博碩士論文 106624009 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:128 、訪客IP:3.16.66.34
姓名 陳芝儀(Chih-Yi Chen)  查詢紙本館藏   畢業系所 應用地質研究所
論文名稱 利用多重水力試驗方法評估場址尺度非受壓含水層異質性特徵
(Systematic assessment of field-scale unconfined aquifer heterogeneity using multiple hydraulic test methods)
相關論文
★ 延散效應對水岩交互作用反應波前的影響★ 序率譜方法制定異質性含水層水井捕集區
★ 跨孔式注氣試驗方法推估異質性非飽和層土壤氣體流動參數★ 現地跨孔式抽水試驗推估異質性含水層水文地質特性
★ iTOUGH2應用於實驗室尺度非飽和土壤參數之推估★ HYDRUS-1D模式應用於入滲試驗推估非飽和土壤特性參數
★ 沿海含水層異質性對海淡水交界面影響之不確定性分析★ 非拘限砂質海岸含水層中潮汐和沙灘坡度水文動力條件影響苯傳輸
★ 利用MODFLOW配合SUB套件推估雲林地區垂向平均長期地層下陷趨勢★ 高雄平原地區抽水引致汙染潛勢評估
★ 利用自然電位法監測淺層土壤入滲歷程★ 利用LiDAR點雲及影像資料決定露頭節理結合面之研究
★ 臺灣西部沿海海水入侵與地下水排出模擬分析★ 三氯乙烯地下水污染場址整治後期傳輸行為分析¬-應用開源FreeFEM++有限元素模式架構
★ 都會地區滯洪池增設礫石樁之入滲效益模擬與分析★ 利用數值模擬探討二氧化碳於異向性及異質性鹽水層之遷移行為
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 精確又有效率地下水污染整治仰賴對場址含水層特性之了解。本研究主要目的是評估現地場址尺度下,各類水力試驗及分析模式的場址參數調查差異,從分析結果獲得一般化的使用原則。本研究試驗場址位於桃園市觀音區,為非受壓含水層(unconfined aquifer),在現地19口井執行跨孔式抽水試驗(cross-hole pumping test)和多深度微水試驗(multi-level slug test),取跨孔式抽水試驗當中抽水井洩降資料,作為單井抽水試驗,以Neuman解析解進行分析;取其中一抽水井與其相對應的觀測井之洩降資料,作為複井抽水試驗;本研究同時將所有抽水井、觀測井資料一併代入SSLE(sequential successive linear estimator)數值模式,則視為水力剖面掃描(hydraulic tomography);多深度微水試驗則透過Hvorslev解析解分析,並利用克利金內插得到場址三維水力傳導係數(hydraulic conductivity, K)分布。透過計算得到現地場址材料之平均流通係數(transmissivity, T)值為66.81m2/day,變異數為0.067,材料之相關長度為3.3m。以此統計結構代入SSLE模式當中作為反推估的參數。結果顯示四種水力試驗能得到相同級數(order)之K值,在資料共享的狀況下,單井、複井抽水試驗以及水力剖面掃描能反推出T值之趨勢,然對應到多深度微水試驗,分布趨勢雖不盡相同,但單井抽水試驗推得T值換算成K值之平均為7.62E-05 m/sec,落在多深度微水試驗推得之K(z)區間內,符合過往文獻提供的一般化材料特性。本研究場址主要材料為卵礫石,各式水力試驗推得之K值雖未落直接落於礫石合理的K值範圍內,但現地地質材料尚含有粉土等細顆粒,因此K值小於礫石之合理範圍實屬合理。研究結果顯示在井數量許可下,水力剖面掃描最能描繪含水層異質性的分布狀況,然而本場址尚屬均質,建議執行單井、複井抽水試驗即可;而多深度微水試驗顯示各井垂直向變異程度很小,建議執行傳統微水試驗即可。本研究亦於數值模式當中針對影響地質材料異質性之參數如變異數、相關長度等進行敏感度測試,針對數值模式的生成場和推估場進行誤差計算;其結果顯示在現地場址異質性條件下,在數值模式當中誤差會落在0.0016~0.0543m2/min之間,相差約一個級數,但是礫石的K值相差至兩個級數都還算合理,因此推測數值模式在現地場址異質性條件下之反推結果屬於合理範圍。
摘要(英) Accurate and efficient remediation policies highly rely on the understanding of hydrogeological conditions at sites. This study aims to conduct assessment of aquifer heterogeneity using multiple hydraulic tests at the same well field, comparing the estimations by different data analysis methods, and then carrying out the insight into the use of hydraulic surveys at the field-scale site. Cross-hole pumping tests and Multi-level slug tests(MLST) are conducted at the well field. The well field has 19 installed wells, including five 4’’ wells that are considered to be the pumping wells. The pumping and slug tests produce multiple types of observations from well field. The single- and multiple-well pumping tests are analyzed with Neuman(1975) model. All the cross-hole hydraulic test data are involved in the sequential successive linear estimator(SSLE) model to estimate the transmissivity(T) distribution. Drawdown data from MLSTs are analyzed with Hvorslev(1951) to obtain the hydraulic conductivity(K) and the results of MLSTs are interpolated with the Kriging method to obtain 3D K distribution. Results of the single-well hydraulic tests show that the mean T value is 66.81m2/day, the variance of ln(T) is 0.067, and results of the MLSTs show that the correlation length of the T field is 3.3m. These results are the input parameters in SSLE for estimating 2D T at the well field. Results show that the K values from four different hydraulic tests are in the same order. Under the same tests data source, the patterns of T heterogeneities are similar for single-well pumping tests, multi-well pumping tests, and SSLE hydraulic tomography. The MLST shows the patterns slightly different from other pumping tests. The results show that the average K value from the single-well pumping tests (7.62E-05 m/sec) is between the minimum and the maximum K(z) values obtained from MLST. The well field is mainly composed of gravel and embedded with fine sand, loam, and silt layers. The obtained K values at the site show relatively smaller than typical K values obtained from previous studies. Results also show that the SSLE can provide the most detailed information of the aquifer heterogeneities. However, the study site is relatively homogeneous. The single and multi-well pumping tests might be sufficient to resolve the aquifer properties at the site. Additionally, the MLSTs show that the lnK variance in the vertical direction is very small, indicating that the depth-averaged slug test can be used to for the well hydraulic test. The sensitivity analysis of SSLE model shows that the T estimated errors for the site is between 0.0016 and 0.0543 m2/min, which is relatively small.
關鍵字(中) ★ 抽水試驗
★ 水力剖面掃描
★ 多深度微水試驗
★ 流通係數
★ 水力傳導係數
★ 異質性
關鍵字(英) ★ Pumping test
★ Hydraulic tomography
★ Multi-level slug test
★ Transmissivity
★ Hydraulic conductivity
★ Heterogeneity
論文目次 摘要 i
Abstract iii
誌謝 v
目錄 vi
圖目錄 ix
表目錄 xi
符號說明 xii
第一章 緒論 1
1-1 研究背景 1
1-2 研究目的 2
1-3 研究流程 3
1-4 論文架構 4
第二章 文獻回顧 5
2-1 微水試驗 5
2-1-1 傳統微水試驗 5
2-1-2 多深度微水試驗 7
2-2 抽水試驗 9
2-2-1 傳統分析方法 9
2-2-2 描繪含水層異質性等各式逆推方法與演進 10
2-2-3 水力剖面掃描 16
第三章 試驗方法與數值模式 22
3-1 試驗場址 22
3-1-1 地理位置 22
3-1-2 區域地質概況 23
3-1-2 井配置圖 28
3-2 試驗方法 28
3-2-1 單井抽水試驗 29
3-2-2 複井抽水試驗 31
3-2-3 水力剖面掃描 33
1. 現地試驗 33
2. 數值模式敏感度測試 35
3-2-4 多深度微水試驗 38
3-3 參數分析方法 39
3-3-1 三維水流控制方程式 39
3-3-2 Neuman(1975)曲線套疊法 40
3-3-3 SSLE模式 44
3-3-4 Hvorslev(1951)分析模式 50
3-3-5 克利金內插 51
第四章 結果與討論 56
4-1 單井抽水試驗 56
4-2 複井抽水試驗 59
4-3 多深度微水試驗 62
4-4 水力剖面掃描 69
4-4-1 現地試驗 69
1. 洩降資料 69
2. 數值模型 71
3. 二維反推結果 72
4-4-2 數值模式敏感度測試 73
4-5 各項試驗結果綜合討論 78
4-5-1 水文參數值 78
4-5-2 趨勢分布 78
4-5-3 尺度關係 80
4-5-4 適用性 81
第五章 結論與建議 83
5-1 結論 83
5-2 建議 85
參考文獻 86
參考文獻 [1] Theis, C. V., “The relation between the lowering of the piezometric surface and the rate and duration of discharge of a well using ground‐water storage”, Eos, Transactions American Geophysical Union, 16(2), 519-524, 1935.
[2] Cooper, H. H., & Jacob, C. E., “A generalized graphical method for evaluating formation constants and summarizing well‐field history”, Eos, Transactions American Geophysical Union, 27(4), 526-534, 1946.
[3] Neuman, S. P., “Effect of partial penetration on flow in unconfined aquifers considering delayed gravity response”, Water resources research, 10(2), 303-312, 1974.
[4] Jim Yeh, T. C., “Stochastic modelling of groundwater flow and solute transport in aquifers”, Hydrological Processes, 6(4), 369-395, 1992.
[5] Yeh, T. C. J., “Scale issues of heterogeneity in vadose-zone hydrology”, Scale dependence and scale invariance in hydrology, 224-265, 1998.
[6] Hvorslev, M. J., “Time lag and soil permeability in ground-water observations”, 1951.
[7] Cooper, H. H., Bredehoeft, J. D., & Papadopulos, I. S., “Response of a finite‐diameter well to an instantaneous charge of water”, Water Resources Research, 3(1), 263-269, 1967.
[8] Bouwer, H., & Rice, R. C., “A slug test for determining hydraulic conductivity of unconfined aquifers with completely or partially penetrating wells”, Water Resources Research, 12(3), 423-428, 1976.
[9] Dagan, G., “A note on packer, slug, and recovery tests in unconfined aquifers”, Water Resources Research, 14(5), 929-934, 1978.
[10] Binkhorst, G. K., & Robbins, G. A., “Conducting and interpreting slug tests in monitoring wells with partially submerged screens”, Groundwater, 36(2), 225-229, 1998.
[11] Hyder, Z., Butler, J. J., McElwee, C. D., & Liu, W., “Slug tests in partially penetrating wells”, Water Resources Research, 30(11), 2945-2957, 1994.
[12] Van der Kamp, G., “Determining aquifer transmissivity by means of well response tests: The underdamped case”, Water Resources Research, 12(1), 71-77, 1976.
[13] Kipp Jr, K. L., “Type curve analysis of inertial effects in the response of a well to a slug test”, Water Resources Research, 21(9), 1397-1408, 1985.
[14] Kabala, Z. J., Pinder, G. F., & Milly, P. C. D., “Analysis of well‐aquifer response to a slug test”, Water Resources Research, 21(9), 1433-1436, 1985.
[15] Springer, R. K., & Gelhar, L. W., “Characterization of large-scale aquifer heterogeneity in glacial outwash by analysis of slug tests with oscillatory response, Cape Cod, Massachusetts”, US Geological Survey Water–Resources Investigation Report, 91, 36-40, 1991.
[16] McElwee, C. D., James. J. Butler (Jr.), & Bohling, G. C., “Nonlinear Analysis of Slug Tests in Highly-permeable Aquifers Using a Hvorslev-type Approach”, Kansas Geological Survey, 1992.
[17] Butler Jr, J. J., The design, performance, and analysis of slug tests, Crc Press, 1997.
[18] Zurbuchen, B. R., Zlotnik, V. A., & Butler, J. J., “Dynamic interpretation of slug tests in highly permeable aquifers”, Water Resources Research, 38(3), 2002.
[19] Butler, J. J., & Zhan, X., “Hydraulic tests in highly permeable aquifers”, Water Resources Research, 40(12), 2004.
[20] Chen, C. S., “An analytic data analysis method for oscillatory slug tests”, Groundwater, 44(4), 604-608, 2006.
[21] Chen, C. S., “An analytical method of analysing the oscillatory pressure head measured at any depth in a well casing”, Hydrological Processes: An International Journal, 22(8), 1119-1124, 2008.
[22] Butler Jr, J. J., Garnett, E. J., & Healey, J. M., “Analysis of slug tests in formations of high hydraulic conductivity”, Groundwater, 41(5), 620-631, 2003.
[23] Widdowson, M. A., Molz, F. J., & Melville, J. G., “An analysis technique for multilevel and partially penetrating slug test data”, Groundwater, 28(6), 937-945, 1990.
[24] Melville, J. G., Molz, F. J., Güven, O., & Widdowson, M. A., “Multilevel slug tests with comparisons to tracer data”, Groundwater, 29(6), 897-907, 1991.
[25] Hinsby, K., Bjerg, P. L., Andersen, L. J., Skov, B., & Clausen, E. V., “A mini slug test method for determination of a local hydraulic conductivity of an unconfined sandy aquifer”, Journal of Hydrology, 136(1-4), 87-106, 1992.
[26] Zlotnik, V. A., & McGuire, V. L., “Multi-level slug tests in highly permeable formations: 1. Modification of the Springer-Gelhar (SG) model”, Journal of Hydrology, 204(1-4), 271-282, 1998.
[27] Zlotnik, V. A., & Zurbuchen, B. R., “Field study of hydraulic conductivity in a heterogeneous aquifer: Comparison of single‐borehole measurements using different instruments”, Water Resources Research, 39(4), 2003.
[28] Sellwood, S. M., Healey, J. M., Birk, S., & Butler Jr, J. J., “Direct‐push hydrostratigraphic profiling: Coupling electrical logging and slug tests”, Groundwater, 43(1), 19-29, 2005.
[29] Zemansky, G. M., & McElwee, C. D., “High‐resolution slug testing”, Groundwater, 43(2), 222-230, 2005.
[30] Ross, H. C., & McElwee, C. D., “Multi-level slug tests to measure 3-D hydraulic conductivity distributions”, Natural Resources Research, 16(1), 67-79, 2007.
[31] 謝云珺,「多深度微水試驗之測試段長度對水力傳導係數影響」,碩士論文,國立中央大學,民國九十八年。
[32] Butler, J. J., & Liu, W., “Pumping tests in nonuniform aquifers: The radially asymmetric case”, Water Resources Research, 29(2), 259-269, 1993.
[33] Wu, C. M., Yeh, T. C. J., Zhu, J., Lee, T. H., Hsu, N. S., Chen, C. H., & Sancho, A. F., “Traditional analysis of aquifer tests: Comparing apples to oranges?”, Water Resources Research, 41(9), 2005.
[34] Yeh, W. W. G., “Review of parameter identification procedures in groundwater hydrology: The inverse problem”, Water Resources Research, 22(2), 95-108, 1986.
[35] McLaughlin, D., & Townley, L. R., “A reassessment of the groundwater inverse problem”, Water Resources Research, 32(5), 1131-1161, 1996.
[36] Kuiper, L. K., “A comparison of several methods for the solution of the inverse problem in two‐dimensional steady state groundwater flow modeling”, Water Resources Research, 22(5), 705-714, 1986.
[37] Zhou, H., Gómez-Hernández, J. J., & Li, L., “Inverse methods in hydrogeology: Evolution and recent trends”, Advances in Water Resources, 63, 22-37, 2014.
[38] Berg, S. J., & Illman, W. A., “Comparison of hydraulic tomography with traditional methods at a highly heterogeneous site”, Groundwater, 53(1), 71-89, 2015.
[39] Neuman, S. P., “Calibration of distributed parameter groundwater flow models viewed as a multiple‐objective decision process under uncertainty”, Water Resources Research, 9(4), 1006-1021, 1973.
[40] Sun N-Z. Inverse problems in groundwater modeling. Dordrecht: Kluwer Academic; 1994. 337p. ISBN 9048144353.
[41] Ponzini, G., & Lozej, A., “Identification of aquifer transmissivities: the comparison model method”, Water Resources Research, 18(3), 597-622, 1982.
[42] Kleinecke, D., “Use of linear programing for estimating geohydrologic parameters of groundwater basins”, Water Resources Research, 7(2), 367-374, 1971.
[43] Navarro, A., “A modified optimization method of estimating aquifer parameters”, Water Resources Research, 13(6), 935-939, 1977.
[44] Irsa, J., & Zhang, Y., “A direct method of parameter estimation for steady state flow in heterogeneous aquifers with unknown boundary conditions”, Water Resources Research, 48(9), 2012.
[45] Kitanidis, P. K., & VoMvoris, E. G., “A geostatistical approach to the inverse problem in groundwater modeling (steady state) and one‐dimensional simulations”, Water resources research, 19(3), 677-690, 1983.
[46] Carrera, J., & Neuman, S. P., “Estimation of aquifer parameters under transient and steady state conditions: 1. Maximum likelihood method incorporating prior information”, Water Resources Research, 22(2), 199-210, 1986.
[47] Medina, A., & Carrera, J., “Coupled estimation of flow and solute transport parameters”, Water Resources Research, 32(10), 3063-3076, 1996.
[48] Kitanidis, P. K., “On the geostatistical approach to the inverse problem”, Advances in Water Resources, 19(6), 333-342, 1996.
[49] De Marsily, G., Lavedan, G., Boucher, M., & Fasanino, G., “Interpretation of interference tests in a well field using geostatistical techniques to fit the permeability distribution in a reservoir model”, Geostatistics for natural resources characterization, Part, 2, 831-849, 1984.
[50] Rubin, Y., Chen, X., Murakami, H., & Hahn, M., “A Bayesian approach for inverse modeling, data assimilation, and conditional simulation of spatial random fields”, Water Resources Research, 46(10), 2010.
[51] Gómez-Hernández, J. J., & Wen, X. H., “Probabilistic assessment of travel times in groundwater modeling”, Stochastic Hydrology and Hydraulics, 8(1), 19-55, 1994.
[52] Sahuquillo, A., Capilla, J. E., Gómez-Hernández, J. J., & Andreu, J., “Conditional simulation of transmissivity fields honoring piezometric data”, Hydraulic engineering software IV, fluid flow modeling, 2, 201-214, 1992.
[53] Gómez-Hernánez, J. J., Sahuquillo, A., & Capilla, J., “Stochastic simulation of transmissivity fields conditional to both transmissivity and piezometric data—I. Theory”, Journal of Hydrology, 203(1-4), 162-174, 1997.
[54] Capilla, J., Gómez-Hernández, J. J., & Sahuquillo, A., “Stochastic simulation of transmissivity fields conditional to both transmissivity and piezometric data 2. Demonstration on a synthetic aquifer”, Journal of hydrology, 203(1-4), 175-188, 1997.
[55] Capilla, J. E., Gömez-Hernández, J. J., & Sahuquillo, A., “Stochastic simulation of transmissivity fields conditional to both transmissivity and piezometric head data—3. Application to the Culebra formation at the waste isolation pilot plan (WIPP), New Mexico, USA”, Journal of Hydrology, 207(3-4), 254-269, 1998.
[56] Wen, X. H., Capilla, J. E., Deutsch, C. V., Gómez-Hernández, J. J., & Cullick, A. S., “A program to create permeability fields that honor single-phase flow rate and pressure data”, Computers & Geosciences, 25(3), 217-230, 1999.
[57] Journel, A. G., “Geostatistics for conditional simulation of ore bodies”, Economic Geology, 69(5), 673-687, 1974.
[58] Hernandez, A. F., Neuman, S. P., Guadagnini, A., & Carrera, J., “Conditioning mean steady state flow on hydraulic head and conductivity through geostatistical inversion”, Stochastic Environmental Research and Risk Assessment, 17(5), 329-338, 2003.
[59] Hernandez, A. F., Neuman, S. P., Guadagnini, A., & Carrera, J., “Inverse stochastic moment analysis of steady state flow in randomly heterogeneous media”, Water Resources Research, 42(5), 2006.
[60] Riva, M., Guadagnini, A., Neuman, S. P., Janetti, E. B., & Malama, B., “Inverse analysis of stochastic moment equations for transient flow in randomly heterogeneous media”, Advances in water resources, 32(10), 1495-1507, 2009.
[61] Riva, M., Panzeri, M., Guadagnini, A., & Neuman, S. P., “Role of model selection criteria in geostatistical inverse estimation of statistical data‐and model‐parameters”, Water Resources Research, 47(7), 2011.
[62] Hastings, W. K., “Monte Carlo sampling methods using Markov chains and their applications”, Biometrika, Volume 57, Issue 1, Pages 97–109, 1970
[63] Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E., “Equation of state calculations by fast computing machines”, The journal of chemical physics, 21(6), 1087-1092, 1953.
[64] Oliver, D. S., Cunha, L. B., & Reynolds, A. C., “Markov chain Monte Carlo methods for conditioning a permeability field to pressure data”, Mathematical Geology, 29(1), 61-91, 1997.
[65] Evensen, G., “Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics”, Journal of Geophysical Research: Oceans, 99(C5), 10143-10162, 1994.
[66] Burgers, G., Jan van Leeuwen, P., & Evensen, G., “Analysis scheme in the ensemble Kalman filter”, Monthly weather review, 126(6), 1719-1724, 1998.
[67] Kalman, R. E., “A new approach to linear filtering and prediction problems”, Journal of basic Engineering, 82(1), 35-45, 1960.
[68] Evensen, G., “The ensemble Kalman filter: Theoretical formulation and practical implementation”, Ocean dynamics, 53(4), 343-367, 2003.
[69] Bertino, L., Evensen, G., & Wackernagel, H., “Sequential data assimilation techniques in oceanography”, International Statistical Review, 71(2), 223-241, 2003.
[70] Chen, Y., & Zhang, D., “Data assimilation for transient flow in geologic formations via ensemble Kalman filter”, Advances in Water Resources, 29(8), 1107-1122, 2006.
[71] Houtekamer, P. L., & Mitchell, H. L., “A sequential ensemble Kalman filter for atmospheric data assimilation”, Monthly Weather Review, 129(1), 123-137, 2001.
[72] Moradkhani, H., Sorooshian, S., Gupta, H. V., & Houser, P. R., “Dual state–parameter estimation of hydrological models using ensemble Kalman filter”, Advances in water resources, 28(2), 135-147, 2005.
[73] Nowak, W., “Best unbiased ensemble linearization and the quasi‐linear Kalman ensemble generator”, Water Resources Research, 45(4), 2009.
[74] Wen, X. H., & Chen, W. H., “Real-time reservoir model updating using ensemble Kalman filter”, In SPE reservoir simulation symposium. Society of Petroleum Engineers, 2005.
[75] Gottlieb, J., & Dietrich, P., “Identification of the permeability distribution in soil by hydraulic tomography”, Inverse Problems, 11(2), 353, 1995.
[76] Butler, J. J., McElwee, C. D., & Bohling, G. C., “Pumping tests in networks of multilevel sampling wells: Motivation and methodology”, Water Resources Research, 35(11), 3553-3560, 1999.
[77] Vasco, D. W., Keers, H., & Karasaki, K., “Estimation of reservoir properties using transient pressure data: An asymptotic approach”, Water Resources Research, 36(12), 3447-3465, 2000.
[78] Yeh, T. C. J., & Liu, S., “Hydraulic tomography: Development of a new aquifer test method”, Water Resources Research, 36(8), 2095-2105, 2000.
[79] Liu, S., Yeh, T. C. J., & Gardiner, R., “Effectiveness of hydraulic tomography: Sandbox experiments”, Water Resources Research, 38(4), 5-1, 2002.
[80] Bohling, G. C., Zhan, X., Butler Jr, J. J., & Zheng, L., “Steady shape analysis of tomographic pumping tests for characterization of aquifer heterogeneities”, Water Resources Research, 38(12), 60-1, 2002.
[81] McDermott, C. I., Sauter, M., & Liedl, R., “New experimental techniques for pneumatic tomographical determination of the flow and transport parameters of highly fractured porous rock samples”, Journal of Hydrology, 278(1-4), 51-63, 2003.
[82] Brauchler, R., Liedl, R., & Dietrich, P., “A travel time based hydraulic tomographic approach”, Water Resources Research, 39(12), 2003.
[83] Li, W., Nowak, W., & Cirpka, O. A., “Geostatistical inverse modeling of transient pumping tests using temporal moments of drawdown”, Water resources research, 41(8), 2005.
[84] Castagna, M., & Bellin, A., “A Bayesian approach for inversion of hydraulic tomographic data”, Water Resources Research, 45(4), 2009.
[85] Cardiff, M., & Barrash, W., “3‐D transient hydraulic tomography in unconfined aquifers with fast drainage response”, Water Resources Research, 47(12), 2011.
[86] Liu, X., & Kitanidis, P. K., “Large‐scale inverse modeling with an application in hydraulic tomography”, Water Resources Research, 47(2), 2011.
[87] Schöniger, A., Nowak, W., & Hendricks Franssen, H. J., “Parameter estimation by ensemble Kalman filters with transformed data: Approach and application to hydraulic tomography”, Water Resources Research, 48(4), 2012.
[88] Zhu, J., & Yeh, T. C. J., “Analysis of hydraulic tomography using temporal moments of drawdown recovery data”, Water Resources Research, 42(2), 2006.
[89] Hoeksema, R. J., & Kitanidis, P. K., “An application of the geostatistical approach to the inverse problem in two‐dimensional groundwater modeling”, Water Resources Research, 20(7), 1003-1020, 1984.
[90] Yeh, T. C. J., Jin, M., & Hanna, S., “An iterative stochastic inverse method: Conditional effective transmissivity and hydraulic head fields”, Water Resources Research, 32(1), 85-92, 1996.
[91] Yeh, T. C. J., Gutjahr, A. L., & Jin, M., “An iterative cokriging‐like technique for ground‐water flow modeling”, Groundwater, 33(1), 33-41, 1995.
[92] Zhang, J., & Yeh, T. C. J., “An iterative geostatistical inverse method for steady flow in the vadose zone”, Water Resources Research, 33(1), 63-71, 1997.
[93] Hughson, D. L., & Yeh, T. C. J., “A geostatistically based inverse model for three-dimensional variably saturated flow”, Stochastic Hydrology and Hydraulics, 12(5), 285-298, 1998.
[94] Hughson, D. L., & Yeh, T. C. J., “An inverse model for three‐dimensional flow in variably saturated porous media”, Water Resources Research, 36(4), 829-839, 2000.
[95] Van Genuchten, M. T., “A closed-form equation for predicting the hydraulic conductivity of unsaturated soils 1”, Soil science society of America journal, 44(5), 892-898, 1980.
[96] Zhu, J., & Yeh, T. C. J., “Characterization of aquifer heterogeneity using transient hydraulic tomography”, Water Resources Research, 41(7), 2005.
[97] Illman, W. A., Craig, A. J., & Liu, X., “Practical issues in imaging hydraulic conductivity through hydraulic tomography”, Groundwater, 46(1), 120-132, 2008.
[98] Liu, X., Illman, W. A., Craig, A. J., Zhu, J., & Yeh, T. C., “Laboratory sandbox validation of transient hydraulic tomography”, Water Resources Research, 43(5), 2007.
[99] Zhao, Z., Illman, W. A., & Berg, S. J., “On the importance of geological data for hydraulic tomography analysis: Laboratory sandbox study”, Journal of Hydrology, 542, 156-171, 2016.
[100] Illman, W. A., Liu, X., & Craig, A., “Steady-state hydraulic tomography in a laboratory aquifer with deterministic heterogeneity: Multi-method and multiscale validation of hydraulic conductivity tomograms”, Journal of Hydrology, 341(3-4), 222-234, 2007.
[101] Illman, W. A., Zhu, J., Craig, A. J., & Yin, D., “Comparison of aquifer characterization approaches through steady state groundwater model validation: A controlled laboratory sandbox study”, Water Resources Research, 46(4), 2010.
[102] Illman, W. A., Berg, S. J., & Zhao, Z., “Should hydraulic tomography data be interpreted using geostatistical inverse modeling? A laboratory sandbox investigation”, Water Resources Research, 51(5), 3219-3237, 2015.
[103] Berg, S. J., & Illman, W. A., “Capturing aquifer heterogeneity: Comparison of approaches through controlled sandbox experiments”, Water Resources Research, 47(9), 2011.
[104] Berg, S. J., & Illman, W. A., “Improved predictions of saturated and unsaturated zone drawdowns in a heterogeneous unconfined aquifer via transient hydraulic tomography: Laboratory sandbox experiments”, Journal of hydrology, 470, 172-183, 2012.
[105] Zhao, Z., Illman, W. A., Yeh, T. C. J., Berg, S. J., & Mao, D., “Validation of hydraulic tomography in an unconfined aquifer: A controlled sandbox study”, Water Resources Research, 51(6), 4137-4155, 2015.
[106] Straface, S., Yeh, T. C., Zhu, J., Troisi, S., & Lee, C. H., “Sequential aquifer tests at a well field, Montalto Uffugo Scalo, Italy”, Water Resources Research, 43(7), 2007.
[107] Bohling, G. C., Butler, J. J., Zhan, X., & Knoll, M. D., “A field assessment of the value of steady shape hydraulic tomography for characterization of aquifer heterogeneities”, Water Resources Research, 43(5), 2007.
[108] Li, W., Englert, A., Cirpka, O. A., & Vereecken, H., “Three‐dimensional geostatistical inversion of flowmeter and pumping test data”, Groundwater, 46(2), 193-201, 2008.
[109] Cardiff, M., Barrash, W., Kitanidis, P. K., Malama, B., Revil, A., Straface, S., & Rizzo, E., “A potential‐based inversion of unconfined steady‐state hydraulic tomography”, Groundwater, 47(2), 259-270, 2009.
[110] Berg, S. J., & Illman, W. A., “Three‐dimensional transient hydraulic tomography in a highly heterogeneous glaciofluvial aquifer‐aquitard system”, Water Resources Research, 47(10), 2011.
[111] Berg, S. J., & Illman, W. A., “Field study of subsurface heterogeneity with steady‐state hydraulic tomography”, Groundwater, 51(1), 29-40, 2013.
[112] Brauchler, R., Hu, R., Dietrich, P., & Sauter, M., “A field assessment of high‐resolution aquifer characterization based on hydraulic travel time and hydraulic attenuation tomography”, Water Resources Research, 47(3), 2011.
[113] Huang, S. Y., Wen, J. C., Yeh, T. C. J., Lu, W., Juan, H. L., Tseng, C. M., ... & Chang, K. C., “Robustness of joint interpretation of sequential pumping tests: Numerical and field experiments”, Water Resources Research, 47(10), 2011.
[114] Cardiff, M., Barrash, W., & Kitanidis, P. K., “A field proof‐of‐concept of aquifer imaging using 3‐D transient hydraulic tomography with modular, temporarily‐emplaced equipment”, Water Resources Research, 48(5), 2012.
[115] Li, W., Englert, A., Cirpka, O. A., Vanderborght, J., & Vereecken, H., “Two‐dimensional characterization of hydraulic heterogeneity by multiple pumping tests”, Water Resources Research, 43(4), 2007.
[116] 黃奕儒,「現地跨孔式抽水試驗推估異質性含水層水文地質特性」,國立中央大學,碩士論文,民國98年。
[117] Illman, W. A., Liu, X., Takeuchi, S., Yeh, T. C. J., Ando, K., & Saegusa, H., “Hydraulic tomography in fractured granite: Mizunami Underground Research site, Japan”, Water resources research, 45(1), 2009.
[118] Castagna, M., Becker, M. W., & Bellin, A., “Joint estimation of transmissivity and storativity in a bedrock fracture”, Water Resources Research, 47(9), 2011.
[119] Cardiff, M., Barrash, W., & Kitanidis, P. K., “Hydraulic conductivity imaging from 3‐D transient hydraulic tomography at several pumping/observation densities”, Water Resources Research, 49(11), 7311-7326, 2013.
[120] Paradis, D., Gloaguen, E., Lefebvre, R., & Giroux, B., “A field proof-of-concept of tomographic slug tests in an anisotropic littoral aquifer”, Journal of Hydrology, 536, 61-73, 2016.
[121] Zha, Y., Yeh, T. C. J., Mao, D., Yang, J., & Lu, W., “Usefulness of flux measurements during hydraulic tomographic survey for mapping hydraulic conductivity distribution in a fractured medium”, Advances in water resources, 71, 162-176, 2014.
[122] Tso, C. H. M., Zha, Y., Yeh, T. C. J., & Wen, J. C., “The relative importance of head, flux, and prior information in hydraulic tomography analysis”, Water Resources Research, 52(1), 3-20, 2016.
[123] Zhao, Z., & Illman, W. A., “On the importance of geological data for three-dimensional steady-state hydraulic tomography analysis at a highly heterogeneous aquifer-aquitard system”, Journal of Hydrology, 544, 640-657, 2007.
[124] Hao, Y., Yeh, T. C. J., Xiang, J., Illman, W. A., Ando, K., Hsu, K. C., & Lee, C. H., “Hydraulic tomography for detecting fracture zone connectivity”, Groundwater, 46(2), 183-192, 2008.
[125] Zhu, J., & Yeh, T. C. J., “Analysis of hydraulic tomography using temporal moments of drawdown recovery data”, Water Resources Research, 42(2), 2006.
[126] Vesselinov, V. V., Neuman, S. P., & Illman, W. A., “Three‐dimensional numerical inversion of pneumatic cross‐hole tests in unsaturated fractured tuff: 2. Equivalent parameters, high‐resolution stochastic imaging and scale effects”, Water Resources Research, 37(12), 3019-3041, 2001.
[127] Zha, Y., Yeh, T. C. J., Illman, W. A., Tanaka, T., Bruines, P., Onoe, H., & Saegusa, H., “What does hydraulic tomography tell us about fractured geological media? A field study and synthetic experiments”, Journal of Hydrology, 531, 17-30, 2015.
[128] Rojstaczer, S., “Determination of fluid flow properties from the response of water levels in wells to atmospheric loading”, Water Resources Research, 24(11), 1927-1938, 1988.
[129] Hsieh, P. A., Bredehoeft, J. D., & Rojstaczer, S. A., “Response of well aquifer systems to earth tides: Problem revisited”, Water Resources Research, 24(3), 468-472, 1988.
[130] Rojstaczer, S., & Riley, F. S., “Response of the water level in a well to earth tides and atmospheric loading under unconfined conditions”, Water Resources Research, 26(8), 1803-1817, 1990.
[131] Davis, E. E., Wang, K., Becker, K., & Thomson, R. E., “Formation‐scale hydraulic and mechanical properties of oceanic crust inferred from pore pressure response to periodic seafloor loading”, Journal of Geophysical Research: Solid Earth, 105(B6), 13423-13435, 2000.
[132] Li, H., Li, G., Cheng, J., & Boufadel, M. C., “Tide‐induced head fluctuations in a confined aquifer with sediment covering its outlet at the sea floor”, Water Resources Research, 43(3), 2007.
[133] Jan, C. D., Chen, T. H., & Huang, H. M., “Analysis of rainfall-induced quick groundwater-level response by using a Kernel function”, Paddy and Water Environment, 11(1-4), 135-144, 2013.
[134] Lin, Y. B., Tan, Y. C., Yeh, T. C. J., Liu, C. W., & Chen, C. H., “A viscoelastic model for groundwater level changes in the Cho‐Shui River alluvial fan after the Chi‐Chi earthquake in Taiwan”, Water resources research, 40(4), 2004.
[135] Yeh, T. C. J., Lee, C. H., Hsu, K. C., Illman, W. A., Barrash, W., Cai, X., Daniels, J., Sudicky, E., Wan, L., Li, G., & Winter, C. L., “A view toward the future of subsurface characterization: CAT scanning groundwater basins”, Water Resources Research, 44(3), 2008.
[136] Yeh, T. C. J., Xiang, J., Suribhatla, R. M., Hsu, K. C., Lee, C. H., & Wen, J. C., “River stage tomography: A new approach for characterizing groundwater basins”, Water resources research, 45(5), 2009.
[137] Wang, Y. L., Yeh, T. C. J., Wen, J. C., Huang, S. Y., Zha, Y., Tsai, J. P., Hao, Y. & Liang, Y., “Characterizing subsurface hydraulic heterogeneity of alluvial fan using riverstage fluctuations”, Journal of Hydrology, 547, 650-663, 2017.
[138] 陳怡葦,「桃園地區活斷層與地形面之研究」,國立彰化師範大學,碩士論文,民國92年。
[139] 何春蓀,台灣地質圖概論-台灣地質圖說明書,經濟部中央地質調查所,1986年。
[140] 塗明寬、陳文政,台灣地質說明書中壢圖幅,經濟部中央地質調查所,1990年。
[141] 郭皇甫,「桃園台地群地形及土壤化育之研究」,國立彰化師範大學,碩士論文,民國102年。
[142] 陳昶宏,「以數值及試驗方法探討非飽和水力特性對非受壓含水層抽水洩降之影響」,國立中央大學,碩士論文,民國106年。
[143] Gutjahr, A. L., “Fast Fourier transforms for random field generation: project report for Los Alamos Grant to New Mexico Tech”, New Mexico Institute of Mining and Technology, PhD Thesis, 1989.
[144] Chen, C. S., Sie, Y. C., & Lin, Y. T., “A Review of the Multilevel Slug Test for Characterizing Aquifer Heterogeneity”, Terrestrial, Atmospheric & Oceanic Sciences, 23(2), 2012.
[145] Cressie, N., “The origins of kriging”, Mathematical geology, 22(3), 239-252, 1990.
[146] Stein, M. L., Interpolation of spatial data: some theory for kriging. Springer Science & Business Media, 2012.
[147] Schwartz, F. W. & Zhang, H., Fundamentals of ground water, John Wiley & Sons, 2002.
[148] Jones, L., Lemar, T., & Tsai, C. T., “Results of two pumping tests in Wisconsin age weathered till in Iowa”, Groundwater, 30(4), 529-538, 1992.
[149] Chen, C. S., & Chang, C. C., “Well hydraulics theory and data analysis of the constant head test in an unconfined aquifer with the skin effect”, Water resources research, 39(5), 2003.
[150] 郭綉娟,「定水頭部分貫穿汲水推估非受壓含水層水文參數之方法」,國立中央大學,碩士論文,民國93年。
[151] 中興工程顧問股份有限公司,台灣地區地下水資源管理決策支援系統建置(2/4),經濟部水利署,民國91年。
指導教授 倪春發(Chuen-Fa Ni) 審核日期 2019-7-25
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明