Scalability of lineament and fracture networks within the crystalline Wiborg Rapakivi Batholith, SE Finland

Multiscale lineament and fracture extraction conducted within the Wiborg Rapakivi Batholith offers insights both into the brittle bedrock structures of the batholith and to the scale-dependence of lineament and fracture analysis results. Multiscale fracture studies from crystalline rocks are sparse even though brittle structures in the crystalline bedrock significantly affect the flow models of fluids, hydrothermal heat and hydrocarbons, and are the main factor controlling the permeability in crystalline rocks. The main goal of this study is to assess the scalability of lineament and fracture networks through statistic characterization of lineament and fracture datasets extracted from four scales of observation using geometric and topological parameters, and by studying the subsequent correlations between the dataset characterizations. The parameters are acquired from both the individual lineaments and fractures and from their respective networks. Brittle bedrock structures were extracted manually using two principle methods: lineament traces were digitized from Light Detection And Ranging (LiDAR) digital elevation models and fracture traces were digitized from drone-based orthophotography of bedrock outcrops. Both extractions result in two-dimensional datasets and, consequently, all characterizations of these datasets along with the scalability analysis results are limited to two dimensions. The crystalline Wiborg Rapakivi Batholith is structurally isotropic and lithologically sufficiently homogeneous so that the effect of both precursor fabrics and lithological variations can be ignored when considering the genesis and emplacement of brittle bedrock structures in the batholith. Scalability analyses conducted within this investigation revealed that the results of lineament and fracture network extractions are always dependent on the scale of observation. Even dimensionless parameters of networks, such as connectivity, were found to follow a scale-dependent trend: The apparent connectivity of a lineament or fracture network decreases as the scale of observation increases. The characterizations of the datasets were used for the interpretation of Wiborg Rapakivi Batholith fracture patterns and paleostresses, which could be compared to Olkiluoto site studies of paleostresses in southern Finland.Viipurin rapakivibatoliitin alueella useassa mittakaavassa tehty lineamenttien ja rakojen kartoitus antaa tietoa sekä batoliitin hauraista kallioperän rakenteista että lineamentti- ja rakokartoituksen tulosten skaalariippuvuudesta. Useassa mittakaavassa tehtävät rakotutkimukset kiteisistä kivistä ovat harvinaisia, vaikka kiteisen kallioperän hauraat rakenteet vaikuttavat vahvasti nesteiden, kaasujen, hydrotermisen lämmön ja hiilivetyjen virtausmalleihin ja ne ovat kiteisen kallioperän permeabiliteetin tärkein kontrolloija. Tämän tutkimuksen tärkein tavoite on lineamentti- ja rakoverkkojen skaalautuvuuden tutkiminen. Tutkiminen tapahtuu ensin karakterisoimalla tilastollisesti lineamentti- ja rakoaineistoja neljästä eri mittakaavasta käyttäen geometrisiä ja topologisia parametrejä, ja sitten tutkimalla aineistojen karakterisointien välisiä korrelaatioita. Parametrit ovat sekä yksittäisten lineamenttien ja rakojen että lineamentti- ja rakoverkkojen parametrejä. Kallioperän hauraat rakenteet kartoitettiin kahdella eri metodilla: lineamenttiviivat digitoitiin laserkeilauskorkeusmalleista (LiDAR DEMs) ja rakoviivat digitoitiin lennokilla otetuista kalliopaljastumien ortomosaiikkikuvista. Molempien kartoitusten tuloksena oli kaksiulotteisia aineistoja, ja tämän takia myös kaikki aineistojen karakterisoinnit ja skaalautuvuusanalyysien tulokset ovat kaksiulotteisia. Kiteinen Viipurin rapakivibatoliitti on rakenteellisesti isotrooppinen ja litologisesti riittävän homogeeninen, jotta sekä edeltävät rakenteet että litologiset vaihtelut voidaan jättää huomioimatta, kun tutkimuksen kohteena on batoliitin hauraiden rakenteiden syntyminen. Tämän tutkimuksen puitteissa tehdyt skaalautuvuusanalyysit osoittivat, että lineamentti- ja rakoverkkokartoitusten tulokset ovat aina riippuvaisia kartoituksen mittakaavasta. Jopa yksiköttömät verkkojen parametrit, kuten verkottuneisuus, seurasi skaalariippuvaista trendiä: Näennäinen lineamentti- tai rakoverkon verkottuneisuus pienenee, kun mittakaava suurenee. Lineamentti- ja rakoaineistojen karakterisointeja käytettiin Viipurin rapakivibatoliitin rakojen muodostamien kuvioiden ja paleostressien tulkintaan. Paleostressitulkintoja voi verrata Olkiluodossa tehtyihin tutkimuksiin paleostresseistä eteläisessä Suomessa

A new subsampling methodology to optimize the characterization of two-dimensional bedrock fracture networks

This paper introduces a new subsampling method to determine the empirical relationships between the areal sampling coverage and the topological-geometric parameters resulting from characterization of two-dimensional outcrop fracture networks. We further developed these relationships into correlations between the total sampling coverage and variance of the characterization result, hence providing insight to the objective uncertainties related to fracture network characterization. The analyses were conducted using two new open-source Python packages: fractopo and fractopo-subsampling, designed for fracture network analysis and subsampling, respectively. We conducted the study on the well-exposed crystalline outcrops of Getaberget, Åland Islands, Finland, where a total of 42499 fracture traces were manually digitized from 13 circular target areas. For the purposes of subsampling, we conducted fracture network characterization for randomly located and sized subsample areas, which locate within the larger target areas.Based on our subsampling results we provide recommendations for the preliminary optimization of areal coverage used in outcrop fracture sampling and the use of our subsampling method for assessing the precision related to the areal fracture network characterization in other previously uncharacterized areas. As an example, we recommend using a total sampling area of 8000with 8 circular sampling areas to define the power-law exponents of fracture traces when conducting outcrop fracture network characterization with drone-based methodology done with similar initial sampling setup in comparable geological environments, as this coverage has shown an acceptable level of precision.</p

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Artificial Intelligence and Cognitive Computing

Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that

An automated fracture trace detection technique using the complex shearlet transform

Representing fractures explicitly using a discrete fracture network (DFN) approach is often necessary to model the complex physics that govern thermo-hydro-mechanical-chemical processes (THMC) in porous media. DFNs find applications in modelling geothermal heat recovery, hydrocarbon exploitation, and groundwater flow. It is advantageous to construct DFNs from the photogrammetry of fractured outcrop analogues as the DFNs would capture realistic, fracture network properties. Recent advances in drone photogrammetry have greatly simplified the process of acquiring outcrop images, and there is a remarkable increase in the volume of image data that can be routinely generated. However, manually digitizing fracture traces is time-consuming and inevitably subject to interpreter bias. Additionally, variations in interpretation style can result in different fracture network geometries, which, may then influence modelling results depending on the use case of the fracture study. In this paper, an automated fracture trace detection technique is introduced. The method consists of ridge detection using the complex shearlet transform coupled with post-processing algorithms that threshold, skeletonize, and vectorize fracture traces. The technique is applied to the task of automatic trace extraction at varying scales of rock discontinuities, ranging from 10° to 102m. We present automatic trace extraction results from three different fractured outcrop settings. The results indicate that the automated approach enables the extraction of fracture patterns at a volume beyond what is manually feasible. Comparative analysis of automatically extracted results with manual interpretations demonstrates that the method can eliminate the subjectivity that is typically associated with manual interpretation. The proposed method augments the process of characterizing rock fractures from outcrops.Applied Geolog

An automated fracture trace detection technique using the complex shearlet transform

Representing fractures explicitly using a discrete fracture network (DFN) approach is often necessary to model the complex physics that govern thermo-hydro-mechanical-chemical processes (THMC) in porous media. DFNs find applications in modelling geothermal heat recovery, hydrocarbon exploitation, and groundwater flow. It is advantageous to construct DFNs from the photogrammetry of fractured outcrop analogues as the DFNs would capture realistic, fracture network properties. Recent advances in drone photogrammetry have greatly simplified the process of acquiring outcrop images, and there is a remarkable increase in the volume of image data that can be routinely generated. However, manually digitizing fracture traces is time-consuming and inevitably subject to interpreter bias. Additionally, variations in interpretation style can result in different fracture network geometries, which, may then influence modelling results depending on the use case of the fracture study. In this paper, an automated fracture trace detection technique is introduced. The method consists of ridge detection using the complex shearlet transform coupled with post-processing algorithms that threshold, skeletonize, and vectorize fracture traces. The technique is applied to the task of automatic trace extraction at varying scales of rock discontinuities, ranging from 10° to 102m. We present automatic trace extraction results from three different fractured outcrop settings. The results indicate that the automated approach enables the extraction of fracture patterns at a volume beyond what is manually feasible. Comparative analysis of automatically extracted results with manual interpretations demonstrates that the method can eliminate the subjectivity that is typically associated with manual interpretation. The proposed method augments the process of characterizing rock fractures from outcrops

An automated fracture trace detection technique using the complex shearlet transform

\u3cp\u3eRepresenting fractures explicitly using a discrete fracture network (DFN) approach is often necessary to model the complex physics that govern thermo-hydro-mechanical-chemical processes (THMC) in porous media. DFNs find applications in modelling geothermal heat recovery, hydrocarbon exploitation, and groundwater flow. It is advantageous to construct DFNs from the photogrammetry of fractured outcrop analogues as the DFNs would capture realistic, fracture network properties. Recent advances in drone photogrammetry have greatly simplified the process of acquiring outcrop images, and there is a remarkable increase in the volume of image data that can be routinely generated. However, manually digitizing fracture traces is time-consuming and inevitably subject to interpreter bias. Additionally, variations in interpretation style can result in different fracture network geometries, which, may then influence modelling results depending on the use case of the fracture study. In this paper, an automated fracture trace detection technique is introduced. The method consists of ridge detection using the complex shearlet transform coupled with post-processing algorithms that threshold, skeletonize, and vectorize fracture traces. The technique is applied to the task of automatic trace extraction at varying scales of rock discontinuities, ranging from 10° to 102m. We present automatic trace extraction results from three different fractured outcrop settings. The results indicate that the automated approach enables the extraction of fracture patterns at a volume beyond what is manually feasible. Comparative analysis of automatically extracted results with manual interpretations demonstrates that the method can eliminate the subjectivity that is typically associated with manual interpretation. The proposed method augments the process of characterizing rock fractures from outcrops.\u3c/p\u3