Integrated Object-Based Image Analysis for semi-automated geological lineament detection in southwest England

Regional lineament detection for mapping of geological structure can provide crucial information for mineral exploration. Manual methods of lineament detection are time consuming, subjective and unreliable. The use of semi-automated methods reduces the subjectivity through applying a standardised method of searching. Object-Based Image Analysis (OBIA) has become a mainstream technique for landcover classification, however, the use of OBIA methods for lineament detection is still relatively under-utilised. The Southwest England region is covered by high-resolution airborne geophysics and LiDAR data that provide an excellent opportunity to demonstrate the power of OBIA methods for lineament detection. Herein, two complementary but stand-alone OBIA methods for lineament detection are presented which both enable semi-automatic regional lineament mapping. Furthermore, these methods have been developed to integrate multiple datasets to create a composite lineament network. The top-down method uses threshold segmentation and sub-levels to create objects, whereas the bottom-up method segments the whole image before merging objects and refining these through a border assessment. Overall lineament lengths are longest when using the top-down method which also provides detailed metadata on the source dataset of the lineament. The bottom-up method is more objective and computationally efficient and only requires user knowledge to classify lineaments into major and minor groups. Both OBIA methods create a similar network of lineaments indicating that semi-automatic techniques are robust and consistent. The integration of multiple datasets from different types of spatial data to create a comprehensive, composite lineament network is an important development and demonstrates the suitability of OBIA methods for enhancing lineament detection

The spatial leave-pair-out cross-validation method for reliable AUC estimation of spatial classifiers

Machine learning based classification methods are widely used in geoscience applications, including mineral prospectivity mapping. Typical characteristics of the data, such as small number of positive instances, imbalanced class distributions and lack of verified negative instances make ROC analysis and cross-validation natural choices for classifier evaluation. However, recent literature has identified two sources of bias, that can affect reliability of area under ROC curve estimation via cross-validation on spatial data. The pooling procedure performed by methods such as leave-one-out can introduce a substantial negative bias to results. At the same time, spatial dependencies leading to spatial autocorrelation can result in overoptimistic results, if not corrected for. In this work, we introduce the spatial leave-pair-out cross-validation method, that corrects for both of these biases simultaneously. The methodology is used to benchmark a number of classification methods on mineral prospectivity mapping data from the Central Lapland greenstone belt. The evaluation highlights the dangers of obtaining misleading results on spatial data and demonstrates how these problems can be avoided. Further, the results show the advantages of simple linear models for this classification task.</p

New computational methods for efficient utilisation of public data

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Turvemaiden digitaalinen kartoitus ja turvepeltolohkojen tunnistaminen

Maatalouden turvemaiden ilmasto- ja vesistöpäästöjen vähentäminen edellyttää turvepeltolohkojen tunnistamista, mutta maaperätieto ei ole ollut riittävän tarkkaa tähän tarkoitukseen. Raportissa esitellyn työn tavoitteena oli tuottaa tarkennettua paikkatietoa turvemaiden esiintymisestä ja paksuudesta turvepeltolohkojen tunnistamiseksi. Uusi paikkatietoaineisto turvemaiden esiintymisestä ja paksuudesta luotiin hyödyntämällä koneoppimismallinnusta. Mallinnus tehtiin Random Forest -menetelmällä. Turpeen esiintymistä selittäviksi aineistoiksi valmisteltiin 117 kpl koko maan kattavia satelliitti- ja lentoalustoilta mitattuja kaukokartoitusaineistoja ja geologista paikkatietoaineistoa. Koneoppimismallin opettamista ja testausta varten koottiin 3,5 miljoonaa maaperähavaintoa, josta 70 % käytettiin mallin opetukseen ja 30 % mallin riippumattomaan testaukseen. Mallinnuksessa ennustettiin turvepaksuusluokkien ≥ 10 cm, ≥ 30 cm, ≥ 40 cm ja > 60 cm esiintymistä 50 m × 50 m rasteriresoluutiossa ja ennusteet tuotettiin maankäyttömuodosta riippumatta kaikille maa-alueille. Malliennusteiden tarkkuus oli korkea. Turvepaksuusluokat pystyttiin erottelemaan muista maalajeista ja turvepaksuusluokista 89–96 % tarkkuudella. Tarkkuudet olivat korkeimmillaan ohuissa turvepaksuusluokissa ja hieman heikompia paksuissa luokissa. Maatalousmailla vähintään 30 cm paksun turvemaan alaksi arvoitiin 273 000 ha, mikä on noin 11 % maatalousmaa-alasta. Tästä pinta-alasta 73 % turvekerros oli > 60 cm. Saamamme arvio maatalousmaiden turvemaiden (≥ 30 cm) pinta-alasta on 8 600 ha suurempi kuin mitä mittakaavaltaan 1:200 000 maaperäkartasta voidaan arvioida. Peltolohkokohtainen tarkastelu osoitti, että turve-ennusteet mahdollistavat turvealan ja -paksuuden arvioimisen yksittäisillä peltolohkoilla. Esimerkiksi turvepeltolohkot, joilla on vähintään 50 % alastaan ≥30 cm paksu turvekerros, tunnistettiin yli 90 % tarkkuudella. Uusi paikkatietoaineisto Turpeen paksuus 1.0/2023 tarkentaa aikaisempaa tietoa turvemaiden esiintymisestä ja paksuudesta koko maassa. Aineiston luokittelutarkkuus ja alueellinen erottelukyky ovat olemassa olevia maaperäkartta-aineistoja parempia ja sen avulla tunnistetaan aikaisemmin kartoittamattomia turvemaita. Yleistarkkuusmetriikat raportoidaan jokaiselle luokittelulle erikseen ja epävarmuuksien hajautuminen on esitetty Random Forest -puiden yksimielisyyden avulla rasterisolukohtaisesti. Uudet turve-ennusteet tuovat uusia mahdollisuuksia maaperään ja maankäyttöön liittyvien toimintojen suunnittelun, ohjaukseen ja vaikutusten arviointiin, sekä tutkimukseen

Maaperän ja siihen liittyvien kasviyhdyskuntien hyperspektrinen etätunnistus ja kaukokartoitus - Spektroskopian sovelluksia boreaalisessa ympäristossä

Hyperspectral close-range and remote sensing techniques have been available to the research community since the 1980’s but applications have focused on forestry and land use. The objective of the study was to explore relevant applications of visible and short wavelength infrared spectroscopy (350−2500 nm) for detection of physical and chemical properties of glacial till soils and plant species communities related to the soil properties in the boreal environment of northern Finland. Empirical single and multivariate regression techniques (MVR) were applied for predicting glacial till soil dielectric permittivity (ε, i.e. soil moisture) and till elemental concentrations from close-range spectrometry. Predictive kernel and neural network based fuzzy classification approaches were applied for classification of data acquired with AISA and HyMap airborne imaging spectrometers. Ordination techniques were used for revealing plant community structures and optimizing the thematic class hierarchal level. The till soil ε was well predicted from VSWIR spectra with the exponential single-spectral variate but also with MVR techniques. The most accurate results were gained with relevance vector machines. Prediction of till soil chemical element concentrations of Al, Ba, Co, Cr, Cu, Fe, Mg, Mn, Ni, V, and Zn was also statistically valid. Soil moisture based site suitability for Scots pine (Pinus sylvestris) from imaging spectroscopic data was moderately successful as the highest area under the receiver operating characteristics curve (AUC) value was 0.741. Site type mapping of aapa peatlands with support vector machines was highly successful with AUC values 0.946−0.999 for bog, sedge fen, and eutrophic fen. Understanding the ε-reflectance relationship would be evident when artificial regeneration to Scots pine, intolerant of wet soils, is considered on clear-cuts with high soil moisture variability. The site suitability on site prepared forest compartments could be predicted using exposed soil pixels in high spatial resolution imagery but also with indirect imaging of soil moisture through understory species patterns. The high success of the peatland site type mapping was attributed to optimization of class hierarchal levels with a constrained ordination based approach which was used to test the spectral and ecological class separability prior to classification. These novel applications of imaging spectroscopic data can readily be applied in practice once cost-effective satellite based data is available. Further research is required to make the close-range spectroscopy operational for quantification of element concentrations to serve forest soil research and mineral potential mapping.Hyperspektiset etä- ja kaukokartoitusmenetelmät ovat olleet tiedeyhteisön aktiivisessa käytössä 1980-luvulta lähtien, mutta sovellukset ovat suurimmaksi osaksi keskittyneet metsätalouden ja maankäytön tarpeisiin. Tässä väitöskirjassa tutkittiin boreaalisessa ympäristössä merkityksellisiä näkyvän valon ja lähi-infrapunaspektroskopian (350−2 500 nm) sovelluksia, joilla pyrittiin tunnistamaan moreenimaiden fysikaalis-kemiallisia ominaisuuksia ja niiden muokkaamia kasviyhdyskuntia Pohjois-Suomessa. Empiiristen yksi- ja monimuuttujaregressiomenetelmien avulla laboratoriossa spektroradiometrillä mitatuista spektreistä mallinnettiin moreenin dielektrisyyttä (Ɛ, vesipitoisuutta) ja alkuaineiden pitoisuuksia. Tukivektorikoneella (SVM) ja neuroverkkomenetelmillä tehtiin sumean logiikan luokitteluja hyperspektrisistä AISA- ja HyMap-lentoaineistoista. Ordinaatiomenetelmillä optimoitiin sopiva hierarkinen taso suoluokille. Moreenien Ɛ ennustettiin luotettavasti heijastusspektreistä yhden muuttujan eksponenttimallilla ja useilla monimuuttujaregressiomalleilla, mutta relevanssivektorikoneilla (RVM) saatiin tarkimmat ennusteet. Moreenin alkuainepitoisuuksia mallinnettiin tilastollisesti luotettavasti useille alkuaineille: Al, Ba, Co, Cr, Cu, Fe, Mg, Mn, Ni, V ja Zn. Maaperän kosteuteen perustuva männyn (Pinus sylvestris) uudistusanalyysi onnistui kohtuullisen luotettavasti, koska mallin oikeellisuutta kuvaavien toimintaominaisuuskäyrien (ROC-käyrä) alapuolelle jäävä alue (AUC) oli männylle soveltuvalle luokalle 0,741. Hyperspektrinen kaukokartoitusaineisto soveltui hyvin myös aapasoiden suotyyppiluokitteluun, koska AUC-arvot vaihtelivat 0,946 ja 0,999:n välillä luokille rahkaneva, neva ja letto. Ymmärrys Ɛ:n ja heijastuskertoimen suhteesta tulee ilmeiseksi silloin, kun arvioidaan männyn uudistamissoveltuvuutta maankosteudeltaan vaihtelevilla avohakkuualueilla. Mänty tulisi uudistaa vain kuiville osa-alueille, jotka voidaan osoittaa luokittelemalla muokkauksessa paljastettuja maapikseleitä. Uudistamissoveltuvuutta voidaan kartoittaa myös epäsuorasti luokittemalla kasviyhdyskuntia kaukokartoitusaineistolta. Suotyyppiluokittelu onnistui hyvin, kun ekologisesti ja spektrisesti eroavat temaattiset luokat optimoitiin rajoitettuun ordinaatioon perustuvalla lähestymisellä ennen spatiaalista luokittelua. Tässä tutkimuksessa osoitettiin uusia hyperspektristen aineistojen sovelluksia, jotka voidaan ottaa käyttöön kuvaavien spektrometrien siirtyessä satelliittialustoille. Moreenien alkuainepitoisuuksien spektriseen ennustamiseen liittyvää tutkimusta tulee jatkaa, ennen kuin sitä voidaan käyttää metsämaa- ja mineraalipotentiaalitutkimuksissa

Detecting Terrain Stoniness From Airborne Laser Scanning Data †

Three methods to estimate the presence of ground surface stones from publicly available Airborne Laser Scanning (ALS) point clouds are presented. The first method approximates the local curvature by local linear multi-scale fitting, and the second method uses Discrete-Differential Gaussian curvature based on the ground surface triangulation. The third baseline method applies Laplace filtering to Digital Elevation Model (DEM) in a 2 m regular grid data. All methods produce an approximate Gaussian curvature distribution which is then vectorized and classified by logistic regression. Two training data sets consisted of 88 and 674 polygons of mass-flow deposits, respectively. The locality of the polygon samples is a sparse canopy boreal forest, where the density of ALS ground returns is sufficiently high to reveal information about terrain micro-topography. The surface stoniness of each polygon sample was categorized for supervised learning by expert observation on the site. The leave-pair-out (L2O) cross-validation of the local linear fit method results in the area under curve A U C = 0 . 74 and A U C = 0 . 85 on two data sets, respectively. This performance can be expected to suit real world applications such as detecting coarse-grained sediments for infrastructure construction. A wall-to-wall predictor based on the study was demonstrated

Biogeochemical anomaly response of circumboreal shrubs and juniper to the Juomasuo hydrothermal Au-Co deposit in northern Finland

Abstract Tree tissue chemistry has proven successful in guiding advanced exploration in the early stages of mineral exploration projects in Arctic and subarctic regions. In this paper, the biogeochemical response of three circumboreal shrubs, crowberry (Empetrum nigrum L.), Labrador tea (Ledum palustre) and bilberry (Vaccinium myrtillus L.), and one conifer, common juniper (Juniperus communis L.), to the underlying hydrothermal Juomasuo Au-Co deposit was assessed in southeast Finnish Lapland. A variety of contrasting spatial multi-elemental anomaly patterns were found for the elements Au and Co, along with Fe, Th, U and rare earth elements, such as Ce, La and Nd, in different plant tissue types over the subcropping lodes and also deep (blind) mineralizations down to a depth of 200 m in two seasonally varying campaigns: in late summer (August) 2013 and early summer (June) 2014. Besides these elements verified by lithogeochemistry, Ag, Bi, Mo, Se, Te, W and Ni exhibited anomalous spatial patterns over the mineralization. Based on the Mann-Whitney-Wilcoxon test, the Au concentrations in twigs/stems of crowberry, bilberry, Labrador tea and common juniper over the mineralization were found to be higher than the background, but the evergreen species gave the most consistent response to the mineralization. This also applied to U, W and Mo, as well as Nd and several other rare earth elements. Using unsupervised clustering with self-organizing maps and k-means, the location of the underlying mineralized zones could be determined with high overall accuracy (70–90%). This indicates that the biogeochemical anomaly patterns over the Juomasuo sulphidic lodes are strong, and the deposit would have been detected from the biogeochemical data even without prior knowledge gained from a diamond drilling campaign. The sampled vascular species are widely distributed over the pan-Arctic and circumboreal terrains, thus demonstrating their considerable significance to mineral exploration for hydrothermal Au ores at northern latitudes

A machine learning approach to tungsten prospectivity modelling using knowledge-driven feature extraction and model confidence

Novel mineral prospectivity modelling presented here applies knowledge-driven feature extraction to a data-driven machine learning approach for tungsten mineralisation. The method emphasises the importance of appropriate model evaluation and develops a new Confidence Metric to generate spatially refined and robust exploration targets. The data-driven Random Forest™ algorithm is employed to model tungsten mineralisation in SW England using a range of geological, geochemical and geophysical evidence layers which include a depth to granite evidence layer. Two models are presented, one using standardised input variables and a second that implements fuzzy set theory as part of an augmented feature extraction step. The use of fuzzy data transformations mean feature extraction can incorporate some user-knowledge about the mineralisation into the model. The typically subjective approach is guided using the Receiver Operating Characteristics (ROC) curve tool where transformed data are compared to known training samples. The modelling is conducted using 34 known true positive samples with 10 sets of randomly generated true negative samples to test the random effect on the model. The two models have similar accuracy but show different spatial distributions when identifying highly prospective targets. Areal analysis shows that the fuzzy-transformed model is a better discriminator and highlights three areas of high prospectivity that were not previously known. The Confidence Metric, derived from model variance, is employed to further evaluate the models. The new metric is useful for refining exploration targets and highlighting the most robust areas for follow-up investigation. The fuzzy-transformed model is shown to contain larger areas of high model confidence compared to the model using standardised variables. Finally, legacy mining data, from drilling reports and mine descriptions, is used to further validate the fuzzy-transformed model and gauge the depth of potential deposits. Descriptions of mineralisation corroborate that the targets generated in these models could be undercover at depths of less than 300 ​m. In summary, the modelling workflow presented herein provides a novel integration of knowledge-driven feature extraction with data-driven machine learning modelling, while the newly derived Confidence Metric generates reliable mineral exploration targets