IMPLEMENTASI ALGORITMA RR-PSO YANG CEPAT, STABIL DAN ROBUST UNTUK INVERSI DISPERSI GELOMBANG RAYLEIGH DAN VERTICAL ELECTRICAL SOUNDING

Akhir-akhir ini, parameter fisis bawah permukaan seperti kecepatan gelombang geser (Vs) dan resistivitas (ρ) banyak dimanfaatkan untuk investigasi geoteknik dan studi lingkungan. Salah satunya, untuk karakterisasi dan monitoring tanggul. Inversi merupakan kunci utama dalam mengestimasi parameter Vs pada analisis dispersi gelombang Rayleigh dan ρ pada vertical electrical sounding (VES). Oleh karena itu, diperlukan algoritma inversi yang cepat, stabil dan robust terhadap noise serta mampu menyediakan informasi ketidakpastian dalam mengestimasi parameter tersebut. Pada penelitian ini, telah dikembangkan algoritma inversi berbasis RR-PSO untuk inversi kurva dispersi dan data VES. Tahap pertama pada penelitian ini, dilakukan simulasi numerik untuk mengatahui kapabilitas algoritma RR-PSO dalam mengoptimasi fungsi multi-modal. Pada simulasi numerik ini, dilibatkan beberapa algoritma lain sebagai pembanding. Setelah itu, algoritma RR-PSO diuji validitasnya dalam menginversi kurva dispersi dan data VES. Pada uji validitas ini, algoritma RR-PSO diimplementasikan pada data sintetik. Data sintetik yang digunakan terdiri dari dua jenis, yaitu data yang bebas noise dan data yang terkontaminasi noise. Uji validitas yang dilakukan meliputi: (1) perhitungan similarity index (SI); (2) estimasi ketidakpastian solusi menggunakan standar deviasi dan jangkauan interkuartil. Pada tahap terakhir, algoritma RR-PSO dimplementasikan pada kurva dispersi dan data VES lapangan tanggul LUSI P.79 – P.82. Hasil inversi yang berupa model Vs dan ρ satu dimensi, digunakan untuk merekonstruksi kondisi bawah permukaan tanggul. Hasil penelitian ini menunjukkan bahwa algoritma RR-PSO memiliki performa yang cepat, stabil dan robust terhadap noise baik pada inversi dispersi gelombang Rayleigh maupun VES. Serta, mampu mengestimasi parameter bawah permukaan dengan tepat. Di samping itu, algoritma ini juga mampu menginterpretasikan keadaan bawah permukaan tanggul dengan baik. Sehingga, dapat digunakan untuk menilai kestabilan tanggul

Algoritma Differential Evolution untuk Estimasi Parameter Sumber Anomali Self-Potential

Self-Potential (SP) anomaly is naturally occurring potential differences due to electrochemical, electro-kinetic, and thermoelectric sources in the subsurface. The Source of SP anomaly can be modeled as a simple-geometry body, e.g: spheres, cylinders, and inclined sheets. The model parameter of SP anomaly is generally estimated using local optimization such as gradient-search-based methods. However, these methods have some drawbacks. Therefore, this problem needs to address using global optimization, namely Differential Evolution (DE) algorithm. DE is one of the metaheuristic algorithms adopting biological evolution in the optimization process. In this work, the DE algorithm is implemented to estimate the parameters of SP anomaly sources.  There are two stages in this work, e.g: synthetic test and field data inversion. In the synthetic test, DE is built and implemented in synthetic data generated from a cylinder body contaminated by noise. This test shows that DE can estimate the parameters of the cylinder body (SP anomaly source) well. In the field data inversion, DE is implemented to estimate the SP Surda anomaly which has been studied by other methods. The results of DE estimation are comparable to the previous studies, and able to provide uncertainty information. DE algorithm can be implemented to characterize the source of SP anomaly for futher study

Inversion of 1-D Resistivity Data using RR-PSO algorithm to identify Shallow Gas in Balikpapan

In recent years shallow gas blowouts have occurred several times in Balikpapan residential areas due to drilling activities for groundwater exploration and geological structures that play as a gas trap. It is necessary to identify structures containing shallow gas by implementing geophysical methods namely Vertical Electrical Sounding (VES). VES is an electrical resistivity method which involves the rapid measurement of variations of the ground resistivity with increasing electrode spaces. The output of this method is a 1-D resistivity model used to identify shallow gas. The 1-D resistivity model can be obtained by the inversion technique. In general, the inversion of VES data is conducted using local optimization method. However, this method has several limitations hence we need to implement a global optimization method in VES data inversion. In this work, the RRPSO algorithm was implemented, which is a global optimization method, in VES data inversion to obtain 1-D resistivity model. First, the RR-PSO algorithm is built and tested to invert synthetic data to evaluate the algorithm's performance. In this stage, the similarity index and several statistical parameters of the inversion results were calculated. After the synthetic test, the algorithm is implemented on field data inversion. The result shows that the RR-PSO algorithm has successfully inverted both the synthetic and field data. In the synthetic test, the similarity index obtained is more than 95%. The 1-D resistivity model from field data inversion indicates at the depth of 20 – 55 m a high resistivity anomaly that is identified as shallow gas. For further study, the RR-PSO algorithm could be implemented for other VES data to construct 2-D resistivity model in the study area (Palm Hills Resident area, south Balikpapan) for imaging the shallow gas presence as a mitigation measure

Inversion of 1-D Resistivity Data using RR-PSO algorithm to identify Shallow Gas in Balikpapan

In recent years shallow gas blowouts have occurred several times in Balikpapan residential areas due to drilling activities for groundwater exploration and geological structures that play as a gas trap. It is necessary to identify structures containing shallow gas by implementing geophysical methods namely Vertical Electrical Sounding (VES). VES is an electrical resistivity method which involves the rapid measurement of variations of the ground resistivity with increasing electrode spaces. The output of this method is a 1-D resistivity model used to identify shallow gas. The 1-D resistivity model can be obtained by the inversion technique. In general, the inversion of VES data is conducted using local optimization method. However, this method has several limitations hence we need to implement a global optimization method in VES data inversion. In this work, the RRPSO algorithm was implemented, which is a global optimization method, in VES data inversion to obtain 1-D resistivity model. First, the RR-PSO algorithm is built and tested to invert synthetic data to evaluate the algorithm's performance. In this stage, the similarity index and several statistical parameters of the inversion results were calculated. After the synthetic test, the algorithm is implemented on field data inversion. The result shows that the RR-PSO algorithm has successfully inverted both the synthetic and field data. In the synthetic test, the similarity index obtained is more than 95%. The 1-D resistivity model from field data inversion indicates at the depth of 20 – 55 m a high resistivity anomaly that is identified as shallow gas. For further study, the RR-PSO algorithm could be implemented for other VES data to construct 2-D resistivity model in the study area (Palm Hills Resident area, south Balikpapan) for imaging the shallow gas presence as a mitigation measure