Adel Asadi

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Verified

Adel verified their affiliation via an institutional email.

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• Doctor of Philosophy
• Postdoctoral Scholar at Stanford University

Mineral exploration is a complex endeavor, often requiring the integration of insights from well known mineralized locations with advanced data-driven approaches to enhance prospectivity mapping. This study addresses the challenges of leveraging historical drilling data, geophysical signatures, and large-scale datasets to inform predictive models f...

Multiple-point geostatistics (MPS) is an established tool for the uncertainty quantification of Earth systems modeling, particularly when dealing with the complexity and heterogeneity of geological data. This study presents a novel pixel-based MPS method for modeling spatial data using advanced machine-learning algorithms. Pixel-based multiple-poin...

A critical challenge in mineral prospectivity mapping is to prioritize new areas for exploration drilling based on estimated depth and grade of potential deposits and the quantified uncertainty of estimations. This research study is aiming for exploiting already available drilling data in conjunction with data of different geophysical variables and...

Landslides, often occurring in mountainous regions and triggered by earthquakes or heavy rainfall, are a major natural disaster. Traditionally, identifying landslides involves manually analyzing optical remote sensing imagery, a process that is both slow and labor-intensive. This study proposes an automatic landslide detection method using advanced...

This study explores a new method to identify and map soil liquefaction areas from aerial images after earthquake events. Traditionally, liquefaction is recorded through field visits as geographic points, leading to incomplete data. Comprehensive mapping of affected areas is crucial for developing accurate prediction models. The research introduces...

Global geospatial liquefaction models are currently being used to predict the probability of liquefaction occurring after an earthquake. However, these models are limited in their ability to generalize to new regions because they are only trained on data from previous events using a global dataset. Additionally, the geology, saturation, and seismic...

Landslides are major natural disasters in mountainous areas, often caused by earthquakes and heavy rainfalls. Traditional manual delineation methods for identifying landslide features using optical imagery are inefficient, highlighting the need for automated detection techniques. Deep Convolutional Neural Networks (CNNs) have emerged as advanced so...

A series of earthquakes occurred in Kumamoto, Japan, in April 2016, which caused numerous landslides. In this study, high-resolution pre-event and post-event optical imagery, plus bi-temporal Synthetic Aperture Radar (SAR) data are paired with geospatial data to train a pixel-based machine learning classification algorithm using logistic regression...

Three different regionalization strategies are presented for geospatial modeling of earthquake-induced soil liquefaction: 1) Ensemble Modeling: An updated global geospatial liquefaction model (GGLM-2017) is developed using Logistic Regression (LR) and is currently used by the US Geological Survey (USGS) as the preferred liquefaction model to map li...

A global geospatial liquefaction model (GGLM‐2017) was previously developed (Zhu et al., 2017) using logistic regression (LR) and is currently used by the U.S. Geological Survey as the preferred liquefaction model to map liquefaction probability immediately after the occurrence of earthquake events. This research proposes an ensemble modeling appro...

Offshore wind turbines (OWTs) are crucial for future energy infrastructure due to their eco-friendly nature. Their performance is greatly influenced by climate change, which affects wind speed and the frequency of extreme weather events. This makes wind speed a vital factor in designing and evaluating offshore wind farm projects. This study focuses...

In the last few decades, with industrial development, massive amounts of heavy metal contaminants have been introduced into the soil as a result of unplanned urbanization and industrial sewage. Diffusion of such substances into the soil may cause changes in geotechnical parameters other than the known environmental impacts. Zinc is a recognized and...

Soil liquefaction often occurs as a secondary hazard during earthquakes and can lead to significant structural and infrastructure damage. Liquefaction is most often documented through field reconnaissance and recorded as point locations. Complete liquefaction inventories across the impacted area are rare but valuable for developing empirical liquef...

Data-driven geospatial liquefaction models are useful tools for regional seis- mic hazard assessments. The models are based on liquefaction occurrence inventories, widely available geospatial variables, and earthquake-specific parameters. Our inventory is updated with geospatial data from non-lique- faction and liquefaction occurrence locations in...

A series of earthquakes, with 7.3 Mw highest intensity, hit Kumamoto, Japan, over a period of two days in April 2016. The earthquakes caused numerous landslides and surface ruptures in the steep volcanic geological environment. In this study, pre- and post-event sets of high-resolution aerial (Geospatial Information Authority of Japan) and satellit...

On February 6th, 2023 (at 01:17:36.1 UTC) a strong M7.8 earthquake with epi-central location 37.17 N and 37.08 E happened approximately 30 km WNW of Gaziantep city, in Southeastern Turkey (CSEM/EMSC), and about twice the distance from the border with Syria. This earthquake was followed by a similar magnitude earthquake (M7.5) approximately nine hou...

Geospatial ground failure models are routinely implemented as part of the Ground Failure tab of the USGS Event page. After the February 2023 Earthquake sequence in Turkey, ground failure maps were disseminated for the Mw7.8 and Mw7.5 events. In addition to the Zhu et al. (2015) and Zhu et al. (2017) models, the authors have an updated geospatial li...

The purpose of his study is to propose a fast pixel-based classification technique based on Logistic Regression algorithm, and to evaluate addition of geospatial and temporal change information to the color imagery in order to increase the classification accuracy in earthquake-induced landslide mapping. Datasets from the 2016 Kumamoto Earthquake in...

Data-driven geospatial liquefaction models are useful tools for real-time post-event impact and regional seismic hazard assessments. Geospatial liquefaction models are based on liquefaction occurrence inventories, widely available geospatial variables and earthquake-specific parameters. We have updated our inventory with geospatial data from non-li...

In the aftermath of an earthquake, data collection is an important part of the response and is used for both loss assessment and data curation for model development. For liquefaction impacts, post-earthquake data collection often relies on field investigations, which are usually spatially limited and incomplete. Field investigations may capture liq...

Multiple-point (geo) statistics (MPS) have wide-scale applications in mineral resource modeling for different commodities. MPS proves effective in capturing the spatial continuity of orebody, lithology, and mineralogy, compared to two-point geostatistics. Although significant large numbers of software packages are available for two-point geostatist...

A series of subsequent earthquakes, with the highest intensity of 7.0 Mw, hit Kumamoto, Japan, continuously over a period of two days in April 2016. The earthquakes caused numerous landslides and surface ruptures in the steep volcanic geological environment, some of them very large in size. In this study, pre- and post-event sets of high-resolution...

In geological remote sensing approaches for estimating evapotranspiration, METRIC method is among the most modern and precise approaches that many positive experiments have been reported regarding its applicability. In this study, the value of actual evapotranspiration (ET) occurred in Marvdasht farmlands, Fars province, Iran, was calculated instan...

Hitherto there have been many studies comparing the usefulness of OLI and ETM+ sensors for linear feature extraction. However, not too much attention has been paid to the differences in the bandwidth of the two sensors. In this study, the suitability of Landsat ETM+ and OLI sensors for automatic detection of linear features by LINE algorithm was co...

Mapping of seismic-induced soil liquefaction is a common practice after earthquakes to help local authorities devise better disaster preparedness plans after a seismic event and to provide data for the research community. Many of those reports provide limited and inconsistent geographic information. Some of the reconnaissance reports provide coordi...

Geostatistical simulation is an established tool for uncertainty quantification of earth systems modeling. Multiple-point statistical (MPS) algorithms are specifically advantageous when dealing with complexity, and heterogeneity of geological data. This study presents a novel pixel-based MPS method for modeling spatial data. The developed computati...

Major oxides like FeO and TiO2 are critical elements available on the lunar surface, and global mapping of such elements suffers from high uncertainty due to the limited availability of trustable information. The idea of this research study is to correlate limited number of experimental geochemical data (i.e. the weight of iron and titanium oxides)...

Recent developments in the image processing approaches and the availability of multi and/or hyper spectral remote sensing data with high spectral, spatial and temporal resolutions have made remote sensing technique of great interest in investigations of geological sciences. One of the biggest advantage of the application of remote sensing in geolog...

One of the serious dangers which threatens human communities especially those who are living in mountainous areas is the occurrence of landslides. Therefore, determination of the areas with potential for landslide events is very important for avoiding establishment of residential areas or industrial facilities. The aim of this study is to provide a...

Rate of Penetration (ROP) estimation is a key parameter in drilling optimization, due to its role in minimizing drilling costs. Several ROP models have been developed which can predict the penetration rate based on physics-based or data-driven techniques. Considering a data-driven approach, the purpose of this research is to apply a Machine Learnin...

Static formation temperature should be determined accurately, especially because it is an essential parameter in petroleum systems modeling. In this study, the commonly used empirical methods for log derived temperature corrections have been reviewed, and a new correlation has been developed by applying an artificial neural networks model to calibr...

To determine rock mechanical properties like uniaxial compressive strength and shear strength accurately, it is required to put considerable time to find and collect suitable samples for laboratory testing. To improve the time and cost efficiency, many empirical relationships have been proposed in literature. The purpose of this study is to develop...

This project focuses on the changes in the characteristics and properties of cement slurry while adding different environmentally friendly additives. To properly implement the cementing job, cement should have the desired properties and characteristics. It should have very low permeability, high strength, sufficient thickening time and pump-ability...

Asmari and Sarvak limestones are two main oil producer formations in Iran and the Middle East. The production and optimal utilization of these reservoirs will have a significant impact on the economy of the petroleum industry. Geomechanical modelling of oil reservoirs are widely used in optimum drilling, production and reservoir compaction. Hence,...

In this study, an artificial neural networks (ANN) model as an artificial intelligence (AI) technique is proposed to determine the formation pore pressure from data of two critical drilling parameters named mechanical specific energy and drilling efficiency. These parameters (MSE and DE) which are closely correlated to differential pressure during...

Development of an Excavation Damaged Zone around an underground excavation can change the physical, mechanical and hydraulic behaviours of the rock mass near the underground space. This paper presents an approach to build a prediction model for the assessment of EDZ based on an artificial intelligence method called artificial neural networks which...

This study focuses on the prediction of tunneling advance rate (AR) for evaluation of tunnel excavation projects. Data sets which are hired for the purpose of this study, came from a tunneling excavation project in Turkey, which was performed by excavators and loaders, to obtain the distribution of geotechnical and construction parameters. Based on...

Due to the advanced mechanization of the methods of excavation, in particular the increasing use of Tunnel Boring Machines (TBM) in underground construction, knowledge regarding rock and soil abrasiveness is critically required. The wear of the TBM disks indeed governs strongly the performance and efficiency of disk cutters, their rate of replaceme...

Roadheaders are very versatile excavation machines used in tunneling, mine development, and mine production for soft to medium strength rock formations. Performance prediction is an important factor for successful roadheader application and generally deals with machine selection, production rate and bit consumption. The main objective of this resea...

The ability to predict the performance of rock drills is important in drilling operations. Drillability is a term used in construction to describe the influence of a number of parameters on the drilling rate (drilling velocity) and the tool wear of the drilling tool. In this evaluation, the drillability term was defined as a penetration rate. This...

It is critical to obtain the rock strength along the wellbore to control drilling problems such as pipe sticking, tight hole, collapse and sand production. The purpose of this research is to predict the uniaxial compressive strength based on data of sonic travel time, formation porosity, density and penetration rate. For prediction of UCS, artifici...

Cerchar abrasion index (CAI) is commonly used to represent rock abrasion for estimation of bit life and wear in various mining and tunneling applications. The test is simple and fast, but there have been some discrepancies in the test results which are related to the type of equipment, condition of the rock surface, operator skills, testing procedu...

The development of an excavation damaged zone (EDZ) around an underground excavation can change the physical, mechanical and hydraulic behaviors of the rock mass near an underground space. This might result in endangering safety, achievement of costs and excavation planed. This paper presents an approach to build a prediction model for the assessme...

Drilling is very common and prerequisite for any earthwork starting from exploration to exploitation of earth resources. Proper drill utilization is always associated with the cost of excavation as well as overall cost of the project. Today applications of drilling require proper identification of operations where a cost reduction is possible. It i...

The wear of the bits on a continuous mining machine is a problem affects mine operators around the world. Rock abrasiveness is the ability of rock to wear down the cutting tool during the interaction between the indenter and the rock in the mechanical rock cutting process. Rock abrasiveness immediately affects the wear of cutting indenters of the d...

Standard Penetration Test (SPT) and Cone penetration Test (CPT) are the most commonly used in situ tests to delineate soil stratigraphy and determine the geotechnical engineering properties of subsurface soils. Several geotechnical design parameters of the soil are associated with the SPT. In contrast, CPT is becoming increasingly more popular for...

The development of an excavation damaged zone around an underground excavation can change the physical, mechanical and hydraulic behaviors of the rock mass near an underground space. This paper presents an approach to build a prediction model for the assessment of EDZ. Rock Engineering Systems was used as an appropriate method for choosing one of t...

Determination of Unconfined Compressive Strength of rock is important in Geotechnical Engineering. Although laboratory test is the most direct way of rock compressive strength estimation, but UCS determination in laboratory is problematic if the rock masses are weathered because obtaining proper core segments is difficult. Hence, using index testin...

In this research, establishment of a good relationship between N and UCS of a rock mass under particular geological circumstances is considered. So, collected data related to the immediate roof rock of coal seams in North-Eastern coal fields of Iran are used in this paper. In order to determine the N and UCS, a significant number of samples were se...