Detection of geobodies in 3D seismic using unsupervised machine learning

In this work, we present a novel, automated method for detecting geobodies in 3D seismic reflection data, helping to reduce interpreter bias and speed up seismic interpretation. A seismic geobody refers to a geometrical, structural, or stratigraphic feature, such as a channel, turbidite fan, or igneous intrusion. Geobodies are subtle seismic features, hard to pick, and their detection is challenging to automate due to their complex 3D geomorphology and diversity of shapes. Nevertheless, the detection and delineation of these structures are essential for improving the understanding of the subsurface as well as building a variety of conceptual models. In our approach, we can rapidly interpret large 3D seismic volumes using point cloud-based segmentation to identify geobodies of interest, including complex stratigraphic features like lobes and channels. By converting the 3D seismic cube into a 3D seismic point cloud (sparse cube), we reduce the volume of data to analyse, which in turn speeds up the detection process. First, we build the 3D point clouds by filtering the seismic reflection volume using different seismic attributes, and then each point in the cloud is segmented into different clusters. The clustering is performed using the unsupervised Density-Based Spatial Clustering of Applications with Noise (DBSCAN) which allows the segmentation of all structures present into delineated objects. The clustered objects can then be characterised by features based on their 3D shape and spatial amplitude distribution. Finally, our method allows the selection of a specific geobody and can retrieve geobodies based on their similarity to exploration targets of interest. The method has been applied successfully to two modern 3D seismic datasets (Falkland Basins) and two types of geobodies: fans and sill intrusions. We demonstrate that our method can scan through a large 3D seismic volume and automatically retrieve likely fan and sill geobodies in a very efficient manner. This approach can be used to scan through large volumes of 3D seismic, looking for a wide variety of geobodiesJames Watt Scholarshi

Three dimensional quantitative textural analysis of nickel sulphide ore using X-ray computed tomography and grey level co-occurrence matrices on drill core

Alongside the global trend to mine and process lower grade and more mineralogically complex ores, there has been an increased awareness of the prevalence of ore heterogeneity. Ore texture - the interrelationship of minerals comprising a rock, has been identified as one of the primary geometallurgical indicators of ore variability. It is well known that a relationship exists between ore texture and the resultant metallurgical performance (ore hardness, throughput, liberation, grade, recovery). Consequently, there exists a need to rapidly, routinely, cost effectively, and reliably quantify ore texture and its variability prior to mining. This information can thereafter be incorporated into the geometallurgical block model and used for decision making informing mine planning, plant operation and optimisation, forecasting, and mine closure. The ability to rapidly, routinely, cost effectively and reliably quantify ore texture remains an ongoing challenge. In this study, the use of 3D X-ray computed tomography (XCT) is proposed as an innovative solution to non-destructively image the internal structure of drill core. Thereafter, an established, discipline independent two dimensional (2D) image analysis technique known as grey level co-occurrence matrices (GLCM) is specially adapted into three dimensions (3D) to quantify ore texture using XCT grey level volumes of drill core

Literature review of the remote sensing of natural resources

A bibliography is presented concerning remote sensing techniques. Abstracts of recent periodicals are included along with author, and keyword indexes

Earthquake damage assessment in urban area from Very High Resolution satellite data

The use of remote sensing within the domain of natural hazards and disaster management has become increasingly popular, due in part to increased awareness of environmental issues, including climate change, but also to the improvement of geospatial technologies and the ability to provide high quality imagery to the public through the media and internet. As technology is enhanced, demand and expectations increase for near-real-time monitoring and images to be relayed to emergency services in the event of a natural disaster. During a seismic event, in particular, it is fundamental to obtain a fast and reliable map of the damage of urban areas to manage civil protection interventions. Moreover, the identification of the destruction caused by an earthquake provides seismology and earthquake engineers with informative and valuable data, experiences and lessons in the long term. An accurate survey of damage is also important to assess the economic losses, and to manage and share the resources to be allocated during the reconstruction phase. Satellite remote sensing can provide valuable pieces of information on this regard, thanks to the capability of an instantaneous synoptic view of the scene, especially if the seismic event is located in remote regions, or if the main communication systems are damaged. Many works exist in the literature on this topic, considering both optical data and radar data, which however put in evidence some limitations of the nadir looking view, of the achievable level of details and response time, and the criticality of image radiometric and geometric corrections. The visual interpretation of optical images collected before and after a seismic event is the approach followed in many cases, especially for an operational and rapid release of the damage extension map. Many papers, have evaluated change detection approaches to estimate damage within large areas (e.g., city blocks), trying to quantify not only the extension of the affected area but also the level of damage, for instance correlating the collapse ratio (percentage of collapsed buildings in an area) measured on ground with some change parameters derived from two images, taken before and after the earthquake. Nowadays, remotely sensed images at Very High Resolution (VHR) may in principle enable production of earthquake damage maps at single-building scale. The complexity of the image forming mechanisms within urban settlements, especially of radar images, makes the interpretation and analysis of VHR images still a challenging task. Discrimination of lower grade of damage is particularly difficult using nadir looking sensors. Automatic algorithms to detect the damage are being developed, although as matter of fact, these works focus very often on specific test cases and sort of canonical situations. In order to make the delivered product suitable for the user community, such for example Civil Protection Departments, it is important to assess its reliability on a large area and in different and challenging situations. Moreover, the assessment shall be directly compared to those data the final user adopts when carrying out its operational tasks. This kind of assessment can be hardly found in the literature, especially when the main focus is on the development of sophisticated and advanced algorithms. In this work, the feasibility of earthquake damage products at the scale of individual buildings, which relies on a damage scale recognized as a standard, is investigated. To this aim, damage maps derived from VHR satellite images collected by Synthetic Aperture Radar (SAR) and optical sensors, were systematically compared to ground surveys carried out by different teams and with different purposes and protocols. Moreover, the inclusion of a priori information, such as vulnerability models for buildings and soil geophysical properties, to improve the reliability of the resulting damage products, was considered in this study. The research activity presented in this thesis was carried out in the framework of the APhoRISM (Advanced PRocedures for volcanIc Seismic Monitoring) project, funded by the European Union under the EC-FP7 call. APhoRISM was aimed at demonstrating that an appropriate management and integration of satellite and ground data can provide new improved products useful for seismic and volcanic crisis management