Metamorphic exploration of an unsupervised clustering program

Machine learning has been becoming increasingly popular and widely-used in various industry domains. The presence of the oracle problem, however, makes it difficult to ensure the quality of this kind of software. Furthermore, the popularity of machine learning and its application has attracted many users who are not experts in this field. In this paper, we report on using a recently introduced method called metamorphic exploration where we proposed a set of hypothesized metamorphic relations for an unsupervised clustering program, Weka, to enhance understanding of the system and its better use

Catchment-based gold prospectivity analysis combining geochemical, geophysical and geological data across northern Australia

The results of a pilot study into the application of an unsupervised clustering approach to the analysis of catchment-based National Geochemical Survey of Australia (NGSA) geochemical data combined with geophysical and geological data across northern Australia are documented. NGSA Mobile Metal Ion (R) (MMI) element concentrations and first and second order statistical summaries across catchments of geophysical data and geological data are integrated and analysed using Self-Organizing Maps (SOM). Input features that contribute significantly to the separation of catchment clusters are objectively identified and assessed. A case study of the application of SOM for assessing the spatial relationships between Au mines and mineral occurrences in catchment clusters is presented. Catchments with high mean Au code-vector concentrations are found downstream of areas known to host Au mineralization. This knowledge is used to identify upstream catchments exhibiting geophysical and geological features that indicate likely Au mineralization. The approach documented here suggests that catchment-based geochemical data and summaries of geophysical and geological data can be combined to highlight areas that potentially host previously unrecognised Au mineralization.The NGSA project was part of the Australian Government’s Onshore Energy Security Program 2006 – 2011, from which funding support is gratefully acknowledged

Digital image processing for automatic lithological mapping using Landsat TM imagery

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Application of physical properties measurements to lithological prediction and constrained inversion of potential field data, Victoria Property, Sudbury, Canada.

In recent years the number of near-surface deposits has decreased significantly; consequently, exploration companies are transitioning from surface-based exploration to subsurface exploration. Geophysical methods are an important tool to explore below the surface. The physical property data are numerical data derived from geophysical measurements that can be analyzed to extract patterns to illustrate how these measurements vary in different geological units. Having knowledge of links between physical properties and geology is potentially useful to obtain more precise understanding of subsurface geology. Firstly, down-hole density, gamma radioactivity, and magnetic susceptibility measurements in five drillholes at the Victoria property, Sudbury, Ontario were analyzed to identify a meaningful pattern of variations in physical property measurements. The measurements grouped into distinct clusters identified by the fuzzy k-means algorithm, which are termed ‘physical log units’. There was a meaningful spatial and statistical correlation between these physical log units and lithological units (or groups of lithological units), as classified by the geologist. The existence of these relationships suggests that it might be possible to train a classifier to produce an inferred function quantifying this link, which can be used to predict lithological units and physical units based on physical property data. A neural network was trained from the lithological information from one hole, and was applied on a new hole with 64% of the rock types being correctly classified when compared with those logged by geologists. This misclassification can occur as a result of overlap between physical properties of rock types. However, the predictive accuracy in the training process rose to 95% when the network was trained to classify the physical log units (which group together the units with overlapping properties). Secondly, lithological prediction based on down-hole physical property measurements was extended from the borehole to three-dimensional space at the Victoria property. Density and magnetic susceptibility models were produced by geologically constrained inversion of gravity and magnetic field data, and a neural network was trained to predict lithological units from the two physical properties measured in seven holes. Then, the trained network was applied on the 3D distribution of the two physical properties derived from the inversion models to produce a 3D litho-prediction model. The lithologies used were simplified to remove potential ambiguities due to overlap of physical properties. The 3D model obtained was consistent with the geophysical data and resulted in a more holistic understanding of the subsurface lithology. Finally, to extract more information from geophysical logs, the density and gamma-ray response logs were analyzed to detect boundaries between lithological units. A derivative method was successfully applied on the down-hole logs, and picked the boundaries between rock types identified by geologists as well as additional information describing variation of physical properties within and between layers not identified by the geologist.Doctor of Philosophy (PhD) in Mineral Deposits and Precambrian Geolog

Automatic organofacies identification by means of Machine Learning on Raman spectra

Funding Information: IFP Energies nouvelles (France) is warmly acknowledgment for kindly providing access to samples, laboratory facilities and unpublished database. Dr. Amalia Spina and Prof. Simonetta Cirilli from the University of Perugia are warmly acknowledged for the high-quality kerogen isolate extraction. This research was funded by: MIUR grants to Roma Tre PhD School in Earth Sciences (XXXIV doctoral cycle, 2018–2021) and IFP Energies nouvelles PhD program. Publisher Copyright: © 2023 The AuthorsPeer reviewedPublisher PD

The Integration of Machine Learning into Automated Test Generation: A Systematic Mapping Study

Context: Machine learning (ML) may enable effective automated test generation. Objective: We characterize emerging research, examining testing practices, researcher goals, ML techniques applied, evaluation, and challenges. Methods: We perform a systematic mapping on a sample of 102 publications. Results: ML generates input for system, GUI, unit, performance, and combinatorial testing or improves the performance of existing generation methods. ML is also used to generate test verdicts, property-based, and expected output oracles. Supervised learning - often based on neural networks - and reinforcement learning - often based on Q-learning - are common, and some publications also employ unsupervised or semi-supervised learning. (Semi-/Un-)Supervised approaches are evaluated using both traditional testing metrics and ML-related metrics (e.g., accuracy), while reinforcement learning is often evaluated using testing metrics tied to the reward function. Conclusion: Work-to-date shows great promise, but there are open challenges regarding training data, retraining, scalability, evaluation complexity, ML algorithms employed - and how they are applied - benchmarks, and replicability. Our findings can serve as a roadmap and inspiration for researchers in this field.Comment: Under submission to Software Testing, Verification, and Reliability journal. (arXiv admin note: text overlap with arXiv:2107.00906 - This is an earlier study that this study extends

Exploratory datamorphic testing of classification applications

Testing has been widely recognised as difficult for AI applications. This paper proposes a set of testing strategies for testing machine learning applications in the framework of the datamorphism testing methodology. In these strategies, testing aims at exploring the data space of a classification or clustering application to discover the boundaries between classes that the machine learning application defines. This enables the tester to understand precisely the behaviour and function of the software under test. In the paper, three variants of exploratory strategies are presented with the algorithms as implemented in the automated datamorphic testing tool Morphy. The correctness of these algorithms are formally proved. The paper also reports the results of some controlled experiments with Morphy that study the factors that affect the test effectiveness of the strategies

Textural and Compositional Characterization of Wadi Feiran Deposits, Sinai Peninsula, Egypt, Using Radarsat-1, PALSAR, SRTM and ETM+ Data

The present work aims at identifying favorable locations for groundwater resources harvesting and extraction along the Wadi Feiran basin, SW Sinai Peninsula, Egypt, in an effort to facilitate new development projects in this area. Landsat ETM+, Radarsat-1 and PALSAR images of Wadi Feiran basin were used in this work to perform multisource data fusion and texture analysis, in order to classify the wadi deposits based on grain size distribution and predominant rock composition as this information may lead to the location of new groundwater resources. An unsupervised classification was first performed on two sets of fused images (i.e., ETM+/Radarsat-1 and ETM+/PALSAR) resulting in five classes (hybrid classes) describing the main alluvial sediments in the wadi system. Some variations in the spatial distribution of individual classes were observed, due to the different spectral and spatial resolutions of Radarsat-1 (C-band, 12.5 m) and PALSAR (L-band, 6.25 m) data. Alluvial deposits are mixtures of parent rocks located further upstream often at a great distance. In order to classify the alluvial deposits in terms of individual rock types (endmembers), a spectral linear unmixing of the optical ETM+ image was performed. Subsequently, each class of the fused (hybrid) images was correlated with (1) individual rock type fractions (endmembers) obtained from spectrally unmixing the ETM+ image, (2) the geocoded and calibrated radar images (Radarsat-1 and PALSAR) and, (3) the slope map generated from the SRTM data. The goal was to determine predominant rock composition, mean backscatter and slope values for each of the five hybrid classes. Backscatter coefficient values extracted from both radar data (C- and L-band) were correlated and checked in the field, confirming that both wavelengths produced more or less similar textural classes that correspond to specific grain or fragment sizes of alluvial deposits. However, comparison of the spatial distribution of matching hybrid classes showed some variations due to the greater discrimination power of surface texture by Radarsat-1 C-band despite its lower spatial resolution. Furthermore, both hybrid classification results showed that regardless of elevation, areas that are covered by fine and moderate grains (fine sand to pebble) and are located along gentle terrains are favorable for groundwater recharge; while areas that are covered by very coarse grains (cobble to boulder) and are located along steep terrains are more likely to be affected by flash floods