Travertine, a building stone extensively employed in...

ABSTRACT. — This work is focused ondetermining the provenance of travertine stonesemployed in the construction of some monuments inUmbria (Italy) from the Etruscan to the Renaissanceage. To this aim we propose a new methodologicalapproach based on the combined use of petrographicobservations and statistical analysis of geochemicaldata. Analyses are performed on samples frommonuments and quarries whose activity isdocumented since ancient times. Statistical analysisis performed using the conventional PrincipalComponent Analysis and two new methods basedArtificial Intelligence (a Self-Organizing Map and aFuzzy Logic System).

Results show that the Principal ComponentAnalysis is a very poor technique to determinatetravertine provenance because of its low discrimi-native power as stated using samples from thedifferent quarries. On the contrary, the two Artifi-cial Intelligence techniques show an excellentdiscriminative power and their application tomonument samples produces very good andconcordant results, although some uncertainties inthe determination of travertine for some monu-ments are observed. These uncertainties can besolved, in most cases, by combining results of thestatistical analysis with petrographic observations.

It is evidenced that a local provenance of traver-

tine employed in the construction of ancient buil-dings is a common feature at any age in the past. Inaddition, it is suggested that a non-local provenan-ce may furnish information on the historical back-ground in which a monument was conceived andbuilt.

Results from this study indicate that the combineduse of Artificial Intelligence techniques and petro-graphic observations is a powerful tool for prove-nance determination of travertine employed in theconstruction of ancient buildings.

RIASSUNTO. — Questo lavoro ha come scopo ladeterminazione della provenienza del travertinoutilizzato per la costruzione di vari monumenti inUmbria (Italia) dal periodo Etrusco fino alRinascimento. A tal fine viene proposto un nuovoapproccio metodologico basato sull’uso combinatodi analisi petrografiche e della statistica applicata aidati geochimici. Sono stati analizzati i campioniprovenienti dai monumenti e da cave la cui attivitàestrattiva è documentata sin dall’antichità. Lastatistica è stata eseguita applicando sia il metodoconvenzionale dell’Analisi delle ComponentiPrincipali sia utilizzando due nuovi metodi basatisull’Intelligenza Artificiale (la Self-Organizing Maped un sistema basato sulla Logica Fuzzy).

I risultati dell’Analisi delle Componenti Princi-pali sui campioni di cava mostrano ampie aree disovrapposizione tra i campioni appartenenti allediverse cave ed evidenziano quindi uno scarso

Per. Mineral. (2004), 73, 151-169 http://go.to/perminSPECIAL ISSUE 3: A showcase of the Italian research in applied petrology

Travertine, a building stone extensively employed in Umbria from Etruscanto Renaissance age: provenance determination using

artificial intelligence technique

MAURIZIO PETRELLI*, DIEGO PERUGINI, BEATRICE MORONI and GIAMPIERO POLI

Departimento di Scienze della Terra, Università di Perugia, Piazza Università, 1, 06100 Perugia, Italy

* Corresponding author, E-mail: [email protected]

An International Journal ofMINERALOGY, CRYSTALLOGRAPHY, GEOCHEMISTRY,ORE DEPOSITS, PETROLOGY, VOLCANOLOGYand applied topics on Environment, Archaeometry and Cultural Heritage

potere discriminativo del metodo. Al contrario, ledue tecniche basate sull’Intelligenza Artificialeevidenziano un elevato potere discriminativo edinoltre l’applicazione di queste due tecniche suicampioni provenenti dai monumenti produce buonirisultati sebbene si evidenzino alcune incertezzenella determinazione della provenienza di alcunicampioni. Le incertezze possono essere risolte nellamaggior parte dei casi combinando i risultati delleanalisi statistiche con quelli relativi alle osservazio-ni petrografiche.

Dalle analisi risulta che l’impiego di travertinolocale in tempi passati fosse un’usanza estremamen-te diffusa. Inoltre, l’utilizzo di travertino di prove-nienza non locale potrebbe fornire informazioni sulcontesto storico in cui il monumento è concepito erealizzato.

I risultati di questo studio indicano che l’usocombinato delle tecniche di Intelligenza Artificiale edegli studi petrografici è un mezzo estremamenteutile nel determinare la provenienza del travertinoimpiegato nella costruzione degli edifici antichi.

KEY WORDS: Archaeometry, travertine, Umbria,artificial intelligence, self-organizing maps, fuzzylogic.

INTRODUCTION

Travertine is one of the most employed andappreciated building stones in Central Italygiven its wide geographic diffusion and easyquarrying. Many travertine deposits with diffe-rent origin, extension, economic validity, andhistorical importance are present in Tuscany,Umbria and Latium (e.g. Cipriani et al., 1977),and a large number of them were sites ofextraction of stones employed in the construc-tion of monuments and works of art in differentperiods in the past (e.g. Rodolico, 1965).

Provenance determination of travertine is acomplex archaeometric problem due to thewide textural and geochemical variabilityinherent to this lithology. Travertine, in fact,is the result of different overlapping proces-ses, the most important being primary encru-station and secondary diagenetic processesthat rapidly evolve in space and time genera-ting a complex dynamical system in conti-nuous evolution (e.g. D’Argenio and Ferreri,

1988). The result is a large lateral and verticalvariability of the textural and geochemicalproperties of the stone with large dis-homoge-neities within a single travertine formation(e.g. Ford and Pedley, 1996; Moroni and Poli,1999).

Because of this heterogeneity, the combina-tion of several methods, including petrographicand geochemical analysis, are needed forprovenance determination studies. Petrographicanalysis are useful for the identification ofdepositional environments and plays a funda-mental role in paleo-geographic reconstruction.Geochemical analyses can be also related to thedepositional environment of travertine. Elabo-ration of geochemical data can be performedusing several statistical approaches. Amongthem, Principal Component Analysis (PCA) iswidely used in archaeometry and many exam-ples are present in literature (e.g. Buxeda et al.,2003a; Hall, 2001). In recent years, new analy-tical methods of data elaboration based on Arti-ficial Intelligence (AI) have been developedand a wide variety of them have been madeavailable in the literature (e.g. Fausett, 1994;Principe et al., 1999). The ability of AImethods to manage large amount of data byusing «learning» algorithms makes them poten-tial powerful methods to be used for archaeo-metric purposes (e.g. Remolà et al., 1996;Lopez-Molinero et al., 2000).

In this work we propose an approach basedon the combined use of petrographic observa-tions and statistical analysis of geochemicaldata using both classical (PCA) and AI statisti-cal methods. We apply this approach cross-correlating travertine samples belonging toquarries whose activity is documented fromancient times with the aim to determine prove-nance of travertine employed in the construc-tion of some works of art in Umbria Region(Italy). In particular this study is focused on theEtruscan Arch and the medieval FontanaMaggiore in Perugia, on several monuments inCorciano, both etruscan and medieval and onthe Orvieto Cathedral, built in the renaissanceage.

M. PETRELLI, D. PERUGINI, B. MORONI and G. POLI152

MATERIALS AND METHODS

Sampling and analysis

Travertine samples were collected frommonuments and ancient quarries mentioned inhistorical documents as sites of extraction (Fig.

1). A total number of 195 travertine samples,91 from monuments and 104 from quarrieswere sampled.

Regarding monuments 24 samples havebeen collected from the Etruscan Arch, 20from the Fontana Maggiore, 7 from «Corciano

Travertine, a building stone extensively employed in Umbria from Etruscan to Renaissance age ... 153

Orvieto

Cetona

Saturnia

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Porano

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Perugia

Fig. 1 – Schematic map showing the location of monuments (A: Fontana Maggiore, B: Etruscan Arch, C: «Corciano Monu-ments», D: Orvieto Cathedral), and travertine quarries (gray shaded regions indicated by black arrows).

Monuments» and 53 from the Orvieto Cathe-dral (Table 1A). Samples from «CorcianoMonuments» were taken from some historicalremnants in the town; in particular 2 samplesare from two lion statues standing on theopposite sides of a flight of steps in the centerof the village and of sure etruscan origin, 2samples from the S. Cristoforo Cathedral, 1sample from the tower of the town, 1 sampleform the Church of S. Maria Assunta and 1sample from the town walls, all of medievalorigin.

Regarding quarries, 8 samples belong to theancient quarry of Santa Sabina near Perugia, 53samples are from a large quarry area in Satur-nia, 5 samples were collected from smalloutcrops near Cetona, and 25 samples were

collected from the «Orvieto district» (Fig. 1).For «Orvieto district» we intend three mainoutcrops located in the surroundings of thetown of Orvieto: Tordimonte (15 samples),Porano (5 samples), and Orvieto (5 samples).

Sampling on monuments has been carriedout in the hidden parts of the buildings usinghammer and chisel with special care in order topreserve the aesthetic value of the monuments.With this method it is possible to collect onlysmall samples (of the order of few cm3) fromeach travertine stone that may not fully repre-sent the whole block. In order to minimize thisproblem several samples from the same traver-tine block have been collected with the aim tohave a representative statistical number ofsamples. Special care was also taken to remove


TABLE 1

A) Number of travertine samples collected from the four studied monuments; B) number of travertine samples collected from quarries;

C) major and trace elements utilized for the application of the SOM and the FLS.

(A)

Monuments Age Locality N. of Samples

Etruscan Arch Etruscan Perugia 24Fontana Maggiore Medieval Perugia 20Corciano Monuments Etruscan - Medieval Corciano (Perugia) 7Orvieto Cathedral Renaissance Orvieto 53Total samples from Monuments 104

(B)

Qarries & Outcrops Type Locality N. of Samples

Santa Sabina quarry Perugia 8Orvieto District outcrop Orvieto 25Cetona outcrop Cetona 5Saturnia quarry Saturnia 53Total samples from quarries 91

(C)

Analyzed chemical elements

Major Elements SiO2, TiO2, Al2O3, Fe2O3, MnO, MgO, CaO, K2O, P2O5, LOITrace Elements V, Cr, Co, Ni, Cu, Zn, Ga, Rb, Sr, Y, Zr, Nb, Ba, La, Pb, Ce, Th

the weathered layers in samples from bothmonuments and quarries.

All samples were characterized for their petro-graphic features and analyzed by X-ray fluore-scence spectroscopy (XRF) to determine majorand trace element abundances (Table 1C).

Analysis of some representative samplesfrom monuments and quarries are reported inTable 2.

Principal Component Analysis (PCA)

Principal Component Analysis (PCA) is astatistical method for reducing the dimensiona-lity of data sets while retaining as much infor-mation as it is possible about the sample popu-lation (Baxter, 1994). This technique consistsin generating a new set of variables called Prin-cipal Components combining the variables ofthe original data set. The first principal compo-nent is the combination of variables thatexplains the greatest amount of variation withinthe data. The second principal component defi-nes the next largest amount of variation and isindependent from the first principal componentand so forth. In practice, the first few PrincipalComponents are able to explain the totalamount of data given by all original variableswith a minimum loss of information.

As drawbacks, PCA results are difficult tointerpret when there are numerous samples orwhen the percentage of explained variance isnot high enough (Remolà et al., 1996). PCAmethod is widely described in literature(Baxter, 1994) and there is an extensive appli-cation of this technique for archaeometricpurposes (e.g. Hall, 2001; Buxeda et al.,2003b).

Artificial Intelligence techniques

Artificial Intelligence is a branch of Sciencefocused on the development of new methods tobe used by computers to find solutions tocomplex problems in human-like fashion. Thisgenerally involves borrowing characteristicsfrom human intelligence and applying them asalgorithms.

There are many different approaches to gene-rate Artificial Intelligence algorithms; amongthem the Artificial Neural Networks (ANNs)are well studied and can be applied to a widevariety of problems, such as signal processing,control problems, pattern recognition, and clas-sification problems (e.g. Fausett, 1994; Bishop,1996; Principe et al., 1999).

An Artificial Neural Network is an informa-tion-processing system that shares some perfor-mance characteristics with biological neuralnetworks. A neural network consists of a largenumber of processing elements called neurons(Fig. 2). Each neuron elaborates elementaryproblems using a function called «activationfunction». Neurons are connected each other bycommunication links, each with an associatedweight (w, Fig. 2). The weights represent theinformation being used by the network to solvea problem. In general the weight of eachneuron is determined in a preliminary phase oftraining in which several examples of solutionsare presented to the network; this phase iscalled «learning» of the network. Neurons aregenerally arranged in layers (Fig. 3) in whichdata are initially acquired and successivelyelaborated.

In this work two techniques based on Artifi-cial Neural Networks are utilized in the attemptto determine provenance of travertine stonesemployed in the construction of the studiedmonuments.

The first technique is known as the Self-Organizing Map (SOM; Kohonen, 1998). It is atool for the visualization of high-dimensionalproblems into regular low-dimensional gridsand it is able to convert complex and non-linearstatistical relationships between high-dimensio-nal data into simple geometric relationships ona low-dimensional display.

The choice of using the SOM approach isrelated to the fact that travertine is a verycomplex lithology having wide geochemicalheterogeneity. In order to keep into account theextreme variability inherent to this lithologymany parameters (i.e., a large number ofchemical elements; Table 1 and 2) must beconsidered at once. The capability of the SOM



TABLE 2

Geochemical analysis of some representative travertine samples from monument and quarries. Major elements (with the exception of MgO determined by wet chemical analyses) analyzed by XRF

with full matrix correction after Franzini and Leoni (1972); trace elements by XRF after Kaye (1965).

Sample Name FM1 FM6 FM7 AE2 AE3 AE4 OC11 OC13 OC33 CM1 CM2 CM3

Site Fontana Fontana Fontana Etruscan Etruscan Etruscan Orvieto Orvieto Orvieto Corciano Corciano CorcianoMaggiore Maggiore Maggiore Arch Arch Arch Cathedral Cathedral Cathedral Monuments Monuments Monuments

Location Perugia Perugia Perugia Perugia Perugia Perugia Orvieto Orvieto Orvieto Corciano Corciano Corciano

Facies MT ST ST PT MT PT DT PT ST MT MT MT

Major Elements

SiO2 0.13 5.84 3.06 0.43 10.44 3.80 10.76 11.31 7.50 0.51 0.88 2.35TiO2 0.00 0.09 0.03 0.01 0.10 0.05 0.04 0.02 0.01 0.01 0.01 0.03Al2O3 0.04 2.24 1.15 0.16 2.85 1.19 1.02 0.83 0.41 0.20 0.38 0.81Fe2O3 0.06 0.82 0.42 0.08 0.70 0.47 0.34 0.24 0.18 0.11 0.14 0.22MnO 0.05 0.06 0.07 0.04 0.06 0.07 0.08 0.04 0.07 0.03 0.02 0.02MgO 1.25 1.28 1.48 0.48 0.86 0.54 0.69 0.89 1.01 0.52 0.30 0.27CaO 54.10 46.85 50.06 54.35 43.07 50.18 46.93 46.01 48.74 54.77 54.68 53.60K2O 0.01 0.41 0.23 0.02 0.72 0.28 0.23 0.26 0.17 0.02 0.05 0.14P2O5 0.59 0.72 0.72 0.56 0.58 0.64 0.20 0.18 0.65 0.39 0.27 0.20LOI 43.77 41.70 42.78 43.87 40.62 42.78 39.39 40.13 41.26 43.44 43.27 42.36

TOT 100.00 100.01 100.00 100.00 100.00 100.00 99.68 99.91 100.00 100.00 100.00 100.00

Trace Elements

V 1 1 1 7 7 7 4 4 6 10 10 10Cr b.d.l. 6 b.d.l. b.d.l. 8 6 2 5 b.d.l. 4 3 6Co b.d.l. 2 b.d.l. 5 3 4 5 1 6 1 1 1Ni b.d.l. b.d.l. b.d.l. 3 4 1 7 3 8 5 0 0Cu 9 15 17 6 2 21 13 17 16 15 16 21Zn 9 16 12 17 26 20 35 29 29 62 20 31Ga 2 2 2 1 2 1 0 1 1 10 14 12Rb 2 16 6 2 22 8 39 19 4 1 2 11Sr 630 1000 1300 510 540 660 1864 1375 1989 579 704 651Y b.d.l. b.d.l. b.d.l. 3 3 3 1 1 0 0 0 1Zr 22 43 43 15 24 24 65 38 49 21 21 26Nb 2 3 3 4 2 3 5 1 1 1 2 2Ba 2 19 5 3 17 16 125 245 21 9 0 22La 36 40 45 21 16 17 15 21 0 6 8 5Pb 2 274 696 8 8 14 13 35 17 9 8 30Ce 1 84 214 5 5 5 11 2 5 3 3 3Th 4 6 8 5 3 3 5 9 4 4 7 4


The precision is better than 15% for V, Cr , Ni, Co, Y, Ga and Nb, better than 10% for Cu, Zn, Rb, La, Pb, Ce, Th, Zr, Ba, and better than 5% for Sr. The accuracy has been tested on international

standards and is better than 10%.

SS1 SS5 SS7 OD1 OD9 OD11 CET1 CET3 CET5 SAT9 SAT25 SAT53

S Sabina S.Sabina S.Sabina Orvieto Orvieto Orvieto Cetona Cetona Cetona Saturnia Saturnia Saturnia quarry quarry quarry District District District Outcrop Outcrop Outcrop quarry quarry quarry

Perugia Perugia Perugia Orvieto Porano Tordimonte Cetona Cetona Cetona Saturnia Saturnia Saturnia

PT MT DT ST PT PT PT ST ST DT MT DT

0.86 0.74 3.64 6.47 13.86 5.73 1.50 8.34 5.84 0.03 0.04 0.070.01 0.01 0.04 0.05 0.21 0.05 0.02 0.13 0.09 b.d.l. b.d.l. 0.000.29 0.23 1.11 1.58 4.70 1.29 0.58 2.86 2.39 0.06 0.07 0.100.12 0.09 0.31 0.58 2.23 0.54 0.30 1.15 0.55 0.02 0.05 0.070.04 0.03 0.04 0.03 0.08 0.02 0.03 0.03 0.02 0.04 0.03 0.040.32 0.39 0.44 0.55 0.97 0.79 0.32 0.72 0.36 0.47 0.43 0.36

54.03 54.11 50.83 48.39 41.11 49.93 53.83 47.10 49.63 55.14 55.13 54.940.05 0.05 0.22 0.44 1.08 0.25 0.06 0.46 0.30 b.d.l. b.d.l. b.d.l.0.55 0.61 0.54 0.27 0.30 0.22 0.09 0.16 0.09 0.47 0.49 0.53

43.73 43.75 42.83 41.43 34.23 41.00 43.23 38.89 40.66 43.78 43.76 43.88

100.00 100.01 100.00 99.79 98.77 99.82 99.96 99.84 99.93 100.01 100.00 99.99

7 7 7 10 52 10 10 15 12 10 7 71 b.d.l. 10 4 6 5 1 29 28 b.d.l. b.d.l. 24 1 b.d.l. 2 6 0 b.d.l. 2 1 7 1 2

b.d.l. b.d.l. 3 b.d.l. 15 4 b.d.l. 15 4 b.d.l. 1 14 7 8 20 24 23 17 20 14 16 15 12

13 9 12 29 46 19 33 48 21 2 9 71 1 1 3 5 1 1 1 4 1 b.d.l. 03 3 7 40 284 31 6 28 22 2 3 3

457 484 493 1651 1716 1272 559 1477 442 880 708 6583 3 4 1 9 1 3 3 13 3 b.d.l. 0

17 16 17 66 132 52 26 58 31 15 13 121 2 1 4 8 4 3 4 4 1 2 13 3 3 188 417 92 10 75 38 2 5 5

11 7 16 19 42 21 19 10 21 17 21 242 6 2 13 228 12 10 9 9 2 7 55 5 5 10 126 9 4 7 6 3 5 52 4 4 5 14 2 3 2 0 b.d.l. b.d.l. 0

technique to reduce and to present graphicallythe solution on two dimensional displaysmakes this technique a potentially useful tool tostudy and characterize travertine.

The used SOM (Petrelli et al., 2003) consi-sts of a two-dimensional regular grid ofneuron W(j,k) (Fig. 4) in which each neuroncontains a number of weights w(j,k)i equal tothe number of elements in the input vectors

Xs. Input vectors are determined by geoche-mical analysis of travertine samples; in parti-cular each travertine sample is described by27-element vector Xs (xs1, xs2, xs3,…, xsn, …,xs27) in which each variable xsn corresponds tothe concentration of the chemical element n inthe sample s (Fig. 4).

Learning is a self-organized process carriedout in cycles, called «epochs» (t). One epochis the period in which all input vectors Xs arepresented to the network once. During thelearning process, the SOM automaticallyadapts itself in such a way that similar inputvectors Xs excite most of the neurons placedtopologically close to each other. The result ofthis operation is a map in which the moresimilar input vectors Xs will be clustered,whereas dissimilar vectors will lie far away inthe map.

The second AI technique is based on theprinciples of Fuzzy Logic (e.g. Klir and Yuan,1996; Yang et al., 2000). Fuzzy Logic theorydeals with classes or groupings of data withboundaries that are not sharply defined andcannot be adequately represented by classicalbinary scheme classifications (e.g. 0-1, black-white). Therefore, Fuzzy Logic methods areuseful for studying processes and phenomena


x

w

xw

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Outputactivationfunction

Inputweights

wi

xi

Fig. 2 – Schematic representation of an artificial neuron; xi represents the inputs and wi are the weights. Data are elaboratedby the activation function which gives the output.

InputLayer

HiddenLayer

OutputLayer

Neurons

Fig. 3 – Typical structure of an Artificial Neural Network;neurons are arranged in layers. The Input layer receives theinformation to be processed, hidden layers elaborate dataand, finally, the output layer gives the result.

characterized by a continuous evolution inspace and time.

The Fuzzy Logic System (FLS) that wedeveloped, works by comparing the chemicalfeatures of samples belonging to monumentsand those of samples belonging from quarries.This technique is detailed in Petrelli et al.(2003) and it is schematically illustrated infigure 5. It consists of a simple network whichcontains a number of input neurons equal tothe number of elements in the input vector Xs(i.e., 27 input neurons, Fig. 5A, Step 1).Quarry sample compositions are used as inter-nal reference database. Differently from theSOM approach, no learning process is utilizedby the FLS. Each input monument sample(Xs) is elaborated by the artificial neurons

whose activation value an varies as a conti-nuous function bounded between 0 to 1 (Fig.5B). The more the concentration of chemicalelements between the input monument sampleand a quarry sample are similar, the more theactivation value of the neuron approachesunity (Fig. 5B). Since analyzed chemicalelements are 27, for each quarry sample thereare 27 activation values, one for each neuron.The sum of these activation values indicatesthe degree of geochemical similarity betweenthe unknown sample and each quarry sampleand it is called Global Activation Value (I,Fig. 5A, Step 2). The procedure ends by asso-ciating the input monument sample to thequarry sample with the highest value of I (Fig.5A, Step 3).


( )1,1

( )1,K

( )J,1

( )J,K

( )1,1 ( )J,1

( )1,K( )J,K

Top Map

N(1,k)

Levelsof weights

N(j,k)= w ,w ,(...),w ,(...),w1(j,k) 2(j,k) n(j,k) 27(j,k)

Neuron placed in cellN (j,k)(j,k)

Xs = , , , , ,x x (...) x (...) xs1 s2 sn s27

SiO2 TiO2 Element n Th

Input Vector Xs

Fig. 4 – Schematic Kohonen SOM represented by a 10x10 block of neurons. Each input vector Xs is elaborated by severalneurons N(j,k). In the case shown in the figure each neuron N(j,k) is represented by a column of five weights [indicated inthe figure as w(1,K)]. The top map displays the final output of the computation.


(...)

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=

=27

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I(a)max I(c)max I(z)max

a1 a2 a3 an a27

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Fig. 5 – A) Schematic representation of the Fuzzy Logic System (FLS). STEP1: the unknown sample is introduced into theFLS as a 27-dimensional input vector Xs and activation values an between each chemical element of the input sample andthe quarry samples are calculated; STEP 2: calculation of the global activation value (I) summing an values; STEP 3: thefinal output of the FLS associates the unknown sample to the quarry having the maximum degree of similarity (i.e., themaximum Imax value). B) Activation function: xs is the chemical concentration of an element in the input sample, xq is theconcentration of the same element in the quarry sample; the lower the absolute value of the differences between xs and xq(i.e. the more the values of concentration are similar in the input and in the quarry sample) the higher is the value an of theactivation function.

PETROGRAPHY

General features

All travertines, both from monuments andoutcrops, are the result of calcium carbonateprecipitation on microphyta, macrophyta andinvertebrate supports. Four main travertinelithotypes were recognized, namely stromatoli-tic, phytohermal, microhermal and detrital(D’Argenio and Ferreri, 1988). Stromatolitic,phytohermal and microhermal travertines derivefrom chemical encrustation on vegetal supports(algal felts, macrophyta and microphyta, respec-tively) in growth position. Detrital travertines,instead, derive from biogenic processes ofcalcium carbonate encrustation involvingmacro- and microphyta fragments belonging tostromatolitic, phytohermal and microhermaltravertines after mechanical transport. Detritaltravertines may contain variable proportions ofarenitic to siltitic calcareous sand.

Regarding monuments, stromatolitic,phytohermal and microhermal travertine isabsent in the samples from Etruscan Arch,Fontana Maggiore and Orvieto Cathedral,respectively. Only microhermal and detritaltravertine is present in the samples from«Corciano Monuments». In outcrops and quar-ries, stromatolitic and microhermal travertine isabsent in the samples from S. Sabina and from«Orvieto district», respectively; only phytoher-mal and stromatolitic travertine is absent in thesamples from Cetona, whereas the four lithoty-pes of travertine are all represented in thesamples from Saturnia. Differences exist in thetype and abundance of the fossil and silicicla-stic component in the samples from differentmonuments and quarries. Table 3 reports asummary of the petrographic characteristics oftravertine samples.

Monuments

The fossil component in the samples frommonuments consists of Characeae oogonia,Gastropoda, Ostracoda and bivalvs. In thesamples from Etruscan Arch only Characeae

oogonia were observed, whereas in somesamples from «Corciano Monuments», and inall samples from Orvieto Cathedral, Gastropo-da and Ostracoda were recognized along withalgal oogonia.

The siliciclastic component is scarse in thesamples from Etruscan Arch, where it consistsexclusively of quartz, and totally absent in thesamples from Corciano and in the microhermaltravertine from Fontana Maggiore, respecti-vely. Stromatolitic and detrital travertines fromFontana Maggiore and all samples from Orvie-to Cathedral contain a much varied siliciclasticfraction. In particular, samples from FontanaMaggiore contain plagioclase, muscovite andpyroxene whereas samples from Orvieto Cathe-dral contain K-feldspar, biotite, chlorite, titani-te and epidote.

The depositional environment of all traverti-nes, as inferred by the presence of Characeaeoogonia in almost all samples, is lacustrine orpaludal (e.g. Ford and Pedley, 1996). Thepresence of magmatic components within thetravertines from Orvieto Cathedral, and withinstromatolitic and detrital travertine samples fromFontana Maggiore is a clear evidence of lacustri-ne sedimentation occurring inside or in the proxi-mity of volcanic areas. Composition of clinopy-roxenes from these samples is similar to that ofclinopyroxenes in volcanic products of theRoman Magmatic Province (Moroni and Poli,2000). In particular, composition of clinopyroxe-nes from the samples from Orvieto Cathedral isconsistent with that of samples from Orvietodistrict outcrops (Moroni and Poli, 2000).

Quarries

The fossil component in the quarry samplesconsists of Characeae oogonia, Gastropoda,Ostracoda and bivalvs. Fossils are totally absentin the samples from Saturnia, and rare in thesamples from Cetona, where they are represen-ted by Ostracoda and Gastropoda, and S. Sabinawhere they consist of Caharaceae oogonia andbivalvs. In the samples from Orvieto district thefossil component is scarce and consists ofCharaceae oogonia, Ostracoda and Gastropoda.


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ract

eris

tics

of t

rave

rtin

e sa

mpl

es fr

om m

onum

ents

and

qua

rrie

s. S

T =

Str

omat

olit

ic T

rave

rtin

e;M

T =

Mic

rohe

rmal

Tra

vert

ine;

PT

= P

hyto

herm

al T

rave

rtin

e; D

T =

Det

rita

l Tra

vert

ine;

f.c.

= fo

ssil

com

pone

nt;

s.c.

= s

ilic

icla

stic

com

pone

nt;

num

bers

ref

er to

the

num

ber

of s

ampl

es;

( )

rare

; —

— n

ot p

rese

nt;

Cha

= C

hara

ceae

; O

str

= O

stra

coda

; G

astr

= G

astr

opod

a; B

iv =

biv

alvs

; Q

z =

qua

rtz;

K-f

ld =

alk

ali-

feld

spar

; B

i = b

ioti

te;

Cpx

= c

lino

pyro

xene

; T

i = ti

tani

te;

Chl

= c

hlor

ite;

Pl =

pla

gioc

lase

; E

p =

epi

dote

; M

s =

mus

covi

te;

Pyr

= p

yroc

last

ic fr

agm

ents

.

MO

NU

ME

NT

SQ

UA

RR

IES

Loc

ality

Peru

gia

Peru

gia

Peru

gia

Orv

ieto

Cet

ona

Satu

rnia

Sant

a Sa

bina

Orv

ieto

Dis

tric

t

Des

crip

tion

Etr

usca

n Fo

ntan

a M

.C

orci

ano

Cat

hedr

alou

tcro

pqu

arry

quar

ryou

tcro

pA

rch

Mon

umen

ts

ST—

8—

212

3—

10

f.c.

—C

ha—

(Ost

r, G

astr

, Cha

)—

——

(Cha

, Ost

r, G

astr

)

s.c.

—Q

z, M

s, C

px,

—(Q

z, K

-fld

, Bi,

Px, M

s)—

——

(Qz,

Px,

K-f

ld, P

l, Py

r, B

i, C

hl)

MT

129

6—

—9

1—

f.c.

Cha

Cha

(Cha

, Gas

tr,

——

—C

ha, B

iv—

Biv

, Ost

r.)

s.c.

——

——

—

PT6

——

135

94

13

f.c.

——

—(O

str,

Gas

tr)

(Ost

r, G

astr

)—

—(O

str,

Gas

tr)

s.c.

Qz

——

Px, Q

z, k

-fld

—

—Q

zQ

z, P

l, Px

(T

i, C

hl, P

l, E

p)(B

i, K

-fld

, Ep,

Ti,

Chl

, Pyr

)

DT

63

119

—32

32

f.c.

Cha

Biv

, Cha

Gas

tr, B

iv, O

str

Ost

r, G

astr

——

Cha

(Ost

r, G

astr

)

s.c.

Qz

Qz,

Pl,

Cpx

—Px

, Bi,

K-f

ld—

—Q

z(P

x, P

yr, Q

z, P

l)

The siliciclastic component is absent in thesamples from Cetona and Saturnia, rare (only afew quartz crystals) in the samples from S. Sabi-na, varied and abundant in the samples from«Orvieto district» where it is represented by thesame mineral phases found in the samples fromthe Cathedral (clinopyroxene, K-feldspar,plagioclase, biotite, chlorite, titanite and epido-te) along with a few pyroclastic fragments.

The depositional environment of travertinesfrom Santa Sabina, «Orvieto district» andCetona is lacustrine as evidenced by thefaunal and algal fossil contents. Travertinefrom «Orvieto district» contains a magmaticsiliciclastic component which is in accordancewith the vicinity of the sampled area to theVulsinian Volcanic District. The characteri-stics of the magmatic component and in parti-cular the presence of pyroclastic fragmentswithin the travertine, indicate lithoclast deri-vation from erosion of pyroclastic formations.The presence of macrophytes and the lack ofany faunal remnants in the travertine fromSaturnia can be assumed as an evidence ofcarbonate deposition in a gentle hydrothermalwater regime.

STATISTICAL ANALYSIS

Principal Components Analysis (PCA)

The PCA technique has been initially appliedto quarry samples in order to establish thediscriminative power of the method beforeapplying it for provenance determinationpurposes. Figure 6 shows the application ofPCA method on quarry samples. All diagramsevidence wide overlapping regions amongsamples belonging to the different quarries.Samples from Saturnia and Santa Sabinaalways cluster as a single group with the onlyexception of figure 6C in which the two quar-ries are quite well discriminated. Orvietodistrict samples show very large scattering withrespect to all Principal Components; this resultsin overlapping regions among these samplesand all other quarry samples.

Similar to Orvieto samples, Cetona samplesare scattered in all diagrams (Fig. 6) and theyshow large overlapping regions with all otherquarry samples.

Artificial Intelligence techniques

Like the PCA method, the two AI systems(SOM and FLS) have been initially tested onquarry samples in order to assess their discrimi-native power.

Application of the Self-Organizing Map (SOM)

Figure 7 shows a SOM for the 91 quarrysamples. The SOM shows that samples belon-ging to each quarry are always well clustered.The only exception is related to a samplebelonging to Cetona which is positioned on theright upper corner of the grid and lies far awayform the other Cetona samples (Fig. 7, blackarrows).

These tests evidence a good discriminativepower of the used SOM regarding quarrysamples. In detail, SOM is able to clustersamples belonging to the same quarry and welldiscriminate samples from different quarries.

The SOM has been applied using samplesfrom both monuments and quarries. Figure 8shows a SOM obtained utilizing the 91 quarrysamples together with the 104 samples frommonuments.

The structure of the SOM shows that Etru-scan Arch samples are clustered with samplesof the quarry of Santa Sabina. Travertinesamples from the Fontana Maggiore are divi-ded in two groups (Fig. 8) and they are notassociated to any quarry. Besides, one sampleis positioned far away from the areas occupiedby other samples of the Fontana Maggiore(Fig. 8, black arrows). «Corciano Monu-ments» samples are clustered as single groupand they are not associated with samplesbelonging to any quarry. Almost all samplesfrom the Orvieto Cathedral and from quarriesof the «Orvieto district» are clustered. Twosamples from Orvieto Cathedral and threesamples from «Orvieto district» quarries are


clustered in a different area (upper rightcorner of the map). No sample belonging tomonuments are clustered With Saturniasamples. Three Cetona samples are clusteredon the upper portion of the map and they arenot grouped with any sample from monu-ments. The two other Cetona samples arepositioned respectively in the lower portionand in the left side of the map.

Application of the Fuzzy Logic System (FLS)

In order to test the discriminative power ofthe FLS, each quarry sample has been excludedfrom the quarry database and has been conside-red as unknown. The procedure has been repea-ted for all quarry samples considering each

sample as an unknown sample. Table 4 showsthe results of the test. The system is capable torecognize the exact provenance of 89 on 91samples with a percentage of success of 97.8%.


-6 -4 -2 0 2 4 6 8 10 12 14 16 18

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

PC1

PC2

-6 -4 -2 0 2 4 6 8 10 12 14 16 18

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

PC1

PC3

-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

PC2

PC3

-6 -4 -2 0 2 4 6 8 10 12 14 16 18

-3

-2

-1

0

1

2

3

4

PC1

PC4

S. Sabina quarrySaturnia quarryOrvieto District

Cetona outcrops

A B

C D

Fig. 6 – Application of Principal Component Analysis to quarry Samples. PC1, PC2, PC3 and PC4 are the first four Princi-pal Components.

TABLE 4.

Results of the application of the FLS on quarrysamples.

Qarry N. of Samples Correct Uncorrect

Santa Sabina 8 8 0Orvieto District 25 25 0Cetona 5 3 2Saturnia 53 53 0Total 91 89 2


SaturniaSaturniaS.SabinaS.Sabina Orvieto dist.Orvieto dist.CetonaCetona

SaturniaSaturniaS.SabinaS.Sabina Orvieto dist.Orvieto dist.CetonaCetona

Corciano Mon.Corciano Mon.

Etruscan ArchEtruscan Arch

Orvieto Cath.Orvieto Cath.Fontana MaggioreFontana Maggiore

5Saturnia

5S. Sabina

6Cetona

Orvieto dist. 4

Fontana Maggiore

Perugia

Orvieto Cathedral

Orvieto

Etruscan Arch

Perugia

S. Sabina 22

Saturnia 2

Corciano Monuments

Corciano

3Saturnia

S.Sabina 2

1

Orvieto dist.

Cetona

2Saturnia

S.Sabina

50Orvieto dist.

1

1

Fig. 7 – SOM for quarry samples. Lines are drawn toevidence the clustering of samples belonging to the samequarry. Black arrows indicate the coordinates of the Cetonasample positioned far away from other Cetona samples.

Fig. 8 – SOM computed using both quarry and monumentsamples. Arrows indicate the sample from the FontanaMaggiore that deviate from the behavior of the other samples.Lines are drawn to evidence the clustering of samples.

Fig. 9 – Schematic report of the results obtained by theapplication of the FLS on monument samples. For eachmonument the number of samples assigned to the differentquarries are reported.

In particular, the system recognizes the prove-nance of all samples belonging to Saturnia,Santa Sabina and «Orvieto district» quarries.Regarding Cetona, 3 samples on 5 are succes-sfully classified. The two remaining samplesare classified as belonging to «Orvietodistrict».

The FLS has been applied on travertinesamples belonging to the four studied monu-ments. Results are schematically reported infigure 9. 22 samples from the Etruscan Archare assigned to the quarry of Santa Sabina and2 to Saturnia. The 53 samples of the OrvietoCathedral are classified as it follows: 50samples are assigned to the «Orvieto district»,2 samples to Saturnia and 1 sample to Santa

Sabina. Regarding the 20 samples belonging tothe Fontana Maggiore very dispersed resultsare obtained: 6 samples are assigned to thequarry of Cetona, 5 to Saturnia, 5 to SantaSabina and 4 to Orvieto District quarries.Regarding «Corciano Monuments», the 2samples from the lion statues are associated toSaturnia, the 2 samples from S. CristoforoCathedral to Santa Sabina and Saturnia, thesample from the tower of the town to SantaSabina, the sample form the Curch of S. MariaAssunta to Saturnia and the sample from thetown walls to Cetona.

DISCUSSION AND CONCLUSIONS

Figure 6 reports results of the PCA forsamples belonging to the quarries and clearlyindicates that there are large overlappingregions for samples belonging to differentquarries. This means that the discriminativepower of PCA is very low, and for this reasonwe discard it as a suitable statistical method forprovenance determination of travertine stones.

The application of the Self-Organizing Mapand the Fuzzy Logic System shows that theyare powerful tools to determine provenance oftravertine stones from ancient buildings. Thetwo systems give, in fact, very concordantresults evidencing a good discriminative powerof both systems that are able to recognize theexact provenance of quarry samples with verylittle uncertainties. The only exception is rela-ted to samples belonging to the quarry of Ceto-na. These samples are quite well discriminatedfrom the other quarry samples by SOMs, butFLS shows some uncertainties and it does notclassify univocally all samples. This occurren-ce may be due to the paucity of samples fromCetona that does not allow to perform a statisti-cally representative analysis.

Although the two systems indicate a goodgeneral agreement, interpretation of SOMsgives, in some cases, additional informationwhich cannot be inferred using the FLS. SOMsstructure, in fact, allows to cluster similarsamples and to separate different ones (Koho-

nen, 1998), whereas FLS associates anyway themonument sample to a quarry for any value ofI. For example, consider some samples from amonument for which there is no quarrysampled. When the monument samples areprocessed by the SOM system they will becluster far away from any existing quarry. Onthe contrary, the same samples processed bythe FLS will be associated to the quarries withthe highest value of I. This evidence induces usto select SOMs as primary technique and useFLS as a test for the results obtained by SOMs.

Application of AI systems (SOM and FLS)on Etruscan Arch samples indicates a prove-nance from the quarry of Santa Sabina, close tothe town of Perugia (Fig. 1). This is in agree-ment with the fact that facies of Etruscan Archand Santa Sabina quarries, as deducible by thefossil and mineralogical contents and by thelithotypes association, are practically the sameand markedly different from those of the otherquarry samples. Therefore results of AI andpetrographic analysis point to a local provenan-ce of travertine. Our results are also in agree-ment with archaeological and historical data. Infact, provenance from a rich basin located nearthe site of construction is postulated by histo-rians (e.g., Defosse, 1980) considering thegreat volume of material necessary to build themonument.

In the case of Orvieto Cathedral, sites ofprovenance of travertine stones are identifiedby AI techniques in quarries occurring in thesurrounding of the town (Tordimonte, Porano,and Orvieto; Fig. 1). These data are in goodaccordance with textural analysis. In particularthey agree for the lithological assemblage, thetype and abundance of the fossil componentand, remarkably, the composition of themagmatic siliciclastic component.

Our results point to a local provenance, fromthe travertine lenses inset in the local pyrocla-stic formation, for all the travertines from theCathedral. It is to note that historical docu-ments (Riccetti, 1988) report that part of thematerial employed in the building of the Cathe-dral was extracted in the area of Cetona. Ouranalysis, instead, shows that no sample


employed in the Cathedral belong to such anarea. In fact, SOM does not evidence any signi-ficant similarity between Cetona and OrvietoCathedral samples and this result is confirmedby FLS. Furthermore, the total absence of anymagmatic siliciclastic component in thesamples from Cetona is in contrast with thepresence of pyroxene, K-feldspar, biotite, chlo-rite, titanite and epidote in the samples fromOrvieto Cathedral (see Table 3).

The hypothesis that Cetona did not providethe travertine for the Cathedral is not comple-tely in contrast with historical sources, since theexact lithology of the materials from eachquarry is not specified in the documents.Therefore it is possible that Cetona providedsome kind of building material other thantravertine or, more likely, that travertine wasreally extracted near Cetona, but due to its lowvalue it was intended for the production of lime.

Regarding Fontana Maggiore results of SOMevidence two clusters of samples not attributa-ble to any of the sampled quarries and outcro-ps. On the other hand, results of FLS point toaffinities between samples from FontanaMaggiore and the four quarry districts takenunder consideration. Although at first sightthese results may appear contradictory, it is tonote, as discussed above, that the great disper-sion of results given by the Fuzzy LogicSystem can be also interpreted as related to thefact that, actually, outcrops of travertineemployed in the Fontana Maggiore have notbeen sampled. Therefore travertine from Fonta-na Maggiore could come from two differentquarry districts or zones of excavation.

Results of petrographic analysis point to thepresence of stromatolithic, microhermal anddetrital travertine lithotypes in FontanaMaggiore. Stromatolithic and detrital traverti-nes are characterized by the presence of amagmatic component, whereas microhermaltravertine lacks of any magmatic minerals(Table 3). Considering this textural evidencetwo groups of samples can be distinguished,the first constituted by stromatolithic and detri-tal travertine, the second represented bymicrohermal travertine, and these groups, on

turn, exactly correspond to the clusters identi-fied by the SOM. Therefore, results of petro-graphic analysis confirm those of AI techni-ques supporting the existence of two zones ofprovenance for the material employed in theFontana Maggiore.

Composition of pyroxenes occurring withinstromatolithic and detrital samples points to aprovenance from travertine deposits inset in thevolcanic units of the Roman Magmatic Provin-ce. However, the SOM system clearly separatesthese samples from those of «Orvieto district»quarries. In addition, values of Sr of thesesamples are in agreement with the values oftravertines from northern Latium (Barbieri etal., 1979). Therefore, a general provenancefrom northern Latium may be invoked for thepyroxene bearing travertine of FontanaMaggiore.

The employment of non-local travertine inthe construction of Fontana Maggiore seems tobe quite surprising considering the intensequarrying activity in Santa Sabina area up torecent times. However, it is important to notethat travertine from Rome was employed in itsconstruction (Ferretti, 2001) supporting theoriginal employment of travertine from Latiumas re-utilized material coming from ancientRoman buildings as in the case of marble(Moroni et al., 2002). The fountain sufferedalso a number of changes and substitutionsduring the centuries in order to remedy thedecay of the constituent material. The employ-ment of different types of travertine in thesubsequent restorations suffered by the monu-ment seems, hence, to be reasonable and theycould be the microhermal samples for which nodefinitive inferences can be advanced.

In the case of the monuments from Corciano,results of SOM evidence a single group ofsamples not attributable to any of the sampledquarries and outcrops. On the other hand,results of FLS point to the presence of affinitiesbetween samples from «Corciano Monuments»and all the four quarry districts taken underconsideration. As in the case of FontanaMaggiore, the great dispersion of results givenby the Fuzzy Logic System can be interpreted


as related to the fact that outcrops of travertineemployed in the «Corciano Monuments» arenot sampled.

Results of textural analysis on the travertinesfrom «Corciano monuments» show great affi-nity of microhermal samples and facies rela-tionships between microhermal and detritalsamples. These results support the hypothesisof a single quarrying area for these travertines.Comparison between the samples from Corcia-no monuments and the quarry samples fromSanta Sabina show general affinities amongmicrohermal travertines, but differences regar-ding the presence of quartz and the relativeabundance of the different lithotypes. Thesesimilarities and differences support, therefore,that the quarrying area was possibly located insurroundings of Santa Sabina.

In conclusion, results of the application ofartificial intelligence techniques and petro-graphic analysis to the determination of prove-nance of travertines led to the identification ofa strictly local provenance for the samples fromEtruscan Arch, Corciano Monuments andOrvieto Cathedral, and a non-local provenancefor at least one group of samples from FontanaMaggiore. Travertine is a material whosecommercial value is generally not so high tosupport the costs of transport over long distan-ces. For this reason, and in consideration of thegreat abundance of travertine formations ofgood quality outcropping all over Central Italy,local provenance can be considered a commonfeature of travertines employed at any age inthe past. Non-local provenance furnishes infor-mation on the historical background in which amonument was conceived and built. In the caseof Fontana Maggiore, for example, provenancefrom ancient Roman monuments clearly testi-fies the great cultural influence of romancustoms over Perugia institutions in the periodin which the Fountain was built.

The obtained results are promising enough toencourage future research based on integratedapproaches involving petrography and Artifi-cial Intelligence technique for archaeometricpurposes.

ACKNOWLEDGMENTS

We are grateful to R. Mazzuoli and an anonymousreferee for helpful suggestions and criticisms inreviewing the manuscript. The editorial handling ofB. Messiga is gratefully acknowledged. This workwas founded by CNR and «Centro di Eccellenza –Tecnologie Scientifiche Applicate alla RicercaArcheologica agli Studi Storico Artistici» grants.

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