013 81,689 133,677Count (number) 1460 483 422


subsurface structure and the composition of rocks in the crust. Astopography, gravity, and magnetic data are mutually independent,

Fig. 9. Distributions of three kinds of lineaments originated from the topography, gravitylegend of the geological map is shown in Fig. 7. Structural features described in Fig. 2 of Gu(1994) are shown in black.

integrated extrapolation based on all three kinds of data reducesthe degree of ambiguity in surface and subsurface crustal struc-tural modeling in the study area.

4. Results and discussion

4.1. Line segments and lineaments

STA-derived line segments and grouped lineaments from thethree grid data sources are shown in Fig. 8, with their correspond-ing statistics summarized in Table 2. Magnetic-derived segmentsand lineaments are the longest, but least frequently occurring,among the three. Segments and lineaments derived from thetopography are the most frequent, but are shorter in length com-pared with those derived from the gravity data. Variations in thelength and frequency of occurrence of the line segments and linea-ments are attributable to the difference in spatial resolutions of thesource data. The segments had average lengths of 13, 58, and60 km and maximum lengths of 108, 485, and 596 km while thelineaments had average lengths of 81, 163, and 305 km and maxi-mum lengths of 312, 593, and 891 km, for the topography, gravity,and magnetic data, respectively. The enhancements in the averageand maximum lengths of the lineaments over the segments provethe validity of the grouping technique applied to the various datain enhancing the connectivity of the linear features (Table 2).

Superimposition of lineaments extrapolated from all 3 datasources and the reference structural features of Keely (1994) and

, and magnetic data and their superimposition on the geological map of Fig. 7. Coloriraud and Bosworth (1999) are shown in pink and those described in Fig. 10 of keely


Guiraud and Bosworth (1999) is shown on Fig. 9. Lineaments fromtwo to three data sources are congruent in many places and showbroad concentrated continuous parallel zones, in spite of differ-ences in spatial resolution and geologic attributes. Topographic lin-eaments showed more abundance in the inland, followed by themagnetic and the gravity lineaments. Gravity and magnetic linea-ments dominate in the Mediterranean Sea. Structural zones alongthe lineaments combined from the 3 data sources agree in orienta-tion and position with the common tectonic features in the region,as well as with the structures mapped in Keely (1994) and Guiraudand Bosworth (1999). They highlight important elements consti-tuting the plate boundaries, intra-plate structural zones, bound-aries of rift basins, and intra-basin structural features (Fig. 9), asexplained in later sections.

Directional trends of the three types of lineaments and theircomposite rose diagram are shown in Fig. 10, with statistics for

Fig. 10. Rose diagrams showing strike frequencies of the lineaments from (a) topographi(d), the colors in each direction sector denote the ratios of the three kinds of lineament

Table 3Statistics for the number (n) and length (l) in km of lineaments derived from topography (marked by bold letters.

Trend Parameter (�) T (n) T (l) G (n)

NW 80–90 25 2200 670–80 16 1675 860–70 14 1246 1050–60 43 3132 2340–50 37 2836 830–40 72 5665 1920–30 87 7368 3110–20 109 8858 34

0–10 78 5895 21NE 0–10 55 4638 22

10–20 121 10,159 4720–30 118 10,131 4030–40 161 13,843 5240–50 167 13,966 6550–60 159 12,551 3860–70 115 9146 3770–80 54 4261 1680–90 29 2443 6

Total 1460 120,013 483

their frequency of occurrence and length summarized in Table 3.In the composite rose diagram, ratios of the counts of individuallineament types to total counts of all types along each directionare calculated. The major trends of lineaments from the three datasources are congruent, which suggests a possible common sourcefor their origin. However, their order of abundance and relative fre-quencies vary greatly in the individual rose diagrams and thecomposite.

The composite rose diagram shows five prominent trends: NE-SW, NNE-SSW, NNW-SSE, NW-SE, and WNW-ESE, arranged coun-ter-clockwise according to decreasing order of abundance(Fig. 10d). The magnetic lineaments show the largest contributionsto the NE-SW, NNE-SSW, NW-SE, and WNW-ESE directions, ar-ranged in decreasing order of contribution. Magnetic lineamentsprobably originate from compositional discontinuity in the under-lying mantle and crust. The topographic lineaments contribute the

c, (b) gravity, and (c) magnetic data, and (d) total of the three kinds of lineaments. Ins: the magnetic lineaments are the most prominent in most sectors.

T), gravity (G), and magnetic (M) data in each directional range. Dominant trends are

G (l) M (n) M (l) Total (n) Total (l)

1068 11 3118 42 63861594 17 5083 41 83521871 12 4100 36 72173565 17 5643 83 12,3401283 5 1722 50 58413495 9 2695 100 11,8556008 4 1442 122 14,8185621 11 3610 154 18,0893200 20 5149 119 14,2442954 17 4911 94 12,5038521 30 9759 198 28,4395975 31 9255 189 25,3618263 30 10,374 243 32,480

11,715 60 18,489 292 44,1706422 61 20,900 258 39,8736446 49 16,027 201 31,6192721 17 4659 87 11.641

967 21 6741 56 10,15181,689 422 133,677 2365 335,379

Fig. 11. Lineament distributions classified into the three fault types (normal, reverse, and strike-slip) and overlaid on the geological map of Fig. 7. Color legend of thegeological map is shown in Fig. 7. Structural features described in Fig. 2 of Guiraud and Bosworth (1999) are shown in pink and those described in Fig. 10 of (Keely, 1994) areshown in black.


most to the NNW-SSE direction, which may indicate shallowercrustal fault characteristics.

4.2. Characterizing the style of faulting along lineament zones

Fault types were identified for all the derived lineaments asshown in Fig. 11. In the Mediterranean Sea, marked with the sub-duction of the African and European plates, lineaments with strike-slip and reverse faulting types are dominant. Reverse faults are ofmarked appearance south of Crete close to the Hellenic arc. Inthe inland areas, lineaments of strike-slip type of faulting showedmore abundance, followed by the normal and then the reversetypes. Lineaments of various fault types can mix or alternate alongthe regional tectonic features in the area. The azimuth and angle ofdip of various interpreted fault types along lineaments and theircomposite are plotted on the rose diagrams and the Schmidt nets(lower hemisphere projection) and shown in Fig. 12. The resultsshow that the most abundant type is the strike-slip type, followedby the normal and then the reverse types. The lineaments of strike-slip and normal fault types are commonly vertical or nearly verti-cal, while the reverse types show wide dip variations in the rangeof 20�–90�. The strike-slip type is abundant relative to the normaland reverse types along the NE, NNE-SSW, and NNW-SSE trends.Strike-slip and normal faulting are predominate and contributenearly equally to the type in the NNE-SSW and NNW-SSE direc-tions. Fault types along the E-W direction vary with location. Asfor the dip direction, normal fault types show a distinctive south-

easterly dipping, while reverse fault types are concentrated inthe opposite (northwesterly) direction. These results agree wellwith the tectonic framework in the region as discussed in the nextsection.

4.3. Geologic characterization of the tectonic trends

The exceptional continuity (Table 2b), co-linearity, and orienta-tion/positional congruence among the topographic, gravity, andmagnetic lineament zones (Fig. 9) and their prominent trendsacross the region indicate a common source of origin. The originis likely lithosphere dynamics that acted during the Neoproterozo-ic era and have been repeatedly reactivated since that time. A note-worthy point is the coincidence with recent (1900–2006) strong(>5 Mb) earthquake epicenters, which are quoted from the dat-abases of the National Earthquake Information Center (NEIC,2009) and the Egyptian National Seismograph Network (ENSN),along the extent of the lineament zones and at their intersections,in particular in the Mediterranean Sea area (Fig. 13). These results,underpinned with the persistence of the trends over several depo-sitional phases (Keely, 1994; Guiraud and Bosworth, 1999) and thegeodynamic evolution of the region, strongly support the repeatedreactivation of tectonism along such deep lineaments to accommo-date the subsequent strain, to control the upward propagation offractures into the younger rocks, and to have acted as the loci forthe development of the younger rift-related geological structures(Klitzsch, 1986; Daly et al., 1989; Meshref, 1990; Keely, 1994;

Fig. 12. Rose diagrams (left) and Schmidt nets (right) for the lineaments of three fault types: (a) normal, (b) reverse, (c) strike-slip, and (d) all the lineaments summing (a–c).In rose diagram of (d), the colors in each direction sector denote the ratios of the three kinds of lineaments: strike-slip and normal types are significant in most directionsectors.


Guiraud and Bellion, 1995; Moustafa, 1997; Unrug, 1997; Guiraudand Bosworth, 1999; Bojar et al., 2002; Abdeen and Greiling, 2005;Bumby and Guiraud, 2005).

In order to highlight the correspondence between the linea-ments (and associated prominent trends) with significant tectonicelements in the region, co-linear, continuous lineaments were se-lected from the concentrated lineament zones in Fig. 9. Then, moreregional zones passing over the study area were delineated.

Namely, we manually transformed overlapping long lineamentsinto one lineament at first, and connected several lineaments hav-ing similar directions by a gentle curve. Resultant Fig. 13 identifiesfive zones and associated trends that characterize crucial plateboundary and intra-plate features including basin boundaries andintra-basin fractures. The five major fault trends (tectonic linea-ments) extrapolated from the concentrated co-linear long linea-ments and the fault types are compared to referenced structural

Fig. 13. Five major fault trends (tectonic lineaments) interpreted from the concentrated co-linear long lineaments. Recent epicenters (1900–2006) with relatively largemagnitude (M > 5), which were derived from the NEIC and ENSN databases, are overlaid with trends on the geological map of Fig. 7. Color legend of the geological map isshown in Fig. 7. Arrows denote the sense of lateral movement reviewed from literatures.


features. This comparison is summarized below in order of thereactivation period, starting from the oldest:

(1) Najd Fault System (NFS) trend (WNW-ESE to NW-SE), posi-tionally corresponds with the magnetic and gravity linea-ments, represents principally Late Precambrian sinistralstrike-slip transpressive shear zones (Stern, 1985). Anotherdistinctive representative of the NFS trending system is itsextraordinary continuity from the southern Sinai to SouthCairo, Wadi Natrun, and the Northwestern MediterraneanSea (Fig. 13). Longacre et al. (2007) demarcated this faultzone in the offshore as a sinistral ocean-continent transformboundary that separates the ocean crust of the southernTethys from the mildly-extended continental crust of north-ern Egypt.

(2) The Trans-African Lineament (TAL) trend (NE-SW) correlateswith the Tethyan shear zones (Keely, 1994) and the Pelu-sium Line (Neev, 1977; Neev et al., 1982). The variablesenses of strike-slip movement induced transtention andtranspression stress fields, by which the TAL-parallel linea-ments became active during the Tethyan rifting (Keely,1994). This TAL trend demarcates also the Red Sea exten-sional faults that have continued to be active from Tertiaryto Recent times (e.g. Cochran and Martinez, 1988).

(3) Red Sea–Gulf of Suez trend (NNW-SSE) correlates with theproto-Clysmic or Erithrean fractures of Keely (1994). TheNNW-SSW trending lineaments chiefly extracted from thetopographic and gravity data are notably situated in theEarly Cretaceous deposits (Fig. 12). This corresponds withthe fact that the Red Sea–Gulf of Suez rifting likely com-menced in the Cretaceous period (Makris and Rihm, 1991)and reached its climax in the Oligocene period, predomi-nantly controlling the linkage of rift-parallel faults in theGulf of Suez (Younes and McClay, 1998; Guiraud and Bos-worth, 1999).

(4) The Gulf of Aqaba–Dead Sea trend (NNE-SSW) demarcatesthe sinistral transtensive Gulf of Aqaba–Dead Sea fault zoneand associated transfer or linking faults which have beenactive from the Middle Miocene to Recent periods (Guiraudand Bosworth, 1999). A mixture of normal and sinistralstrike-slip movements was confirmed along the westernmargin of the Gulf of Aqaba by Ben-Avraham (1985), whichagrees well with the estimated fault types in the presentstudy.

(5) Eastern Mediterranean Basin (EMB) related-trends are rep-resented generally by a perpendicular conjugate set of zonesoriented nearly E-W resulting from compressional stressfields and N-S resulting from extensional ones associated


with the northward movement of Africa and still active tilltoday. Lineaments of these trends were derived jointly fromthe gravity and magnetic anomalies in the Mediterraneanoffshore area dominated by reverse and strike-slip types(Fig. 11). E-W striking lineaments are commonly concen-trated near the southern boundary of the EMB, while the lesscommon N-S oriented lineaments represent intra-basin fea-tures. The EMB trend’s agreement with the well-known Hel-lenic and the Cyprean Arc (See Fig. 7) is striking.

5. Conclusions

Sophisticated techniques for digital processing and integrationof remotely-sensed data at various scales over wide compositionaland depth variations enabled, for the first time, successful auto-matic mapping of significant tectonic lineaments that agree wellwith known tectonic fabrics in the region. Three methods weredeveloped: ATMDS for enhancing line features in the grid data; re-vised STA for extraction and concatenation of line segments; and atechnique for modeling fault types along lineaments. ATMDSproved to be effective because the line segments were extractedthoroughly regardless of the gradient of slopes in the grid data.Further, it is an automated one-step process that saves time and ef-forts and is not biased by the subjectivity that may arise from theinteractive setting of the azimuth and the altitude of one-direc-tional source of illumination and the step-wise superimpositionof the resulting shaded grids from several directions. By comparingthe three edge detectors founded on typical principles, the highcapability for detecting line segments that satisfied the criterionof straight linearity along valley features irrespective of the shadeintensities with STA was demonstrated using the shaded grid data.Long lineaments were represented by concatenating the short linesegments derived from the grids and smoothing the connectioninto a gentle curve using B-spline. Effectiveness of estimating ori-entations and fault types (normal, reverse, and strike-slip) usingthe lineaments was also demonstrated by comparison to regionalfault structures.

A case study of the adequacy of applying the developed auto-mated techniques to the area covering Egypt and adjacent areasdemonstrated their effectiveness in identifying large-scale tectonicstructures based on integration of lineaments extracted fromtopography, gravity, and aeromagnetic grid data. The continuouslineaments corresponded well, both in position and orientation,with the five referenced significant structural features and can beused to update them: the Najd Fault System, Trans-African Linea-ment, the proto-Clysmic or Erthrean fractures, the Gulf of Aqaba-Dead Sea fault zone, and Eastern Mediterranean Basin structures,which are plate boundaries and intra-plate features of shear zones,boundaries of basins, and intra-basin fractures. The continuities ofthe five structures and the faulting types were brought out in moredetail by this study. The tectonic significance of lineaments wasconfirmed by the concentration of the epicenters of recent(1900–2006) large seismic activity (M > 5) along the broad linea-ment zones and at their intersections.

In spite of the differences in the main geologic attribute as wellas the varied depth range of the three grid data sources, the corre-spondence of the lineaments from all three sources over much ofthe region implies the persistence of the prominent trends overwide horizontal and vertical ranges and provides strong supportfor a structural inheritance model. In this model, basement struc-tural zones have been repeatedly reactivated accompanying thedevelopment and upward propagation of the fractures into thePhanerozoic, which was underpinned with the discontinuities inthe crust’s sediment thickness. The abundance of five prominenttrends (NE-SW, NNE-SSW, NNW-SSE, NW-SE, and WNW-ESE)seems to be related to the frequency of their reactivation periods.

Future research will focus on applying the developed tech-niques on fine scale various potential field data where detailedstructural information would be available for local limited areas.This can maximize the reliability of the results, in particular forthe fault type modeling, and make the validation process mucheasier.

Acknowledgements

We are grateful to the anonymous reviewers for their construc-tive review comments that greatly improved the manuscript.

References

Abdeen, M.M., Greiling, R.O., 2005. A quantitative structural study of late Pan-African compressional deformation in the central Eastern Desert (Egypt) duringGondwana assembly. Gondwana Research 8 (4), 457–471.

Argialas, D.P., Mavrantza, O.D., 2004. Comparison of edge detection and Houghtransform techniques for the extraction of geologic features. InternationalArchives of Photogrammetry, Remote Sensing and Spatial Information Sciences34 (Part B3), 790–795.

Austin, J.R., Blenkinsop, T.G., 2008. The Cloncurry Lineament: Geophysical andgeological evidence for a deep crustal structure in the Eastern Succession of theMount Isa Inlier. Precambrian Research 163 (1-2), 50–68.

Becker, J.J., Sandwell, D.T., Smith, W.H.F., Braud, J., Binder, B., Depner, J., Fabre, D.,Factor, J., Ingalls, S., Kim, S-H., Ladner, R., Marks, K., Nelson, S., Pharaoh, A.,Sharman, G., Trimmer, R., von Rosenburg, J., Wallace, G., Weatherall, P., 2009.Global bathymetry and elevation data at 30 arc seconds resolution:SRTM30_PLUS. Marine Geodesy 32 (4), 355–371.

Ben-Avraham, Z., 1985. Structural framework of the Gulf of Elat (Aqaba). Journal ofGeophysical Research 90 (B1), 703–726.

Blinn, J.F., 1977. Models of Light Reflection for Computer Synthesized Pictures. Proc.4th Annual Conference on Computer Graphics and Interactive Techniques,SIGGRAPH 77, 192–198.

Bojar, A.V., Fritz, H., Kargl, S., Unzog, W., 2002. Phanerozoic tectonothermal historyof the Arabian–Nubian Shield in the Eastern Desert of Egypt: evidence fromfission track and paleostress data. Journal of African Earth Sciences 34 (3-4),191–202.

Boucher, R.K., 1995. The relevance of lineament tectonics to hydrocarbonoccurrences in the Cooper and Eromanga Basins, South Australia. Journal ofPetroleum Exploration Society of Australia 21, 69–75.

Bui-Tuong, P., 1975. Illumination for computer generated pictures. Communicationsof the Association of Computing Machinery 18 (6), 311–317.

Bumby, A.J., Guiraud, R., 2005. The Geodynamic setting of the Phanerozoic Basins ofAfrica. Journal of African Earth Sciences 43 (1-3), 1–12.

Burns, J.B., Hanson, A.R., Riseman, E.M., 1986. Extracting straightlines. IEEETransactions on Pattern Analysis and Machine Intelligence 8 (4), 425–455.

CGMW: Commission for the Geological Map of the World (2006). Structural andkinematic map of the World, 1:50 M scale, A. Haghipour, CGMW/UNESCO

Cochran, J.R., Martinez, F., 1988. Structure and tectonics of the northern Red Sea:Catching a continental margin between rifting and drifting. Tectonophysics 150(1-2), 1–32.

Comanicu, D., Meer, P., 2002. Mean shift: A robust approach toward feature spaceanalysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (5),603–619.

Cooper, G.R.J., 2003. Feature detection using sun shading. Computers & Geosciences29 (8), 941–948.

Daly, M.C., Chorowicz, J., Fairhead, J.D., 1989. Rift basin evolution in Africa: theinfluence of reactivated steep basement shear zones. Geological Society,London, Special Publications 44 (1), 309–334.

Dix, O.R., Jackson, M.P.A., 1981. Statistical analysis of lineaments and their relationto fracturing, faulting, and halokinesis in the East Texas Basin. Bureau ofEconomic Geology, The University of Texas at Austin, Austin TX, Report 110, 30.

Fichler, C., Rundhovde, E., Olesen, O., Saether, B.M., Ruelatten, H., Lundin, E., Dore,A.G., 1999. Regional tectonic interpretation of image enhanced gravity andmagnetic data covering the mid-Norweigian shelf and adjacent mainland.Tectonophysics 306 (2), 183–197.

Foley, J.D., van Dam, A., Feiner, K., Hughes, J.F., 1990. Computer Graphics: Principlesand Practice, 2nd ed. Addison-Wesley, Reading, MA.

Grompone von Gioi, R., Jakubowicz, J., Morel, J., Randall, G., 2010. LSD: a fast linesegment detector with a false detection control. IEEE Transactions on PatternAnalysis and Machine Intelligence 32 (4), 722–732.

Guild, P.W., 1974. Distribution of metallogenic provinces in relation to major earthfeatures: Schriftenreihe der erdwissenschaftlichen Kommission derÖsterreichischen Akademie der Wissenschaften Band 1, pp. 10–24.

Guiraud, R., Bellion, Y., 1995. Late Carboniferous to Recent Geodynamic Evolution ofthe West Gondwanian Cratonic Tethyan Margins. In: Nairn, A., Dercourt, J.,Vrielynck, B. (Eds.), The Tethys Ocean. The Ocean Basins and Margins. PlenumPress, New York, pp. 101–124.

Guiraud, R., Bosworth, W., 1999. Phanerozoic geodynamic evolution of northeasternAfrica and the northwestern Arabia platform. Tectonophysics 315 (1-4), 73–108.


Horn, B.K.P., 1982. Hill shading and the reflectance map. Geo-Processing 2, 65–146.Keely, M.L., 1994. Phanerozoic evolution of the basins of Northern Egypt and

adjacent areas. Geologische Rundschau 83 (4), 728–742.Keeley, M.L., Massoud, M.S., 1998. Tectonic controls on the petroleum geology of NE

Africa. Geological Society, London, Special Publications 132, 265–281.Klitzsch, E., 1986. Plate tectonics and cratonal geology in Northeast Africa (Egypt,

Sudan). Geologische Rundschau 75 (3), 755–768.Koike, K., Nagano, S., Ohmi, M., 1995. Lineament analysis of satellite images using a

Segment Tracing Algorithm (STA). Computers & Geosciences 21 (9), 1091–1104.Koike, K., Ichikawa, Y., 2006. Spatial correlation structures of fracture systems for

identifying a scaling law and modeling fracture distributions. Computers &Geosciences 32 (8), 1079–1095.

Koike, K., Nagano, S., Kawaba, K., 1998. Construction and analysis of interpretedfracture planes through combination of satellite-derived lineaments and digitalelevation model data. Computers & Geosciences 24 (6), 573–583.

Koike, K., Kouda, R., Ueki, T., 2001. Characterizing fracture systems of Kyushu,southwest Japan through satellite-image derived lineaments superimposed ontopographic and lithologic data. Bulletin of the Geological Survey of Japan 52(9), 405–423.

Kudo, T., Yamamoto, A., Nohara, T., Kinoshita, H., Shichi, R., 2004. Variations ofgravity anomaly roughness in Chugoku district, Japan: relationship withdistributions of topographic lineaments. Earth Planets Space 56 (5), e5–e8.

Longacre, M., Bentham, P., Hanbal, I., Cotton, J., Edwards, R., 2007. New CrustalStructure of the Eastern Mediterranean Basin: Detailed Integration andModeling of Gravity, Magnetic, Seismic Refraction, and Seismic ReflectionData. EGM Innovation in EM, Gravity and Magnetic Methods: a new Perspectivefor Exploration, Capri, Italy, April 15–18.

Makris, J., Rihm, R., 1991. Shear-controlled evolution of the Red sea: pull apartmodel. Tectonophysics 198 (2-4), 441–466.

Masoud, A.A., Koike, K., 2006. Tectonic architecture through LANDSAT-7 ETM+/SRTM DEM-derived lineaments and relationship to the hydrogeologic setting inSiwa Region, NW Egypt. Journal of African Earth Sciences 45 (4–5), 467–477.

Masoud, A.A, Koike, K., 2009. Use of Bézier and B-Spline Curves for SmartRepresentation of Lineaments Auto-detected from DEM. InternationalAssociation of Mathematical Geology (IAMG09): Computational Methods forthe Earth, Energy and Environmental Sciences, Stanford University, California,USA, 23–28 August, (on CD-ROM).

Masoud, A., Koike, K., Teng, Y., 2007. Geothermal Reservoir CharacterizationIntegrating Spatial GIS Models of Temperature, Geology, and Fractures. Proc.12th Conference of International Association for Mathematical Geology, Beijing,China, August 26-31, (on CD-ROM).

Maus, S., Barckhausen, U., Berkenbosch, H., Bournas, N., Brozena, J., Childers, V.,Dostaler, F., Fairhead, J.D., Finn, C., von Frese, R.R.B., Gaina, C., Golynsky, S.,Kucks, R., Lühr, H., Milligan, P., Mogren, S., Müller, D., Olesen, O., Pilkington, M.,Saltus, R., Schreckenberger, B., Thébault, E., Caratori Tontini, F., 2009. EMAG2: A2-arc-minute resolution Earth Magnetic Anomaly Grid compiled from satellite,airborne and marine magnetic measurements. Geochemistry, Geophysics,Geosystems 10 (Q08005). doi:10.1029/2009GC002471.

Maus, S., Sazonova, T., Hemant, K., Fairhead, J.D., Ravat, D., 2007. Nationalgeophysical data center candidate for the world digital magnetic anomalymap. Geochemistry, Geophysics, Geosystems 8 (Q06017). doi:10.1029/2007GC001643.

McNutt, M., 1979. Compensation of oceanic topography: an application of theresponse function technique to the Surveyor area. Journal of GeophysicalResearch 84 (B13), 7589–7598.

Meer, P., Georgescu, B., 2001. Edge detection with embedded confidence. IEEETransactions on Pattern Analysis and Machine Intelligence 23 (12), 1351–1365.

Meshref, W.M., 1990. Tectonic Framework. In: Said, R. (Ed.), The Geology of Egypt.A.A. Balkema Publishers, Rotterdam, Netherlands, pp. 113–156.

Milbury, A.E.C., Smrekar, S.E., Raymond, C.A., Schubert, G., 2007. Lithosphericstructure in the east region of Mars’dichotomy boundary. Planetary and SpaceScience 55 (3), 280–288.

Morris, K., 1991. Using knowledge-base rules to map the three-dimensional natureof geologic features. Photogrammetric Engineering & Remote Sensing 57 (9),1209–1216.

Moustafa, A.R., 1997. Controls on the development and evolution of transfer zones:the influence of basement structure and sedimentary thickness in the Suez riftand Red Sea. Journal of Structural Geology 19 (6), 755–768.

Neev, D., 1977. The Pelusium Line–a major transcontinental shear. Tectonophysics38 (3-4), T1–T8.

Neev, D., Hall, J.K., Saul, J.M., 1982. The Pelusium Megashear System across Africaand associated lineament swarms. Journal of Geophysical Research 87 (B2),1015–1030.

National Earthquake Information Center (NEIC), 2009. US Geological Survey, http://neic.usgs.gov. (Accessed 17 June 2009).

Oakey, G., 1994. A structural fabric defined by topographic lineaments: Correlationwith Tertiary deformation of Ellesmere and Axel Heiberg Islands, CanadianArctic. Journal of Geophysical Research 99 (B10), 0148–0227.

Omar, G.I., Steckler, M.S., 1995. Fission-track evidence on the initial opening of theRed Sea: two pulses, no propagation. Science 270 (5240), 1341–1344.

Prautzsch, H., Boehm, W., Paluszny, M., 2002. Bézier and B-Spline Techniques.Springer-Verlag, New York.

Prechtel, N., 2000. Operational Analytical Hill Shading within an Advanced ImageProcessing System. In: Proceedings of the 2nd Workshop on High MountainCartography, Kartographische Bausteine 18. Technische Universität Dresden,Dresden, pp. 85–98.

Rowan, L.C., Bowers, T.L., 1995. Analysis of linear features mapped in Landsatthematic mapper and side-looking airborne radar images of the Reno 1 degreeby 2 degree Quadrangles, Nevada and California; implications for mineralresource studies. Photogrammetric Engineering & Remote Sensing 61 (6), 749–759.

Rowland, J.V., Sibson, R.H., 2004. Structural controls on hydrothermal flow in asegmented rift system, Taupo Volcanic Zone, New Zealand. Geofluids 4 (4), 259–283.

Salem, A., Furuya, S., Aboud, E., Elawadi, E., Jotaki, H, Ushijima, K., 2005. Subsurfacestructural mapping using gravity data of Hohi Geothermal Area, CentralKyushu, Japan. Proceedings World Geothermal Congress, Antalya, Turkey, onCD-ROM.

Sandwell, D.T., Smith, W.H.F., 1997. Marine gravity anomaly from Geosat and ERS-1satellite altimetry. Journal of Geophysical Research 102, 10039-10050. Data isavailable at http://topex.ucsd.edu/cgi-bin/get_data.cgi. (Accessed 12 June2009).

Sibson, R.H., 1986a. Earthquakes and lineament infrastructure. PhilosophicalTransactions of the Royal Society, London A 317 (1539), 63–79.

Sibson, R.H., 1986b. Brecciation processes in fault zones; inferences fromearthquake rupturing. Pure Applied Geophysics 124 (1-2), 159–175.

Smith, W.H.F., Sandwell, D.T., 1994. Bathymetric prediction from dense satellitealtimetry and sparse shipboard bathymetry. Journal of Geophysical Research 99(B11), 21803–21824.

Stern, R.J., 1985. The Najd Fault System, Saudi Arabia and Egypt: a Late Precambrianrift system? Tectonics 4 (5), 497–511.

Süzen, M.L., Toprak, V., 1998. Filtering of satellite images in geological lineamentanalyses: an application to a fault zone in Central Turkey. International Journalof Remote Sensing 19 (6), 1101–1114.

Tripathi, N., Gokhale, K., Siddiqu, M., 2000. Directional morphological imagetransforms for lineament extraction from remotely sensed images.International Journal of Remote Sensing 21 (17), 3281–3292.

Unrug, R., 1997. Rodinia to Gondwana; the geodynamic map of Gondwanasupercontinent assembly. GSA Today 7 (1), 1–6.

Warner, T.A., 1997. Integration of remotely sensed geobotanical and structuralmethods for hydrocarbon exploration in West-central West Virginia. Finalreport for DOE-sponsored Research Contract DE-FG21-95MC32159.Morgantown, WV, USA, 10 p.

Yoëli, P., 1967. The mechanisation of analytical hill shading. The CartographicJournal 4 (2), 82–88.

Younes, A., McClay, K., 1998. Role of basement fabric on Miocene rifting in the Gulfof Suez–Red Sea. In: Proc. 14th Petroleum Conference, October Exploration Vol.1. Egyptian General Petroleum Corporation, Cairo, pp. 35–50.

Zhou, X., Dorrer, E., 1995. An Adaptive Algorithm of Shaded-Relief Images fromDEMs Based on Wavelet Transforms. Digital Photogrammetry and RemoteSensing ’95, SPIE Proceedings Series 2646, 212–24.

Ziegler, P.A., Roure, F., 1999. Petroleum systems of Alpine-Mediterranean foldbeltsand basins, in: Durand, B., Jolivet, L., Horváth, F., Séranne, M. (ed.), TheMediterranean Basins: Tertiary extension within the Alpine orogen. GeologicalSociety, London, Special Publications 156, pp. 517–540.

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