Mapping Allochemical Limestone Formations in Hazara, Pakistan Using Google Cloud Architecture: Application of Machine-Learning Algorithms on Multispectral Data

Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area.

Automatic Identification of Calcareous Lithologies Using Support Vector Machines, Borehole Logs and Fractal Dimension of Borehole Electrical Imaging

In this research algorithms of support vector machine (SVM) and a logic function were applied to identify automatically sections of carbonate rocks in wells located in the former Barco Concession, Catatumbo Basin - Colombia. During training stages the SVMs use neutron, photoelectric factor and gamma ray logs as input; also mean and variance of resistivity acquired for image tool and fractal dimension of resistive images. The first SVM employs in the training stage intervals manually interpreted of fossiliferous limestone, performed by a specialized geologist integrating information of core-logs correlation of a pilot well; afterwards, in classification stages, this SVM automatically recognizes intervals with fossiliferous limestone only using logs data of any well of the field. The second SVM was also trained with nuclear logs, resistivity and fractal dimension, but in this case, with information of intervals composed of calcareous shales interbedded with limestone, recognizing automatically these rock associations during classification stage without interpretations of a geologist as input data. Additionally, a logic function was applied to intervals with photoelectric factor ≥ 4 and all sections not classified by the SVMs were grouped as laminated calcareous rocks. The SVMs and logic function show accuracy of 98.76 %, 94.02 % and 94.60 % respectively in six evaluated wells and might be applied to other wells in the field that have the same dataset. This methodology is highly dependent of the data quality and all intervals affected by bad borehole condition have to be removed prior its application in order to avoid wrong interpretations. Finally, the whole model has to be recalibrated to be applied in other fields of the basin.

Siltstone: a key to cyclicity in mixed siliciclastic–carbonate successions: example from Upper Ordovician Kope Formation (Ohio, Indiana, Kentucky)

The Upper Ordovician (mid-Katian) Kope Formation provides an example of how a detailed study of fine-grained siliciclastic sediment can provide sedimentological insights. The Kope Formation is an exemplar of mixed siliciclastic–carbonate cyclicity; however, most of the sedimentological research to date has focused on the fossiliferous limestone beds. Conversely, this paper provides a detailed investigation of siltstone within 42 metre-scale cycles from 26 locations spanning a large geographic area. The objective of the study is twofold: (i) to confirm the mineralogical nature of silt-sized sediment. Currently, silt-sized sediment is irregularly defined as being either carbonate or siliciclastic rich. This paper uses X-ray diffraction, thin-section, and elemental analyses to determine the mineralogy of the silt; (ii) to determine the stratigraphic distribution of siltstone beds. If a distinctive and reoccurring distribution of siltstone can be found, then it can provide additional information about environmental energy changes that led to cyclic fossiliferous limestone formation. This study finds that silt within the Kope Formation is siliciclastic and siltstone, therefore, records periodic minor pulses of coarser terrigenous sediment. Predominantly, metre-scale limestone–shale cycles show an increasing abundance of siltstone beds in the upper half of the shale interval, with a marked decrease just before the capping limestone. This finding supports the idea that limestone formation occurred during slowing sedimentation, perhaps at a time of maximum sea-level rise. Furthermore, decametre-scale fluctuations in siltstone abundance are similar in pattern to previously documented faunal variations, indicating that fauna assemblage might be more controlled by turbidity and not depth.

Early late Visean ammonoid faunas from the Jadar Block (NW Serbia)

Early late Visean ammonoid faunas from the Jadar Block (NW Serbia)The outcrop at Milivojevića Kamenjar in Družetić (Jadar Block, Vardar Zone, NW Serbia), which exposes a fossiliferous limestone olistolith, is one of the key sites for Carboniferous stratigraphy and paleogeography in the Balkan Peninsula. Its age has been debated several times, and re-examination of the succession was required. Based on ammonoids and conodonts, an interval spanning from the latest Devonian to the basal Serpukhovian is represented. From the early late Visean portion of the section, the new ammonoid genus and speciesUbites filipovicigen. nov. et sp. nov. is described.Entogonites tetragonus(Kullmann, 1962), a formerly misinterpreted ammonoid species, is revised.

Seabed habitat on the south-eastern Australian continental shelf: context, vulnerability and monitoring

A hierarchical approach to mapping seabed habitat is presented. A provincial scale survey that included hydrography and geology provided the context for interpreting habitat use and vulnerability. A megascale map, developed in cooperation with local fishers, identified major seabed features (kilometres to 10s of kilometres). Vulnerability of a feature was defined as its resistance to physical modification and its resilience, or capacity to recover, on removal of the modifier. Vulnerability was assessed from geological, biological and oceanological properties. Inner-shelf sandstone and limestone reefs that were exposed and weathered during the last ice age, and shelf-break bryozoan patch reefs, appear to be the most vulnerable of the hard-grounds to physical disturbance. In contrast, larger, high-relief, outer-shelf fossiliferous limestone reefs appear relatively invulnerable to physical disturbance from fishing. Megascale features were the focus of detailed physical and biological sampling at the mesoscale level (10 m to km), the level of resolution necessary for establishing baseline conditions and monitoring change. The hierarchical approach used here to map seabed habitat amalgamates scientific and fishers’ information. Approached in this way, habitat mapping has the potential to build a common framework of knowledge on which effective spatial management can be based.