Neural computing in geophysics

McCormack, Michael D.

doi:10.1190/1.1436771

Cited by 70 publications

(23 citation statements)

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“…ANN has been extensively used in earth sciences including geophysics exploration, iron cap recognition, and oil and gas discrimination (McCorak 1991;Cai et al 1994;Lv and Zhao 1998). ANN brings a new approach to predicting the geologic structures without the necessity of prior knowledge of the relationships between the various affecting factors.…”

Section: Ann-based Small Structure Prediction Modelmentioning

confidence: 98%

Application of artificial neural network technology to predicting small faults and folds in coal seams, China

Zeng

Liu

Zhang

et al. 2016

Sustain. Water Resour. Manag.

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Small geologic structures pose a great threat to production safety of the coal mines in China. Many water hazards and rock collapses are related to these small geologic structures. Accurate prediction of these structures relies on multiple lines of evidence including coal seam dip, thickness change, amount of gas accumulated, water flow changes, temperature, fracture type and degree of fragmentation of coal seams. Through the use of artificial neural network technology, this article presents a working method for forecasting small geologic structures in coal mines. The methods are applied to Zhangcun Coal Mine, China. A nonlinear model consisting of coal seam dip and thickness is constructed to predict the small structures in the front of working faces. The predictions are verified by field data. The distribution characteristics of the small structures can be accurately predicted in the coal seam extraction process as long as data of the controlling factors are accurately collected.

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Section: Ann-based Small Structure Prediction Modelmentioning

confidence: 98%

Application of artificial neural network technology to predicting small faults and folds in coal seams, China

Zeng

Liu

Zhang

et al. 2016

Sustain. Water Resour. Manag.

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“…Neural networks have been widely used for several years in geophysical studies [5]. One of neural network algorithms is a probabilistic neural network (PNN).…”

Section: Pnn Pore Pressure Predictionmentioning

confidence: 99%

Pore pressure prediction using probabilistic neural network: case study of South Sumatra Basin

Haris

Sitorus

Riyanto

2017

IOP Conf. Ser.: Earth Environ. Sci.

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“…ANN had been widely used in various areas of geophysics, such as crossequalization of seismic time-lapse surveys (Ibrahim et al, 2002) identification of seismic crew noise (Buffenmyer et al, 2000), identification of seismic arrival types , seismic attribute calibration (Johnston, 1993), seismic inversion (Poulton et al, 1992., Roth and Tarantola, 1994., Boadu, 1998, fractured reservoir characterization (Ouenes, 2000), first arrival or arrival picking (Murat and Rudman, 1992;Mc Cormack et al, 1993;Mac Bath, 1995, 1997), seismic event classification (Dystart and Pulli, 1990), earthquake prediction (Feng et al, 1997), discrimination of genuine from spurious seismic events in mines (Finnie, 1999), as intelligence amplification tool (Poulton, 2002), locating buried objects (Brown and Poulton, 1996), borehole resistivity modeling (Zhang et al, 2002), lithology log estimation (McCormack, 1991;Rogers et al, 1992;Chen and Fang, 1993;, log interpretation (Pezeshk et al, 1996), permeability prediction from well logs (Rogers et al, 1992;Huang et al, 1996), reservoir parameter estimation (Aminzadeh et al, 2000;Lingireddy, 1998), reservoir characterization (An and Moon, 1993), prediction of transient water levels in subsurface (Coppola et al, 2003), run off prediction (Elshorbagy and Simonovic, 2000), characterization of aquifer properties (Rizzo and Dougherty, 1994), in modeling soil water retention curves (Schaap and Boulten, 1996), in simulating the rainfall-runoff (Abrahart and Kneale, 1997;Abrahart and See, 1988;Hsu et al, 1995;Smith and Eli, 1995)...…”

Section: Introductionmentioning

confidence: 99%

Neural Network Based Automated Interpretation Algorithm for Combined Geophysical Soundings in Coastal Zones

Sastry

Tesfakiros

2006

Environ Monit Assess

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The fresh water availability in coastal aquifers is an important problem faced by a major part of world's population dwelling there. For in situ and dynamic characterization of seawater encroachment into coastal aquifers, electrical geophysical methods are better suited. However, the resolving power of such data in distinguishing saline sands from moist clays in the subsurface is very poor. To meet this aspect and also the problem of analyzing voluminous data sets, we propose a feed forward back-propagation neural network (BPNN) based approach for the analysis of combined vertical electrical and induced polarization soundings. Our method is tested on synthetic data computed from available geo-electric sections and prevailing subsurface geological information of coastal aquifers of East Coast of India. The synthetic data are comprised of 18 combined soundings spread over five profiles. 15 out of 18 are used for training the BPNN, while 3 are used for testing. The trained BPNN (one node each in each of the input and output layers and 18 hidden nodes) showed 84.85% accuracy in testing phase for distinguishing clays from saline sands. Our method is also tested on real data concerning a shaly aquifer in Bahia, Brazil yielding an overall accuracy of 84.9%, comparable to that of synthetic case; thereby validating our approach.

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Neural computing in geophysics

Cited by 70 publications

References 0 publications

Application of artificial neural network technology to predicting small faults and folds in coal seams, China

Application of artificial neural network technology to predicting small faults and folds in coal seams, China

Pore pressure prediction using probabilistic neural network: case study of South Sumatra Basin

Neural Network Based Automated Interpretation Algorithm for Combined Geophysical Soundings in Coastal Zones

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