期刊名称：Prisma Sains: Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram
印刷版ISSN：2338-4530
电子版ISSN：2540-7899
出版年度：2019
卷号：7
期号：2
页码：138-147
DOI：10.33394/j-ps.v7i2.1766
出版社：IKIP Mataram, Fakultas Pendidikan Matematika dan IPA
摘要：[Title: Moving Average Filter for Time Lapse Gravity Anomaly Separation]. Interpretation of time-lapse gravity anomaly due to fluid injection in the reservoir is difficult when it mixed with shallow source anomalies. To solve the kind of problem, anomaly separation is performed with the method of Moving Average Filter. This study was conducted to obtain an effective method to separate the gravity anomaly of shallow sources on time-lapse gravity anomaly. Trials are conducted on anomaly models derived from reservoirs with three distinct depths that are 300, 600 and 900 meters. This forward model is then mixed with gravitational anomaly from the shallow source obtained from the field data. The mixed anomaly is then separated by a Moving Average filter. Results show that Moving Average filters can separate the shallow effect from the deep source anomaly and are effective up to a depth of 900 meters. The research is also beneficial for classroom learning in the computer programming class based on Matlab.
其他摘要：[Title: Moving Average Filter for Time Lapse Gravity Anomaly Separation]. Interpretation of time-lapse gravity anomaly due to fluid injection in the reservoir is difficult when it mixed with shallow source anomalies. To solve the kind of problem, anomaly separation is performed with the method of Moving Average Filter. This study was conducted to obtain an effective method to separate the gravity anomaly of shallow sources on time-lapse gravity anomaly. Trials are conducted on anomaly models derived from reservoirs with three distinct depths that are 300, 600 and 900 meters. This forward model is then mixed with gravitational anomaly from the shallow source obtained from the field data. The mixed anomaly is then separated by a Moving Average filter. Results show that Moving Average filters can separate the shallow effect from the deep source anomaly and are effective up to a depth of 900 meters. The research is also beneficial for classroom learning in the computer programming class based on Matlab.
