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Lake Toba is located in the province of North Sumatra, Indonesia which is the largest lake in southeast Asia and is the largest volcanic-type lake in the world. The lake’s countenance level is decreased and this can have a negative impact on utilization of power plants and agricultural irrigation. This study will see whether the water level is decreased due to the influence of El Niño activity. The methods used are statistical and descriptive analysis. The data used is the water level of Lake Toba in period of 1957-2016, precipitation data uses NOAA Climate Prediction Center (CPC) data with a resolution of 0.50° × 0.50° from January 1957 to December 2013 (56 years) to analyze rainfall anomalies. El Niño’s activities have an effect on Lake Toba water level, where the moderate El Niño > 12 months duration resulted in a reduction of 1.5-2 m, while the strong El Niño with a duration of > 10 months resulted in a decline of 2-2.5 m.

This research quantitatively and qualitatively analyzes the factors responsible for the water level variations in Lake Toba, North Sumatra Province, Indonesia. According to several studies carried out from 1993 to 2020, changes in the water level were associated with climate variability, climate change, and human activities. Furthermore, these studies stated that reduced rainfall during the rainy season due to the El Niño Southern Oscillation (ENSO) and the continuous increase in the maximum and average temperatures were some of the effects of climate change in the Lake Toba catchment area. Additionally, human interventions such as industrial activities, population growth, and damage to the surrounding environment of the Lake Toba watershed had significant impacts in terms of decreasing the water level. However, these studies were unable to determine the factor that had the most significant effect, although studies on other lakes worldwide have shown these factors are the main causes of fluctuations or decreases in water levels. A simulation study of Lake Toba's water balance showed the possibility of having a water surplus until the mid-twenty-first century. The input discharge was predicted to be greater than the output; therefore, Lake Toba could be optimized without affecting the future water level. However, the climate projections depicted a different situation, with scenarios predicting the possibility of extreme climate anomalies, demonstrating drier climatic conditions in the future. This review concludes that it is necessary to conduct an in-depth, comprehensive, and systematic study to identify the most dominant factor among the three that is causing the decrease in the Lake Toba water level and to describe the future projected water level.

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Lombok and Nusa Tenggara are one of the regions in Indonesia that have quite complex tectonic arrangements. With this order, not infrequently in the region there are often natural disaster phenomena. One of the most striking things is the occurrence of a series of earthquakes of magnitude Mw > 5.0 that rocked the northern region of the Island of Lombok on 29 July 2018 (Mw = 6.4), 5 August 2018 (Mw = 6.9), 9 August 2018 (Mw = 5.9), 19 August 2018 (Mw = 6.3 and 6.9) and 25 August 2018 (Mw = 5.5). This study aims to identify the condition of tectonic structures under the surface of the earthquake occurring region using the tomography method. This method utilizes earthquake travel time data recorded at 15 BMKG seismic stations. The tomogram results show the contrast of anomalous values in the Vp and Vs models. The contrast of the anomalous is associated with the presence of fault structures that have an allowance angle of about ± 20-30° and a thrust fault type, verified by focal mechanism ball data. This thrust fault is then indicated as the cause of the occurrence of earthquakes with magnitude Mw > 5.0, which shook the northern part of the island of Lombok in July-August 2018. According to the tomography result, the fault is located closer to the north side of Lombok Island than Flores Back arc Thrust.

Abnormal pore pressure can cause some problems during the drilling process such as a blowout or sticking pipe while drilling. Pore pressure prediction may prevent the drilling hazard, especially in carbonate field that known as a complex reservoir. It is useful for mud weight determination to prevent blowout and sticking pipe while drilling. This study focuses on predicting pore pressure values and maps it through 3D seismic data. The field is carbonate reservoir which known as a heterogeneous formation with shale above the reservoir. Due to the difference of lithologies, the two different empirical parameter is used in each lithology for Eaton equation. The pore pressure prediction then correlates with the seismic attribute using a neural network method. The input parameter of the Eaton is sonic and density log. Then, the result of Eaton’s method is calibrated by leak-off test (LOT) and repeat formation test (RFT), hence the results are more accurate and verified. Then, the pore pressure is correlated to acoustic impedance, shear impedance, seismic frequency, and seismic amplitude to create a subsurface model by the neural network machine learning. The result shows that the pore pressure prediction of the model is verified by the measured pore pressure well-log data with good accuracy up to 90%. The combination method of Eaton and neural network was proven to be able to predict and map pore pressure distribution in a complex carbonate field.

It has done a study of porosity prediction by using neural network. The study uses 2D seismic data post-stack time migration (PSTM) and 2 well data at field “T”. The objective is determining distribution of porosity. Porosity in carbonate reservoir is actually heterogeneous, complex and random. To face the complexity the neural network method has been implemented. The neural network algorithm uses probabilistic neural network based on best seismic attributes. It has been selected by using multi-attribute method with has high correlation. The best attributes which have been selected are amplitude envelope, average frequency, amplitude weighted phase, integrated absolute amplitude, acoustic impedance, and dominant frequency. The attribute is used as input to probabilistic neural network method process. The result porosity prediction based on probabilistic neural network use non-linear equation obtained high correlation coefficient 0.86 and approach actual log. The result has a better correlation than using multi-attribute method with correlation 0.58. The value of distribution porosity is 0.05–0.3 and it indicates the heterogeneous porosity distribution generally from the bottom to up are decreasing value.

The seismoelectric method is a new means of characterizing near surface aquifers and appears to be able to detect significant changes in permeability or pore fluid chemistry. Seismic energy is used to probe the properties of porous media via the electrokinetic effect by measuring the resulting electrical potential on the surface.Two types of signal are observed in practice: signals recorded simultaneously by many sensors due to the seismic wave encountering a change in sub-surface properties, and signals that are produced when the seismic wave is near a sensor. The first type of signal is the desired response, and the second type of signal is interference. As the second type of signal exhibits move-out with seismic velocities it can be removed with filtering in the F-K or t -p domain. In practice, the limited number of acquisition channels typically available and the strength of these unwanted signals compared to the desired signals limits the effectiveness of these methods.We propose and demonstrate a solution to this problem by combining shot records from 24 sensors at different shot positions to create a virtual 120 channel shot record that allows velocity or move-out dependent filters to perform more effectively. The application of this method of data collection and processing has allowed us to reliably detect seismoelectric signals originating from depths of up to 120 metres.