A PROPOSED MULTIMODAL CONVOLUTIONAL NEURAL NETWORKS (CNNS) SOLUBLE SALTS MAPPING SYSTEM OF THE SUBSURFACE: THE CASE OF THE WALL PAINTING AT THE ROYAL BOX AT HERODIUM, ISRAEL
Keywords: Wall Painting, Conservation, Salt Weathering, non-destructive testing (NDT), soluble salts mapping, SRCNN, convolutional neural network (CNN), Deep Learning
Abstract. In 2008, excavations at Herodium revealed magnificent secco wall paintings and stucco decorations adorning the central chamber at the top of the royal theatre. The wall paintings, dated to the first century B.C.E., have been preserved up to a height of 6 meters. However, shortly after the discovery, salts weathering and structural faults caused severe damages to the decorations. The conservation process to restore the wall paintings lasted almost a decade. These efforts helped stabilize the state of wall painting, but in a very fragile manner, while the deterioration factors are still present, any slight change in the condition of the enclosure, could damage the paintings. This study is aimed at assisting the conservators in developing a tool that will offer a glance to the hidden threats at the subsurface, and by that help protect historic monuments from salt weathering. This paper will describe an innovative methodology with particular emphasis on novel multimodal convolutional neural networks (CNNs) technologies to process data of non-destructive testing (NDT) for detection and mapping soluble salts at the subsurface of ancient wall paintings. Prior to preforming the system protocol in situ, a laboratory simulation was carried out to study thermochemical behaviour of soluble salts, chlorides and sulphates, within different subsets. The preliminary results of the simulation will be presented in this paper.