applied sciences

Article A Stratigraphic Prediction Method Based on Machine Learning

Cuiying Zhou 1,2, Jinwu Ouyang 1,2, Weihua Ming 1,2, Guohao Zhang 1,2, Zichun Du 1,2 and Zhen Liu 1,2,*

1 Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China 2 Guangdong Engineering Research Centre for Major Infrastructure Safety, Guangzhou 510275, China * Correspondence: [email protected]



Received: 20 June 2019; Accepted: 27 August 2019; Published: 29 August 2019


Abstract: Simulation of a geostratigraphic unit is of vital importance for the study of geoinformatics, as well as geoengineering planning and design. A traditional method depends on the guidance of expert experience, which is subjective and limited, thereby making the effective evaluation of a stratum simulation quite impossible. To solve this problem, this study proposes a machine learning method for a geostratigraphic series simulation. On the basis of a recurrent neural network, a sequence model of the stratum type and a sequence model of the stratum thickness is successively established. The performance of the model is improved in combination with expert-driven learning. Finally, a machine learning model is established for a geostratigraphic series simulation, and a three-dimensional (3D) geological modeling evaluation method is proposed which considers the stratum type and thickness. The results show that we can use machine learning in the simulation of a series. The series model based on machine learning can describe the real situation at wells, and it is a complimentary tool to the traditional 3D geological model. The prediction ability of the model is improved to a certain extent by including expert-driven learning. This study provides a novel approach for the simulation and prediction of a series by 3D geological modeling.

Keywords: recurrent neural network; series simulation; three-dimensional geological modeling; expert-driven learning

1. Introduction A geostratigraphic structure is the result of multiple factors in the course of the evolution of Earth’s history, forming a complex morphology and irregular distribution. Geological bodies have spatially successive relationships, thus forming a series of strata with different lateral extensions and thicknesses. A geostratigraphic series is spatially uncertain due to the variations in sequence and the number and thickness of the stratum layers. Within the rock-soil mass extending from the top of the bedrock (referring to lithified rock that underlies unconsolidated surface sediments, conglomerates or regolith) to the surface, only one layer or dozens of layers can be present. There can be a few layers, and each can be different. At the same time, the thickness of the strata also varies considerably, from tens of centimeters to hundreds of meters. Different geotechnical bodies have different physical, chemical, and mechanical properties, and weak stratum conditions directly threaten the safety of engineering construction and operation. A geostratigraphic series model with high reliability is helpful to understand the geological conditions of a construction area, providing far-reaching practical guidance for site planning and selection, engineering construction, environmental assessment, cost savings, and operational risk reduction. Therefore, building a series model and accurately describing the spatial distribution of strata have become important topics in the field of geology and engineering geology.

Appl. Sci. 2019, 9, 3553; doi:10.3390/app9173553 www.mdpi.com/journal/applsci Appl. Sci. 2019, 9, 3553 2 of 29

To understand the geological structure, many techniques and methods have been developed to describe, simulate, and model strata [1–6]. With the introduction of the Glass Earth [7] concept and geological data, interdisciplinary theoretical integration and application research is being carried out. The most representative traditional method of simulating the stratum structure is three-dimensional (3D) geological modeling, such as that with the B-rep model [8], octree model [9], tri-prism model [10] and geochron concepts [11–14]. However, the traditional method relies on the guidance of expert knowledge and experience in the selection of assumptions, parameters, and data interpolation methods, which are subjective and limited [15]. Assumptions about the borehole data distribution must be made, and it is difficult to effectively evaluate the stratum simulation results. Machine learning [16–18] has been widely used in various fields of geology. The machine learning method does not make too many assumptions about the data but selects a model according to the data characteristics. Then, the machine learning method divides the data into a training set and a test set and constantly adjusts the parameters to obtain better accuracy. Machine learning is more concerned with the predictive power of models [19]. In the fields of geology and engineering, there have been numerous research and application examples in different fields [20–25]. Rodriguez-Galiano et al. conducted a study on mineral exploration based on a decision tree [26]. Porwal et al. used radial function and neural network to evaluate potential maps in mineral exploration [27]. Zhang studied the relationships between chemical elements and magmatite and between the sedimentary rock lithology and sedimentary rock minerals by using a multilayer perceptron and back propagation (BP) neural network [28]. Zhang et al. predicted karst collapse based on the Gaussian process [29]. Chaki et al. carried out an inversion of reservoir parameters by combining well logging and seismic data [30]. Gaurav combined machine learning, pattern recognition, and multivariate geostatistics to estimate the final recoverable shale gas volume [31]. Sha et al. used a convolutional neural network to characterize unfavorable geological bodies and surface issues, etc. [32]. Generally, machine learning research on stratum distributions based on drilling data is in its infancy. To solve the above problems, this study explores the feasibility and reliability of machine learning through the simulation of a geostratigraphic series and proposes a machine learning geostratigraphic series simulation method. This method does not rely on subjective factors, and it is based on the principle of a recurrent neural network [33,34] to establish a stratum simulation model. This method can determine the stratum information accurately. The predictive power of machine learning models is examined with expert-driven mechanism based on supervisory learning [35]. Compared with the traditional 3D geological modeling method, this study shows that the proposed method can better describe the real situation. This study provides a novel approach for the simulation and prediction of a geostratigraphic series. This work has far-reaching practical significance for the accurate description of the spatial distribution of lithologic features and guidance of site selection, engineering construction, and environmental assessment.

2. Geostratigraphic Series Simulation Method Based on Machine Learning

2.1. Geostratigraphic Series A sequence refers to a series of data of a system at a specific sampling interval. In reality, sequences are a very common form of data. For example, strata have a certain thickness, and a certain stratum may be distributed throughout the whole field or only locally (namely, the stratum division). Stratum information can be interpreted as a sequence. Therefore, the strata can be regarded as a vertically oriented spatial sequence, as shown in Figure1. The simulation of a geostratigraphic series is based on the learning results of borehole data to predict the geostratigraphic series at any point in the study area, including the stratum type and thickness of each layer in the sequence. Appl. Sci. 2019, 9, 3553 3 of 29 Appl. Sci. 2019, 9, x FOR PEER REVIEW 3 of 32

Figure 1. Geostratigraphic series diagram. Figure 1. Geostratigraphic series diagram. 2.2. Stratum Data Reconstruction Schemes Based on Machine Learning 2.2. Stratum Data Reconstruction Schemes Based on Machine Learning Drilling data reconstruction schemes based on machine learning include data normalization, data segmentation,Drilling data data reconstruction filling, and data schemes coding. based on machine learning include data normalization, data segmentation, data filling, and data coding. 2.2.1. Stratum Data Normalization 2.2.1. Stratum Data Normalization Data normalization refers to the process of compressing data into a small interval, and the intervalData is normalization usually taken asrefers [0,1] to or the [ 1,1]. process Data of normalization compressing is data essentially into a asmall linear interval, transformation. and the − intervalData normalization is usually taken does as not [0, change1] or [−1, the 1]. variationData normalization suppress and is essentially sequence a of linear the data.transformation. There are Datamany normalization common means does of not data change normalization, the variation such suppress as linear and normalization, sequence of the and data. inverse There cotangentare many commonnormalization. means In of this data study, normalization, the most common such methodas linear of linearnormalization, normalization and isinverse adopted. cotangent For any normalization.data point, the programIn this study, determines the most the common spatial coordinates method of linear and the normalization maximum and is adopted. minimum For values any (Xdatamax point,and X themin ,program respectively) determines of the stratum the spatial thickness coordinates after traversing and the maximum all the borehole and minimum data. The values above (Xlinearmax and normalization Xmin, respectively) is applied of the by stratum using Equation thickness (1): after traversing all the borehole data. The above linear normalization is applied by using Equation (1): X = (X Xmin)/(Xmax Xmin) (1) X = (X− − Xmin) / (Xmax −− Xmin) (1) where X is the result ofof normalization.normalization.

2.2.2. Drilling Data Segmentation and Equalization Machine learning learning is is used used to to ensure ensure that that the the desi designedgned model model achieves achieves good good prediction prediction results results in bothin both the the training training set set and and the the test test set. set. Therefore, Therefore, before before machine machine learning, learning, the the original original drilling data must be divided into training data and test data. This process is called data segmentation. To To ensure the effectiveness effectiveness of machine learning, randomness and uniformity of the data distribution should be ensured during sampling of the training data andand testtest data.data. To ensure the eeffectivenessffectiveness ofof the training data, we adopt a random replication strategy for small samples. We We randomly randomly select select data data from from boreholes boreholes with different different numbers of geological layers to improve the replication eeffect.ffect. This This method is is us useded to comprehensively study data with different different characteristics, improve the prediction ability ability of of a model for different different number numberss of geological layers, increase thethe numbernumber ofof di differentfferent layers layers represented represented by by nearby nearby drilling drilling data, data, and and artificially artificially upgrade upgrade the thetraining training sample sample data data at the at equilibriumthe equilibrium level. level. This Th approachis approach of artificially of artificially replicating replicating small small data types data typesis known is known as over as sampling over sampling [36]. [36]. 2.2.3. Geostratigraphic Series Filling 2.2.3. Geostratigraphic Series Filling When a recurrent neural network (RNN) is used to process sequential problems, input data are When a recurrent neural network (RNN) is used to process sequential problems, input data are received at every moment, and output is produced after the hidden layer has finished processing the received at every moment, and output is produced after the hidden layer has finished processing the data. Therefore, the input and output of an RNN are equal in length, and it is difficult to process data. Therefore, the input and output of an RNN are equal in length, and it is difficult to process input data of different lengths at the same time. In drilling data, the number of layers in each borehole Appl. Sci. 2019, 9, 3553 4 of 29 input data of different lengths at the same time. In drilling data, the number of layers in each borehole varies, and the geostratigraphic series is nonuniform. Therefore, the use of an RNN for batch training using stratum data requires filling at the tail of the geostratigraphic series without changing the original sequence of the geostratigraphic series and extending all geostratigraphic series to the same length [37]. Before training, in addition to adding a start of sequence (SOS) to the geostratigraphic series, an end of sequence (EOS) must be added to the geostratigraphic series. For each training set, the sampling process stops when the termination marker appears in the equal length geostratigraphic series output of the RNN. As two virtual stratum types, the initiation and termination markers participate in the RNN training process via the input and output. The initiation markers represent the beginning of geostratigraphic series prediction, while the termination markers represent the end of the series prediction. The introduction of termination markers teaches the RNN model to predict when a sequence will end and overcomes the shortcomings of processing unequally long sequences by the RNN. In addition, the RNN model can conduct geostratigraphic series simulations with different numbers of layers at any location in the research area.

2.2.4. Stratum Coding Based on One-Hot Encoding In machine learning tasks, data characteristics are not always continuous values, such as coordinates. One-hot encoding is a data processing method used to address discrete features. In geology, stratum types are finite and countable, regardless of the criteria used to divide the strata. Therefore, the set of geostratigraphic series elements is determined after crossing all the borehole data, in addition to obtaining the maximum value of each feature and the number of layers. To facilitate the search and mathematical representation, in this study, each stratum is represented by a unique digital identification [38].

2.3. Geostratigraphic Series Simulation Based on a Recurrent Neural Network

2.3.1. Establishment of the Sequence Model of the Stratum Type The model in geostratigraphic series prediction uses the RNN as the core of the neural network. The structure is shown in Figure2. In the machine learning tasks, the input data are coordinated in a stratum, while the output result is the simulation result of the stratum type model corresponding to the given coordinates. Since the RNN does not have a state hidden from the previous moment at the current moment, it is necessary to assign the initial state of the hidden layer neurons in the RNN before each training run. The input coordinates are the common attributes of all the strata in a geostratigraphic series, and it guides the whole process of RNN simulation of the geostratigraphic series. Therefore, the assignment process establishes the correlation between the input coordinates and RNN, guiding the geostratigraphic series simulation from the beginning. The content of the assignment is determined by the input information. After the input layer receives the coordinates of the borehole and the basic elevation information, the coordinate input information is increased from the original three dimensions to the number of dimensions equal to the number of neurons. It serves as the initial state of the hidden layer neurons in the RNN. Appl.Appl. Sci. Sci. 20192019, ,99, ,x 3553 FOR PEER REVIEW 5 5of of 32 29 Appl. Sci. 2019, 9, x FOR PEER REVIEW 5 of 32

Figure 2. Schematic diagram of the stratum type model. Figure 2. Schematic diagram of the stratum type model.

At each moment, the RNN receives input of the neuron state and stratum information from the At each moment, the RNN receives input of the neuron state and stratum information from the previous moment, and outputs the judgement of the stratum type through hidden layer calculations. previous moment, and outputs the judgement of the stratum stratum type type th throughrough hidden hidden layer layer calculations. calculations. By introducing an n-dimensional correct value vector, each item in the weight vector represents the By introducing an n-dimensional correct value vector, each item in the weight vector represents the possibility of a certain stratum. The larger the value is, the higher the probability of a certain stratum. possibility of a certain stratum. The The larger larger the the value value is, is, the the higher higher the the probability probability of of a a certain certain stratum. stratum. Thus, the most likely stratum is the predicted value at that moment. Repeating the above process and Thus, the most likely stratum is the predicted valuevalue at that moment. Repeating the above process and removing the termination marker in the output, we can obtain the model’s simulation results for the removing the termination marker in the output, we can obtain the model’s simulation results for the input coordinate information of the geostratigraphic series. input coordinate information ofof thethe geostratigraphicgeostratigraphic series.series.

2.3.2.2.3.2. Establishment Establishment of of the the Series Series Model Model of of the the Stratum Stratum Thickness Thickness 2.3.2. Establishment of the Series Model of the Stratum Thickness Sequence-to-sequenceSequence-to-sequence (or (or seq2seq) seq2seq) learning learning has has been been widely widely used used in in the the processing processing of of machine machine Sequence-to-sequence (or seq2seq) learning has been widely used in the processing of machine translationtranslation and and speech recognition,recognition, alsoalso knownknown as as the the encoder-decoder encoder-decoder network. network. It It maps maps sequences sequences as translation and speech recognition, also known as the encoder-decoder network. It maps sequences asinput input to to output output sequences sequences through through deep deep neural neural networks. networks. The The seq2seq seq2seq model model is is shown shown in in Figure Figure3. as input to output sequences through deep neural networks. The seq2seq model is shown in Figure 3.This This process process includes includes two two steps, steps, input input encoding encodi andng outputand output decoding decoding and these and two these links two are links handled are 3. This process includes two steps, input encoding and output decoding and these two links are handledby the encoder by the and encoder decoder, and respectively. decoder, respectively. The encoder The is responsible encoder is for responsible converting for a variable-length converting a handled by the encoder and decoder, respectively. The encoder is responsible for converting a variable-lengthinput series into input a fixed-length series into vector. a fixed-length This fixed-length vector. This vector fixed-length contains vector information contains about information the input variable-length input series into a fixed-length vector. This fixed-length vector contains information aboutseries. the The input encoder series. is responsible The encoder for decoding is responsible this fixed-length for decoding vector andthis generating fixed-length a variable-length vector and about the input series. The encoder is responsible for decoding this fixed-length vector and generatingoutput series a variable-length according to the output information series according content the to vectorthe information represents. content the vector represents. generating a variable-length output series according to the information content the vector represents.

FigureFigure 3. 3. TheThe sequence-to-sequence sequence-to-sequence (seq2seq) (seq2seq) model. model. Figure 3. The sequence-to-sequence (seq2seq) model.

InIn contrast contrast to to the the traditional traditional R RNN,NN, the the seq2seq seq2seq architecture architecture does does not not require require input input and and generates generates In contrast to the traditional RNN, the seq2seq architecture does not require input and generates outputoutput at at every every moment. moment. Instead, Instead, the the algorithm algorithm converts converts the the input input series series of ofthe the stratum stratum types types into into a output at every moment. Instead, the algorithm converts the input series of the stratum types into a vector with the help of the encoder, and then outputs the results through the decoder. In other words, vector with the help of the encoder, and then outputs the results through the decoder. In other words, Appl. Sci. 2019, 9, 3553 6 of 29 Appl. Sci. 2019, 9, x FOR PEER REVIEW 6 of 32 Appl. Sci. 2019, 9, x FOR PEER REVIEW 6 of 32 seq2seqa vector carries with the more help information of the encoder, when and making then outputs predictions the results than throughthe traditional the decoder. RNN and In other infers words, the seq2seq carries more information when making predictions than the traditional RNN and infers the outputseq2seq content carries based more on information the input series when as making a whole. predictions than the traditional RNN and infers the output content based on the input series as a whole. outputIn this content study, based two onRNNs the are input used series as the as aencoder whole. and decoder which are connected to each other. In this study, two RNNs are used as the encoder and decoder which are connected to each other. Seq2seqIn thisis now study, widely two RNNsused to are process used asmachine the encoder translation and decoder and speech which recognition are connected problems, to each thus, other. Seq2seq is now widely used to process machine translation and speech recognition problems, thus, weSeq2seq apply isit nowto the widely layer thickness used to process recognition machine problem, translation that is and to say, speech given recognition the geostratigraphic problems, thus, series we we apply it to the layer thickness recognition problem, that is to say, given the geostratigraphic series xapply = [x1, itx2, to x3, the …,xn], layer thicknessan equal-length recognition thickness problem, sequence that d is = to [d1, say, d2, given d3, …,dn] the geostratigraphic is generated. N series is the x x = [x1, x2, x3, …,xn], an equal-length thickness sequence d = [d1, d2, d3, …,dn] is generated. N is the length= [x1, of x2, the x3, sequence... ,xn], an (i.e., equal-length the total number thickness of strata sequence at that d = point).[d1, d2, The d3, encoder... ,dn] receives is generated. the type N is length of the sequence (i.e., the total number of strata at that point). The encoder receives the type informationthe length ofof the the sequence current stratum (i.e., the at total each number moment, of strata n times at that in point).total. After The the encoder input receives has been the information of the current stratum at each moment, n times in total. After the input has been completelytype information received, of the hidden current state, stratum at the at eachlast mo moment,ment of n the times encoder, in total. is Aftertaken theas the input initial has state been completely received, the hidden state, at the last moment of the encoder, is taken as the initial state tocompletely guide the decoder. received, Then, the hidden the decoder state, atoutputs the last th momente thickness of theof each encoder, layer is step-by-step. taken as the The initial above state to guide the decoder. Then, the decoder outputs the thickness of each layer step-by-step. The above processto guide and the model decoder. structure Then, are the shown decoder in outputs Figure 4. the thickness of each layer step-by-step. The above process and model structurestructure areare shownshown inin FigureFigure4 4..

Figure 4. Structure diagram of the two-layer prediction network. Figure 4. Structure diagram of the two-layer prediction network. 2.3.3.2.3.3. Establishment Establishment of of the the Geostratigraphic Geostratigraphic Series Series Modeling Modeling 2.3.3. Establishment of the Geostratigraphic Series Modeling TheThe stratum stratum thickness thickness model model uses uses real real stratum stratum type type data data in the in thetraining training process. process. In practice, In practice, the The stratum thickness model uses real stratum type data in the training process. In practice, the realthe stratum real stratum type is type unknown, is unknown, and the and output the outputsequence sequence of the stratum of the stratumtype model type should model be should used as be real stratum type is unknown, and the output sequence of the stratum type model should be used as theused judgement as the judgement basis. The basis. output The of outputthe stratum of the type stratum model type is connected model is connected with the encoder with the of encoder the layer of the judgement basis. The output of the stratum type model is connected with the encoder of the layer thicknessthe layer model. thickness We model. can obtain We cana complete obtain a geostratigraphic complete geostratigraphic series model. series The model. simulation The simulationsequence thickness model. We can obtain a complete geostratigraphic series model. The simulation sequence ofsequence the layer of thickness the layer is thickness shown in is Figures shown in5 and Figures 6. 5 and6. of the layer thickness is shown in Figures 5 and 6.

FigureFigure 5. 5. DiagramDiagram of of the the neural neural network network structure structure for for stratum stratum prediction. prediction. Figure 5. Diagram of the neural network structure for stratum prediction. Appl. Sci. 2019, 9, 3553 7 of 29 Appl. Sci. 2019, 9, x FOR PEER REVIEW 7 of 32

FigureFigure 6. 6. SimulationSimulation process process of of the the stratum thicknessthickness sequence.sequence. 2.4. Evaluation Method of Stratum Type Series Simulation 2.4. Evaluation Method of Stratum Type Series Simulation The stratum accuracy, the series edit distance, and the geostratigraphic series similarity based onThe the stratum edit distance accuracy, are used the series to evaluate edit distance, the simulation and the performance geostratigraphic of the series series similarity models of based the on stratumthe edit type. distance are used to evaluate the simulation performance of the series models of the stratumThe type. stratum accuracy is the simplest evaluation index. By comparing elements at corresponding positionsThe stratum of the accuracy simulated is sequence the simplest and evaluation the real geostratigraphic index. By comparing series, the elements proportion at corresponding of the same positionsnumber of of the strata simulated in the total sequence number and of strata the real was ge calculatedostratigraphic by Equation series, (2):the proportion of the same number of strata in the total number of strata was calculated by Equation (2): Correct stratum number (2) The edit distance is a standard that is used to measure the similarity of series. The edit distance represents the minimum number of edit operations$required\; for\; one\; series\; to\; be\; converted\; into\; another\; series\; after\; insertion,\; deletion,\; and\; replacement.when\; editing\; two\; series\; with\; the\; same\; distance.\; To\; better\; describe\; the\; closeness\; of\; series,\; the\; following\; Equation\; (3)\; is\; used\; in\; the\; calculation\; of\; the\frac{D(S,\; T)\; L(S,\; T)\; =\; 1\; (3)\; -\; max(\; S\; ,\; T\; )\; |\; |\; |\; |\; (2)\; where\; D(S,\text{ Correct stra T) represents the edit distance betweentum number}}{t1\; =\; [silt,\; finesand,\; silt,\; clay,\; silt,\; clay]\; and\; t2\; =\; [miscellaneous\; fill,\; sand,\; fine\; sand,\; silt,\; clay].\; In\; order\; to\; convert\; t1\; to\; t2,\; the\; implementation\; process\; of\; the\; minimum\text{Total formation number of test� operation times is as follows: 1. Replace the first “silt” with “sand”;A0;data}}$

Appl. Sci.3. 2019Remove, 9, x FOR the PEER last “clay”;REVIEW 10 of 32 4. Delete the final “silt”.

AlthoughThroughout the transition the above from four stepsone toseries replace, to another delete, and through insert operations,several insertions, the geostratigraphic deletions, and substitutionsseries t1 changed has many to series possibilities, t2. Thus, thethe twoediting geostratigraphic distance D series(S, T) of between edit distance the D(S,two T)series is 4. is always unique. Althoughthetransitionfromoneseriestoanotherthroughseveralinsertions, deletions, andsubstitutions has many possibilities, the editing distance D(S, T) between the two series is always unique. 3. Results and Discussions 3. Results and Discussions 3.1. Study of the Regional Geology and Data Reconstruction Schemes 3.1. Study of the Regional Geology and Data Reconstruction Schemes The Theresearch research area area is located is located in in a acity city in in east easternern ChinaChina withwith a a plain plain topography. topography. The The soil soil in the in the studystudy area areais mainly is mainly composed composed of of sandy sandy soil, soil, cohesive cohesive soil,soil, and and silty silty soil. soil. The The local local strata strata are siltare andsilt and silty siltysoil. soil.The Theresearch research data data come come from from the the city’s city’s geological survey survey work. work. There There is ais total a total of 1386 of 1386 boreholeborehole datasets, datasets, and and all allthe the boreholes boreholes terminate terminate on thethe bedrockbedrock surface. surface. A A total total of 13of stratum13 stratum types types werewere determined. determined. These These boreholes boreholes are are nonuniformly nonuniformly di distributedstributed in in an an area area of of 3882 3882 square square kilometers, kilometers, as shownas shown in Figure in Figure 7. 7 .

FigureFigure 7. 7.DistributionDistribution of of the the boreholes inin the the study study area. area.

UsingUsing the reconstruction the reconstruction scheme scheme of of the the stratum stratum data proposedproposed in in this this study, study, the the drilling drilling data data are are reconstructed.reconstructed. The The specific specific operation operation process process is is as as follows: 1. Data normalization: In this study, the borehole data are used and the x coordinates, y coordinates, 1. Data normalization: In this study, the borehole data are used and the x coordinates, y hole elevation, and stratum thickness are continuous values. After reviewing all the borehole data, coordinates,it is found hole that elevation, the coordinates and stratum of the borehole thickne datass are used continuous the Xi’an 80 values. coordinate After system, reviewing and their all the boreholevalue data, reaches it theis found millions, that while the thecoordinates elevation ofof the the orifice borehole and thedata thickness used the of theXi’an strata 80 are coordinate only system,within and 100 their m. value The di reachesfference betweenthe millions, each characteristicwhile the elevation is large andof the can orifice be up and to tens the of thickness thousands. of the strataTo are ensuring only within the same 100 dimension,m. The difference the above between borehole each data characteristic characteristics is arelarge compressed and can be into up the to tens of thousands.interval of [0,1]To byensuring linear normalization the same processing.dimension, the above borehole data characteristics are compressed2. Drilling into the data interval segmentation of [0,1] andby linear equalization: normalization In this study, processing. the training data and test data are selected2. Drilling randomly data segmentation according to theand ratio equalization: of 4:1 among In allthis drilling study, points, the training and the data data and are balancedtest data are selectedaccording randomly to the according number of to layers. the ratio The spatial of 4:1 positionsamong all of thedrilling training points, data and testthe datadata are are shown balanced accordingin Figure to 8the. number of layers. The spatial positions of the training data and test data are shown in Figure 8. Appl. Sci. 2019, 9, 3553 9 of 29 Appl. Sci. 2019, 9, x FOR PEER REVIEW 11 of 32

Figure 8. Schematic diagram of the training da datata and test data distributions.

Figure 88 showsshows the location location distributions distributions of of the the training training data data and and test test data data in the in thestudy study area areaafter afterthe original the original drilling drilling data data are aresegmented segmented into into training training data data and and test test data, data, where where the the red red symbols represent thethe trainingtraining data, data, and and the the green green symbols symbols represent represent the testthe data.test data. The positions,The positions, plotting plotting scale, andscale, geographic and geographic coordinates coordinates in Figure in Figure8 are the 8 are same the as same in Figure as in 7Figure. 7. 3. Stratum coding:coding: According According to to the the statistics, statistics, the the borehole borehole stratum stratum data used,data inused, this in study, this containstudy, acontain total of a 13total types of of13 stratatypes andof strata 15 types and of 15 initiation types of and initiation termination and termination markers artificially markers introducedartificially inintroduced the subsequent in the geostratigraphicsubsequent geostratigraphic series. The numbers series. The zero numbers to 14 were zero assigned, to 14 were and assigned, vectorization and wasvectorization carried out was by carried one-hot out encoding. by one-hot The numberencoding and. The coding number vectors and ofcoding the stratum vectors types of the are stratum shown intypes Table are1. shown in Table 1.

Table 1.1. Strata numbers and one-hot vectors.

Stratum StratumTypes Types Number Number Coding Coding VectorVector clay clay 0 0 (1,(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0,0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0) 0) silt silt 1 1 (0,(0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) 0) plain fillplain fill 2 2 (0,(0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0) 0) miscellaneous fill 3 (0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) miscellaneoussilty fill sand 3 4 (0,(0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,0, 0,0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0) 0) silty sandsilty clay 4 5 (0,(0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,0, 0,0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0) 0) silty claymucky soil 5 6 (0,(0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,0, 0,0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0) 0) mucky soilmucky clay 6 7 (0,(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,0, 0,0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0) 0) old city fill 8 (0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0) mucky clay 7 (0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0) clay sand inclusion 9 (0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0) old city fill mud 8 10 (0,(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,1, 0,0, 1, 0, 0, 0, 0, 0, 0, 0, 0) 0) clay sand inclusionmedium sand 9 11 (0,(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0,0, 0,1, 0, 0, 1, 0, 0, 0, 0, 0, 0) 0) mudintermediate fine sand 10 12 (0,(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0,0, 0, 1, 0, 0, 1, 0, 0, 0, 0) 0) medium sandstart mark 11 13 (0,(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0,0, 0, 0, 0, 1, 0, 0, 1, 0, 0) 0) end mark 14 (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1) intermediate fine 12 (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0) sand 4. Geostratigraphicstart mark series filling: 13 According (0,to the0, 0, statistics, 0, 0, 0, 0, 0, the 0, maximum0, 0, 0, 0, 1, number0) of strata in the study dataend is 10.mark Therefore, the 14filling length of (0, the 0, 0, geostratigraphic 0, 0, 0, 0, 0, 0, 0, series 0, 0, 0, should 0, 1) be larger than 10 layers. For simplicity, the termination marker is used here to fill all geostratigraphic series to the 11th layer.4. Geostratigraphic Suppose that series all stratum filling: types According of a borehole to the statistics, are clay, silt,the siltmaximum sand, clay, number and muckyof strata clay, in andthe study the corresponding data is 10. Therefore, number vectorthe filling is expressed length of asthe (0,1,4,0,7). geostratigraphic The termination series should marker be denotedlarger than by 10 layers. For simplicity, the termination marker is used here to fill all geostratigraphic series to the 11th layer. Suppose that all stratum types of a borehole are clay, silt, silt sand, clay, and mucky clay, Appl. Sci. 2019, 9, 3553 10 of 29 the number 14 is repeatedly added at the end of the vector until the length of the numbered vector is 11. Finally, the geostratigraphic series data input of the machine learning model is obtained by replacing each item in the numbered vector with the corresponding one-hot encoding vector.

3.2. Machine Learning Simulation Result Analysis We have implemented the proposed algorithms written by Python software in the computer. Part of the algorithm code is as follows:

1. class CrossLoss(nn.Module): 2. def __init__(self,ignore_index = 0): 3. super(CrossLoss, self).__init__() 4. self.ignore_index = ignore_index 5. self.criterion = nn.CrossEntropyLoss(ignore_index = 0) 6. def forward(self, input, target): 7. ind = (target ! = self.ignore_index).float() 8. num_all = torch.sum(ind).data[0] 9. #print(target) 10. size0 = target.size(0) 11. size1 = target.size(1) 12. temp = target.cpu().data 13. for i in range(size0): 14. for j in range(size1): 15. temp[i,j] = depthLabel(temp[i,j]) 16. pred = torch.mul(input,ind).long() 17. temp = temp.long() 18. loss = self.criterion(pred, temp) 19. return loss, num_all

As the procedure may be further commercialized, it is not suitable to make it all public for the time being. Information about the algorithm’s computer performance is as follows:

CPU: Intel Core i7-4790k @ 4.00GHz quad-core; • Memory: 32 GB; • VGA card: Nvidia GeForce GTX 770(2GB). • 3.2.1. Training and Verification of the Stratum Type Series Model (1) Model Training The cross-entropy loss function is used to describe the performance of the model in the training process. Figure9 shows that as the number of training rounds increases, the loss value decreases continuously. However, the gradient of the loss curve begins to decrease after several cycles, and the amplitude of change gradually decreases. The final loss value fluctuates in a small range and tends to be stable. Appl. Sci. 2019, 9, x FOR PEER REVIEW 12 of 32 and the corresponding number vector is expressed as (0,1,4,0,7). The termination marker denoted by the number 14 is repeatedly added at the end of the vector until the length of the numbered vector is 11. Finally, the geostratigraphic series data input of the machine learning model is obtained by replacing each item in the numbered vector with the corresponding one-hot encoding vector.

3.2. Machine Learning Simulation Result Analysis We have implemented the proposed algorithms written by Python software in the computer. Part of the algorithm code is as follows: 1 class CrossLoss(nn.Module): 2 def __init__(self,ignore_index = 0): 3 super(CrossLoss, self).__init__() 4 self.ignore_index = ignore_index 5 self.criterion = nn.CrossEntropyLoss(ignore_index = 0) 6 def forward(self, input, target): 7 ind = (target ! = self.ignore_index).float() 8 num_all = torch.sum(ind).data[0] 9 #print(target) 10 size0 = target.size(0) 11 size1 = target.size(1) 12 temp = target.cpu().data 13 for i in range(size0): 14 for j in range(size1): 15 temp[i,j] = depthLabel(temp[i,j]) 16 pred = torch.mul(input,ind).long() 17 temp = temp.long() 18 loss = self.criterion(pred, temp) 19 return loss, num_all As the procedure may be further commercialized, it is not suitable to make it all public for the time being. Information about the algorithm’s computer performance is as follows: • CPU: Intel Core i7-4790k @ 4.00GHz quad-core; • Memory: 32 GB; • VGA card: Nvidia GeForce GTX 770(2GB).

3.2.1. Training and Verification of the Stratum Type Series Model (1) Model Training The cross-entropy loss function is used to describe the performance of the model in the training process. Figure 9 shows that as the number of training rounds increases, the loss value decreases continuously. However, the gradient of the loss curve begins to decrease after several cycles, and the Appl.amplitude Sci. 2019 of, 9 ,change 3553 gradually decreases. The final loss value fluctuates in a small range and 11tends of 29 to be stable.

Appl. Sci. 2019, 9, x FOR PEER REVIEW 13 of 32

Figure 9. Loss curve of the first first 50 training rounds.

The model hashas completedcompleted mostmost of of its its loss loss reduction reduction after after 50 50 training training rounds, rounds, as as shown shown in Figurein Figure 10. After10. After 50 rounds, 50 rounds, the lossthe functionloss function tends tends to be stable,to be stable, and the and model the ismodel slowly is learningslowly learning from the from training the trainingdata. The data. specific The declinespecific indecline the loss in functionthe loss function is listed inis listed Table2 in. Table 2.

Figure 10. Loss curve after 500 training rounds.

Table 2. Statistical table of the loss decline. Table 2. Statistical table of the loss decline.

RoundRound NumberNumber 50 50 500 Loss valuevalue 0.483226 0.374167 CumulativeCumulative declinedecline 0.327009 0.436068 Cumulative decline 40.36% 53.82% Cumulative decline 40.36% 53.82%

(2) Model Test The trainedtrained and and finally finally stable stable model model was was tested, tested, and theand coordinate the coordinate information information of the test of boreholethe test boreholedata was data inputted was successively.inputted successively. The position The ofposition the termination of the termination marker inmarker the simulated in the simulated stratum stratumtype sequence type sequence output by output the model by the was model searched was searched and intercepted. and intercepted. All the elementsAll the elements before the before first thetermination first termination marker were marker taken were as thetaken stratum as the prediction stratum prediction series. By comparingseries. By comparing the predicted the value predicted with valuethe real with value the one-to-one,real value one-to-one, the single-layer the single-layer accuracy of accuracy the geostratigraphic of the geostratigraphic series is tested. series Thenis tested. the Thensimilarity the similarity between thebetween prediction the prediction sequence sequence and the real and geostratigraphic the real geostratigraph series isic evaluated series is evaluated by using bythe using edit distance the edit algorithm.distance algorithm. The accuracy of stratum type simulation varies varies with with the the training training round, round, as as shown shown in in Figure Figure 11.11. Figure 11 showsshows that that as as the the number number of of training training roun roundsds increases, increases, the the overall overall prediction prediction ability ability of the of modelthe model continues continues to improve, to improve, and and the the accuracy accuracy of of the the stratum stratum type type and and geostratigraphic series prediction is rapidly improved. The accuracy of the final final stratum type prediction was stable at 59.86%. As the loss function function curve curve change changes,s, the the accuracy accuracy curve curve increases increases gr gradually.adually. The The accuracy accuracy achieved achieved in thein the first first 50 50rounds rounds is almost is almost the the same same as asthe the final final accuracy. accuracy. The prediction of a single stratum is the firstfirst stepstep inin establishingestablishing a spatial stratum distribution model. In In addition addition to to the the accurate accurate prediction prediction of ofa single a single stratum, stratum, it is it of is greater of greater concern concern whether whether the model can make an accurate overall prediction of the geostratigraphic series in the study area. Then, the edit distance algorithm is used to evaluate the similarity between the simulated sequence and the real geostratigraphic series. If the edit distance between the prediction sequence and the real geostratigraphic series is larger than one, the prediction failed and will not be considered. The edit distance changes are shown in Figure 12. Appl. Sci. 2019, 9, 3553 12 of 29 the model can make an accurate overall prediction of the geostratigraphic series in the study area. Then, the edit distance algorithm is used to evaluate the similarity between the simulated sequence andAppl. the Sci. real 2019 geostratigraphic, 9, x FOR PEER REVIEW series. If the edit distance between the prediction sequence and the14 real of 32 geostratigraphic series is larger than one, the prediction failed and will not be considered. The edit distanceAppl. Sci. changes 2019, 9, x areFORshown PEER REVIEW in Figure 12. 14 of 32

Figure 11. Variation diagram of the simulation accuracy of the RNN model.

Figure 11. Variation diagram of the simulation accuracy of the RNN model. Figure 11. Variation diagram of the simulation accuracy of the RNN model.

FigureFigure 12. 12.Variation Variation curve curve of of the the geostratigraphic geostratigraphic series series edit edit distance distance with with time. time.

In Figure 12, the lower curve indicates that the edit distance is zero, i.e., the proportion of the In Figure Figure12, the 12. lower Variation curve curve indicates of the geostratigraphic that the edit distance series edit is distancezero, i.e., with the time. proportion of the number of boreholes in the predicted result in the test set is exactly equal to the real result. The above number of boreholes in the predicted result in the test set is exactly equal to the real result. The above curve indicates the proportion of the number of boreholes within an edit distance of one, i.e., the model curveIn indicates Figure 12, the the proportion lower curve of theindicates number that of the bore edholesit distance within is an zero, edit i.e., distance the proportion of one, i.e., of the makes no more than one wrong prediction in the whole sequence prediction process. The predicted numbermodel makes of boreholes no more in thethan predicted one wrong result prediction in the test in set the is exactlywhole sequenceequal to the prediction real result. process. The above The sequence can be converted into a real stratum sequence by a single insertion, replacement, or deletion curvepredicted indicates sequence the canproportion be converted of the into number a real of stra boretumholes sequence within by an a singleedit distance insertion, of replacement,one, i.e., the operation. In the end, the former curve converges to 35.2%, while the latter curve converges to 74%. modelor deletion makes operation. no more Inthan the one end, wrong the formerprediction curve in theconverges whole sequenceto 35.2%, predictionwhile the process.latter curve The Because the number of layers is different, it is difficult to accurately describe the similarity between predictedconverges sequence to 74%. can be converted into a real stratum sequence by a single insertion, replacement, the predicted series and the real result by applying the edit distance alone. Therefore, the similarity or deletionBecause operation. the number In ofthe layers end, theis different, former curveit is difficult converges to accuratelyto 35.2%, describewhile the the latter similarity curve calculation equation based on the edit distance is adopted. The variation curve of the predicted series convergesbetween the to predicted74%. series and the real result by applying the edit distance alone. Therefore, the similarity with the number of training rounds is shown in Figure 13. similarityBecause calculation the number equation of layers based is ondifferent, the edit it distanceis difficult is adopted.to accurately The describevariation the curve similarity of the betweenpredicted the series predicted similarity series with and the the number real result of training by applying rounds the is shown edit distance in Figure alone. 13. Therefore, the similarity calculation equation based on the edit distance is adopted. The variation curve of the predicted series similarity with the number of training rounds is shown in Figure 13. Appl. Sci. 2019, 9, x FOR PEER REVIEW 15 of 32

Appl. Sci. 2019, 9, 3553 13 of 29 Appl. Sci. 2019, 9, x FOR PEER REVIEW 15 of 32

Figure 13. Similarity curve of the geostratigraphic series.

In Figure 13, with an increase in training rounds, the overall prediction ability of the model is continuously improved,Figure and the 13. averageSimilarity similarity curve of the curve geostratigraphic between the series. predicted series and the actual geostratigraphic series also gradually increases and finally converges to 70.9%. This result shows that InIn FigureFigure 1313,, withwith anan increaseincrease inin trainingtraining rounds,rounds, thethe overalloverall predictionprediction ability of the modelmodel is model accuracy continuously improves with increasing training rounds in the learning process and continuously improved,improved, andand thethe averageaverage similaritysimilarity curvecurve between the predicted seriesseries and the actual gradually establishes the correlation between the elevation information and the geostratigraphic geostratigraphic seriesseries alsoalso graduallygradually increasesincreases andand finallyfinally convergesconverges toto 70.9%.70.9%. This result shows that series in the study area. model accuracy continuously improves with increasing training rounds in the learning process andand (3) Testing the Effect of Expert-Driven Learning gradually establishesestablishes the the correlation correlation between between the the elevation elevation information information and theand geostratigraphic the geostratigraphic series To improve the learning performance of the RNN and test the effect of expert-driven learning, inseries the studyin the area.study area. this study conducted the training and testing of the expert-driven model based on supervisory (3)(3) TestingTesting thethe EEffectffect ofof Expert-DrivenExpert-Driven LearningLearning learning in accordance with four ratios using the same dataset. The four expert ratios are 1/3, 1/2, 2/3 To improveimprove the the learning learning performance performance of of the the RNN RNN and and test test the the effect effect of expert-driven of expert-driven learning, learning, this and 1, i.e., expert-driven learning is carried out once every three rounds, once every two rounds, and studythis study conducted conducted the training the training and testing and testing of the expert-drivenof the expert-driven model basedmodel on based supervisory on supervisory learning twice every three rounds, and the entire training process is conducted in the form of expert-driven inlearning accordance in accordance with four with ratios four using ratios the using same dataset.the same The dataset. four expertThe four ratios expert are 1ratios/3, 1/2, are 2/ 31/3, and 1/2, 1, i.e.,2/3 learning. expert-drivenand 1, i.e., expert-driven learning is carriedlearning out is carried once every out threeonce every rounds, three once rounds, every twoonce rounds, every two and rounds, twice every and Figures 14–17 show the loss function curves of expert-driven learning using different factors. threetwice rounds,every three and therounds, entire and training the entire process training is conducted process inis theconducted form of in expert-driven the form of learning.expert-driven Since the model is based on the prediction results of both expert-driven learning and non-expert- learning.Figures 14–17 show the loss function curves of expert-driven learning using different factors. driven learning, the loss function is banded in the first three figures. The model obtained a higher SinceFigures the model 14–17 is based show on the the loss prediction function results curves of bothof expert-driven expert-driven learning learning us anding non-expert-driven different factors. descent gradient under the guidance of correct monitoring signals as compared with the ordinary learning,Since the themodel loss is function based on is bandedthe prediction in the firstresult threes of figures.both expert-driven The model obtainedlearning aand higher non-expert- descent RNN model. The larger the proportion of expert-driven learning in the learning process is, the higher gradientdriven learning, under the the guidance loss function of correct is banded monitoring in th signalse first three as compared figures. withThe model the ordinary obtained RNN a model.higher the rate of loss reduction. When expert-driven learning is completely adopted, the model loss Thedescent larger gradient the proportion under the of guidance expert-driven of correct learning monitoring in the learning signals processas compared is, the with higher the the ordinary rate of function curve decreases the fastest. Almost all of the gradient descent is completed within the first lossRNN reduction. model. The When larger expert-driven the proportion learning of expert-drive is completelyn learning adopted, in the the learning model process loss function is, the higher curve 50 training rounds. decreasesthe rate of the loss fastest. reduction. Almost allWhen of the expert-driven gradient descent learning is completed is completely within theadopted, first 50 trainingthe model rounds. loss function curve decreases the fastest. Almost all of the gradient descent is completed within the first 50 training rounds.

FigureFigure 14. 14.Expert-driven Expert-driven learning learning with with a a factor factor of of 1 /1/3.3.

Figure 14. Expert-driven learning with a factor of 1/3. Appl. Sci. 2019, 9, x FOR PEER REVIEW 16 of 32 Appl. Sci. 2019, 9, x FOR PEER REVIEW 16 of 32 Appl. Sci. 2019, 9, x FOR PEER REVIEW 16 of 32 Appl.Appl. Sci. Sci.2019 2019, 9, ,9 3553, x FOR PEER REVIEW 1416 ofof 29 32

Figure 15. Expert-driven learning with a factor of ½. Figure 15. Expert-driven learning with a factor of ½. Figure 15. Expert-driven learning with a factor of ½. Figure 15. Expert-driven learning with a factor of1 ½. Figure 15. Expert-driven learning with a factor of 2 .

Figure 16. Expert-driven learning with a factor of 2/3. Figure 16. Expert-driven learning with a factor of 2/3. FigureFigure 16. 16.Expert-driven Expert-driven learning learning with with a a factor factor of of 2 /2/3.3. Figure 16. Expert-driven learning with a factor of 2/3.

Figure 17. Full expert-driven learning. FigureFigure 17. 17.Full Full expert-driven expert-driven learning. learning. Figure 17. Full expert-driven learning. The single-stratum accuracy rate curve in each test round is shown in Figures 18–21. TheThe single-stratum single-stratum accuracy accuracy rateFigure rate curve curve 17. Full in in each expert-driveneach test test round round learning. is is shown shown in in Figures Figures 18 18–21.–21. The single-stratum accuracy rate curve in each test round is shown in Figures 18–21. The single-stratum accuracy rate curve in each test round is shown in Figures 18–21.

FigureFigure 18. 18.Expert-driven Expert-driven learning learning with with a a factor factor of of 1 /1/3.3. Figure 18. Expert-driven learning with a factor of 1/3. Figure 18. Expert-driven learning with a factor of 1/3. Figure 18. Expert-driven learning with a factor of 1/3. Appl. Sci. 2019, 9, x FOR PEER REVIEW 17 of 32

Appl. Sci. 2019, 9, x FOR PEER REVIEW 17 of 32 Appl. Sci. 2019, 9, x FOR PEER REVIEW 17 of 32 Appl.Appl. Sci. Sci.2019 2019, 9,, 9 3553, x FOR PEER REVIEW 1517 of of 29 32

Figure 19. Expert-driven learning with a factor of ½.

Figure 19. Expert-driven learning with a factor of ½.

Figure 19. Expert-driven learning with a factor of ½. 1 FigureFigure 19. 19.Expert-driven Expert-driven learning learning with with a a factor factor of of2 .½.

Figure 20. Expert-driven learning with a factor of 2/3.

Figure 20. Expert-driven learning with a factor of 2/3.

FigureFigure 20. 20.Expert-driven Expert-driven learning learning with with a a factor factor of of 2 /2/3.3. Figure 20. Expert-driven learning with a factor of 2/3.

Figure 21. Full expert-driven learning.

FigureFigure 21. 21.Full Full expert-driven expert-driven learning. learning.

The accuracy of the model simulationFigure 21. Full results expert-driven under different learning. tutor ratios is shown in Table 3 below.TheThe accuracy accuracy of of the the model model simulation simulationFigure 21. results Full results expert-driven under under different different learning. tutor ratiostutor ratios is shown is shown in Table in3 below.Table 3 below.The accuracy of the model simulation results under different tutor ratios is shown in Table 3 below.The accuracy of TabletheTable model 3. 3.Stratum Stratum simulation type type accuracy accuracy results under undeunderr di differentff differenterent expert expert tutor ratios. ratios. ratios is shown in Table 3 below. Table 3. Stratum type accuracy under different expert ratios. ExpertExpert Ratio Ratio 0 0 1/3 1/3 1/21/2 2/32/3 1 1 Table 3. Stratum type accuracy under different expert ratios. Maximum value 61.42% 63.83% 64.82% 63.40% 64.82% ExpertMaximum RatioTable value 3. Stratum 61.42% 0 type accuracy 63.83%1/3 unde 64.82%r different1/2 63.40% expert ratios.2/3 64.82% 1 MaximumSteadyExpertSteady valueRatio value value 59.86% 59.86%61.42% 0 60.00%63.83% 60.00%1/3 62.41%62.41%64.82%1/2 61.13% 61.13%63.40%2/3 60.42% 60.42%64.82% 1 Expert Ratio 0 1/3 1/2 2/3 1 MaximumSteady value value 59.86%61.42% 60.00%63.83% 62.41%64.82% 61.13%63.40% 60.42%64.82% FiguresMaximum 22–25Steady show value value the proportion 59.86%61.42% of the60.00%63.83% drilling data62.41%64.82% with edit distances61.13%63.40% of zero and60.42%64.82% one in the Figures 22–25 show the proportion of the drilling data with edit distances of zero and one in the total test data Steadybetween value the prediction 59.86% series of60.00% the model and62.41% the real geostratigraphic61.13% series.60.42% Detailed total testFigures data between22–25 show the predictionthe proportion series of of the the drilling model data and thewith real edit geostratigraphic distances of zero series. and Detailedone in the statisticsFigures are shown22–25 show in Table the 4.proportion of the drilling data with edit distances of zero and one in the statisticstotal test are data shown between in Table the 4prediction. series of the model and the real geostratigraphic series. Detailed Figures 22–25 show the proportion of the drilling data with edit distances of zero and one in the statisticstotal test dataare shown between in Tablethe prediction 4. series of the model and the real geostratigraphic series. Detailed statisticstotal test dataare shown between in Tablethe prediction 4. series of the model and the real geostratigraphic series. Detailed statistics are shown in Table 4.

Figure 22. Expert-driven learning with a factor of 1/3.

Appl. Sci. 2019, 9, x FOR PEER REVIEW 18 of 32

Appl. Sci. 2019, 9, x FOR PEER REVIEW 18 of 32 Appl. Sci. 2019, 9, x FOR PEER REVIEWFigure 22. Expert-driven learning with a factor of 1/3. 18 of 32 Appl. Sci. 2019, 9, x FOR PEER REVIEW 18 of 32 Figure 22. Expert-driven learning with a factor of 1/3. Figure 22. Expert-driven learning with a factor of 1/3. Appl. Sci. 2019, 9, 3553 16 of 29 Figure 22. Expert-driven learning with a factor of 1/3.

Figure 23. Expert-driven learning with a factor of 2/3.

Figure 23. Expert-driven learning with a factor of 2/3. Figure 23. Expert-driven learning with a factor of 2/3. FigureFigure 23. 23.Expert-driven Expert-driven learning learning with with a a factor factor of of 2 /2/3.3.

Figure 24. Expert-driven learning with a factor of ½.

Figure 24. Expert-driven learning with a factor of ½. Figure 24. Expert-driven learning with a factor of1 ½. Figure 24. Expert-driven learning with a factor of 2 . Figure 24. Expert-driven learning with a factor of ½.

Figure 25. Full expert-driven learning. Figure 25. Full expert-driven learning. Figure 25. Full expert-driven learning. Table 4. Statistical resultsFigure of 25. the Full edit expert-driven distance under learning. the different expert ratios. Table 4. Statistical results of the edit distance under the different expert ratios. Figure 25. Full expert-driven learning. Table 4. StatisticalExpert Ratio results of the edit distance 0 under 1/3 the different 1/2 expert 2/3 ratios. 1 Table 4. Statistical results of the edit distance under the different expert ratios. Expert RatioMaximum value 037.2% 1/ 339.6% 1/ 239.2% 2/3 39.6% 1 36.4% EditTable Distance 4. StatisticalExpert = 0 RatioMaximum results of value the edit 37.2% distance 0 39.6% under 1/3 the 39.2% different 1/2 39.6% expert 2/3 36.4% ratios. 1 Edit DistanceExpert= 0 RatioSteady value 35.2% 0 38% 1/3 38.4% 1/2 38.4% 2/3 35.6% 1 SteadyMaximum value value 35.2% 37.2% 38% 39.6% 38.4% 39.2% 38.4% 39.6% 35.6% 36.4% Expert Ratio 0 1/3 1/2 2/3 1 Edit Distance = 0 MaximumMaximum value value 76% 37.2% 76% 77.2% 39.6% 77.2% 76.4% 39.2%76.4% 77.2% 39.6%77.2% 76.4% 36.4%76.4% EditEdit Distance Distance =<= 0 1 Steady value 35.2% 38% 38.4% 38.4% 35.6% SteadySteadyMaximum value value value 74% 35.2%37.2% 74% 75.6% 39.6% 75.6%38% 75.6% 38.4%39.2%75.6% 75.6% 38.4%39.6%75.6% 73.6% 35.6%36.4%73.6% Edit Distance = 0 Maximum value 76% 77.2% 76.4% 77.2% 76.4% Edit Distance <= 1 MaximumSteady value value 35.2% 76% 77.2%38% 76.4%38.4% 77.2%38.4% 76.4%35.6% Edit Distance <= 1 Steady value 74% 75.6% 75.6% 75.6% 73.6% The similarity curves betweenMaximum the prediction value 76%series of 77.2% the model 76.4% and 77.2%the real 76.4%geostratigraphic The similarityEdit Distance curves <= between 1 Steady the value prediction series 74% of 75.6% the model 75.6% and the75.6% real geostratigraphic73.6% series under different expert ratiSteadyos is shown value in Figures 74% 26–29. 75.6% 75.6% 75.6% 73.6% seriesThe under similarity different curves expert between ratios is shownthe prediction in Figures seri 26es– 29of. the model and the real geostratigraphic The similarity curves between the prediction series of the model and the real geostratigraphic series under different expert ratios is shown in Figures 26–29. seriesThe under similarity different curves expert between ratios is the shown prediction in Figures seri es26–29. of the model and the real geostratigraphic series under different expert ratios is shown in Figures 26–29.

Appl. Sci. 2019, 9, x FOR PEER REVIEW 19 of 32 FigureFigure 26. 26.Expert-driven Expert-driven learning learning with with a a factor factor of of 1 /1/3.3.

Figure 26. Expert-driven learning with a factor of 1/3. Figure 26. Expert-driven learning with a factor of 1/3. Figure 26. Expert-driven learning with a factor of 1/3.

FigureFigure 27. 27.Expert-driven Expert-driven learning learning with with a a factor factor of of 2 /2/3.3.

Figure 28. Expert-driven learning with a factor of ½.

Figure 29. Full expert-driven learning.

The statistics of series similarity under different expert ratios are shown in Table 5.

Table 5. Statistical results of the series similarity.

Expert Ratio 0 1/3 1/2 2/3 1 Maximum value 71.85% 73.60% 73.95% 73.98% 72.51% Steady value 70.91% 72.64% 73.57% 73.09% 71.68%

It can be seen that adopting the expert-driven learning mechanism is helpful to improve the performance of test models for stratum type series simulation based on machine learning, as shown in Table 5. However, the amplitude of the improvement effect is not significant. The expert-driven model can accelerate the convergence of the learning curve, and the higher the expert ratio is, the faster the model will reach stability. From the highest and stable values of the various indicators in the different models, it is not the rule that the higher the expert ratio is, the better the effect will be. The ultimate performance of full expert-driven learning was only slightly better than that of the RNN model. The best results were obtained by using a partial expert-driven learning strategy model.

3.2.2. Training and Verification of the Stratum Thickness Series Model (1) Layer Thickness Simulation Based on Multi-Category Classification The layer thickness of the study area is divided into six stratum thickness intervals as follows: within 3 m, 3 m to 5 m, 5 m to 10 m, 10 m to 20 m, 20 m to 30 m, and above 30 m. Stratum thickness series simulation based on multi-category classification also needs to be numbered and coded for the different stratum thicknesses, as shown in Table 6.

Appl. Sci. 2019, 9, x FOR PEER REVIEW 19 of 32 Appl. Sci. 2019, 9, x FOR PEER REVIEW 19 of 32

Appl. Sci. 2019, 9, 3553 Figure 27. Expert-driven learning with a factor of 2/3. 17 of 29 Figure 27. Expert-driven learning with a factor of 2/3.

Figure 28. Expert-driven learning with a factor of ½. 1 FigureFigure 28. 28.Expert-driven Expert-driven learning learning with with a a factor factor of of2 ½..

FigureFigure 29. 29.Full Full expert-driven expert-driven learning. learning. Figure 29. Full expert-driven learning. TheThe statistics statistics of of series series similarity similarity under under di difffferenterent expert expert ratios ratios are are shown shown in in Table Table5. 5. The statistics of series similarity under different expert ratios are shown in Table 5. TableTable 5. 5.Statistical Statistical results results of of the the series series similarity. similarity. Table 5. Statistical results of the series similarity. ExpertExpert Ratio Ratio 0 0 1/ 1/33 1/1/22 2/2/33 1 1 Expert Ratio 0 1/3 1/2 2/3 1 MaximumMaximum value value 71.85%71.85% 73.60%73.60% 73.95%73.95% 73.98%73.98% 72.51%72.51% MaximumSteadySteady value value value 70.91% 70.91%71.85% 72.64%72.64%73.60% 73.57%73.57%73.95% 73.09%73.09%73.98% 71.68%71.68%72.51% Steady value 70.91% 72.64% 73.57% 73.09% 71.68%

ItIt can can be be seen seen that that adopting adopting the the expert-driven expert-driven learning learning mechanism mechanism is is helpful helpful to to improve improve the the performanceIt can be of seen test modelsthat adopting for stratum the expert-driven type series simulation learning mechanismbased on machine is helpful learning, to improve as shown the performanceperformance of of test test models models for for stratum stratum type type series series simulation simulation based based on on machine machine learning, learning, as as shown shown inin Table Table5. 5. However, However, the the amplitude amplitude of of the the improvement improvement e ffeffectect is is not not significant. significant. The The expert-driven expert-driven modelin Table can 5. accelerateHowever, thethe convergenceamplitude of ofthe the improvem learning entcurve, effect and is thenot highersignificant. the expertThe expert-driven ratio is, the modelmodel can can accelerate accelerate the the convergence convergence of the of learningthe learning curve, curve, and theand higher the higher the expert the expert ratio is, ratio the fasteris, the thefaster model the willmodel reach will stability.reach stability. From theFrom highest the highest and stable and stable values values of the of various the various indicators indicators in the in thefaster different the model models, will itreach is not stability. the rule From that thethe highhighester the and expert stable ratio values is, theof thebetter various the effect indicators will be. in dithefferent different models, models, it is notit is thenot rulethe rule that that the the higher high theer the expert expert ratio ratio is, is, the the better better the the e ffeffectect will will be. be. TheThe ultimate ultimate performance performance of of full full expert-driven expert-driven learning learning was was only only slightly slightly better better than than that that of of the the RNN RNN model.The ultimate The best performance results were of fullobtained expert-driven by using learninga partial was expert-driven only slightly learning better thanstrategy that model. of the RNN model.model. The The best best results results were were obtained obtained by by using using a a partial partial expert-driven expert-driven learning learning strategy strategy model. model. 3.2.2.3.2.2. Training Training and and Verification Verification of of the the Stratum Stratum Thickness Thickness Series Series Model Model 3.2.2. Training and Verification of the Stratum Thickness Series Model (1)(1) Layer Layer Thickness Thickness Simulation Simulation Based Based on on Multi-Category Multi-Category Classification Classification (1) Layer Thickness Simulation Based on Multi-Category Classification TheThe layer layer thickness thickness of of the the study study area area is is divided divided into into six six stratum stratum thickness thickness intervals intervals asas follows: follows: The layer thickness of the study area is divided into six stratum thickness intervals as follows: withinwithin 3 3 m, m, 3 3 m m to to 5 5 m, m, 5 5 m m to to 10 10 m, m, 10 10 m m to to 20 20 m, m, 20 20 m m to to 30 30 m, m, and and above above 30 30 m. m. Stratum Stratum thickness thickness within 3 m, 3 m to 5 m, 5 m to 10 m, 10 m to 20 m, 20 m to 30 m, and above 30 m. Stratum thickness seriesseries simulation simulation based based on on multi-category multi-category classification classification also also needs needs to to be be numbered numbered and and coded coded for for the the series simulation based on multi-category classification also needs to be numbered and coded for the didifferentfferent stratum stratum thicknesses, thicknesses, as as shown shown in in Table Table6. 6. different stratum thicknesses, as shown in Table 6.

Table 6. Code of the layer thickness type.

Layer Thickness Type Stratum Thickness Interval Coded Vector Coding Number <3 m 0 [1, 0, 0, 0, 0, 0, 0] 3–5 m 1 [0, 1, 0, 0, 0, 0, 0] 5–10 m 2 [0, 0, 1, 0, 0, 0, 0] 10–20 m 3 [0, 0, 0, 1, 0, 0, 0] 20–30 m 4 [0, 0, 0, 0, 1, 0, 0] >30 m 5 [0, 0, 0, 0, 0, 1, 0] initiation mark 6 [0, 0, 0, 0, 0, 0, 1] Appl. Sci. 2019, 9, x FOR PEER REVIEW 20 of 32

Table 6. Code of the layer thickness type.

Layer Thickness Stratum Thickness Interval Coded Vector Type Coding Number <3 m 0 [1, 0, 0, 0, 0, 0, 0] 3–5 m 1 [0, 1, 0, 0, 0, 0, 0] 5–10 m 2 [0, 0, 1, 0, 0, 0, 0] 10–20 m 3 [0, 0, 0, 1, 0, 0, 0] 20–30 m 4 [0, 0, 0, 0, 1, 0, 0] >30 m 5 [0, 0, 0, 0, 0, 1, 0] Appl. Sci. 2019, 9, 3553 18 of 29 initiation mark 6 [0, 0, 0, 0, 0, 0, 1]

Before thethe outputoutput of of the the model model is generated,is generated, the encoderthe encoder has received has received a complete a complete series of series stratum of stratumtypes, that types, is, thethat total is, the number total number of stratum of stratum layers atlayers the predictionat the prediction point ispoint known. is known. Therefore, Therefore, there thereis no needis no need to add to aadd termination a termination marker marker for thefor layerthe layer thickness thickness interval. interval. Only Only an an initiation initiation mark mark is isintroduced introduced as theas the starting starting point point of the of decoder’s the decoder’s simulated simulated layer thicknesslayer thickness sequence. sequence. After all After outputs all outputsof the model of the are model completed, are completed, a series equal a series to theequal number to the of number layers isof intercepted layers is intercepted as the prediction as the predictionsequence of sequence the layer of thickness. the layer thickness. (2) Model Training andand TestingTesting The stratum stratum thickness thickness series series model model adopts adopts the the seq2 seq2seqseq architecture architecture and and uses uses the the drilling drilling data data in thein the training training set set for for training. training. To To accurately accurately reflect reflect the the actual actual performance performance of of the the model, model, the the highest accuracy andand averageaverage accuracy accuracy of of the the model model in the in testthe settest were set compared.were compared. After each After round each of round training, of training,the model the was model tested, was and tested, the and test resultsthe test wereresults recorded. were recorded. After training After training 500 rounds, 500 rounds, the loss the curve loss curveof the of model the model is shown is shown in Figure in Figure 30, and 30, theand changes the changes in prediction in prediction accuracy accuracy are are shown shown in Figurein Figure 31. 31.As theAs numberthe number of training of training rounds rounds increases, increases, the prediction the prediction performance performance of the of model the model increases increases slowly slowlyand finally and convergesfinally converges to 63.53%. to 63.53%.

Figure 30. Loss function curve.

Figure 31. Prediction accuracy curve of the layer thickness. Figure 31. Prediction accuracy curve of the layer thickness. (3) Testing the Effect of Expert-Driven Learning (3) Testing the Effect of Expert-Driven Learning (3)To furtherTesting improvethe Effect the of Expert-Driven accuracy of the Learning model and improve the prediction ability of the model To further improve the accuracy of the model and improve the prediction ability of the model for theTo stratumfurther thicknessimprove the category, accuracy this of section the model conducts and improve expert-driven the prediction model based ability on of supervisory the model for the stratum thickness category, this section conducts expert-driven model based on supervisory forlearning the stratum in diff erentthickness proportions category, and this compares section co thenducts learning expert-driven effect to determine model based the modelon supervisory with the learning in different proportions and compares the learning effect to determine the model with the learninghighest accuracy in different and proportions the greatest and prediction compares ability. the learning In this section, effect to the determine expert ratios the model adopted with by the highest accuracy and the greatest prediction ability. In this section, the expert ratios adopted by the highestseq2seq accuracy model in theand learning the greatest process prediction are 1/3, 1ability./2, 2/3, In and this 1. Thesection, accuracy the expert performance ratios adopted of the di byfferent the seq2seq model in the learning process are 1/3, 1/2, 2/3, and 1. The accuracy performance of the seq2seqmodels inmodel test data in the is provided learning inprocess Table7 .are 1/3, 1/2, 2/3, and 1. The accuracy performance of the different models in test data is provided in Table 7. Table 7. Prediction accuracy of the layer thickness.

Expert Ratio 0 1/3 1/2 2/3 1 Maximum value 65.07% 73.05% 80.08% 75.60% 70.07% Steady value 63.53% 70.07% 75.05% 72.62% 67.94%

Table7 shows the highest value of the results achieved in the test data and the final stable value after convergence, based on the different expert ratios. As we can see from the test results, with the increase in the proportion of expert-driven learning, the accuracy of the model in terms of the test data first increases and then decreases. In addition, the models that do not adopt expert-driven learning and Appl. Sci. 2019, 9, x FOR PEER REVIEW 21 of 32 Appl. Sci. 2019, 9, x FOR PEER REVIEW 21 of 32

Table 7. Prediction accuracy of the layer thickness. Table 7. Prediction accuracy of the layer thickness. Expert Expert 0 1/3 1/2 2/3 1 Ratio 0 1/3 1/2 2/3 1 Ratio Maximum Maximum 65.07% 73.05% 80.08% 75.60% 70.07% value 65.07% 73.05% 80.08% 75.60% 70.07% value Steady Steady 63.53% 70.07% 75.05% 72.62% 67.94% value 63.53% 70.07% 75.05% 72.62% 67.94% value

Table 7 shows the highest value of the results achieved in the test data and the final stable value Table 7 shows the highest value of the results achieved in the test data and the final stable value after convergence, based on the different expert ratios. As we can see from the test results, with the after convergence, based on the different expert ratios. As we can see from the test results, with the increase in the proportion of expert-driven learning, the accuracy of the model in terms of the test increase in the proportion of expert-driven learning, the accuracy of the model in terms of the test dataAppl. Sci.first2019 increases, 9, 3553 and then decreases. In addition, the models that do not adopt expert-driven19 of 29 data first increases and then decreases. In addition, the models that do not adopt expert-driven learning and completely adopt expert-driven learning do not achieve the highest accuracy. Clearly, learning and completely adopt expert-driven learning do not achieve the highest accuracy. Clearly, the relationship between the expert ratio and the prediction accuracy rate is not simply a positive thecompletely relationship adopt between expert-driven the expert learning ratio do and not the achieve prediction the highest accuracy accuracy. rate is Clearly, not simply the relationship a positive correlation. The loss function of 50% expert-driven learning and the training process is shown in correlation.between the The expert loss ratio function and the of prediction 50% expert-drive accuracyn ratelearning is not and simply the atraining positive process correlation. is shown The loss in Figure 32. When 50% expert-driven learning is applied, the stable value of the layer thickness Figurefunction 32. of When 50% expert-driven 50% expert-driven learning learning and the is training applied, process the stable is shown value in of Figure the layer 32. When thickness 50% prediction accuracy is 75.05%, and the highest value is 80.08%, which is the best model performance predictionexpert-driven accuracy learning is 75.05%, is applied, and the the stable highest value valu ofe theis 80.08%, layer thickness which is prediction the best model accuracy performance is 75.05%, in the test set, as shown in Figure 33. At this point, the prediction ability of the model for unknown inand the the test highest set, as valueshown is in 80.08%, Figure 33. which At this isthe point, best the model prediction performance ability of in the the model test set, for asunknown shown data is the greatest, which is consistent with the experience with the stratum type identification model. datain Figure is the 33 greatest,. At this which point, is the consistent prediction with ability the experience of the model with for the unknown stratum datatype isidentification the greatest, model. which Therefore, expert-driven learning can improve the prediction ability of the model and accelerate Therefore,is consistent expert-driven with the experience learning with can the improve stratum th typee prediction identification ability model. of the Therefore, model and expert-driven accelerate convergence, but it is not the rule that the higher the expert ratio is, the better the performance of the convergence,learning can improve but it is not the the prediction rule that ability the higher of the the model expert and ratio accelerate is, the better convergence, the performance but it is not of the model. model.rule that the higher the expert ratio is, the better the performance of the model.

Figure 32. Loss curve of 50% expert-driven learning. Figure 32. Loss curve of 50% ex expert-drivenpert-driven learning.

FigureFigure 33. 33. AccuracyAccuracy of of 50% 50% expert-driven expert-driven learning. learning. Figure 33. Accuracy of 50% expert-driven learning.

TheThe final final results results show show that that the the maximum maximum accuracy accuracy of of the the layer layer thickness thickness model model is is 80.85% 80.85% under under The final results show that the maximum accuracy of the layer thickness model is 80.85% under thethe 50% 50% expert expert ratio, ratio, which which accurately accurately predicts predicts the the layer thickness in the test data. the 50% expert ratio, which accurately predicts the layer thickness in the test data. 3.2.3. Verification of the Geostratigraphic Series Model 3.2.3. Verification of the Geostratigraphic Series Model 3.2.3. Verification of the Geostratigraphic Series Model To verify the true prediction ability of the geostratigraphic series model, the stratum data in the To verify the true prediction ability of the geostratigraphic series model, the stratum data in the test boreholeTo verify data the aretrue used prediction for practical ability testing, of the andgeostr theatigraphic differences series between model, the simulatedthe stratum series data output in the test borehole data are used for practical testing, and the differences between the simulated series testby the borehole model anddata the are real used geostratigraphic for practical testing, series are and compared. the differences Selected between examples the of simulated the real borehole series output by the model and the real geostratigraphic series are compared. Selected examples of the real outputstratum by conditions the model and and prediction the real geostratigraphic results of machine series learning are compared. are shown Selected in Table examples8. of the real borehole stratum conditions and prediction results of machine learning are shown in Table 8. boreholeTable stratum8 shows conditions that by comparing and prediction the prediction results of results machine of the learning model are with shown the real in Table borehole 8. data, the machine learning model based on the seq2seq architecture has a high accuracy in stratum type prediction. According to the statistics, in all data of the test set, the machine learning model accurately simulates 62.98% of the stratum types, and the similarity between the simulated sequence and the real stratum sequence is 72.16%. In addition, the accuracy rate of the stratum thickness prediction is 74.04%, which basically realizes the determination of the stratum thickness in the study area, as shown in Table9. In conclusion, the machine learning model based on a recurrent neural network can accurately simulate the real stratum situation in the study area, and its feasibility is verified. Appl. Sci. 2019, 9, 3553 20 of 29

Table 8. Comparison of the real borehole stratum and machine learning prediction results.

The Real Borehole Strata Prediction Results of Machine Learning Number Stratum Thickness Sequence Stratum Thickness Sequence Stratum Type Sequence Stratum Type Sequence (m) (m) 1 silt, clay 0.3, 3.9 floury soil, clay, plain fill within 3 m, within 3 m, 3–5 m 2 clay 2 clay within 3 m 3 miscellaneous fill 0.6 plain fill 5–10 m 4 plain fill, clay 3.1, 9.8 plain fill, clay within 3 m, 5–10 m miscellaneous fill, plain fill, mucky soil, within 3 m, within 3 m, within 3 m, within 3 5 miscellaneous fill, clay, mucky soil, plain fill, clay 1.2, 1.3, 1.5, 2.4, 13.3 plain fill, clay m, 10–20 m within 3 m, within 3 m, within 3 m, within 3 6 floury soil, silty clay, plain fill, clay, plain fill, clay 1.0, 0.5, 2.5, 1.2, 0.3, 3.6 floury soil, plain fill, clay, plain fill, clay m 5–10 m 7 miscellaneous fill, plain fill, clay 0.7, 3.0, 4.5 miscellaneous fill, plain fill, clay within 3 m, within 3 m, 3–5 m 8 miscellaneous fill, clay 0.6, 4.0 miscellaneous fill within 3 m 9 miscellaneous fill, plain fill, clay 0.5, 1.0, 11.9 miscellaneous fill, plain fill, clay within 3 m, within 3 m, 10–20 m 10 miscellaneous fill, clay 1.0, 9.8 miscellaneous fill, clay within 3 m, 5–10 m 11 miscellaneous fill, silt, plain fill, clay 4.1, 11.2, 7.0, 10.0 miscellaneous fill, plain fill, clay within 3 m, 10–20 m, 5–10 m 12 floury soil, plain fill, mucky soil, clay 0.5, 6.7, 1.2, 8.6 floury soil, plain fill, plain fill, clay within 3 m, within 3 m, within 3 m, 5–10 m 13 silt, clay 0.4, 6.6 floury soil, clay within 3 m, 5–10 m 14 silt, clay 0.4, 10.4 floury soil, clay within 3 m, 5–10 m 15 miscellaneous fill, silt, plain fill, clay 0.7, 1.9, 3.4, 24.0 miscellaneous fill, floury soil, plain fill, clay within 3 m, within 3 m, within 3 m, 20–30 m miscellaneous fill soil, plain fill soil, old city miscellaneous fill, floury soil, plain fill, old within 3 m, within 3 m, within 3 m, 5–10 m, 16 1.2, 2.6, 6.5, 13.0 miscellaneous fill soil, clay town fill, clay 10–20 m 17 miscellaneous fill soil, plain fill soil, clay 0.5, 2.8, 10.2 miscellaneous fill, plain fill, clay within 3 m, within 3 m, 10–20 m 18 miscellaneous fill soil, plain fill soil, clay 2.1, 0.8, 12.9 miscellaneous fill, plain fill, clay within 3 m, within 3 m, 10–20 m, Appl. Sci. 2019, 9, 3553 21 of 29 Appl. Sci. 2019, 9, x FOR PEER REVIEW 23 of 32

Table 8 shows that by comparing the prediction results of the model with the real borehole data, Table 9. Statistical results of the geostratigraphic series model simulations. the machine learning model based on the seq2seq architecture has a high accuracy in stratum type prediction.Stratum According Type Accuracyto the statistics, Average in all Sequencedata of the Similarity test set, the Stratummachine Thickness learning Accuracymodel accurately simulates 62.98%62.98% of the stratum types, and the 72.16% similarity between the simulated 74.04% sequence and the real stratum sequence is 72.16%. In addition, the accuracy rate of the stratum thickness prediction is 3.3.74.04%, Three-Dimensional which basically Geological realizes Modeling the determination and Testing of the stratum thickness in the study area, as shown in Table 9. 3.3.1. Three-Dimensional Geological Modeling Table 9. Statistical results of the geostratigraphic series model simulations. To further test the geostratigraphic series simulation effect based on machine learning, this section comparesStratum the Type geostratigraphic Accuracy series Average simulation Sequence method Similarity based on machine Stratum learning Thickness with the Accuracy traditional method based62.98% on 3D geological modeling. On72.16% the basis of the training data, a 3D 74.04% geological model of the research area is constructed by using the triangulated irregular network (TIN) 3D geological modelingIn conclusion, method [ 39the]. machine The 3D learning geological model model based is consistent on a recurrent with theneural real network strata at can the accurately borehole locations,simulate the and real it can stratum directly situation show the in complexthe study geological area, and structureits feasibility and theis verified. spatial distributions of the rock and soil masses comprehensively. 3.3. Three-DimensionalThe main steps for Geol theogical construction Modeling the and 3D Testing geological model in this study are as follows: 1. Drilling treatment: According to the geological conditions and drilling stratification data, the3.3.1. strata Three-Dimensional are classified and Geological integrated, Modeling and the strata are preliminarily sorted from top to bottom. 2.To Interpolation further test meshthe geostratigraphic generation: Using series Delaunay’s simulation triangulation effect based and on subdivision machine learning, algorithms, this asection TIN mesh compares is generated, the geostratigraphic as shown in Figure series 34simulation. method based on machine learning with the traditional3. Network method refinement: based on The 3D generated geological irregular mode triangularling. On interpolationthe basis of networkthe training is adjusted data, a until 3D thegeological accuracy model meets of the the requirements. research area is constructed by using the triangulated irregular network (TIN) 3D geological4. Uniform modeling drilling series:method All [39]. drilling The 3D holes geological are traversed model andis consistent a uniform with geostratigraphic the real strata seriesat the isborehole established locations, by considering and it can special directly stratum show conditions the complex such geological as missing structure data and reversals.and the spatial Then, accordingdistributions to theof the unified rock and geostratigraphic soil masses comprehensively. series, the original stratification of all borehole data is transformedThe main into steps a unified for the stratification construction ofthe the 3D borehole geological series, model as shownin this study in Figure are 35as .follows: If a stratum is not included1. Drilling in thetreatment: original According data of the to borehole, the geological its layer conditions thickness and is set drilling to zero. stratification data, the strata5. are Spatial classified interpolation: and integrated, For each and layer the ofstrata the uniformare preliminarily drilling series, sorted the from Kriging top to method bottom. is used to calculate2. Interpolation the elevation mesh at generation: the top and bottomUsing Delaunay’s of the layer triangulation in the interpolation and subdivision grid. If the algorithms, elevation of a theTIN top mesh layer is isgenerated, the same as as shown that of in the Figure bottom 34. layer, this layer does not exist. 6.3. StratumNetwork construction: refinement: IfThe the generated elevation of irregular the top and triangular bottom ofinterpolatio the stratumn network are different, is adjusted the top anduntil bottom the accuracy can be meets connected the requirements. with adjacent points to the interpolation point to form a stratum of the 3D model,4. Uniform as shown drilling in Figure series: 36 All. drilling holes are traversed and a uniform geostratigraphic series is established7. Inspection: by considering The generated special 3D stratum model isconditions inspected, such and as the missing model data is adjusted and reversals. according Then, to experienceaccording andto the geological unified characteristics.geostratigraphic series, the original stratification of all borehole data is transformed8. Model into generation: a unified Astratification 3D stratum of model the borehole is rendered, series, and as theshown redundant in Figure parts 35. If are a stratum removed, is whilenot included only the in research the original area isdata maintained. of the borehole, its layer thickness is set to zero.

FigureFigure 34.34. InterpolationInterpolation networknetwork diagram.diagram. Appl. Sci. 2019, 9, x FOR PEER REVIEW 24 of 32

Appl. Sci. 2019, 9, 3553 22 of 29 Appl. Sci. 2019, 9, x FOR PEER REVIEW 24 of 32

Figure 35. Unified geostratigraphic series diagram.

5. Spatial interpolation: For each layer of the uniform drilling series, the Kriging method is used to calculate the elevation at the top and bottom of the layer in the interpolation grid. If the elevation of the top layer is the same as that of the bottom layer, this layer does not exist. 6. Stratum construction: If the elevation of the top and bottom of the stratum are different, the top and bottom can be connected with adjacent points to the interpolation point to form a stratum of the 3D model, as shown in Figure 36. 7. Inspection: The generated 3D model is inspected, and the model is adjusted according to experience and geological characteristics.

8. Model generation: A 3D stratum model is rendered, and the redundant parts are removed, Figure 35. Unified geostratigraphic series diagram. while only the research areaFigure is maintained. 35. Unified geostratigraphic series diagram.

5. Spatial interpolation: For each layer of the uniform drilling series, the Kriging method is used to calculate the elevation at the top and bottom of the layer in the interpolation grid. If the elevation of the top layer is the same as that of the bottom layer, this layer does not exist. 6. Stratum construction: If the elevation of the top and bottom of the stratum are different, the top and bottom can be connected with adjacent points to the interpolation point to form a stratum of the 3D model, as shown in Figure 36. 7. Inspection: The generated 3D model is inspected, and the model is adjusted according to experience and geological characteristics. 8. Model generation: A 3D stratum model is rendered, and the redundant parts are removed, while only the research area is maintained.

Figure 36. Schematic diagram of stratum construction. Figure 36. Schematic diagram of stratum construction. The method to determine the boundary conditions of the model is as follows: According to boundaryThe method on the mapto determine of the study the boundary area, boundary conditions points of arethe selectedmodel is atas appropriatefollows: According distances. to Theboundary boundary on the points map areof the used study as area, the control boundary points points of are the selected estimated at appropriate stratigraphic distances. boundaries. The Then,boundary these points control are points used areas the connected control points successively of the estimated to form a closedstratigraphic polygon. boundaries. The closed Then, polygon these iscontrol used aspoints the boundaryare connected of the successively estimated to stratum. form a cl Afterosed polygon. determining The closed the estimated polygon stratigraphicis used as the boundary,boundary weof extendedthe estimated the area stratum. of the borehole After determining to the boundary the ofestimated the estimated stratigraphic stratum andboundary, eventually we establishedextended the the entirearea of 3D the geological borehole model. to the boundary of the estimated stratum and eventually establishedThe whole the processentire 3D of geological 3D geological model. model modeling, from borehole data processing to the final The whole process of 3D geological model modeling, from borehole data processing to the final generationAppl. of Sci. the 2019 model,, 9, x FOR is PEER shown REVIEW in Figure 37 below. 25 of 32 generation of the model, is shown in Figure 37 below. Figure 36. Schematic diagram of stratum construction.

The method to determine the boundary conditions of the model is as follows: According to boundary on the map of the study area, boundary points are selected at appropriate distances. The boundary points are used as the control points of the estimated stratigraphic boundaries. Then, these control points are connected successively to form a closed polygon. The closed polygon is used as the boundary of the estimated stratum. After determining the estimated stratigraphic boundary, we extended the area of the borehole to the boundary of the estimated stratum and eventually established the entire 3D geological model. The whole process of 3D geological model modeling, from borehole data processing to the final generation of the model, is shownFigureFigure 37.in 37.WorkflowFigure Workflow 37 ofbelow.of 3D geological geological modeling. modeling.

Finally, a 3D geological model of the research area is constructed (as shown in Figure 38) and sectioned. The stratum types and series after sectioning are shown in Figures 39–41.

Figure 38. Three-dimensional geological model. The 3D geological model software was developed by our own team.

Figure 39. Three-dimensional boreholes.

Figure 40. Borehole and stratum distributions. Appl.Appl. Sci.Sci. 20192019,, 99,, xx FORFOR PEERPEER REVIEWREVIEW 2525 ofof 3232

Appl. Sci. 2019, 9, 3553 23 of 29 FigureFigure 37.37. WorkflowWorkflow ofof 3D3D geologicalgeological modeling.modeling.

Finally,Finally, aa 3D3D3D geologicalgeologicalgeological modelmodel ofof thethe researchresearch areaarea isis constructedconstructed (as(as shownshown inin FigureFigure 3838)38)) andand sectioned.sectioned. The The stratum stratum types types and and series series after after sectioning sectioning areare shownshown inin FiguresFigures 3939–41.39–41.–41.

FigureFigure 38.38. Three-dimensionalThree-dimensional geologicalgeological model.model. The The 3D 3D geological geological model model software software was was developed developed by by ourour ownown team.team.

FigureFigure 39.39. Three-dimensionalThree-dimensional boreholes. boreholes.

Appl. Sci. 2019, 9, x FOR PEER REVIEWFigureFigure 40.40. BoreholeBorehole and and stratum stratum distributions.distributions. 26 of 32

Figure 41. Geological section.

3.3.2. Three-Dimensional Geological Model VerificationVerification At the samesame positionspositions as as the the data data in in Section Section 3.2.3 3.2., the3, the borehole borehole coordinate coordinate information information is input is input into theinto 3D the geological 3D geological model. model. Then Then the comparison the comparis predictionon prediction results results between between the 3D the geological 3D geological model andmodel the and real the borehole real borehole stratum stratum are obtained, are obtained, as shown as shown in Table in 10 Table. 10. Appl. Sci. 2019, 9, 3553 24 of 29

Table 10. Comparison of the real borehole stratum conditions and 3D geological modeling prediction results.

The Real Borehole Strata Prediction Results of 3D Geological Modeling Number Stratum Thickness Sequence Stratum Thickness Sequence Stratum Type Sequence Stratum Type Sequence (m) (m) 1 silt, clay 0.3, 3.9 clay, silt 0.3, 3.9 2 clay 2 miscellaneous fill 2.0 3 miscellaneous fill 0.6 miscellaneous fill 0.6 4 plain fill, clay 3.1, 9.8 miscellaneous fill 13.5 5 miscellaneous fill, clay, mucky soil, plain fill, clay 1.2, 1.3, 1.5, 2.4, 13.3 miscellaneous fill, clay, mucky soil, silt 1.2, 1.3, 3.9, 13.3 6 floury soil, silty clay, plain fill, clay, plain fill, clay 1.0, 0.5, 2.5, 1.2, 0.3, 3.6 plain fill, silt clay, silt, clay, silt 1, 0.5, 2.5, 1.2, 3.9 7 miscellaneous fill, plain fill, clay 0.7, 3.0, 4.5 miscellaneous fill, silt 0.7, 8.5 8 miscellaneous fill, clay 0.6, 4.0 miscellaneous fill 4.6 9 miscellaneous fill, plain fill, clay 0.5, 1.0, 11.9 miscellaneous fill, silt 0.5, 0.5 10 miscellaneous fill, clay 1.0, 9.8 miscellaneous fill 12.2 11 miscellaneous fill, silt, plain fill, clay 4.1, 11.2, 7.0, 10.0 miscellaneous fill, silt 2.8, 25.2 12 floury soil, plain fill, mucky soil, clay 0.5, 6.7, 1.2, 8.6 plain fill, silt 0.5, 16.5 13 silt, clay 0.4, 6.6 plain fill 7 14 silt, clay 0.4, 10.4 plain fill 10.9 15 miscellaneous fill, silt, plain fill, clay 0.7, 1.9, 3.4, 24.0 miscellaneous fill, plain fill, silt, silt 0.7, 1.9, 3.4, 24 miscellaneous fill soil, plain fill soil, old city miscellaneous fill, plain fill, old city 16 1.2, 2.6, 6.5, 13.0 1.2, 2.6, 22.5 miscellaneous fill soil, clay miscellaneous fill soil 17 miscellaneous fill soil, plain fill soil, clay 0.5, 2.8, 10.2 miscellaneous fill, plain fill 0.5, 13 18 miscellaneous fill soil, plain fill soil, clay 2.1, 0.8, 12.9 miscellaneous fill, silt, clay 2.1, 0.8, 12.9 Appl. Sci. 2019, 9, x FOR PEER REVIEW 28 of 32 Appl. Sci. 2019, 9, 3553 25 of 29 From Table 10, the 3D geological model performs poorly in terms of the number of layers, stratumFrom type, Table and 10, sequence the 3D geological similarity, model but performs it can better poorly predict in terms the of stratum the number thickness. of layers, When stratum the type,prediction and sequence of the stratum similarity, type butis accurate, it can better the corresponding predict the stratum thickness thickness. prediction When is the close prediction to the real of thevalue. stratum type is accurate, the corresponding thickness prediction is close to the real value. Some borehole data are randomly selected in the training set, and the borehole coordinate information isis inputinput intointo thethe 3D 3D geological geological model model to to obtain obtain the the stratum stratum sequence sequence prediction prediction results results of theof the borehole borehole points. points. According According to the to statistics,the statistics, in all in the all data the ofdata the of test the set, test the set, 3D the geological 3D geological model accuratelymodel accurately simulates simulates 30.78% of30.78% the stratum of the types,stratum and types, the similarity and the similarity between thebetween simulated the seriessimulated and theseries real and geostratigraphic the real geostratigraphic series is 32.27%. series Inis addition,32.27%. In the addition, accuracy the rate accuracy of the stratumrate of the thickness stratum is 64.52%,thickness as is shown 64.52%, in as Table shown 11. in Table 11.

Table 11. Statistics of 3D geological model prediction results.

Stratum TypeType Accuracy AverageAverage Sequence Sequence Similarity StratumStratum Thickness Thickness Accuracy Accuracy Similarity Accuracy 30.78% 32.27% 64.52% 30.78% 32.27% 64.52%

Comparing TablesTables9 and9 and 11, the11, predictionthe prediction results results histogram histogram of machine of machine learning andlearning 3D geological and 3D modelinggeological ismodeling obtained is in obtained terms of in the terms stratum of the type, stratum average type, series average similarity, series similarity, and stratum and thickness stratum accuracy,thickness asaccuracy, shown inas Figureshown 42 in. Figure 42.

Figure 42.42. ComparisonComparison histogram histogram of theof the prediction prediction results resu of machinelts of machine learning learning and 3D geologicaland 3D geological modeling. modeling. Figure 42 shows that there is a certain difference in accuracy between the geostratigraphic series modelsFigure based 42 shows on 3D that geological there is modelinga certain difference and machine in accuracy learning. between Generally, the geostratigraphic these two methods series canmodels describe based theon real3D geological stratum situation modeling well. and machine The model learning. based Generally, on machine these learning two methods has a good can simulationdescribe the e ffrealect stratum in terms situation of the stratum well. The type, model and allbased its corresponding on machine learning indexes has are a superiorgood simulation to those ofeffect the in traditional terms of 3Dthe geological stratum type, model. and The all machineits corresponding learning modelindexes provides are superior stratum to informationthose of the bytraditional predicting 3D thegeological layer thicknesses model. The within machine the stratalearning and model it is slightly provides more stratum accurate information than the 3Dby geologicalpredicting model.the layer thicknesses within the strata and it is slightly more accurate than the 3D geological model. 3.4. Evaluation of 3D Geological Modeling Based on the Geostratigraphic Series Model 3.4. EvaluationConsidering of 3D the Geological actual performance Modeling Based of the on machine the Geostratigraphic learning model Series in the Model prediction of the stratum typeConsidering and stratum thickness,the actual thisperformance study proposes of the anmachine evaluation learning algorithm model for in a the 3D geologicalprediction model.of the Instratum the absence type and of real stratum data guidance, thickness, the this learning study proposes results based an evaluation on the machine algorithm learning for modela 3D geological represent themodel. accuracy In the of absence geological of real modeling. data guidance, For any the geostratigraphic learning results series, based the on reliabilitythe machine evaluation learning process model isrepresent described the below. accuracy of geological modeling. For any geostratigraphic series, the reliability evaluationThe evaluation process is objectsdescribed are below. divided into a stratum type series and stratum thickness series. The geostratigraphicThe evaluation objects series modelare divided generates into outputa stratu inm the type same series position, and stratum including thickness stratum series. type andThe stratumgeostratigraphic thickness series series. model generates output in the same position, including stratum type and stratum thickness series. Appl. Sci. 2019, 9, 3553 26 of 29 Appl. Sci. 2019, 9, x FOR PEER REVIEW 29 of 32 Appl. Sci. 2019, 9, x FOR PEER REVIEW 29 of 32 The similarity of the stratum type series calculated by the edit distance algorithm is used as the The similarity of the stratum type series calculated by the edit distance algorithm is used as the evaluation index. evaluation index. Comparing the layer thickness series, if the 3D layer thickness is the same as the most likely Comparing the layer thickness series, if the 3D layer thickness is the same as the most likely thickness, the score is one; if the 3D layer thickness is the same as the second most likely thickness, thickness, the score is one; if the 3D layer thicknessthickness is the same as the second most likely thickness, the score is 0.5; otherwise, the score is zero. the score is 0.5; otherwise, the score isis zero.zero. The scores are added, and the score sum is divided by the 3D series length, which is then used The scores areare added,added, andand thethe score score sum sum is is divided divided by by the the 3D 3D series series length, length, which which is thenis then used used as as the layer thickness evaluation index. The average values of the type evaluation index and thickness theas the layer layer thickness thickness evaluation evaluation index. index. The The average average values values of of the the type type evaluationevaluation indexindex andand thickness evaluation index are calculated, and the reliability score of this point in the 3D geological model is evaluation index are calculated, and the reliability score of this point in the 3D geological model is obtained. If the reliability score is higher than 0.5, the simulation of the real stratum is considered to obtained. If the reliability score is higher than 0.5, the simulation of the real stratum is considered to be reliable. be reliable. The calculation process of this algorithm consists of two parts, the type evaluation index and the The calculation process of this algorithm consists of two parts, the type evaluation index and the layer thickness evaluation index. The reliability score is the average of these two indexes. The range layer thicknessthickness evaluationevaluation index. index. TheThe reliability reliability score score is is the the average average of of these these two two indexes. indexes. The The range range of of reliability scores calculated by this algorithm is [0,1], representing the matching degree between reliabilityof reliability scores scores calculated calculated by by this this algorithm algorithm is [0,1], is [0,1], representing representing the the matching matching degree degree between between the the evaluation object and the empirical cognition of the machine learning model. The higher the evaluationthe evaluation object object and theand empirical the empirical cognition cognition of the machineof the machine learning learning model. The model. higher The the higher reliability the reliability score is, the closer the evaluation object and the model are in predicting the stratum scorereliability is, the score closer is, thethe evaluation closer the objectevaluation and the object model and are the in predictingmodel are thein predicting stratum distribution the stratum of distribution of this point. thisdistribution point. of this point. The test borehole provides the real stratum data, and its evaluation result should be higher than The test borehole provides the real stratum data, and its evaluation result should be higher than that of the 3D model. Moreover, if the stratum distribution of a point in the 3D model is similar to the that of the 3D model. Moreover, Moreover, if the stratum distributiondistribution of a point in the 3D model is similar to the real situation, the scoring result will be similar to the result of the real stratum. To test the feasibility real situation, thethe scoringscoring resultresult will will be be similar similar to to the the result result of of the the real real stratum. stratum. To To test test the the feasibility feasibility of of the evaluation algorithm based on the 3D geological model, this study uses the algorithm to theof the evaluation evaluation algorithm algorithm based based on the on 3D the geological 3D geological model, model, this study this uses study the uses algorithm the algorithm to calculate to calculate the reliability score of the test borehole data and the 3D geological model. The calculation thecalculate reliability the reliability score of the score test boreholeof the test data borehole and the data 3D geological and the 3D model. geological The calculation model. The and calculation statistical and statistical results show that the average reliability score of the test borehole data is 0.6293, which resultsand statistical show that results the averageshow that reliability the average score reliabilit of the testy score borehole of the data test isborehole 0.6293, data which is is0.6293, higher which than is higher than that of the 3D geological model, as shown in Table 12. In addition, the reliability scores thatis higher of the than 3D that geological of the 3D model, geological as shown model, in as Table shown 12. in In Table addition, 12. In the addition, reliability the scoresreliability of the scores test of the test boreholes are mostly higher than 0.5, while those of the 3D geological model are mainly boreholesof the test areboreholes mostly are higher mostly than higher 0.5, while than those0.5, while of the those 3D geologicalof the 3D geological model are model mainly are below mainly 0.5, below 0.5, as shown in the Figures 43 and 44. asbelow shown 0.5, inas the shown Figures in the 43 Figuresand 44. 43 and 44. Table 12. Average reliability of the test borehole data and 3D geological model. TableTable 12. Average reliability of the test borehole datadata andand 3D3D geologicalgeological model.model. Test Borehole Data Three-Dimensional Geological Model Test BoreholeBorehole Data Data Three-Dimensional Three-Dimensional Geological Geological Model Model Average reliability 0.6293 0.3205 AverageAverage reliability reliability 0.6293 0.6293 0.3205 0.3205

Figure 43. Histogram of the reliability index frequency of the 3D geological model. Figure 43. Histogram of the reliability index frequency of the 3D geological model.

Figure 44. HistogramHistogram of the borehole data reliability index frequency. Figure 44. Histogram of the borehole data reliability index frequency. In conclusion, the evaluation method of 3D geological modeling based on the geostratigraphic In conclusion, the evaluation method of 3D geological modeling based on the geostratigraphic series model is feasible in this study. series model is feasible in this study. Appl. Sci. 2019, 9, 3553 27 of 29

In conclusion, the evaluation method of 3D geological modeling based on the geostratigraphic series model is feasible in this study.

4. Conclusions (1) In view of the disadvantages of the traditional simulation method of the structure of a geostratigraphic series, this study proposes a method based on the principle of a recurrent neural network. This method has the advantage of not relying on subjective factors such as assumptions and expert experience. Moreover, this approach can effectively evaluate geostratigraphic series simulation results in terms of characteristics such as the stratum thickness, stratum type, and stratum sequence. In the process of stratum simulation, utilizing expert-driven learning can improve both the learning efficiency and the predictive ability of the model. (2) A complete machine learning model for geostratigraphic series simulation is established, and a model-based 3D geological modeling evaluation method is designed. This study provides a novel approach for the simulation and prediction of geostratigraphic series with 3D geological modeling. This work has far-reaching practical significance for the accurate description of the spatial distributions of geological features and guidance of site selection, engineering construction, and environmental assessment. (3) The series model based on machine learning can describe the real situation at wells, and is a complimentary tool to the traditional 3D geological model. This study directly shows that machine learning is feasible and reliable in geostratigraphic series simulation. Additionally, our research provides new ideas and references for the popularization of machine learning in other fields of geology and engineering, especially 3D geological modeling.

Author Contributions: Z.L., conceptualization, methodology, data curation, formal analysis; writing—original draft preparation, writing—review and editing, project administration, funding acquisition; C.Z., conceptualization, methodology, writing—original draft preparation, supervision, project administration, funding acquisition; J.O., data curation, formal analysis, writing—original draft preparation and editing; W.M., writing—original draft preparation; Z.D., G.Z., formal analysis, writing—original draft preparation. Funding: This research presented is funded by the Provincial Science and Technology Project of Guangdong Province (Grant no. 2016B010124007), the Science and Technology Youth Top-Notch Talent Project of Guangdong Special Support Program (Grant no. 2015 TQ01Z344) and the Guangzhou Science and Technology Project (Grant no. 201803030005). Acknowledgments: The authors would like to thank the anonymous reviewers for their very constructive and helpful comments. Conflicts of Interest: The authors declare no conflict of interest.

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