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

Abstract

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.

A review on advancements in lithological mapping utilizing machine learning algorithms and remote sensing data

“… [ 84 ] SVM/NB/K-NN/RF ASTER/Landsat 8 OLI/Sentinel-1/Sentinel-2A. The combination of ASTER and simulated panchromatic Sentinel-2A data showing the most efficient result [ 85 ] RF/SVM/NB/Classification and regression tree (CART) ASTER-L1T/Landsat-8//Sentinel-2 The utilization of CART for the classification of Landsat-8 data resulted in an impressive accuracy of 99.63%, which showed strong agreement with field validation. [ 86 ] SAM/ML/MD ASTER Ml and MD resulted in improved differentiation of various geological facies.…”

A Fusion of Feature-Oriented Principal Components of Multispectral Data to Map Granite Exposures of Pakistan

“…FPCS can be further used by selecting a limited number of bands from the highresolution hyperspectral data for mapping dimension stones. The proposed methodology can also be used for data annotation [65] in supervised techniques for lithological mapping. Mineralogy is also used to predict geotechnical properties [38,66,67]; therefore, SWIR2 bands/components identifying accessory minerals could play an important role in modelling geotechnical parameters via mineralogy through RS.…”

Chromite-Bearing Peridotite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Luobusa Area, Tibet, China

“…Decorrelation stretch (DS) is a type of color stretch used to solve the problem of overly concentrated image tone changes or decreased image saturation. It reduces the correlation between spectra and is widely used as a means of spectral enhancement [23,70,71]. For example, Abram used the 7-5-4 band decorrelation stretch with TM data to identify ultrabasic rocks when mapping the Oman ophiolite [22].…”