PREDIKSI SEBARAN TITIK API KEBAKARAN HUTAN GAMBUT MENGGUNAKAN WAVELET DAN BACKPROPAGATION (STUDI KASUS PROVINSI KALIMANTAN TENGAH)

Salah satu penyebab dari bencana kabut asap serta kerusakan iklim khususnya di Palangka Raya, Kalimantan Tengah adalah kebakaran hutan gambut. Ada banyak kerugian yang ditimbulkan oleh bencana kabut asap ini antara lain adalah munculnya peningkatan penderita yang terkena infeksi saluran pernapasan (ISPA) karena mutu udara tercemar dan lain sebagainya. Kebakaran hutan gambut sulit diatasi karena lokasi kebakaran yang jauh dari akses. Penelitian ini berfokus dalam membangun sistem prediksi sebaran titik api kebakaran hutan gambut dengan memanfaatkan citra satelit. Dalam perancangan sistem prediksi sebaran titik api ini menggunakan wavelet orthogonal untuk pengolahan awal citra satelit dalam memetakan sebaran titik api serta metode backpropagation untuk identifikasi pola sebaran titik api dalam sistem ini. Dari hasil data pengujian yang telah dilakukan untuk prediksi titik api diperoleh citra dengan dekomposisi menggunakan wavelet Haar memiliki nilai akurasi pengenalan titik api dengan presentase tertinggi yaitu 90 %. Kebaruan dari sistem ini adalah suatu sistem prediksi sebaran titik api kebakaran hutan gambut yang dapat dimanfaatkan sebagai salah satu upaya pencegahan kebakaran hutan gambut khususnya di Palangka Raya, Kalimantan Tengah

A new approach to spatial data interpolation using higher-order statistics

Interpolation techniques for spatial data have been applied frequently in various fields of geosciences. Although most conventional interpolation methods assume that it is sufficient to use first- and second-order statistics to characterize random fields, researchers have now realized that these methods cannot always provide reliable interpolation results, since geological and environmental phenomena tend to be very complex, presenting non-Gaussian distribution and/or non-linear inter-variable relationship. This paper proposes a new approach to the interpolation of spatial data, which can be applied with great flexibility. Suitable cross-variable higher-order spatial statistics are developed to measure the spatial relationship between the random variable at an unsampled location and those in its neighbourhood. Given the computed cross-variable higher-order spatial statistics, the conditional probability density function is approximated via polynomial expansions, which is then utilized to determine the interpolated value at the unsampled location as an expectation. In addition, the uncertainty associated with the interpolation is quantified by constructing prediction intervals of interpolated values. The proposed method is applied to a mineral deposit dataset, and the results demonstrate that it outperforms kriging methods in uncertainty quantification. The introduction of the cross-variable higher-order spatial statistics noticeably improves the quality of the interpolation since it enriches the information that can be extracted from the observed data, and this benefit is substantial when working with data that are sparse or have non-trivial dependence structures

STOCHASTIC INVERSION INTEGRATING SEISMIC DATA, LITHO-FACIES PHYSICAL PROPERTIES, AND MULTIPLE-POINT GEOSTATISTICS FOR RESERVOIR CHARACTERIZATION

We proposed a novel seismic inversion approach that integrates the physical properties of litho-facies, and geophysical data, within the multiple-point geostatistical frameworks to reduce the uncertainty in predictions of litho-facies spatial arrangement away from wells or control points. The litho-facies groups (rock-type) in the well locations are defined and conditioned to the distribution of elastic properties, including P-wave velocity (Vp) and facies density (ρ) in the well locations. A conceptual geological model (training image) is utilized within a wavelet-based multiple-point geostatistical simulation (WAVESIM) algorithm to generate litho-facies realizations. In our inversion algorithm, the forward model is created by implementing the bivariate Kernel density estimation technique of the litho-facies properties (Vp and ρ) that are distributed in the well locations. The inversion approach is an iterative process, where a particular number of elastic properties (Vp and ρ) for each WAVESIM realization are drawn, and then the forward model was utilized to create synthetic seismograms. For each generated set of the WAVESIM realizations, a series of synthetic seismograms are produced, and one realization is selected that provides the best-match synthetic seismogram compared to the input seismic data using crosscorrelation function. Our inversion technique was successfully applied to synthetic and field datasets. The results demonstrate the efficiency of our inversion approach to characterize highly heterogeneous reservoirs

A Novel Pixel-based Multiple-Point Geostatistical Simulation Method for Stochastic Modeling of Earth Resources

Uncertainty is an integral part of modeling Earth\u27s resources and environmental processes. Geostatistical simulation technique is a well-established tool for uncertainty quantification of earth systems modeling. Multiple-point statistical (MPS) algorithms are specifically advantageous when dealing with the complexity and heterogeneity of geological data. MPS algorithms take advantage of using training images to mimic physical reality. This research presents a novel and efficient pixel-based multiple-point geostatistical simulation method for mineral resource modeling. Pixel-based simulation implies the sequential modeling of individual points on the simulation grid by borrowing spatial information from the training image and honoring conditioning data points. The developed method borrows information by integrating multiple machine learning algorithms, including Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithms. For automation and to ensure high-quality realizations, multiple optimizations, and parameter tuning strategies were introduced. The proposed methodology proved its applicability by accurate reproduction of complex geological features honoring conditioning data while maintaining reasonable computational time. The model is validated by simulating a variety of categorical and continuous variables for both two and three-dimensional cases and conditional and unconditional simulations. As a three-dimensional case study for categorical stochastic modeling, the proposed method is applied to a gold deposit for orebody modeling. The proposed algorithm can be applied to a variety of contexts, including but not limited to petroleum reservoir characterization, seismic inversion, mineral resources modeling, gap-filling in remote sensing, and climate modeling. The developed model can be extended for spatio-temporal modeling, multivariate simulation, non-stationary modeling, and super-resolution realizations

ORTHOGONAL WAVELET FUNCTION FOR COMPRESSION SATELLITE IMAGERY OF PEAT FOREST FIRES

Background: In the process of digital image data representation, constrained the number of data volumes are required. One of the main sources of information in data processing of imagery is satellite imagery. Some applications of remote sensing technology requires a good quality image but in small size. Purpose: This study focuses on image compression is done to reduce the size of the image needs. However, the information contained in the image retained its existence. Method: In this study, using 17 orthogonal wavelet function used to reduce data satellite images of peat forest fires. Then, 17 of these orthogonal wavelet functions are compared with the parameter measurement i.e. PSNR (Peak Signal to Noise Ratio) and compression ratio. The benchmark of image compression is seen from the largest PSNR and large compression ratio Finding: Based on orthogonal wavelet function testing, then the Haar (daubechies 1) wavelet function results obtained has the highest PSNR for all level of decomposition on all test image i.e 50.783 dB for test image 1, 50.954 dB for image 2 and 49.855 dB for image 3. For the highest compression ratio on all test image is a function of wavelet symlet 8 i.e 97.00% for image 1, 97.05% for image 2 and 96.90% for image 3. Originality value: Satellite imagery that has been reduced would contribute to facilitating the processing of data as well as data input for the creation of digital image processing for system detection peat forest fires hotspots