Knowledge-Based Segmentation Of Texture Images With Application To Seismic Data Interpretation

1988 ◽  
Author(s):  
Zhen Zhang ◽  
M. Simaan
Keyword(s):  
Seismic Data ◽  
Data Interpretation ◽  
Knowledge Based

Initial results from the Intelligent Monitoring System

1990 ◽  
Vol 80 (6B) ◽  
pp. 1852-1873 ◽  
Author(s):  
Steven R. Bratt ◽  
Henry J. Swanger ◽  
Richard J. Stead ◽  
Floriana Ryall ◽  
Thomas C. Bache
Keyword(s):  
Knowledge Base ◽  
Monitoring System ◽  
Seismic Data ◽  
Data Interpretation ◽  
Local Network ◽  
Intelligent Monitoring ◽  
Knowledge Based ◽  
Interpretation Process ◽  
The University ◽  
Initial Results

Abstract The Intelligent Monitoring System (IMS) integrates advanced technologies in a knowledge-based distributed system that automates most of the seismic data interpretation process. Results from IMS during its first 8 weeks of operation (1 October through 25 November 1989) are analyzed to evaluate its performance. During this test period, the IMS processed essentially all data recorded by the NORESS and ARCESS high-frequency arrays in Norway. The emphasis was on detection and location of regional events within 2,000 km of these arrays. All events were reviewed and corrected if necessary by a skilled analyst. The final IMS Bulletin for the period includes 1,580 regional events (∼280 events/day). Approximately 55 per cent were smaller than MLg 1, with the largest just over MLg 3. Comparison of IMS locations in southern Finland and northwestern USSR (800 to 900 km from both arrays) with event locations from the University of Helsinki's local network bulletin are used to assess the detection and location capabilities of the system. Two or more phases (minimum needed to locate) were detected for 96 per cent of the events with magnitude greater than 2.5. The median separation between the IMS and Helsinki locations for all common events was 23.5 km. A consistent bias in arrival-time and azimuth residuals was observed for events in small geographic areas, indicating that refined travel-time models and path corrections could further improve location accuracy. The knowledge base in this first version of IMS was based on analysis of NORESS data, and many of the errors in interpretation corrected by the analysts can be attributed to differences encountered when this knowledge is used to interpret ARCESS data. Nevertheless, nearly 60 per cent of the events appearing in the final bulletin are automatic solutions approved without change or moved (by analyst corrections) less than 25 km from the automatic locations. The IMS had the most difficulty interpreting the overlapping signals generated by closely spaced explosions commonly detonated at mines in the Kola Peninsula and northern Sweden. Using the knowledge acquisition facilities included in the system, the deficiencies responsible for these and other errors are isolated, leading to development of new knowledge to be incorporated in the next version of the IMS knowledge base.

Fault Detection by Using Instance Segmentation

2021 ◽  
Author(s):  
Donglin Zhu ◽  
Lei Li ◽  
Rui Guo ◽  
Shifan Zhan
Keyword(s):  
Fault Detection ◽  
Seismic Data ◽  
Semantic Segmentation ◽  
Data Interpretation ◽  
Machine Learning Methods ◽  
Segmentation Algorithms ◽  
Direct Recognition ◽  
First Time ◽  
Seismic Images ◽  
Instance Segmentation

Abstract Fault detection is an important, but time-consuming task in seismic data interpretation. Traditionally, seismic attributes, such as coherency (Marfurt et al., 1998) and curvature (Al-Dossary et al., 2006) are used to detect faults. Recently, machine learning methods, such as convolution neural networks (CNNs) are used to detect faults, by applying various semantic segmentation algorithms to the seismic data (Wu et al., 2019). The most used algorithm is U-Net (Ronneberger et al., 2015), which can accurately and efficiently provide probability maps of faults. However, probabilities of faults generated by semantic segmentation algorithms are not sufficient for direct recognition of fault types and reconstruction of fault surfaces. To address this problem, we propose, for the first time, a workflow to use instance segmentation algorithm to detect different fault lines. Specifically, a modified CNN (LaneNet; Neven et al., 2018) is trained using automatically generated synthetic seismic images and corresponding labels. We then test the trained CNN using both synthetic and field collected seismic data. Results indicate that the proposed workflow is accurate and effective at detecting faults.

scholarly journals Seismic Data Interpretation and Identification of Hydrocarbon-Bearing Zones of Rajian Area, Pakistan

Minerals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 891
Author(s):  
Naveed Ahmad ◽  
Sikandar Khan ◽  
Eisha Fatima Noor ◽  
Zhihui Zou ◽  
Abdullatif Al-Shuhail
Keyword(s):  
Seismic Data ◽  
Foreland Basin ◽  
Data Interpretation ◽  
Indus Basin ◽  
Subsurface Structure ◽  
Vertical Wells ◽  
Salt Range ◽  
Seismic Sections ◽  
The Given ◽  
Triangular Zone

The present study interprets the subsurface structure of the Rajian area using seismic sections and the identification of hydrocarbon-bearing zones using petrophysical analysis. The Rajian area lies within the Upper Indus Basin in the southeast (SE) of the Salt Range Potwar Foreland Basin. The marked horizons are identified using formation tops from two vertical wells. Seismic interpretation of the given 2D seismic data reveals that the study area has undergone severe distortion illustrated by thrusts and back thrusts, forming a triangular zone within the subsurface. The final trend of those structures is northwest–southeast (NW–SE), indicating that the area is part of the compressional regime. The zones interpreted by the study of hydrocarbon potential include Sakessar limestone and Khewra sandstone. Due to the unavailability of a petrophysics log within the desired investigation depths, lithology cross-plots were used for the identification of two potential hydrocarbon-bearing zones in one well at depths of 3740–3835 m (zone 1) and 4015–4100 m (zone 2). The results show that zone 2 is almost devoid of hydrocarbons, while zone 1 has an average hydrocarbon saturation of about 11%.

Improving 4D Seismic Data Interpretation using Geostatistical Filtering

2005 ◽  
Author(s):  
Carlos Eduardo Abreu ◽  
Nathalie Lucet and Philippe Nivlet ◽  
Jean-Jacques Royer
Keyword(s):  
Seismic Data ◽  
Data Interpretation ◽  
4D Seismic

scholarly journals Upper Jurassic carbonate buildups in the Miechów Trough, Southern Poland – insights from seismic data interpretation

2019 ◽  
Author(s):  
Łukasz Słonka ◽  
Piotr Krzywiec
Keyword(s):  
Transition Zone ◽  
Seismic Data ◽  
Late Jurassic ◽  
Data Interpretation ◽  
Upper Jurassic ◽  
Carbonate Sediments ◽  
Northern Margin ◽  
Depositional System ◽  
Carbonate Buildups ◽  
Shelf Sedimentation

Abstract. The geometry and internal architecture of the Upper Jurassic carbonate depositional system in the epicontinental basin of western and central Europe, and within the northern margin of the Tethyan shelf are hitherto only partly recognised, especially in areas with thick Cretaceous and younger cover such as the Miechów Trough. In such areas, seismic data are indispensable for analysis of a carbonate depositional system, in particular for identification of the carbonate buildups and the enveloping strata. The study area is located in the central part of the Miechów Trough that in the Late Jurassic was situated within the transition zone between the Polish part of western and central European epicontinental basin and the Tethys Ocean. This paper presents the results of interpretation of 2D seismic data calibrated by deep wells that document the presence of large Upper Jurassic carbonate buildups. The lateral extent of particular structures is in the range of 400–1000 m, and their heights are in range of 150–250 m. Interpretation of seismic data revealed that the depositional architecture of the subsurface Upper Jurassic succession in the Miechów Trough is characterised by the presence of large carbonate buildups surrounded by basinal (bedded) limestone-marly deposits. These observations are compatible with depositional characteristics of well-recognised Upper Jurassic carbonate sediments that crop out in the adjacent Kraków-Częstochowa Upland. The presented study provides new information about carbonate open shelf sedimentation within the transition zone in the Late Jurassic, which proves the existence of much more extensive system of organic buildups which flourished in this part of the basin. Obtained results, due to high quality of available seismic data, provide also an excellent generic reference point for seismic studies of carbonate buildups from other basins and of different ages.

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