The Alto Molise area belongs to the Apennine orogenic belt, which has been developing since the Late Cretaceous as a result of the Europe–Africa collision. This area is characterized by the complex superposition of different palaeogeographic domains that developed during the Mesozoic along the passive southern margin of Tethys Ocean. These domains have been subdivided into four main structural units; from shallowest to deepest they are: the Sannio pelagic basin, the Latium-Abruzzi carbonate platform, the Molise pelagic basin and the Apulia carbonate platform Units. Until the present study, the tectonic relationship among these units was thought to have been mainly determined by Early Messinian to Late Pliocene thrusting. This paper reports the results of a new geometric and kinematic analysis of the Alto Molise area. This new interpretation of the Alto Molise structural style indicates that strike-slip tectonics played a primary role during post-Pliocene times, greatly modifying the previous Alto Molise thrust structures. Two main tectonic events are recorded, consisting of the Pliocene thrusting of the Molise domain onto the Apulia platform followed by the post-Pliocene disruption and rotation of the pre-existing compressional structures by N–S-oriented, high-angle, right-lateral strike-slip faults. These faults are manifest in the Apulia platform as narrow shear zones, but propagate towards the surface into wider belts of strike-slip and oblique-slip deformation. We have compared our results with published structural, palaeomagnetic and anisotropy of magnetic susceptibility (AMS) data from the Central Apennines. This comparison suggests that the superposition of the younger strike-slip tectonic deformation on the older fold and thrust structures in the Alto Molise area is consistent with block-rotation of a segmented orogen, applied to the whole Apennine system.
The alternative relationships that can exist between a mountain front and the adjacent foreland basin have been recognized for many years. However, seismic reflection data from such areas are commonly of poor quality and therefore structural models may contain large uncertainties. In view of scientific and commercial interest in mountain belts, we have reviewed the methods for discriminating between alternative interpretations using a case study from the Montagna dei Fiori in the central Apennines, Italy. In this area Mesozoic and Tertiary carbonate sediments are juxtaposed with a foredeep basin containing up to 7 km of Messinian and Plio-Pleistocene siliciclastic sediments. A new structural model for this area demonstrates how the structures in this area form a kinematically closed system in which displacement is transferred from the thrust belt to blind structures beneath the present-day foreland. Growth strata show that Pliocene shortening was initially rapid (15 mm a −1 ) followed by slower rates during the final stages of deformation. Variations in structural elevation indicate a component of basement involvement during thrusting, and this is further supported by magnetic modelling. The results illustrate the interaction of thin- and thick-skinned structures in the central Apennines, and the methods for discriminating between alternative structural models.
Coward M. P., Dietrich D. & Park R. G. (eds) 1989. Alpine Tectonics. Geological Society Special Publication no. 45. vi + 450 pp. Published for the Geological Society by Blackwell Scientific Publications. Price £65.00 (hard covers). ISBN 0 632 02508 5. - Volume 127 Issue 5
<p>Machine learning is being presented as a new solution for a wide range of geoscience problems. Primarily machine learning has been used for 3D seismic data processing, seismic facies analysis and well log data correlation. The rapid development in technology with open-source artificial intelligence libraries and the accessibility of affordable computer graphics processing units (GPU) makes the application of machine learning in geosciences increasingly tractable. However, the application of artificial intelligence in structural interpretation workflows of subsurface datasets is still ambiguous. This study aims to use machine learning techniques to classify images of folds and fold-thrust structures. Here we show that convolutional neural networks (CNNs) as supervised deep learning techniques provide excellent algorithms to discriminate between geological image datasets. Four different datasets of images have been used to train and test the machine learning models. These four datasets are a seismic character dataset with five classes (faults, folds, salt, flat layers and basement), folds types with three classes (buckle, chevron and conjugate), fault types with three classes (normal, reverse and thrust) and fold-thrust geometries with three classes (fault bend fold, fault propagation fold and detachment fold). These image datasets are used to investigate three machine learning models. One Feedforward linear neural network model and two convolutional neural networks models (Convolution 2d layer transforms sequential model and Residual block model (ResNet with 9, 34, and 50 layers)). Validation and testing datasets forms a critical part of testing the model&#8217;s performance accuracy. The ResNet model records the highest performance accuracy score, of the machine learning models tested. Our CNN image classification model analysis provides a framework for applying machine learning to increase structural interpretation efficiency, and shows that CNN classification models can be applied effectively to geoscience problems. The study provides a starting point to apply unsupervised machine learning approaches to sub-surface structural interpretation workflows.</p>
'%3e%3cpath fill='%23012169' d='M0 0v30h60V0z'/%3e%3cpath stroke='%23fff' stroke-width='6' d='m0 0 60 30m0-30L0 30'/%3e%3cpath stroke='%23C8102E' stroke-width='4' d='m0 0 60 30m0-30L0 30' clip-path='url(%23b)'/%3e%3cpath stroke='%23fff' stroke-width='10' d='M30 0v30M0 15h60'/%3e%3cpath stroke='%23C8102E' stroke-width='6' d='M30 0v30M0 15h60'/%3e%3c/g%3e%3c/svg%3e)
