EARTHQUAKE CLOUDS AND
SHORT TERM PREDICTION

Earthquake Vapor Model & Precise Prediction

- July 9, 2016 -

As the latest update for those who are interested in earthquake research, Earthquake Vapor Model & Precise Prediction will be published around late July or August,2016.  In spite of the torment of my cancer, no funding, insufficient empirical data and conservative traditionalists’ uunreasonable accusations and obstruction,  I have finally completed “Earthquake Vapor Model & Precise Prediction”,  after 26 years’ persistence and extensive earthquake research.   Here is the book cover and its preface.  I will keep you posted with the publishing date.  Thank you for your interests!



   

 

 Preface 

 

    Earthquakes are thought to defy predictions.  Indeed, numerous prediction methods have been attempted by generations of geologists.  These methods are based on correlations of phenomena (such as animal behavior, tidal behavior, radon generation, ground shift, electromagnetism, foreshocks) with earthquakes.  However, none of these methods have yielded useful predictions, because they are based on correlations instead of mechanisms.  That is, an impending earthquake may or may not lead to these putative precursors, and phenomena other than earthquakes may also cause these phenomena.    

    In this book I propose a new theory – earthquake vapor theory – that can be used to generate precise and accurate earthquake predictions.  This theory proposes that when a huge rock is stressed by external (including man-made) forces, its weak parts break first. This induces crevices into which ground water percolates.  Water expansion, contraction, friction and erosion further weaken the rock.  Friction during ground movement heats the ground water and eventually generates vapor at high temperature and pressure.  This vapor erupts from an impending hypocenter to the surface by the crevices and a nozzle, and rises up to form an “earthquake cloud” when encountering cold temperature at a higher altitude or a geoeruption (geothermal eruption) as its heat dissipates a preexisting cloud.  

     Earthquake clouds and geoeruptions can be differentiated from other geological or weather phenomena because they are vapor-based, suddenly appear, and have a fixed source, high temperature and high pressure.  The location of eruption predicts the epicenter, the vapor amount predicts the magnitude, and the time after a complete eruption empirically predicts the time of the earthquake within days. 

    Using this theory, I predicted 63 independent earthquakes to the United States Geological Survey from 1994 to 2001.  Each prediction has three definite windows of time, area and magnitude. More than 60% of these predictions are all correct.  An evaluation on this set shows that random guesses will have only a chance of 0.001 to achieve the same level of success. I also made over 1500 predictions to the public from 1999 to 2007. The Bam cloud is representative.  It appeared suddenly from and insisted at a nozzle in Bam, Iran for 26 hours on December 20~21, 2003 when surface temperature increased 5oC downwind. The Bam prediction on December 25 is excellent. An M6.8 earthquake hit Bam exactly on December 26 and was the only one in the predicted area and magnitude in the record of about 3000 years. The United Nations published my paper “Bam Earthquake Prediction & Space Technology” in its 2004 yearbook (Seminars of the United Nations Programme on Space Applications 16), and shared this book to its all member states in Vienna in early 2005. The United States Patent and Trademark Department published “Method of precise earthquake prediction and prevention of mysterious air and sea accidents” (Patent US 08068985B) in 2011.      

    Despite the promise of the Earthquake Vapor Theory, I have received no fund to overcome technical difficulties associated with satellite data, temperature data, and earthquake data. I hope that this book will help the mankind to solve these solvable problems and be able to predict all devastating earthquakes.   

    I acknowledge LinYing Fang, WenYing Shou, Abdolreza Ansari Amoli, Andy M Han, ChengNan Xu, Sri Ram, JianJun Xia, XiaoKun Chen, Yan Fang, ShuiZhen Ye, JuanJuan Shao, the Lee & Hayes Office, and the Han Office for support or help.  I thank international sources, media, scholars, and people for data, information, reports, inspiration, and donation.

 

 

 

 

 

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