quantitativebiostratigraphy ofthetaranakibasin ......paleozoic terrane history and evolution in the...

AAPG Bulletin, v. 85, no. 8 (August 2001), pp. 1469–1498 1469

Quantitative biostratigraphyof the Taranaki Basin,New Zealand: A deterministicand probabilistic approachRoger A. Cooper, James S. Crampton, J. Ian Raine,Felix M. Gradstein, Hugh E. G. Morgans, Peter M. Sadler,C. Percy Strong, David Waghorn, and Graeme J. Wilson

ABSTRACT

A quantitative biostratigraphic analysis of the Paleocene to lowerMiocene of the Taranaki Basin has enabled high precision in cor-relation, zonation, and assessment of depositional history. Bio-stratigraphic range-end events, based on 493 taxa in cuttings sam-ples from eight wells, representing foraminifera, nannofossils,dinoflagellates, and miospores, were culled to 87 range-top eventsthat were then analyzed by deterministic (constrained optimization[CONOP]) and probabilistic (ranking and scaling [RASC]) tech-niques. All except 16 of the events are found to have relatively goodbiostratigraphic reliability. The RASC probable sequence and prob-abilistic zonation give the best estimate of the sequence of eventsand zones to be encountered in any new well in the basin and aprecise biostratigraphic scale for future exploration. The CONOPcomposite section, which matches well with that derived by con-ventional graphic correlation (GRAPHCOR), is readily related toprevious zonations based on maximum ranges of taxa but gives anorder-of-magnitude greater precision. CONOP provides a precisecorrelation framework and reveals marked variation in thickness ofstages across the basin. When the composite section is calibratedagainst the time scale, basinwide changes in depositional rate arerevealed. The upper Eocene and Oligocene mark an interval of slowdeposition, whereas the Miocene marks a sharp increase in depo-sition. The time-calibrated composite section enables unconformi-ties and changes in depositional rate found in individual wells to beprecisely estimated. Many new unconformities are indicated, par-ticularly in the Paleocene and Eocene.

INTRODUCTION

The Taranaki Basin (Figure 1) is New Zealand’s only producinghydrocarbon province. It contains a Late Cretaceous–Cenozoic

Copyright �2001. The American Association of Petroleum Geologists. All rights reserved.

Manuscript received November 8, 1999; revised manuscript received August 21, 2000; final acceptanceOctober 10, 2000.

AUTHORS

Roger A. Cooper � Institute of Geological andNuclear Sciences, P.O. Box 30368, Lower Hutt, NewZealand; [email protected]

Roger A. Cooper is interested in time-scale developmentmethodology and the application of quantitative methods instratigraphy. He is a project leader in the Institute ofGeological and Nuclear Sciences (formerly, New ZealandGeological Survey). Other interests include the systematics,biostratigraphy, evolution, and ecology of graptolites;Ordovician correlation and time-scale development; and earlyPaleozoic terrane history and evolution in the southwestPacific.

James S. Crampton � Institute of Geologicaland Nuclear Sciences, P.O. Box 30368, Lower Hutt, NewZealand; [email protected]

James Crampton leads the Geological Time andEnvironmental Change Programme at the Institute ofGeological and Nuclear Sciences. His research is focussedprimarily on the stratigraphy, molluscan biostratigraphy, andgeological history of Cretaceous basins of eastern NewZealand. Other research interests include the use ofquantitative techniques in time-scale development and theuse of quantitative, biometric techniques to describe complexevolutionary changes in problematic fossil groups.

J. Ian Raine � Institute of Geological and NuclearSciences, P.O. Box 30368, Lower Hutt, New Zealand;[email protected]

J. Ian Raine has been a palynologist-biostratigrapher at theNew Zealand Geological Survey and its successororganization, the Institute of Geological and Nuclear Sciences,since 1974. He received his Ph.D. from Australian NationalUniversity in 1976. His principal research interests areterrestrial palynology of the Mesozoic and Cenozoic of NewZealand and Antarctica and application of computertechniques in biostratigraphy and taxonomy. He has beenclosely associated with the New Zealand Fossil Record File, aprimary database of fossil occurrence data.

Felix M. Gradstein � Oslo University,Department of Geology, P.O. Box 1047 Blindern, N-0316Oslo, Norway; [email protected]

Felix M. Gradstein (retired) is chairman of the InternationalCommission of Stratigraphy (ICS), research fellow at theuniversities of London, Oslo, and Amsterdam, and industryliaison for the Ocean Drilling Project. He, and his long-termassociate Frits Agterberg, developed the probabilistic methodof biostratigraphy (RASC) to crack complex bug correlations inpetroleum basins while working for the Geological Survey ofCanada. The newest version of RASC was sponsored by SagaPetroleum.

Hugh E. G. Morgans � Institute of Geologicaland Nuclear Sciences, P.O. Box 30368, Lower Hutt, NewZealand; [email protected]

Hugh Morgans is a micropaleontologist with the Institute ofGeological and Nuclear Sciences and specializes inforaminiferal biostratigraphy of the Paleogene and Miocene.He is interested in the application of quantitative foraminiferalprocessing and analysis for paleoenvironment interpretation

1470 Quantitative Biostratigraphy of the Taranaki Basin

sedimentary succession resting unconformably on an erosional sur-face of varied relief (King and Thrasher, 1996), cut across a base-ment comprising Paleozoic and Mesozoic sedimentary, volcanic,plutonic, and metamorphic rocks (Mortimer et al., 1997).

The basin has had a complex evolutionary history (King andThrasher, 1996). From the Late Cretaceous to the Paleocene itwas part of an intracontinental rift-transform system, contempo-raneous with opening of the Tasman Sea, and deposition wasmainly confined to a series of fault-controlled half grabens (Figure2). From the Paleocene to the Oligocene it formed part of thepost-rift/drift passive margin, progressively subsiding and forminga deep embayment in the western margin of the New Zealandland mass, open to the New Caledonia Basin and Tasman Sea.Hydrogen-rich coals and carbonaceous mudstones accumulated inthe fault-controlled basins and on the post-drift passive marginand are the main source rocks in the Taranaki Basin. A regionalparaconformity developed in the early Oligocene, possibly a cor-relative of the middle Oligocene Marshall paraconformity that iswidespread throughout New Zealand (Carter, 1985), that repre-sents a final phase of tectonic stability following the passive mar-gin deposition. Rapid subsidence during the Oligocene causedfoundering of the continental margin and marks the onset ofa new tectonic regime: the initial development of the presentAustralian-Pacific plate boundary. A widespread basal Oligoceneto Miocene carbonate sheet (Tikorangi Formation and its equiv-alents) forms a distinctive seismic and lithological marker horizonthroughout the basin.

During the Miocene, thrusting along the Taranaki fault systemin the east deepened the basin and supplied an influx of fine clasticsediments in response to the development of the Hikurangi sub-duction zone and uplift of the axial region of New Zealand. Thewestern margin of the basin, in contrast, became and has remainedrelatively stable tectonically and is known as the Western StablePlatform. A submarine andesitic volcanic field having several erup-tive centers developed in the northern part of the basin in the lateMiocene. As the plate boundary through New Zealand becamestrongly transpressional during the Pliocene–Pleistocene, and upliftof the axial ranges became more rapid, a flood of coarse clasticsediment entered the basin from the east and south. During thisphase, northeastern parts of the basin underwent subsidence andextensional faulting and are best described as occupying a back-arctectonic setting.

As a result of this complex history, there are numerous depo-sitional breaks, condensed intervals and local unconformities, con-temporaneous faults and folds, and lateral facies changes, generatinga complex stratigraphy (King and Thrasher, 1996). Biostratigraphyis an essential tool for interpreting the depositional history of thebasin and has, in the past, been applied using traditional methodsof stratigraphic range and assemblage biozonation, within the NewZealand chronostratigraphic system of local stages and series.Within the basin, Cretaceous to Miocene strata are largely confined

ACKNOWLEDGEMENTS

This article is Institute of Geological and Nuclear Sciencescontribution 1798. We thank George Scott, Peter King, andtwo AAPG Bulletin reviewers, Gregg Blake and Peter Webb,for their constructive comments on the manuscript.

and, integrated with magnetostratigraphic and isotopicsignatures, for refined biostratigraphy.

Peter M. Sadler � Department of EarthSciences, University of California, Riverside, California,92521; [email protected]

Pete Sadler develops the CONOP9 correlation software aspart of his research program in quantitative stratigraphy andthe completeness of the stratigraphic record at University ofCalifornia Riverside, where he is a professor of geology. Petereceived B.Sc. degree and Ph.D. from the University of Bristol,England. He was a postdoctoral researcher at the Universityof Gottingen before moving to California.

C. Percy Strong � Institute of Geological andNuclear Sciences, P.O. Box 30368, Lower Hutt, NewZealand; [email protected]

Percy Strong received his B.S. degree from the College ofWooster (Ohio) in 1964 and his Ph.D. from University ofWashington in 1969. From 1969 to 1975 he was assistantprofessor of geology at Mt. Union College (Alliance, Ohio). In1975 he joined the New Zealand Geological Survey as amicropaleontologist and has continued in this position duringthe organization’s evolution into the Institute of Geologicaland Nuclear Sciences. His main research interests are LateCretaceous and early Paleogene foraminiferal biostratigraphyand foraminiferal events at the Cretaceous–Tertiary boundary.

David Waghorn � Premier & Shell Pakistan B.V.,Jang Building, Fazal-e-Haq Rd., Blue Area, Islamabad,Pakistan

David Waghorn is chief geologist with Premier ExplorationPakistan Ltd. He received a Ph.D. in geology from VictoriaUniversity of Wellington, New Zealand, in 1983. Since then hehas worked as an exploration and development geologist inAustralia, New Zealand, southeast Asia, and Pakistan. He is amember of AAPG, the Society of Petroleum Engineers, andthe Society of Professional Well Log Analysts. He maintains aninterest in calcareous nannofossil biostratigraphy, is amember of the International Nannoplankton Association(INA), and has worked on practical applications of nannofossilbiostratigraphy to operational and development aspects of thepetroleum industry.

Graeme J. Wilson � Institute of Geological andNuclear Sciences, P.O. Box 30368, Lower Hutt, NewZealand; [email protected]

Graeme J. Wilson gained his B.Sc. and M.Sc. degrees fromVictoria University of Wellington, New Zealand, and his Ph.D.from the University of Nottingham, England. Since 1964 hehas worked as a palynologist with the New ZealandGeological Survey and its successors, specializing in Mesozoicand Cenozoic dinoflagellates mainly from New Zealand,Antarctica, the southwest Pacific, Patagonia, and westernEurope. He has also published on spores and pollen from latePaleozoic to Pleistocene sequences.

Cooper et al. 1471

Figure 1. Taranaki Basin, showing location of the eight wells used in the present study. The depositional environments shown arefor the late Eocene and are from King and Thrasher (1996).

to the subsurface, and much stratigraphic informa-tion is available from the more than 80 explorationwells. Several key wells were summarized by Kingand Thrasher (1996, appendix 3). The well depthsgiven in the present article are depths below rotarytable.

Purpose of the Present Study

The present study has the following aims: (1) toachieve a high-precision biostratigraphic subdivisionand correlation for the central-western Taranaki Basin,a statistically based biozonation scheme, and a standardcomposite sequence of events; (2) to assess the reli-ability of taxa and events for correlation and zona-

tion within the basin; (3) to assess depositional ratesacross the basin; and (4) to compare the probabilisticand deterministic methods of quantitative stratigraphicanalysis.

Value for Petroleum Exploration and Development

The quantitative methods described here enable agreatly improved precision in dating and correlation ofwell sequences; the precision matches or exceeds thatobtainable from seismic correlations (Cooper et al.,2000). The composite sections and correlation frame-work can be progressively improved by the addition ofnew sections and taxa as they become available. Theprobable sequence gives the most probable order


Figure 2. Schematic distribution of main lithofacies and proven reservoir rocks in Taranaki Basin (modified from King and Thrasher[1996], figure 6.3). Main source rocks are coals and carbonaceous muds in the fluvial and coastal sand systems, of Late Cretaceousto Paleocene age.

of events to be encountered in any new well. Thereliability of biostratigraphic events used for correla-tion can be evaluated quantitatively.

The time-calibrated composite described hereinenables age-depth curves to be constructed that locateand date stratigraphic unconformities with a precisionnot previously available. Sediment accumulation ratescan be estimated with improved precision in any singlewell section. The methods outlined are applicable toany sedimentary basin with reasonable biostratigraphiccontrol.

QUANTITAT IVE STRATIGRAPHICMETHODS

Biostratigraphy is primarily concerned with strati-graphic subdivision, zonation, and correlation, using

biostratigraphic events. We use the term “event” re-peatedly in this article and define it as follows. Achange through time in an ancient flora or fauna, suchas the extinction of a species, is a “biological event.”The stratigraphic record of this event, such as the ob-served top of the range of the species in a section, isthe corresponding “biostratigraphic event.” Biostrati-graphic events can be poor estimators (in time) of thecausal biological event, and the quantitative methodsdescribed here offer two alternative approaches to im-proving or interpreting the observed event so that itis more useful in zonation and correlation (Table 1).Unless otherwise qualified in the following discussion,the term “event” refers to a biostratigraphic event, ei-ther observed or interpreted. Many other kinds ofstratigraphic events (sedimentary, e-log, seismic, iso-topic) exist, and these are qualified appropriatelywhere used.

Cooper et al. 1473

Table 1. Biological Events (Signal Generators),Biostratigraphic Events (the Recorded Signal), and TheirInterpretation

HistoricalHappening Observation Interpretation

Biological event Biostratigraphic event

Extinction ofspecies A

Observed rangetop of speciesA in a section

Adjusted (extended)range top of speciesA, derived by CONOPanalysis

Averaged range top ofspecies A, derived byRASC analysis

In many biostratigraphic successions there are alarge number of events that potentially can be used.Where several sections are to be correlated and thenumber of events in each section is relatively large, itbecomes difficult to obtain the best correlation or zo-nation using all of the available biostratigraphic data,that is, all of the events in all of the sections. In part,this is because the stratigraphic order of biostrati-graphic events commonly varies from section to sec-tion as a result of a variety of factors such as incompletesampling or preservation, true variations in the distri-butions of fossil taxa, misidentifications, and mixingof sediments. The number of possible correlationschemes becomes enormous, and finding the most eco-nomical or best-fitting scheme (i.e., the scheme thathas the fewest contradictions or smallest net strati-graphic misplacement of events) is a huge computa-tional task. For these reasons, conventional biostratig-raphy relies heavily on use of “key” fossils, or “zone”fossils, that have proven reliable over an extended pe-riod of use, that is, in many trials. Although it is rec-ognized that in the Taranaki Basin, detailed biostrati-graphic study for more than 60 years has establishedan exceptionally large database of biostratigraphicevents (mainly in the form of unpublished rangecharts)—particularly foraminiferal events—that areuseful for ordering strata, in many basins much, or evenmost, of the available biostratigraphic information isnot used. Contradictions are minimized or avoided byeliminating all but a widely spaced subset of events.The frequency of event contradictions is generally in-versely related to the stratigraphic separation of events.The task of finding the best-fit correlation scheme us-ing all of the available data is referred to here as the“correlation problem.”

Quantitative stratigraphy provides a means of de-riving zonation schemes, precise correlation schemes,composite sections of biostratigraphic events, proba-bilistic sequences of events, measures of event vari-ance, and assessment of depositional history, based onall the available stratigraphic data in all stratigraphicsections. The reliability of key fossils and of existingzonal and correlation schemes can be tested against thefull data set. Quantitative stratigraphy is generally ap-plied within a single sedimentary basin where the num-ber of well sections and number of events that can beused in correlation are both large. In the present con-text, only biostratigraphic events are used, but othercorrelation events, such as ash beds and magnetic re-versals, can be integrated at a later stage.

A wide range of quantitative and semiquantitativetechniques is now available (Brower, 1981; Edwards,1982a, b, 1984; Gradstein et al., 1985; Tipper, 1988).The methods used here represent the two opposingphilosophies (Edwards, 1982a), or approaches, in bio-stratigraphy, which can be labeled deterministic andprobabilistic. Deterministic methods seek the totalstratigraphic ranges of taxa, whereas probabilisticmethods seek the most probable range (Figure 3; Table2). Deterministic models assume that inconsistenciesin the order of range-end events are due to missing

Figure 3. Illustration of the difference between average andmaximum ranges of a species (taxon A) in eight stratigraphicsections. Probabilistic methods seek the average stratigraphicrange, deterministic methods seek the total range.


tion. For comparison, we also derived a composite sec-tion for the Taranaki data using traditional graphic cor-relation (GRAPHCOR). Terms used in the presentpaper are defined in Table 3. Where confined to samplesets from within a basin, both deterministic and prob-abilistic methods fail to recognize basinwide isochro-nous depositional events such as depositional gaps anddepositional rate changes. These can be detected onlyby reference to an outside standard section or timescale. For this reason we have constructed a time-calibrated composite section to measure depositionalrates within the basin.

Composite and Probabilistic Sequences

Both CONOP and RASC produce a generalized se-quence of biostratigraphic events based on the individ-ual sequences represented in the set of well sections.CONOP seeks the maximum stratigraphic range foreach taxon in the basin, whereas RASC seeks the mostcommonly occurring range (Figures 3, 4; Table 2). Thisdifference is important to remember where comparingthe outputs from the two programs. The generalizedsequence produced by CONOP is analogous to the

data. Probabilistic models assume that such inconsis-tencies are the result of random deviations from someaverage succession. The best-known deterministicmethod is graphic correlation (Shaw’s method), whichhad been used mainly in the petroleum exploration in-dustry but now is used widely around the world in avariety of applications (Edwards, 1978, 1984; Sweet,1984, 1988; Prell et al., 1986; Cooper and Lindholm,1990; McLeod, 1991; Mann and Lane, 1995). One dis-advantage of the method is that it is labor intensive,even when using a PC program such as GRAPHCOR(Hood, 1986), especially where the numbers of eventsand sections are large. It requires section-by-sectioncomparison with the composite in successive rounds ofcorrelation. The automated graphic correlation tech-nique, constrained optimization (CONOP), developedby Kemple et al. (1995), however, treats all sectionsand events simultaneously. The best known of theprobabilistic methods is ranking and scaling (RASC)for zonation, and correlation and standard deviationcalculation (CASC) for correlation (Gradstein et al.,1985; Agterberg, 1990).

CONOP and RASC are used in the present studyand are discussed in more detail in the following sec-

Table 2. Comparison of the Three Quantitative Stratigraphic Techniques Used in This Study

GRAPHCOR CONOP RASC

Deterministic method, graphic correlation Deterministic method, constrainedoptimization

Probabilistic method, ranking and scaling

Uses event order and spacing Uses event order and spacing Uses event order onlyLarge data sets require much labor Can process large data sets readily Can process large data sets very quicklyRequires selection of an initial “standard”section, then section-by-sectioncomparison with the composite inrepeated rounds

Treats all sections and eventssimultaneously

Treats all sections and events simultaneously

LOC fitting in section-by-section plots,partially automated

Fully automated Fully automated

Attempts to find maximum stratigraphicrange of taxa among the sections

Attempts to find maximumstratigraphic range of taxa amongthe sections

Attempts to find average stratigraphic rangeof taxa among the sections

Builds a composite by interpolation ofmissing events in successive section-by-section plots via the LOC

Uses simulated annealing to findeither the “best,” or a very good,multidimensional line of correlationand composite sequence

Uses scores of order relationships todetermine single most probable sequenceof events

Relative spacing of events in thecomposite is derived from originalstratigraphic spacing

Relative spacing of events in thecomposite is derived from originalstratigraphic spacing

Relative spacing of events is derived frompairwise crossover frequency

Does not correlate sections automatically Correlates sections automatically Automatic correlation of sections by sisterprogram (CASC) using RASC output

Cooper et al. 1475

composite section (or composite standard section) ofgraphic correlation. The generalized sequence ofRASC is probabilistic and is referred to here as theprobable sequence (equivalent to the optimum se-quence of Gradstein et al., 1985). The events depictedin the generalized sequences (i.e., the CONOP com-posite sequence and RASC probable sequence), al-though identified by the same taxon names, are not thesame events. The difference between the RASC prob-able sequence and the CONOP composite sequence,based on the same taxa in the same wells, is in part dueto this fact. The extent to which they match reflects

the extent to which the maximum range differs fromthe average range in a systematic way across the basin.

Another reason for the difference is that an eventis placed in the RASC scaled sequence relative to otherevents, whereas it is placed in the CONOP compositesequence relative to its level, in meters, in the com-posite section. The CONOP composite section incor-porates an estimate of interevent stratigraphic spacing;it is comparable with, but different from, the RASCscaled probable sequence. Both are discussed in thefollowing sections. In this discussion “stratigraphic po-sition” refers to the position (in meters) of an event in

Table 3. Brief Definitions of Terms Used in the Present Article

Term Definition

Biological event A change in ancient flora or fauna, such as the extinction, origination, or peakin abundance of a species

Biostratigraphic event, event (unqualified) The stratigraphic record of a biological event, such as the top of the observedstratigraphic range of the fossil species in a section; here also embraces theadjusted, or interpreted, range top by RASC and CONOP

Stratigraphic order The order of events in a stratigraphic sequence of eventsStratigraphic level (or position), event level The position (in meters) of a stratigraphic event in a stratigraphic sectionPenalty (CONOP) Sum of the adjustments (in meters) in stratigraphic position of an event in

each of the well sections when the ranges of taxa are adjusted to match thecomposite section (CONOP)

Normalized mean penalty Total penalty score for all wells, of an event, normalized for frequency ofoccurrence and section thickness (CONOP)

Event variance Measure of the extent to which an event varies in stratigraphic order (RASC)or stratigraphic position (CONOP) from section to section

Event reliability Measure of the biostratigraphic reliability of an event based on the eventvariance measures by RASC and CONOP (Figure 13)

Well variance Sum of the variance of all events in a well section. Either sum of normalizedmean penalty scores (CONOP), or Kendall’s tau statistic (RASC); gives anestimate of the net difference between the well and the composite

Composite sequence Best estimate of the true stratigraphic order of (range-end) events, based onobserved stratigraphic ranges in local sections (CONOP, GRAPHCOR)

Composite section Best estimate of the true stratigraphic order and spacing of (range-end)events, based on observed stratigraphic ranges in local sections (CONOP,GRAPHCOR)

Probable sequence, or ranked probable sequence That sequence in which events are placed in their most probable stratigraphicorder, based on observed order in local sections

Scaled probable sequence A ranked probable sequence in which events are given their most probablerelative stratigraphic spacing, based on observed stratigraphic order in localsections

Simulated annealing A very efficient heuristic searching routine that finds either the best solution ora very good solution to a correlation problem, for which the number ofpossible solutions is so large that an exhaustive search is not practical (seeKemple et al. 1995, 76–77)


a well section, whereas “stratigraphic order” refers tothe relative position of the event in the sequence ofevents.

Both the composite and probable sequences be-come more reliable the more wells that are added tothe analysis. New events and new wells can be addedto an existing composite, including physical eventssuch as e-log markers, ash bands, and so forth. The twomethods enable the derivation of integrated zonalschemes incorporating a range of taxa and event types.For convenience in the present study, both the com-posite section and RASC scaled sequence are scaled ina downhole direction, that is, with the lowest valuesfor the stratigraphically highest events, regardless ofthe conventions of the various techniques.

Constrained Optimization (CONOP)

This is a comparatively new technique developed byKemple et al. (1995) that automates the time-consuming graphic correlation procedure, using the PCprogram CONOP (Sadler, 1999). Like graphic corre-lation, it uses event order and stratigraphic spacing be-tween events. Rather than finding the single best so-lution to the correlation problem (among severalstratigraphic sections) via an extensive search, whichcan be a huge computational task, it uses the simulatedannealing heuristic search (Kemple et al., 1995) to findthe best solution for moderately large data sets (up to10 sections and 100 taxa) and very good solutions for

very large or highly contradictory data sets. This canbe tested in successive runs with the same or differentrun parameters. A multidimensional line of correlation(LOC) is fitted simultaneously to all points in all sec-tions and represents the solution to the correlationproblem. Unlike GRAPHCOR, CONOP fits the lineautomatically and does not need selection of an initialstandard section. CONOP run parameters can be spec-ified, and runs on the one data set by different opera-tors give closely similar results, whereas the results ofGRAPHCOR analyses by different operators can differdepending on choices made by the operator in LOCplacement. The placement of highly variable events inthe composite section by both CONOP and GRAPH-COR can vary widely in successive analyses of the onedata set.

The composite section of events, or estimated“true” history of the original biological events as re-corded among the well sections, is that (hypothetical)sequence of ordered and spaced biostratigraphic eventsthat causes the smallest net disruption or “penalty”when the ranges of taxa in each of the well sections areadjusted to match it (Kemple et al., 1995). We assumethat inconsistency of event order among the wells iscaused by incomplete stratigraphic ranges of taxa insome of the wells (incomplete local ranges). As in con-ventional graphic correlation, local ranges for eachtaxon are extended to bring them into alignment withthe maximum range recorded among all wells. Thecomposite section carries the lowest net penalty asmeasured by the net extension of taxon ranges amongall the wells. Unlike RASC, which treats all events,whether range tops or bases, in the same way, CONOPextends tops upward and bases downward to achievea best fit. For large data sets CONOP may generateseveral equally best-fit composite sections.

PenaltyThe penalty can be measured in several ways. Thecomposite section resulting from the analysis dependson the type of penalty measure used. In the presentstudy, we have found that where penalty is measuredby the net stratigraphic thickness, measured in meters,of all event adjustments (called the “interval option” inCONOP), the composite section closely approximatesthat derived by conventional graphic correlation. Al-ternatively, the penalty can be measured by the num-ber of contradictory pairwise event orderings expressedas a percentage of the number of observed orderings(the rascal option). This imitates the probabilisticRASC method and, not surprisingly, produces a prob-

Figure 4. RASC scaled probable sequence and CONOP com-posite section of stratigraphic range tops for four taxa (A–D) ineight stratigraphic sections.

Cooper et al. 1477

able sequence similar to that of RASC. The simulatedannealing algorithm used by CONOP, however, is notefficient for this approach, and in the following discus-sion, unless otherwise indicated, we have used the in-terval option in CONOP analysis.

The mean penalty score assigned to an event (i.e.,total penalty divided by the number of wells in whichthe event occurs) is an indication of its inconsistencyin stratigraphic position among the wells and is ex-pressed in meters. Where normalized for difference inthickness among the wells, it provides an approximatemeasure of event variance in stratigraphic positionacross the basin. The mean penalty score is approxi-mate because it is influenced by differences in depo-sitional rate within individual wells. The measurementof the penalty in meters favors those sequences re-corded in relatively thick sections. An alternative mea-sure (called the “level option” in CONOP) counts fos-siliferous horizons (observed event levels) within therange extensions, rather than thickness. It favors thesequences in the most fossiliferous or most intenselysampled sections.

Ranking and Scaling (RASC)

Ranking and scaling is a probabilistic technique thatwas inspired, in part, by the pioneering work of Hay(1972). The RASC program was developed by Agter-berg, Gradstein, and others between 1978 and 1985(Gradstein et al., 1985; Agterberg, 1990; Gradsteinand Agterberg, 1998). The version of the program usedhere, version 15, was designed and written by Agter-berg and Gradstein in 1996. The program yields theprobable or average sequence of events in the set ofwells (ranking) and estimates the average distance be-tween successive events, on an arbitrary time scale, inthe probable sequence (scaling).

The ranking procedure places the events in order.In its simplest form, ranking proceeds by the pairwisecomparison of each event with all others to determinerelative frequencies of event order, that is, the propor-tion of wells in which an event occurs above all otherevents. Hence, in the resulting probable sequence, anevent A that occurs above event B was observed mostoften to be above, rather than below, event B in thegiven sample of wells. A problem arises where ordinalrelationships for three or more events are mutually in-consistent such that, for example, event A most com-monly occurs before B, B before C, and C before A.Clearly there is no unique ranking solution for thesethree events. Program RASC uses a “presorting” or

“probabilistic ranking” algorithm and weights on themost commonly occurring pairs of events to solve thisproblem (for computational details see Agterberg[1990]). During operation of program RASC, the userdetermines the number of wells in which an event andan event pair must occur to be retained within the anal-ysis. Clearly, there is little value in calculating the av-erage position of an event that occurs in only two orthree wells.

The scaling procedure determines the relativespacing of events. Scaling uses the frequency of cross-over (i.e., mismatch) between pairs of events amongthe well sequences and assumes that this frequencyis inversely proportional to distance apart in relativetime (CONOP’s composite sequences routinely cor-roborate this assumption). An event pair with nocrossover is assigned an arbitrary distance equal totwo standard deviations. Results are displayed as adendrogram. Large interevent distances, correspond-ing to minimal crossover, between adjacent eventsare inferred to mark missing strata, unfavorable fa-cies, or major change in the biota. They are goodplaces in the sequence to establish assemblage zoneboundaries because they separate packages containingevents that are rarely found in the wrong package.Events within each package (or assemblage zone),however, are commonly found out of order with theother events in the package. This approach resemblesthe unitary association method of Geux (1977,1991). In some cases, calculated interevent distancesmay be negative, and this requires some reorderingof the probable sequence to yield positive distances.Up to five such reorderings are permitted by the pro-gram. Consequently, the scaled probable sequencemay differ slightly from the ranked probable se-quence. In the following discussion, the “probablesequence” refers to the ranked probable sequence un-less otherwise stated.

Variance MeasuresOne of the powerful features of RASC is the facilityto estimate the reliability of individual events or wellsrelative to the probable sequence. This can be used totest for reworking, misidentification, nonobservation,and contamination. The program applies several objec-tive tests of reliability:

1. Scatter plots of individual wells against the probablesequence

2. A “step model” that scores penalties for reversals oforder between a well and the probable sequence


3. A “normality test” for deviation of an event in a wellsequence from its position in the scaled probablesequence, assuming all events in the probable se-quence have equal variance and are normallydistributed

4. A “variance analysis” whereby data for individualwells are compared with the probable sequence(ranked or scaled) using curve fitting techniques toyield an estimate of the standard deviation of eachevent

All tests tend to yield comparable results. Note thatthe normality test always assigns 1% of all events anout-of-place probability of 99%, and 5% of all eventsan out-of-place probability of 95%.

Events that have a small standard deviation or lowscores in the step model are likely to be the most usefulin correlation. In addition, if wells are arranged in asystematic paleogeographic order, the results of vari-

ance analysis can help identify events that are dia-chronous across a basin.

All RASC measures of variance are influenced bythe spacing of events in time. Closely spaced events arefound out of order more frequently and thus incur ahigher standard deviation than events spaced widely intime, even though all events may have the same vari-ance in time. For this reason, the RASC measures areapproximate measures of true variance in time. In ad-dition, in the present study there are only four to eightoccurrences per event, and standard deviations shouldtherefore also be regarded as approximate.

WELLS AND DATA USED IN THE ANALYSIS

The eight Taranaki Basin wells chosen for the presentstudy (Figure 5; Table 4) were drilled between 1975and 1984. They were selected for their richness in fos-

Figure 5. Taranaki wells used in the present study. The wells are aligned at the base of the Tikorangi Formation (early Oligocene).The original biostratigraphy is shown for comparison with new biostratigraphy in Figure 12. The wells are ordered, left to right, fromshoreward to basinward. For stage name abbreviations at right, refer to stages listed in Figure 2.

Cooper et al. 1479

sils and, from the initial studies, stratigraphic com-pleteness. They represent a range of depositional set-tings from inner shelf to slope and basin (Figure 1).Their biostratigraphy has been described in various un-published well reports and subsequent analyses by atleast seven paleontologists of the Institute of Geologi-cal and Nuclear Sciences and is largely based on wellcuttings. The time interval selected for the presentstudy is early Paleocene to early Miocene. The qualityof the biostratigraphic data is variable because the de-gree of detail in documenting the biostratigraphy var-ied from well to well and within individual wells. Also,the taxonomic concepts of several species havechanged. For these reasons many of the original sam-ples were reexamined for the present study, and se-lected further cuttings samples were prepared and ex-amined. Calcareous nannofossils were not examinedpreviously in several of the wells and have been addedto the present study.

Four hundred and ninety-three taxa, representingforaminifera, nannofossils, dinoflagellates, spores, andpollen were present in the eight wells, providing a totalof 986 range-end events for correlation (i.e., first or lastoccurrences). This data set was culled to improve itsquality. Uncertain or qualified identifications were re-moved, reducing the list to 351 taxa. All range baseswere removed because caving problems were presentin several of the wells, making range bases unreliable.There were thus 351 usable events (range tops). Fur-ther culling was not necessary for CONOP and, in fact,probably excluded useful correlation data. For the pur-poses of comparing the techniques (at least, to com-pare their composite sequences), a common data setwas needed. Those events that were present in fewerthan four of the eight wells were removed because thevariance analysis of RASC becomes meaningless witha low frequency of occurrence. The data set wasthereby reduced to 178 events and was run with bothRASC and CONOP.

About 25 events emerged from this first stage ofanalysis as being extremely inconsistent in positionfrom well to well and were removed from the data setin a final stage of the cull, along with some other eventsbased on taxa known to be unreliable, for example,strongly facies-controlled taxa (mostly benthic fora-minifera), or range tops known elsewhere to range be-yond the youngest levels sampled in the wells. The finaldata set therefore comprises 87 events and, even afterthe culling, retains considerable inconsistency. Figure6 graphically illustrates the problem faced by the bio-stratigrapher in dating well sequences from cuttingsTa

ble4.

Sum

mar

yof

Wel

lDat

aan

dW

ellN

etVa

rianc

e

Wel

lDa

teDr

illed

Top

ofSa

mpl

edIn

terv

alBa

seof

Sam

pled

Inte

rval

Thick

ness

ofSa

mpl

edIn

terv

alNum

bero

fEv

ents

Num

bero

fEv

entL

evel

s

Nor

mal

ized

Net

Pena

lty(C

ONOP)

Kend

all’s

Tau

Stat

istic

(RAS

C)

Wel

lPe

rform

ance

,CO

NOP*

Wel

lpe

rform

ance

,RA

SC*

1Ki

wa

1981

3640

2795

845

3719

33.0

0.77

57

2W

itior

a19

8441

6528

5013

1571

3543

.80.

836

43

Turi

1975

4002

1933

2069

5228

24.9

0.78

36

4Ta

iman

a19

83–1

984

4102

3100

1002

7242

60.9

0.84

83

5Ta

ne19

7634

8725

0398

476

3631

.00.

844

36

Wai

nui

1981

3700

2800

900

6926

49.5

0.75

78

7Ar

iki

1983

–198

442

2028

5013

7067

2717

.20.

881

18

Tang

aroa

1980

3912

2750

1162

6433

20.2

0.81

25

*1is

best.


samples, even after culling to remove the most incon-sistent events. The final data set therefore provides agood test for the quantitative techniques being tested.The event levels and the number of events at eachlevel, for each well, are given in Figure 7.

Measures of Well Quality

In Table 4, two measures of well net variance are given.The CONOP normalized net penalty score for the well(in meters) is the sum of event adjustments determinedby CONOP in the best solution, normalized for wellthickness and number of event levels in the well. Thosewells that conform most closely to the composite havethe lowest scores. The Kendall’s tau statistic is a rankcorrelation coefficient that is calculated from theRASC penalty scores for each well. It expresses thedegree of correlation with the RASC probable se-quence (1 � perfect positive correlation).

Figure 6. Stratigraphic range tops of the 87 most reliable taxa used for quantitative analysis in the eight Taranaki wells. Wellsaligned at the base of the Tikorangi Formation (approximately base Oligocene).

The best-performing wells in CONOP are Ariki-1and Tangaroa-1. In these wells, events are adjusted bythe smallest amount necessary to achieve the best cor-relation. The worst performer is Taimana-1. Ariki-1 isalso the best performer in RASC, indicating that, inthis well, event order most closely matches the prob-able sequence. The worst performer in RASC isWainui-1. Overall, Ariki-1 performs best and Wainui-1 worst.

COMPOSITE SEQUENCE FOR THETARANAKI BASIN

The CONOP composite section (Figures 8, 9; Table5) gives the best estimate of the true stratigraphicranges of taxa, based on the well sections. This estimateis consistent with the aims of conventional biostratig-raphy based on range-end events, which are to estab-

Cooper et al. 1481

lish zonation and correlation schemes based on the(maximum) ranges of taxa. The RASC scaled probablesequence (Figures 8, 10), however, gives the mostprobable order of observed events and event spacing.As already discussed, it is particularly useful for futureexploration drilling in the basin.

Where only the order of taxa is compared in thetwo methods, that is, the RASC ranked probable se-quence compared with the CONOP composite se-quence (Figure 8A), the accordance is very good ex-cept for the middle part of the studied interval wheresome events become scattered. This is probably due tothe very close spacing of events in the middle (late Eo-cene) part of the succession: a small stratigraphic dis-placement of an event in a well incurs a low CONOPpenalty, but it jumps many other events, incurring ahigh RASC variance (Figure 9). The performance of

Figure 7. Event horizons used in the present study (wells, showing levels and number of events).

taxa under the two methods, CONOP and RASC, inboth order and spacing of events (Figure 8B), shows aremarkably linear distribution of points considering thedifferent methodologies employed (Figure 4). Thescaled probable (RASC) and composite (CONOP) se-quences are clearly closely comparable except for somescatter near the top of the succession, suggesting thatdifferences between maximum ranges and averageranges of taxa are relatively consistent across the basin.In the middle part of the succession the broad scatterof events is reduced, although CONOP places eventsslightly higher than RASC. From this plot it wouldbe possible to derive a combined composite, con-structed from the distribution of events along the prin-cipal component or eigenvector of the bivariate distri-bution. We regard such a hybrid composite to be ofdoubtful value, however, because it would be difficult


to interpret and evaluate. For this reason, we have notconstructed such a combined composite.

For comparison, the RASC scaled probable se-quence and CONOP composite section are eachplotted against the composite derived by traditionalgraphic correlation (GRAPHCOR), using the Ariki-1well sequence as the initial standard sequence andtwo rounds of correlation (Figure 8C, D). Goodagreement with the CONOP composite is achieved(Figure 8C), as might be expected, except thatCONOP displaces events in the middle part of thesequence to a higher stratigraphic level. The reasonsfor this are not clear but are likely to be related tothe fact that in the GRAPHCOR analysis, a straightline regression (LOC), or minimally segmented line,was used, whereas CONOP fits a multisegmented re-gression line with as many segments as required toallow it to pass through, or close to, all points (Kem-ple et al., 1995). Good agreement with the RASCscaled probable sequence also results (Figure 8D) ex-cept for some scatter in the upper part of the suc-

cession. The RASC scaled probable sequence andgraphic correlation composite are also compared withthe CONOP composite in Figure 9. Note that theGRAPHCOR process implicitly weights sections andevents (choosing starting section and LOC place-ment), whereas no weighting has been employedhere in RASC or CONOP.

These comparisons suggest that the automatedmethods, RASC and CONOP, produce sequencessimilar to the composite standard section of traditionalgraphic correlation. For the present study we use theRASC probable sequence as the best guide to the mostprobable sequence and spacing of events that would beexpected in a new well (see following discussion) andfor deriving a probabilistic zonation. The CONOPcomposite is the easiest to relate to existing zonalschemes and stages and to the international time scaleand is used for those purposes here. Both the CONOPcomposite and the RASC probable sequence providea finely calibrated scale for correlation and basin anal-ysis. Both can be improved in reliability by adding

Figure 8. Bivariate plots ofcomposite and probable se-quences. (A) RASC rankedprobable sequence plottedagainst CONOP composite se-quence. (B) RASC scaled proba-ble sequence and CONOP com-posite section. (C) GRAPHCORcomposite section and CONOPcomposite section. (D) RASCscaled probable sequence andGRAPHCOR composite section.The figures show how the taxacompare under the varioustechniques; see text for expla-nation. Order of events is com-pared in (A), order and spacingof events is compared in (B–D).Correlation coefficients aregiven for each: r � Pearsons’srank, and r� � Spearman’s.Scale units for the compositesections and scaled probablesections in (B–D) are explainedin the text. In (A) the scales forthe x and y axes represent therank order of events.

1484Quantitative

Biostratigraphyofthe

TaranakiBasin

Table 5. Dictionary Listing of Taxa in Alphabetical Order*

CONOPRASC

GRAPHCOR

EventDictionaryNumber Event (Range Tops)

No. ofWells

ContainingEvent

EventOrder

inCompositeSequence

NormalizedMeanPenalty

EventLevelin

CompositeSection

EventAgein

Time-ScaledComposite

Section(Ma)

EventOrder

inRankedProbableSequence

StandardDeviation

EventLevelin

ScaledProbableSequence

EventOrder

inComposite

Section

EventLevelin

CompositeSection

Eventreliability

(fromFigure13)**

1 Allomorphina conica 4 77 107.6 355 50 76 4.05 6.79 74 3948 22 Anomalinoides semiteres 7 25 111.9 166 33 32 5.53 4.28 23 3522 23 Apectodinium homomorphum 5 81 63.8 416 55 82 2.73 7.60 83 4055 14 Bulimina subbortonica 5 66 9.6 285 44 64 2.13 5.86 66 3921 15 Cassidium fragile 4 86 16.0 496 61 85 1.35 8.45 82 4038 16 Catapsydrax dissimilis 5 7 109.6 63 17 14 1.91 1.20 14 3100 17 Cepekiella lumina 5 64 48.9 278 43 65 2.00 5.97 65 3919 18 Chiasmolithus altus 6 19 19.9 142 29 18 1.63 2.42 21 3496 19 Chiasmolithus expansus 7 57 9.2 226 37 49 1.91 4.81 56 3810 1

10 Chiasmolithus oamaruensis 8 30 8.0 180 35 28 3.33 3.69 25 3565 111 Chiasmolithus solitus 7 55 123.4 219 37 58 3.88 5.22 58 3844 212 Cibicides parki 8 24 33.4 166 33 25 3.16 3.67 19 3489 113 Cibicides semiperforatus 5 41 80.4 188 35 43 4.81 4.56 48 3661 214 Coccolithus eopelagicus 8 11 4.2 93 20 8 1.76 0.31 11 3052 115 Coccolithus formosus 8 33 4.9 181 35 24 2.66 3.56 31 3588 116 Corannulus germanicus 5 37 51.4 183 35 34 1.93 4.34 46 3655 117 Cyclicargolithus abisectus 8 10 2.6 86 19 7 1.75 0.20 7 3006 118 Cyclicargolithus floridanus 8 8 0.0 70 17 6 1.99 0.00 8 3006 119 Cyclicargolithus marismontium 7 53 46.8 213 36 45 2.78 4.64 50 3725 120 Daktylethra punctulata 4 54 85.9 214 37 51 1.97 5.00 51 3725 121 Deflandrea convexa 4 68 17.6 291 45 68 1.93 6.08 69 3938 122 Deflandrea leptodermata 5 27 47.8 169 34 27 4.11 3.90 28 3566 123 Discoaster barbadiensis 7 56 94.4 224 37 53 3.06 5.17 57 3810 125 Discoaster kuepperi 5 74 45.0 324 48 73 1.43 6.59 71 3946 126 Discoaster lodoensis 5 58 102.9 229 38 67 2.36 5.88 61 3865 127 Discoaster saipanensis 8 40 22.8 186 35 31 2.40 4.13 36 3608 128 Discoaster tanii nodifer 8 21 24.9 152 31 26 2.42 3.64 18 3380 1

Cooperet

al.1485

29 Enneadocysta partridgei 7 51 27.3 207 36 48 2.93 4.79 37 3608 130 Globanomalina wilcoxensis 4 78 32.1 359 51 79 1.90 7.14 78 3976 131 Globigerapsis index 8 22 12.9 163 33 23 3.05 3.33 20 3489 132 Globigerina angiporoides minima 4 59 0.0 235 38 54 4.28 4.93 54 3797 133 Globigerina boweri 5 72 19.7 322 48 71 2.50 6.27 68 3937 134 Globigerina euapertura 8 16 8.4 121 24 16 1.51 1.61 16 3316 135 Globigerina triloculinoides 6 75 83.9 334 49 75 3.33 6.82 72 3946 136 Globigerinoides bisphericus 4 15 2.8 113 22 12 0.40 0.77 5 2967 1†

37 Globigerinoides trilobus 6 2 60.2 0 16 2 1.89 0.37 1 2850 138 Globoquadrina dehiscens 8 1 87.7 0 16 3 1.77 0.40 4 2900 139 Globorotalia centralis 5 42 183.6 190 35 55 3.89 5.15 53 3729 240 Globorotalia incognita 4 6 55.0 46 17 5 1.39 0.33 12 3093 141 Globorotalia miozea 5 4 41.6 26 16 1 1.86 0.31 3 2900 142 Globorotalia nana 8 5 109.6 33 16 13 2.92 0.88 6 2980 143 Globorotalia praescitula 5 3 50.4 12 16 4 1.75 0.10 2 2850 144 Helicosphaera compacta 8 32 56.6 181 35 29 8.34 4.27 29 3574 345 Helicosphaera euphratis 8 9 35.6 75 18 10 2.18 0.68 13 3097 146 Helicosphaera lophota 6 50 34.6 206 36 46 3.72 4.61 49 3672 149 Homotryblium tasmaniense 5 69 103.2 301 46 77 3.25 6.72 77 3965 150 Hystrichokolpoma wilsonii 4 48 68.1 205 36 40 6.68 4.70 42 3632 351 Impagidinium cassiculum 4 79 0.0 364 51 78 1.20 7.23 79 4005 1†

52 Karreriella novozealandica 4 17 12.0 136 28 17 2.16 1.94 17 3332 153 Lanternithus minutus 8 29 0.5 177 34 21 1.98 3.26 26 3565 154 Malvacipollis subtilis 5 26 324.7 166 33 52 8.88 4.85 47 3660 355 Membranophoridium perforatum 4 71 16.1 319 48 70 0.78 6.20 75 3949 1†

56 Micrantholithus vesper 5 34 152.8 182 35 47 8.46 4.84 34 3594 357 Morozovella crater 6 73 43.2 324 48 72 2.37 6.29 73 3946 158 Nannotetrina cristata 6 65 42.8 280 44 63 3.74 5.82 62 3889 159 Neococcolithes dubius 8 52 48.8 210 36 44 1.94 4.66 38 3608 160 Nothofagidites flemingii 5 49 55.6 205 36 35 4.82 4.55 43 3632 261 Palaeocystodinium golzowense 5 83 0.0 449 58 83 1.30 8.17 84 4132 1†

62 Palaeoperidinium pyrophorum 4 87 0.0 506 62 87 1.36 8.86 86 4220 1†

63 Pemma basquense basquense 6 36 118.3 183 35 42 3.11 4.70 45 3635 164 Polycladolithus operosus 4 35 73.0 183 35 39 3.87 4.52 41 3630 165 Pseudogloboquadrina primitiva 7 61 17.8 250 40 59 2.84 5.34 60 3864 166 Rectuvigerina postprandia 6 23 10.0 163 33 22 1.09 3.31 30 3578 1†

67 Reticulofenestra coenura 8 38 36.6 184 35 38 1.85 4.23 39 3608 1

1486Quantitative

Biostratigraphyofthe

TaranakiBasin

Table 5. Continued

CONOPRASC

GRAPHCOR

EventDictionaryNumber Event (Range Tops)

No. ofWells

ContainingEvent

EventOrder

inCompositeSequence

NormalizedMeanPenalty

EventLevelin

CompositeSection

EventAgein

Time-ScaledComposite

Section(Ma)

EventOrder

inRankedProbableSequence

StandardDeviation

EventLevelin

ScaledProbableSequence

EventOrder

inComposite

Section

EventLevelin

CompositeSection

Eventreliability

(fromFigure13)**

68 Reticulofenestra dictyoda 7 63 135.0 264 42 62 6.10 5.80 63 3889 369 Reticulofenestra hampdenensis 8 39 54.1 184 35 36 3.17 4.20 40 3608 170 Reticulofenestra oamaruensis 5 20 68.3 147 30 30 6.68 3.96 33 3589 371 Reticulofenestra reticulata 8 45 13.5 199 36 33 2.60 4.11 32 3588 172 Reticulofenestra scissura 8 14 38.6 106 21 15 2.26 1.34 10 3050 173 Reticulofenestra umbilica 8 28 0.0 177 34 20 1.89 3.05 24 3555 174 Rhabdosphaera spinula 7 31 82.4 180 35 41 5.03 4.57 27 3565 275 Rotaliatina sulcigera 8 18 29.8 138 28 19 1.24 2.40 22 3522 176 Senegalinium dilwynense 4 84 55.1 471 59 84 1.91 8.45 85 4176 177 Sphenolithus dissimilis 4 12 0.0 100 20 11 1.13 0.65 15 3252 1†

78 Sphenolithus heteromorphus 4 13 23.5 101 21 9 2.90 0.51 9 3006 179 Sphenolithus radians 7 46 223.8 202 36 60 5.46 5.43 52 3725 380 Sphenolithus spiniger 7 44 114.9 196 35 50 4.72 4.84 35 3604 281 Spinizonocolpites prominatus 4 70 16.2 301 46 69 1.56 5.89 70 3944 182 Thalmannammina subturbinata 4 76 72.5 347 50 74 3.03 6.82 76 3952 183 Turbiosphaera galatea 4 82 0.0 433 56 81 1.70 7.53 80 4015 184 Uvigerina wanzea 4 60 8.4 247 40 57 1.48 5.27 59 3849 185 Vaginulinopsis waiparaensis 5 67 121.4 289 45 66 4.56 6.27 67 3936 286 Vozzhennikovia angulata 4 85 24.4 481 60 86 1.80 8.86 87 4220 187 Vulvulina bortonica 4 43 113.3 195 35 56 6.18 4.79 55 3798 388 Wetzeliella hampdenensis 5 47 11.3 203 36 37 3.49 4.33 44 3632 189 Wetzeliella spinulosa 6 80 8.6 393 53 80 2.04 7.34 81 4024 190 Wilsonidium echinosuturatum 4 62 17.9 261 41 61 2.05 5.52 64 3895 1

*Columns to the right summarize event order and event level in the RASC scaled probable sequence and CONOP composite section, and variance measures.**The event reliability measure, derived from Figure 13, is as follows: 1 � good, 2 � suspect, 3 � highly suspect. The best seven events are indicated by a dagger.

Cooper et al. 1487

further sequences, and both can be extended by addingyounger or older sequences.

Note that the scale of the composite section ingraphic correlation is the stratigraphic thickness scale(in meters) of the initial standard section (or standardreference section) used in the analysis. The CONOPcomposite section, however, is scaled in arbitrary unitsbased on the stratigraphic thicknesses in all sections.The units are chosen to be a better proxy for time thanthe thickness units of any one section. CONOP offersseveral compositing options. For this case study, thecomposite was built from the best-resolved parts of thelocal sections as follows. The species ranges in eachsection were augmented and extended to fit the com-posite sequence (to correct for incompleteness). Next,the overall thickness of each section was rescaled tothe number of events that it spans (a better measureof time span). Finally, each interevent spacing in thecomposite was set to the maximum value found in therescaled sections. The RASC scaled probable sequenceis scaled in relative units that represent the degree ofstratigraphic crossover between all pairs of events andis not tied in any way to original stratigraphic spacingof events. To the extent that the degree of crossover(“out-of-order-ness”) reflects the closeness of events intime, the RASC scaled units are a proxy for time units.

Time-Calibrated Composite Section

The CONOP composite section averages out the dif-ferences in depositional rates among the wells, whereasthe RASC probable sequence finds the average strati-graphic order and is independent of depositional rate.Neither, however, detects basinwide sedimentaryevents such as hiatuses unless an external frame of ref-erence is used. Most events used in the present analysisare well known in other depositional basins in NewZealand, and many have well-established stratigraphicranges, providing an external stratigraphic referenceframework. In Figure 11, the expected stratigraphiclevels of 57 of the events, in terms of the New Zealandstages and their calibrated ages, are plotted againsttheir levels in the Taranaki Basin composite. The stagesare well tied to the chronometric time scale (Berggrenet al., 1995; Cande and Kent, 1995; Morgans et al.,1996) and so provide a good approximation of a timeaxis for the diagram. The points follow a curvilineardistribution except for five events that are anomalouslydiscordant and are ignored. The smoothed line is a lo-cally estimated sum of squares (LOESS) regressionfunction (e.g., Cleveland, 1979; Chambers et al.,

1983), with a span factor of 0.3, that was computedusing the statistical program S-plus v. 5 (Mathsoft,1998). This method was chosen because it makes no apriori assumptions about underlying relationships be-tween depth and age and because the resulting curveimplies basinwide depositional rate changes that areconsistent with known stratigraphy (see following dis-cussion [King and Thrasher, 1996]).

The regression allows the CONOP composite sec-tion to be rescaled to an approximately linear timescale that is independent of basinwide changes in dep-ositional rate (Figure 11). We refer to it here as a “time-calibrated composite.” The regression also allows thestage boundaries to be projected into the compositefrom the time scale axis. These boundaries are thoseused in the correlation diagram (Figure 12). The time-calibrated composite is a powerful tool for basin ex-ploration and is here applied to measuring depositionalrates in the individual wells (see following discussion).The method can be used for developing a time scale(Gradstein et al., 1985; Agterberg, 1990) and enablesconfidence limits to be calculated on stage boundaries.

Reliability of Events

High variability of some events from well to well (Fig-ure 6), both in stratigraphic order and in stratigraphicposition, makes those events difficult to use for bio-stratigraphy. The RASC standard deviations for the 87taxa used in the present study and the CONOP nor-malized mean penalty score each give a rough measureof event variance (Table 5). The two measures arecompared in Figure 13. The relatively poor correlationarises because both methods are only approximate andthey respond differently to uneven event spacing. Dur-ing intervals of rapid sedimentation, events that areclosely spaced in time are relatively widely separatedstratigraphically, whereas during intervals of slow sed-imentation, the same events are closely spaced strati-graphically. For a given amount of stratigraphic vari-ability measured in meters, that is, for a given CONOPpenalty score, the events incur a higher RASC standarddeviation in the second case than in the first. This issimply because more event levels are transgressed inthe condensed section than in the thick section.

For the 87 taxa in the Taranaki Basin, the CONOPnormalized mean penalty is the more conservativemeasure of event variance (Figure 13). Events arespaced along the RASC axis more evenly thanalong the CONOP axis, where they cluster at the lowend. Although both measures are approximate, the

Cooper et al. 1489

Figure 10. RASC scaled probable sequence and dendrogram based on scaled distances between events. The dendrogram identifiesdiscrete clusters of events that are separated by relatively large interfossil distances. Boundaries between clusters, in many cases,correspond to a missing section, facies changes, or formation boundaries. The clusters are used to define 18 interval zones. Interpre-tation of these zones in terms of New Zealand stages is shown on the left of the diagram (see text for discussion of method).

Figure 11. Fifty-seven eventsin the CONOP composite sec-tion plotted against their ex-pected distribution in time,based on their known strati-graphic age elsewhere (errorbars shown). Chronometric cali-bration follows Morgans et al.(1996). Regression line-fitting isexplained in the text. Anoma-lous events (open circles) werenot used. Inset shows methodof interpolation of events (Aand B) into time scale to pro-duce the time-calibrated com-posite section and the projec-tion of epoch boundaries fromthe time scale into the compos-ite section. Events shown alongthe y axis are present in theCONOP composite but are notused in construction of thetime-calibrated composite.

biostratigraphic reliability of events that score highlyon both scales can be regarded as suspect. Similarly,those that have low scores on the two scales can beregarded as most reliable. This should be a useful guidefor future use of these taxa in conventional biostratig-raphy. In Figure 13 events are broadly scattered, butthere is at least a general accordance between the twomeasures (r� 0.65). We have arbitrarily classified taxainto three grades of reliability (Figure 13; Table 5). Six-teen events are labeled “suspect” or “highly suspect.”They include the foraminifers Allomorphina conica,Globorotalia centralis, and Vaginulinopsis waiparaensis,and the coccoliths Chiasmolithus solitus, Helicosphaeracompacta, Sphenolithus radians, and S. spiniger, gener-ally regarded as biostratigraphically reliable. The pres-ent study suggests only that among the eight Taranakiwells, the stratigraphic position of range tops for these

taxa is highly variable; they may be reliable elsewhere.Seventy-one events, however, are seen as relatively re-liable, and these include several species not generallyused for biostratigraphy. Seven events are singled outas being of extremely low variability on both scales(marked with a dagger symbol in the last column ofTable 5) and therefore highly consistent in position andlevel among the eight wells.

Reliability of the Composite Section

From the scatter of events in Figure 11, it is clear thatstratigraphic position of many events in Taranaki dif-fers to some degree from that determined for the coun-try as a whole. This is not surprising because the Tar-anaki sequence derived here is based on only eight wellsections. It is not likely that the highest known range


Figure 12. The CONOP best solution to the correlation of well successions. Event positions in the well successions are the “placedlevels” (i.e., the adjusted levels). Note that the five-point moving average separates events that would otherwise be clustered at thesame level. Unless there were more than 10 events at one level, each line represents a single event. The wells are zeroed at the baseof the Tikorangi Formation. Apparent unconformities at the top or base of a well section are unreliable.

top for every species is recorded among the eight Tar-anaki wells, and it is also possible that, in Taranaki,some species disappeared from the basin earlier thanelsewhere. The composite section of range-top events

should therefore be regarded only as the best estimateof true stratigraphic position of events based on theeight wells. The composite is improved and becomesmore reliable by the addition of more sections, par-

Cooper et al. 1491

Figure 12. Continued.

ticularly the addition of onshore measured sections, atwhich time range bases can be added. Ultimately, thecomposite can be expanded to include sections fromother basins and become a true national composite.Physical events such as ash bands, seismic markers, and

e-log markers can be added: both RASC and CONOPhave the facility to treat such isochronous events asfixed with respect to the composite.

Events near the top and base of the composite andprobable sequences are less well constrained than those


Figure 13. RASC standard deviation plotted against theCONOP normalized mean penalty for 87 events in the eightTaranaki wells. The relative reliability of events for biostratig-raphy is indicated.

in the middle part, which have many events both aboveand below to help constrain them. For this reason re-liability of the composite and probable sequences di-minishes near the extremes.

Several apparently reliable events appear in un-expected order in the composite and probable se-quences. For example, the foraminifer Globigerinoidestrilobus is ancestral to G. bisphericus, but the two lastappearance events appear in the “wrong” order in boththe CONOP and RASC sequences (Figures 9, 10).Both events, and that of G. bisphericus in particular,have low variabilities (Table 5). Similarly, range topsof the foraminifers Globorotalia praescitula and G.miozea have low variabilities and an ancestor-descen-dant relationship that is apparently contradicted bytheir order in the composite and probable sequences.Both examples can be explained in several non–mu-tually exclusive ways. The G. trilobus–G. bisphericustransition lies on a morphological cline, and speciesboundaries may have been inconsistently placed by dif-ferent paleontologists during examination of wells, re-sulting in apparently out-of-order relationships. In ad-dition, following the evolution of G. bisphericus, bothspecies coexisted for some time, and it is possiblethat locally, within the Taranaki Basin, the descendantbecame extinct before the ancestor. Likewise, theG. praescitula–G. miozea evolutionary event is also

transitional and may have been subject to the sameidentification uncertainties noted previously. Lastly,some range tops might have been extended upward bysediment reworking, in particular in the younger partsof the study interval. Both these event pairs warrantfurther study in other Taranaki wells.

In the RASC scaled probable sequence and den-drogram (Figure 10), the “reliable” foraminfer Globo-quadrina dehiscens is known to range into the upperMiocene (ca. 10 Ma), but on Figure 10 its range top isinferred to be lower to middle Miocene (between 17.0and 15.5 Ma). This probably results from the errone-ous interpretation of a well-known abundance mini-mum for this species that occurs within the middleAltonian (Scott et al., 1995) and marks the apparentlast occurrence event within the sampled interval, butnot the true range top for the species. Another problemconcerns the last occurrence of the “reliable” foramin-ifer Globigerapsis index that marks, by definition, thebase of the Whaingaroan Stage (34.3 Ma) (Morgans etal., 1996). On Figure 10, however, this event is in-ferred to lie within the lower Whaingaroan (ca. 32.5Ma). Reasons for this anomaly cannot be determinedhere, but the range of G. index is known to be diach-ronous on a regional scale, and it is possible that theabsolute age assigned to the Runangan-Whaingaroanboundary by Morgans et al. (1996) is somewhat tooold.

For the preceding reasons, until more biostrati-graphic sequences are built into the analysis, the com-posite and probable sequences and the location of stageboundaries within them should be regarded as reliableonly for the central-western part of the Taranaki Basinwherein most of the wells are located.

Probabilistic Zonation for Taranaki Basin

Figure 10 shows the RASC zonation based on thescaled probable sequence. As noted previously, thisdiffers slightly from the ranked probable sequence, butfor the purposes of the present discussion, these dif-ferences are not significant. Eighteen interval zoneshave been identified, based on the dendrogram ofscaled distances. These zones are separated by rela-tively large interevent distances that, in many cases,correspond to missing section, facies changes, or for-mation boundaries. Chronostratigraphic correlationsof these zones with the New Zealand time scale ofMorgans et al. (1996) have been achieved using a pro-cedure similar to that used to generate the time-calibrated composite (cf. Figure 11). Fifty-seven of the

Cooper et al. 1493

events with expected stratigraphic levels in terms ofNew Zealand stages were plotted against their own po-sitions in the scaled probable sequence. The bivariatedata were smoothed using a LOESS regression, andthis curve was used to predict the “true” level of theRASC zonal boundaries in the New Zealand timescale. Using this scale, it is seen that the greatest inter-event distance, separating zones 5 and 6 and the lowerand upper Whaingaroan Stage (early–middle Oligo-cene), lies immediately above or within a stratigraphicgap (probably equivalent to the Marshall paraconform-ity) (Carter, 1985) in all wells except Turi-1 and Ariki-1 (Figure 14).

The probable sequence represents the most likelyorder of events that would be encountered in a newwell. The zones identified could form the basis for arobust “natural” zonation for the Taranaki Basin, butthey are not formalized here.

DEPOSIT IONAL RATES

The best indicator of changes in overall depositionalrate across the Taranaki Basin is the plot of the com-posite section against the time scale (Figure 11). De-viations from a straight regression line indicate devia-tions from a uniform depositional rate—the steeper theline, the faster the rate. Deposition through the Paleo-cene and Eocene was mainly mudstone with minorsandstone (Turi Formation) and was followed by achange in facies to carbonates (Tikorangi and Otaraoaformations) in the basal Oligocene (lower Whainga-roan Stage). In that part of the basin covered in thisstudy, the Tikorangi Formation is mainly foraminiferallimestone, marl, and calcareous mudstone (basinal fa-cies). This unit generally marks a significant drop indepositional rate that was maintained through theOligocene–basal Miocene (Duntroonian and Waitak-ian stages) with deposition of calcareous mudstone(Taimana Formation). Low depositional rates are in-dicated in Figure 11 by a comparatively low slope tothe regression line between about 34 and 20 Ma. In theearly Miocene (Altonian Stage), the depositional rateaccelerated markedly with the increased tempo ofplate boundary tectonism to the east and an accom-panying flood of reworked fine-grained clastics (Man-ganui Formation). This acceleration in depositionalrate is indicated in Figure 11 by a sharp increase inslope of the regression line at about 18 Ma.

Where the time-calibrated composite section isplotted against the CONOP-placed levels for events

in a well, a composite age-depth plot for the well isachieved (see also Cooper et al. [2000]). This givesthe best estimate of depositional rate per well (Fig-ure 14A–H). Clear patterns of changing depositionalrate can be seen. Intervals of slow deposition or non-deposition or erosion (here collectively referred toas “unconformities”) are marked by ledges (orbenches) in the regression lines, as in conventionalgraphic correlation.

All previously recognized unconformities (Kingand Thrasher, 1996) are detected except for one, inTaimana-1 (marked G in Figure 14D), which needsfurther investigation. Previously undetected uncon-formities, however, are present in all wells (markedN in Figure 14). In addition, several of the previ-ously recognized unconformities are significantly re-defined (marked A in Figure 14). The age-depthplots appear to be relatively sensitive indicators ofthe age and duration of unconformities.

The base of the Tikorangi Formation is markedby an unconformity that excludes the latest Eocene,early Oligocene, or both, in all wells except forTuri-1 and Tangaroa-1, indicating that the Marshallparaconformity, known to be developed extensivelyin the eastern and southern part of the basin (Kingand Thrasher, 1996), also extends widely throughthe central-western part of the basin. In Tangaroa-1there is, instead, an unconformity within the Tiko-rangi Formation. A major (10 m.y.) unconformity,representing the late Oligocene and early Miocene,is present in Tane-1, near the base of the TaimanaFormation. Unconformities are present within themiddle Eocene and in the Paleocene (Turi Forma-tion) in all wells except for Taimana-1 where, how-ever, there is a lack of control in the lower part.The well age-depth plots (Figure 14) indicate thatdepositional breaks are more common in most wellsthan previously thought. In some wells (Tane-1,Tangaroa-1, Kiwa-1, Witiora-1, Turi-1), if the plotsare correct, there is as much, or more, time repre-sented by hiatuses as by deposition of sediment. Fur-ther investigation is warranted.

Ledges marked with an asterisk in Figure 14A–H are possibly artifacts and are of two types. First,because the events used are range tops, a ledge atthe top of a well section is unreliable and is disre-garded. Second, in the lower parts of the well sec-tions for Kiwa-1, Taimana-1, and Wainui-1, thereare relatively few sampled levels. A relatively largenumber of events must be placed by CONOP, andthere are few levels to use. This results in bunching


of events, resembling unconformities. They are rec-ognizable by the presence of an overlying thick stra-tal interval with no data (Taimana-1 in Figure 14D).

During the Paleocene and Eocene, deposition isrelatively slow in the three northern wells (Turi-1,

Figure 14. Well age-depth plots. Adjusted levels (“placed levels”) by CONOP for all events plotted against the time-calibratedcomposite section are shown for each well (A–H). The ledges represent unconformities. Formation name abbreviations are as follows:Mo � Moki Formation equivalent, Ma � Manganui Formation, Ta � Taimana formation, Ti � Tikorangi Formation, Tn � TangaroaFormation, Tu � Turi Formation, Ka � Kaimiro Formation, B � basement. Unconformities marked N are new; those marked A aresignificantly redefined. The interval marked G in Taimana-1 is shown as an unconformity by King and Thrasher (1996). Featuresmarked with an asterisk are artifacts; see text for explanation.

Ariki-1, Tangaroa-1) whereas it is rapid in all otherwells. During the late Eocene–Oligocene, however,deposition is relatively slow and interrupted by hia-tuses or erosion in all wells except for the three north-ern wells.

Cooper et al. 1495

Figure 14. Continued.

CORRELATION OF TARANAKI WELLS

High-precision correlation of the eight Taranaki wellsequences by CONOP (Figure 11) is based on 87 bio-stratigraphic events. This scheme requires the least netreadjustment of events from their observed levels inthe well sequences. A five-point moving averagesmoothing has been used to separate lines bunched at

the same horizon. All events are thus separated in eachof the wells, unless there are more than 10 events atthe one level, to more clearly reveal the intervals ofrelatively condensed deposition and unconformities.When interpolating missing events, CONOP choosesthe nearest matching event level rather than creating anew event level. Thus there is artificial bunching ofevents to some degree. In the single-well age-depth


plots (Figure 14), however, no smoothing is appliedbecause the aim is to define ledges, or hiatuses, indeposition.

The CONOP correlation of wells shown in Figure12 reveals precise correlation of levels within stages. Itportrays changes in thickness of stages across the basin,as represented in the wells. Most stages vary widely inthickness from well to well. The Mangaorapan Stage isthe most uniform in thickness across the basin. Thefigure also reveals intrastage unconformities and con-densed intervals. Many of the unconformities discussedpreviously are readily seen, particularly the condensedinterval in the late Eocene in Tane-1 and Witiora-1;however, the clearest indicators of unconformities inthe wells are the age-depth plots (Figure 14).

Note that unconformities at the top (or base) ofa section are likely to be artifacts produced byabsence of overlying (or underlying) beds. The cor-relation agrees in general with the original (conven-tional) biostratigraphic scheme but provides an order-of-magnitude more precision.

COMPARISON OF PROBABIL IST IC ANDDETERMINIST IC TECHNIQUES

The relative merits of the various approaches toquantitative biostratigraphic subdivision and correla-tion have been discussed in several articles (Edwards1982a, b; Gradstein et al., 1985; Geux, 1991). Thepresent study has enabled us to compare the two au-tomated quantitative stratigraphic techniques, RASCand CONOP, using the same database. The RASCprobable sequence and CONOP composite sequenceare remarkably similar, and both compare well withclassical graphic correlation (Figures 8, 9). RASCgives the most probable order of events for the eightTaranaki wells and a probabilistic zonation, both ofwhich should be useful for future exploration.CONOP gives the best approximation of the truestratigraphic range of taxa for the eight wells, whichis most convenient for relating to previous zonalschemes and range charts. Where rescaled to thetime scale, it enables the depositional rates in wellsequences to be determined and the age and durationof unconformities to be accurately estimated. Bothtechniques enable a high-precision correlation of wellsuccessions, based on all events. The CONOP cor-relation, based on maximum ranges, is most readilyrelated to existing zonal schemes and New Zealandstages and is used here.

Both techniques enable the biostratigraphic reli-ability of events to be estimated. RASC measures theircrossover (or out-of-order) frequency as a standard de-viation, whereas CONOP measures the net strati-graphic adjustment required in the best solution to thecorrelation problem. Taken together, the two measuresgive the best estimate of overall biostratigraphic reli-ability for each event.

The probabilistic and deterministic methods aretherefore not alternatives but are complementary. Thefullest understanding of the biostratigraphy and depo-sitional history of the basin comes from deployment ofboth techniques.

CONCLUSIONS

1. Drill holes in the Taranaki Basin provide a large vol-ume of biostratigraphic data of moderate to lowquality. Quantitative stratigraphic techniques en-able greatly improved precision in correlation, as-sessment of depositional history, reliability in zo-nation, and accuracy in fossil dating compared toconventional biostratigraphy. The methods thusdemonstrate that without any additional expendi-ture on data acquisition, a large additional amountof high-quality information can be extracted fromthe data.

2. The probable sequence of RASC and the compositesequence of CONOP compare well with the com-posite sequence of classical graphic correlation, in-dicating that the automated methods can serve as asubstitute for the labor-intensive graphic correla-tion. The composite and probable sequences pro-vide valuable standards for biostratigraphic datingand correlation in the basin. These sequences be-come even more reliable with the addition of fur-ther wells and onshore sections, which also increasethe number of events that can be used for dating,correlation, and zonation.

3. A large number of taxa (70) have reasonably goodreliability for correlation in the Paleocene to lowerMiocene of the Taranaki Basin. Some of these taxahave not previously been regarded as biostrati-graphically reliable or useful. Similarly, some taxa,previously regarded as reliable, do not perform wellin the analysis and should be treated with suspicionin the Taranaki Basin.

4. The time-calibrated composite section is a particu-larly powerful tool for detecting unconformitieswhere plotted against the CONOP-placed levels in

Cooper et al. 1497

the well sections. The age-depth plots provide asensitive indicator of depositional rate and of theage and duration of unconformities. Many previ-ously undetected unconformities have been re-vealed, particularly in the Eocene and Paleocene,while all previously postulated unconformities ex-cept for one were detected.

5. The CONOP correlation of well sections gives anorder-of-magnitude more precision than availablefrom conventional biostratigraphy and clearlyshows the changing thickness of stages across thebasin.

6. The two automated methods, RASC and CONOP,are not alternatives but are complementary tech-niques. To get the most information out of low-quality but voluminous data, both techniquesshould be used. RASC provides the most probableorder of events to be encountered in any new wellto be drilled, and a probabilistic zonation of events,whereas CONOP provides a correlation of well suc-cessions that can readily be related to other zonalschemes and to the time scale. Both CONOP andRASC/CASC provide greatly increased precision incorrelation as well as an assessment of the biostrati-graphic reliability of events.

7. Future work should include adding further well andonshore sections to improve the reliability of thecomposite, adding younger sequences to extend thestratigraphic range of the composite, and testing fordifferences between shallow-water and deep-waterfacies. Also, nonbiological stratigraphic events canbe added to the composite. The newly detected in-dications of unconformities and condensed intervalsshould be related to lithologic and geophysical logsand seismic surveys for verification.

REFERENCES CITED

Agterberg, F. P., 1990, Automated stratigraphic correlation: devel-opments in palaeontology and stratigraphy, v. 13: Amsterdam,Elsevier, 424 p.

Agterberg, F. P., and F. M. Gradstein, 1996, RASC and CASC: bio-stratigraphic zonation and correlation software, version 15: F. P.Agterberg and F. M. Gradstein.

Berggren, W. A., D. V. Kent, C. C. Swisher, and M.-P. Aubry, 1995,A revised Cenozoic geochronology and chronostratigraphy: So-ciety for Sedimentary Geology Special Publication, v. 54,p. 129–212.

Brower, J. C., 1981, Quantitative biostratigraphy, inD. F. Merriam,ed., Computer applications in the Earth sciences: New York,Plenum, p. 63–103.

Cande, S. C., and D. V. Kent, 1995, Revised calibration of the geo-magnetic polarity timescale for the late Cretaceous and Ceno-zoic: Journal of Geophysical Research, v. 100, p. 6093–6095.

Carter, R. M., 1985, The mid-Oligocene Marshall paraconformity,New Zealand: coincidence with global eustatic sea-level fall orrise?: Journal of Geology, v. 93, p. 359–371.

Chambers, J. M., W. S. Cleveland, B. Kleiner, and P. A. Tukey, 1983,Graphical methods for data analysis: Belmont, California,Wadsworth, 395 p.

Cleveland, W. S., 1979, Robust locally weighted regression andsmoothing scatterplots: Journal of American Statistics Associ-ation, v. 74, p. 829–836.

Cooper, R. A., and K. Lindholm, 1990, A precise worldwide corre-lation of Early Ordovician graptolite sequences: GeologicalMagazine, v. 127, p. 497–525.

Cooper, R. A., J. S. Crampton, and C. I. Uruski, 2000, The time-calibrated composite—a powerful tool in basin exploration:2000 New Zealand Petroleum Conference Proceedings, p. 346–354.

Edwards, L. E, 1978, Range charts and no-space graphs: Computersand Geosciences, v. 4, p. 247–255.

Edwards, L. E., 1982a, Quantitative biostratigraphy: the methodsshould suit the data, in J. M. Cubitt and R. A. Reyment, eds.,Quantitative stratigraphic correlation: New York, John Wiley,p. 45–60.

Edwards, L. E., 1982b, Numerical and semi-objective biostratigra-phy: review and predictions: Proceedings of the Third NorthAmerican Paleontological Convention, v. 1, p. 147–152.

Edwards, L. E., 1984, Insights on why graphic correlation (Shaw’smethod) works: Journal of Geology, v. 92, p. 583–597.

Geux, J.,1977, Une nouvelle method d’analyse biochronologique,note preliminaire: Bulletin de la Societe Vaudoise des SciencesNaturelles, v. 351, p. 73, and Bulletin des Laboratoires de Geo-logie, Mineralogie, Geophysique et du Musee Geologique del’Universite de Lausanne, v. 224, p. 309–321.

Geux, J., 1991, Biochronological correlations: Berlin, Springer-Verlag, 250 p.

Gradstein, F. M., and F. P. Agterberg, 1998, Uncertainty in strati-graphic correlation, in F. M. Gradstein, K. O. Sandvik, and N. J.Milton, eds., Sequence stratigraphy—concepts and applica-tions: Norwegian Petroleum Society Special Publication, v. 8,p. 9–29.

Gradstein, F. M., F. P. Agterberg, J. C. Brower, and W. S. Schwar-zacher, 1985, Quantitative stratigraphy: Paris and Dordrecht,Unesco and Reidel, 598 p.

Hay, W. W., 1972, Probabilistic stratigraphy: Eclogae GeologicaeHelvetiae, v. 65, p. 255–266.

Hood, K. C., 1986, GRAPHCOR—interactive graphic correlationsoftware, version 2.2: Kenneth C. Hood.

Kemple, W. G., P. M. Sadler, and D. J. Strauss, 1995, Extendinggraphic correlation to many dimensions: stratigraphic correla-tion as constrained optimisation, in K. O. Mann, H. R. Lane,and P. A. Scholle, eds., Graphic correlation: SEPM Special Pub-lication 53, p. 65–82.

King, P. R., and G. P. Thrasher, 1996, Cretaceous–Cenozoic geologyand petroleum systems of the Taranaki Basin, New Zealand:Institute of Geological and Nuclear Sciences Monograph, v. 13,243 p.

Mann, K. O., and H. R. Lane, 1995, Graphic correlation: SEPMSpecial Publication 53, 263 p.

Mathsoft, 1998, S-plus software, version 5: Seattle, Mathsoft.McLeod, N., 1991, Punctuated anagenesis and the importance of

stratigraphy to paleontology: Paleobiology, v. 17, p. 167–188.

Morgans, H. E. G., G. H. Scott, A. G. Beu, I. G. Graham, T. C.Mumme, W. St George, and C. P. Strong, 1996, New ZealandCenozoic timescale (version 11/96): Institute of Geological andNuclear Sciences Science Report, v. 96, no. 38, p. 1–12.

Mortimer, N., A. J. Tulloch, and T. R. Ireland, 1997, Basement


geology of Taranaki and Wanganui basins, New Zealand: NewZealand Journal of Geology and Geophysics, v. 40, p. 223–236.

Prell, W. L., J. Imbrie, J. J. Morley, N. G. Pisias, N. J. Shackleton,and H. F. Streeter, 1986, Graphic correlation of oxygen isotopestratigraphy applications to the Late Quaternary: Paleocean-ography, v. 1, no. 2, p. 137–162.

Sadler, P. M., 1999, Constrained optimisation approaches to thestratigraphic correlation and seriation problems: a user’s guideand reference manual to the CONOP program family: River-side, California, Peter M. Sadler, 142 p.

Scott, G. H., C. S. Nelson, and H. H. Stone, 1995, Planktic fora-miniferal events in early Miocene Zones N.6 and N.7 at south-west Pacific DSDP Site 593: relation with climatic changes in

oxygen isotope Zone Mi1b: Marine Micropaleontology, v. 25,p. 29–45.

Sweet, W. C., 1984, Graphic correlation of upper Middle and UpperOrdovician rocks, North American Midcontinental Province,USA, in D. L. Bruton, ed., Aspects of the Ordovician System:Paleontological Contributions of the University of Oslo, v. 295,p. 23–35.

Sweet, W. C., 1988, Mohawkian and Cincinnatian chronostratigra-phy: Bulletin of the New York State Museum, v. 462, p. 84–90.

Tipper, J. C., 1988, Techniques for quantitative stratigraphic cor-relations: a review and annotated bibliography: GeologicalMagazine, v. 125, p. 475–494.

quantitativebiostratigraphy ofthetaranakibasin ......paleozoic terrane history and evolution in the...

Documents