Modeling and classification of marine sediment using...
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Modeling and classification of marine sediment using multivariate statistics and hybrid neural computation 1 Saumen Maiti * , 2 and Maheshwar Ojha 1 Department of Applied Geophysics, Indian School of Mines, Dhanbad-826004 2 CSIR-National Geophysical Research Institute, Hyderabad-500007 Email: [email protected] Keywords Bearing Sea; Lithology; Bayesian neural network; Hybrid Monet Carlo; Evidence program; Downhole data. Summary A novel approach based on the concept of hybrid neural network (HNN) has been used for classifying sediment types using downhole log data acquired during Integrated Ocean Drilling Program (IODP) Expedition 323 in the Bering Sea Slope region. Prior to applying supervised classification, the multi-variate based statistical analysis was carried out to know the cluster which supports the present analysis. The stochastic Markov Chain Monte Carlo (MCMC)/Hybrid Monte Carlo (HMC) learning paradigm has been used to constrain the lithology boundaries using density, density porosity, gamma ray, sonic P-wave velocity and electrical resistivity at the hole U1344A. The hybrid results based on HMC algorithm (BNN.HMC) resolves finer structures at certain depths in addition to main lithology such as silty clay, diatom clayey silt and sandy silt. The present analysis provides detail pictures of sediment information from a depth ranging between 615 to 655 meter below seafloor (mbsf) of no core recovery zone. The present analysis demonstrates that the hybrid approach renders robust means for the classification of complex lithology successions at the hole U1344A, which could be very useful for other studies and understanding the oceanic sediment inhomogeneity and structural discontinuities. Introduction The Ocean Drilling Program (ODP) and the Integrated Ocean Drilling Program (IODP) are long term international scientific missions to explore ocean basin and oceanic crust by direct drilling (Benaouda et al., 1999). Coring and downhole logging were carried out during the IODP Expedition 323 in 2009 to understand the pliocene and pleistocene paleoceanographic changes in the Bering Sea (Takahashi et al., 2011a). Although main source of information is derived from core recovery, downhole log information can be potential alternative since core recovery is often partial and very often core properties are changed by partial or full mixing with mud fluid. The geophysical properties can be converted into lithological terms by assigning class based on well log properties. Geoscientists/log analysts have been engaged in classifying lithology units from the recorded geophysical well log data using the conventional method like graphical cross-plotting and other statistical techniques (Rogers et al., 1992). In the graphical cross-plotting technique (Pickett, 1963; Gassaway et al., 1989), two or more logs are cross-plotted to yield lithologies. Multivariate statistical methods such as principal component and cluster analyses (Wolff and Pelissier-Combescure, 1982), and discriminant function analysis (Delfiner et al., 1987; Busch et al., 1987) have invariably been used to study borehole data. During the recent past, artificial neural networks (ANNs) based techniques have extensively been applied in almost all branches of geophysics (Van der Baan and Jutten, 2000; Poulton, 2001). It has also been applied successfully in well log analysis (Baldwin et al., 1990; Rogers et al., 1992; Helle et al., 2001; Benaouda et al., 1999; Aristodempu et al., 2005; Maiti et al., 2007, Maiti and Tiwari, 2009, 2010a, 2010b; Ojha and Maiti, 2013). The method has its inherent ability to extract relation between input and output variables by learning through examples, even if, there is no explicit linear deterministic relationship between the measured litholog properties to the lithology units. Here, we have developed a Bayesian neural network based on supervised classification program to classify lithology and applied the method on the IODP Expedition 323 downhole data (Fig. 1). Prior to neural network based supervised classification, the un-supervised classification using cluster analysis and principal component analysis were performed to obtain information about the data dimensionality and the number of class that supports the classification scheme. These results would provide a better understanding of the oceanic crustal inhomogeneity and structural discontinuity over the Bearing sea slope region. Study area IODP Expedition 323 in 2009 is the first expedition to convalesce deep, continuous sections of sediment from the Bering Sea (Fig. 1). Lithologically, the oceanic crust at the drill site consists of three main lithologic units: silty clay, diatom clayey silt and sandy silt, with minor occurrence of varying abundances of foraminifers, nannofossils, and sponge spicules which alternate rhythmically (Takahashi et al., 2011a, b; Wehrmann et al., 2011). The sections are 11th Biennial International Conference & Exposition
Transcript of Modeling and classification of marine sediment using...
Modeling and classification of marine sediment using multivariate statistics and hybrid neuralcomputation
1Saumen Maiti*, 2and Maheshwar Ojha1Department of Applied Geophysics, Indian School of Mines, Dhanbad-826004
2CSIR-National Geophysical Research Institute, Hyderabad-500007Email: [email protected]
KeywordsBearing Sea; Lithology; Bayesian neural network;Hybrid Monet Carlo; Evidence program; Downhole data.
Summary
A novel approach based on the concept of hybrid neuralnetwork (HNN) has been used for classifying sedimenttypes using downhole log data acquired during IntegratedOcean Drilling Program (IODP) Expedition 323 in theBering Sea Slope region. Prior to applying supervisedclassification, the multi-variate based statistical analysiswas carried out to know the cluster which supports thepresent analysis. The stochastic Markov Chain Monte Carlo(MCMC)/Hybrid Monte Carlo (HMC) learning paradigmhas been used to constrain the lithology boundaries usingdensity, density porosity, gamma ray, sonic P-wavevelocity and electrical resistivity at the hole U1344A. Thehybrid results based on HMC algorithm (BNN.HMC)resolves finer structures at certain depths in addition tomain lithology such as silty clay, diatom clayey silt andsandy silt. The present analysis provides detail pictures ofsediment information from a depth ranging between 615 to655 meter below seafloor (mbsf) of no core recovery zone.The present analysis demonstrates that the hybrid approachrenders robust means for the classification of complexlithology successions at the hole U1344A, which could bevery useful for other studies and understanding the oceanicsediment inhomogeneity and structural discontinuities.
Introduction
The Ocean Drilling Program (ODP) and the IntegratedOcean Drilling Program (IODP) are long term internationalscientific missions to explore ocean basin and oceanic crustby direct drilling (Benaouda et al., 1999). Coring anddownhole logging were carried out during the IODPExpedition 323 in 2009 to understand the pliocene andpleistocene paleoceanographic changes in the Bering Sea(Takahashi et al., 2011a). Although main source ofinformation is derived from core recovery, downhole loginformation can be potential alternative since core recoveryis often partial and very often core properties are changedby partial or full mixing with mud fluid. The geophysicalproperties can be converted into lithological terms byassigning class based on well log properties.Geoscientists/log analysts have been engaged in classifyinglithology units from the recorded geophysical well log data
using the conventional method like graphical cross-plottingand other statistical techniques (Rogers et al., 1992). In thegraphical cross-plotting technique (Pickett, 1963;Gassaway et al., 1989), two or more logs are cross-plottedto yield lithologies. Multivariate statistical methods such asprincipal component and cluster analyses (Wolff andPelissier-Combescure, 1982), and discriminant functionanalysis (Delfiner et al., 1987; Busch et al., 1987) haveinvariably been used to study borehole data.
During the recent past, artificial neural networks (ANNs)based techniques have extensively been applied in almostall branches of geophysics (Van der Baan and Jutten, 2000;Poulton, 2001). It has also been applied successfully in welllog analysis (Baldwin et al., 1990; Rogers et al., 1992;Helle et al., 2001; Benaouda et al., 1999; Aristodempu etal., 2005; Maiti et al., 2007, Maiti and Tiwari, 2009, 2010a,2010b; Ojha and Maiti, 2013). The method has its inherentability to extract relation between input and outputvariables by learning through examples, even if, there is noexplicit linear deterministic relationship between themeasured litholog properties to the lithology units.
Here, we have developed a Bayesian neural network basedon supervised classification program to classify lithologyand applied the method on the IODP Expedition 323downhole data (Fig. 1). Prior to neural network basedsupervised classification, the un-supervised classificationusing cluster analysis and principal component analysiswere performed to obtain information about the datadimensionality and the number of class that supports theclassification scheme. These results would provide a betterunderstanding of the oceanic crustal inhomogeneity andstructural discontinuity over the Bearing sea slope region.
Study area
IODP Expedition 323 in 2009 is the first expedition toconvalesce deep, continuous sections of sediment from theBering Sea (Fig. 1). Lithologically, the oceanic crust at thedrill site consists of three main lithologic units: silty clay,diatom clayey silt and sandy silt, with minor occurrence ofvarying abundances of foraminifers, nannofossils, andsponge spicules which alternate rhythmically (Takahashi etal., 2011a, b; Wehrmann et al., 2011). The sections are
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pervasively bioturbated, and laminated intervals are rare.Overall, sedimentation on the Bering slope is characterizedby a higher influence of both siliciclastic material deliveredby ice sheets and terrigenous sedimentation derived fromthe continental shelf and slope, which are indented by someof the largest submarine canyons in the world. Detailedinformation of IODP expedition 323 data and itsgeophysical significance are well addressed in earlier paper(Takahashi et al., 2011a, b; Wehrmann et al., 2011).Theborehole data are sampled at 0.1524m (6 inch) intervals.Total depth of the U1344A hole is 712 m.
Figure 1: Location of IODP 323 drilling and coring sites.
Methods
Sediment clustering/Lithology classification is of two types(i) unsupervised classification and (ii) supervisedclassification. In supervised classification, classifier istrained from training patterns (input/downhole logs tooutput pairs/sediment classes) whereas in unsupervisedclassification (clustering), there are no training data withknown class labels. A clustering algorithm explores thesimilarity between the patterns and places similar patternsin a cluster.
For supervised classification, Multi-layer–Perceptron(MLP) with hybrid algorithm (conventional and Bayesianwith hybrid Monte Carlo optimization) has been useful forclassification from a set of input and output data pairs(Maiti et al. 2009). It has been popular in solving patternclassification problem due to the fact that it can classifyoverlapping signal/data vector through nonlinear mappingfrom a set of correct training patterns examples. The MLPmodel consists of an input layer, output layer and at least anintermediate hidden layer (there may be numerous hiddenlayer) between the input and the output layer. Layers areconnected by synaptic weights. Output of the input layer isthen fed to the input of the hidden layer and the output of
the hidden layer is then fed to the input of the next layer/output layer (Fig. 2).
Figure 2: Topology of Hybrid Neural Networks.
Cluster Analysis (CA)
The K-means unsupervised cluster analysis is applied tothe well log data of hole U1344A. The initial cluster centeris considered from 2 to 10 that provides sum of squareerror from 65,766 to 4644 with maximum iteration fixed at500. The general error history suggest that sum of squareerror can be minimized with large number of cluster centerwhile adequate iteration is considered. Theoretically sumof square error can be made zero if we consider clustercenter equal to the number of data points. Here mostsatisfactory results are shown by keeping tradeoff betweencluster center and sum-of–square error in Fig. 3a.
In that the following parameters are supported (i) numberof data points=4065, (ii) number of cluster unit=3, (iii)number of iteration=500 and (iv) sum of squareerror=7569. Fig. 3a suggests that the classification issatisfactory except at the common boundary among thelithology of silty clay, diatom clayey silt and sandy silt.The dendogram (tree-based) classification results of totaldata sets shows that there are three litho-unit present in thedata set (Fig. 3b). From the dendogram based cluster Fig.3 it is clear that sample 11, 9, 6, 8, 7, 10, 16, 19, 2, 13, 14forms a cluster unit 1; sample 15, 27, 28, 30, 29 formscluster unit 2 and sample 1, 3, 4, 5, 12, 17, 18, 20, 21, 22,23, 24, 25, 26 forms cluster unit 3.
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Figure 3: Cluster analysis of IODP log data.
Principal Component Analysis (PCA)
The principal component analysis is applied to the entiredata set of hole 1344A. Here the total variables are five (i)density (ii) porosity (iii) gamma ray (iv) seismic p-wavevelocity and (v) resistivity. The cross correlation panelsuggests that resistivity log has almost zero correlation withdensity and gamma ray log (Fig. 4a).
The seismic P-wave velocity log correlated negatively withresistivity log but correlates moderately positively withgamma ray log. The density log is strongly negativelycorrelated with porosity log. The density log correlatesmoderately positively with the gamma ray and seismic P-wave velocity log. Fig. 4b suggests that 99% variance ofthe original data are explained by two first principalcomponents. So data can be represented by two principalcomponents without losing much information. Thus themultivariable data set can be visualized with the help oftwo coordinate axis.
Fig. 5a demonstrates the role of each principal component(PC) for classification of the sediment type. Fig. 6a and 1suggest that the gamma ray plays significant role inlithology classification. The gamma ray log has significantnegative input in PC(1). In PC(2) seismic P-wave velocity
and resistivity log have dominant role but in oppositedirection. In PC(3) porosity log has role opposite to thedensity, seismic P-wave velocity and resistivity log. InPC(4) density log has role opposite to the porosity, seismicP-wave velocity and resistivity log.
The PCA based classification result among silty clay,diatom clayey silt and sandy silt for the depth 93-712 mbsfinterval is demonstrated in Fig. 5b.
Figure 4: Correlation and variance analysis for IODP logdata.
Hybrid Neural Networks (HNNs)
For neural network application, the networks are trained ontraining samples. The neural network training pairs(input/output) was generated according to range of physicalproperties given in Table 1. The network was designedaccording to the present work and the available trainingsamples. Following Nabney (2004), we appropriately chose
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the initial prior hyper-parameters value 1.0 , 0.50and low tolerance value (10-7) for the weight optimization.The sampling phase of MCMC simulations are run for 500-iterations and the first 200-iterations are discarded to ensurethe simulation attain the equilibrium distribution. Althoughit is somewhat difficult to know when the simulationreaches the equilibrium (Neal, 1996) we run numeroussimulations to ensure the stability of the output results.
Figure 5: PCA analysis of IODP log data
Results
After successful training using the appropriate controllingparameters, the total real data of the 1344A bore hole depthranging from 93.62 to 712.97 mbsf depth were used asinput to trained neural network. Note that used geophysicalwell log data are continuous and uninterrupted in theinterval.
The Fig. 6 exhibit the posterior probability distribution in a3-coloum gray-shaded matrix with black representing 1 andwhite representing 0 (Figs. 6). The present BNN.HMCmodel confirms, in general, the presence of silty clay,diatom clayey silt and sandy silt within the logging unit 1(100-330 m). In the depth interval 93.62-130.04 mbsf there
is presence of silty clay suggested by the BNN.HMCmodel. There is presence of sandy clay as suggested by theneural network model at depth interval 140-144 mbsf. Atdepth interval 160-179 mbsf the presence of diatom clayeysilt is suggested by neural network model and wellsupported by other two neural based models. In the depthinterval 180-265 mbsf dominance of sandy silt is suggestedby the present model and the presence of sandy silt is wellcorrelated with BNN.SCG and ANN.SCG. Further deep atthe depth interval 268-286 mbsf the presence of diatomclayey silt is suggested by the present HMC-based model(Fig. 6).
Figure 6: Sediment classification using hybrid learningbeneath ocean floor.
It is important to note that at some of the depth interval suchas 290-295 m there is bimodal response of the model. Inthose cases the largest node value even if it is notsignificantly larger than the second largest node value isconsidered as the final for lithology interpretation. Thepresence of clayey silt is confirmed at depth interval 300-315 mbsf. At depth interval 319-325 mbsf neural HMCbased network suggests the presence of diatom clayey silt.The patterns of the changes of lithology are well describedby the physical significance of the well log (Fig. 6). Forinstance the presence of sandy silt is represented bycomparatively low density value, high density derivedporosity value, low gamma ray intensity value, and lowseismic P-wave velocity value than the silty clay (Fig. 6).The silty clay is represented by comparatively high density,gamma ray intensity, low density derived porosity and highseismic P-wave velocity than the both diatom clayey silt andsandy silt. The well log response of density, porosity,gamma ray intensity and seismic P-wave velocity for diatom
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clayey silt falls in between the respective response of siltyclay and sandy silt. The electrical resistivity reflects thefracture fluid filled porous zones thus in general its responseis opposite to the response of density derived porosity.
Looking at deeper depth of the well 1344A, BNN.HMCshows the presentence of silty clay at the depth interval330-400 mbsf except at depth interval 374-376 mbsf wherethe presence of sandy silt is suggested. A drop in seismicprimary wave velocity (Vp) and secondary wave velocity(Vs) at 376 mbsf can be correlated with a reflector at 4.73 stwo way travel times (twt) which can be extended up dip tohigh amplitudes that have been interpreted as free gastrapped at the bottom of the gas hydrate stability field(Takahashi et al., 2011a, b; Wehrmann et al., 2011).Slightly higher velocity and resistivity trends and lowerdipole waveform amplitudes above the reflector, as well aslower chlorinity values measured on several pore watersamples, suggest that some amount of gas hydrate might bepresent (Takahashi et al., 2011a). If we go further deep inthe depth interval 403-414 mbsf, the model suggested thepresence of sandy silt. In the depth interval 415-420 mbsfthe lithology changes from sandy silt to diatom clayey silt.Further deep in the depth interval 420-460 mbsf there isstrong bimodal signature between silty clay and diatomclayey silt. The lithology is drawn based on the maximuma posteriori probability (map) which is alternative sequencebetween silty clay and diatom clayey silt in the interval(Fig. 6).
In the depth interval of 460-576 mbsf, there is a presenceof silty clay represented by dominant uni-modal signatureof model. The presence of diatom clayey silt is suggestedin the depth interval of 577-591 mbsf. From depth betweenof 592 and 606 mbsf there is dominant mode ofdistribution of sandy silt. The dominance of diatom clayeysilt is occurred at the depth interval of 607-618 mbsf. In thelast deeper part of the 1344A hole section, there is cleardominance of the silty clay in the depth interval of 620-712mbsf except the intervals of 667-669 and 670-676 mbsf.Note that the average core recovery in the five holes was92%, but only 87% in hole U1344A and there is significantgaps in recovery within the depth ranging between ~615and 655 mbsf. The caliper log in hole U1344A indicatesthat the borehole was irregular and significantly larger thanthe bit size above ~300 mbsf. But the good agreementbetween the gamma ray and density logs, and the porosityand gamma ray core measurements suggest that the dataare of good quality (Takahashi et al., 2011a, b; Wehrmannet al., 2011). The lithology change is defined by a sharpincrease in P-wave velocity (Vp), gamma ray and density,as well as a significant change in the trends of all logs.Most logs display variability with depth of wider amplitudeand lower frequency than in the upper units, suggesting asignificant change in the deposition history and rates.
Conclusions
Our main conclusions are as follows:
1. The HMC based BNN technique is an efficient andcost-effective tool to solve lithology classificationproblem from borehole log data.
2. The HMC based BNN technique developed here isrobust and stable up to handle 10% correlated noisydata for the present case.
3. A comparative study of hybrid Monte Carlo basedBayesian neural network results are well correlatedwith Bayesian network optimized by scaled conjugategradient method and conventional neural networkoptimized by scaled conjugate gradient method. Inaddition to that of known lithology unit the methodbased on HMC based BNN is also able to predictlithology where no core recovery is occurred. Themethod also able to model the succession of some finerstructures, which may be of geophysical significance,hitherto not recognized in previous work.
4. Generally such findings may have significantimplications for understanding crustal in-homogeneityof the oceanic crust.
5. Because of its computational efficiency and ability toremember the solution the method can be furtherexploited for analyzing large number of borehole datain other area of interest.
6. Finally, some deviations among the different neuralnetwork approach seem to be interesting and shouldprovide a basis for more detailed investigations lookinginto the geological significance of finer structuresintervening into the bigger geological units.
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Acknowledgements
Authors of respective institute are thankful to theirDirectors, Indian School of Mines, Dhanbad and CSIR-NGRI, Hyderabad for their kind permission to publish thework. Partial financial benefit from the Ministry of EarthSciences, Govt. of India, New Delhi, India, is alsothankfully acknowledged.
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