lidar as remote sensor

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Journal of Sedimentary Research, 2011, v. 81, 339–347 Research Article DOI: 10.2110/jsr.2011.31 LIDAR INTENSITY AS A REMOTE SENSOR OF ROCK PROPERTIES DARRIN BURTON, DALLAS B. DUNLAP, LESLI J. WOOD, AND PETER P. FLAIG Bureau of Economic Geology, 10100 Burnet Road, Austin, Texas 78758, U.S.A. e-mail: [email protected] ABSTRACT: Lidar collects high-resolution spatial data, making it a popular tool for outcrop investigations; however, few of these studies utilize lidar’s spectral capability. Lidar scanners commonly collect intensity returns (power returned/power emitted) that are influenced primarily by distance and target reflectivity, with lesser influence from angle of incidence, roughness, and environmental conditions. Application of distance normalization results in values that approximate target reflectivity. At the near-infrared wavelength of lidar, quartz-rich sandstones are more reflective than clay-rich mudstones. Scans of unweathered core and weathered outcrop were collected to investigate the relationship between lithology and lidar intensity. In unweathered, laboratory samples, intensity shows an inverse relationship to wt. % clay and are positively correlated to wt. % combined quartz, plagioclase, and K-feldspar. A similar relationship was also observed in scans of lightly weathered outcrop, although weathering and moisture diminished intensity contrast between sand-rich and shale-rich intervals. Thus, lidar intensity is a possible remote sensor of lithology, particularly in remotely located and inaccessible outcrops. INTRODUCTION Terrestrial lidar (light detection and ranging) has become a popular tool for outcrop investigation and modeling (Bellian et al. 2005; McCaffrey et al. 2005; Pringle et al. 2006; Trinks et al. 2005; Bonnaffe et al. 2007; Labourdette and Jones 2007; Buckley et al. 2008; White and Jones 2008; Jones et al. 2009; Rotevatn et al. 2009; Klise et al. 2009; Wawryzniec et al. 2009). Lidar uses the time of flight and angle of emission to accurately (within centimeters) calculate the x, y, and z location of a point on a targeted surface. Lidar scanners can collect thousands of points per second, allowing the rapid compilation of a ‘‘cloud’’ of points approximating the shape of the targeted surface (Optech 2009). Point clouds are used to generate digital outcrop models (DOMs) that allow geologists to better quantify and better reproduce the results of traditional outcrop studies (Bellian et al. 2005). Producing high- resolution (centimeter or greater) DOMs, which can cover kilometers of continuous outcrop, has greatly contributed to the popularity of lidar as a tool for geologic investigation (Jones et al. 2008; Jones et al. 2009). Digital outcrop models are ideal for reservoir studies because they enable geologists to simultaneously study the outcrop at a variety of scales and viewing angles in a virtual environment, while allowing the investigator to quantify and spatially reference stratigraphic geometries and relationships (Pringle et al. 2006; Enge et al. 2007; Janson et al. 2007; Pranter et al. 2007; Jones et al. 2008; Rotevatn et al. 2009). Lidar data have been used to extract detailed measurements of both structural (Ahlgren and Holmlund 2003; Rotevatn et al. 2009; Jones et al. 2008; Olariu et al 2008) and stratigraphic features (Bellian et al. 2005; Janson et al 2007; Lee et al. 2007; Jones et al. 2008; Klise et al. 2009). Digitally recorded data can be used to condition reservoir models (Pringle et al. 2006; Enge et al. 2007; Janson et al. 2007; Pranter et al. 2007; Jones et al. 2008; Rotevatn et al. 2009) and forward seismic models to outcrop data (Janson et al. 2007). FIG. 1.—NASA JPL library spectroscopy (from Baldridge et al. 2008), solid sample data showing median (solid line) and quartiles (dashed lines) for shale (gray) and sandstone (black). The central dashed line is approximates the wavelength of terrestrial lidar. Note the spectral separability between sandstones and shale at lidar wavelengths. Copyright E 2011, SEPM (Society for Sedimentary Geology) 1527-1404/11/081-339/$03.00

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Page 1: Lidar as remote sensor

Journal of Sedimentary Research, 2011, v. 81, 339–347

Research Article

DOI: 10.2110/jsr.2011.31

LIDAR INTENSITY AS A REMOTE SENSOR OF ROCK PROPERTIES

DARRIN BURTON, DALLAS B. DUNLAP, LESLI J. WOOD, AND PETER P. FLAIGBureau of Economic Geology, 10100 Burnet Road, Austin, Texas 78758, U.S.A.

e-mail: [email protected]

ABSTRACT: Lidar collects high-resolution spatial data, making it a popular tool for outcrop investigations; however, few ofthese studies utilize lidar’s spectral capability. Lidar scanners commonly collect intensity returns (power returned/poweremitted) that are influenced primarily by distance and target reflectivity, with lesser influence from angle of incidence,roughness, and environmental conditions. Application of distance normalization results in values that approximate targetreflectivity. At the near-infrared wavelength of lidar, quartz-rich sandstones are more reflective than clay-rich mudstones.Scans of unweathered core and weathered outcrop were collected to investigate the relationship between lithology and lidarintensity. In unweathered, laboratory samples, intensity shows an inverse relationship to wt. % clay and are positivelycorrelated to wt. % combined quartz, plagioclase, and K-feldspar. A similar relationship was also observed in scans oflightly weathered outcrop, although weathering and moisture diminished intensity contrast between sand-rich and shale-richintervals. Thus, lidar intensity is a possible remote sensor of lithology, particularly in remotely located and inaccessibleoutcrops.

INTRODUCTION

Terrestrial lidar (light detection and ranging) has become a populartool for outcrop investigation and modeling (Bellian et al. 2005;McCaffrey et al. 2005; Pringle et al. 2006; Trinks et al. 2005; Bonnaffeet al. 2007; Labourdette and Jones 2007; Buckley et al. 2008; White andJones 2008; Jones et al. 2009; Rotevatn et al. 2009; Klise et al. 2009;Wawryzniec et al. 2009). Lidar uses the time of flight and angle ofemission to accurately (within centimeters) calculate the x, y, and z

location of a point on a targeted surface. Lidar scanners can collectthousands of points per second, allowing the rapid compilation of a‘‘cloud’’ of points approximating the shape of the targeted surface(Optech 2009). Point clouds are used to generate digital outcrop models(DOMs) that allow geologists to better quantify and better reproduce theresults of traditional outcrop studies (Bellian et al. 2005). Producing high-resolution (centimeter or greater) DOMs, which can cover kilometers ofcontinuous outcrop, has greatly contributed to the popularity of lidar as atool for geologic investigation (Jones et al. 2008; Jones et al. 2009).

Digital outcrop models are ideal for reservoir studies because they enablegeologists to simultaneously study the outcrop at a variety of scales andviewing angles in a virtual environment, while allowing the investigator toquantify and spatially reference stratigraphic geometries and relationships(Pringle et al. 2006; Enge et al. 2007; Janson et al. 2007; Pranter et al. 2007;Jones et al. 2008; Rotevatn et al. 2009). Lidar data have been used toextract detailed measurements of both structural (Ahlgren and Holmlund2003; Rotevatn et al. 2009; Jones et al. 2008; Olariu et al 2008) andstratigraphic features (Bellian et al. 2005; Janson et al 2007; Lee et al. 2007;Jones et al. 2008; Klise et al. 2009). Digitally recorded data can be used tocondition reservoir models (Pringle et al. 2006; Enge et al. 2007; Janson etal. 2007; Pranter et al. 2007; Jones et al. 2008; Rotevatn et al. 2009) andforward seismic models to outcrop data (Janson et al. 2007).

FIG. 1.—NASA JPL library spectroscopy (from Baldridge et al. 2008), solidsample data showing median (solid line) and quartiles (dashed lines) for shale(gray) and sandstone (black). The central dashed line is approximates thewavelength of terrestrial lidar. Note the spectral separability between sandstonesand shale at lidar wavelengths.

Copyright E 2011, SEPM (Society for Sedimentary Geology) 1527-1404/11/081-339/$03.00

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In addition to providing detailed spatial data, lidar scanners record theintensity: the power of the backscattered signal relative to the power ofthe emitted signal (Song et al. 2002; Pfeifer et al. 2008). The strength ofthe reflected signal is related to the geometry between the laser emitterand the targeted surface, and the reflective character of the target at thewavelength of the laser (Lutz et al. 2003; Ahokas et al. 2006; Coren andSterazi 2006; Hasegawa 2006; Mazzarini et al. 2006; Starek et al. 2006;Hofle and Pfiefer 2007; Kukko et al. 2008; Pesci et al. 2008). Geometricvariables, such as range to the target and angle of incidence, can belargely corrected for use in physical or data-driven models (Song et al.2002; Luzum et al. 2004; Ahokas et al 2006; Mazzarini et al. 2006; Hofleand Pfiefer 2007; Kaasalinen et al. 2008). Geometrically normalizedintensities are proportional to target reflectivity (Song et al. 2002; Ahokas

et al. 2006; Coren and Sterazi 2006; Pfiefer et al. 2008); therefore,materials with different reflective properties can be distinguished byvariation in the intensity (Song et al. 2002; Lutz et al. 2003; Ahokas et al.2006; Brennan and Webster 2006; Starek et al. 2006; Hofle and Pfiefer2007). Intensity has been used as a remote sensing and surfaceclassification tool in archeology (Coren et al. 2005), forestry (Brennanand Webster 2006; Donoghue et al. 2007; Antonarakis et al. 2008),glaciology (Lutz et al. 2003), volcanology (Mazzarini et al. 2006; Pesci etal. 2008), hydrology (Klise et al. 2009), and other fields of study (Song etal. 2002; Luzum et al. 2004; Hasegawa et al. 2006; Long et al. 2006; Chustet al. 2008; Wawrzyniec et al. 2009). However, lidar-based outcrop studieshave largely ignored intensity data, focusing primarily on spatialinformation.

FIG. 2.—The maximum, average, and mini-mum intensity series from outcrop data for A)raw data, B) Optech Irlis 3D software, and C)statistical distance normalization. Normalizationproduced more consistent intensity values withrespect to distance. The gap in the series from190 m to 260 m is the location of theSagavanirktok River, with a gravel bar from100 m to 190 m and the outcrop from 260 m to445 m.

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The use of lidar for surface classification of lithology is a largelyundeveloped application of lidar mapping techniques. Pesci et al. (2006)used intensity to map and identify different stratigraphic units exposed inthe walls of a volcanic crater. Their study shows that intensity is firmlyrelated to the physical and chemical properties of the reflecting target.They also suggest that stratigraphic studies using intensity can beextended to nonvolcanic outcrops. Bellian et al. (2005) used intensity incombination with other attributes to enhance detection of lithologicvariation. Klise et al. (2009) showed that intensity and multiscan stackingeasily differentiates gravel from sandy facies. Another recent studyshowed an inverse linear correlation between intensity and the hydrogenweight percent (clay content proxy) in a carbonate-dominated succession(Franceschi et al. 2009). All of these studies point to lidar intensity as apotential technique for discriminating lithology. Understanding theapparent relationship between rock properties and intensity could greatlyincrease the value of lidar data in outcrop studies.

ROCK-PROPERTY CONTROLS ON REFLECTIVITY

Reflectance of a granular surface is complexly controlled by thecomposition of individual grains, their weight fraction, and grain size(Clark and Roush 1984; Clark 1999; Bowitz and Ehling 2008). Whenphotons emitted from the lidar laser source encounter the granularsurface of an outcrop, some is absorbed while some is scattered (Clark1999). Bright grains (e.g., quartz) scatter most photons, while darksurfaces (e.g., coal) absorb the majority of photons. Theoretically,sandstones containing a high percentage of quartz should be morereflective than shale. For example, spectral data from NASA’s JetPropulsion Laboratory (Fig. 1) shows a large difference in the NIRreflectivity of quartz-rich sandstones and clay-rich shale (Baldridge etal. 2008). If a significant mineralogical difference exists in thecomposition of adjacent sandstone and shale in outcrop, these aremost likely spectrally distinguishable, and this difference in near-infrared reflectivity of sandstone and shale may be detectable usinglidar intensity returns.

The research presented here tests the hypothesis that lidar reflectiveintensity is influenced by lithology in clastic rocks by scanning both fresh

and weathered surfaces, normalizing for distance, and comparingnormalized intensity to lithology. Fresh rock samples came fromsubsurface core, and outcrops were scanned to investigate intensityreturns from weathered surfaces.

METHODS

Two experiments were conducted to investigate the relationshipbetween intensity and lithology. Both experiments were conducted usingthe Bureau of Economic Geology’s Optech Ilris 3D laser scanner. Thisunit uses a 1500 nm (eye-safe) wavelength laser, with a range of 800 m(, 2,625 ft) for target with 20% reflectivity (Optech 2009). Optech (2009)reports a raw range accuracy of 7 mm (0.28 in) at 100 m (, 330 ft) forthis unit. Raw intensities and ranges are collected in an equal-angle array.The laser beam divergence is 0.17 mrad, but the minimum spot spacingdivergence is 0.02 mrad (Optech 2009). Scanning with point spacing nearthe spot size (calculated from beam divergence) decreases the time ofacquisition while covering nearly the entire targeted surface. Scanningnear the minimum spot spacing is often unnecessary (many repeatmeasurements), but improves the signal-to-noise ratio.

Both physical and environmental conditions impact intensity values.The most important physical factors are distance and target reflectivity,with angle of incidence and roughness being secondary. Environmentalconditions such as humidity, dust, and variations in air density can alsoaffect intensity. When lidar is acquired under favorable conditions (stillair, low humidity), and after the geometric variables are corrected,intensity becomes proportional to reflectivity (Lutz et al. 2003; Ahokas etal. 2006; Hofle and Pfeifer 2007; Pfeifer et al. 2007). An extensivetreatment of lidar correction theory and methods can be found in theremote-sensing literature (Song et al. 2002; Luzum et al. 2004; Ahokas etal. 2006; Coren and Sterazi 2006; Mazzarini et al. 2006; Hofle and Pfeifer2007; Pfeifer et al. 2007; Pfeifer et al 2008). In agreement with Pfeifer et al.(2007), it was observed that the Optech ILRIS intensity data does notfollow simple physical principles. However, when raw intensities varysystematically with distance, data-driven normalizations can be developedthat achieve intensities which approximate reflectivity (Hofle and Pfiefer2007).

FIG. 3.—Boxes of core were used in experiment 1. A) Shale in the core corresponds to low intensities B) The intensity grayscale is 0 (black) to 255 (white).

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FIG. 4.—Logged stratigraphy, gamma-raylog, and intensity log from experiment 1.

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For both experiments one and two, distance-normalized intensitieswere used. In the first experiment, subsurface core from ‘‘behind-the-outcrop’’ was scanned. The core used in this study is from the ExxonProduction Research Sego Canyon 2 well in eastern Utah. Boxes of corewere set up at , 60 meters (, 200 ft.) from the lidar unit at an angle of45–40u from perpendicular to the path of the laser. At this distance thelaser spot covered an area of about 1.5 square-centimeters (, 0.6 in2) andoverlapping points were acquired every 5 millimeters (, 0.2 in). Distanceto the target and angle of incidence stayed roughly the same throughoutthe experiment. Because there was no appreciable change in geometrybetween the scanner and targets, the Optech scanner software correctionwas considered adequate. Using this method for correction, intensityreturns remained consistent from one scan to the next.

For experiment two, five scans were taken along an outcrop on theSagavanirktok River from a distance of approximately 300 m (, 985 ft).Raw intensities from these data show a strong fade with distance (Fig. 2).Normalization using the scanner software also shows a fade with distanceacross the outcrop, but possibly more telling is the large amount ofvariability introduced by the scanner correction (Fig. 2). As a result, adata-driven statistical normalization was used (Reyes et al. 2009).Histograms of intensities were generated for each distance bin (everymeter). If enough data points were available, the histograms could berepresented by a Gaussian distribution. This Gaussian approximationwas used to obtain a median for each bin. This median was subtractedfrom 100 (approximate median for all data), and this value was used as amultiplier to shift intensity histograms to a normalized value. The rawdata were reread to shift the median output value for each bin. A similarmultiplier was calculated and used to shift the maximum and minimumvalue for each bin. Statistically normalized intensities show very little fadewith distance, median values are very consistent, and variability is muchlower than scanner normalized intensities (Fig. 2).

EXPERIMENT 1

Lidar cans of the Sego Canyon 2 core were visualized in Polyworks,and a strip of points 10 cm (, 4 in.) wide was extracted from each coresegment (Fig. 3). Data points were adjusted to the proper depth asmarked on the core boxes. Normalized intensity data from the core scanswere combined to make a lidar intensity log.

Quantitative mineralogy from X-ray diffraction and core descriptionswere used to provide lithologic and mineralogical information forcomparison to lidar intensity. In addition, intensity was compared tothe log suite from the Sego Canyon 2 well.

Results

Quantitative analysis through experimentation shows that lithology is aprimary control on intensity returns after distance normalization. Theintensity data show that sandstones are more reflective than mudstones(Fig. 3). Figure 4 includes intensity and gamma ray log for the SegoCanyon 2 well and the corresponding stratigraphy for the SegoSandstone. Intensity lows correspond to shale-rich interval and togamma-ray highs, while intensity highs coincide with sandstones andlow gamma-ray values. Differences between the gamma-ray and lidar logscan be partially explained by the difference in resolution between the twotools. Lidar has a higher resolution than gamma-ray logs, and is moresensitive to lithologic variation. From this experiment it is clear that lidarintensity is related to lithology.

Mineralogy from core plugs (Wendlandt and Bhuyan 1990) was alsocompared to intensity in the Sego Canyon 2 well core. The plots inFigure 5 show the correlation between intensity and shale (clay) andproxies for sandstone (combined quartz, plagioclase, and K-spar) inweight percent. Intensity exhibits a log-linear relationship to both weightpercent clay (R2 5 .6981 and p value % 0.01) and weight percent ofcombined quartz, k-spar, and plagioclase, a proxy for percent sand(R2 5 .7729 and p-value % .01). This regression analysis shows thatmuch of variation in corrected intensity in the Sego Canyon 2 core can beexplained by the difference in mineralogical composition betweensandstone and shale.

EXPERIMENT 2

The results of experiment two are in strong agreement with experimentone, and suggest intensity as an effective remote sensor of lithology. ThePrince Creek Formation at Sagwon Bluffs, located along the Sagava-nirktok River on the North Slope of Alaska, consists of fine- to coarse-grained sandstone, siltstone, mudstone, carbonaceous shale, and coal.

FIG. 5.—Mineralogy A) wt. % clay and B) wt % combined quartz, plagioclase,and K-feldspar (from Wendlandt and Bhuyan, 1990) compared with lidar intensityof the Sego Canyon 2 core.

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Depositional environments include meandering rivers, levees, crevassesplays, lakes, swamps, and soil-forming environments (Flaig and van derKolk 2009). Palynomorph biostratigraphy indicates that the entiresuccession is early Paleocene in age (Frederiksen et al. 1996; Mull et al.2003). Distance-normalized scans from the Prince Creek Formation weremerged and interpreted in Polyworks. For qualitative analysis, a 5 cm(2 in.) strip of intensities was extracted from a section of clean outcropand compared to a lithologic measured section. Data points werecollected in 10 cm (3.9 in.) radius circles on a regular grid, with minorvariation to include thinner units (Fig. 6). Intensities were binned bylithology (interpreted using photos, measured sections, and lidar spatialand spectral data) for quantitative statistical analysis.

Results

Qualitatively, there is a general relationship between intensity andlithology (Fig. 7). The intensity image shows clearly distinguishable unitsof sandstone, mudstone, and coal (Fig. 6). Weathered, wet, and coveredoutcrops were avoided. A t test was performed to asses weather the meansfor the population of sandstone and shale were statistically different fromeach other. The t test results show that the intensity population forsandstone is significantly higher than the intensity population formudstone, t(11398) 5 127.06, p % 0.001. A histogram of all data pointscollected from unweathered outcrop shows a distinct trimodal distribu-tion (Fig. 8). Each mode related to the median of each lithologic intensity

population. Thus, coals are less reflective than either sandstones ormudstones. Experiment two visual and statistical analysis confirms thehypothesis that sandstones and mudstones (and also coal) can bedistinguished using lidar intensity data.

WEATHERING AND MOISTURE

An important difference between experiments one and two is theanalysis of unweathered core in experiment one verses naturallyweathered outcrop in experiment two. During the course of this study,intensity images from many settings were viewed, and it became obviousthat weathering had a significant impact on intensity returns. Typically,weathering of sandstones (e.g., desert varnish) decreases intensity returns.Additionally, Fe oxides along fractures have higher-intensity returns thansurrounding shale. Weathered mudstone beds are commonly recessedrelative to the surrounding sandstone. As a result these thin beds mightnot be contacted by the lidar-emitted laser, and therefore, are notidentifiable in the intensity image. Although the effects of weathering arenot quantitatively assessed, weathering did influence intensity returns ofoutcrop surfaces. Surface coverings such as dust, mud cake, and lichenalso act to diminish returns. Lidar intensity can be used to discriminatelithology only from relatively clean, unweathered outcrop surfaces. As aresult, lithology can most easily be discerned from intensity returns inareas where physical weathering dominates (e.g., alpine and tundrasettings).

FIG. 6.—Intensity image (0–255) of the PrinceCreek Formation, with bright sandstones, darkershales, and very dark coals. The data used for theintensity log (Fig. 7) is marked by a blue line,and the red circles are the data points within theimage that were included in the statisticalanalysis. Snow banks, water, and saturated rocksare seen to return low intensities in the ravines.

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In addition to weathering, moisture also impacts intensity. At thewavelength used in these experiments, water is a strong absorber (Long etal. 2006; Franceschi et al. 2009). In lidar scans from the North Slope ofAlaska (Fig. 6) the impact of water and ice on intensity is clear. Water-saturated rock and soil in ravines exhibit lower intensities than thesurrounding outcrop, and no returns were received from the snow patcheson the upper hill slope. Marked intensity lows and unusable intensitieshave also been observed in scanned seacliffs (personal observation).Because of the negative effects of moisture on intensity returns, lidardiscrimination of lithology is not effective in wet conditions (e.g., highhumidity or rainy conditions).

APPLICATION

The results of this study highlight the applicability of lidar toquantitative stratigraphic studies in clastic outcrops. As shown by thisresearch, quantitative estimates of weight percent sandstone or shale inoutcrop can be made from calibrated intensity images. Lidar is especiallyuseful in studies of inaccessible outcrop exposures. Intensity data couldalso be employed in aerial lidar surveys to map clastic stratigraphy andquantify geometries on a regional scale (e.g., Cretaceous and Paleoceneoutcrops in northern Alaska).

A useful application of the relationship between intensity andlithology is the production ‘‘pseudo-gamma ray’’ logs from lidar data(Fig. 9). The best-fit regression (a power function) is shown in the graphin Figure 9 relating intensity (x) to gamma-ray (y) values for the SegoCanyon 2 well. The pseudo-gamma ray log in Figure 9 was generatedfrom the regression equation, and is very similar to the actual down-holegamma ray log (Fig. 4). This method of simulating gamma raysignatures of clastic reservoir analogs is faster, and spatially moreaccurate, than traditional methods using a hand-held gamma-rayspectrometer.

The application of intensity as a remote sensor of rock properties is notlimited to sand, clay, and shale. Other studies have shown its applicabilityin distinguishing between clay-rich and carbonate-rich lithologies(Franceschi et al. 2009) and differentiation of sand from gravel (Kliseet al. 2009). We have observed a correlation between rock properties andlithology in a number of settings. For example, in the tar sands ofAlberta, Canada, bitumen saturation diminishes intensity returns, andbitumen-stained sand can be distinguished from unsaturated sand andsilt. In the Tocito Sandstone of New Mexico, sandstones rich inglauconite return less of the emitted signal than adjacent sandstoneswithout glauconite. In the future, lidar intensity will likely be used as aproxy for a variety of rock properties (for example lithology, bitumensaturation, and sandstone composition). Any such applications will at aminimum need adjustment for distance fade, and calibration toquantitative sampling to be valid.

CONCLUSIONS

Lidar is used in many disciplines as a remote-sensing tool toquickly acquire spatial and spectral data. Although lidar has becomea popular tool for outcrop investigation, studies have largely ignoredthe spectral capabilities of intensity returns from lidar surveys.Experimental scans on the Sego Canyon 2 well show a correlationbetween lidar intensity, weight percent clay, and weight percentcombined quartz, K-spar, and plagioclase. Similarly, lidar intensity issensitive to lithology in clastic outcrops that are lightly unweatheredand dry.

Our study shows that lidar can provide a number of new geologicremote-sensing applications in clastic outcrops. Intensity data can be usedto discriminate between different lithologies and simulate gamma-raylogs, as well as to quantify stratigraphy. Our findings are particularlyapplicable to inaccessible outcrops and regional belts.

ACKNOWLEDGMENTS

This research is made possible by the generosity of the members of theQuantitative Clastic Laboratory Industrial Associates program, whichincludes Anadarko, BHP Billiton, BG Group, Cairn, ExxonMobil,Marathon, Resold YPF, Shell, Statoil, and Woodside. Programming andsignal processing was done by Reuben Reyes. Much thanks to the reviewers,Lynn Soregan, R.R. Jones, and Tim Wawrzyniec, and to editor JohnSouthard. Their edits and suggestions greatly improved the quality of thispaper.

FIG. 7.—Lithology log (from measured section) and lidar intensity log of thePrince Creek Formation.

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FIG. 9.—A ‘‘pseudo’’ gamma-ray log of theSego Sandstone in the Sego Canyon 2 wellgenerated from lidar intensity returns, and across plot of intensity and gamma ray values.

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Received 6 April 2010; accepted 2 December 2010.

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