高雄寶來竹林地區慢滑移深層山崩的微地震監測與分析

Microearthquake monitoring of slow-moving deep-seated Chulin landslide in Baolai, Kaohsiung

褚臻(Jen Chu)

指導教授 ： 胡植慶

共同指導教授 ： 黃信樺(Hsin-Hua Huang)

國立臺灣大學/理學院/地質科學研究所/碩士(2022年)

https://doi.org/10.6342/NTU202203284

摘要

山崩是高危害度的地質災害之一，臺灣山脈高聳、坡陡的地形特徵容易受到地震、降雨等因素而造成山崩的發生，對於人民的生活及財產造成威脅。坡體在破裂或滑動的過程中會產生地動訊號，藉由分析這些地動訊號我們可以監測與了解坡體內部的變形破裂與滑動行為。
竹林地區位於高雄寶來村，地層以板岩及硬頁岩夾薄層砂岩組成，岩性脆弱，容易受到地震及降雨等因素而破裂。中央地質調查所將此區域劃為山崩敏感區，並將一部份區域劃為大規模崩塌災害潛勢地區。本研究自2019年7月起在此區域架設了17台地震測站做長期監測，監測其山崩坡體的微地震活動。監測期間參考中央研究院地球科學所佈置在南坡的GNSS資料，發現在2021年8月盧碧颱風侵台時的強降雨造成研究區域明顯的滑動，約有20-30公分的滑動量；參考經濟部中央地質調查所佈置的GNSS資料，也發現位於冠部有2個測站於此段時間有約60公分的滑動量。因此本研究初步以人工判識波形的方式找出可能的微地震事件，搜尋2021年8月的資料並辨識出53筆微地震事件。後續利用網格搜尋法對這53筆挑出的事件做微地震定位，了解其在空間上的分布特性。定位結果顯示大部分的微地震事件發生在本研究區域南坡的位置，深度較淺，但多數微地震仍在滑移面以下的區域，這有可能是地震定位一般在垂直深度上的誤差較大，或者代表當坡體在滑動時，滑移面以下也可能破裂變形，產生微地震的訊號。
由於利用人工判識微地震事件非常耗時且難以找到所有發生的事件，因此，本研究進一步利用模板掃描法，將12個定位品質較佳的微地震事件作為樣本，掃描2019年7月至2021年12月的每日連續波形來系統性的找出所有與樣本相似的事件，建立完整的微地震目錄。與雨量資料、GNSS資料及區域地震資料做分析與比對，並將微地震掃描結果分為三類，第一類的微地震掃描結果顯示在坡體滑動前，微地震數量有明顯上升，表示坡體可能已經有緩慢形變破裂的情形，因此，微地震的上升可能是滑動前的徵兆，或可做為未來山崩預警的指標。第二類的微地震掃描結果與雨量較有相關，與地震地動程度較不相關，在颱風、豪雨事件過後，可能是雨水滲入地表，使坡體之摩擦力下降，以及孔隙水壓上升，造成微地震數量的增加。此類在2021年8月盧碧颱風侵台時微地震數量也有顯著上升的情形，但在坡體滑動期間的數量卻是減少的，推測可能是強降雨或溪水暴漲導致訊噪比降低，因而降低微地震事件的偵測能力；或是坡體滑動時的應力釋放導致微地震數目的下降。第三類的微地震掃描結果中，有兩個微地震事件的震源位置在研究區域以外，看不出與雨量、地震有關聯，另一個微地震事件雖然發生在研究區域內，但未有上述提到的特性，也較看不出有其他特性。本研究展示了利用地震學監測坡體內部的微地震活動有機會了解坡體滑動的時空行為特性，進而幫助山崩潛勢區的預警與減災。

關鍵字

竹林山崩 ； 大規模深層山崩 ； 微地震 ； 微地震定位 ； 模板掃描法

並列摘要

Landslide is one of the most dangerous geological disasters and poses a threat to human lives and properties. Steep topography in mountainous area in Taiwan is prone to landslides induced by frequent earthquakes or heavy rainfall. The deformation or failure of unstable slopes will produce internal microearthquakes and generate seismic signals to be recorded. Analyzing these seismic signals would therefore provide an opportunity to understanding the deformation and sliding behavior along the slip plane of potential landslides. The Chulin area is located in Baolai village, Kaohsiung. The lithology is mainly composed of slate and argillite with thin bedded metasandstone. Central Geological Survey has classified this area as landslide sensitive area, and a part of area has classified as potential large-scale deep-seated landslides. To monitor its slope stability, a seismic network of 17 stations was operated since late 2019 by Institute of Earth Sciences, Academia Sinica. During the time, displacements of 20-30 cm were observed by the GNSS stations on the southern flank installed by the Institute of Earth Sciences and about 60 cm were observed by the GNSS stations on the crown part installed by the Central Geological Survey in August 2021 when Typhoon Lupit brought the prolonged heavy rainfall. We thus focus on searching related seismic signals of this typhoon event in August and manually identified a total of 53 microearthquake events. We then located these 53 events using modified source scanning method. The results show that most of the events located in the southern flank at shallow depths but mostly below the slip plane. This may indicate typically larger location errors in the vertical direction or implies that the sliding-related cracks could develop not only along but also below the slip plane.
We further select 12 well-located events as templates and apply the matched filter method to scan daily continuous waveform to systematically find similar events from late 2019 through 2021. In comparison with rainfall, ground shaking, and GNSS data, the 12 templates are classified into 3 categories. In the first category, a rapid increase of microearthquake number observed prior to the slope displacement, which may serve as a precursory indicator for landslide early warning. The second category demonstrates a seasonal variation in microearthquake number during rainy seasons, but no clear relationship with the ground shaking of regional earthquakes. This probably because the infiltration of the rain water into the slope give rise the pore pressure and reduce the friction of pre-existing fractures for the increase of microearthquakes. During the Typhoon Lupit, the number of microearthquake also increases before the slope moved but rapidly decreases during the movement. The decrease in microearthquake number may result from the low signal to noise ratio due to the heavy rainfall and river flooding, or result from the stress release of the slope sliding. The third category includes two microearthquakes outside our monitored landslide area and shows no clear characteristic as the previous two. This study demonstrates that using seismology to track the internal microearthquakes of the slope could potentially provide a new means to understanding the spatiotemporal behavior of slope deformation and possibly helping with the early warning and hazard mitigation to landslide-sensitive areas.

並列關鍵字

Chulin landslide ； Large-scale deep-seated landslides ； Microearthquake ； Microearthquake location ； Waveform matched filter

參考文獻

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