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Engineering Geology

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Investigation of land subsidence with the combination of distributed fiberoptic sensing techniques and microstructure analysis of soils

Kai Gua,b,d,⁎, Bin Shia,⁎, Chun Liua,b, Hongtao Jiangc, Tianbin Lid, Jinghong Wua

a School of Earth Sciences and Engineering, Nanjing University, Nanjing 210046, ChinabNanjing University High-Tech Institute at Suzhou, Suzhou 215123, Chinac School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210046, Chinad State Key Laboratory of Geohazard Prevention and Geoenvironmental Protection, Chengdu University of Technology, Chengdu 610059, China

A R T I C L E I N F O

Keywords:Land subsidenceDFOS techniquesSEMMIPSu-Xi-Chang area

A B S T R A C T

Land subsidence is a global phenomenon caused by a variety of factors, and there are still many challenges inland subsidence monitoring. The results of an investigation of land subsidence using the combination ofDistributed Fiber Optic Sensing (DFOS) techniques and microstructure analysis of soil are here presented. TwoDFOS techniques: Brillouin optical time domain reflectometry (BOTDR) and fiber Bragg grating (FBG), weredeveloped in two boreholes (namely GM03 and SZ01) in Su-Xi-Chang area, China, to monitor movement ofstrata crossed by the borehole and pore pressure in some specific strata. Scanning electron microscope (SEM) andMercury intrusion porosimetry (MIP) were employed to quantify and characterize the microstructures of un-disturbed soils from the boreholes. Based on the BOTDR monitoring results, the annual levels of land subsidencein GM03 and SZ01 are ~1.2mm and ~2.9mm, respectively and the movement history of strata were clearlytracked. For both boreholes, the main contributors to subsidence are two aquitards (Ad2 and Ad3) that existadjacent to the second aquifer (Af2). The results obtained by FBG-based sensors indicate good agreement withBOTDR and it confirms that the deformation of aquifer is significantly controlled by pore pressure. SEM imageswere analyzed with Pore/Particle and Crack Analysis System (PCAS). Three statistical parameters includingaverage form factor (ffavg), fractal dimension (Df) and area probability distribution index (b), were used todescribe the geometric characteristics of pores. It is found that larger average form factor, larger fractal di-mension and higher proportion of large pores in soil microstructures favors land subsidence. MIP results confirmthat area probability distribution index can be used for the estimation of strata compression potential in landsubsidence investigations.

1. Introduction

Land subsidence has occurred in many countries, due to the ex-cessive withdrawal of groundwater and hydrocarbon fluids (Chai et al.,2004; Conway, 2016; Galloway and Burbey, 2011; Hu et al., 2004;Phien-wej et al., 2006; Teatini et al., 2005; Tosi et al., 2009; Xue et al.,2005; Ye et al., 2016; Zhang et al., 2014a). In China, Su-Xi-Chang areawhich is located in the lower reaches of the Yangtze River, has beensuffering from severe land subsidence due to extraction of groundwaterfor domestic and industrial uses since the 1960s (Shi et al., 2012; Shiet al., 2008; Shi et al., 2007; Wang et al., 2009). In 2002, the maximumcumulative subsidence of Su-Xi-Chang area reached 2.0m (Hu, 2011).Since 2005, great effort has been made to mitigate subsidence in thearea, including prohibitions of groundwater withdrawal and im-plementation of artificial recharge of aquifer systems. Currently, land

subsidence in this region has been reduced to rates< 10mm per year(mm/y) in most areas (Hu, 2011; Ye et al., 2016).

Implementation of monitoring networks is of great necessity tocontrol and prevent land subsidence. In 2012, the State Council of thePeople's Republic of China approved and the Ministry of Land andResources and the Ministry of Water Resources issued the “Nationalland subsidence prevention and control program (2011~2020)(NLSPCP)” aiming to implement monitoring networks in all subsidence-affected regions in China. In the Su-Xi-Chang area, a monitoring net-work has been installed using ground-based techniques (including le-veling, borehole extensometers and observation wells) and space-basedtechniques (including GPS and InSAR). More monitoring branch pointsusing these techniques are under construction since the NLSPCP ap-proval. It should be noted that, there are still challenges concerningthese monitoring techniques. Leveling, GPS and InSAR techniques

https://doi.org/10.1016/j.enggeo.2018.04.004Received 14 December 2016; Received in revised form 3 March 2018; Accepted 3 April 2018

⁎ Corresponding authors at: School of Earth Sciences and Engineering, Nanjing University, Nanjing 210046, China.E-mail addresses: gukai@nju.edu.cn (K. Gu), shibin@nju.edu.cn (B. Shi).

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provide land surface elevations without indicating the specific corre-sponding displacements of each aquifer and aquitard. Extensometerswhich provide precise and accurate measurements to an accuracy of0.01–0.1 mm are not only costly, but yield only data on displacementsbetween several pre-determined depth intervals. With respect to thecomplexity of aquifer-aquitard system and the slow subsidence rate inSu-Xi-Chang area, an alternative monitoring technique for land sub-sidence with low cost and high accuracy is required.

In the last few decades, distributed fiber optic sensing (DFOS)techniques have been significantly deployed in health monitoring ofcivil and geotechnical structures and early warning of geo-hazards(Lopez-Higuera et al., 2011; Selker et al., 2006; Shi et al., 2004; Sunet al., 2014; Zhu et al., 2012). The unique advantages of DFOS includeanti-electromagnetic interference, anti-corrosion, waterproof, long-dis-tance monitoring and others. For DFOS, the optical fiber is the sensor,and the light waves is the signal carrier. The distribution of strain,temperature and other parameters along the entire fiber can be ob-tained. Attempts at applications of DFOS techniques in land subsidencemonitoring were recently reported. Wu et al. (2015) investigated landsubsidence by applying DFOS in a borehole in Suzhou, China, and theyfound that DFOS can clearly identify compressed strata along the wholeborehole. Ikeda et al. (2015) used fiber optics to monitor in-situ com-paction in deep reservoirs although not many details were reported. Ascan be seen, DFOS enables better understanding of the deformationhistory, deformation level, and rate of each soil layer, which is essentialto evaluation of land subsidence.

Regarding the mechanism of land subsidence, fluid withdrawal isthe main cause. It induces an increase in effective stress on soil solidskeleton and eventually, the compression of soil. Macroscopic com-pression of soils attends the decrease in soil porosity (Chuhan et al.,2003; Revil et al., 2002; Tan et al., 2010). With the irreversible re-duction in the porosity of some soil layers, soil compression often slowsdown and finally stop. Gong et al. (2009) investigated the microscopiccharacteristics of soft clay in Shanghai and found that it had pre-dominant effects on land subsidence even without fluctuations ingroundwater level. Cui and Tang (2010) also illustrated the relationshipbetween pore structure and land subsidence in Shanghai. Pore mor-phology and pore size distribution have been shown to have markedinfluence on the compressibility of soils (Griffiths and Joshi, 1991;Griffiths and Joshi, 1989a; Hong et al., 2006; Low et al., 2008). Scan-ning electron microscope (SEM) images and mercury intrusion por-osimetry (MIP) are frequently used in parallel to investigate micro-structural characteristics of soils (Delage, 2010; Hong et al., 2006; Liand Zhang, 2009; Monroy et al., 2010; Romero, 2013; Yanful andSimms, 2004). SEM provides visualized information on the

microstructure of soils although there are still some difficulties in re-presenting individual pores in three dimensions. Quantification andcharacterization of pores, using image processing rather than observa-tion by naked eyes, involves averaging procedure, perhaps with ac-ceptable imprecision. MIP provides data on the pore size distribution(PSD) of soil and can sometimes, be used to estimate volume change aswell.

The paper is focused on provision of better understanding of landsubsidence mechanisms using a combination of DFOS techniques andmicrostructural analyses of soil. DFOS was used to obtain the spatialand temporal distributions of complex soil layer deformations in twoboreholes located in the Su-Xi-Chang area. SEM and MIP analyses wereperformed on undisturbed soil samples from the two boreholes.Processing of SEM images was performed using the Pore/Particle andCrack Analysis System (PCAS). Linking of soil deformation to micro-structural changes allows estimation of land subsidence potential, withthe ultimate aim of devising effective control measures.

2. Boreholes for land subsidence monitoring

In January 2015 and July 2012, two boreholes namely GM03 andSZ01were drilled for an integrated investigation of land subsidence inGuangming Village, Wuxi, and Shengze Middle School, Suzhou, re-spectively (Fig. 1). They are two points in the monitoring network in-stalled in this area since NLSPCP. GM03 is 148m in depth and 129mmin diameter, and SZ01 is 200m in depth and 129mm in diameter. Thegeological record of the area reveals that the deposits crossed by in thetwo boreholes are mainly marine and continental sediments with re-spect to the periods of marine transgression and regression since theQuaternary. The complex aquifer-aquitard system consists of inter-calated sands, silts, silty clays and clays, which make precise delinea-tion of strata rather difficult. Fig. 2 illustrates geomedia stratificationand the aquifer division of GM03 and SZ01. There are three aquitardsand two confined aquifers in GM03, and four aquitards and threeconfined aquifers in SZ01. However, both the aquitards and the aquifersare inhomogeneous in lithology as illustrated in Fig. 2 which alsopresents the thickness of each strata and associated intercalated soillayers.

3. Distributed fiber optic sensing techniques for land subsidencemonitoring

Generally, DFOS can be classified into two categories: (1) full-dis-tributed techniques based on Rayleigh, Brillouin and Raman scatteringsuch as Rayleigh optical time-domain reflectometry (ROTDR), Rayleigh

Fig. 1. Location of the boreholes in Wuxi and Suzhou, China.

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optical frequency-domain reflectometry (ROFDR), Brillouin opticaltime domain reflectometry/analysis (BOTDR/A), Brillouin optical fre-quency domain analysis (BOFDA), etc. and (2) quasi-distributed tech-niques such as fiber Bragg grating (FBG). In this study, BOTDR andFBG-based strain and stress sensors were used.

3.1. Working principle of BOTDR and optical fiber sensor installed

For BOTDR, the variation of strain on the optical fiber causes afrequency shift of the Brillouin back-scattered light. The relationshipbetween the shift and the strain is expressed as:

= + ⎛⎝

⎞⎠

ν ε ν dνdε

ε( ) (0)B BB

(1)

where νB(ε) is the Brillouin frequency under the strain (ε), νB(ε) is theBrillouin frequency shift without strain (ε); and dνB/dε is strain coeffi-cient and the proportional coefficient of strain at a wavelength of1.55 μm is about 0.5 GHz/% (strain). Based on the principle providedby Eq. (1), the strain distribution along the sensing optical fiber can bemeasured. The Brillouin frequency shift is also affected by temperaturebut that can be ignored when the temperature change is smaller than2 °C (Zhang et al., 2004).

In land subsidence, deformation of the sensing cable caused by soilcompression or rebound can be calculated from the obtained strainaccording to the following equation:

∫=D ε h dhΔ ( )h

h

1

2

(2)

where ΔD is the deformation (elongation or compression) from depth h1to depth h2, and ε(h) is the strain along the depth.

In this study, a metal-reinforced single-core cable (MRC) was usedto monitor strata deformation. It was developed and produced byNanzee Sensing Technology Ltd. Suzhou. Calibration on MRC wasperformed by Lu (2013) using the BOTDA technique. It has a mea-surement accuracy of± 7 με. Fig. 3 presents the comparison betweenthe calculated deformation based on strain data on MRC and the actualdeformation. The sheath and metal reinforcement caused small strainlosses on the fiber, therefore a slightly smaller calculated deformationwas obtained Lu (2013). Noting that ~6% strain loss occurs when usingMRC, it should be accounted for during data processing.

3.2. Working principle of FBG and FBG-based sensors

A change in axial strain or temperature alters the Bragg wavelengthand can be estimated by:

= − + +λλ

p ε α ξ TΔ (1 )Δ ( )ΔB

B

eff(3)

where ΔλB is the change in the Bragg wavelength due to applied strainand temperature changes, λB is the original Bragg wavelength under astrain-free and 0 °C condition, peff is the photo-elastic parameter and αand ξ are the thermal expansion and thermo-optic coefficients, re-spectively. Commercial FBG sensors usually have calibration functionson objective parameters and measured wavelength, therefore stress,temperature and others applied on FBG sensors can be calculated.

Two kinds of FBG-based sensors obtained from Nanzee SensingTechnology Ltd. Suzhou were used in this study: (1) FBG-based ex-tensometer and (2) FBG-based osmometer. The technical parameters forthese sensors are presented in Table 1. For the FBG-based extensometer,

Fig. 2. Segmentation of aquifer-aquitard system and strata distribution inGM03 and SZ01.

Fig. 3. Calibration of metal-reinforced single-core cable (MRC).

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the relationship between the measured displacement and the wave-length is:

= − − −D K P P P P[( ) ( )]s s T T0 0 (4)

where D is the measured displacement, Ks is a constant about the ratioof displacement and Bragg wavelength, Ps is the measured Bragg wa-velength, P0 is the initial Bragg wavelength of sensor, PT is the wave-length caused by temperature, and PT0 is the initial wavelength causedby temperature.

For the FBG-based osmometer, the relationship between the mea-sured pore pressure and the wavelength is:

= −p K P P( )0 (5)

where p is the pore pressure, K is a constant about the ratio of porepressure and Bragg wavelength, P is the measured Bragg wavelength, P0is the initial Bragg wavelength of the sensor.

3.3. Demodulators

The BOTDR analyzer used in this study is N8511 Optical FiberTemperature/Strain Analyzer which was produced by ADVANTEST Co.Ltd. of Japan. Its minimum spatial resolution is 1m, the readout re-solution accuracy is 5 cm, and the strain measurement accuracyis± 50 με. Although BOTDR is of lower accuracy than BOTDA, itssingle-ended monitoring model is more suitable for field investigations.The SM125 FBG demodulator produced by Micron Optics InternationalCo. Ltd., America, was used to obtain FBG data. SM125 is a kind of fullspectrum measurement equipment with a precision up to 1 pm.

3.4. Monitoring scheme on land subsidence using DFOS

Fig. 4 illustrates the general monitoring scheme using DFOS tech-niques. A borehole for monitoring was drilled to bedrock. Then thesensing cable was installed in a borehole using a heavy guider whichcould drag the cables down to the bottom and also keep cables tightduring installation. The borehole was then backfilled with a mixture ofgravel, sand and clay to its brim. As long as it was ensured that thebackfill in the borehole was compacted to stability which could take upto several weeks, the initial strain of the embedded cables was mea-sured. It should be noted that pretensioning must be performed to in-crease tension strain on cables. Therefore, in this project, the cableswere kept in tension during the whole installation process.

4. Strata deformation characterization

The initial strain distributions on the cable in GM03 and SZ01wererecorded on March 9, 2015 and December 25, 2012, respectively. Theseinitial stain data were subtracted from the latter data when calculatingthe soil deformation of the monitoring period. The data acquisitioninterval was about 1–3months. It should be noted that, strain data atshallow depth (< 13.5m) was not adopted here due to the weak cou-pling of cable and soil within this depth.

4.1. Results from GM03

Fig. 5a shows the spatial and temporal deformation features inGM03. Generally, strata deformation in GM03 was not significant. No

clear variation of strain can be detected from Ad1 to Af1 (13.5–78.1 m).Distribution of negative strain (in the range of 0 ~ 150 με) was observedin strata deeper than 78.1m (Ad2, Af2 and Ad3) which suggests thatthere was slight compression within this range. Deformation data ob-tained by BOTDR and FBG from 50m to 65m is presented in Fig. 5b.There is generally good agreement between the deformations obtainedby the two techniques although slight differences are observed whichcould be due to the slight inaccuracies of both BOTDR and FBG in smalldeformation measurements.

Fig. 6 shows the total deformation along the borehole. As was ex-pected, the measurements reveal seasonal fluctuations in the totalsubsidence, which is consistent with the results of earlier studies in thisarea (Shi et al., 2007). In the first spring, the subsidence increasedsignificantly and reached 6.2mm in the summer (July 2015). In thefollowing autumn and winter, the level of subsidence gradually de-creased with the rebound of some strata and reached a peak in April2016. Thereafter subsidence increased. Previous studies indicate thatthe variation of subsidence is attributed to the fluctuations in ground-water level (Chai et al., 2004; Chen et al., 2003; Zhang et al., 2014b).The groundwater level fell in the spring and summer, inducing largereffective stresses on the soil skeleton than in the autumn and wintermonths when the groundwater level rose. Fig. 6 also shows the de-formation of each strata calculated from strain measurements. It is clearthat the subsidence in GM03 is mainly attributable to the deformationof Ad2, Af2 and Ad3. Table 2 is a summary of the temporal variation ofdeformation of each strata with the monthly and annual rates calcu-lated. As revealed, Ad2, Af2 and Ad3 had similar contribution to thesubsidence from summer to autumn (i.e. from July to December). AfterNovember 2015, however, the compression of Ad2 decreased until arebound occurred on January 26, 2016. Compression of Af2 and Ad3reduced after the summer until the next April, indicating a slight re-bound during this period, but there was still some irreversible com-pression. Slight difference on the rates of deformation can be observedamong Ad2, Af2 and Ad3. But they presented good synchronization inthe trend of deformation (i.e. compression in spring and summer andrebound in autumn and winter). For the whole soil profile, the annualcompression was very small, only ~1.2 mm. Ad2 represents elasticdeformation since it almost recovered to the initial thickness after aone-year time period. Af2 and Ad3 were the effective contributors tothe observed subsidence.

4.2. Results from SZ01

The strain distribution in SZ01 during the three-year time period isshown in Fig. 7. The compression mainly occurred in Ad2 and Ad3,which were adjacent to Af2. The strain distribution shows clear tran-sition at the interfaces of Ad2-Af2 and Af2-Ad3. Fig. 8 illustrates thetotal and individual deformation of strata. A much severer subsidencecan be observed here than in GM03. The total compression also fluc-tuated with changing seasons. While the compression could occur al-most during the entire year, the rebound only occurred within a shortperiod (i.e. January 2014 to March 2014, and March 2015 to June2015), which was quite different from GM03. In SZ01, the 13.3 m thickAf2 was the pumping confined aquifer that and presented a smallcompression, which turned out to be elastic. In this area, the ground-water withdrawal was most severe in the summer but the compressionof Ad2 and Ad3 could last until the winter due to the low hydraulicpermeability. Also, the maximum deformation of Ad2 and Ad3 occurredaround winter or spring, followed with a slight rebound. Much of thecompression in Ad2 was observed to be irreversible as the level of re-bound only accounted for ~17%–30% of the strata's deformation. Incontrast, nearly half of the deformation of Ad3 was reversible. Themonthly and annual deformations of all strata are summarized inTable 3. The different changes in each strata due to the diversity inlithology and initial thickness, were identified. Ad3 had smaller com-pression speed and larger rebound speed than Ad2, due to a small

Table 1Technical parameters of FBG-based extensometer and osmometer.

Extensometer Osmometer

Scale 0–50mm 0–600 kPaAccuracy 0.05mm 0.6 kPaReflectivity ≥90% ≥90%Sensor size Φ26 * 346mm Φ50 * 22mm

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hydraulic gradient for drainage. Also, the annual deformation of theprofile was ~2.9 mm, much larger than that of GM03 but still at a veryslow level. The annual deformation of Ad2 was almost three times thatof Ad3.

The deformation and pore pressure within Af2 was obtained usingthe FBG-based extensometer installed at 73.9–88.9m and the FBG-based osmometer installed at 88.9 m, respectively. Fig. 9 representscorrelations of the deformation of Af2 (specifically, 73.9–88.9m) withthe pore pressure. It is clear that soil pore pressure has significant in-fluence on the deformation of Af2. There is likely a good correspondingrelationship between the increments of deformation and pore pressureas illustrated in Fig. 9b: a positive incremental pore pressure usuallycorresponds to a negative incremental displacement and vice versa. Thedata recorded by FBG sensors agree well with the mechanism of landsubsidence (i.e. the decrease in pore pressure causes compression of soiland the increase in pore pressure mitigates soil compression) and de-monstrate that land subsidence monitoring using FBG sensors is rea-sonably accurate.

5. Soil microstructure characterization

In order to obtain data on the microstructural characteristics ofsoils, undisturbed soil samples were collected during the drilling ofboreholes and prepared for SEM and MIP analyses. In all, 32 and 33samples were collected from GM03 and SZ01, respectively. Fig. 10ashows the grain size distributions of samples from the two boreholes.The clay separates for total 55 samples varied between 3% and 45%.

Fig. 10b shows the uniformity coefficient (Cu) and curvature coefficient(Cc) of samples. Samples with data in the gray area with Cu larger than5 and Cc between 1 and 3 are of good sorting while others are not. Soilsthat are poorly sorted tend to exhibit diversity in microstructure.

5.1. Scanning electron microscope (SEM) analysis

5.1.1. MethodThe samples used for SEM investigation (~20×5×5mm in size)

were carefully cut from sample blocks using a wire saw. Then, theywere freeze-dried followed by vacuum drying for 48 h. The SEM imageswere captured with a Hitachi SU3500 instrument. To quantitativelyanalyze the pores in these images, image processing and statisticalmethods were applied. The Particle/Pore and Crack Analysis System(PCAS) which can characterize and quantify the micro-porosity ofsamples was used. The details of the system has been published by Liuet al. (2011). Fig. 11 illustrates shows an example of the SEM imageprocessing using PCAS. A magnification of 1000 of the images wasadopted here according to the MIP results (shows later). At this mag-nification, the side length of each pixel is 0.1 μm which ensures thatmost of the pores are recognized by the analysis system. During thesegmentation of an image, a threshold (T) was carefully selected totransfer the original image (Fig. 11a) to the binary image (Fig. 11b). Ascan be seen from Fig. 11b, a SEM image of soils tend to be complex withpores of diverse sizes. At a certain magnification, some very small pores(e.g. intra-aggregate pores) cannot be accurately identified such thatthe statistics of pore sizes in an image could be skewed. The PCAS

Fig. 4. Monitoring scheme for land subsidence using DFOS technologies. (a) Sensing cable installation; (b) Metal-reinforced single core cable for full-distributedmonitoring; (c)&(d) FBG-based osmometer and FBG-based extensometer for quasi-distributed monitoring; (e) Heavy guider for sensor installation.

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allows the selection of a minimum analyzed pore area (so called“minimum area”), under which small pores in binary SEM image will beremoved, and hence intra-aggregate pores which are not significantlyaffected by the mechanical compression and noise can be removed.Fig. 11c and Fig. 11d show an example provided by changing minimumarea from 50 pixels to 300 pixels. Finally, every pore larger than theminimum area can be recognized by the system with information onarea, perimeter, length, width and long-axis direction.

Three statistical parameters were used to describe the geometriccharacteristics of the pores, namely average form factor (ffavg), areaprobability distribution index (b) and fractal dimension (Df).

1. The form factor describes the circularity and roughness of individual

pores with a maximum value of 1. The average form factors (Eq. (6))evaluates the complexity of pores in the profile. The smaller ffavg,the more complex the pores.

=∑ =ff

πS Cn

4 /avg

in

i i12

(6)

where n is the number of pores in SEM image, S is pore area and C ispore perimeter.

2. Df was developed with the purpose of describing shapes of variouscomplexities (Cox and Budhu, 2008; Dathe et al., 2001; Moore andDonaldson, 1995). It gives the autocorrelation of the profile or therate of change in length (perimeter) in response to a change in the

Fig. 5. Strain distribution obtained by BOTDR in GM03 from April 2015 to July 2016.

Fig. 6. Total and individual time-related deformation distributions of each stratum in GM03.

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scale of measurement (area). The relationship between Df and ff isshown in Eq. (7).

= −ff aS Df1 (7)

where a equals to 4π-10-2c, c is a constant.

3. The concept of b can be generally illustrated by Fig. 12. If pore areasare divided into several area ranges (e.g. 0–a1, a1–a2, a2–a3, etc.),the probability density of pores within a specific area range can bedetermined by counting the number of pores with area values thatfall within each area range. The probability distribution function, f(S), describes the density of pores within a specific area range, ex-pressed by the power function:

= −f S aS( ) b (8)

where b is the area probability distribution index and a is determinedby b according to the following equation:

= −−− −a b

S1

(2 1)n b b(1 )0

1 (9)

5.1.2. Results on SEM analysisThe change of micropores due to compression or consolidation has

been well documented (Aplin et al., 2006; Delage, 2010; Delage andLefebvre, 1984; Griffiths and Joshi, 1991; Griffiths and Joshi, 1990;Griffiths and Joshi, 1989b). When a pressure increment was applied tothe soil, the largest pores in the sample changed in shape and pro-gressively decreased in size. Also, the profile of the pore became pro-gressively smoother. On the other hand, the smaller pores remainedunaffected by the mechanical processes. In this process, the form factordescribes the shape of pores. The area probability distribution indexdescribes the pore area distribution. The smoothing effect on the profileof pores derived by increasing pressures on soil is reflected in theprogressively decreasing values of fractal dimension.

5.1.2.1. Form factor. Fig. 13 shows the average form factors (ffavg) ofsamples from GM03 and SZ01. Within each main strata, the meanvalues were also calculated. Generally, ffavg ranges from 0.35 to 0.50.The diversity in lithology induced a scattered distribution of formfactors. For GM03, significant larger ffavg are observed in the deeperthree layers (i.e. Ad2, Af2 and Ad3) than in the shallower two layers

Table 2Amount and rate of deformation of each stratum in GM03.

Date Deformation (mm) Monthly deformation (mm/m) Annual deformation (mm/y)⁎

Total Ad2 Af2 Ad3 Total Ad2 Af2 Ad3 Total Ad2 Af2 Ad3

2015/4/10 −0.3 0.02 −0.26 −0.04 – – – – −1.19 −0.01 −0.55 −0.672015/6/14 −4.3 −1.19 −1.43 −1.51 −1.82 −0.56 −0.54 −0.682015/7/31 −6.2 −1.86 −2.10 −1.87 −1.26 −0.43 −0.43 −0.232015/11/1 −4.8 −1.72 −1.42 −1.28 +0.47 +0.05 +0.22 +0.192016/1/26 −2.0 −0.57 −1.00 −0.70 +0.95 +0.40 +0.15 +0.202016/4/29 −1.5 0.02 −0.81 −0.71 +0.18 +0.19 +0.06 +0.002016/7/7 −4.0 −0.64 −1.45 −1.21 −1.08 −0.29 −0.28 −0.22

Note: “−” indicates compression and “+” indicates rebound.⁎ Calculated from 2015/4/10 to 2016/4/29.

Fig. 7. Strain distribution obtained by BOTDR in SZ01 from March 2013 to April 2016.

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(i.e. Ad1 and Ad1). This implies that micropores in the deeper threelayers are generally of higher level of circularity. In SZ01, ffavg of Ad2and Ad3 was 0.454 and 0.458, respectively, and slightly larger than theform factor of Af2. This also indicates that micropores in Af2 arerougher than pores in Ad2 and Ad3.

5.1.2.2. Fractal dimension. Small pores are usually rounder with largerform factors than larger pores. When pore area increases, the shapebecomes more complex and irregular. As illustrated by Eq. (7), thefractal dimension (Df) actually describes the variation of porecomplexity when the pore area increases. Fig. 14 shows the fractaldimension of soil samples determined by PCAS. The average values ofeach strata are also presented. For GM03, the deeper three strata showlarger fractal dimension (i.e. 1.184, 1.194 and 1.205) than theshallower two layers (1.183 and 1.177). Large pores which weremore compressible, are also rougher in Ad2, Af2 and Ad3. As for soilsin SZ01, however, an opposite trend is observed. Df of Ad2 and Ad3 issmaller than Af2, where deformation was insignificant. It is attributableto the relatively small density of sample from Af2 analyzed whichconsisted of 25% clay and 75% silt. The compressibility is smaller thanthose of soils in the aquitards. The high proportion of silt may decreasethe density of soil and therefore, increase the fractal dimension,according to Moore and Donaldson (1995) who compared fractal

dimensions of soil sections with different comparative densities andfound that decreasing comparative density caused increase in porefractal dimension.

5.1.2.3. Area probability distribution index. Delage and Lefebvre (1984)found that the compression index of a natural clay is related to thelargest existing pore sizes at a given consolidation pressure increment.Since large pores are more susceptible to mechanical processes, thequantification of large pores in microstructures is essential tounderstanding of the microscopic deformation. The area probabilitydistribution indices of samples from GM03 and SZ01 are given inFig. 15. By calculating the mean value of samples within each mainlayer, smaller area probability distribution indices of Ad2, Af2 and Ad3(1.90, 1.73 and 1.89, respectively) than Ad1 and Af1 (2.09 and 2.02,respectively) can be observed. This illustrates that the deeper threelayers had higher large area pore probability density in themicrostructure, according to the concept of area probabilitydistribution index (see Fig. 12). It agrees well with the observedlarger deformation of the deeper three layers relative to the shallowerlayers detected by DFOS (Fig. 5). Soils in the phreatic layer are loose,hence, the small area probability distribution indices obtained althoughthere is no reliable deformation data. SEM samples in SZ01 were mademainly from Ad2, Af2 and Ad3. The average area probability

Fig. 8. Total and individual time-related deformation distributions of each stratum in SZ01.

Table 3Amount and rate of deformation of each stratum in SZ01.

Date Deformation(mm) Monthly deformation (mm/m) Annual deformation (mm/y)⁎

Total Ad2 Af2 Ad3 Total Ad2 Af2 Ad3 Total Ad2 Af2 Ad3

2013/3/8 +0.5 −0.18 +0.43 +0.81 – – – – −2.91 −2.27 −0.30 −0.742013/4/22 −1.3 −1.03 +0.88 −0.12 −1.22 −0.57 +0.30 −0.622013/5/22 −2.6 −1.47 −0.26 −0.06 −1.29 −0.44 −1.14 +0.062013/9/11 −11.8 −5.84 −2.09 −3.23 −2.46 −1.17 −0.49 −0.852013/12/26 −14.0 −7.12 −1.44 −4.13 −0.63 −0.36 +0.18 −0.262014/3/9 −7.5 −5.88 0.24 −1.81 +2.66 +0.51 +0.69 +0.962014/4/29 −9.0 −6.25 −0.44 −1.99 −0.89 −0.22 −0.40 −0.112014/7/11 −12.1 −7.29 −1.50 −2.79 −1.24 −0.43 −0.44 −0.332014/11/13 −13.5 −8.50 −1.49 −3.33 −0.34 −0.29 0.00 −0.132015/3/5 −16.0 −9.66 −1.27 −4.14 −0.68 −0.31 +0.06 −0.222015/4/11 −13.6 −7.83 −1.30 −3.57 +1.95 +1.49 −0.02 +0.462015/6/4 −11.0 −7.69 −0.56 −2.42 +1.45 +0.07 +0.41 +0.642015/11/5 −13.7 −8.90 −0.69 −3.90 −0.53 −0.24 −0.02 −0.292016/1/9 −12.7 −8.67 −0.57 −3.21 +0.47 +0.11 +0.05 +0.322016/4/11 −9.9 −7.79 −0.01 −2.31 +0.88 +0.29 +0.18 +0.29

Note: “−” indicates compression and “+” indicates rebound.⁎ 2013/9/11–2013/12/26 data was not presented due to small variation.

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distribution indices of the three strata also indicates that there are morelarge pores in Ad2 and Ad3 and therefore, these two strata were morecompressed than Af2.

5.2. Mercury intrusion porosimetry (MIP) measurements

5.2.1. MethodThe basic concept of MIP technique is that a non-wetting fluid, such

as mercury, does not enter a porous medium unless a pressure applied.

The equivalent pore entrance size diameter d is related to the appliedpressure p through the Washburn equation assuming cylindrical poresin the calculation:

= −p σ θd

4 cos

where σ is the surface tension of the intruding liquid (0.484 N/m formercury) and θ is the mercury-soil contact angle (140° is adopted here).

MIP measurements were performed using an AutoPore IV 9500Micromeritics with a maximum injection pressure of 275MPa. Samplesfor MIP testing (~1–2 g) were carefully cut from the sample blocksusing a wire saw. Then, they were freeze-dried followed by vacuumdrying for 48 h.

5.2.2. Results on MIP analysisFig. 16 shows the MIP results of four silty clay samples and three

clay samples, respectively. Data on sorting of these samples all plot inthe red box in Fig. 10, indicating that they have similar grain sizedistribution curves. The highly saturated undisturbed soils display es-sentially unimodal PSD curves. For silty clay samples, GM13 and GM22have a similar predominant pore diameter of ~0.5 μm. GM23 andGM40 have larger predominant pore diameter, ~3 μm and ~2 μm, re-spectively. This implies that GM13 and GM22 have smaller large poreprobability density than GM23 and GM40. The clay samples generallyhave smaller predominant pore diameters, ~0.5 μm, ~0.02 μm and~0.3 μm for GM25, GM32 and GM47, respectively. For these samples,the pore probability densities on the right side of the predominant porediameters are at low levels.

As mentioned above, the minimum pore area is 50 pixels in SEManalysis (the side length of each pixel is 0.1 μm), hence, the pores ob-served in the images have a minimum entrance pore diameter of 0.8 μmassuming that they are round. In this case, the entrance diameter ofpores characterized by PCAS in SEM image should be larger than 0.8 μmin MIP. Previous studies have reported that there is qualitative agree-ment between the size of pores measured by SEM and MIP (Bartoliet al., 1999; Yanful and Simms, 2004), although MIP usually reportssmaller pore sizes than obtained in two-dimensional SEM analysis be-cause MIP measures pore entrance diameter. Fig. 17 illustrates the re-lationship between area probability distribution index by SEM and theproportion of intruded mercury with diameter larger than 0.8 μm to thetotal intruded mercury. Samples with smaller area probability dis-tribution indices have greater density of pores with large pore sizediameter than those with larger indices. Since smaller area probabilitydistribution index indicates greater quantity of large pores, there isgood agreement between the area probability distribution index from

Fig. 9. Correlation of deformation and pore pressure in Af2. (a) Relation be-tween deformation and pore pressure; (b) Relation between incremental de-formation and incremental pore pressure, the incremental value was the dif-ference between the monitoring date and its previous date.

Fig. 10. Physical properties of soil samples from GM03 and SZ01, (a) Grain size distributions; (b) uniformity coefficient vs curvature coefficient.

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SEM and the pore size distribution from MIP.Delage (2010) suggested a parameter, Cp, that defines the slope of

the cumulative PSD curve at its inflection point. This is obtained from apore size diameter/intruded mercury void ratio figure with a reverse x-axis as shown in this study. This parameter characterizes the densityfunction of the pore population: the higher the value of Cp, the bettergraded the pore distribution (Juang and Holtz, 1986). The area prob-ability distribution index (b) has a similar significance since it alsocharacterizes the distribution density of pores. The relationship be-tween Cp and b is presented in Fig. 18, where a good negative corre-lation between these two parameters is observed. The significant dif-ference in Cp between the silty clay samples and clay samples is evidentin the better pore size grading in clay soils. Delage (2010) also inter-preted the linear relationship between the density function coefficient,Cp and the compression coefficient, Cc when he investigated the com-pressibility of some Eastern Canada sensitive clays using the micro-structural approach. Unfortunately, the relationship between Cc and bcannot be evaluated in this study due to the absence of compressioncoefficient of soils represented, but they appear to have a negativecorrelation (i.e. a small b corresponds to a large Cc).

6. Discussion

As demonstrated by the results presented above, the application ofDFOS techniques in land subsidence monitoring allows the observationof the movement of strata at any depth along a borehole with the ad-vantage of enabling in distributed monitoring. It is like installing“neuro” for strata to record changes of strain, temperature and othersalong optical fibers. However, this work focuses on the deformation ofthe main aquitards and confined aquifers. For the case of multi-layeredaquifer-aquitard systems, more details about the source layers of thesubsidence would provide a better picture about deformation amountand rate, contribution of each strata to total deformation, and eventransition interfaces (Fig. 7), hence a better understanding on theprocess of land subsidence. Most importantly, these results also wouldbe very useful to accurate land subsidence numerical simulation, gov-ernment policy-making and other engineering related issues.

As demonstrated herein, observation of soil microstructures usingSEM and MIP provides good understanding of the movement of strataobtained by DFOS techniques because the microstructure affects thecompressibility of soils. Generally, there are good correlations ofparameters obtained from microstructure characterization with thedeformation of strata, although correlative relationships are usually toomulti-factored for practical use. This challenge derives from the di-versity of soils, the sampling frequency in boreholes, the characteriza-tion standard on microstructure and others. Nevertheless, strata thatcontributes larger deformation in land subsidence tends to exhibitlarger average form factor, larger fractal dimension and higher pro-portion of large pores in the microstructures of the soils that constituteitem than those contribute less. Essentially, area probability distribu-tion index could be used to estimate the compression potential of soilsbecause mechanical compression only affects large pores (Delage et al.,2006; Delage and Lefebvre, 1984). The determination of large poreproportions in deformable strata allows estimation of land subsidencepotential. Romero (2013) suggested an approach to separate large pores(inter-aggregate pores) from small pores (intra-aggregate pores) usingthe dominant peak of a unimodal PSD curve obtained MIP in saturatedclay or clayey soil. This may be used to evaluate large pore proportions

Fig. 11. Original SEM image and PCAS proceeded images. (a) Original SEM image at a magnification of ×1000; (b) Binary image at T=49, black area for soilparticles and white area for pores; (c) Pore characterization with a minimum area of 50 pixels; (d) Pore characterization with a minimum area of 300 pixels.

Fig. 12. Concept of area probability distribution index, b.

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in soils from aquitards where prominent subsidence occurs. In thisstudy, the sizes of pores were estimated by pixel numbers but no spe-cific limiting sizes were given due to limited MIP tests performed. Wuet al. (2014) proposed a method to evaluate the compaction potentialby comparing the porosity of soil (obtained from SEM image) at eachdepth with the porosity of soil which was close to bedrock and alreadyhalf-diagenesis. Although this may have limitations in other researcharea, the setting of reference parameters provides a good idea in eval-uating the compression potential of soils.

7. Conclusions

The combination of DFOS techniques and microstructural analyseswas applied in land subsidence investigation in two boreholes drilled inthe Su-Xi-Chang area, China. The following conclusions can be drawnfrom the results obtained in this study.

(1) The annual amount of land subsidence in GM03 is ~1.2mm, and itsdeformation mainly comes from Ad2, Af2 and Ad3. The deforma-tion of these strata varies seasonally: compressed in spring andsummer with rebound in autumn and winter. In a one-year period,the deformation of Af2 was elastic while irreversible compressionoccurred in Ad2 and Af3.

(2) The land subsidence in SZ01 is faster than that of GM03 and reachesreaching ~2.9 mm/y. Ad2 and Ad3 were the main contributors tothe recorded subsidence. With the case of GM03 being different,strata rebound only occurs in a short period in winter or spring inthe sort of the strata. The results from FBG-based extensometer andFBG-based osmometer installed at Af2, confirm that the deforma-tion of Af2 is greatly dependent on the pore pressure in this stratum.Af2 shows elastic deformation during the monitoring period.

(3) The microstructures of undisturbed soils from GM03 and SZ01 in-dicate that the strata that contribute larger factors of deformation inland subsidence exhibits larger average form factor, larger fractal

Fig. 13. Form factor of sample determined by PCAS.

Fig. 14. Fractal dimension of sample determined by PCAS.

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dimension and higher proportion of large pores in soil micro-structures.

(4) There is also good qualitative agreement between MIP results andSEM images characterized with PCAS. Area probability distributionindex obtained by PCAS which indicates that this combined methodcan be used for the estimation of strata compression potential.

(5) The combination of DFOS techniques and microstructural analysesis of great potential in land subsidence investigation through

enabling better understanding of relevant processes and more ac-curate estimation of land subsidence trend, although this methodstill has challenges as discussed in this paper.

Acknowledgements

This research was performed with financial support from NaturalScience Foundation of China (NSFC, No. 41230636, 41502274,

Fig. 15. Area probability distribution index of sample determined by PCAS.

Fig. 16. PSD curves and density function for samples. (a) Silty clays; (b) Clays.

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41372265), Natural Science Foundation of Jiangsu Province (No.BK20150389), China Postdoctoral Science Foundation (CPSF, No.2015M580414), Open Foundation of State Key Laboratory ofGeohazard Prevention and Geoenvironmental Protection, ChengduUniversity of Technology (No. SKLGP2016K010) are greatly appre-ciated. The authors also want to thank Prof. Hilary I. Inyang for thelanguage improvement.

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