The first archaeogeophysical survey at the ancient site of Parion (Biga-Canakakle) is presented in this paper. During the summer of 2006 we conducted an electrical resistivity tomography survey at the necropolis. Resistivity data were collected along parallel profiles using Wenner-Schlumberger array and electrode spacing of 1 m. A two-dimensional least squares algorithm based on the smoothness-constrained technique inverted the apparent resistivity data. MATLAB-based visualization tools displayed resistivity distribution of the subsurface in three-dimensional volume. These tools yield volumetric resistivity images by combining two-dimensional model resistivity sections. Thus, both the horizontal and vertical extent of the anomalous zones were displayed. This visualization technique revealed high resistivity zones at depths ranging from 0.25 to 2.5 m at the investigation area. Taking into account archaeological evidence, these higher resistivity zones are the most promising locations for archaeological excavation.

/pdf/3d-resistivity-imaging-of-buried-tombs-at-the-parion-34p6erzgpp.pdf

3D resistivity imaging of buried tombs at the Parion necropolis (NW Turkey)

Ground penetrating radar (GPR) is a non-destructive geophysical method that uses radar pulses to image the subsurface. Notwithstanding that it is particularly promising for soil studies, GPR is characterised by notoriously difficult automated data analysis. Hence, the focus of this chapter is to provide the reader with a deep understanding of the state of the art and open issues in the field of GPR data processing techniques as well as of the interesting application of GPR in the field of civil engineering. In particular, we present an overview on noise suppression, deconvolution, migration, attribute analysis and classification techniques for GPR data.

GPR data processing techniques

Attenuation analysis of real GPR wavelets: The equivalent amplitude spectrum (EAS)

The nature of archaeology is complex, and is not confined to the humanistic world, but can easily spread to scientific one. Nowadays, it is not uncommon to find archaeologists using new technologies in order to gather, analyze, and interpret data and disseminate the results. It is clear that every aspect of the work of the archaeologists is now actively revolutionized by modern technologies. In this framework, the use of machine learning techniques proves to be extremely interesting. New opportunities for the classification of artifacts, the prediction of site location, and the standardization of remains analysis can now be offered to archaeology. Although the introduction of machine learning in archaeology dates back to the 1970s, an extensive use in day-to-day work is still far away. Probably, the greatest obstacle is the need for a trained archaeologist expert in machine learning. While the improvements that machine learning could introduce in archaeology are clear, the role of the archaeologist in machine learning applications is not. This limitation should be removed increasing the knowledge in the archaeological community. This work means to offer an answer to this need providing descriptions of the main algorithms as well as their applications in the archaeological community. 

https://link.springer.com/content/pdf/bfm:978-3-030-60016-7/1?pdf=chapter%20toc

Machine Learning: A Novel Tool for Archaeology

An important part of many site investigations is the geomechanical assessment of the rock mass in terms of a classification system which is independent of the user. All classification systems used to date are based on observation of the rock mass and the determination of simple engineering parameters on intact rock specimens. In this paper, the use of the seismic properties of the rock mass to assess the geomechanical performance of the rock mass are examined and illustrated by three case histories. (The investigation of Altnabraec granite, Caithness, Middle chalk at Mundford, Norfolk, and Dalradian schists.) It is shown that precise values of uniaxial compressive strength cannot be predicted from compressional wave velocity determinations, although rock types can be classified qualitatively (for example, weak to very weak or strong to very strong). For a specific lithology, the compressional wave velocity can be related to the degree of fracturing and an approximate relationship with RQD (rock quality designation) established.(A) For the covering abstract of the conference see IRRD 832042.

Rock mass assessment from seismic measurements . field testing in engineering geology. proceedings of the twenty-fourth annual conference of the engineering group of the geological society, sunderland polytechnic, september 4-8 1988

Classification techniques such as the self-organizing-maps (SOM) network are proposed as a tool for efficient interpretation of geophysical data obtained from archaeological investigation. This methodology was useful for the interpretation of georadar data from the archaeological site of Aptera, Chania, Greece where an integrated geophysical survey covered 9730 m2. The survey was realized under the auspices of the 25th Ephorate of Prehistoric and Classical Antiquities, Ministry of Culture. Soil resistance and magnetic gradient maps, resistivity and georadar sections and 3D images, aerial and ground photos, archaeological and geologic maps were registered in a GIS project. The geophysical maps display the ancient city plan. Resistivity depth slices exhibit a stone corridor at a depth of 0.34 m. Three-dimensional inversion was applied on electrical tomography data collected along parallel survey lines. The resulted three-dimensional image reveals a water pipeline. Instantaneous and geometric attributes calculated from the migrated georadar sections over a roman cistern were subsequently classified using SOM. The resulted classified 3D georadar image displays the arc-shaped roof of the cistern as well as stone pipelines which supplied the cistern with rain water.

Application of Classification Methods on Geophysical Data from the Archaeological Site of Aptera, Chania, Greece

Η ποσοτική eκτίμηση των χαρακτηριστικών παραμέτρων της ισχυρής eδαφικής κίνησης στους eπιφανeιακούς γeωλογικούς σχηματισμούς έχeι ιδιαίτeρη σημασία στην τeχνική σeισμολογία και την eδαφομηχανική. Στο έργο “GEO  -CHARACTERIZATION” ΘΑΛΗΣ, μeλeτώνται οι γeωτeχνικές και γeωφυσικές τeχνικές ως μέθοδοι διeρeύνησης των γeωτeχνικών παραμέτρων του υπeδάφους. Eνώ οι γeωτeχνικές δοκιμές παρέχουν μe ακρίβeια τις γeωτeχνικές παραμέτρους, η κατασκeυή των γeωτρήσeων και η eκτίμηση των γeωτeχνικών παραμέτρων, eίναι ιδιαίτeρα δαπανηρή, ασαφής και χρονοβόρα διαδικασία. Οι γeωφυσικές μέθοδοι eίναι αποτeλeσματικές στη διeρeύνηση του υπeδάφους, αλλά τα αποτeλέσματά τους δeν μπορούν άμeσα να μeταφραστούν σe μeταβολή των γeωτeχνικών παραμέτρων. Μe σκοπό τη πληρέστeρη μeλέτη του υπeδάφους, σe σταθμούς eπιταχυνσιογράφων του E.Δ.E. (Eθνικού Δικτύου Eπιταχυνσιογράφων), θα  eκτeλeστούν  συμπληρωματικές  γeωτeχνικές  και τeχνικογeωλογικές eργασίeς  eνώ ταυτόχρονα θα eκτeλeστούν γeωφυσικές διασκοπήσeις. Το σύνολο των δeδομένων θα eπιτρέ   ψeι  τη  συσχέτιση  των  φυσικομηχανικών  παραμέτρων  (από  eργαστηριακές/eπιτόπου δοκιμές) μe τις γeωφυσικές παραμέτρους. Eπιπλέον, τα δeδομένα αυτά θα eπιτρέψουν τη dιeρeύνηση γeωτeχνικών προβλημάτων καθώς και την απόκριση των σχηματισμών που δομούν την πeριοχή σe eνδeχόμeνη δυναμική – σeισμική φόρτιση, eνώ θα προταθούν και τοπικά   κανονικοποιημένα eλαστικά φασμάτα σχeδιασμούτα οποία θα συγκριθούν μe τα αντίστοιχα φάσματα του Eυρωκόδικα 8 (EC8).

/pdf/geo-characterization-of-selected-areas-in-crete-greece-32rt9jsqlj.pdf

"Geo-characterization" of selected areas in Crete, Greece, towards realistic assessment of seismic design actions

A systematic workflow is proposed which includes ground penetrating radar data pre-processing and attribute calculation. The attributes are classified using a competitive artificial neural network, namely Self Organizing Maps (SOM). This workflow is applied to 3-D ground penetrating radar datasets acquired with the PulseEkko1000 system from a tank model and the archaeological site of Aptera, Chania, Crete, Greece. This proposed methodology, proved to be useful for distinguishing different pipes from the surrounding medium with high resolution and buried relics.

GPR Data Interpretation Using Self Organizing Maps

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Application of Classification Methods on Geophysical Data from the Archaeological Site of Aptera, Chania, Greece

"Geo-characterization" of selected areas in Crete, Greece, towards realistic assessment of seismic design actions

GPR Data Interpretation Using Self Organizing Maps