A comparison of seismic saltbody interpretation via neural networks at sample and pattern levels

Saltbodies are important subsurface structures that have significant implications for hydrocarbon accumulation and sealing in petroleum reservoirs, and accurate saltbody imaging and delineation is now greatly facilitated with the availability of three-dimensional seismic surveying. However, with the growing demand for larger survey coverage and higher imaging resolution, the size of seismic data is increasing dramatically. Correspondingly, manual saltbody interpretation fails to offer an efficient solution, particularly in exploration areas of complicated salt intrusion history. Recently, artificial intelligence is attracting great attention from geoscientists who desire to utilize the popular machine learning technologies for evolving the interpretational tools capable of mimicking an experienced interpreter's intelligence. This study first implements two popular machine learning tools, the multi-layer perceptron and the convolutional neural network, for delineating seismic saltbodies at sample and pattern levels, respectively, then compares their performance through applications to the synthetic SEAM seismic volume, and moreover tentatively investigates what contributes to the better convolutional neural network delineation. Specifically, the multi-layer perceptron scheme is capable of efficiently utilizing an interpreter's knowledge by selecting, pre-conditioning and integrating a set of seismic attributes that best highlight the target saltbodies, whereas the convolutional neural network scheme makes it possible for saltbody delineation directly from seismic amplitude and thus significantly reduces the dependency on attribute selection from interpreters. It is concluded that the better performance from the convolutional neural network scheme results from two factors. First, the convolutional neural network builds the mapping relationship between the seismic signals and the saltbodies using the original seismic amplitude instead of manually selected seismic attributes, so that the negative impact of using less representative attributes is virtually eliminated. Second and more importantly, the convolutional neural network defines, learns and identifies the saltbodies by utilizing local seismic reflection patterns, so that the seismic noises and processing artefacts of distinct patterns are effectively identified and excluded.     

Semi-automatic fault/fracture interpretation based on seismic geometry analysis

Fault and fracture interpretation is a fundamental but essential tool for subsurface structure mapping and modelling from 3D seismic data. The existing methods for semi-automatic/automatic fault picking are primarily based on seismic discontinuity analysis that evaluates the lateral changes in seismic waveform and/or amplitude, which is limited by its low resolution on subtle faults and fractures without apparent vertical displacements in seismic images. This study presents an innovative workflow for computer-aided fault/fracture interpretation based on seismic geometry analysis. First, the seismic curvature and flexure attributes are estimated for highlighting both the major faults and the subtle fractures in a seismic volume. Then, fault probability is estimated from the curvature and flexure volumes for differentiation between the potential faults and non-faulting features in the geometric attributes. Finally, the seeded fault picking is implemented for interpreting the target faults and fractures guided by the knowledge of interpreters to avoid misinterpretation and artefacts in the presence of faulting complexities as well as coherent seismic noises. Applications to two 3D seismic volumes from the Netherlands North Sea and the offshore New Zealand demonstrate the added values of the proposed method in imaging and picking the subtle faults and fractures that are often overlooked in the conventional seismic discontinuity analysis and the following fault-interpretation procedures.     

Integrated model for earthquake risk assessment using neural network and analytic hierarchy process:Aceh province,Indonesia

Catastrophic natural hazards,such as earthquake,pose serious threats to properties and human lives in urban areas.Therefore,earthquake risk assessment(ERA)is indispensable in disaster management.ERA is an integration of the extent of probability and vulnerability of assets.This study develops an integrated model by using the artificial neural network–analytic hierarchy process(ANN–AHP)model for constructing the ERA map.The aim of the study is to quantify urban population risk that may be caused by impending earthquakes.The model is applied to the city of Banda Aceh in Indonesia,a seismically active zone of Aceh province frequently affected by devastating earthquakes.ANN is used for probability mapping,whereas AHP is used to assess urban vulnerability after the hazard map is created with the aid of earthquake intensity variation thematic layering.The risk map is subsequently created by combining the probability,hazard,and vulnerability maps.Then,the risk levels of various zones are obtained.The validation process reveals that the proposed model can map the earthquake probability based on historical events with an accuracy of 84%.Furthermore,results show that the central and southeastern regions of the city have moderate to very high risk classifications,whereas the other parts of the city fall under low to very low earthquake risk classifications.The findings of this research are useful for government agencies and decision makers,particularly in estimating risk dimensions in urban areas and for the future studies to project the preparedness strategies for Banda Aceh.     

The effects of reflection coefficient of the harbour sidewall on the performance of floating breakwaters

This paper studies the interaction of linear water waves with a moored floating breakwater with a leeward boundary composed from a vertical wall. This describes a real modelling for the case of ports in contrary to the problems of unbounded domains. It involves the application of a partial reflection boundary condition for this sidewall. In fact, the partial reflection problem in practical application is of great importance in the design of a harbour or breakwater and mainly for short waves. The reflection coefficient of the harbour boundary (sidewall) plays an important role in modifying the performance of the floating breakwater. Moreover, it reduces the resonant peaks appearing inside the ports due to the energy accumulation in an enclosed domain. The effects of the variation of structural parameters of the breakwater on the transmitted wave height are discussed in details for various values of partial reflection.     

Observations on geomorphology,transportation and distribution of sediments in western Lebanon and its continental shelf and slope regions

The geomorphology, drainage systems and predominant lithologies of western Lebanon are briefly described as a background for sediment transportation into the eastern Mediterranean. Fragmentary past and current bathymetric work over offshore Lebanon is reviewed and a simplified compilation bathymetric map correlating and interpreting the different survey results is presented. It reveals a narrow continental shelf about 3 km in width with two wider areas in the north and south; the shelf break occurs generally at about 100 m depth. Submarine valleys are common, most of which are seaward continuations of important land valleys but some prolong land-fault trends. Many continue down the continental slope, dividing into “distributaries”. Hydrological data indicate that over 2,525 × 10⁶ m³ of surface-water runoff annually discharges into the sea through the perennial streams of western Lebanon. A high proportion of the country exposes carbonate rocks but a number of the stream basins drain appreciable areas of soft clastics; preliminary work suggests that these contribute appreciable loads into the Mediterranean. Overall marine net sediment transport is northwards by the longshore current and predominant WSW-ENE directed waves, but investigations in progress indicate that the submarine valleys provide avenues for cross-shelf and down-slope movement for much sediment.     

Three-dimensional curvature analysis of seismic waveforms and its interpretational implications

The idea of curvature analysis has been widely used in subsurface structure interpretation from three-dimensional seismic data (e.g., fault/fracture detection and geomorphology delineation) by measuring the lateral changes in the geometry of seismic events. However, such geometric curvature utilizes only the kinematic information (two-way traveltime) of the available seismic signals. While analysing the dynamic information (waveform), the traditional approaches (e.g., complex trace analysis) are often trace-wise and thereby fail to take into account the seismic reflector continuity and deviate from the true direction of geologic deposition, especially for steeply dipping formations. This study proposes extending the three-dimensional curvature analysis to the waveforms in a seismic profile, here denoted as the waveform curvature, and investigates the associated implications for assisting seismic interpretation. Applications to the F3 seismic dataset over the Netherlands North Sea demonstrate the added values of the proposed waveform curvature analysis in four aspects. First, the capability of the curvature operator in differentiating convex and concave bending allows automatic decomposition of a seismic image by the reflector types (peaks, troughs and zero crossings), which can greatly facilitate computer-aided horizon interpretation and modelling from three-dimensional seismic data. Second, the signed minimum curvature offers a new analytical approach for estimating the fundamental and important reflector dip attribute by searching the orientation associated with least waveform variation. Third, the signed maximum curvature makes it possible to analyse the seismic signals along the normal direction of the reflection events. Finally, the curvature analysis promotes the frequency bands of the seismic signals and thereby enhances the apparent resolution on identifying and interpreting subtle seismic features.