A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Leveraging Artificial Intelligence and Geomechanical Data for Accurate Shear Stress Prediction in CO2 Sequestration within Saline Aquifers (Smart Proxy Modeling)

This research builds upon the success of a previous project that used a Smart Proxy Model (SPM) to predict pressure and saturation in Carbon Capture and Storage (CCS) operations into saline aquifers. The Smart Proxy Model is a data-driven machine learning model that can replicate the output of a sophisticated numerical simulation model for each time step in a short amount of time, using Artificial Intelligence (AI) and large volumes of subsurface data. This study aims to develop the Smart Proxy Model further by incorporating geomechanical datadriven techniques to predict shear stress by using a neural network, specifically through supervised learning, to construct Smart Proxy Models, which are critical to ensuring the safety and effectiveness of Carbon Capture and Storage operations. By training the Smart Proxy Model with reservoir simulations that incorporate varying geological properties and geomechanical data, we will be able to predict the distribution of shear stress. The ability to accurately predict shear stress is crucial to mitigating the potential risks associated with Carbon Capture and Storage operations. The development of a geomechanical Smart Proxy Model will enable more efficient and reliable subsurface modeling decisions in Carbon Capture and Storage operations, ultimately contributing to the safe and effective storage of CO2 and the global effort to combat climate change

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

Advances in Remote Sensing-based Disaster Monitoring and Assessment

Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones